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18 Qualitative Research Examples

qualitative research examples and definition, explained below

Qualitative research is an approach to scientific research that involves using observation to gather and analyze non-numerical, in-depth, and well-contextualized datasets.

It serves as an integral part of academic, professional, and even daily decision-making processes (Baxter & Jack, 2008).

Methods of qualitative research encompass a wide range of techniques, from in-depth personal encounters, like ethnographies (studying cultures in-depth) and autoethnographies (examining one’s own cultural experiences), to collection of diverse perspectives on topics through methods like interviewing focus groups (gatherings of individuals to discuss specific topics).

Qualitative Research Examples

1. ethnography.

Definition: Ethnography is a qualitative research design aimed at exploring cultural phenomena. Rooted in the discipline of anthropology , this research approach investigates the social interactions, behaviors, and perceptions within groups, communities, or organizations.

Ethnographic research is characterized by extended observation of the group, often through direct participation, in the participants’ environment. An ethnographer typically lives with the study group for extended periods, intricately observing their everyday lives (Khan, 2014).

It aims to present a complete, detailed and accurate picture of the observed social life, rituals, symbols, and values from the perspective of the study group.

Example of Ethnographic Research

Title: “ The Everyday Lives of Men: An Ethnographic Investigation of Young Adult Male Identity “

Citation: Evans, J. (2010). The Everyday Lives of Men: An Ethnographic Investigation of Young Adult Male Identity. Peter Lang.

Overview: This study by Evans (2010) provides a rich narrative of young adult male identity as experienced in everyday life. The author immersed himself among a group of young men, participating in their activities and cultivating a deep understanding of their lifestyle, values, and motivations. This research exemplified the ethnographic approach, revealing complexities of the subjects’ identities and societal roles, which could hardly be accessed through other qualitative research designs.

Read my Full Guide on Ethnography Here

2. Autoethnography

Definition: Autoethnography is an approach to qualitative research where the researcher uses their own personal experiences to extend the understanding of a certain group, culture, or setting. Essentially, it allows for the exploration of self within the context of social phenomena.

Unlike traditional ethnography, which focuses on the study of others, autoethnography turns the ethnographic gaze inward, allowing the researcher to use their personal experiences within a culture as rich qualitative data (Durham, 2019).

The objective is to critically appraise one’s personal experiences as they navigate and negotiate cultural, political, and social meanings. The researcher becomes both the observer and the participant, intertwining personal and cultural experiences in the research.

Example of Autoethnographic Research

Title: “ A Day In The Life Of An NHS Nurse “

Citation: Osben, J. (2019). A day in the life of a NHS nurse in 21st Century Britain: An auto-ethnography. The Journal of Autoethnography for Health & Social Care. 1(1).

Overview: This study presents an autoethnography of a day in the life of an NHS nurse (who, of course, is also the researcher). The author uses the research to achieve reflexivity, with the researcher concluding: “Scrutinising my practice and situating it within a wider contextual backdrop has compelled me to significantly increase my level of scrutiny into the driving forces that influence my practice.”

Read my Full Guide on Autoethnography Here

3. Semi-Structured Interviews

Definition: Semi-structured interviews stand as one of the most frequently used methods in qualitative research. These interviews are planned and utilize a set of pre-established questions, but also allow for the interviewer to steer the conversation in other directions based on the responses given by the interviewee.

In semi-structured interviews, the interviewer prepares a guide that outlines the focal points of the discussion. However, the interview is flexible, allowing for more in-depth probing if the interviewer deems it necessary (Qu, & Dumay, 2011). This style of interviewing strikes a balance between structured ones which might limit the discussion, and unstructured ones, which could lack focus.

Example of Semi-Structured Interview Research

Title: “ Factors influencing adherence to cancer treatment in older adults with cancer: a systematic review “

Citation: Puts, M., et al. (2014). Factors influencing adherence to cancer treatment in older adults with cancer: a systematic review. Annals of oncology, 25 (3), 564-577.

Overview: Puts et al. (2014) executed an extensive systematic review in which they conducted semi-structured interviews with older adults suffering from cancer to examine the factors influencing their adherence to cancer treatment. The findings suggested that various factors, including side effects, faith in healthcare professionals, and social support have substantial impacts on treatment adherence. This research demonstrates how semi-structured interviews can provide rich and profound insights into the subjective experiences of patients.

4. Focus Groups

Definition: Focus groups are a qualitative research method that involves organized discussion with a selected group of individuals to gain their perspectives on a specific concept, product, or phenomenon. Typically, these discussions are guided by a moderator.

During a focus group session, the moderator has a list of questions or topics to discuss, and participants are encouraged to interact with each other (Morgan, 2010). This interactivity can stimulate more information and provide a broader understanding of the issue under scrutiny. The open format allows participants to ask questions and respond freely, offering invaluable insights into attitudes, experiences, and group norms.

Example of Focus Group Research

Title: “ Perspectives of Older Adults on Aging Well: A Focus Group Study “

Citation: Halaweh, H., Dahlin-Ivanoff, S., Svantesson, U., & Willén, C. (2018). Perspectives of older adults on aging well: a focus group study. Journal of aging research .

Overview: This study aimed to explore what older adults (aged 60 years and older) perceived to be ‘aging well’. The researchers identified three major themes from their focus group interviews: a sense of well-being, having good physical health, and preserving good mental health. The findings highlight the importance of factors such as positive emotions, social engagement, physical activity, healthy eating habits, and maintaining independence in promoting aging well among older adults.

5. Phenomenology

Definition: Phenomenology, a qualitative research method, involves the examination of lived experiences to gain an in-depth understanding of the essence or underlying meanings of a phenomenon.

The focus of phenomenology lies in meticulously describing participants’ conscious experiences related to the chosen phenomenon (Padilla-Díaz, 2015).

In a phenomenological study, the researcher collects detailed, first-hand perspectives of the participants, typically via in-depth interviews, and then uses various strategies to interpret and structure these experiences, ultimately revealing essential themes (Creswell, 2013). This approach focuses on the perspective of individuals experiencing the phenomenon, seeking to explore, clarify, and understand the meanings they attach to those experiences.

Example of Phenomenology Research

Title: “ A phenomenological approach to experiences with technology: current state, promise, and future directions for research ”

Citation: Cilesiz, S. (2011). A phenomenological approach to experiences with technology: Current state, promise, and future directions for research. Educational Technology Research and Development, 59 , 487-510.

Overview: A phenomenological approach to experiences with technology by Sebnem Cilesiz represents a good starting point for formulating a phenomenological study. With its focus on the ‘essence of experience’, this piece presents methodological, reliability, validity, and data analysis techniques that phenomenologists use to explain how people experience technology in their everyday lives.

6. Grounded Theory

Definition: Grounded theory is a systematic methodology in qualitative research that typically applies inductive reasoning . The primary aim is to develop a theoretical explanation or framework for a process, action, or interaction grounded in, and arising from, empirical data (Birks & Mills, 2015).

In grounded theory, data collection and analysis work together in a recursive process. The researcher collects data, analyses it, and then collects more data based on the evolving understanding of the research context. This ongoing process continues until a comprehensive theory that represents the data and the associated phenomenon emerges – a point known as theoretical saturation (Charmaz, 2014).

Example of Grounded Theory Research

Title: “ Student Engagement in High School Classrooms from the Perspective of Flow Theory “

Citation: Shernoff, D. J., Csikszentmihalyi, M., Shneider, B., & Shernoff, E. S. (2003). Student engagement in high school classrooms from the perspective of flow theory. School Psychology Quarterly, 18 (2), 158–176.

Overview: Shernoff and colleagues (2003) used grounded theory to explore student engagement in high school classrooms. The researchers collected data through student self-reports, interviews, and observations. Key findings revealed that academic challenge, student autonomy, and teacher support emerged as the most significant factors influencing students’ engagement, demonstrating how grounded theory can illuminate complex dynamics within real-world contexts.

7. Narrative Research

Definition: Narrative research is a qualitative research method dedicated to storytelling and understanding how individuals experience the world. It focuses on studying an individual’s life and experiences as narrated by that individual (Polkinghorne, 2013).

In narrative research, the researcher collects data through methods such as interviews, observations , and document analysis. The emphasis is on the stories told by participants – narratives that reflect their experiences, thoughts, and feelings.

These stories are then interpreted by the researcher, who attempts to understand the meaning the participant attributes to these experiences (Josselson, 2011).

Example of Narrative Research

Title: “Narrative Structures and the Language of the Self”

Citation: McAdams, D. P., Josselson, R., & Lieblich, A. (2006). Identity and story: Creating self in narrative . American Psychological Association.

Overview: In this innovative study, McAdams et al. (2006) employed narrative research to explore how individuals construct their identities through the stories they tell about themselves. By examining personal narratives, the researchers discerned patterns associated with characters, motivations, conflicts, and resolutions, contributing valuable insights about the relationship between narrative and individual identity.

8. Case Study Research

Definition: Case study research is a qualitative research method that involves an in-depth investigation of a single instance or event: a case. These ‘cases’ can range from individuals, groups, or entities to specific projects, programs, or strategies (Creswell, 2013).

The case study method typically uses multiple sources of information for comprehensive contextual analysis. It aims to explore and understand the complexity and uniqueness of a particular case in a real-world context (Merriam & Tisdell, 2015). This investigation could result in a detailed description of the case, a process for its development, or an exploration of a related issue or problem.

Example of Case Study Research

Title: “ Teacher’s Role in Fostering Preschoolers’ Computational Thinking: An Exploratory Case Study “

Citation: Wang, X. C., Choi, Y., Benson, K., Eggleston, C., & Weber, D. (2021). Teacher’s role in fostering preschoolers’ computational thinking: An exploratory case study. Early Education and Development , 32 (1), 26-48.

Overview: This study investigates the role of teachers in promoting computational thinking skills in preschoolers. The study utilized a qualitative case study methodology to examine the computational thinking scaffolding strategies employed by a teacher interacting with three preschoolers in a small group setting. The findings highlight the importance of teachers’ guidance in fostering computational thinking practices such as problem reformulation/decomposition, systematic testing, and debugging.

Read about some Famous Case Studies in Psychology Here

9. Participant Observation

Definition: Participant observation has the researcher immerse themselves in a group or community setting to observe the behavior of its members. It is similar to ethnography, but generally, the researcher isn’t embedded for a long period of time.

The researcher, being a participant, engages in daily activities, interactions, and events as a way of conducting a detailed study of a particular social phenomenon (Kawulich, 2005).

The method involves long-term engagement in the field, maintaining detailed records of observed events, informal interviews, direct participation, and reflexivity. This approach allows for a holistic view of the participants’ lived experiences, behaviours, and interactions within their everyday environment (Dewalt, 2011).

Example of Participant Observation Research

Title: Conflict in the boardroom: a participant observation study of supervisory board dynamics

Citation: Heemskerk, E. M., Heemskerk, K., & Wats, M. M. (2017). Conflict in the boardroom: a participant observation study of supervisory board dynamics. Journal of Management & Governance , 21 , 233-263.

Overview: This study examined how conflicts within corporate boards affect their performance. The researchers used a participant observation method, where they actively engaged with 11 supervisory boards and observed their dynamics. They found that having a shared understanding of the board’s role called a common framework, improved performance by reducing relationship conflicts, encouraging task conflicts, and minimizing conflicts between the board and CEO.

10. Non-Participant Observation

Definition: Non-participant observation is a qualitative research method in which the researcher observes the phenomena of interest without actively participating in the situation, setting, or community being studied.

This method allows the researcher to maintain a position of distance, as they are solely an observer and not a participant in the activities being observed (Kawulich, 2005).

During non-participant observation, the researcher typically records field notes on the actions, interactions, and behaviors observed , focusing on specific aspects of the situation deemed relevant to the research question.

This could include verbal and nonverbal communication , activities, interactions, and environmental contexts (Angrosino, 2007). They could also use video or audio recordings or other methods to collect data.

Example of Non-Participant Observation Research

Title: Mental Health Nurses’ attitudes towards mental illness and recovery-oriented practice in acute inpatient psychiatric units: A non-participant observation study

Citation: Sreeram, A., Cross, W. M., & Townsin, L. (2023). Mental Health Nurses’ attitudes towards mental illness and recovery‐oriented practice in acute inpatient psychiatric units: A non‐participant observation study. International Journal of Mental Health Nursing .

Overview: This study investigated the attitudes of mental health nurses towards mental illness and recovery-oriented practice in acute inpatient psychiatric units. The researchers used a non-participant observation method, meaning they observed the nurses without directly participating in their activities. The findings shed light on the nurses’ perspectives and behaviors, providing valuable insights into their attitudes toward mental health and recovery-focused care in these settings.

11. Content Analysis

Definition: Content Analysis involves scrutinizing textual, visual, or spoken content to categorize and quantify information. The goal is to identify patterns, themes, biases, or other characteristics (Hsieh & Shannon, 2005).

Content Analysis is widely used in various disciplines for a multitude of purposes. Researchers typically use this method to distill large amounts of unstructured data, like interview transcripts, newspaper articles, or social media posts, into manageable and meaningful chunks.

When wielded appropriately, Content Analysis can illuminate the density and frequency of certain themes within a dataset, provide insights into how specific terms or concepts are applied contextually, and offer inferences about the meanings of their content and use (Duriau, Reger, & Pfarrer, 2007).

Example of Content Analysis

Title: Framing European politics: A content analysis of press and television news .

Citation: Semetko, H. A., & Valkenburg, P. M. (2000). Framing European politics: A content analysis of press and television news. Journal of Communication, 50 (2), 93-109.

Overview: This study analyzed press and television news articles about European politics using a method called content analysis. The researchers examined the prevalence of different “frames” in the news, which are ways of presenting information to shape audience perceptions. They found that the most common frames were attribution of responsibility, conflict, economic consequences, human interest, and morality.

Read my Full Guide on Content Analysis Here

12. Discourse Analysis

Definition: Discourse Analysis, a qualitative research method, interprets the meanings, functions, and coherence of certain languages in context.

Discourse analysis is typically understood through social constructionism, critical theory , and poststructuralism and used for understanding how language constructs social concepts (Cheek, 2004).

Discourse Analysis offers great breadth, providing tools to examine spoken or written language, often beyond the level of the sentence. It enables researchers to scrutinize how text and talk articulate social and political interactions and hierarchies.

Insight can be garnered from different conversations, institutional text, and media coverage to understand how topics are addressed or framed within a specific social context (Jorgensen & Phillips, 2002).

Example of Discourse Analysis

Title: The construction of teacher identities in educational policy documents: A critical discourse analysis

Citation: Thomas, S. (2005). The construction of teacher identities in educational policy documents: A critical discourse analysis. Critical Studies in Education, 46 (2), 25-44.

Overview: The author examines how an education policy in one state of Australia positions teacher professionalism and teacher identities. While there are competing discourses about professional identity, the policy framework privileges a  narrative that frames the ‘good’ teacher as one that accepts ever-tightening control and regulation over their professional practice.

Read my Full Guide on Discourse Analysis Here

13. Action Research

Definition: Action Research is a qualitative research technique that is employed to bring about change while simultaneously studying the process and results of that change.

This method involves a cyclical process of fact-finding, action, evaluation, and reflection (Greenwood & Levin, 2016).

Typically, Action Research is used in the fields of education, social sciences , and community development. The process isn’t just about resolving an issue but also developing knowledge that can be used in the future to address similar or related problems.

The researcher plays an active role in the research process, which is normally broken down into four steps: 

  • developing a plan to improve what is currently being done
  • implementing the plan
  • observing the effects of the plan, and
  • reflecting upon these effects (Smith, 2010).

Example of Action Research

Title: Using Digital Sandbox Gaming to Improve Creativity Within Boys’ Writing

Citation: Ellison, M., & Drew, C. (2020). Using digital sandbox gaming to improve creativity within boys’ writing. Journal of Research in Childhood Education , 34 (2), 277-287.

Overview: This was a research study one of my research students completed in his own classroom under my supervision. He implemented a digital game-based approach to literacy teaching with boys and interviewed his students to see if the use of games as stimuli for storytelling helped draw them into the learning experience.

Read my Full Guide on Action Research Here

14. Semiotic Analysis

Definition: Semiotic Analysis is a qualitative method of research that interprets signs and symbols in communication to understand sociocultural phenomena. It stems from semiotics, the study of signs and symbols and their use or interpretation (Chandler, 2017).

In a Semiotic Analysis, signs (anything that represents something else) are interpreted based on their significance and the role they play in representing ideas.

This type of research often involves the examination of images, sounds, and word choice to uncover the embedded sociocultural meanings. For example, an advertisement for a car might be studied to learn more about societal views on masculinity or success (Berger, 2010).

Example of Semiotic Research

Title: Shielding the learned body: a semiotic analysis of school badges in New South Wales, Australia

Citation: Symes, C. (2023). Shielding the learned body: a semiotic analysis of school badges in New South Wales, Australia. Semiotica , 2023 (250), 167-190.

Overview: This study examines school badges in New South Wales, Australia, and explores their significance through a semiotic analysis. The badges, which are part of the school’s visual identity, are seen as symbolic representations that convey meanings. The analysis reveals that these badges often draw on heraldic models, incorporating elements like colors, names, motifs, and mottoes that reflect local culture and history, thus connecting students to their national identity. Additionally, the study highlights how some schools have shifted from traditional badges to modern logos and slogans, reflecting a more business-oriented approach.

15. Qualitative Longitudinal Studies

Definition: Qualitative Longitudinal Studies are a research method that involves repeated observation of the same items over an extended period of time.

Unlike a snapshot perspective, this method aims to piece together individual histories and examine the influences and impacts of change (Neale, 2019).

Qualitative Longitudinal Studies provide an in-depth understanding of change as it happens, including changes in people’s lives, their perceptions, and their behaviors.

For instance, this method could be used to follow a group of students through their schooling years to understand the evolution of their learning behaviors and attitudes towards education (Saldaña, 2003).

Example of Qualitative Longitudinal Research

Title: Patient and caregiver perspectives on managing pain in advanced cancer: a qualitative longitudinal study

Citation: Hackett, J., Godfrey, M., & Bennett, M. I. (2016). Patient and caregiver perspectives on managing pain in advanced cancer: a qualitative longitudinal study.  Palliative medicine ,  30 (8), 711-719.

Overview: This article examines how patients and their caregivers manage pain in advanced cancer through a qualitative longitudinal study. The researchers interviewed patients and caregivers at two different time points and collected audio diaries to gain insights into their experiences, making this study longitudinal.

Read my Full Guide on Longitudinal Research Here

16. Open-Ended Surveys

Definition: Open-Ended Surveys are a type of qualitative research method where respondents provide answers in their own words. Unlike closed-ended surveys, which limit responses to predefined options, open-ended surveys allow for expansive and unsolicited explanations (Fink, 2013).

Open-ended surveys are commonly used in a range of fields, from market research to social studies. As they don’t force respondents into predefined response categories, these surveys help to draw out rich, detailed data that might uncover new variables or ideas.

For example, an open-ended survey might be used to understand customer opinions about a new product or service (Lavrakas, 2008).

Contrast this to a quantitative closed-ended survey, like a Likert scale, which could theoretically help us to come up with generalizable data but is restricted by the questions on the questionnaire, meaning new and surprising data and insights can’t emerge from the survey results in the same way.

Example of Open-Ended Survey Research

Title: Advantages and disadvantages of technology in relationships: Findings from an open-ended survey

Citation: Hertlein, K. M., & Ancheta, K. (2014). Advantages and disadvantages of technology in relationships: Findings from an open-ended survey.  The Qualitative Report ,  19 (11), 1-11.

Overview: This article examines the advantages and disadvantages of technology in couple relationships through an open-ended survey method. Researchers analyzed responses from 410 undergraduate students to understand how technology affects relationships. They found that technology can contribute to relationship development, management, and enhancement, but it can also create challenges such as distancing, lack of clarity, and impaired trust.

17. Naturalistic Observation

Definition: Naturalistic Observation is a type of qualitative research method that involves observing individuals in their natural environments without interference or manipulation by the researcher.

Naturalistic observation is often used when conducting research on behaviors that cannot be controlled or manipulated in a laboratory setting (Kawulich, 2005).

It is frequently used in the fields of psychology, sociology, and anthropology. For instance, to understand the social dynamics in a schoolyard, a researcher could spend time observing the children interact during their recess, noting their behaviors, interactions, and conflicts without imposing their presence on the children’s activities (Forsyth, 2010).

Example of Naturalistic Observation Research

Title: Dispositional mindfulness in daily life: A naturalistic observation study

Citation: Kaplan, D. M., Raison, C. L., Milek, A., Tackman, A. M., Pace, T. W., & Mehl, M. R. (2018). Dispositional mindfulness in daily life: A naturalistic observation study. PloS one , 13 (11), e0206029.

Overview: In this study, researchers conducted two studies: one exploring assumptions about mindfulness and behavior, and the other using naturalistic observation to examine actual behavioral manifestations of mindfulness. They found that trait mindfulness is associated with a heightened perceptual focus in conversations, suggesting that being mindful is expressed primarily through sharpened attention rather than observable behavioral or social differences.

Read my Full Guide on Naturalistic Observation Here

18. Photo-Elicitation

Definition: Photo-elicitation utilizes photographs as a means to trigger discussions and evoke responses during interviews. This strategy aids in bringing out topics of discussion that may not emerge through verbal prompting alone (Harper, 2002).

Traditionally, Photo-Elicitation has been useful in various fields such as education, psychology, and sociology. The method involves the researcher or participants taking photographs, which are then used as prompts for discussion.

For instance, a researcher studying urban environmental issues might invite participants to photograph areas in their neighborhood that they perceive as environmentally detrimental, and then discuss each photo in depth (Clark-Ibáñez, 2004).

Example of Photo-Elicitation Research

Title: Early adolescent food routines: A photo-elicitation study

Citation: Green, E. M., Spivak, C., & Dollahite, J. S. (2021). Early adolescent food routines: A photo-elicitation study. Appetite, 158 .

Overview: This study focused on early adolescents (ages 10-14) and their food routines. Researchers conducted in-depth interviews using a photo-elicitation approach, where participants took photos related to their food choices and experiences. Through analysis, the study identified various routines and three main themes: family, settings, and meals/foods consumed, revealing how early adolescents view and are influenced by their eating routines.

Features of Qualitative Research

Qualitative research is a research method focused on understanding the meaning individuals or groups attribute to a social or human problem (Creswell, 2013).

Some key features of this method include:

  • Naturalistic Inquiry: Qualitative research happens in the natural setting of the phenomena, aiming to understand “real world” situations (Patton, 2015). This immersion in the field or subject allows the researcher to gather a deep understanding of the subject matter.
  • Emphasis on Process: It aims to understand how events unfold over time rather than focusing solely on outcomes (Merriam & Tisdell, 2015). The process-oriented nature of qualitative research allows researchers to investigate sequences, timing, and changes.
  • Interpretive: It involves interpreting and making sense of phenomena in terms of the meanings people assign to them (Denzin & Lincoln, 2011). This interpretive element allows for rich, nuanced insights into human behavior and experiences.
  • Holistic Perspective: Qualitative research seeks to understand the whole phenomenon rather than focusing on individual components (Creswell, 2013). It emphasizes the complex interplay of factors, providing a richer, more nuanced view of the research subject.
  • Prioritizes Depth over Breadth: Qualitative research favors depth of understanding over breadth, typically involving a smaller but more focused sample size (Hennink, Hutter, & Bailey, 2020). This enables detailed exploration of the phenomena of interest, often leading to rich and complex data.

Qualitative vs Quantitative Research

Qualitative research centers on exploring and understanding the meaning individuals or groups attribute to a social or human problem (Creswell, 2013).

It involves an in-depth approach to the subject matter, aiming to capture the richness and complexity of human experience.

Examples include conducting interviews, observing behaviors, or analyzing text and images.

There are strengths inherent in this approach. In its focus on understanding subjective experiences and interpretations, qualitative research can yield rich and detailed data that quantitative research may overlook (Denzin & Lincoln, 2011).

Additionally, qualitative research is adaptive, allowing the researcher to respond to new directions and insights as they emerge during the research process.

However, there are also limitations. Because of the interpretive nature of this research, findings may not be generalizable to a broader population (Marshall & Rossman, 2014). Well-designed quantitative research, on the other hand, can be generalizable.

Moreover, the reliability and validity of qualitative data can be challenging to establish due to its subjective nature, unlike quantitative research, which is ideally more objective.

Compare Qualitative and Quantitative Research Methodologies in This Guide Here

In conclusion, qualitative research methods provide distinctive ways to explore social phenomena and understand nuances that quantitative approaches might overlook. Each method, from Ethnography to Photo-Elicitation, presents its strengths and weaknesses but they all offer valuable means of investigating complex, real-world situations. The goal for the researcher is not to find a definitive tool, but to employ the method best suited for their research questions and the context at hand (Almalki, 2016). Above all, these methods underscore the richness of human experience and deepen our understanding of the world around us.

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  • What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on 4 April 2022 by Pritha Bhandari . Revised on 30 January 2023.

Qualitative research involves collecting and analysing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analysing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, and history.

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Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography, action research, phenomenological research, and narrative research. They share some similarities, but emphasise different aims and perspectives.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

  • Observations: recording what you have seen, heard, or encountered in detailed field notes.
  • Interviews:  personally asking people questions in one-on-one conversations.
  • Focus groups: asking questions and generating discussion among a group of people.
  • Surveys : distributing questionnaires with open-ended questions.
  • Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
  • You take field notes with observations and reflect on your own experiences of the company culture.
  • You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
  • You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves ‘instruments’ in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analysing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

  • Prepare and organise your data. This may mean transcribing interviews or typing up fieldnotes.
  • Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
  • Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorise your data.
  • Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
  • Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analysing qualitative data. Although these methods share similar processes, they emphasise different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

  • Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

  • Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

  • Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

  • Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analysing and interpreting their data. Qualitative research suffers from:

  • Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

  • Subjectivity

Due to the researcher’s primary role in analysing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

  • Limited generalisability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalisable conclusions because the data may be biased and unrepresentative of the wider population .

  • Labour-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

There are five common approaches to qualitative research :

  • Grounded theory involves collecting data in order to develop new theories.
  • Ethnography involves immersing yourself in a group or organisation to understand its culture.
  • Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
  • Phenomenological research involves investigating phenomena through people’s lived experiences.
  • Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

  • Prepare and organise your data.
  • Review and explore your data.
  • Develop a data coding system.
  • Assign codes to the data.
  • Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Types of Qualitative Research

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

  • Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
  • Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
  • Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
  • Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
  • Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
  • Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
  • Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

  • Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
  • Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
  • Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
  • Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
  • Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
  • Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
  • Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

  • Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
  • Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
  • Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
  • Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
  • Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
  • Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
  • Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

  • Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
  • Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
  • Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
  • Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

  • Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
  • Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
  • Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
  • Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
  • Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

  • Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
  • Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
  • Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
  • Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
  • Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
  • Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

  • Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
  • Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
  • Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
  • Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
  • Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
  • Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

  • Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
  • Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
  • Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
  • Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
  • Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
  • Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

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Qualitative Research : Definition

Qualitative research is the naturalistic study of social meanings and processes, using interviews, observations, and the analysis of texts and images.  In contrast to quantitative researchers, whose statistical methods enable broad generalizations about populations (for example, comparisons of the percentages of U.S. demographic groups who vote in particular ways), qualitative researchers use in-depth studies of the social world to analyze how and why groups think and act in particular ways (for instance, case studies of the experiences that shape political views).   

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Qualitative research: methods and examples

Last updated

13 April 2023

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Qualitative research involves gathering and evaluating non-numerical information to comprehend concepts, perspectives, and experiences. It’s also helpful for obtaining in-depth insights into a certain subject or generating new research ideas. 

As a result, qualitative research is practical if you want to try anything new or produce new ideas.

There are various ways you can conduct qualitative research. In this article, you'll learn more about qualitative research methodologies, including when you should use them.

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  • What is qualitative research?

Qualitative research is a broad term describing various research types that rely on asking open-ended questions. Qualitative research investigates “how” or “why” certain phenomena occur. It is about discovering the inherent nature of something.

The primary objective of qualitative research is to understand an individual's ideas, points of view, and feelings. In this way, collecting in-depth knowledge of a specific topic is possible. Knowing your audience's feelings about a particular subject is important for making reasonable research conclusions.

Unlike quantitative research , this approach does not involve collecting numerical, objective data for statistical analysis. Qualitative research is used extensively in education, sociology, health science, history, and anthropology.

  • Types of qualitative research methodology

Typically, qualitative research aims at uncovering the attitudes and behavior of the target audience concerning a specific topic. For example,  “How would you describe your experience as a new Dovetail user?”

Some of the methods for conducting qualitative analysis include:

Focus groups

Hosting a focus group is a popular qualitative research method. It involves obtaining qualitative data from a limited sample of participants. In a moderated version of a focus group, the moderator asks participants a series of predefined questions. They aim to interact and build a group discussion that reveals their preferences, candid thoughts, and experiences.

Unmoderated, online focus groups are increasingly popular because they eliminate the need to interact with people face to face.

Focus groups can be more cost-effective than 1:1 interviews or studying a group in a natural setting and reporting one’s observations.

Focus groups make it possible to gather multiple points of view quickly and efficiently, making them an excellent choice for testing new concepts or conducting market research on a new product.

However, there are some potential drawbacks to this method. It may be unsuitable for sensitive or controversial topics. Participants might be reluctant to disclose their true feelings or respond falsely to conform to what they believe is the socially acceptable answer (known as response bias).

Case study research

A case study is an in-depth evaluation of a specific person, incident, organization, or society. This type of qualitative research has evolved into a broadly applied research method in education, law, business, and the social sciences.

Even though case study research may appear challenging to implement, it is one of the most direct research methods. It requires detailed analysis, broad-ranging data collection methodologies, and a degree of existing knowledge about the subject area under investigation.

Historical model

The historical approach is a distinct research method that deeply examines previous events to better understand the present and forecast future occurrences of the same phenomena. Its primary goal is to evaluate the impacts of history on the present and hence discover comparable patterns in the present to predict future outcomes.

Oral history

This qualitative data collection method involves gathering verbal testimonials from individuals about their personal experiences. It is widely used in historical disciplines to offer counterpoints to established historical facts and narratives. The most common methods of gathering oral history are audio recordings, analysis of auto-biographical text, videos, and interviews.

Qualitative observation

One of the most fundamental, oldest research methods, qualitative observation , is the process through which a researcher collects data using their senses of sight, smell, hearing, etc. It is used to observe the properties of the subject being studied. For example, “What does it look like?” As research methods go, it is subjective and depends on researchers’ first-hand experiences to obtain information, so it is prone to bias. However, it is an excellent way to start a broad line of inquiry like, “What is going on here?”

Record keeping and review

Record keeping uses existing documents and relevant data sources that can be employed for future studies. It is equivalent to visiting the library and going through publications or any other reference material to gather important facts that will likely be used in the research.

Grounded theory approach

The grounded theory approach is a commonly used research method employed across a variety of different studies. It offers a unique way to gather, interpret, and analyze. With this approach, data is gathered and analyzed simultaneously.  Existing analysis frames and codes are disregarded, and data is analyzed inductively, with new codes and frames generated from the research.

Ethnographic research

Ethnography  is a descriptive form of a qualitative study of people and their cultures. Its primary goal is to study people's behavior in their natural environment. This method necessitates that the researcher adapts to their target audience's setting. 

Thereby, you will be able to understand their motivation, lifestyle, ambitions, traditions, and culture in situ. But, the researcher must be prepared to deal with geographical constraints while collecting data i.e., audiences can’t be studied in a laboratory or research facility.

This study can last from a couple of days to several years. Thus, it is time-consuming and complicated, requiring you to have both the time to gather the relevant data as well as the expertise in analyzing, observing, and interpreting data to draw meaningful conclusions.

Narrative framework

A narrative framework is a qualitative research approach that relies on people's written text or visual images. It entails people analyzing these events or narratives to determine certain topics or issues. With this approach, you can understand how people represent themselves and their experiences to a larger audience.

Phenomenological approach

The phenomenological study seeks to investigate the experiences of a particular phenomenon within a group of individuals or communities. It analyzes a certain event through interviews with persons who have witnessed it to determine the connections between their views. Even though this method relies heavily on interviews, other data sources (recorded notes), and observations could be employed to enhance the findings.

  • Qualitative research methods (tools)

Some of the instruments involved in qualitative research include:

Document research: Also known as document analysis because it involves evaluating written documents. These can include personal and non-personal materials like archives, policy publications, yearly reports, diaries, or letters.

Focus groups:  This is where a researcher poses questions and generates conversation among a group of people. The major goal of focus groups is to examine participants' experiences and knowledge, including research into how and why individuals act in various ways.

Secondary study: Involves acquiring existing information from texts, images, audio, or video recordings.

Observations:   This requires thorough field notes on everything you see, hear, or experience. Compared to reported conduct or opinion, this study method can assist you in getting insights into a specific situation and observable behaviors.

Structured interviews :  In this approach, you will directly engage people one-on-one. Interviews are ideal for learning about a person's subjective beliefs, motivations, and encounters.

Surveys:  This is when you distribute questionnaires containing open-ended questions

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  • What are common examples of qualitative research?

Everyday examples of qualitative research include:

Conducting a demographic analysis of a business

For instance, suppose you own a business such as a grocery store (or any store) and believe it caters to a broad customer base, but after conducting a demographic analysis, you discover that most of your customers are men.

You could do 1:1 interviews with female customers to learn why they don't shop at your store.

In this case, interviewing potential female customers should clarify why they don't find your shop appealing. It could be because of the products you sell or a need for greater brand awareness, among other possible reasons.

Launching or testing a new product

Suppose you are the product manager at a SaaS company looking to introduce a new product. Focus groups can be an excellent way to determine whether your product is marketable.

In this instance, you could hold a focus group with a sample group drawn from your intended audience. The group will explore the product based on its new features while you ensure adequate data on how users react to the new features. The data you collect will be key to making sales and marketing decisions.

Conducting studies to explain buyers' behaviors

You can also use qualitative research to understand existing buyer behavior better. Marketers analyze historical information linked to their businesses and industries to see when purchasers buy more.

Qualitative research can help you determine when to target new clients and peak seasons to boost sales by investigating the reason behind these behaviors.

  • Qualitative research: data collection

Data collection is gathering information on predetermined variables to gain appropriate answers, test hypotheses, and analyze results. Researchers will collect non-numerical data for qualitative data collection to obtain detailed explanations and draw conclusions.

To get valid findings and achieve a conclusion in qualitative research, researchers must collect comprehensive and multifaceted data.

Qualitative data is usually gathered through interviews or focus groups with videotapes or handwritten notes. If there are recordings, they are transcribed before the data analysis process. Researchers keep separate folders for the recordings acquired from each focus group when collecting qualitative research data to categorize the data.

  • Qualitative research: data analysis

Qualitative data analysis is organizing, examining, and interpreting qualitative data. Its main objective is identifying trends and patterns, responding to research questions, and recommending actions based on the findings. Textual analysis is a popular method for analyzing qualitative data.

Textual analysis differs from other qualitative research approaches in that researchers consider the social circumstances of study participants to decode their words, behaviors, and broader meaning. 

qualitative research studies examples

Learn more about qualitative research data analysis software

  • When to use qualitative research

Qualitative research is helpful in various situations, particularly when a researcher wants to capture accurate, in-depth insights. 

Here are some instances when qualitative research can be valuable:

Examining your product or service to improve your marketing approach

When researching market segments, demographics, and customer service teams

Identifying client language when you want to design a quantitative survey

When attempting to comprehend your or someone else's strengths and weaknesses

Assessing feelings and beliefs about societal and public policy matters

Collecting information about a business or product's perception

Analyzing your target audience's reactions to marketing efforts

When launching a new product or coming up with a new idea

When seeking to evaluate buyers' purchasing patterns

  • Qualitative research methods vs. quantitative research methods

Qualitative research examines people's ideas and what influences their perception, whereas quantitative research draws conclusions based on numbers and measurements.

Qualitative research is descriptive, and its primary goal is to comprehensively understand people's attitudes, behaviors, and ideas.

In contrast, quantitative research is more restrictive because it relies on numerical data and analyzes statistical data to make decisions. This research method assists researchers in gaining an initial grasp of the subject, which deals with numbers. For instance, the number of customers likely to purchase your products or use your services.

What is the most important feature of qualitative research?

A distinguishing feature of qualitative research is that it’s conducted in a real-world setting instead of a simulated environment. The researcher is examining actual phenomena instead of experimenting with different variables to see what outcomes (data) might result.

Can I use qualitative and quantitative approaches together in a study?

Yes, combining qualitative and quantitative research approaches happens all the time and is known as mixed methods research. For example, you could study individuals’ perceived risk in a certain scenario, such as how people rate the safety or riskiness of a given neighborhood. Simultaneously, you could analyze historical data objectively, indicating how safe or dangerous that area has been in the last year. To get the most out of mixed-method research, it’s important to understand the pros and cons of each methodology, so you can create a thoughtfully designed study that will yield compelling results.

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Home Market Research

Qualitative Research Methods: Types, Analysis + Examples

Qualitative Research

Qualitative research is based on the disciplines of social sciences like psychology, sociology, and anthropology. Therefore, the qualitative research methods allow for in-depth and further probing and questioning of respondents based on their responses. The interviewer/researcher also tries to understand their motivation and feelings. Understanding how your audience makes decisions can help derive conclusions in market research.

What is qualitative research?

Qualitative research is defined as a market research method that focuses on obtaining data through open-ended and conversational communication .

This method is about “what” people think and “why” they think so. For example, consider a convenience store looking to improve its patronage. A systematic observation concludes that more men are visiting this store. One good method to determine why women were not visiting the store is conducting an in-depth interview method with potential customers.

For example, after successfully interviewing female customers and visiting nearby stores and malls, the researchers selected participants through random sampling . As a result, it was discovered that the store didn’t have enough items for women.

So fewer women were visiting the store, which was understood only by personally interacting with them and understanding why they didn’t visit the store because there were more male products than female ones.

Gather research insights

Types of qualitative research methods with examples

Qualitative research methods are designed in a manner that helps reveal the behavior and perception of a target audience with reference to a particular topic. There are different types of qualitative research methods, such as in-depth interviews, focus groups, ethnographic research, content analysis, and case study research that are usually used.

The results of qualitative methods are more descriptive, and the inferences can be drawn quite easily from the obtained data .

Qualitative research methods originated in the social and behavioral research sciences. Today, our world is more complicated, and it is difficult to understand what people think and perceive. Online research methods make it easier to understand that as it is a more communicative and descriptive analysis .

The following are the qualitative research methods that are frequently used. Also, read about qualitative research examples :

Types of Qualitative Research

1. One-on-one interview

Conducting in-depth interviews is one of the most common qualitative research methods. It is a personal interview that is carried out with one respondent at a time. This is purely a conversational method and invites opportunities to get details in depth from the respondent.

One of the advantages of this method is that it provides a great opportunity to gather precise data about what people believe and their motivations . If the researcher is well experienced, asking the right questions can help him/her collect meaningful data. If they should need more information, the researchers should ask such follow-up questions that will help them collect more information.

These interviews can be performed face-to-face or on the phone and usually can last between half an hour to two hours or even more. When the in-depth interview is conducted face to face, it gives a better opportunity to read the respondents’ body language and match the responses.

2. Focus groups

A focus group is also a commonly used qualitative research method used in data collection. A focus group usually includes a limited number of respondents (6-10) from within your target market.

The main aim of the focus group is to find answers to the “why, ” “what,” and “how” questions. One advantage of focus groups is you don’t necessarily need to interact with the group in person. Nowadays, focus groups can be sent an online survey on various devices, and responses can be collected at the click of a button.

Focus groups are an expensive method as compared to other online qualitative research methods. Typically, they are used to explain complex processes. This method is very useful for market research on new products and testing new concepts.

3. Ethnographic research

Ethnographic research is the most in-depth observational research method that studies people in their naturally occurring environment.

This method requires the researchers to adapt to the target audiences’ environments, which could be anywhere from an organization to a city or any remote location. Here, geographical constraints can be an issue while collecting data.

This research design aims to understand the cultures, challenges, motivations, and settings that occur. Instead of relying on interviews and discussions, you experience the natural settings firsthand.

This type of research method can last from a few days to a few years, as it involves in-depth observation and collecting data on those grounds. It’s a challenging and time-consuming method and solely depends on the researcher’s expertise to analyze, observe, and infer the data.

4. Case study research

T he case study method has evolved over the past few years and developed into a valuable quality research method. As the name suggests, it is used for explaining an organization or an entity.

This type of research method is used within a number of areas like education, social sciences, and similar. This method may look difficult to operate; however , it is one of the simplest ways of conducting research as it involves a deep dive and thorough understanding of the data collection methods and inferring the data.

5. Record keeping

This method makes use of the already existing reliable documents and similar sources of information as the data source. This data can be used in new research. This is similar to going to a library. There, one can go over books and other reference material to collect relevant data that can likely be used in the research.

6. Process of observation

Qualitative Observation is a process of research that uses subjective methodologies to gather systematic information or data. Since the focus on qualitative observation is the research process of using subjective methodologies to gather information or data. Qualitative observation is primarily used to equate quality differences.

Qualitative observation deals with the 5 major sensory organs and their functioning – sight, smell, touch, taste, and hearing. This doesn’t involve measurements or numbers but instead characteristics.

Explore Insightfully Contextual Inquiry in Qualitative Research

Qualitative research: data collection and analysis

A. qualitative data collection.

Qualitative data collection allows collecting data that is non-numeric and helps us to explore how decisions are made and provide us with detailed insight. For reaching such conclusions the data that is collected should be holistic, rich, and nuanced and findings to emerge through careful analysis.

  • Whatever method a researcher chooses for collecting qualitative data, one aspect is very clear the process will generate a large amount of data. In addition to the variety of methods available, there are also different methods of collecting and recording the data.

For example, if the qualitative data is collected through a focus group or one-to-one discussion, there will be handwritten notes or video recorded tapes. If there are recording they should be transcribed and before the process of data analysis can begin.

  • As a rough guide, it can take a seasoned researcher 8-10 hours to transcribe the recordings of an interview, which can generate roughly 20-30 pages of dialogues. Many researchers also like to maintain separate folders to maintain the recording collected from the different focus group. This helps them compartmentalize the data collected.
  • In case there are running notes taken, which are also known as field notes, they are helpful in maintaining comments, environmental contexts, environmental analysis , nonverbal cues etc. These filed notes are helpful and can be compared while transcribing audio recorded data. Such notes are usually informal but should be secured in a similar manner as the video recordings or the audio tapes.

B. Qualitative data analysis

Qualitative data analysis such as notes, videos, audio recordings images, and text documents. One of the most used methods for qualitative data analysis is text analysis.

Text analysis is a  data analysis method that is distinctly different from all other qualitative research methods, where researchers analyze the social life of the participants in the research study and decode the words, actions, etc. 

There are images also that are used in this research study and the researchers analyze the context in which the images are used and draw inferences from them. In the last decade, text analysis through what is shared on social media platforms has gained supreme popularity.

Characteristics of qualitative research methods

Characteristics of qualitative research methods - Infographics| QuestionPro

  • Qualitative research methods usually collect data at the sight, where the participants are experiencing issues or research problems . These are real-time data and rarely bring the participants out of the geographic locations to collect information.
  • Qualitative researchers typically gather multiple forms of data, such as interviews, observations, and documents, rather than rely on a single data source .
  • This type of research method works towards solving complex issues by breaking down into meaningful inferences, that is easily readable and understood by all.
  • Since it’s a more communicative method, people can build their trust on the researcher and the information thus obtained is raw and unadulterated.

Qualitative research method case study

Let’s take the example of a bookstore owner who is looking for ways to improve their sales and customer outreach. An online community of members who were loyal patrons of the bookstore were interviewed and related questions were asked and the questions were answered by them.

At the end of the interview, it was realized that most of the books in the stores were suitable for adults and there were not enough options for children or teenagers.

By conducting this qualitative research the bookstore owner realized what the shortcomings were and what were the feelings of the readers. Through this research now the bookstore owner can now keep books for different age categories and can improve his sales and customer outreach.

Such qualitative research method examples can serve as the basis to indulge in further quantitative research , which provides remedies.

When to use qualitative research

Researchers make use of qualitative research techniques when they need to capture accurate, in-depth insights. It is very useful to capture “factual data”. Here are some examples of when to use qualitative research.

  • Developing a new product or generating an idea.
  • Studying your product/brand or service to strengthen your marketing strategy.
  • To understand your strengths and weaknesses.
  • Understanding purchase behavior.
  • To study the reactions of your audience to marketing campaigns and other communications.
  • Exploring market demographics, segments, and customer care groups.
  • Gathering perception data of a brand, company, or product.

LEARN ABOUT: Steps in Qualitative Research

Qualitative research methods vs quantitative research methods

The basic differences between qualitative research methods and quantitative research methods are simple and straightforward. They differ in:

  • Their analytical objectives
  • Types of questions asked
  • Types of data collection instruments
  • Forms of data they produce
  • Degree of flexibility



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Chapter 1. Introduction

“Science is in danger, and for that reason it is becoming dangerous” -Pierre Bourdieu, Science of Science and Reflexivity

Why an Open Access Textbook on Qualitative Research Methods?

I have been teaching qualitative research methods to both undergraduates and graduate students for many years.  Although there are some excellent textbooks out there, they are often costly, and none of them, to my mind, properly introduces qualitative research methods to the beginning student (whether undergraduate or graduate student).  In contrast, this open-access textbook is designed as a (free) true introduction to the subject, with helpful, practical pointers on how to conduct research and how to access more advanced instruction.  

Textbooks are typically arranged in one of two ways: (1) by technique (each chapter covers one method used in qualitative research); or (2) by process (chapters advance from research design through publication).  But both of these approaches are necessary for the beginner student.  This textbook will have sections dedicated to the process as well as the techniques of qualitative research.  This is a true “comprehensive” book for the beginning student.  In addition to covering techniques of data collection and data analysis, it provides a road map of how to get started and how to keep going and where to go for advanced instruction.  It covers aspects of research design and research communication as well as methods employed.  Along the way, it includes examples from many different disciplines in the social sciences.

The primary goal has been to create a useful, accessible, engaging textbook for use across many disciplines.  And, let’s face it.  Textbooks can be boring.  I hope readers find this to be a little different.  I have tried to write in a practical and forthright manner, with many lively examples and references to good and intellectually creative qualitative research.  Woven throughout the text are short textual asides (in colored textboxes) by professional (academic) qualitative researchers in various disciplines.  These short accounts by practitioners should help inspire students.  So, let’s begin!

What is Research?

When we use the word research , what exactly do we mean by that?  This is one of those words that everyone thinks they understand, but it is worth beginning this textbook with a short explanation.  We use the term to refer to “empirical research,” which is actually a historically specific approach to understanding the world around us.  Think about how you know things about the world. [1] You might know your mother loves you because she’s told you she does.  Or because that is what “mothers” do by tradition.  Or you might know because you’ve looked for evidence that she does, like taking care of you when you are sick or reading to you in bed or working two jobs so you can have the things you need to do OK in life.  Maybe it seems churlish to look for evidence; you just take it “on faith” that you are loved.

Only one of the above comes close to what we mean by research.  Empirical research is research (investigation) based on evidence.  Conclusions can then be drawn from observable data.  This observable data can also be “tested” or checked.  If the data cannot be tested, that is a good indication that we are not doing research.  Note that we can never “prove” conclusively, through observable data, that our mothers love us.  We might have some “disconfirming evidence” (that time she didn’t show up to your graduation, for example) that could push you to question an original hypothesis , but no amount of “confirming evidence” will ever allow us to say with 100% certainty, “my mother loves me.”  Faith and tradition and authority work differently.  Our knowledge can be 100% certain using each of those alternative methods of knowledge, but our certainty in those cases will not be based on facts or evidence.

For many periods of history, those in power have been nervous about “science” because it uses evidence and facts as the primary source of understanding the world, and facts can be at odds with what power or authority or tradition want you to believe.  That is why I say that scientific empirical research is a historically specific approach to understand the world.  You are in college or university now partly to learn how to engage in this historically specific approach.

In the sixteenth and seventeenth centuries in Europe, there was a newfound respect for empirical research, some of which was seriously challenging to the established church.  Using observations and testing them, scientists found that the earth was not at the center of the universe, for example, but rather that it was but one planet of many which circled the sun. [2]   For the next two centuries, the science of astronomy, physics, biology, and chemistry emerged and became disciplines taught in universities.  All used the scientific method of observation and testing to advance knowledge.  Knowledge about people , however, and social institutions, however, was still left to faith, tradition, and authority.  Historians and philosophers and poets wrote about the human condition, but none of them used research to do so. [3]

It was not until the nineteenth century that “social science” really emerged, using the scientific method (empirical observation) to understand people and social institutions.  New fields of sociology, economics, political science, and anthropology emerged.  The first sociologists, people like Auguste Comte and Karl Marx, sought specifically to apply the scientific method of research to understand society, Engels famously claiming that Marx had done for the social world what Darwin did for the natural world, tracings its laws of development.  Today we tend to take for granted the naturalness of science here, but it is actually a pretty recent and radical development.

To return to the question, “does your mother love you?”  Well, this is actually not really how a researcher would frame the question, as it is too specific to your case.  It doesn’t tell us much about the world at large, even if it does tell us something about you and your relationship with your mother.  A social science researcher might ask, “do mothers love their children?”  Or maybe they would be more interested in how this loving relationship might change over time (e.g., “do mothers love their children more now than they did in the 18th century when so many children died before reaching adulthood?”) or perhaps they might be interested in measuring quality of love across cultures or time periods, or even establishing “what love looks like” using the mother/child relationship as a site of exploration.  All of these make good research questions because we can use observable data to answer them.

What is Qualitative Research?

“All we know is how to learn. How to study, how to listen, how to talk, how to tell.  If we don’t tell the world, we don’t know the world.  We’re lost in it, we die.” -Ursula LeGuin, The Telling

At its simplest, qualitative research is research about the social world that does not use numbers in its analyses.  All those who fear statistics can breathe a sigh of relief – there are no mathematical formulae or regression models in this book! But this definition is less about what qualitative research can be and more about what it is not.  To be honest, any simple statement will fail to capture the power and depth of qualitative research.  One way of contrasting qualitative research to quantitative research is to note that the focus of qualitative research is less about explaining and predicting relationships between variables and more about understanding the social world.  To use our mother love example, the question about “what love looks like” is a good question for the qualitative researcher while all questions measuring love or comparing incidences of love (both of which require measurement) are good questions for quantitative researchers. Patton writes,

Qualitative data describe.  They take us, as readers, into the time and place of the observation so that we know what it was like to have been there.  They capture and communicate someone else’s experience of the world in his or her own words.  Qualitative data tell a story. ( Patton 2002:47 )

Qualitative researchers are asking different questions about the world than their quantitative colleagues.  Even when researchers are employed in “mixed methods” research ( both quantitative and qualitative), they are using different methods to address different questions of the study.  I do a lot of research about first-generation and working-college college students.  Where a quantitative researcher might ask, how many first-generation college students graduate from college within four years? Or does first-generation college status predict high student debt loads?  A qualitative researcher might ask, how does the college experience differ for first-generation college students?  What is it like to carry a lot of debt, and how does this impact the ability to complete college on time?  Both sets of questions are important, but they can only be answered using specific tools tailored to those questions.  For the former, you need large numbers to make adequate comparisons.  For the latter, you need to talk to people, find out what they are thinking and feeling, and try to inhabit their shoes for a little while so you can make sense of their experiences and beliefs.

Examples of Qualitative Research

You have probably seen examples of qualitative research before, but you might not have paid particular attention to how they were produced or realized that the accounts you were reading were the result of hours, months, even years of research “in the field.”  A good qualitative researcher will present the product of their hours of work in such a way that it seems natural, even obvious, to the reader.  Because we are trying to convey what it is like answers, qualitative research is often presented as stories – stories about how people live their lives, go to work, raise their children, interact with one another.  In some ways, this can seem like reading particularly insightful novels.  But, unlike novels, there are very specific rules and guidelines that qualitative researchers follow to ensure that the “story” they are telling is accurate , a truthful rendition of what life is like for the people being studied.  Most of this textbook will be spent conveying those rules and guidelines.  Let’s take a look, first, however, at three examples of what the end product looks like.  I have chosen these three examples to showcase very different approaches to qualitative research, and I will return to these five examples throughout the book.  They were all published as whole books (not chapters or articles), and they are worth the long read, if you have the time.  I will also provide some information on how these books came to be and the length of time it takes to get them into book version.  It is important you know about this process, and the rest of this textbook will help explain why it takes so long to conduct good qualitative research!

Example 1 : The End Game (ethnography + interviews)

Corey Abramson is a sociologist who teaches at the University of Arizona.   In 2015 he published The End Game: How Inequality Shapes our Final Years ( 2015 ). This book was based on the research he did for his dissertation at the University of California-Berkeley in 2012.  Actually, the dissertation was completed in 2012 but the work that was produced that took several years.  The dissertation was entitled, “This is How We Live, This is How We Die: Social Stratification, Aging, and Health in Urban America” ( 2012 ).  You can see how the book version, which was written for a more general audience, has a more engaging sound to it, but that the dissertation version, which is what academic faculty read and evaluate, has a more descriptive title.  You can read the title and know that this is a study about aging and health and that the focus is going to be inequality and that the context (place) is going to be “urban America.”  It’s a study about “how” people do something – in this case, how they deal with aging and death.  This is the very first sentence of the dissertation, “From our first breath in the hospital to the day we die, we live in a society characterized by unequal opportunities for maintaining health and taking care of ourselves when ill.  These disparities reflect persistent racial, socio-economic, and gender-based inequalities and contribute to their persistence over time” ( 1 ).  What follows is a truthful account of how that is so.

Cory Abramson spent three years conducting his research in four different urban neighborhoods.  We call the type of research he conducted “comparative ethnographic” because he designed his study to compare groups of seniors as they went about their everyday business.  It’s comparative because he is comparing different groups (based on race, class, gender) and ethnographic because he is studying the culture/way of life of a group. [4]   He had an educated guess, rooted in what previous research had shown and what social theory would suggest, that people’s experiences of aging differ by race, class, and gender.  So, he set up a research design that would allow him to observe differences.  He chose two primarily middle-class (one was racially diverse and the other was predominantly White) and two primarily poor neighborhoods (one was racially diverse and the other was predominantly African American).  He hung out in senior centers and other places seniors congregated, watched them as they took the bus to get prescriptions filled, sat in doctor’s offices with them, and listened to their conversations with each other.  He also conducted more formal conversations, what we call in-depth interviews, with sixty seniors from each of the four neighborhoods.  As with a lot of fieldwork , as he got closer to the people involved, he both expanded and deepened his reach –

By the end of the project, I expanded my pool of general observations to include various settings frequented by seniors: apartment building common rooms, doctors’ offices, emergency rooms, pharmacies, senior centers, bars, parks, corner stores, shopping centers, pool halls, hair salons, coffee shops, and discount stores. Over the course of the three years of fieldwork, I observed hundreds of elders, and developed close relationships with a number of them. ( 2012:10 )

When Abramson rewrote the dissertation for a general audience and published his book in 2015, it got a lot of attention.  It is a beautifully written book and it provided insight into a common human experience that we surprisingly know very little about.  It won the Outstanding Publication Award by the American Sociological Association Section on Aging and the Life Course and was featured in the New York Times .  The book was about aging, and specifically how inequality shapes the aging process, but it was also about much more than that.  It helped show how inequality affects people’s everyday lives.  For example, by observing the difficulties the poor had in setting up appointments and getting to them using public transportation and then being made to wait to see a doctor, sometimes in standing-room-only situations, when they are unwell, and then being treated dismissively by hospital staff, Abramson allowed readers to feel the material reality of being poor in the US.  Comparing these examples with seniors with adequate supplemental insurance who have the resources to hire car services or have others assist them in arranging care when they need it, jolts the reader to understand and appreciate the difference money makes in the lives and circumstances of us all, and in a way that is different than simply reading a statistic (“80% of the poor do not keep regular doctor’s appointments”) does.  Qualitative research can reach into spaces and places that often go unexamined and then reports back to the rest of us what it is like in those spaces and places.

Example 2: Racing for Innocence (Interviews + Content Analysis + Fictional Stories)

Jennifer Pierce is a Professor of American Studies at the University of Minnesota.  Trained as a sociologist, she has written a number of books about gender, race, and power.  Her very first book, Gender Trials: Emotional Lives in Contemporary Law Firms, published in 1995, is a brilliant look at gender dynamics within two law firms.  Pierce was a participant observer, working as a paralegal, and she observed how female lawyers and female paralegals struggled to obtain parity with their male colleagues.

Fifteen years later, she reexamined the context of the law firm to include an examination of racial dynamics, particularly how elite white men working in these spaces created and maintained a culture that made it difficult for both female attorneys and attorneys of color to thrive. Her book, Racing for Innocence: Whiteness, Gender, and the Backlash Against Affirmative Action , published in 2012, is an interesting and creative blending of interviews with attorneys, content analyses of popular films during this period, and fictional accounts of racial discrimination and sexual harassment.  The law firm she chose to study had come under an affirmative action order and was in the process of implementing equitable policies and programs.  She wanted to understand how recipients of white privilege (the elite white male attorneys) come to deny the role they play in reproducing inequality.  Through interviews with attorneys who were present both before and during the affirmative action order, she creates a historical record of the “bad behavior” that necessitated new policies and procedures, but also, and more importantly , probed the participants ’ understanding of this behavior.  It should come as no surprise that most (but not all) of the white male attorneys saw little need for change, and that almost everyone else had accounts that were different if not sometimes downright harrowing.

I’ve used Pierce’s book in my qualitative research methods courses as an example of an interesting blend of techniques and presentation styles.  My students often have a very difficult time with the fictional accounts she includes.  But they serve an important communicative purpose here.  They are her attempts at presenting “both sides” to an objective reality – something happens (Pierce writes this something so it is very clear what it is), and the two participants to the thing that happened have very different understandings of what this means.  By including these stories, Pierce presents one of her key findings – people remember things differently and these different memories tend to support their own ideological positions.  I wonder what Pierce would have written had she studied the murder of George Floyd or the storming of the US Capitol on January 6 or any number of other historic events whose observers and participants record very different happenings.

This is not to say that qualitative researchers write fictional accounts.  In fact, the use of fiction in our work remains controversial.  When used, it must be clearly identified as a presentation device, as Pierce did.  I include Racing for Innocence here as an example of the multiple uses of methods and techniques and the way that these work together to produce better understandings by us, the readers, of what Pierce studied.  We readers come away with a better grasp of how and why advantaged people understate their own involvement in situations and structures that advantage them.  This is normal human behavior , in other words.  This case may have been about elite white men in law firms, but the general insights here can be transposed to other settings.  Indeed, Pierce argues that more research needs to be done about the role elites play in the reproduction of inequality in the workplace in general.

Example 3: Amplified Advantage (Mixed Methods: Survey Interviews + Focus Groups + Archives)

The final example comes from my own work with college students, particularly the ways in which class background affects the experience of college and outcomes for graduates.  I include it here as an example of mixed methods, and for the use of supplementary archival research.  I’ve done a lot of research over the years on first-generation, low-income, and working-class college students.  I am curious (and skeptical) about the possibility of social mobility today, particularly with the rising cost of college and growing inequality in general.  As one of the few people in my family to go to college, I didn’t grow up with a lot of examples of what college was like or how to make the most of it.  And when I entered graduate school, I realized with dismay that there were very few people like me there.  I worried about becoming too different from my family and friends back home.  And I wasn’t at all sure that I would ever be able to pay back the huge load of debt I was taking on.  And so I wrote my dissertation and first two books about working-class college students.  These books focused on experiences in college and the difficulties of navigating between family and school ( Hurst 2010a, 2012 ).  But even after all that research, I kept coming back to wondering if working-class students who made it through college had an equal chance at finding good jobs and happy lives,

What happens to students after college?  Do working-class students fare as well as their peers?  I knew from my own experience that barriers continued through graduate school and beyond, and that my debtload was higher than that of my peers, constraining some of the choices I made when I graduated.  To answer these questions, I designed a study of students attending small liberal arts colleges, the type of college that tried to equalize the experience of students by requiring all students to live on campus and offering small classes with lots of interaction with faculty.  These private colleges tend to have more money and resources so they can provide financial aid to low-income students.  They also attract some very wealthy students.  Because they enroll students across the class spectrum, I would be able to draw comparisons.  I ended up spending about four years collecting data, both a survey of more than 2000 students (which formed the basis for quantitative analyses) and qualitative data collection (interviews, focus groups, archival research, and participant observation).  This is what we call a “mixed methods” approach because we use both quantitative and qualitative data.  The survey gave me a large enough number of students that I could make comparisons of the how many kind, and to be able to say with some authority that there were in fact significant differences in experience and outcome by class (e.g., wealthier students earned more money and had little debt; working-class students often found jobs that were not in their chosen careers and were very affected by debt, upper-middle-class students were more likely to go to graduate school).  But the survey analyses could not explain why these differences existed.  For that, I needed to talk to people and ask them about their motivations and aspirations.  I needed to understand their perceptions of the world, and it is very hard to do this through a survey.

By interviewing students and recent graduates, I was able to discern particular patterns and pathways through college and beyond.  Specifically, I identified three versions of gameplay.  Upper-middle-class students, whose parents were themselves professionals (academics, lawyers, managers of non-profits), saw college as the first stage of their education and took classes and declared majors that would prepare them for graduate school.  They also spent a lot of time building their resumes, taking advantage of opportunities to help professors with their research, or study abroad.  This helped them gain admission to highly-ranked graduate schools and interesting jobs in the public sector.  In contrast, upper-class students, whose parents were wealthy and more likely to be engaged in business (as CEOs or other high-level directors), prioritized building social capital.  They did this by joining fraternities and sororities and playing club sports.  This helped them when they graduated as they called on friends and parents of friends to find them well-paying jobs.  Finally, low-income, first-generation, and working-class students were often adrift.  They took the classes that were recommended to them but without the knowledge of how to connect them to life beyond college.  They spent time working and studying rather than partying or building their resumes.  All three sets of students thought they were “doing college” the right way, the way that one was supposed to do college.   But these three versions of gameplay led to distinct outcomes that advantaged some students over others.  I titled my work “Amplified Advantage” to highlight this process.

These three examples, Cory Abramson’s The End Game , Jennifer Peirce’s Racing for Innocence, and my own Amplified Advantage, demonstrate the range of approaches and tools available to the qualitative researcher.  They also help explain why qualitative research is so important.  Numbers can tell us some things about the world, but they cannot get at the hearts and minds, motivations and beliefs of the people who make up the social worlds we inhabit.  For that, we need tools that allow us to listen and make sense of what people tell us and show us.  That is what good qualitative research offers us.

How Is This Book Organized?

This textbook is organized as a comprehensive introduction to the use of qualitative research methods.  The first half covers general topics (e.g., approaches to qualitative research, ethics) and research design (necessary steps for building a successful qualitative research study).  The second half reviews various data collection and data analysis techniques.  Of course, building a successful qualitative research study requires some knowledge of data collection and data analysis so the chapters in the first half and the chapters in the second half should be read in conversation with each other.  That said, each chapter can be read on its own for assistance with a particular narrow topic.  In addition to the chapters, a helpful glossary can be found in the back of the book.  Rummage around in the text as needed.

Chapter Descriptions

Chapter 2 provides an overview of the Research Design Process.  How does one begin a study? What is an appropriate research question?  How is the study to be done – with what methods ?  Involving what people and sites?  Although qualitative research studies can and often do change and develop over the course of data collection, it is important to have a good idea of what the aims and goals of your study are at the outset and a good plan of how to achieve those aims and goals.  Chapter 2 provides a road map of the process.

Chapter 3 describes and explains various ways of knowing the (social) world.  What is it possible for us to know about how other people think or why they behave the way they do?  What does it mean to say something is a “fact” or that it is “well-known” and understood?  Qualitative researchers are particularly interested in these questions because of the types of research questions we are interested in answering (the how questions rather than the how many questions of quantitative research).  Qualitative researchers have adopted various epistemological approaches.  Chapter 3 will explore these approaches, highlighting interpretivist approaches that acknowledge the subjective aspect of reality – in other words, reality and knowledge are not objective but rather influenced by (interpreted through) people.

Chapter 4 focuses on the practical matter of developing a research question and finding the right approach to data collection.  In any given study (think of Cory Abramson’s study of aging, for example), there may be years of collected data, thousands of observations , hundreds of pages of notes to read and review and make sense of.  If all you had was a general interest area (“aging”), it would be very difficult, nearly impossible, to make sense of all of that data.  The research question provides a helpful lens to refine and clarify (and simplify) everything you find and collect.  For that reason, it is important to pull out that lens (articulate the research question) before you get started.  In the case of the aging study, Cory Abramson was interested in how inequalities affected understandings and responses to aging.  It is for this reason he designed a study that would allow him to compare different groups of seniors (some middle-class, some poor).  Inevitably, he saw much more in the three years in the field than what made it into his book (or dissertation), but he was able to narrow down the complexity of the social world to provide us with this rich account linked to the original research question.  Developing a good research question is thus crucial to effective design and a successful outcome.  Chapter 4 will provide pointers on how to do this.  Chapter 4 also provides an overview of general approaches taken to doing qualitative research and various “traditions of inquiry.”

Chapter 5 explores sampling .  After you have developed a research question and have a general idea of how you will collect data (Observations?  Interviews?), how do you go about actually finding people and sites to study?  Although there is no “correct number” of people to interview , the sample should follow the research question and research design.  Unlike quantitative research, qualitative research involves nonprobability sampling.  Chapter 5 explains why this is so and what qualities instead make a good sample for qualitative research.

Chapter 6 addresses the importance of reflexivity in qualitative research.  Related to epistemological issues of how we know anything about the social world, qualitative researchers understand that we the researchers can never be truly neutral or outside the study we are conducting.  As observers, we see things that make sense to us and may entirely miss what is either too obvious to note or too different to comprehend.  As interviewers, as much as we would like to ask questions neutrally and remain in the background, interviews are a form of conversation, and the persons we interview are responding to us .  Therefore, it is important to reflect upon our social positions and the knowledges and expectations we bring to our work and to work through any blind spots that we may have.  Chapter 6 provides some examples of reflexivity in practice and exercises for thinking through one’s own biases.

Chapter 7 is a very important chapter and should not be overlooked.  As a practical matter, it should also be read closely with chapters 6 and 8.  Because qualitative researchers deal with people and the social world, it is imperative they develop and adhere to a strong ethical code for conducting research in a way that does not harm.  There are legal requirements and guidelines for doing so (see chapter 8), but these requirements should not be considered synonymous with the ethical code required of us.   Each researcher must constantly interrogate every aspect of their research, from research question to design to sample through analysis and presentation, to ensure that a minimum of harm (ideally, zero harm) is caused.  Because each research project is unique, the standards of care for each study are unique.  Part of being a professional researcher is carrying this code in one’s heart, being constantly attentive to what is required under particular circumstances.  Chapter 7 provides various research scenarios and asks readers to weigh in on the suitability and appropriateness of the research.  If done in a class setting, it will become obvious fairly quickly that there are often no absolutely correct answers, as different people find different aspects of the scenarios of greatest importance.  Minimizing the harm in one area may require possible harm in another.  Being attentive to all the ethical aspects of one’s research and making the best judgments one can, clearly and consciously, is an integral part of being a good researcher.

Chapter 8 , best to be read in conjunction with chapter 7, explains the role and importance of Institutional Review Boards (IRBs) .  Under federal guidelines, an IRB is an appropriately constituted group that has been formally designated to review and monitor research involving human subjects .  Every institution that receives funding from the federal government has an IRB.  IRBs have the authority to approve, require modifications to (to secure approval), or disapprove research.  This group review serves an important role in the protection of the rights and welfare of human research subjects.  Chapter 8 reviews the history of IRBs and the work they do but also argues that IRBs’ review of qualitative research is often both over-inclusive and under-inclusive.  Some aspects of qualitative research are not well understood by IRBs, given that they were developed to prevent abuses in biomedical research.  Thus, it is important not to rely on IRBs to identify all the potential ethical issues that emerge in our research (see chapter 7).

Chapter 9 provides help for getting started on formulating a research question based on gaps in the pre-existing literature.  Research is conducted as part of a community, even if particular studies are done by single individuals (or small teams).  What any of us finds and reports back becomes part of a much larger body of knowledge.  Thus, it is important that we look at the larger body of knowledge before we actually start our bit to see how we can best contribute.  When I first began interviewing working-class college students, there was only one other similar study I could find, and it hadn’t been published (it was a dissertation of students from poor backgrounds).  But there had been a lot published by professors who had grown up working class and made it through college despite the odds.  These accounts by “working-class academics” became an important inspiration for my study and helped me frame the questions I asked the students I interviewed.  Chapter 9 will provide some pointers on how to search for relevant literature and how to use this to refine your research question.

Chapter 10 serves as a bridge between the two parts of the textbook, by introducing techniques of data collection.  Qualitative research is often characterized by the form of data collection – for example, an ethnographic study is one that employs primarily observational data collection for the purpose of documenting and presenting a particular culture or ethnos.  Techniques can be effectively combined, depending on the research question and the aims and goals of the study.   Chapter 10 provides a general overview of all the various techniques and how they can be combined.

The second part of the textbook moves into the doing part of qualitative research once the research question has been articulated and the study designed.  Chapters 11 through 17 cover various data collection techniques and approaches.  Chapters 18 and 19 provide a very simple overview of basic data analysis.  Chapter 20 covers communication of the data to various audiences, and in various formats.

Chapter 11 begins our overview of data collection techniques with a focus on interviewing , the true heart of qualitative research.  This technique can serve as the primary and exclusive form of data collection, or it can be used to supplement other forms (observation, archival).  An interview is distinct from a survey, where questions are asked in a specific order and often with a range of predetermined responses available.  Interviews can be conversational and unstructured or, more conventionally, semistructured , where a general set of interview questions “guides” the conversation.  Chapter 11 covers the basics of interviews: how to create interview guides, how many people to interview, where to conduct the interview, what to watch out for (how to prepare against things going wrong), and how to get the most out of your interviews.

Chapter 12 covers an important variant of interviewing, the focus group.  Focus groups are semistructured interviews with a group of people moderated by a facilitator (the researcher or researcher’s assistant).  Focus groups explicitly use group interaction to assist in the data collection.  They are best used to collect data on a specific topic that is non-personal and shared among the group.  For example, asking a group of college students about a common experience such as taking classes by remote delivery during the pandemic year of 2020.  Chapter 12 covers the basics of focus groups: when to use them, how to create interview guides for them, and how to run them effectively.

Chapter 13 moves away from interviewing to the second major form of data collection unique to qualitative researchers – observation .  Qualitative research that employs observation can best be understood as falling on a continuum of “fly on the wall” observation (e.g., observing how strangers interact in a doctor’s waiting room) to “participant” observation, where the researcher is also an active participant of the activity being observed.  For example, an activist in the Black Lives Matter movement might want to study the movement, using her inside position to gain access to observe key meetings and interactions.  Chapter  13 covers the basics of participant observation studies: advantages and disadvantages, gaining access, ethical concerns related to insider/outsider status and entanglement, and recording techniques.

Chapter 14 takes a closer look at “deep ethnography” – immersion in the field of a particularly long duration for the purpose of gaining a deeper understanding and appreciation of a particular culture or social world.  Clifford Geertz called this “deep hanging out.”  Whereas participant observation is often combined with semistructured interview techniques, deep ethnography’s commitment to “living the life” or experiencing the situation as it really is demands more conversational and natural interactions with people.  These interactions and conversations may take place over months or even years.  As can be expected, there are some costs to this technique, as well as some very large rewards when done competently.  Chapter 14 provides some examples of deep ethnographies that will inspire some beginning researchers and intimidate others.

Chapter 15 moves in the opposite direction of deep ethnography, a technique that is the least positivist of all those discussed here, to mixed methods , a set of techniques that is arguably the most positivist .  A mixed methods approach combines both qualitative data collection and quantitative data collection, commonly by combining a survey that is analyzed statistically (e.g., cross-tabs or regression analyses of large number probability samples) with semi-structured interviews.  Although it is somewhat unconventional to discuss mixed methods in textbooks on qualitative research, I think it is important to recognize this often-employed approach here.  There are several advantages and some disadvantages to taking this route.  Chapter 16 will describe those advantages and disadvantages and provide some particular guidance on how to design a mixed methods study for maximum effectiveness.

Chapter 16 covers data collection that does not involve live human subjects at all – archival and historical research (chapter 17 will also cover data that does not involve interacting with human subjects).  Sometimes people are unavailable to us, either because they do not wish to be interviewed or observed (as is the case with many “elites”) or because they are too far away, in both place and time.  Fortunately, humans leave many traces and we can often answer questions we have by examining those traces.  Special collections and archives can be goldmines for social science research.  This chapter will explain how to access these places, for what purposes, and how to begin to make sense of what you find.

Chapter 17 covers another data collection area that does not involve face-to-face interaction with humans: content analysis .  Although content analysis may be understood more properly as a data analysis technique, the term is often used for the entire approach, which will be the case here.  Content analysis involves interpreting meaning from a body of text.  This body of text might be something found in historical records (see chapter 16) or something collected by the researcher, as in the case of comment posts on a popular blog post.  I once used the stories told by student loan debtors on the website as the content I analyzed.  Content analysis is particularly useful when attempting to define and understand prevalent stories or communication about a topic of interest.  In other words, when we are less interested in what particular people (our defined sample) are doing or believing and more interested in what general narratives exist about a particular topic or issue.  This chapter will explore different approaches to content analysis and provide helpful tips on how to collect data, how to turn that data into codes for analysis, and how to go about presenting what is found through analysis.

Where chapter 17 has pushed us towards data analysis, chapters 18 and 19 are all about what to do with the data collected, whether that data be in the form of interview transcripts or fieldnotes from observations.  Chapter 18 introduces the basics of coding , the iterative process of assigning meaning to the data in order to both simplify and identify patterns.  What is a code and how does it work?  What are the different ways of coding data, and when should you use them?  What is a codebook, and why do you need one?  What does the process of data analysis look like?

Chapter 19 goes further into detail on codes and how to use them, particularly the later stages of coding in which our codes are refined, simplified, combined, and organized.  These later rounds of coding are essential to getting the most out of the data we’ve collected.  As students are often overwhelmed with the amount of data (a corpus of interview transcripts typically runs into the hundreds of pages; fieldnotes can easily top that), this chapter will also address time management and provide suggestions for dealing with chaos and reminders that feeling overwhelmed at the analysis stage is part of the process.  By the end of the chapter, you should understand how “findings” are actually found.

The book concludes with a chapter dedicated to the effective presentation of data results.  Chapter 20 covers the many ways that researchers communicate their studies to various audiences (academic, personal, political), what elements must be included in these various publications, and the hallmarks of excellent qualitative research that various audiences will be expecting.  Because qualitative researchers are motivated by understanding and conveying meaning , effective communication is not only an essential skill but a fundamental facet of the entire research project.  Ethnographers must be able to convey a certain sense of verisimilitude , the appearance of true reality.  Those employing interviews must faithfully depict the key meanings of the people they interviewed in a way that rings true to those people, even if the end result surprises them.  And all researchers must strive for clarity in their publications so that various audiences can understand what was found and why it is important.

The book concludes with a short chapter ( chapter 21 ) discussing the value of qualitative research. At the very end of this book, you will find a glossary of terms. I recommend you make frequent use of the glossary and add to each entry as you find examples. Although the entries are meant to be simple and clear, you may also want to paraphrase the definition—make it “make sense” to you, in other words. In addition to the standard reference list (all works cited here), you will find various recommendations for further reading at the end of many chapters. Some of these recommendations will be examples of excellent qualitative research, indicated with an asterisk (*) at the end of the entry. As they say, a picture is worth a thousand words. A good example of qualitative research can teach you more about conducting research than any textbook can (this one included). I highly recommend you select one to three examples from these lists and read them along with the textbook.

A final note on the choice of examples – you will note that many of the examples used in the text come from research on college students.  This is for two reasons.  First, as most of my research falls in this area, I am most familiar with this literature and have contacts with those who do research here and can call upon them to share their stories with you.  Second, and more importantly, my hope is that this textbook reaches a wide audience of beginning researchers who study widely and deeply across the range of what can be known about the social world (from marine resources management to public policy to nursing to political science to sexuality studies and beyond).  It is sometimes difficult to find examples that speak to all those research interests, however. A focus on college students is something that all readers can understand and, hopefully, appreciate, as we are all now or have been at some point a college student.

Recommended Reading: Other Qualitative Research Textbooks

I’ve included a brief list of some of my favorite qualitative research textbooks and guidebooks if you need more than what you will find in this introductory text.  For each, I’ve also indicated if these are for “beginning” or “advanced” (graduate-level) readers.  Many of these books have several editions that do not significantly vary; the edition recommended is merely the edition I have used in teaching and to whose page numbers any specific references made in the text agree.

Barbour, Rosaline. 2014. Introducing Qualitative Research: A Student’s Guide. Thousand Oaks, CA: SAGE.  A good introduction to qualitative research, with abundant examples (often from the discipline of health care) and clear definitions.  Includes quick summaries at the ends of each chapter.  However, some US students might find the British context distracting and can be a bit advanced in some places.  Beginning .

Bloomberg, Linda Dale, and Marie F. Volpe. 2012. Completing Your Qualitative Dissertation . 2nd ed. Thousand Oaks, CA: SAGE.  Specifically designed to guide graduate students through the research process. Advanced .

Creswell, John W., and Cheryl Poth. 2018 Qualitative Inquiry and Research Design: Choosing among Five Traditions .  4th ed. Thousand Oaks, CA: SAGE.  This is a classic and one of the go-to books I used myself as a graduate student.  One of the best things about this text is its clear presentation of five distinct traditions in qualitative research.  Despite the title, this reasonably sized book is about more than research design, including both data analysis and how to write about qualitative research.  Advanced .

Lareau, Annette. 2021. Listening to People: A Practical Guide to Interviewing, Participant Observation, Data Analysis, and Writing It All Up .  Chicago: University of Chicago Press. A readable and personal account of conducting qualitative research by an eminent sociologist, with a heavy emphasis on the kinds of participant-observation research conducted by the author.  Despite its reader-friendliness, this is really a book targeted to graduate students learning the craft.  Advanced .

Lune, Howard, and Bruce L. Berg. 2018. 9th edition.  Qualitative Research Methods for the Social Sciences.  Pearson . Although a good introduction to qualitative methods, the authors favor symbolic interactionist and dramaturgical approaches, which limits the appeal primarily to sociologists.  Beginning .

Marshall, Catherine, and Gretchen B. Rossman. 2016. 6th edition. Designing Qualitative Research. Thousand Oaks, CA: SAGE.  Very readable and accessible guide to research design by two educational scholars.  Although the presentation is sometimes fairly dry, personal vignettes and illustrations enliven the text.  Beginning .

Maxwell, Joseph A. 2013. Qualitative Research Design: An Interactive Approach .  3rd ed. Thousand Oaks, CA: SAGE. A short and accessible introduction to qualitative research design, particularly helpful for graduate students contemplating theses and dissertations. This has been a standard textbook in my graduate-level courses for years.  Advanced .

Patton, Michael Quinn. 2002. Qualitative Research and Evaluation Methods . Thousand Oaks, CA: SAGE.  This is a comprehensive text that served as my “go-to” reference when I was a graduate student.  It is particularly helpful for those involved in program evaluation and other forms of evaluation studies and uses examples from a wide range of disciplines.  Advanced .

Rubin, Ashley T. 2021. Rocking Qualitative Social Science: An Irreverent Guide to Rigorous Research. Stanford : Stanford University Press.  A delightful and personal read.  Rubin uses rock climbing as an extended metaphor for learning how to conduct qualitative research.  A bit slanted toward ethnographic and archival methods of data collection, with frequent examples from her own studies in criminology. Beginning .

Weis, Lois, and Michelle Fine. 2000. Speed Bumps: A Student-Friendly Guide to Qualitative Research . New York: Teachers College Press.  Readable and accessibly written in a quasi-conversational style.  Particularly strong in its discussion of ethical issues throughout the qualitative research process.  Not comprehensive, however, and very much tied to ethnographic research.  Although designed for graduate students, this is a recommended read for students of all levels.  Beginning .

Patton’s Ten Suggestions for Doing Qualitative Research

The following ten suggestions were made by Michael Quinn Patton in his massive textbooks Qualitative Research and Evaluations Methods . This book is highly recommended for those of you who want more than an introduction to qualitative methods. It is the book I relied on heavily when I was a graduate student, although it is much easier to “dip into” when necessary than to read through as a whole. Patton is asked for “just one bit of advice” for a graduate student considering using qualitative research methods for their dissertation.  Here are his top ten responses, in short form, heavily paraphrased, and with additional comments and emphases from me:

  • Make sure that a qualitative approach fits the research question. The following are the kinds of questions that call out for qualitative methods or where qualitative methods are particularly appropriate: questions about people’s experiences or how they make sense of those experiences; studying a person in their natural environment; researching a phenomenon so unknown that it would be impossible to study it with standardized instruments or other forms of quantitative data collection.
  • Study qualitative research by going to the original sources for the design and analysis appropriate to the particular approach you want to take (e.g., read Glaser and Straus if you are using grounded theory )
  • Find a dissertation adviser who understands or at least who will support your use of qualitative research methods. You are asking for trouble if your entire committee is populated by quantitative researchers, even if they are all very knowledgeable about the subject or focus of your study (maybe even more so if they are!)
  • Really work on design. Doing qualitative research effectively takes a lot of planning.  Even if things are more flexible than in quantitative research, a good design is absolutely essential when starting out.
  • Practice data collection techniques, particularly interviewing and observing. There is definitely a set of learned skills here!  Do not expect your first interview to be perfect.  You will continue to grow as a researcher the more interviews you conduct, and you will probably come to understand yourself a bit more in the process, too.  This is not easy, despite what others who don’t work with qualitative methods may assume (and tell you!)
  • Have a plan for analysis before you begin data collection. This is often a requirement in IRB protocols , although you can get away with writing something fairly simple.  And even if you are taking an approach, such as grounded theory, that pushes you to remain fairly open-minded during the data collection process, you still want to know what you will be doing with all the data collected – creating a codebook? Writing analytical memos? Comparing cases?  Having a plan in hand will also help prevent you from collecting too much extraneous data.
  • Be prepared to confront controversies both within the qualitative research community and between qualitative research and quantitative research. Don’t be naïve about this – qualitative research, particularly some approaches, will be derided by many more “positivist” researchers and audiences.  For example, is an “n” of 1 really sufficient?  Yes!  But not everyone will agree.
  • Do not make the mistake of using qualitative research methods because someone told you it was easier, or because you are intimidated by the math required of statistical analyses. Qualitative research is difficult in its own way (and many would claim much more time-consuming than quantitative research).  Do it because you are convinced it is right for your goals, aims, and research questions.
  • Find a good support network. This could be a research mentor, or it could be a group of friends or colleagues who are also using qualitative research, or it could be just someone who will listen to you work through all of the issues you will confront out in the field and during the writing process.  Even though qualitative research often involves human subjects, it can be pretty lonely.  A lot of times you will feel like you are working without a net.  You have to create one for yourself.  Take care of yourself.
  • And, finally, in the words of Patton, “Prepare to be changed. Looking deeply at other people’s lives will force you to look deeply at yourself.”
  • We will actually spend an entire chapter ( chapter 3 ) looking at this question in much more detail! ↵
  • Note that this might have been news to Europeans at the time, but many other societies around the world had also come to this conclusion through observation.  There is often a tendency to equate “the scientific revolution” with the European world in which it took place, but this is somewhat misleading. ↵
  • Historians are a special case here.  Historians have scrupulously and rigorously investigated the social world, but not for the purpose of understanding general laws about how things work, which is the point of scientific empirical research.  History is often referred to as an idiographic field of study, meaning that it studies things that happened or are happening in themselves and not for general observations or conclusions. ↵
  • Don’t worry, we’ll spend more time later in this book unpacking the meaning of ethnography and other terms that are important here.  Note the available glossary ↵

An approach to research that is “multimethod in focus, involving an interpretative, naturalistic approach to its subject matter.  This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them.  Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives." ( Denzin and Lincoln 2005:2 ). Contrast with quantitative research .

In contrast to methodology, methods are more simply the practices and tools used to collect and analyze data.  Examples of common methods in qualitative research are interviews , observations , and documentary analysis .  One’s methodology should connect to one’s choice of methods, of course, but they are distinguishable terms.  See also methodology .

A proposed explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation.  The positing of a hypothesis is often the first step in quantitative research but not in qualitative research.  Even when qualitative researchers offer possible explanations in advance of conducting research, they will tend to not use the word “hypothesis” as it conjures up the kind of positivist research they are not conducting.

The foundational question to be addressed by the research study.  This will form the anchor of the research design, collection, and analysis.  Note that in qualitative research, the research question may, and probably will, alter or develop during the course of the research.

An approach to research that collects and analyzes numerical data for the purpose of finding patterns and averages, making predictions, testing causal relationships, and generalizing results to wider populations.  Contrast with qualitative research .

Data collection that takes place in real-world settings, referred to as “the field;” a key component of much Grounded Theory and ethnographic research.  Patton ( 2002 ) calls fieldwork “the central activity of qualitative inquiry” where “‘going into the field’ means having direct and personal contact with people under study in their own environments – getting close to people and situations being studied to personally understand the realities of minutiae of daily life” (48).

The people who are the subjects of a qualitative study.  In interview-based studies, they may be the respondents to the interviewer; for purposes of IRBs, they are often referred to as the human subjects of the research.

The branch of philosophy concerned with knowledge.  For researchers, it is important to recognize and adopt one of the many distinguishing epistemological perspectives as part of our understanding of what questions research can address or fully answer.  See, e.g., constructivism , subjectivism, and  objectivism .

An approach that refutes the possibility of neutrality in social science research.  All research is “guided by a set of beliefs and feelings about the world and how it should be understood and studied” (Denzin and Lincoln 2005: 13).  In contrast to positivism , interpretivism recognizes the social constructedness of reality, and researchers adopting this approach focus on capturing interpretations and understandings people have about the world rather than “the world” as it is (which is a chimera).

The cluster of data-collection tools and techniques that involve observing interactions between people, the behaviors, and practices of individuals (sometimes in contrast to what they say about how they act and behave), and cultures in context.  Observational methods are the key tools employed by ethnographers and Grounded Theory .

Research based on data collected and analyzed by the research (in contrast to secondary “library” research).

The process of selecting people or other units of analysis to represent a larger population. In quantitative research, this representation is taken quite literally, as statistically representative.  In qualitative research, in contrast, sample selection is often made based on potential to generate insight about a particular topic or phenomenon.

A method of data collection in which the researcher asks the participant questions; the answers to these questions are often recorded and transcribed verbatim. There are many different kinds of interviews - see also semistructured interview , structured interview , and unstructured interview .

The specific group of individuals that you will collect data from.  Contrast population.

The practice of being conscious of and reflective upon one’s own social location and presence when conducting research.  Because qualitative research often requires interaction with live humans, failing to take into account how one’s presence and prior expectations and social location affect the data collected and how analyzed may limit the reliability of the findings.  This remains true even when dealing with historical archives and other content.  Who we are matters when asking questions about how people experience the world because we, too, are a part of that world.

The science and practice of right conduct; in research, it is also the delineation of moral obligations towards research participants, communities to which we belong, and communities in which we conduct our research.

An administrative body established to protect the rights and welfare of human research subjects recruited to participate in research activities conducted under the auspices of the institution with which it is affiliated. The IRB is charged with the responsibility of reviewing all research involving human participants. The IRB is concerned with protecting the welfare, rights, and privacy of human subjects. The IRB has the authority to approve, disapprove, monitor, and require modifications in all research activities that fall within its jurisdiction as specified by both the federal regulations and institutional policy.

Research, according to US federal guidelines, that involves “a living individual about whom an investigator (whether professional or student) conducting research:  (1) Obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or  (2) Obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens.”

One of the primary methodological traditions of inquiry in qualitative research, ethnography is the study of a group or group culture, largely through observational fieldwork supplemented by interviews. It is a form of fieldwork that may include participant-observation data collection. See chapter 14 for a discussion of deep ethnography. 

A form of interview that follows a standard guide of questions asked, although the order of the questions may change to match the particular needs of each individual interview subject, and probing “follow-up” questions are often added during the course of the interview.  The semi-structured interview is the primary form of interviewing used by qualitative researchers in the social sciences.  It is sometimes referred to as an “in-depth” interview.  See also interview and  interview guide .

A method of observational data collection taking place in a natural setting; a form of fieldwork .  The term encompasses a continuum of relative participation by the researcher (from full participant to “fly-on-the-wall” observer).  This is also sometimes referred to as ethnography , although the latter is characterized by a greater focus on the culture under observation.

A research design that employs both quantitative and qualitative methods, as in the case of a survey supplemented by interviews.

An epistemological perspective that posits the existence of reality through sensory experience similar to empiricism but goes further in denying any non-sensory basis of thought or consciousness.  In the social sciences, the term has roots in the proto-sociologist August Comte, who believed he could discern “laws” of society similar to the laws of natural science (e.g., gravity).  The term has come to mean the kinds of measurable and verifiable science conducted by quantitative researchers and is thus used pejoratively by some qualitative researchers interested in interpretation, consciousness, and human understanding.  Calling someone a “positivist” is often intended as an insult.  See also empiricism and objectivism.

A place or collection containing records, documents, or other materials of historical interest; most universities have an archive of material related to the university’s history, as well as other “special collections” that may be of interest to members of the community.

A method of both data collection and data analysis in which a given content (textual, visual, graphic) is examined systematically and rigorously to identify meanings, themes, patterns and assumptions.  Qualitative content analysis (QCA) is concerned with gathering and interpreting an existing body of material.    

A word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data (Saldaña 2021:5).

Usually a verbatim written record of an interview or focus group discussion.

The primary form of data for fieldwork , participant observation , and ethnography .  These notes, taken by the researcher either during the course of fieldwork or at day’s end, should include as many details as possible on what was observed and what was said.  They should include clear identifiers of date, time, setting, and names (or identifying characteristics) of participants.

The process of labeling and organizing qualitative data to identify different themes and the relationships between them; a way of simplifying data to allow better management and retrieval of key themes and illustrative passages.  See coding frame and  codebook.

A methodological tradition of inquiry and approach to analyzing qualitative data in which theories emerge from a rigorous and systematic process of induction.  This approach was pioneered by the sociologists Glaser and Strauss (1967).  The elements of theory generated from comparative analysis of data are, first, conceptual categories and their properties and, second, hypotheses or generalized relations among the categories and their properties – “The constant comparing of many groups draws the [researcher’s] attention to their many similarities and differences.  Considering these leads [the researcher] to generate abstract categories and their properties, which, since they emerge from the data, will clearly be important to a theory explaining the kind of behavior under observation.” (36).

A detailed description of any proposed research that involves human subjects for review by IRB.  The protocol serves as the recipe for the conduct of the research activity.  It includes the scientific rationale to justify the conduct of the study, the information necessary to conduct the study, the plan for managing and analyzing the data, and a discussion of the research ethical issues relevant to the research.  Protocols for qualitative research often include interview guides, all documents related to recruitment, informed consent forms, very clear guidelines on the safekeeping of materials collected, and plans for de-identifying transcripts or other data that include personal identifying information.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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  • Published: 27 May 2020

How to use and assess qualitative research methods

  • Loraine Busetto   ORCID: 1 ,
  • Wolfgang Wick 1 , 2 &
  • Christoph Gumbinger 1  

Neurological Research and Practice volume  2 , Article number:  14 ( 2020 ) Cite this article

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This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 , 8 , 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 , 10 , 11 , 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

figure 1

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.


Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

figure 2

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

figure 3

From data collection to data analysis

Attributions for icons: see Fig. 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 , 25 , 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

figure 4

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].


While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 , 32 , 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 , 38 , 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.


While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Availability of data and materials

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Busetto, L., Wick, W. & Gumbinger, C. How to use and assess qualitative research methods. Neurol. Res. Pract. 2 , 14 (2020).

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qualitative research studies examples

Qualitative Research: Characteristics, Design, Methods & Examples

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Qualitative research is a type of research methodology that focuses on gathering and analyzing non-numerical data to gain a deeper understanding of human behavior, experiences, and perspectives.

It aims to explore the “why” and “how” of a phenomenon rather than the “what,” “where,” and “when” typically addressed by quantitative research.

Unlike quantitative research, which focuses on gathering and analyzing numerical data for statistical analysis, qualitative research involves researchers interpreting data to identify themes, patterns, and meanings.

Qualitative research can be used to:

  • Gain deep contextual understandings of the subjective social reality of individuals
  • To answer questions about experience and meaning from the participant’s perspective
  • To design hypotheses, theory must be researched using qualitative methods to determine what is important before research can begin. 

Examples of qualitative research questions include: 

  • How does stress influence young adults’ behavior?
  • What factors influence students’ school attendance rates in developed countries?
  • How do adults interpret binge drinking in the UK?
  • What are the psychological impacts of cervical cancer screening in women?
  • How can mental health lessons be integrated into the school curriculum? 


Naturalistic setting.

Individuals are studied in their natural setting to gain a deeper understanding of how people experience the world. This enables the researcher to understand a phenomenon close to how participants experience it. 

Naturalistic settings provide valuable contextual information to help researchers better understand and interpret the data they collect.

The environment, social interactions, and cultural factors can all influence behavior and experiences, and these elements are more easily observed in real-world settings.

Reality is socially constructed

Qualitative research aims to understand how participants make meaning of their experiences – individually or in social contexts. It assumes there is no objective reality and that the social world is interpreted (Yilmaz, 2013). 

The primacy of subject matter 

The primary aim of qualitative research is to understand the perspectives, experiences, and beliefs of individuals who have experienced the phenomenon selected for research rather than the average experiences of groups of people (Minichiello, 1990).

An in-depth understanding is attained since qualitative techniques allow participants to freely disclose their experiences, thoughts, and feelings without constraint (Tenny et al., 2022). 

Variables are complex, interwoven, and difficult to measure

Factors such as experiences, behaviors, and attitudes are complex and interwoven, so they cannot be reduced to isolated variables , making them difficult to measure quantitatively.

However, a qualitative approach enables participants to describe what, why, or how they were thinking/ feeling during a phenomenon being studied (Yilmaz, 2013). 

Emic (insider’s point of view)

The phenomenon being studied is centered on the participants’ point of view (Minichiello, 1990).

Emic is used to describe how participants interact, communicate, and behave in the research setting (Scarduzio, 2017).

Interpretive analysis

In qualitative research, interpretive analysis is crucial in making sense of the collected data.

This process involves examining the raw data, such as interview transcripts, field notes, or documents, and identifying the underlying themes, patterns, and meanings that emerge from the participants’ experiences and perspectives.

Collecting Qualitative Data

There are four main research design methods used to collect qualitative data: observations, interviews,  focus groups, and ethnography.


This method involves watching and recording phenomena as they occur in nature. Observation can be divided into two types: participant and non-participant observation.

In participant observation, the researcher actively participates in the situation/events being observed.

In non-participant observation, the researcher is not an active part of the observation and tries not to influence the behaviors they are observing (Busetto et al., 2020). 

Observations can be covert (participants are unaware that a researcher is observing them) or overt (participants are aware of the researcher’s presence and know they are being observed).

However, awareness of an observer’s presence may influence participants’ behavior. 

Interviews give researchers a window into the world of a participant by seeking their account of an event, situation, or phenomenon. They are usually conducted on a one-to-one basis and can be distinguished according to the level at which they are structured (Punch, 2013). 

Structured interviews involve predetermined questions and sequences to ensure replicability and comparability. However, they are unable to explore emerging issues.

Informal interviews consist of spontaneous, casual conversations which are closer to the truth of a phenomenon. However, information is gathered using quick notes made by the researcher and is therefore subject to recall bias. 

Semi-structured interviews have a flexible structure, phrasing, and placement so emerging issues can be explored (Denny & Weckesser, 2022).

The use of probing questions and clarification can lead to a detailed understanding, but semi-structured interviews can be time-consuming and subject to interviewer bias. 

Focus groups 

Similar to interviews, focus groups elicit a rich and detailed account of an experience. However, focus groups are more dynamic since participants with shared characteristics construct this account together (Denny & Weckesser, 2022).

A shared narrative is built between participants to capture a group experience shaped by a shared context. 

The researcher takes on the role of a moderator, who will establish ground rules and guide the discussion by following a topic guide to focus the group discussions.

Typically, focus groups have 4-10 participants as a discussion can be difficult to facilitate with more than this, and this number allows everyone the time to speak.


Ethnography is a methodology used to study a group of people’s behaviors and social interactions in their environment (Reeves et al., 2008).

Data are collected using methods such as observations, field notes, or structured/ unstructured interviews.

The aim of ethnography is to provide detailed, holistic insights into people’s behavior and perspectives within their natural setting. In order to achieve this, researchers immerse themselves in a community or organization. 

Due to the flexibility and real-world focus of ethnography, researchers are able to gather an in-depth, nuanced understanding of people’s experiences, knowledge and perspectives that are influenced by culture and society.

In order to develop a representative picture of a particular culture/ context, researchers must conduct extensive field work. 

This can be time-consuming as researchers may need to immerse themselves into a community/ culture for a few days, or possibly a few years.

Qualitative Data Analysis Methods

Different methods can be used for analyzing qualitative data. The researcher chooses based on the objectives of their study. 

The researcher plays a key role in the interpretation of data, making decisions about the coding, theming, decontextualizing, and recontextualizing of data (Starks & Trinidad, 2007). 

Grounded theory

Grounded theory is a qualitative method specifically designed to inductively generate theory from data. It was developed by Glaser and Strauss in 1967 (Glaser & Strauss, 2017).

 This methodology aims to develop theories (rather than test hypotheses) that explain a social process, action, or interaction (Petty et al., 2012). To inform the developing theory, data collection and analysis run simultaneously. 

There are three key types of coding used in grounded theory: initial (open), intermediate (axial), and advanced (selective) coding. 

Throughout the analysis, memos should be created to document methodological and theoretical ideas about the data. Data should be collected and analyzed until data saturation is reached and a theory is developed. 

Content analysis

Content analysis was first used in the early twentieth century to analyze textual materials such as newspapers and political speeches.

Content analysis is a research method used to identify and analyze the presence and patterns of themes, concepts, or words in data (Vaismoradi et al., 2013). 

This research method can be used to analyze data in different formats, which can be written, oral, or visual. 

The goal of content analysis is to develop themes that capture the underlying meanings of data (Schreier, 2012). 

Qualitative content analysis can be used to validate existing theories, support the development of new models and theories, and provide in-depth descriptions of particular settings or experiences.

The following six steps provide a guideline for how to conduct qualitative content analysis.
  • Define a Research Question : To start content analysis, a clear research question should be developed.
  • Identify and Collect Data : Establish the inclusion criteria for your data. Find the relevant sources to analyze.
  • Define the Unit or Theme of Analysis : Categorize the content into themes. Themes can be a word, phrase, or sentence.
  • Develop Rules for Coding your Data : Define a set of coding rules to ensure that all data are coded consistently.
  • Code the Data : Follow the coding rules to categorize data into themes.
  • Analyze the Results and Draw Conclusions : Examine the data to identify patterns and draw conclusions in relation to your research question.

Discourse analysis

Discourse analysis is a research method used to study written/ spoken language in relation to its social context (Wood & Kroger, 2000).

In discourse analysis, the researcher interprets details of language materials and the context in which it is situated.

Discourse analysis aims to understand the functions of language (how language is used in real life) and how meaning is conveyed by language in different contexts. Researchers use discourse analysis to investigate social groups and how language is used to achieve specific communication goals.

Different methods of discourse analysis can be used depending on the aims and objectives of a study. However, the following steps provide a guideline on how to conduct discourse analysis.
  • Define the Research Question : Develop a relevant research question to frame the analysis.
  • Gather Data and Establish the Context : Collect research materials (e.g., interview transcripts, documents). Gather factual details and review the literature to construct a theory about the social and historical context of your study.
  • Analyze the Content : Closely examine various components of the text, such as the vocabulary, sentences, paragraphs, and structure of the text. Identify patterns relevant to the research question to create codes, then group these into themes.
  • Review the Results : Reflect on the findings to examine the function of the language, and the meaning and context of the discourse. 

Thematic analysis

Thematic analysis is a method used to identify, interpret, and report patterns in data, such as commonalities or contrasts. 

Although the origin of thematic analysis can be traced back to the early twentieth century, understanding and clarity of thematic analysis is attributed to Braun and Clarke (2006).

Thematic analysis aims to develop themes (patterns of meaning) across a dataset to address a research question. 

In thematic analysis, qualitative data is gathered using techniques such as interviews, focus groups, and questionnaires. Audio recordings are transcribed. The dataset is then explored and interpreted by a researcher to identify patterns. 

This occurs through the rigorous process of data familiarisation, coding, theme development, and revision. These identified patterns provide a summary of the dataset and can be used to address a research question.

Themes are developed by exploring the implicit and explicit meanings within the data. Two different approaches are used to generate themes: inductive and deductive. 

An inductive approach allows themes to emerge from the data. In contrast, a deductive approach uses existing theories or knowledge to apply preconceived ideas to the data.

Phases of Thematic Analysis

Braun and Clarke (2006) provide a guide of the six phases of thematic analysis. These phases can be applied flexibly to fit research questions and data. 

Template analysis

Template analysis refers to a specific method of thematic analysis which uses hierarchical coding (Brooks et al., 2014).

Template analysis is used to analyze textual data, for example, interview transcripts or open-ended responses on a written questionnaire.

To conduct template analysis, a coding template must be developed (usually from a subset of the data) and subsequently revised and refined. This template represents the themes identified by researchers as important in the dataset. 

Codes are ordered hierarchically within the template, with the highest-level codes demonstrating overarching themes in the data and lower-level codes representing constituent themes with a narrower focus.

A guideline for the main procedural steps for conducting template analysis is outlined below.
  • Familiarization with the Data : Read (and reread) the dataset in full. Engage, reflect, and take notes on data that may be relevant to the research question.
  • Preliminary Coding : Identify initial codes using guidance from the a priori codes, identified before the analysis as likely to be beneficial and relevant to the analysis.
  • Organize Themes : Organize themes into meaningful clusters. Consider the relationships between the themes both within and between clusters.
  • Produce an Initial Template : Develop an initial template. This may be based on a subset of the data.
  • Apply and Develop the Template : Apply the initial template to further data and make any necessary modifications. Refinements of the template may include adding themes, removing themes, or changing the scope/title of themes. 
  • Finalize Template : Finalize the template, then apply it to the entire dataset. 

Frame analysis

Frame analysis is a comparative form of thematic analysis which systematically analyzes data using a matrix output.

Ritchie and Spencer (1994) developed this set of techniques to analyze qualitative data in applied policy research. Frame analysis aims to generate theory from data.

Frame analysis encourages researchers to organize and manage their data using summarization.

This results in a flexible and unique matrix output, in which individual participants (or cases) are represented by rows and themes are represented by columns. 

Each intersecting cell is used to summarize findings relating to the corresponding participant and theme.

Frame analysis has five distinct phases which are interrelated, forming a methodical and rigorous framework.
  • Familiarization with the Data : Familiarize yourself with all the transcripts. Immerse yourself in the details of each transcript and start to note recurring themes.
  • Develop a Theoretical Framework : Identify recurrent/ important themes and add them to a chart. Provide a framework/ structure for the analysis.
  • Indexing : Apply the framework systematically to the entire study data.
  • Summarize Data in Analytical Framework : Reduce the data into brief summaries of participants’ accounts.
  • Mapping and Interpretation : Compare themes and subthemes and check against the original transcripts. Group the data into categories and provide an explanation for them.

Preventing Bias in Qualitative Research

To evaluate qualitative studies, the CASP (Critical Appraisal Skills Programme) checklist for qualitative studies can be used to ensure all aspects of a study have been considered (CASP, 2018).

The quality of research can be enhanced and assessed using criteria such as checklists, reflexivity, co-coding, and member-checking. 


Relying on only one researcher to interpret rich and complex data may risk key insights and alternative viewpoints being missed. Therefore, coding is often performed by multiple researchers.

A common strategy must be defined at the beginning of the coding process  (Busetto et al., 2020). This includes establishing a useful coding list and finding a common definition of individual codes.

Transcripts are initially coded independently by researchers and then compared and consolidated to minimize error or bias and to bring confirmation of findings. 

Member checking

Member checking (or respondent validation) involves checking back with participants to see if the research resonates with their experiences (Russell & Gregory, 2003).

Data can be returned to participants after data collection or when results are first available. For example, participants may be provided with their interview transcript and asked to verify whether this is a complete and accurate representation of their views.

Participants may then clarify or elaborate on their responses to ensure they align with their views (Shenton, 2004).

This feedback becomes part of data collection and ensures accurate descriptions/ interpretations of phenomena (Mays & Pope, 2000). 

Reflexivity in qualitative research

Reflexivity typically involves examining your own judgments, practices, and belief systems during data collection and analysis. It aims to identify any personal beliefs which may affect the research. 

Reflexivity is essential in qualitative research to ensure methodological transparency and complete reporting. This enables readers to understand how the interaction between the researcher and participant shapes the data.

Depending on the research question and population being researched, factors that need to be considered include the experience of the researcher, how the contact was established and maintained, age, gender, and ethnicity.

These details are important because, in qualitative research, the researcher is a dynamic part of the research process and actively influences the outcome of the research (Boeije, 2014). 

Reflexivity Example

Who you are and your characteristics influence how you collect and analyze data. Here is an example of a reflexivity statement for research on smoking. I am a 30-year-old white female from a middle-class background. I live in the southwest of England and have been educated to master’s level. I have been involved in two research projects on oral health. I have never smoked, but I have witnessed how smoking can cause ill health from my volunteering in a smoking cessation clinic. My research aspirations are to help to develop interventions to help smokers quit.

Establishing Trustworthiness in Qualitative Research

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability.

Credibility in Qualitative Research

Credibility refers to how accurately the results represent the reality and viewpoints of the participants.

To establish credibility in research, participants’ views and the researcher’s representation of their views need to align (Tobin & Begley, 2004).

To increase the credibility of findings, researchers may use data source triangulation, investigator triangulation, peer debriefing, or member checking (Lincoln & Guba, 1985). 

Transferability in Qualitative Research

Transferability refers to how generalizable the findings are: whether the findings may be applied to another context, setting, or group (Tobin & Begley, 2004).

Transferability can be enhanced by giving thorough and in-depth descriptions of the research setting, sample, and methods (Nowell et al., 2017). 

Dependability in Qualitative Research

Dependability is the extent to which the study could be replicated under similar conditions and the findings would be consistent.

Researchers can establish dependability using methods such as audit trails so readers can see the research process is logical and traceable (Koch, 1994).

Confirmability in Qualitative Research

Confirmability is concerned with establishing that there is a clear link between the researcher’s interpretations/ findings and the data.

Researchers can achieve confirmability by demonstrating how conclusions and interpretations were arrived at (Nowell et al., 2017).

This enables readers to understand the reasoning behind the decisions made. 

Audit Trails in Qualitative Research

An audit trail provides evidence of the decisions made by the researcher regarding theory, research design, and data collection, as well as the steps they have chosen to manage, analyze, and report data. 

The researcher must provide a clear rationale to demonstrate how conclusions were reached in their study.

A clear description of the research path must be provided to enable readers to trace through the researcher’s logic (Halpren, 1983).

Researchers should maintain records of the raw data, field notes, transcripts, and a reflective journal in order to provide a clear audit trail. 

Discovery of unexpected data

Open-ended questions in qualitative research mean the researcher can probe an interview topic and enable the participant to elaborate on responses in an unrestricted manner.

This allows unexpected data to emerge, which can lead to further research into that topic. 

The exploratory nature of qualitative research helps generate hypotheses that can be tested quantitatively (Busetto et al., 2020).


Data collection and analysis can be modified and adapted to take the research in a different direction if new ideas or patterns emerge in the data.

This enables researchers to investigate new opportunities while firmly maintaining their research goals. 

Naturalistic settings

The behaviors of participants are recorded in real-world settings. Studies that use real-world settings have high ecological validity since participants behave more authentically. 


Time-consuming .

Qualitative research results in large amounts of data which often need to be transcribed and analyzed manually.

Even when software is used, transcription can be inaccurate, and using software for analysis can result in many codes which need to be condensed into themes. 


The researcher has an integral role in collecting and interpreting qualitative data. Therefore, the conclusions reached are from their perspective and experience.

Consequently, interpretations of data from another researcher may vary greatly. 

Limited generalizability

The aim of qualitative research is to provide a detailed, contextualized understanding of an aspect of the human experience from a relatively small sample size.

Despite rigorous analysis procedures, conclusions drawn cannot be generalized to the wider population since data may be biased or unrepresentative.

Therefore, results are only applicable to a small group of the population. 

Extraneous variables

Qualitative research is often conducted in real-world settings. This may cause results to be unreliable since extraneous variables may affect the data, for example:

  • Situational variables : different environmental conditions may influence participants’ behavior in a study. The random variation in factors (such as noise or lighting) may be difficult to control in real-world settings.
  • Participant characteristics : this includes any characteristics that may influence how a participant answers/ behaves in a study. This may include a participant’s mood, gender, age, ethnicity, sexual identity, IQ, etc.
  • Experimenter effect : experimenter effect refers to how a researcher’s unintentional influence can change the outcome of a study. This occurs when (i) their interactions with participants unintentionally change participants’ behaviors or (ii) due to errors in observation, interpretation, or analysis. 

What sample size should qualitative research be?

The sample size for qualitative studies has been recommended to include a minimum of 12 participants to reach data saturation (Braun, 2013).

Are surveys qualitative or quantitative?

Surveys can be used to gather information from a sample qualitatively or quantitatively. Qualitative surveys use open-ended questions to gather detailed information from a large sample using free text responses.

The use of open-ended questions allows for unrestricted responses where participants use their own words, enabling the collection of more in-depth information than closed-ended questions.

In contrast, quantitative surveys consist of closed-ended questions with multiple-choice answer options. Quantitative surveys are ideal to gather a statistical representation of a population.

What are the ethical considerations of qualitative research?

Before conducting a study, you must think about any risks that could occur and take steps to prevent them. Participant Protection : Researchers must protect participants from physical and mental harm. This means you must not embarrass, frighten, offend, or harm participants. Transparency : Researchers are obligated to clearly communicate how they will collect, store, analyze, use, and share the data. Confidentiality : You need to consider how to maintain the confidentiality and anonymity of participants’ data.

What is triangulation in qualitative research?

Triangulation refers to the use of several approaches in a study to comprehensively understand phenomena. This method helps to increase the validity and credibility of research findings. 

Types of triangulation include method triangulation (using multiple methods to gather data); investigator triangulation (multiple researchers for collecting/ analyzing data), theory triangulation (comparing several theoretical perspectives to explain a phenomenon), and data source triangulation (using data from various times, locations, and people; Carter et al., 2014).

Why is qualitative research important?

Qualitative research allows researchers to describe and explain the social world. The exploratory nature of qualitative research helps to generate hypotheses that can then be tested quantitatively.

In qualitative research, participants are able to express their thoughts, experiences, and feelings without constraint.

Additionally, researchers are able to follow up on participants’ answers in real-time, generating valuable discussion around a topic. This enables researchers to gain a nuanced understanding of phenomena which is difficult to attain using quantitative methods.

What is coding data in qualitative research?

Coding data is a qualitative data analysis strategy in which a section of text is assigned with a label that describes its content.

These labels may be words or phrases which represent important (and recurring) patterns in the data.

This process enables researchers to identify related content across the dataset. Codes can then be used to group similar types of data to generate themes.

What is the difference between qualitative and quantitative research?

Qualitative research involves the collection and analysis of non-numerical data in order to understand experiences and meanings from the participant’s perspective.

This can provide rich, in-depth insights on complicated phenomena. Qualitative data may be collected using interviews, focus groups, or observations.

In contrast, quantitative research involves the collection and analysis of numerical data to measure the frequency, magnitude, or relationships of variables. This can provide objective and reliable evidence that can be generalized to the wider population.

Quantitative data may be collected using closed-ended questionnaires or experiments.

What is trustworthiness in qualitative research?

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability. 

Credibility refers to how accurately the results represent the reality and viewpoints of the participants. Transferability refers to whether the findings may be applied to another context, setting, or group.

Dependability is the extent to which the findings are consistent and reliable. Confirmability refers to the objectivity of findings (not influenced by the bias or assumptions of researchers).

What is data saturation in qualitative research?

Data saturation is a methodological principle used to guide the sample size of a qualitative research study.

Data saturation is proposed as a necessary methodological component in qualitative research (Saunders et al., 2018) as it is a vital criterion for discontinuing data collection and/or analysis. 

The intention of data saturation is to find “no new data, no new themes, no new coding, and ability to replicate the study” (Guest et al., 2006). Therefore, enough data has been gathered to make conclusions.

Why is sampling in qualitative research important?

In quantitative research, large sample sizes are used to provide statistically significant quantitative estimates.

This is because quantitative research aims to provide generalizable conclusions that represent populations.

However, the aim of sampling in qualitative research is to gather data that will help the researcher understand the depth, complexity, variation, or context of a phenomenon. The small sample sizes in qualitative studies support the depth of case-oriented analysis.

Boeije, H. (2014). Analysis in qualitative research. Sage.

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Qualitative Study


  • 1 University of Nebraska Medical Center
  • 2 GDB Research and Statistical Consulting
  • 3 GDB Research and Statistical Consulting/McLaren Macomb Hospital
  • PMID: 29262162
  • Bookshelf ID: NBK470395

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypothenar to further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a standalone study, purely relying on qualitative data, or part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and applications of qualitative research.

Qualitative research, at its core, asks open-ended questions whose answers are not easily put into numbers, such as "how" and "why." Due to the open-ended nature of the research questions, qualitative research design is often not linear like quantitative design. One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. Phenomena such as experiences, attitudes, and behaviors can be complex to capture accurately and quantitatively. In contrast, a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a particular time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify, and it is essential to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore "compete" against each other and the philosophical paradigms associated with each other, qualitative and quantitative work are neither necessarily opposites, nor are they incompatible. While qualitative and quantitative approaches are different, they are not necessarily opposites and certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated.

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Qualitative Methods in Health Care Research

Vishnu renjith.

School of Nursing and Midwifery, Royal College of Surgeons Ireland - Bahrain (RCSI Bahrain), Al Sayh Muharraq Governorate, Bahrain

Renjulal Yesodharan

1 Department of Mental Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Judith A. Noronha

2 Department of OBG Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Elissa Ladd

3 School of Nursing, MGH Institute of Health Professions, Boston, USA

Anice George

4 Department of Child Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Healthcare research is a systematic inquiry intended to generate robust evidence about important issues in the fields of medicine and healthcare. Qualitative research has ample possibilities within the arena of healthcare research. This article aims to inform healthcare professionals regarding qualitative research, its significance, and applicability in the field of healthcare. A wide variety of phenomena that cannot be explained using the quantitative approach can be explored and conveyed using a qualitative method. The major types of qualitative research designs are narrative research, phenomenological research, grounded theory research, ethnographic research, historical research, and case study research. The greatest strength of the qualitative research approach lies in the richness and depth of the healthcare exploration and description it makes. In health research, these methods are considered as the most humanistic and person-centered way of discovering and uncovering thoughts and actions of human beings.


Healthcare research is a systematic inquiry intended to generate trustworthy evidence about issues in the field of medicine and healthcare. The three principal approaches to health research are the quantitative, the qualitative, and the mixed methods approach. The quantitative research method uses data, which are measures of values and counts and are often described using statistical methods which in turn aids the researcher to draw inferences. Qualitative research incorporates the recording, interpreting, and analyzing of non-numeric data with an attempt to uncover the deeper meanings of human experiences and behaviors. Mixed methods research, the third methodological approach, involves collection and analysis of both qualitative and quantitative information with an objective to solve different but related questions, or at times the same questions.[ 1 , 2 ]

In healthcare, qualitative research is widely used to understand patterns of health behaviors, describe lived experiences, develop behavioral theories, explore healthcare needs, and design interventions.[ 1 , 2 , 3 ] Because of its ample applications in healthcare, there has been a tremendous increase in the number of health research studies undertaken using qualitative methodology.[ 4 , 5 ] This article discusses qualitative research methods, their significance, and applicability in the arena of healthcare.

Qualitative Research

Diverse academic and non-academic disciplines utilize qualitative research as a method of inquiry to understand human behavior and experiences.[ 6 , 7 ] According to Munhall, “Qualitative research involves broadly stated questions about human experiences and realities, studied through sustained contact with the individual in their natural environments and producing rich, descriptive data that will help us to understand those individual's experiences.”[ 8 ]

Significance of Qualitative Research

The qualitative method of inquiry examines the 'how' and 'why' of decision making, rather than the 'when,' 'what,' and 'where.'[ 7 ] Unlike quantitative methods, the objective of qualitative inquiry is to explore, narrate, and explain the phenomena and make sense of the complex reality. Health interventions, explanatory health models, and medical-social theories could be developed as an outcome of qualitative research.[ 9 ] Understanding the richness and complexity of human behavior is the crux of qualitative research.

Differences between Quantitative and Qualitative Research

The quantitative and qualitative forms of inquiry vary based on their underlying objectives. They are in no way opposed to each other; instead, these two methods are like two sides of a coin. The critical differences between quantitative and qualitative research are summarized in Table 1 .[ 1 , 10 , 11 ]

Differences between quantitative and qualitative research

Qualitative Research Questions and Purpose Statements

Qualitative questions are exploratory and are open-ended. A well-formulated study question forms the basis for developing a protocol, guides the selection of design, and data collection methods. Qualitative research questions generally involve two parts, a central question and related subquestions. The central question is directed towards the primary phenomenon under study, whereas the subquestions explore the subareas of focus. It is advised not to have more than five to seven subquestions. A commonly used framework for designing a qualitative research question is the 'PCO framework' wherein, P stands for the population under study, C stands for the context of exploration, and O stands for the outcome/s of interest.[ 12 ] The PCO framework guides researchers in crafting a focused study question.

Example: In the question, “What are the experiences of mothers on parenting children with Thalassemia?”, the population is “mothers of children with Thalassemia,” the context is “parenting children with Thalassemia,” and the outcome of interest is “experiences.”

The purpose statement specifies the broad focus of the study, identifies the approach, and provides direction for the overall goal of the study. The major components of a purpose statement include the central phenomenon under investigation, the study design and the population of interest. Qualitative research does not require a-priori hypothesis.[ 13 , 14 , 15 ]

Example: Borimnejad et al . undertook a qualitative research on the lived experiences of women suffering from vitiligo. The purpose of this study was, “to explore lived experiences of women suffering from vitiligo using a hermeneutic phenomenological approach.” [ 16 ]

Review of the Literature

In quantitative research, the researchers do an extensive review of scientific literature prior to the commencement of the study. However, in qualitative research, only a minimal literature search is conducted at the beginning of the study. This is to ensure that the researcher is not influenced by the existing understanding of the phenomenon under the study. The minimal literature review will help the researchers to avoid the conceptual pollution of the phenomenon being studied. Nonetheless, an extensive review of the literature is conducted after data collection and analysis.[ 15 ]


Reflexivity refers to critical self-appraisal about one's own biases, values, preferences, and preconceptions about the phenomenon under investigation. Maintaining a reflexive diary/journal is a widely recognized way to foster reflexivity. According to Creswell, “Reflexivity increases the credibility of the study by enhancing more neutral interpretations.”[ 7 ]

Types of Qualitative Research Designs

The qualitative research approach encompasses a wide array of research designs. The words such as types, traditions, designs, strategies of inquiry, varieties, and methods are used interchangeably. The major types of qualitative research designs are narrative research, phenomenological research, grounded theory research, ethnographic research, historical research, and case study research.[ 1 , 7 , 10 ]

Narrative research

Narrative research focuses on exploring the life of an individual and is ideally suited to tell the stories of individual experiences.[ 17 ] The purpose of narrative research is to utilize 'story telling' as a method in communicating an individual's experience to a larger audience.[ 18 ] The roots of narrative inquiry extend to humanities including anthropology, literature, psychology, education, history, and sociology. Narrative research encompasses the study of individual experiences and learning the significance of those experiences. The data collection procedures include mainly interviews, field notes, letters, photographs, diaries, and documents collected from one or more individuals. Data analysis involves the analysis of the stories or experiences through “re-storying of stories” and developing themes usually in chronological order of events. Rolls and Payne argued that narrative research is a valuable approach in health care research, to gain deeper insight into patient's experiences.[ 19 ]

Example: Karlsson et al . undertook a narrative inquiry to “explore how people with Alzheimer's disease present their life story.” Data were collected from nine participants. They were asked to describe about their life experiences from childhood to adulthood, then to current life and their views about the future life. [ 20 ]

Phenomenological research

Phenomenology is a philosophical tradition developed by German philosopher Edmond Husserl. His student Martin Heidegger did further developments in this methodology. It defines the 'essence' of individual's experiences regarding a certain phenomenon.[ 1 ] The methodology has its origin from philosophy, psychology, and education. The purpose of qualitative research is to understand the people's everyday life experiences and reduce it into the central meaning or the 'essence of the experience'.[ 21 , 22 ] The unit of analysis of phenomenology is the individuals who have had similar experiences of the phenomenon. Interviews with individuals are mainly considered for the data collection, though, documents and observations are also useful. Data analysis includes identification of significant meaning elements, textural description (what was experienced), structural description (how was it experienced), and description of 'essence' of experience.[ 1 , 7 , 21 ] The phenomenological approach is further divided into descriptive and interpretive phenomenology. Descriptive phenomenology focuses on the understanding of the essence of experiences and is best suited in situations that need to describe the lived phenomenon. Hermeneutic phenomenology or Interpretive phenomenology moves beyond the description to uncover the meanings that are not explicitly evident. The researcher tries to interpret the phenomenon, based on their judgment rather than just describing it.[ 7 , 21 , 22 , 23 , 24 ]

Example: A phenomenological study conducted by Cornelio et al . aimed at describing the lived experiences of mothers in parenting children with leukemia. Data from ten mothers were collected using in-depth semi-structured interviews and were analyzed using Husserl's method of phenomenology. Themes such as “pivotal moment in life”, “the experience of being with a seriously ill child”, “having to keep distance with the relatives”, “overcoming the financial and social commitments”, “responding to challenges”, “experience of faith as being key to survival”, “health concerns of the present and future”, and “optimism” were derived. The researchers reported the essence of the study as “chronic illness such as leukemia in children results in a negative impact on the child and on the mother.” [ 25 ]

Grounded Theory Research

Grounded theory has its base in sociology and propagated by two sociologists, Barney Glaser, and Anselm Strauss.[ 26 ] The primary purpose of grounded theory is to discover or generate theory in the context of the social process being studied. The major difference between grounded theory and other approaches lies in its emphasis on theory generation and development. The name grounded theory comes from its ability to induce a theory grounded in the reality of study participants.[ 7 , 27 ] Data collection in grounded theory research involves recording interviews from many individuals until data saturation. Constant comparative analysis, theoretical sampling, theoretical coding, and theoretical saturation are unique features of grounded theory research.[ 26 , 27 , 28 ] Data analysis includes analyzing data through 'open coding,' 'axial coding,' and 'selective coding.'[ 1 , 7 ] Open coding is the first level of abstraction, and it refers to the creation of a broad initial range of categories, axial coding is the procedure of understanding connections between the open codes, whereas selective coding relates to the process of connecting the axial codes to formulate a theory.[ 1 , 7 ] Results of the grounded theory analysis are supplemented with a visual representation of major constructs usually in the form of flow charts or framework diagrams. Quotations from the participants are used in a supportive capacity to substantiate the findings. Strauss and Corbin highlights that “the value of the grounded theory lies not only in its ability to generate a theory but also to ground that theory in the data.”[ 27 ]

Example: Williams et al . conducted a grounded theory research to explore the nature of relationship between the sense of self and the eating disorders. Data were collected form 11 women with a lifetime history of Anorexia Nervosa and were analyzed using the grounded theory methodology. Analysis led to the development of a theoretical framework on the nature of the relationship between the self and Anorexia Nervosa. [ 29 ]

Ethnographic research

Ethnography has its base in anthropology, where the anthropologists used it for understanding the culture-specific knowledge and behaviors. In health sciences research, ethnography focuses on narrating and interpreting the health behaviors of a culture-sharing group. 'Culture-sharing group' in an ethnography represents any 'group of people who share common meanings, customs or experiences.' In health research, it could be a group of physicians working in rural care, a group of medical students, or it could be a group of patients who receive home-based rehabilitation. To understand the cultural patterns, researchers primarily observe the individuals or group of individuals for a prolonged period of time.[ 1 , 7 , 30 ] The scope of ethnography can be broad or narrow depending on the aim. The study of more general cultural groups is termed as macro-ethnography, whereas micro-ethnography focuses on more narrowly defined cultures. Ethnography is usually conducted in a single setting. Ethnographers collect data using a variety of methods such as observation, interviews, audio-video records, and document reviews. A written report includes a detailed description of the culture sharing group with emic and etic perspectives. When the researcher reports the views of the participants it is called emic perspectives and when the researcher reports his or her views about the culture, the term is called etic.[ 7 ]

Example: The aim of the ethnographic study by LeBaron et al . was to explore the barriers to opioid availability and cancer pain management in India. The researchers collected data from fifty-nine participants using in-depth semi-structured interviews, participant observation, and document review. The researchers identified significant barriers by open coding and thematic analysis of the formal interview. [ 31 ]

Historical research

Historical research is the “systematic collection, critical evaluation, and interpretation of historical evidence”.[ 1 ] The purpose of historical research is to gain insights from the past and involves interpreting past events in the light of the present. The data for historical research are usually collected from primary and secondary sources. The primary source mainly includes diaries, first hand information, and writings. The secondary sources are textbooks, newspapers, second or third-hand accounts of historical events and medical/legal documents. The data gathered from these various sources are synthesized and reported as biographical narratives or developmental perspectives in chronological order. The ideas are interpreted in terms of the historical context and significance. The written report describes 'what happened', 'how it happened', 'why it happened', and its significance and implications to current clinical practice.[ 1 , 10 ]

Example: Lubold (2019) analyzed the breastfeeding trends in three countries (Sweden, Ireland, and the United States) using a historical qualitative method. Through analysis of historical data, the researcher found that strong family policies, adherence to international recommendations and adoption of baby-friendly hospital initiative could greatly enhance the breastfeeding rates. [ 32 ]

Case study research

Case study research focuses on the description and in-depth analysis of the case(s) or issues illustrated by the case(s). The design has its origin from psychology, law, and medicine. Case studies are best suited for the understanding of case(s), thus reducing the unit of analysis into studying an event, a program, an activity or an illness. Observations, one to one interviews, artifacts, and documents are used for collecting the data, and the analysis is done through the description of the case. From this, themes and cross-case themes are derived. A written case study report includes a detailed description of one or more cases.[ 7 , 10 ]

Example: Perceptions of poststroke sexuality in a woman of childbearing age was explored using a qualitative case study approach by Beal and Millenbrunch. Semi structured interview was conducted with a 36- year mother of two children with a history of Acute ischemic stroke. The data were analyzed using an inductive approach. The authors concluded that “stroke during childbearing years may affect a woman's perception of herself as a sexual being and her ability to carry out gender roles”. [ 33 ]

Sampling in Qualitative Research

Qualitative researchers widely use non-probability sampling techniques such as purposive sampling, convenience sampling, quota sampling, snowball sampling, homogeneous sampling, maximum variation sampling, extreme (deviant) case sampling, typical case sampling, and intensity sampling. The selection of a sampling technique depends on the nature and needs of the study.[ 34 , 35 , 36 , 37 , 38 , 39 , 40 ] The four widely used sampling techniques are convenience sampling, purposive sampling, snowball sampling, and intensity sampling.

Convenience sampling

It is otherwise called accidental sampling, where the researchers collect data from the subjects who are selected based on accessibility, geographical proximity, ease, speed, and or low cost.[ 34 ] Convenience sampling offers a significant benefit of convenience but often accompanies the issues of sample representation.

Purposive sampling

Purposive or purposeful sampling is a widely used sampling technique.[ 35 ] It involves identifying a population based on already established sampling criteria and then selecting subjects who fulfill that criteria to increase the credibility. However, choosing information-rich cases is the key to determine the power and logic of purposive sampling in a qualitative study.[ 1 ]

Snowball sampling

The method is also known as 'chain referral sampling' or 'network sampling.' The sampling starts by having a few initial participants, and the researcher relies on these early participants to identify additional study participants. It is best adopted when the researcher wishes to study the stigmatized group, or in cases, where findings of participants are likely to be difficult by ordinary means. Respondent ridden sampling is an improvised version of snowball sampling used to find out the participant from a hard-to-find or hard-to-study population.[ 37 , 38 ]

Intensity sampling

The process of identifying information-rich cases that manifest the phenomenon of interest is referred to as intensity sampling. It requires prior information, and considerable judgment about the phenomenon of interest and the researcher should do some preliminary investigations to determine the nature of the variation. Intensity sampling will be done once the researcher identifies the variation across the cases (extreme, average and intense) and picks the intense cases from them.[ 40 ]

Deciding the Sample Size

A-priori sample size calculation is not undertaken in the case of qualitative research. Researchers collect the data from as many participants as possible until they reach the point of data saturation. Data saturation or the point of redundancy is the stage where the researcher no longer sees or hears any new information. Data saturation gives the idea that the researcher has captured all possible information about the phenomenon of interest. Since no further information is being uncovered as redundancy is achieved, at this point the data collection can be stopped. The objective here is to get an overall picture of the chronicle of the phenomenon under the study rather than generalization.[ 1 , 7 , 41 ]

Data Collection in Qualitative Research

The various strategies used for data collection in qualitative research includes in-depth interviews (individual or group), focus group discussions (FGDs), participant observation, narrative life history, document analysis, audio materials, videos or video footage, text analysis, and simple observation. Among all these, the three popular methods are the FGDs, one to one in-depth interviews and the participant observation.

FGDs are useful in eliciting data from a group of individuals. They are normally built around a specific topic and are considered as the best approach to gather data on an entire range of responses to a topic.[ 42 Group size in an FGD ranges from 6 to 12. Depending upon the nature of participants, FGDs could be homogeneous or heterogeneous.[ 1 , 14 ] One to one in-depth interviews are best suited to obtain individuals' life histories, lived experiences, perceptions, and views, particularly while exporting topics of sensitive nature. In-depth interviews can be structured, unstructured, or semi-structured. However, semi-structured interviews are widely used in qualitative research. Participant observations are suitable for gathering data regarding naturally occurring behaviors.[ 1 ]

Data Analysis in Qualitative Research

Various strategies are employed by researchers to analyze data in qualitative research. Data analytic strategies differ according to the type of inquiry. A general content analysis approach is described herewith. Data analysis begins by transcription of the interview data. The researcher carefully reads data and gets a sense of the whole. Once the researcher is familiarized with the data, the researcher strives to identify small meaning units called the 'codes.' The codes are then grouped based on their shared concepts to form the primary categories. Based on the relationship between the primary categories, they are then clustered into secondary categories. The next step involves the identification of themes and interpretation to make meaning out of data. In the results section of the manuscript, the researcher describes the key findings/themes that emerged. The themes can be supported by participants' quotes. The analytical framework used should be explained in sufficient detail, and the analytic framework must be well referenced. The study findings are usually represented in a schematic form for better conceptualization.[ 1 , 7 ] Even though the overall analytical process remains the same across different qualitative designs, each design such as phenomenology, ethnography, and grounded theory has design specific analytical procedures, the details of which are out of the scope of this article.

Computer-Assisted Qualitative Data Analysis Software (CAQDAS)

Until recently, qualitative analysis was done either manually or with the help of a spreadsheet application. Currently, there are various software programs available which aid researchers to manage qualitative data. CAQDAS is basically data management tools and cannot analyze the qualitative data as it lacks the ability to think, reflect, and conceptualize. Nonetheless, CAQDAS helps researchers to manage, shape, and make sense of unstructured information. Open Code, MAXQDA, NVivo, Atlas.ti, and Hyper Research are some of the widely used qualitative data analysis software.[ 14 , 43 ]

Reporting Guidelines

Consolidated Criteria for Reporting Qualitative Research (COREQ) is the widely used reporting guideline for qualitative research. This 32-item checklist assists researchers in reporting all the major aspects related to the study. The three major domains of COREQ are the 'research team and reflexivity', 'study design', and 'analysis and findings'.[ 44 , 45 ]

Critical Appraisal of Qualitative Research

Various scales are available to critical appraisal of qualitative research. The widely used one is the Critical Appraisal Skills Program (CASP) Qualitative Checklist developed by CASP network, UK. This 10-item checklist evaluates the quality of the study under areas such as aims, methodology, research design, ethical considerations, data collection, data analysis, and findings.[ 46 ]

Ethical Issues in Qualitative Research

A qualitative study must be undertaken by grounding it in the principles of bioethics such as beneficence, non-maleficence, autonomy, and justice. Protecting the participants is of utmost importance, and the greatest care has to be taken while collecting data from a vulnerable research population. The researcher must respect individuals, families, and communities and must make sure that the participants are not identifiable by their quotations that the researchers include when publishing the data. Consent for audio/video recordings must be obtained. Approval to be in FGDs must be obtained from the participants. Researchers must ensure the confidentiality and anonymity of the transcripts/audio-video records/photographs/other data collected as a part of the study. The researchers must confirm their role as advocates and proceed in the best interest of all participants.[ 42 , 47 , 48 ]

Rigor in Qualitative Research

The demonstration of rigor or quality in the conduct of the study is essential for every research method. However, the criteria used to evaluate the rigor of quantitative studies are not be appropriate for qualitative methods. Lincoln and Guba (1985) first outlined the criteria for evaluating the qualitative research often referred to as “standards of trustworthiness of qualitative research”.[ 49 ] The four components of the criteria are credibility, transferability, dependability, and confirmability.

Credibility refers to confidence in the 'truth value' of the data and its interpretation. It is used to establish that the findings are true, credible and believable. Credibility is similar to the internal validity in quantitative research.[ 1 , 50 , 51 ] The second criterion to establish the trustworthiness of the qualitative research is transferability, Transferability refers to the degree to which the qualitative results are applicability to other settings, population or contexts. This is analogous to the external validity in quantitative research.[ 1 , 50 , 51 ] Lincoln and Guba recommend authors provide enough details so that the users will be able to evaluate the applicability of data in other contexts.[ 49 ] The criterion of dependability refers to the assumption of repeatability or replicability of the study findings and is similar to that of reliability in quantitative research. The dependability question is 'Whether the study findings be repeated of the study is replicated with the same (similar) cohort of participants, data coders, and context?'[ 1 , 50 , 51 ] Confirmability, the fourth criteria is analogous to the objectivity of the study and refers the degree to which the study findings could be confirmed or corroborated by others. To ensure confirmability the data should directly reflect the participants' experiences and not the bias, motivations, or imaginations of the inquirer.[ 1 , 50 , 51 ] Qualitative researchers should ensure that the study is conducted with enough rigor and should report the measures undertaken to enhance the trustworthiness of the study.


Qualitative research studies are being widely acknowledged and recognized in health care practice. This overview illustrates various qualitative methods and shows how these methods can be used to generate evidence that informs clinical practice. Qualitative research helps to understand the patterns of health behaviors, describe illness experiences, design health interventions, and develop healthcare theories. The ultimate strength of the qualitative research approach lies in the richness of the data and the descriptions and depth of exploration it makes. Hence, qualitative methods are considered as the most humanistic and person-centered way of discovering and uncovering thoughts and actions of human beings.

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Research Article

"You don’t get side effects from social prescribing”—A qualitative study exploring community pharmacists’ attitudes to social prescribing

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom

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Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Writing – original draft, Writing – review & editing

Roles Writing – review & editing

Affiliation Independent Research Pharmacist, United Kingdom

Roles Writing – original draft, Writing – review & editing

Affiliation Nuffield Department of Primary Care Science, University of Oxford, Oxford, United Kingdom

Roles Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

Roles Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

  • Adam Pattison Rathbone, 
  • Harry Pearson, 
  • Oluwafunmi Akinyemi, 
  • Nia Cartwright, 
  • Stephanie Tierney, 
  • Gill Rowlands, 
  • Laura Lindsey


  • Published: May 16, 2024
  • Reader Comments

Table 1

Social prescribing is an approach that enables the referral of patients to non-clinical support and places a focus on holistic care. This study explored views of community pharmacists regarding social prescribing in pharmacies.

Study design

A qualitative phenomenological approach was used.

A convenience sample of eleven community pharmacists from Northern England were recruited via social media (Twitter, Facebook) and took part in a semi-structured, one-to-one qualitative interviews that asked about their knowledge of social prescribing, the advantages of community pharmacist involvement and any barriers they predicted to its implementation. Interviews were transcribed verbatim and thematically analysed.

The sample included largely male pharmacists (63.3%) with less than five years’ experience (45.5%) and included pharmacists working as employees (63.6%), locums (27.3%) and owners (9%) in both chain (36%) and independent stores (54.5%). The main findings indicate an enthusiasm for but limited understanding of social prescribing. Factors which appeared to influence involvement were training requirements and time available to complete an additional service in busy pharmacies. Opportunities centred on the broader pharmacy team’s role to optimise health outcomes.


The findings indicate pharmacists may be an underused resource due to a poor understanding of the full scale and scope of social prescribing beyond health promotion, lifestyle interventions. Further work is needed to explore the transferability of the findings to the broader pharmacy workforce to understand how social prescribing can be positioned within pharmacy practice.

Citation: Rathbone AP, Pearson H, Akinyemi O, Cartwright N, Tierney S, Rowlands G, et al. (2024) "You don’t get side effects from social prescribing”—A qualitative study exploring community pharmacists’ attitudes to social prescribing. PLoS ONE 19(5): e0301076.

Editor: Simon White, Keele University, UNITED KINGDOM

Received: September 18, 2023; Accepted: March 9, 2024; Published: May 16, 2024

Copyright: © 2024 Rathbone et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: There are ethical and legal restrictions on sharing the de-identified data set. Participants did not give explicit consent for the de-identified data set to be shared as participants were told the data would be kept confidential. Anonymized data is held at an online repository under embargo. This restriction is imposed by the University Ethics Committee. Please contact [email protected] for further information.

Funding: The author(s0 received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.


Social prescribing has been described as: “ a means for health-care workers to connect patients to a range of non-clinical services in the community to improve health and well-being . Social prescribing can help to address the underlying causes of patients’ health and well-being issues , as opposed to simply treating symptoms .” [ 1 ]. It has become a cornerstone of healthcare policy in the UK [ 2 ] and overseas [ 3 ]. Patients are often referred for social prescribing by a General Practitioner (GP), who contacts a link worker to meet with the patient to identify appropriate support for their non-medical needs [ 3 ]. Exercise classes, arts and crafts, volunteering, gardening, and cookery classes, as well as accommodation and debt management services, are among the types of support patients might be connected to by a link worker [ 4 – 6 ].

It is argued that social prescribing can be useful for people living with long-term health conditions, mental health problems, socioeconomic struggles and social isolation–the latter of which has become more prevalent following the COVID-19 pandemic [ 7 ]. There is some evidence suggesting engagement in social prescribing may reduce demand for non-elective healthcare [ 8 ] and GP attendances [ 9 ]. Hence, it may be a means of addressing demand on overstretched healthcare services, supporting the broader well-being of vulnerable, socioeconomically disadvantaged communities. However, it should be noted, claims social prescribing reduces health inequalities for socioeconomically deprived communities are still considered contentious and their impact on reducing demand of some healthcare services, such as pharmacies, is not known [ 10 – 13 ].

Approaches to expanding access to social prescribing are being explored in the UK through ‘proactive social prescribing’ [ 14 ], where patient populations are screened by primary care professionals to identify target groups with unmet needs. Other examples of improving access to social prescribing are schemes such as digital self-referrals, where an app matches patients with appropriate support in the community [ 15 ]. In addition, an appeal for further healthcare professionals, including pharmacists, to take a role in social prescribing has been made [ 16 ].

Pharmacies offer open access to healthcare for a wide range of people, in both rural and urban settings. Pharmacies are known to be more accessible than GPs in areas of high socioeconomic deprivation [ 17 ]. Evidence suggests during the pandemic in the UK in 2020, over a third of patients visited their community pharmacy instead of their GP practice [ 18 ] although it was unclear if this prevented follow-up visits to GP practices. Although pharmacies are reporting high workloads [ 19 – 21 ], their accessibility makes them suitable for healthcare initiatives to reduce demand on existing health services [ 15 , 17 ]. Despite this, funding for pharmacies in the UK is focused primarily on dispensing medications rather than patient-facing services [ 22 ]. Recent changes to policy, such as the NHS Long Term Plan, Healthy Living Pharmacy Framework, Pharmacy First and the Community Pharmacy Consultation Scheme, indicate pharmacies will be an increasingly important place for the delivery of healthcare services, both urgent and preventative, in the future [ 2 , 23 – 26 ].

The evidence evaluating social prescribing interventions in pharmacy is limited. A systematic review in 2017 found little evidence of the efficacy of social prescribing in community settings, due to the short-term nature of the evaluations [ 27 ]. Other work focusing specifically on pharmacies found similarly limited literature [ 28 ]. To improve the existing evidence for social prescribing in pharmacy, evaluations must start from the foundations and work up; identifying capabilities, opportunities and motivations of pharmacy teams as well as the impact on patients, community groups and other health and social care professionals in the system. Little is known about pharmacists’ awareness and understanding of social prescribing and what factors influence their involvement in this non-clinical activity [ 28 , 29 ]. What evidence does exist suggests workload and funding may limit the involvement of pharmacists, and that these professionals may have limited awareness of what social prescribing is [ 29 ]. This existing evidence is based on quantitative methods and, thus, did not provide a detailed description of pharmacists’ experiences of social prescribing in practice. Hence, the aim of our study was to explore community pharmacists’ experiences, perspectives and attitudes to social prescribing in practice.

A qualitative phenomenological approach was adopted, which drew on the Capability Opportunity Motivation–Behaviour (COM-B) model [ 30 ]. A phenomenological approach allowed the study to focus on community pharmacists’ experiences of what happens in practice and how it happens [ 31 ]. Specifically, the COM-B model was used to create a topic guide to use during interviews and added structure to the findings following the identification of themes. As evidence in relation to pharmacists’ roles in social prescribing is limited, an exploratory design was appropriate [ 30 ].

Participants and recruitment

A convenience sampling method was adopted. A form was posted on social media (Twitter and Facebook) to allow participants to self-screen against inclusion criteria. The criteria included having experience working as a community pharmacist in Northern England, conversant in English, and capacity to consent to research. Snowball sampling was also used to recruit participants to the study.

The form was created and posted on social media via the research team (APR, HP, LL) outlining the study. Users completed screening questions for eligibility and were prompted to give an email address and telephone number to be contacted. The decision to limit to Northern England was pragmatic as researchers were based there. It also allowed the study to recruit pharmacists practicing in areas of high deprivation, where pharmacists are known to be more readily accessed by patients than in areas of low deprivation [ 17 ]. A participant information sheet was provided to interviewees in advance, and verbal consent was taken prior to participation, which was recorded by the interviewer (HP) and witnessed by the supervisor (APR).

Methods of data collection

Semi-structured interviews were conducted via the online platforms Zoom and Microsoft Teams, and over the telephone between Monday 5th October 2020 and Friday 29 th January 2021. The semi-structured nature of the interviews allowed for an in-depth exploration of pharmacists’ views, which would be unobtainable via a survey [ 32 ]. Interviews were conducted at a time convenient to the participant. Interviews lasted between 30 and 45 minutes (average = 39 minutes). A topic guide was used (see Tables 1 and 2 ) that included questions such as: i) What are your experiences of social prescribing? ii) What do you understand as the advantages of social prescribing in community pharmacy? iii) What do you think are the barriers to implementing social prescribing in community pharmacy?


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One-to-one interviews were conducted by a final year pharmacy student (HP). He was trained by experienced qualitative health researchers (LL, GR, APR). Interviews were audio-recorded and transcribed manually by one author (HP) with a 10% sample quality checked by listening back to the audio and reading the transcript (APR) [ 33 , 34 ]. Transcripts were anonymised by removing the names of participants, people, and places [ 33 , 34 ]. Data collection ceased at the point of theoretical data sufficiency [ 35 ], which occurred after ten interviews; one additional interview was conducted to confirm this was the case. Theoretical data sufficiency relates to the point in the study at which no new findings are being identified in the data. This was operationalised in the study through regular weekly supervision meetings during data collection and analysis to interrogate, explore and identify when no new findings were being found. This point indicates the research team had access to sufficient data to draw conclusions, though due to the nature of qualitative inquiry, further findings may be found by new researchers looking at the same data.

Data processing and analysis

Transcripts were imported into NVivo and inductive thematic analysis was completed by three researchers (HP, APR, LL) using the method outlined by Braun and Clarke (33). The first step of this analysis began with familiarisation with the data, next there was generation of initial codes, then clusters were created, and finally themes. A constant comparative approach was adopted, which meant codes, clusters and themes were compared with one another and findings interrogated in data presentation meetings involving the authors. This process was underpinned by a phenomenological understanding of experiences, which focuses on what the essence of an experience is and how this happens–i.e., what was happening and how was it happening. The COM-B Model was then used to contextualise the themes to link what happened to behavioural theory.

As part of the analysis, participants were categorised based on their role within community pharmacy and the length of time since registration. Participants with experience of less than five years were classified as ‘pharmacists with less experience’. This classification is in line with The Royal Pharmaceutical Society (RPS) Foundation Pharmacist Framework [ 36 ]. Participants with experience greater than this were categorised as ‘pharmacists with more experience’. This follows the RPS Advanced Pharmacy Framework [ 37 ]. Credibility was defined as the ability of the findings of the study to be reasonably believed and dependability was defined as the ability to trust the research process was carried out accurately. Credibility and dependability were established by involving more than one person in the analysis (sometimes referred to as analytical triangulation), through presentation and discussion of data at regular coding meetings with the research team. Weekly meetings were also used to ensure senior authors (LL, GR, APR) provided suitable training, support, supervision and accountability to the team (HP, NC). The study processes and findings were also reviewed by external collaborators (OA, ST) which further enhanced the credibility and dependability of the findings.


Reflexivity allows research authors to become aware of, respond to and acknowledge how their own personal characteristics, identity and perspectives influences research [ 38 ]. In this study, the authors came from working class, middle class and upper middle-class backgrounds, where mostly White and came from Britain. Two authors were not from Britain and two authors were not White. Three authors were pharmacists, one was a general practitioner and one a psychologist. Four authors had completed, and one author was completing, a PhD. The research was led by a team based in a School of Pharmacy and this meant members of the research team may have been well known to participants as former educators (LL, APR) or colleagues (HP, OA). This meant there was a shared understanding of language and terminology between participants and researchers which enriched the subjectivity of the study during data collection. However, other members of the research team (NC, ST, GR) were less professionally connected to the pharmacy sector and so provided an objective perspective during data analysis and interpretation.

Research ethics

Ethical approval for this study was obtained through Newcastle University (reference number 6162/2020).

Participant demographics

Eleven participants were recruited and demographics are summarised in Table 1 .

Thematic findings

Findings are described below, with codes, clusters and themes shown in Table 2 . Data extracts describe findings in participants’ own words. Quotes denote if participants were employee, locum or owner pharmacists and which ‘type’ of pharmacy they worked in–either an ‘independent’ pharmacy which refers to a small chain, local, or single pharmacy business or a ‘multiple’ pharmacy chain which refers to a large, multi-national pharmacy corporation with many pharmacy businesses operating under a single banner.

Theme 1) Varied knowledge and understanding of social prescribing.

Most participants seemed to have an awareness of and enthusiasm for social prescribing, although they reported little knowledge of it. The setting where participants worked, their status or level of experience did not appear to influence knowledge or reported beliefs about social prescribing. Participants who were aware of social prescribing appeared to know about it either from involvement in a social prescribing event, through prescribing community-based, non-clinical support themselves, or having heard about the process in previous employment.

“ I was running group clinics…where we don’t just talk about their medicines, we talk about interventions like exercise, or lifestyle advice or diet. It was much more informal, but we would make recommendations to patients like a Tai Chi class for example, that you would benefit from, or you might be better off doing some core strengthening exercises given your type of arthritis.” Participant 6 (Pharmacist, Independent)

Despite a limited understanding, pharmacists appeared to believe they had capability to support social prescribing. However, they appeared to view it as a clinician-led approach, focusing on the physical symptoms of disease, rather than a person-centred approach to address socioeconomic factors of health and well-being, directed by the patient to address their specific needs. This indicated social prescribing was being conflated with public health promotion, lifestyle campaigns.

“ I can really see where [social prescribing] would fit in that remit, so the kind of physical and the recommendation for physical activity, how it can help with a number of different medical conditions… we’ve got a really good knowledge base of different health conditions and generally kind of how the body works. So why not use that and I don’t think we’re using it a lot at the minute.” Participant 1 (Locum, Independent)

These findings demonstrate the nuance of pharmacists’ approach to social prescribing, in that, enthusiasm toward social prescribing was reported, but that this appeared to be based on an understanding of social prescribing as an aspect of health promotion and lifestyle interventions based on physical disease states, rather than socio-economic circumstances of the patient.

Theme 2) Factors influencing involvement in social prescribing.

Concern about the economics of a pharmacy businesses, the balance of workload and funding, was a recurring factor which appeared to shape participants’ thoughts about involvement in social prescribing. Participants reported the busy nature of community pharmacy and highlighted how much additional time would be needed to engage with social prescribing.

“ Well with the increase of both dispensing items, the more and more consultations that we are having to do, as well as the fact some stores due to cuts [to funding] have had to get rid of managers, that then falls on the pharmacist’s desk, there’s a lot less time for patient-pharmacist discussions. So timing is going to be a major issue I think.” Participant 11 (Employee, Multiple)

Additional demands and additional time pressure, following on from the relentless experience encountered during the pandemic for many pharmacists through involvement in vaccination and increased workload, were concerns participants shared.

“ I certainly have worked over the COVID-19 pandemic in community myself, I know how ridiculously busy we’ve been. You know to try and fit in another additional service on top of all of the ones that are already being offered… but I think currently I’ve never known pharmacy this busy in my entire career…” Participant 6 (Employee, Independent)

Hesitancy also related to patients’ responses to being offered social prescribing in a pharmacy setting.

“ They might feel embarrassed to accept that help. And they might find it quite intrusive, they might not expect a pharmacist to be involved and…nobody wants to be categorised as a vulnerable or isolated patient particularly.” Participant 4 (Employee, Multiple)

Participants reported feeling that larger ‘multiples’ companies had greater resources and financial capital and would therefore find it easier to implement social prescribing services than independent organisations.

“ And the small pharmacies I worry that because they’re so, their [funding is] so tight they are trying to make the best they can being an independent that they won’t sort of have the capacity necessary to widen to some of these sorts of wider societal things that they can have input in.” Participant 5 (Employee, Multiple)

Conversely, others suggested larger organisations with more capital may focus on profits rather than supporting patient communities, unlike independent organisations.

“ I’ve worked for [supermarket pharmacy 1] and [supermarket pharmacy 2] before which are bigger companies, and I know they’re much more focused around [funding]…rather than the kind of community support and health advice [in social prescribing]. I think an independent might do it because of the benefit to the community and to be seen to be giving extra services which might attract and keep their customer base.” Participant 6 (Employee, Independent)

The only participant who was a pharmacy owner (and therefore responsible for organisational structure and financial targets of a community pharmacy business) reported social prescribing was an individual, professional decision of the pharmacist in charge, rather than the priorities of the business or owner. This appeared to diminish the role of organizational policy, working conditions (such as opening times and staffing levels), and the funding landscape, suggesting engagement with social prescribing will come down to personal preference of the individual pharmacist.

“ There’s no [funding] difference between the individual pharmacists, whoever they work for. So, under those circumstances it doesn’t matter if it’s an independent or a multiple pharmacy, they will organise themselves differently, but it’ll come down to the individuals, not the policy of the owner.” Participant 10 (Owner, Independent)

Collectively, these findings indicate pharmacists’ motivations to deliver social prescribing services are influenced by access to appropriate levels of economic capital and resources to manage workload and patient expectations.

Theme 3) Outcomes of social prescribing in community pharmacy.

This theme describes social prescribing as an opportunity for pharmacies to improve patient outcomes by involving all members of the pharmacy team, not just pharmacists. The inclusion of all staff into social prescribing was raised by participants. The knowledge and trust shared with patients was considered to make them a good resource to facilitate social prescribing. Participants felt dispensing staff, delivery drivers, and pharmacy technicians, as well as pharmacists, represented valuable assets to facilitate social prescribing, if given appropriate training and links to social prescribing networks.

Participants appeared to clearly understand the accessibility of pharmacy and highlighted the patient-centeredness of pharmacies, in comparison to other healthcare settings for patients, was aligned to social prescribing principles.

“ We are the most accessible healthcare professional in every community, and patients know they can just pop in for that source of advice. We have a lot more time [than other healthcare professionals] to tailor to individual patients” Participant 11 (Employee, Multiple)

Participants reported valuing the role that social prescribing could play in improving health outcomes for patients, lessening the need for medication and expensive treatments.

“ …you’ve got the obvious benefits to the patients around outcomes…it might be that they are prescribed metformin for type 2 diabetes, which alongside social prescribing around diet and exercise…as a result of the diet and exercise intervention that the whole…type 2 diabetes will be better off.” Participant 5 (Employee, Multiple)

Additionally, participants appeared to recognise opportunities to improve patient care by providing an alternative to medications.

“ You know it’s got loads of benefits for patients because you know you don’t get side effects from social prescribing.” Participant 6 (Employee, Independent)

Pharmacistsreported the need to work with others who are already social prescribing to learn, share best practice and develop a common understanding.

“ Ultimately though this isn’t something pharmacies could just do on their own, we need to be linked up with other people doing this, like is there a national body of social prescribers or like standardised training about how to do it? If we knew more about social prescribers we would be linked in with that network more.” Participant 10 (Owner, Independent)

Collectively this theme demonstrates complexity in pharmacists’ views of the outcomes of social prescribing, primarily being reliant on the social capital pharmacists have with patients and other staff in their premises but also on building social capital by engaging with other social prescribing networks and experts.

Discussion and conclusion

Summary of findings.

The key finding of this study is participants appeared to recognise, understand and value social prescribing as a means of supporting patients’ health and well-being, but misunderstood social prescribing as a form of disease-focused, public health promotion. Limited training, experience and resources to facilitate social prescribing in practice were identified as learning needs in this study. Participants reported willing to be involved in social prescribing, reporting interests to better understand the process of social prescribing and expressing beliefs that this could expand the current role of community pharmacists and their team members. Many participants reported limited exposure to or involvement with social prescribing in current practice and education. This indicates a need for further collaboration and involvement in social prescribing networks. Professional bodies may also need to support education, learning and training of pharmacists and their teams to implement social prescribing services. The unique accessibility of community pharmacy teams and the rapport they have with their patients were seen as opportunities to contribute to social prescribing to improve patient outcomes.

A strength of the study is it provides a conceptualization of pharmacists’ understanding of social prescribing. The study met theoretical data sufficiency and used qualitative methods to identify insights. Additionally, the sample included pharmacists from a range of practice settings across North East England, which means the findings may be transferable to different contexts of practice. However, using convenience sampling meant these findings may not include the range of views across the pharmacy profession–particularly from those outside of North East England. Additionally, recruitment via social media introduces self-selection bias (whereby pharmacists with little interest in social prescribing would have been recruited) which may positively skew the findings in terms of participants’ reported willingness and enthusiasm for social prescribing rather than the reported limited exposure and understanding of it.

Comparison to existing literature

The findings presented here add to the literature, demonstrating pharmacists are enthusiastic, but do not fully appreciate the scope and impact of social prescribing. The findings are congruent with a survey completed by 120 pharmacists, showing poor understanding of social prescribing, and the need for increased staff training and funding [ 29 ]. Existing literature has suggested pharmacists could adopt multiple roles to implement social prescribing–as screeners, identifiers, link workers or providers of social interventions [ 28 , 39 ] to reduce the demand on existing health services [ 8 ]. However, with such a limited understanding shown in this research, the role pharmacists could adopt to implement social prescribing at present may be limited.

Some existing literature has stated that the impact of social prescribing may be overestimated [ 13 ]. A key reason for this, put forward by Gibson, Pollard (13), using a Bourdieuan lens, is focused on patients’ structural contexts; access to economic, social and cultural capital influences engagement with social prescribing interventions. Our study extends the argument from patients to pharmacists, highlighting that structural context also influences professional engagement with social prescribing interventions. Our study demonstrated that pharmacists have little cultural, economic and social capital to invest in social prescribing—their conceptualisation of it is limited (cultural capital), funding is poor and workload is high (economic capital) and their connections to professional social prescribing networks and bodies is poor (social capital) [ 40 , 41 ]. This may hinder the capability, opportunity and motivation for pharmacists to engage in social prescribing. Further research such as feasibility and pilot studies, as well as trials, are needed to understand and consider the effectiveness of pharmacists and their teams bridging the gap between health and social care to help communities most in need.

Implications for policy and practice

The NHS has made a commitment to increase social prescribing activity and expand the number of link workers [ 2 , 42 ]. Pharmacists, with adequate economic, social and cultural capital, could support this—either by identifying patients for referral to link workers or providing link worker services ‘in house’ [ 28 ]. However, this study has shown that although pharmacists are interested in social prescribing, it appears to be positioned within current pharmacy practice as ‘healthy lifestyle changes’, ‘health promotion’ and ‘public health’ initiative, rather than supporting patients to deal with broader socioeconomic determinants of health, such as poor housing, economic hardship, and abusive relationships–which many link workers currently deal with through social prescribing [ 9 , 10 , 43 ]. If pharmacists are going to refer patients to social prescribing, then additional training, access and engagement with link workers will be needed to upskill the current workforce. Furthermore, establishing ways to build social connections of pharmacists with those involved in delivering social prescribing are required. If pharmacists themselves are going to act as link workers ‘in house’, then the findings suggest a much greater effort will be needed to enable them to have the skills, expertise, supervision and support structures to build their cultural capital to deal with non-clinical social issues to optimise health outcomes. Our findings show pharmacists believe they know what social prescribing is but their beliefs are not aligned to what social prescribing link workers actually do in reality. It shows there is a gap between pharmacists’ beliefs and social prescribing practice. This provides a very specific target for educators and policy makers to create an intervention to change pharmacists’ perceptions of social prescribing from a ‘healthy lifestyle intervention’ to a new praxis of ‘social pharmaceutical care’. This raises questions for policy makers and practitioners, and the profession as a whole–is social prescribing something community pharmacy teams want to do, given current high workloads in the sector?

This study aimed to explore community pharmacists’ experiences of social prescribing. It has shown how they recognise and value social prescribing, but currently have limited understanding, training, experience and resources to incorporate it into their practice. These findings provide an insight into pharmacists understanding but may not be generalisable or transferable. Further work is therefore needed to explore if, when and how pharmacists and their teams could engage with social prescribing.


The authors would like to thank the participants for taking part in this study.

  • 1. World Health Organization. A Toolkit on How to Implement Social Prescribing Available at: accessed 3rd July 20232022.
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  • 16. Local Government Association. Just what the doctor ordered. Social Prescribing-a guide for local authorities. London: Local Government Association. 2016.
  • 18. Burns C. Third of patients visited community pharmacies in place of their GP during the COVID-19 pandemic, NPA survey finds. Available at accessed 3rd July 20232023.
  • 22. Briefing 010/20: Community Pharmacy Funding in 2020/21 [Internet]. Available accessed 3rd July 2023; 2020
  • 23. Community Pharmacy England. Pharmacy funding—Community Pharmacy England vailable from: accessed 11th July 20232022.
  • 25. NHS England. NHS Community Pharmacist Consultant Service (CPCS)—integrating pharmacy into urgent care Available at accessed 2nd January 20242023.
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  • 41. Bourdieu P. Distinction: A Social Critique of the Judgement of Taste. Cambridge: Harvard University Press; 1984.
  • 42. NHS England. NHS Longterm Workforce Plan. Available at accessed 3rd July 2023: 2023.
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Use of qualitative methods in evaluation studies.

  • Namita Ranganathan Namita Ranganathan University of Delhi
  •  and  Toolika Wadhwa Toolika Wadhwa Shyama Prasad Mukherji College for Women
  • Published online: 26 April 2019

Evaluation studies typically comprise research endeavors that are undertaken to investigate and gauge the effectiveness of a program, an institution, or individuals working in educational contexts, such as teachers, students, administrators, and other stakeholders in education. Usually, research studies in this genre use empirical methods to evaluate educational practices and systems. Alternatively, they may take up theoretical reflections on new policies, programs, and systems. An evaluation study requires a rigorous design and method of assessment to focus on the specific context and set of issues that it targets. In general, research studies that attempt to evaluate a program, an individual, or an institution place emphasis on checking their efficacy. They do not seek to find explanations that have led to the level of efficacy that the variables under study may have achieved. Thus, quite often, they are contested as not being full-fledged research.

Evaluation studies use a variety of methods. The choice of method depends on the area of study as well as the research questions. An evaluation study may thus fall within the qualitative or quantitative paradigms. Often, a mixed method approach is used. The purpose of the study plays a significant role in deciding the method of inquiry and analysis. Establishing the probability, plausibility, and adequacy of the program can be some of the main aims of evaluation studies. This implies as well that the programs, institutions, or individuals under study would have an impact on the course and direction of future programs and practices. An evaluation study is thus of vital importance to ensure that appropriate decisions can be made about efficacy, transferability to different contexts, and difficulties and challenges to be faced in subsequent applications.

Evaluation studies in India have been done in a vast range of areas that include program evaluation, impact studies, evaluations of specific interventions, performance outcome assessments, and the like. Some examples of studies undertaken by the government and the development sector in this regard are the following: assessment of interventions for adolescence education; impact studies of interventions, programs, and policies launched for education of minorities, including girls; and evaluation of performance outcomes stemming from programs for education of the marginalized.

The key challenges in evaluation studies are to gather accurate data in order to establish reliable outcomes, to establish clear relationships between the outcomes and the interventions being studied, and to safeguard against researcher bias.

  • evaluation studies
  • program evaluation
  • qualitative evaluation
  • outcome-based evaluation
  • project evaluation
  • inferring qualitative trends

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Types of market research: Methods and examples


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Here at GWI we publish a steady stream of blogs, reports, and other resources that dig deep into specific market research topics.

But what about the folks who’d appreciate a more general overview of market research that explains the big picture? Don’t they deserve some love too?

Of course they do. That’s why we’ve created this overview guide focusing on types of market research and examples. With so many market research companies to choose from, having a solid general understanding of how this sector works is essential for any brand or business that wants to pick the right market research partner.

So with that in mind, let’s start at the very beginning and get clear on…

Market research definition

At the risk of stating the slightly obvious, market research is the gathering and analyzing of data on consumers, competitors, distributors, and markets. As such it’s not quite the same as consumer research , but there’s significant overlap.

Market research matters because it can help you take the guesswork out of getting through to audiences. By studying consumers and gathering information on their likes, dislikes, and so on, brands can make evidence-based decisions instead of relying on instinct or experience. 

qualitative research studies examples

What is market research?

Market research is the organized gathering of information about target markets and consumers’ needs and preferences. It’s an important component of business strategy and a major factor in maintaining competitiveness.

If a business wants to know – really know – what sort of products or services consumers want to buy, along with where, when, and how those products and services should be marketed, it just makes sense to ask the prospective audience. 

Without the certainty that market research brings, a business is basically hoping for the best. And while we salute their optimism, that’s not exactly a reliable strategy for success.

What are the types of market research?

Primary research .

Primary research is a type of market research you either conduct yourself or hire someone to do on your behalf.

A classic example of primary research involves going directly to a source – typically customers or prospective customers in your target market – to ask questions and gather information about a product or service. Interviewing methods include in-person, online surveys, phone calls, and focus groups.

The big advantage of primary research is that it’s directly focused on your objectives, so the outcome will be conclusive, detailed insights – particularly into customer views – making it the gold standard.

The disadvantages are it can be time-consuming and potentially costly, plus there’s a risk of survey bias creeping in, in the sense that research samples may not be representative of the wider group.

Secondary research 

Primary market research means you collect the data your business needs, whereas the types of market research known as secondary market research use information that’s already been gathered for other purposes but can still be valuable. Examples include published market studies, white papers, analyst reports, customer emails, and customer surveys/feedback.

For many small businesses with limited budgets, secondary market research is their first choice because it’s easier to acquire and far more affordable than primary research.

Secondary research can still answer specific business questions, but with limitations. The data collected from that audience may not match your targeted audience exactly, resulting in skewed outcomes. 

A big benefit of secondary market research is helping lay the groundwork and get you ready to carry out primary market research by making sure you’re focused on what matters most.

qualitative research studies examples

Qualitative research

Qualitative research is one of the two fundamental types of market research. Qualitative research is about people and their opinions. Typically conducted by asking questions either one-on-one or in groups, qualitative research can help you define problems and learn about customers’ opinions, values, and beliefs.

Classic examples of qualitative research are long-answer questions like “Why do you think this product is better than competitive products? Why do you think it’s not?”, or “How would you improve this new service to make it more appealing?”

Because qualitative research generally involves smaller sample sizes than its close cousin quantitative research, it gives you an anecdotal overview of your subject, rather than highly detailed information that can help predict future performance.

Qualitative research is particularly useful if you’re developing a new product, service, website or ad campaign and want to get some feedback before you commit a large budget to it.

Quantitative research

If qualitative research is all about opinions, quantitative research is all about numbers, using math to uncover insights about your audience. 

Typical quantitative research questions are things like, “What’s the market size for this product?” or “How long are visitors staying on this website?”. Clearly the answers to both will be numerical.

Quantitative research usually involves questionnaires. Respondents are asked to complete the survey, which marketers use to understand consumer needs, and create strategies and marketing plans.

Importantly, because quantitative research is math-based, it’s statistically valid, which means you’re in a good position to use it to predict the future direction of your business.

Consumer research 

As its name implies, consumer research gathers information about consumers’ lifestyles, behaviors, needs and preferences, usually in relation to a particular product or service. It can include both quantitative and qualitative studies.

Examples of consumer research in action include finding ways to improve consumer perception of a product, or creating buyer personas and market segments, which help you successfully market your product to different types of customers.

Understanding consumer trends , driven by consumer research, helps businesses understand customer psychology and create detailed purchasing behavior profiles. The result helps brands improve their products and services by making them more customer-centric, increasing customer satisfaction, and boosting bottom line in the process.

Product research 

Product research gives a new product (or indeed service, we don’t judge) its best chance of success, or helps an existing product improve or increase market share.

It’s common sense: by finding out what consumers want and adjusting your offering accordingly, you gain a competitive edge. It can be the difference between a product being a roaring success or an abject failure.

Examples of product research include finding ways to develop goods with a higher value, or identifying exactly where innovation effort should be focused. 

Product research goes hand-in-hand with other strands of market research, helping you make informed decisions about what consumers want, and what you can offer them.

Brand research  

Brand research is the process of gathering feedback from your current, prospective, and even past customers to understand how your brand is perceived by the market.

It covers things like brand awareness, brand perceptions, customer advocacy, advertising effectiveness, purchase channels, audience profiling, and whether or not the brand is a top consideration for consumers.

The result helps take the guesswork out of your messaging and brand strategy. Like all types of market research, it gives marketing leaders the data they need to make better choices based on fact rather than opinion or intuition.

Market research methods 

So far we’ve reviewed various different types of market research, now let’s look at market research methods, in other words the practical ways you can uncover those all-important insights.

Consumer research platform 

A consumer research platform like GWI is a smart way to find on-demand market research insights in seconds.

In a world of fluid markets and changing attitudes, a detailed understanding of your consumers, developed using the right research platform, enables you to stop guessing and start knowing.

As well as providing certainty, consumer research platforms massively accelerate speed to insight. Got a question? Just jump on your consumer research platform and find the answer – job done.

The ability to mine data for answers like this is empowering – suddenly you’re in the driving seat with a world of possibilities ahead of you. Compared to the most obvious alternative – commissioning third party research that could take weeks to arrive – the right consumer research platform is basically a magic wand.

Admittedly we’re biased, but GWI delivers all this and more. Take our platform for a quick spin and see for yourself.

And the downside of using a consumer research platform? Well, no data set, however fresh or thorough, can answer every question. If you need really niche insights then your best bet is custom market research , where you can ask any question you like, tailored to your exact needs.

Face-to-face interviews 

Despite the rise in popularity of online surveys , face-to-face survey interviewing – using mobile devices or even the classic paper survey – is still a popular data collection method.

In terms of advantages, face-to-face interviews help with accurate screening, in the sense the interviewee can’t easily give misleading answers about, say, their age. The interviewer can also make a note of emotions and non-verbal cues. 

On the other hand, face-to-face interviews can be costly, while the quality of data you get back often depends on the ability of the interviewer. Also, the size of the sample is limited to the size of your interviewing staff, the area in which the interviews are conducted, and the number of qualified respondents within that area.

Social listening 

Social listening is a powerful solution for brands who want to keep an ear to the ground, gathering unfiltered thoughts and opinions from consumers who are posting on social media. 

Many social listening tools store data for up to a couple of years, great for trend analysis that needs to compare current and past conversations.

Social listening isn’t limited to text. Images, videos, and emojis often help us better understand what consumers are thinking, saying, and doing better than more traditional research methods. 

Perhaps the biggest downside is there are no guarantees with social listening, and you never know what you will (or won’t) find. It can also be tricky to gauge sentiment accurately if the language used is open to misinterpretation, for example if a social media user describes something as “sick”.

There’s also a potential problem around what people say vs. what they actually do. Tweeting about the gym is a good deal easier than actually going. The wider problem – and this may shock you – is that not every single thing people write on social media is necessarily true, which means social listening can easily deliver unreliable results.

Public domain data 

Public domain data comes from think tanks and government statistics or research centers like the UK’s National Office for Statistics or the United States Census Bureau and the National Institute of Statistical Sciences. Other sources are things like research journals, news media, and academic material.

Its advantages for market research are it’s cheap (or even free), quick to access, and easily available. Public domain datasets can be huge, so potentially very rich.

On the flip side, the data can be out of date, it certainly isn’t exclusive to you, and the collection methodology can leave much to be desired. But used carefully, public domain data can be a useful source of secondary market research.

Telephone interviews 

You know the drill – you get a call from a researcher who asks you questions about a particular topic and wants to hear your opinions. Some even pay or offer other rewards for your time.

Telephone surveys are great for reaching niche groups of consumers within a specific geographic area or connected to a particular brand, or who aren’t very active in online channels. They’re not well-suited for gathering data from broad population groups, simply because of the time and labor involved.

How to use market research 

Data isn’t an end in itself; instead it’s a springboard to make other stuff happen. So once you’ve drawn conclusions from your research, it’s time to think of what you’ll actually do based on your findings.

While it’s impossible for us to give a definitive list (every use case is different), here are some suggestions to get you started.

Leverage it . Think about ways to expand the use – and value – of research data and insights, for example by using research to support business goals and functions, like sales, market share or product design.

Integrate it . Expand the value of your research data by integrating it with other data sources, internal and external. Integrating data like this can broaden your perspective and help you draw deeper insights for more confident decision-making.

Justify it . Enlist colleagues from areas that’ll benefit from the insights that research provides – that could be product management, product development, customer service, marketing, sales or many others – and build a business case for using research.

How to choose the right type of market research 

Broadly speaking, choosing the right research method depends on knowing the type of data you need to collect. To dig into ideas and opinions, choose qualitative; to do some testing, it’s quantitative you want.

There are also a bunch of practical considerations, not least cost. If a particular approach sounds great but costs the earth then clearly it’s not ideal for any brand on a budget.

Then there’s how you intend to use the actual research, your level of expertise with research data, whether you need access to historical data or just a snapshot of today, and so on.

The point is, different methods suit different situations. When choosing, you’ll want to consider what you want to achieve, what data you’ll need, the pros and cons of each method, the costs of conducting the research, and the cost of analyzing the results. 

Market research examples

Independent agency Bright/Shift used GWI consumer insights to shape a high-impact go-to-market strategy for their sustainable furniture client, generating £41K in revenue in the first month. Here’s how they made the magic happen .

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Research Writing and Analysis

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Jump to DSE Guide

Purpose statement overview.

The purpose statement succinctly explains (on no more than 1 page) the objectives of the research study. These objectives must directly address the problem and help close the stated gap. Expressed as a formula:

qualitative research studies examples

Good purpose statements:

  • Flow from the problem statement and actually address the proposed problem
  • Are concise and clear
  • Answer the question ‘Why are you doing this research?’
  • Match the methodology (similar to research questions)
  • Have a ‘hook’ to get the reader’s attention
  • Set the stage by clearly stating, “The purpose of this (qualitative or quantitative) study is to ...

In PhD studies, the purpose usually involves applying a theory to solve the problem. In other words, the purpose tells the reader what the goal of the study is, and what your study will accomplish, through which theoretical lens. The purpose statement also includes brief information about direction, scope, and where the data will come from.

A problem and gap in combination can lead to different research objectives, and hence, different purpose statements. In the example from above where the problem was severe underrepresentation of female CEOs in Fortune 500 companies and the identified gap related to lack of research of male-dominated boards; one purpose might be to explore implicit biases in male-dominated boards through the lens of feminist theory. Another purpose may be to determine how board members rated female and male candidates on scales of competency, professionalism, and experience to predict which candidate will be selected for the CEO position. The first purpose may involve a qualitative ethnographic study in which the researcher observes board meetings and hiring interviews; the second may involve a quantitative regression analysis. The outcomes will be very different, so it’s important that you find out exactly how you want to address a problem and help close a gap!

The purpose of the study must not only align with the problem and address a gap; it must also align with the chosen research method. In fact, the DP/DM template requires you to name the  research method at the very beginning of the purpose statement. The research verb must match the chosen method. In general, quantitative studies involve “closed-ended” research verbs such as determine , measure , correlate , explain , compare , validate , identify , or examine ; whereas qualitative studies involve “open-ended” research verbs such as explore , understand , narrate , articulate [meanings], discover , or develop .

A qualitative purpose statement following the color-coded problem statement (assumed here to be low well-being among financial sector employees) + gap (lack of research on followers of mid-level managers), might start like this:

In response to declining levels of employee well-being, the purpose of the qualitative phenomenology was to explore and understand the lived experiences related to the well-being of the followers of novice mid-level managers in the financial services industry. The levels of follower well-being have been shown to correlate to employee morale, turnover intention, and customer orientation (Eren et al., 2013). A combined framework of Leader-Member Exchange (LMX) Theory and the employee well-being concept informed the research questions and supported the inquiry, analysis, and interpretation of the experiences of followers of novice managers in the financial services industry.

A quantitative purpose statement for the same problem and gap might start like this:

In response to declining levels of employee well-being, the purpose of the quantitative correlational study was to determine which leadership factors predict employee well-being of the followers of novice mid-level managers in the financial services industry. Leadership factors were measured by the Leader-Member Exchange (LMX) assessment framework  by Mantlekow (2015), and employee well-being was conceptualized as a compound variable consisting of self-reported turnover-intent and psychological test scores from the Mental Health Survey (MHS) developed by Johns Hopkins University researchers.

Both of these purpose statements reflect viable research strategies and both align with the problem and gap so it’s up to the researcher to design a study in a manner that reflects personal preferences and desired study outcomes. Note that the quantitative research purpose incorporates operationalized concepts  or variables ; that reflect the way the researcher intends to measure the key concepts under study; whereas the qualitative purpose statement isn’t about translating the concepts under study as variables but instead aim to explore and understand the core research phenomenon.  

Best Practices for Writing your Purpose Statement

Always keep in mind that the dissertation process is iterative, and your writing, over time, will be refined as clarity is gradually achieved. Most of the time, greater clarity for the purpose statement and other components of the Dissertation is the result of a growing understanding of the literature in the field. As you increasingly master the literature you will also increasingly clarify the purpose of your study.

The purpose statement should flow directly from the problem statement. There should be clear and obvious alignment between the two and that alignment will get tighter and more pronounced as your work progresses.

The purpose statement should specifically address the reason for conducting the study, with emphasis on the word specifically. There should not be any doubt in your readers’ minds as to the purpose of your study. To achieve this level of clarity you will need to also insure there is no doubt in your mind as to the purpose of your study.

Many researchers benefit from stopping your work during the research process when insight strikes you and write about it while it is still fresh in your mind. This can help you clarify all aspects of a dissertation, including clarifying its purpose.

Your Chair and your committee members can help you to clarify your study’s purpose so carefully attend to any feedback they offer.

The purpose statement should reflect the research questions and vice versa. The chain of alignment that began with the research problem description and continues on to the research purpose, research questions, and methodology must be respected at all times during dissertation development. You are to succinctly describe the overarching goal of the study that reflects the research questions. Each research question narrows and focuses the purpose statement. Conversely, the purpose statement encompasses all of the research questions.

Identify in the purpose statement the research method as quantitative, qualitative or mixed (i.e., “The purpose of this [qualitative/quantitative/mixed] study is to ...)

Avoid the use of the phrase “research study” since the two words together are redundant.

Follow the initial declaration of purpose with a brief overview of how, with what instruments/data, with whom and where (as applicable) the study will be conducted. Identify variables/constructs and/or phenomenon/concept/idea. Since this section is to be a concise paragraph, emphasis must be placed on the word brief. However, adding these details will give your readers a very clear picture of the purpose of your research.

Developing the purpose section of your dissertation is usually not achieved in a single flash of insight. The process involves a great deal of reading to find out what other scholars have done to address the research topic and problem you have identified. The purpose section of your dissertation could well be the most important paragraph you write during your academic career, and every word should be carefully selected. Think of it as the DNA of your dissertation. Everything else you write should emerge directly and clearly from your purpose statement. In turn, your purpose statement should emerge directly and clearly from your research problem description. It is good practice to print out your problem statement and purpose statement and keep them in front of you as you work on each part of your dissertation in order to insure alignment.

It is helpful to collect several dissertations similar to the one you envision creating. Extract the problem descriptions and purpose statements of other dissertation authors and compare them in order to sharpen your thinking about your own work.  Comparing how other dissertation authors have handled the many challenges you are facing can be an invaluable exercise. Keep in mind that individual universities use their own tailored protocols for presenting key components of the dissertation so your review of these purpose statements should focus on content rather than form.

Once your purpose statement is set it must be consistently presented throughout the dissertation. This may require some recursive editing because the way you articulate your purpose may evolve as you work on various aspects of your dissertation. Whenever you make an adjustment to your purpose statement you should carefully follow up on the editing and conceptual ramifications throughout the entire document.

In establishing your purpose you should NOT advocate for a particular outcome. Research should be done to answer questions not prove a point. As a researcher, you are to inquire with an open mind, and even when you come to the work with clear assumptions, your job is to prove the validity of the conclusions reached. For example, you would not say the purpose of your research project is to demonstrate that there is a relationship between two variables. Such a statement presupposes you know the answer before your research is conducted and promotes or supports (advocates on behalf of) a particular outcome. A more appropriate purpose statement would be to examine or explore the relationship between two variables.

Your purpose statement should not imply that you are going to prove something. You may be surprised to learn that we cannot prove anything in scholarly research for two reasons. First, in quantitative analyses, statistical tests calculate the probability that something is true rather than establishing it as true. Second, in qualitative research, the study can only purport to describe what is occurring from the perspective of the participants. Whether or not the phenomenon they are describing is true in a larger context is not knowable. We cannot observe the phenomenon in all settings and in all circumstances.

Writing your Purpose Statement

It is important to distinguish in your mind the differences between the Problem Statement and Purpose Statement.

The Problem Statement is why I am doing the research

The Purpose Statement is what type of research I am doing to fit or address the problem

The Purpose Statement includes:

  • Method of Study
  • Specific Population

Remember, as you are contemplating what to include in your purpose statement and then when you are writing it, the purpose statement is a concise paragraph that describes the intent of the study, and it should flow directly from the problem statement.  It should specifically address the reason for conducting the study, and reflect the research questions.  Further, it should identify the research method as qualitative, quantitative, or mixed.  Then provide a brief overview of how the study will be conducted, with what instruments/data collection methods, and with whom (subjects) and where (as applicable). Finally, you should identify variables/constructs and/or phenomenon/concept/idea.

Qualitative Purpose Statement

Creswell (2002) suggested for writing purpose statements in qualitative research include using deliberate phrasing to alert the reader to the purpose statement. Verbs that indicate what will take place in the research and the use of non-directional language that do not suggest an outcome are key. A purpose statement should focus on a single idea or concept, with a broad definition of the idea or concept. How the concept was investigated should also be included, as well as participants in the study and locations for the research to give the reader a sense of with whom and where the study took place. 

Creswell (2003) advised the following script for purpose statements in qualitative research:

“The purpose of this qualitative_________________ (strategy of inquiry, such as ethnography, case study, or other type) study is (was? will be?) to ________________ (understand? describe? develop? discover?) the _________________(central phenomenon being studied) for ______________ (the participants, such as the individual, groups, organization) at __________(research site). At this stage in the research, the __________ (central phenomenon being studied) will be generally defined as ___________________ (provide a general definition)” (pg. 90).

Quantitative Purpose Statement

Creswell (2003) offers vast differences between the purpose statements written for qualitative research and those written for quantitative research, particularly with respect to language and the inclusion of variables. The comparison of variables is often a focus of quantitative research, with the variables distinguishable by either the temporal order or how they are measured. As with qualitative research purpose statements, Creswell (2003) recommends the use of deliberate language to alert the reader to the purpose of the study, but quantitative purpose statements also include the theory or conceptual framework guiding the study and the variables that are being studied and how they are related. 

Creswell (2003) suggests the following script for drafting purpose statements in quantitative research:

“The purpose of this _____________________ (experiment? survey?) study is (was? will be?) to test the theory of _________________that _________________ (compares? relates?) the ___________(independent variable) to _________________________(dependent variable), controlling for _______________________ (control variables) for ___________________ (participants) at _________________________ (the research site). The independent variable(s) _____________________ will be generally defined as _______________________ (provide a general definition). The dependent variable(s) will be generally defined as _____________________ (provide a general definition), and the control and intervening variables(s), _________________ (identify the control and intervening variables) will be statistically controlled in this study” (pg. 97).

Sample Purpose Statements

  • The purpose of this qualitative study was to determine how participation in service-learning in an alternative school impacted students academically, civically, and personally.  There is ample evidence demonstrating the failure of schools for students at-risk; however, there is still a need to demonstrate why these students are successful in non-traditional educational programs like the service-learning model used at TDS.  This study was unique in that it examined one alternative school’s approach to service-learning in a setting where students not only serve, but faculty serve as volunteer teachers.  The use of a constructivist approach in service-learning in an alternative school setting was examined in an effort to determine whether service-learning participation contributes positively to academic, personal, and civic gain for students, and to examine student and teacher views regarding the overall outcomes of service-learning.  This study was completed using an ethnographic approach that included observations, content analysis, and interviews with teachers at The David School.
  • The purpose of this quantitative non-experimental cross-sectional linear multiple regression design was to investigate the relationship among early childhood teachers’ self-reported assessment of multicultural awareness as measured by responses from the Teacher Multicultural Attitude Survey (TMAS) and supervisors’ observed assessment of teachers’ multicultural competency skills as measured by the Multicultural Teaching Competency Scale (MTCS) survey. Demographic data such as number of multicultural training hours, years teaching in Dubai, curriculum program at current school, and age were also examined and their relationship to multicultural teaching competency. The study took place in the emirate of Dubai where there were 14,333 expatriate teachers employed in private schools (KHDA, 2013b).
  • The purpose of this quantitative, non-experimental study is to examine the degree to which stages of change, gender, acculturation level and trauma types predicts the reluctance of Arab refugees, aged 18 and over, in the Dearborn, MI area, to seek professional help for their mental health needs. This study will utilize four instruments to measure these variables: University of Rhode Island Change Assessment (URICA: DiClemente & Hughes, 1990); Cumulative Trauma Scale (Kira, 2012); Acculturation Rating Scale for Arabic Americans-II Arabic and English (ARSAA-IIA, ARSAA-IIE: Jadalla & Lee, 2013), and a demographic survey. This study will examine 1) the relationship between stages of change, gender, acculturation levels, and trauma types and Arab refugees’ help-seeking behavior, 2) the degree to which any of these variables can predict Arab refugee help-seeking behavior.  Additionally, the outcome of this study could provide researchers and clinicians with a stage-based model, TTM, for measuring Arab refugees’ help-seeking behavior and lay a foundation for how TTM can help target the clinical needs of Arab refugees. Lastly, this attempt to apply the TTM model to Arab refugees’ condition could lay the foundation for future research to investigate the application of TTM to clinical work among refugee populations.
  • The purpose of this qualitative, phenomenological study is to describe the lived experiences of LLM for 10 EFL learners in rural Guatemala and to utilize that data to determine how it conforms to, or possibly challenges, current theoretical conceptions of LLM. In accordance with Morse’s (1994) suggestion that a phenomenological study should utilize at least six participants, this study utilized semi-structured interviews with 10 EFL learners to explore why and how they have experienced the motivation to learn English throughout their lives. The methodology of horizontalization was used to break the interview protocols into individual units of meaning before analyzing these units to extract the overarching themes (Moustakas, 1994). These themes were then interpreted into a detailed description of LLM as experienced by EFL students in this context. Finally, the resulting description was analyzed to discover how these learners’ lived experiences with LLM conformed with and/or diverged from current theories of LLM.
  • The purpose of this qualitative, embedded, multiple case study was to examine how both parent-child attachment relationships are impacted by the quality of the paternal and maternal caregiver-child interactions that occur throughout a maternal deployment, within the context of dual-military couples. In order to examine this phenomenon, an embedded, multiple case study was conducted, utilizing an attachment systems metatheory perspective. The study included four dual-military couples who experienced a maternal deployment to Operation Iraqi Freedom (OIF) or Operation Enduring Freedom (OEF) when they had at least one child between 8 weeks-old to 5 years-old.  Each member of the couple participated in an individual, semi-structured interview with the researcher and completed the Parenting Relationship Questionnaire (PRQ). “The PRQ is designed to capture a parent’s perspective on the parent-child relationship” (Pearson, 2012, para. 1) and was used within the proposed study for this purpose. The PRQ was utilized to triangulate the data (Bekhet & Zauszniewski, 2012) as well as to provide some additional information on the parents’ perspective of the quality of the parent-child attachment relationship in regards to communication, discipline, parenting confidence, relationship satisfaction, and time spent together (Pearson, 2012). The researcher utilized the semi-structured interview to collect information regarding the parents' perspectives of the quality of their parental caregiver behaviors during the deployment cycle, the mother's parent-child interactions while deployed, the behavior of the child or children at time of reunification, and the strategies or behaviors the parents believe may have contributed to their child's behavior at the time of reunification. The results of this study may be utilized by the military, and by civilian providers, to develop proactive and preventive measures that both providers and parents can implement, to address any potential adverse effects on the parent-child attachment relationship, identified through the proposed study. The results of this study may also be utilized to further refine and understand the integration of attachment theory and systems theory, in both clinical and research settings, within the field of marriage and family therapy.

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  • Published: 18 May 2024

Identifying primary care clinicians’ preferences for, barriers to, and facilitators of information-seeking in clinical practice in Singapore: a qualitative study

  • Mauricette Moling Lee 1 , 2 ,
  • Wern Ee Tang 3 ,
  • Helen Elizabeth Smith 4 &
  • Lorainne Tudor Car 1 , 5  

BMC Primary Care volume  25 , Article number:  172 ( 2024 ) Cite this article

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Metrics details

The growth of medical knowledge and patient care complexity calls for improved clinician access to evidence-based resources. This study aimed to explore the primary care clinicians’ preferences for, barriers to, and facilitators of information-seeking in clinical practice in Singapore.

A convenience sample of ten doctors and ten nurses was recruited. We conducted semi-structured face-to-face in-depth interviews. The interviews were recorded, transcribed verbatim, and analysed using thematic content analysis.

Of the 20 participants, eight doctors and ten nurses worked at government-funded polyclinics and two doctors worked in private practice. Most clinicians sought clinical information daily at the point-of-care. The most searched-for information by clinicians in practice was less common conditions. Clinicians preferred evidence-based resources such as clinical practice guidelines and UpToDate®. Clinical practice guidelines were mostly used when they were updated or based on memory. Clinicians also commonly sought answers from their peers. Furthermore, clinicians frequently use smartphones to access the Google search engine and UpToDate® app. The barriers to accessing clinical information included the lack of time, internet surfing separation of work computers, limited search functions in the organisation’s server, and limited access to medical literature databases. The facilitators of accessing clinical information included convenience, easy access, and trustworthiness of information sources.

Most primary care clinicians in our study sought clinical information at the point-of-care daily and reported increasing use of smartphones for information-seeking. Future research focusing on interventions to improve access to credible clinical information for primary care clinicians at the point-of-care is recommended.

Trial registration

This study has been reviewed by NHG Domain Specific Review Board (NHG DSRB) (the central ethics committee) for ethics approval. NHG DSRB Reference Number: 2018/01355 (31/07/2019).

Peer Review reports

Primary care clinicians provide the bulk of care to patients in primary care settings. In Singapore, there are 23 polyclinics and about 1,800 General Practitioner (GP) clinics with private GPs providing primary care for about 80% of the population [ 1 ]. The primary care clinicians provide primary care services at community polyclinics and private medical clinics around Singapore [ 1 ]. The polyclinics are formed by three healthcare groups – National Healthcare Group, National University Health System, and SingHealth [ 1 ]. These polyclinics served various populations in Singapore's central, northern, north-eastern, western, and eastern parts [ 1 ]. Every day, clinicians make many clinical decisions, ranging from diagnosis and prognosis to treatment and patient management [ 2 , 3 ]. However, to provide consistent high-quality patient care, such clinical judgments must be informed by existing trustworthy medical evidence [ 4 , 5 , 6 ]. To meet their information needs, clinicians seek relevant information from various sources of information [ 3 ]. Searching for and using the information to meet information needs has been described as information-seeking behaviour [ 7 , 8 , 9 ].

Previous research showed that clinicians often raise questions about patient care in their practice [ 10 ]. Half of those questions are left unanswered. Identifying what information primary care clinicians need, how they search for required information and how they adopt it into practice is essential in ensuring safe and high-quality patient care [ 11 , 12 ]. While there are reports of information-seeking behaviour in primary care from other countries [ 2 , 8 , 13 , 14 ], similar reports in Singapore are limited.

Clinicians may consult several sources to support their decisions, including clinical practice guidelines (CPGs), journal articles, peers, and more [ 3 ]. However, there is a wide variation in the adoption of evidence-based practices across healthcare disciplines, which could lead to poorer primary care outcomes [ 8 , 12 , 15 , 16 , 17 , 18 , 19 ]. To mitigate this, a commonly employed approach is the development of CPGs, clinical pathways, or care guides [ 20 ]. They offer a structured, reliable, and consistent approach to healthcare evidence dissemination and reduce unnecessary clinical practice variation [ 21 ]. However, CPGs are costly to develop and update, context-specific, and unevenly adopted across various healthcare systems [ 22 ]. CPG's uptake is affected by diverse factors such as presentation formats, time pressures, reputability, and ownership [ 14 , 23 ]. Conversely, other sources of clinical practice-related information may not be as valid, credible, or current as CPGs.

Increasingly, healthcare professionals worldwide use their smartphones as an important channel for clinical information [ 24 , 25 , 26 , 27 ], using them to access websites, mobile apps or communicate with peers [ 28 ]. The use of electronic resources improves clinicians' knowledge and behaviour as well as patients' outcomes [ 29 ]. However, evidence on how smartphones are used at the point-of-care, particularly for evidence-seeking, is limited. Singapore, with a total population of 5.92 million as of the end of June 2023 [ 30 ], is one of the countries with the highest smartphone usage among its residents, with approximately 5.72 million (97%) users in 2023 [ 31 ]. Correspondingly, smartphones may be an important information-seeking channel among primary care clinicians. However, the increasing cyber threats worldwide may lead to internet surfing separation as a common security measure.

Institutional policies limiting access to computers at the point-of-care deter clinicians from seeking information and disrupt their workflow [ 32 ]. Due to patient data privacy breaches, the Singapore Ministry of Health introduced internet surfing separation as a security measure in July 2018 in all public healthcare institutions in Singapore [ 33 ]. Internet surfing separation stands for the restrictions on internet access and browsing which were enforced in Singapore public healthcare institutions in 2018 due to patient data privacy breaches [ 33 ]. This has limited the internet access of primary care clinicians at the workplace. Since its introduction, the Internet has not been accessible from any of the clinic's desktop computers and has been available through a few work laptops with limited availability to the polyclinic staff. At the time that this research was conducted, primary care clinicians in the public healthcare sector in Singapore did not have access to the internet from their work computers. Clinicians rely on evidence-based information to make informed decisions about patient care [ 4 , 5 , 6 ]. When access to online resources is restricted, clinicians may struggle to receive current and correct information, thus jeopardising patient safety and the quality of care offered [ 11 , 12 ]. Therefore, we sought to understand how primary care clinicians were addressing their clinical information needs when their work computers were not available to access evidence-based resources online. This study aimed to explore the primary care clinicians’ preferences for, barriers to, and facilitators of information-seeking in clinical practice in Singapore.

A qualitative study consisting of semi-structured face-to-face in-depth interviews was used to explore the primary care clinicians’ preferences for, barriers to, and facilitators of information-seeking in clinical practice in Singapore. The interviews were conducted between August and November 2019 at two polyclinics and two private clinics in Singapore.

The study was approved by the institutional ethics committee (NHG DSRB Reference Number: 2018/01355). All participants read the study information sheet before providing written consent. This study followed the Consolidated Criteria for Reporting Qualitative Research guidelines [ 34 ] [see Additional file 1].

Participants and recruitment

We included primary care doctors and registered nurses from the polyclinics and private primary care practices aged ≥ 21 years who were fluent in English. We employed convenience sampling in this study. Prospective participants were recruited from various polyclinics through personal contacts and advertisements. Five potential participants were contacted but did not respond to the invitation, two potential participants declined participation in this study and one potential participant resigned before the commencement of the study and hence did not participate in the study.

Data collection

The interviews were conducted by a female researcher (MML) in designated private meeting rooms or consultation rooms at various polyclinics or the respective consultation rooms of the private practice. MML was provided with sufficient details, resources, and training on qualitative research before the study commencement. Before the start of the interview, the researcher introduced herself, stated the aim of the interview, explained confidentiality, and obtained informed consent and permission to use a digital voice recorder. The interviewees could pause the interviews due to professional responsibilities at any time. MML conducted the interviews using an interview guide based on a review of the relevant literature and team discussions [ 10 ] [see Additional file 2]. The interview topics included the type of questions during clinical encounters, commonly employed sources of clinical information, frequency and timing of information-seeking, satisfaction with existing information sources, use of CPGs, barriers to information-seeking, and reliability of obtained information. All interview sessions lasted not more than 60 minutes with a mean interview time of 25 minutes and were digitally recorded and transcribed. Field notes were taken during the interviews for further analysis. Data saturation, defined as no new themes arising after three consecutive interviews [ 35 ], was achieved after 20 interviews, therefore we stopped recruitment at 20 participants. Participants were compensated with a SGD25 voucher and a meal upon completion of the interview.

Data analysis

The qualitative data were analysed using Burnard’s method, a structured approach for thematic content analysis established in 1991 [ 36 ]. Burnard's method includes fourteen stages for categorising and coding interview transcripts [ 36 ] [see Additional file 4]. Types of questions were analysed using Ely’s classification [ 37 ]. Burnard's method enhances understanding of the information-seeking behaviour patterns found by Ely's approach by doing a comprehensive evaluation. Ely et al. (2000) developed an approach for categorising clinician queries about patient care [ 37 ]. Clinical questions in primary care were divided into several main categories. For example, the three most common categories of questions based on Ely’s approach were "What is the drug of choice for condition x?", "What is the cause of symptom x?" and "What test is indicated in situation x?" [ 37 ]. Ely et al. (2000) framework was used by the study team to gain a better understanding of clinicians' information needs and to identify the types of questions they had about patient care. It was used mainly to facilitate the study team’s discussion. The study team did not adopt the categories. The analysis was done independently and in parallel by two researchers (MML and LTC). First, the researchers familiarised themselves with the transcripts by reading them multiple times. Second, the initial codes were proposed. Third, the themes were derived from the codes. Fourth, the researchers discussed and combined their themes for comparison. Finally, they reached a consensus on the themes and how to define them. Apart from the initial stages of being acquainted with the transcripts and recommended initial codes, to streamline our codes, related codes were consolidated into more comprehensive headings. This process allows us to organise them more effectively under pertinent subthemes. For example, various information sources that were mentioned by the participants such as evidence-based resources, non-evidence-based resources, and colleagues have all been merged into a subtheme titled "popular information sources" [see Additional file 3]. This process was done iteratively through several rounds. The final list of themes and subthemes was created by removing repeated or similar subthemes. Two other study team members independently created a list of headings without using the first study team member's list. Three lists were discussed and improved to increase validity and reduce researcher bias. Finally, we employed abstraction by developing a basic description of the phenomenon under investigation to establish the final subthemes and themes. Tables 1 and 2 illustrate how these stages were conducted.

Table 1 illustrates that the previous "subtheme" for "rare condition" was "most searched information in clinical practice," but it has been revised to "the type of information needs" to include numerous codes such as pharmacology and others following additional discussion with study team members. A third reviewer HES acted as an arbiter. The coding of transcripts was performed using a word processor. A predetermined classification system was not employed since there was insufficient research to inform the clinicians' perceptions of information-seeking behaviour in Singapore. In particular, the dynamic identification of themes from data was facilitated using an inductive approach. Burnard's method was applied inductively to establish categories and abstraction through open coding illustrated in Tables 1 and 2 . No single method of analysis is appropriate for every type of interview data [ 36 ]. Burnard’s method focuses on a systematic approach to thematic content analysis, which can improve qualitative research objectivity and transparency [ 36 ]. As descriptive studies can investigate perceived barriers to and facilitators of adopting new behaviours [ 38 ], a more descriptive set of themes was appropriate for the study's objectives, and it is consistent with Burnard's method [ 36 ].

A total of 20 clinicians were recruited. Eight doctors and 10 nurses were working in the polyclinics. All nurses and three doctors who participated in this study were females. The demographics of the clinicians is represented in Table  3 . Demographics of clinicians ( N  = 20).

Thematic analysis

Three distinct themes were derived from the analysis of the interview data, 1) the choice of information sources, 2) accessing information sources, and 3) the role of evidence in information-seeking [see Additional file 3]. This is represented in Fig.  1 . Themes and subthemes derived from the interviews.

figure 1

Themes and subthemes derived from the interviews

1) The choice of information sources

Is a theme that encompasses different sources clinicians in our study used to seek and gather information. Clinicians' preferred choice of information sources in five subthemes: popular information sources, CPGs as an information source, internet as an information source, peers as an information source and accessing online information using smartphones

Popular information sources

Clinicians mentioned that their first choice point-of-care evidence-based online sources were UpToDate®, an evidence-based resource that helps clinicians make decisions and informs their practice [ 39 ], CPGs and the Monthly Index of Medical Specialties, followed by PubMed (Medline) and continuing medication education sources. A non-evidence-based information source, the Google search engine was commonly mentioned as well. Lastly, clinicians often mentioned consulting their colleagues:

“I will Google, look for images and compare…I tell them that I’m looking because I am not sure, and I want to just confirm…sometimes even show them the photo on my phone, to ensure…what they saw, the rash…might have already disappeared is…what I suspect it is.” Doctor02.
“I commonly I would search…this app that I have on my phone is called UpToDate®, right…because it’s the most easiest…easily accessible source of information…I’ll just type the whole lot into…the Lexicomp component of the UpToDate® and then from there it tells me whether the drugs have interactions, what kind of interactions.” Doctor07.

CPGs as an information source

 Clinicians mentioned that CPGs did not apply to all patients. Doctors described CPGs as evidence-based resources, designed to be safe and most relevant to practice as a baseline reference. Doctors considered CPGs lengthy at times and there was a need to apply clinical discretion when using them. Doctors also mentioned that CPGs focused sometimes on cost-effectiveness instead of the quality of care:

“I think they are useful in summarising the latest evidence and what…is recommended, especially if they are local clinical practice guidelines, then it’s tailored to our own population…And keeping in mind perhaps the cost sensitivities, cost effectiveness” Doctor02.

Nurses said that they saw CPGs as a standard of practice for clinicians and an easy resource to refer to. However, some nurses said that they found CPGs difficult to access and outdated:

“…but it’s not so…easy to access…because you have to…enter certain keywords, and sometimes it’s not that keyword that’s going to churn out all the information you see…like, try a few times…want to make sure that…I’m doing things correctly…following the guidelines…just quickly…log into the intranet and…search for the information.” Nurse01.

If nurses had difficulty accessing CPGs, they said that they tended to seek doctors’ opinion:

“It’s very informative. It’s quite clear, easy to refer to…in certain special cases…not stated in the book, we will still have to seek…doctor’s opinion” Nurse07.

Internet as an information source

Clinicians mentioned that the internet provided access to clinical information for practice. However, clinicians mentioned that it was important to ensure that the information was well-grounded and dependable:

“…some…information might not be…so trustworthy…takes…a little additional filtering process before…I can say this is a reliable source or not…some of the websites…more opinion-based…very high…chance of bias…the reference from that writing…written at the bottom where I can do…cross-checking…I think the credibility…for this…article written is slightly higher.” Doctor01.
“If only you have an internet, you can always show it to the patient also. For example, when I search for some information, I can even help in patient education…for now, I feel it is a bit harder…And then I have to rely on my phone to use the UpToDate®.” Doctor03.

Peers as an information source

Clinicians mentioned approaching peers who were available to seek a second opinion on their clinical questions. They also mentioned that they tended to approach experts:

“…it’s really a case-to-case basis and it depends if the colleagues around…Also it depends on the proximity of the colleague. If the colleague knows a lot but…busy in another room on another level then I might approach next door colleagues instead.” Doctor06. “I think most of time, if we are going to get our information immediately, we’ll call one of our colleagues here…discuss the case…we’ll come to a consensus, what will be the best for our kind of patient…contribute to the informed decision immediately.” Doctor01.

Accessing online information using smartphones

Clinicians mentioned that their smartphones were convenient for accessing information for practice. For instance, accessing the UpToDate® app and Google search engine using smartphones:

“…commonly I would search…this app that I have on my phone is called UpToDate®…because it’s the most easiest…easily accessible source of information…I’ll just type the whole lot into…the Lexicomp component of…UpToDate® and then from there it tells me whether the drugs have interactions.” Doctor07.
“I will go on the internet…if I needed information about…certain medical conditions…Just definitions, just to have an idea of, you know… Correct, pure Google.” Nurse01.

2) Accessing information sources

Is a theme that encompasses different aspects of information-seeking and access by clinicians in our study. Factors influencing clinicians’ utilisation of information sources in five subthemes: type of information needs, the timing and frequency of information needs, the timing and frequency of using CPGs, information-seeking facilitators and information-seeking barriers.

The type of information needs

Clinicians mentioned that they commonly sought information on less common health areas such as unusual skin rashes, rare diseases, paediatrics, women’s health, medications, and at times concerning all clinical areas:

“Drug information…maybe dosing and everything…when we are prescribing for paediatric…we also see female patients who are pregnant…Lactating, and all… contraindicated” Doctor03.
“Other ones that I would search for would be if the patient comes in with very…unusual presentations.” Doctor07.

The timing and frequency of information needs

Clinicians explained that they commonly seek clinical information daily or several times a week. They said that they either seek information at the point-of-care or at home:

“I will look at least weekly once…It’s of my own interest…Not during working times, most of the time…When we are travelling, in MRT…Sometimes at home also.” Nurse10.
“Not so many cases…It’s quite rare, actually…Because most of our cases are quite common…we still can deal with…Yes…Maybe once a few weeks…Once a month…When I have concerns or any doubts…After patient left…yes. Maybe, sometimes…And after the doctors consult.” Nurse05.

The timing and frequency of using CPGs

Clinicians said that they commonly use CPGs daily or when there was a change or update to the CPGs:

“…day to day, because all these guidelines I’m familiar with, it’s in my memory…internally we do have guidelines for certain acute conditions.” Doctor02.

Clinicians discussed convenience, easy access, the trustworthiness of information, having colleagues who are specialists, and being keen to keep up-to-date as the facilitators to seeking clinical information:

Information-seeking facilitators

“I find…clinical practice guidelines quite useful…since it’s on our terminal. I do open that up to look at it…it does give us quite a convenient and no fuss way to be able to access them on our terminal while we are seeking information whether during or even after consults.” Doctor06.
“work instructions…Policies and protocols…Intranet…So I just want to make sure that…I’m doing things correctly, that I’m, you know, following the guidelines. So I’ll just quickly enter, you know, log into the intranet and just search for the information…The information that’s on the intranet has, you know, been validated by an expert, you know…So that’s why I rely heavily on it.” Nurse01.

Information-seeking barriers

Clinicians mentioned that internet surfing separation, the lack of time, limited access to medical literature databases, and limited search function in the organisation’s server were barriers to seeking clinical information:

“The information I know is there…But it’s not so easy to search for…Not user-friendly, not very exhaustive…Sometimes you just have to…trial-and-error…different keywords.” Nurse01.

Additionally, clinicians frequently mentioned using smartphones to access clinical information. Consequently, doctors said that they were worried that using smartphones during a clinical consultation might make them seem unprofessional to patients:

“I need to explain to the patient that…I am using my phone because I don’t have internet access or may appear rude to the patient; I am surfing my phone in the middle of the consult.” Doctor02.

Doctors reported that they were also concerned about their privacy when they showed their smartphones to their patients:

“…sometimes…you don’t want to show your phone to them(patients) also…Because sometimes you may have other notifications.” Doctor05.

3) The role of evidence in information-seeking

Is a theme that explores the role of evidence in clinicians' information-seeking in our study. The value of scientific research for clinicians seeking information in two subthemes: the importance of trustworthy information sources and employing evidence-based information sources.

The importance of trustworthy information sources

Clinicians agreed that peer-reviewed clinical information was reliable. Additionally, doctors expressed trust in clinical information if there were frequent updates of the content:

“…they(UpToDate®) do put…the date of which they have updated the articles…it’s from multiple sources…citations and…management…seems quite sound.” Doctor06.
“The information that’s on the intranet has…been validated by an expert.” Nurse01.

Employing evidence-based information sources

Clinicians mentioned that emphasising the importance of evidence in patient care and building an evidence-based culture in the workplace helps to encourage the use of evidence-based information sources in practice.

“I don’t have any concrete kind of suggestions now but…perhaps find some ways to sustain interest…to remind us that we’re doing this for best of patients.” Doctor06.
“If I have discussions with my peers regarding cases then I will, like, refer back to the…to…the CPG and things like that…I think the conference…or the…forums they are also a very good source of information.” Nurse03.

To our knowledge, this is the first study conducted in Singapore to investigate the primary care clinicians’ preferences for, barriers to, and facilitators of information-seeking in clinical practice. Clinicians’ mostly researched information on conditions such as unusual skin rashes, rare diseases, paediatrics, and women's health. Most clinicians searched clinical information at the point-of-care daily for a variety of reasons, including personal interest, clarification of doubts, or self-improvement. Sources of information included CPGs, online evidence-based resources, the internet, peers, and smartphones. Although CPGs were clinicians' preferred sources of information, they did not refer to them regularly and only did so in memory or when the guidelines were updated. We also found that using smartphones for seeking clinical information was commonly reported among clinicians. The barriers to primary care clinicians’ information-seeking process were the lack of time, internet surfing separation of work computers, limited search function of their organisation’s server, and limited access to medical literature databases. The facilitators to primary care clinicians’ information-seeking process were convenience, ease of access, and the trustworthiness of the information sources.

Like other studies [ 3 , 8 , 20 , 40 , 41 ], we found that the choice of information sources was affected by the trustworthiness and availability of resources. CPGs were preferred among clinicians as they were written by experts or specialists in their field. However, some clinicians felt that CPGs were too lengthy to be used at the point-of-care, outdated, and difficult to locate on their organisation's server. Additionally, clinicians only referred to CPGs recalled from memory or when they were updated. This highlights the importance of providing an alternative evidence-based clinical resource that is succinct and easy to refer to at the point-of-care [ 42 ]. Using medical apps for the provision of point-of-care summaries may mitigate the challenges of using CPGs for clinical information. Correspondingly, clinicians in the polyclinics commonly referred to the UpToDate® app provided by their organisation as a point-of-care resource they could use on their smartphones. Evidence-based point-of-care resources are commonly presented in key point summaries, follow formal categorisation of medical conditions, and provide references [ 43 ]. Limited research has shown that it was beneficial to integrate UpToDate® searches into daily clinical practice [ 42 ]. Additionally, the American Accreditation Commission International's @TRUST programme is one framework designed to encourage trustworthy online content. It is an invaluable resource for both individuals looking for health information online and organisations attempting to deliver trustworthy content [ 44 ]. However, continual efforts are required to encourage its use and ensure that individuals have access to accurate and reliable health information online. Therefore, future studies should investigate the quality of existing medical apps in providing point-of-care summaries and the effects of their use in the primary care setting.

We also found that clinicians were seeking clinical information on their smartphones. This is not surprising as Singapore’s public healthcare institutions enforce internet surfing separation on work computers. Furthermore, with the high penetration of smartphones in Singapore [ 45 ], these devices became the next best alternative for clinicians to seek online clinical information. Clinicians in the polyclinics frequently cited using UpToDate® app and the Google search engine on their smartphones. Similar to another study [ 46 ], we found that doctors often used Google images on their smartphones to identify less common rashes. Additionally, our study found that clinicians use Google images to educate patients. However, clinicians in the polyclinic reported privacy and professionalism concerns as barriers to using smartphones for clinical consultations. These findings were consistent with a systematic review assessing the challenges and opportunities of using mobile devices by healthcare professionals [ 47 ]. Despite the internet surfing separation in public healthcare institutions in Singapore and the availability various information sources, we found similar barriers to clinicians seeking clinical information with other studies [ 3 , 20 , 48 ]. Future research may focus on addressing specific barriers to using various mobile devices by primary care clinicians at the point-of-care.

Finally, smartphones may be an important information-seeking channel for healthcare professionals, and the hospital or government may be forced to establish legislation to protect healthcare professionals who use smartphones in clinical practice. Compliance with legislation governing smartphone use at work may be examined during the evaluation process for healthcare professionals. Guidelines on smartphone use among healthcare professionals can be tailored to individual conditions, such as patients' permission to share medically sensitive information via text. As a result, guidelines could be based on best practice claims and common actionable statements. Additionally, this study suggests that clinicians have, for the most part, been left to navigate information access on their responsibility, which may not be the most effective. Developing a more robust culture of evidence-based medicine within the organisation is essential and ought to be explicitly promoted moving forward. It could be beneficial for clinicians to receive organised training on effective information-seeking strategies and resources.

Our study has several strengths and limitations. Our strength is that we employed an in-depth interview approach and an open-ended style of questioning. The interactive nature of our interviews provided richer context and room for free responses from the interviewees. We were then able to critically scrutinise the conversations and provide insights that were helpful in the final analysis of themes.

There are several limitations. Firstly, we did not explore the influence of gender and age in the participants’ information-seeking behaviour, which has been demonstrated in other research in this area [ 14 ]. Secondly, the study was limited by environmental factors in the workplace, such as internet and information access. Finally, there may be possible social desirability bias, whereby the participants may have presented responses that were more socially appropriate than their actual thoughts on the issues explored during the interviews.

We found that clinicians frequently sought answers to clinical queries arising from patient care. However, the choice of information sources was influenced by the trustworthiness and availability of the resources. Clinicians in the polyclinic commonly reported using their smartphones for practice. Using UpToDate® app and Google search engine was commonly cited as their preferred clinical information sources due to its convenience and accessibility. While our findings may have been reported in other contexts, there are significant and novel elements when compared to healthcare around the world. For example, the implementation of internet surfing separation in public healthcare institutions raises concerns regarding clinicians' usage of smartphones, as well as their privacy and professionalism. This may lead us to examine the need for some regulation and training on the use of smartphones among clinicians, as well as the necessity to investigate this further from the patient's perspective. Future studies to improve access to evidence-based clinical information sources other than CPGs should be explored to address the information needs of primary care clinicians. Studies examining trustworthiness and effectiveness of using app-based point-of-care information summaries and exploring the impact of using mobile devices for information-seeking by clinicians at the point-of-care will also be useful to address the information-seeking needs of primary care clinicians. Furthermore, Large Language Model (LLM)-based artificial intelligence (AI) systems, such as ChatGPT, are increasingly being developed and used. They are used in various disciplines, including healthcare. Some, such as AMIE (Articulate Medical Intelligence Explorer) and Pathways Language Model (Med-PaLM 2), have been developed specifically for healthcare [ 49 , 50 , 51 ]. More research into the usage of AI among clinicians is needed to assure trust, dependability, and ethical conduct.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available due the fact that all data obtained during the course of this study is strictly confidential and will be kept by the study team at the end of the study for at least 6 years and disposed of according to the Personal Data Protection Act in Singapore. Data are however available from Associate Professor Tang Wern Ee (co-author) upon reasonable request and with permission of the ethics committee of National Healthcare Group Domain Specific Review Board (the central ethics committee).


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This study is funded by Seedcorn Grant Centre for Primary Health Care Research and Innovation, a joint Lee Kong Chian School of Medicine, and the National Healthcare Group Polyclinics Initiative.

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Mauricette Moling Lee

Clinical Research Unit, National Health Group Polyclinics (HQ), 3 Fusionopolis Link, Nexus @ One-North, Singapore, 138543, Singapore

Wern Ee Tang

Family Medicine and Primary Care, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Novena Campus Clinical Sciences, Building 11 Mandalay Road, Singapore, 308232, Singapore

Helen Elizabeth Smith

Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, UK

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Lorainne Tudor Car conceived the idea for this study. Tang Wern Ee contributed to the design of the work and the acquisition of the data. Mauricette Lee collected the data, analysed it and wrote the manuscript with support from Tang Wern Ee, Helen Smith, and Lorainne Tudor Car. Lorainne Tudor Car and Tang Wern Ee supervised the project.

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This study was approved by the National Healthcare Group Domain Specific Review Board (the central ethics committee). National Healthcare Group Domain Specific Review Board Reference Number: 2018/01355. Informed consent was obtained from all participants. All methods were carried out in accordance with relevant guidelines and regulations, in accordance with the Declaration of Helsinki.

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Lee, M.M., Tang, W.E., Smith, H.E. et al. Identifying primary care clinicians’ preferences for, barriers to, and facilitators of information-seeking in clinical practice in Singapore: a qualitative study. BMC Prim. Care 25 , 172 (2024).

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BMC Primary Care

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qualitative research studies examples

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Exploring shared decision-making needs in lung cancer screening among high-risk groups and health care providers in China: a qualitative study

  • Xiujing Lin 1 ,
  • Fangfang Wang 1 ,
  • Yonglin Li 1 ,
  • Fang Lei 2 ,
  • Weisheng Chen 3 ,
  • Rachel H. Arbing 4 ,
  • Wei-Ti Chen   ORCID: 4 &
  • Feifei Huang   ORCID: 1  

BMC Cancer volume  24 , Article number:  613 ( 2024 ) Cite this article

Metrics details

The intricate balance between the advantages and risks of low-dose computed tomography (LDCT) impedes the utilization of lung cancer screening (LCS). Guiding shared decision-making (SDM) for well-informed choices regarding LCS is pivotal. There has been a notable increase in research related to SDM. However, these studies possess limitations. For example, they may ignore the identification of decision support and needs from the perspective of health care providers and high-risk groups. Additionally, these studies have not adequately addressed the complete SDM process, including pre-decisional needs, the decision-making process, and post-decision experiences. Furthermore, the East-West divide of SDM has been largely ignored. This study aimed to explore the decisional needs and support for shared decision-making for LCS among health care providers and high-risk groups in China.

Informed by the Ottawa Decision-Support Framework, we conducted qualitative, face-to-face in-depth interviews to explore shared decision-making among 30 lung cancer high-risk individuals and 9 health care providers. Content analysis was used for data analysis.

We identified 4 decisional needs that impair shared decision-making: (1) LCS knowledge deficit; (2) inadequate supportive resources; (3) shared decision-making conceptual bias; and (4) delicate doctor-patient bonds. We identified 3 decision supports: (1) providing information throughout the LCS process; (2) providing shared decision-making decision coaching; and (3) providing decision tools.


This study offers valuable insights into the decisional needs and support required to undergo LCS among high-risk individuals and perspectives from health care providers. Future studies should aim to design interventions that enhance the quality of shared decision-making by offering LCS information, decision tools for LCS, and decision coaching for shared decision-making (e.g., through community nurses). Simultaneously, it is crucial to assess individuals’ needs for effective deliberation to prevent conflicts and regrets after arriving at a decision.

Peer Review reports

Low-dose computed tomography (LDCT) is an effective tool for early lung cancer detection and has been proven to enhance survival rates in individuals at high-risk for lung cancer [ 1 , 2 ]. However, global LDCT usage is limited, with only 2-35% of eligible individuals undergoing screening [ 3 , 4 , 5 , 6 , 7 ], in contrast to 16-68% of eligible candidates undergoing colorectal cancer screening [ 8 ]. Improvements in LDCT screening rates for high-risk groups have been modest. The intricate balance between the advantages and risks of LDCT impedes the utilization of lung cancer screening (LCS) [ 9 ]. Notably, compared to their non-screened counterparts, high-risk individuals who underwent LDCT had a remarkable 24% decrease in lung cancer mortality [ 2 ]. However, the benefits of LDCT come with potential drawbacks, such as radiation-induced cancer, needless examinations, invasive procedures stemming from false positives, overdiagnosis, incidental discoveries, and psychological burdens [ 10 ]. These complexities render the LDCT screening decision-making process multifaceted and reliant on personal preferences. Hence, guiding high-risk groups toward well-informed choices regarding LCS is pivotal and represents a substantial mechanism for advancing the secondary prevention of lung cancer.

Shared decision-making is defined as “a collaborative approach for health care providers and patients in making informed health decisions”, which involves considering evidence regarding the benefits and risks of medical options, as well as individuals’ preferences and values [ 11 ]. This decision-making process allows both health care providers and individuals as well as their family members to engage in deliberation which leads to identifying the most appropriate decision for the situation [ 12 ]. Multiple guidelines strongly recommend shared decision-making as an essential step before patients undergo LDCT. Shared decision-making is also stipulated as a prerequisite for LDCT reimbursement by the Centers for Medicare and Medicaid Services in the United States [ 13 , 14 , 15 , 16 ]. Regrettably, the utilization of shared decision-making in clinical practice is currently not optimal [ 17 , 18 ]. Patients do not know what LDCT is, and they often report a lack of about the risks and benefits of LDCT. As a result, patients often have concerns about the risks of LDCT, and health care providers frequently fail to inquire about individuals’ preferences [ 19 ]. Consequently, there has been a notable increase in the literature focusing on barriers to shared decision-making from the perspectives of both health care providers and lung cancer high-risk groups. For example, studies have shown that the barriers to shared decision-making include different perceptions about the use of shared decision-making and a lack of time to communicate with providers. However, there are some limitations in terms of methodology and the comparative nature of the studies that focus on LCS shared decision-making. First, previously published studies focused on identifying barriers to shared decision-making and neglected decision support from physicians and patients. For instance, one study found that a lack of professionalism in health care providers is a barrier to shared decision-making, yet no studies have examined specific LCS shared decision-making decision supports for health care providers [ 19 ]. Second, current research centers on short-term decision-making experiences, such as cognitive consequences experienced immediately following shared decision-making. However, studies have not adequately addressed the complete shared decision-making process – pre-decisional needs, the decision-making process itself, and post-decision experiences, such as decision regret. Third, the COVID-19 pandemic has introduced a new risk of LDCT usage (exposure to the health-care environment) [ 20 ]. The added risk alters the benefit-risk ratio of LDCT under pre-COVID-19 guideline recommendations. Fourth, shared decision-making, developed in Western societies, is rarely discussed in China. The national climate and medical systems of China and Western countries differ greatly [ 21 ], and the lack of evidence on LCS shared decision-making in China indicates a need for an assessment of shared decision-making in those who require LDCT.

This study aimed to explore the decisional needs and decision support of shared decision-making for LCS among Chinese high-risk individuals and their health care providers using data collected through in-depth one-on-one interviews.

Theoretical framework

The Ottawa Decision-Support Framework (ODSF) is an evidence-based conceptual framework that is structured around three key components [ 22 ]: (1) assessing decisional needs, such as insufficient knowledge, complex decision types, and limited resources; (2) providing decision support, which encompasses clinical counseling, decision-making tools, and decision coaching; and (3) evaluating decisional outcomes, which includes assessing the quality of the decision-making process and its impact. According to the ODSF, successful decision support should be guided by an assessment of the individual’s knowledge and his/her ability to make his/her own decision to reduce their unmet needs and achieve a final health decision with the support of health care providers and family members. The ODSF has been successfully used within several populations with health needs to guide health decisions and provide decision support [ 23 , 24 ].

This qualitative study emphasizes the “who, what, and where” of events or experiences [ 25 ]. The central research question posed was, “What are the decisional needs and supports of LCS shared decision-making among individuals at high-risk of lung cancer and health care providers?” Consequently, a descriptive qualitative approach was deemed appropriate for exploring the decisional needs and supports for LCS shared decision-making among individuals at high-risk of lung cancer and health care providers [ 26 ]. This descriptive qualitative study adhered to the Consolidated Criteria for Reporting Qualitative Studies (COREQ) checklist [ 27 ]. Ethical approval for this study was obtained from the ethics committee of Fujian Medical University (Approval No. 2,023,098).

Inclusion and exclusion criteria

Aligned with the guidelines for the early detection of lung cancer in China [ 14 ], the inclusion criteria used for the high-risk group for lung cancer were as follows: (a) aged between 50 and 74 years; (b) had at least one of the following risk factors for lung cancer: a smoking history ≥ 30 pack-years, which includes current smokers or individuals who quit smoking within the last 15 years; prolonged exposure to passive smoking (living or working with smokers for 20 years or more); a history of COPD; a history of occupational exposure to asbestos, radon, beryllium, chromium, cadmium, nickel, silicon, soot, or coal soot for a minimum of 1 year; or a family history of lung cancer; (c) verbal confirmation of undergoing LCS shared decision-making; (d) undergone LDCT within the past 5 years; (e) Able to converse in Mandarin; (f) absence of cognitive or psychological disorders; and (g) willingness to share their personal stories. The exclusion criteria used for the high-risk group for lung cancer were as follows: (a) previous history of lung cancer; and (b) cognitive or psychological disorders (such as depression and anxiety). The inclusion criteria used for health care providers were as follows: (a) certified physicians or nurses; (b) expertise in LCS; and (c) willingness to share their experiences. Healthcare providers who were receiving external training were excluded from participation in the study.

Qualitative data collection

The data were collected from March 2023 to May 2023. A purposive sampling method was used to identify and recruit individuals at high-risk for lung cancer, as well as local health care providers from five community healthcare centers and two surgical oncology departments of tertiary hospitals. Study flyers provided information on the purpose of the study and the inclusion and exclusion criteria and were distributed to potential participants on site. After participants expressed their interest in the study, they were screened for eligibility to participate and their informed consent was secured. Next, a one-on-one interview was scheduled and a questionnaire was completed by participants to obtain their demographic data (gender, age, residential area, smoking status, etc.). One-on-one interviews were conducted in Mandarin, digitally recorded, with study data stored on a passworded encrypted laptop. Each interview lasted approximately 20 to 40 min. A private room in the clinic was used for all the in-depth interviews.

The interview questions were formulated based on the ODSF and after a comprehensive literature review [ 28 ], with extensive discussions among researchers of the study (Feifei Huang, PhD, RN, Professor, specializing in lung cancer prevention and psycho-oncology; Weisheng Chen, MD, specializing in lung cancer prevention, diagnosis and treatment; and Wei-Ti Chen PhD, RN, CNM, FAAN, specializing in intervention design and qualitative data collection). To ensure the acceptability and credibility of the interview guide, the interview questions were pilot tested with four participants in total, including two health care providers and two individuals at high-risk of lung cancer. As a result, some misconceptions regarding the interview questions were identified and subsequently modified. For instance, we replaced the term “decision tools” with “patient decision aids” to help participants to better understand the posed questions. The final interview questions are outlined in Table 1 . Tables 2 and 3 summarize key demographic data collected on the high-risk individuals and health care providers, respectively.

The sample size was determined by data saturation, that is, recruitment ended at the point where no new themes emerged from the participants’ experiences [ 29 ]. Data saturation was reached at approximately the twenty-seventh in-depth interview with a high-risk lung cancer individual, with another three high-risk lung cancer individuals being interviewed to ensure that the data reached complete saturation. Data saturation was reached at approximately the seventh in-depth interview with healthcare providers, with another two healthcare providers interviewed to ensure data saturation.

Data analysis

Since the interviews were conducted in Mandarin, a bilingual coding technique was used to keep the data in the original Chinese format, and the coding assignments were in English (e.g., decision negotiation). To ensure accuracy and minimize potential translation errors, two bilingual researchers (Chinese and English) reviewed and confirmed the translations [ 30 ]. The process of data analysis began with data collection. To analyze the data, content analysis was guided by the ODSF and Nvivo software version 12 was used [ 31 ]. The classification of themes was performed both inductively (derived from the quotes of research participants) and deductively (derived from the ODSF theoretical framework) under the principle of complementarity. The detailed steps of the data analysis process are illustrated in Fig. 1 .

figure 1

Directed content analysis flowchart


Credibility, dependability, confirmability and transferability were employed to assure the trustworthiness of this study’s findings [ 32 ]. To enhance credibility, the researcher dedicated ample time to establishing meaningful interactions with the participants, thereby building trust for effective data collection. Regarding dependability, two researchers cross-checked and rectified codes that did not precisely reflect participants’ perspectives. Furthermore, an audit trail and reflexivity techniques were used during the data analysis process, which included tracking the interview and data analysis notes and memos. To ensure confirmability, the supervisor reviewed and selected quotations, codes, and categories, thereby validating the accuracy of the coding process. In terms of transferability, participants were purposefully selected from both urban and rural areas to incorporate a wide range of perspectives. Herein, a comprehensive description of the entire research process is presented to facilitate reproducibility of the study.

Out of a total of 44 participants consented, five participants (4 high-risk individuals and 1 health care provider) dropped out of the study due to their busy schedules and lack of interest in participating. A total of 39 eligible volunteers composed the study sample. Among them, 30 individuals were classified as at high-risk for lung cancer with an average age of 61.27 ± 7.92 years, while nine health care providers had an average age of 36.78 ± 7.45 years. Five health care provider participants specialized in lung cancer prevention, diagnosis, and treatment, and four specialized in general medical education and community cancer screening education. Detailed demographic information on the participants can be found in Tables  2 and 3 .

A total of 546 unique codes related to LCS shared decision-making were identified. Following the framework of the ODSF, participants’ decisional needs and supports for shared decision-making were categorized (refer to Fig.  2 ; Table  4 ).

figure 2

Participants’ viewpoints on shared decision-making based on ODSF

Decisional needs

We identified four categories related to the theme of decisional needs, including LCS knowledge deficits, inadequate supportive resources, shared decision-making conceptual bias, and delicate doctor-patient bonds.

Theme 1: LCS knowledge deficit

Many high-risk study participants expressed that they did not have access to reliable and authoritative medical information. Many of the high-risk participants shared their inability to access LCS-related information and their limited capacity to distinguish accurate LCS information from misinformation. Furthermore, participants mentioned that a negative personal view of life influenced their active engagement in shared decision-making with health care providers and/or family, which diminished their comprehensive understanding of LCS.

“Some people are negative, they believe God’s will can decide everything, so when they faced a decision, they will ask the gods instead of making a decision according to their actual situation” H13 (high-risk individual, female, 53 years-old).

Theme 2: inadequate supportive resources

Participants emphasized that shared decision-making was hindered by financial, transportation and time-related barriers to hospital visits. Furthermore, unfamiliarity with the process of seeking medical treatment also presented an obstacle to shared decision-making. Notably, participants expressed negative emotions related to the LDCT test which influenced their shared decision-making. In particular, the LDCT process was not well received by individuals who had claustrophobia. Participants described feeling claustrophobic during the process of the imagological examination. The requirement for patients to lie flat during the examination, combined with the confined and dim space, can lead to feelings of depression and suffocation. Additionally, the machine’s noise and concerns about potential risks (such as radiation and false positives) from having LDCT scans may have heightened patients’ negative emotions and fears.

“Since I smoke, I’m always scared of getting bad test results. If the results are bad, it’s just really scary, I don’t think I have the sanity to make shared decisions with my doctors. I need help.” H11 (a high-risk individual, female, 54 years-old).
“I struggle with claustrophobia, and every time I have a test, I feel really trapped. It would be difficult for me to have shared decision-making when I have a claustrophobia. It felt like my mind was blank.” H12 (a high-risk individual, male, 52 years-old).

Several participants mentioned experiencing anxiety regarding the test results. They expressed their apprehension about potential adverse outcomes and indicated that this anxiety affected their ability to engage in shared decision-making with their doctors. Moreover, after experiencing claustrophobia, some participants expressed that they felt an inability to make shared decisions with their doctors in a rational manner.

Theme 3: Shared decision-making conceptual bias

Some participants mentioned that they were not familiar with the specific term ‘shared decision-making’. Health care providers shared the perspective that excessive communication with the high-risk group about their condition might lead to a refusal of subsequent treatment, potentially jeopardizing their health.

“I believe that when it comes to professional matters, it’s best to rely on trained professionals. Most patients don’t have expert medical knowledge, and even if they do, they might be hesitant about certain exams. That, in my opinion, doesn’t do much good for their health.” M8 (a general practitioner, female, 36 years-old).

Additionally, participants had misconceptions about shared decision-making. For example, health care providers had misconceptions about shared decision-making in LDCT screenings – some believed that shared decision-making meant merely providing information about the benefits and risks of LDCT; others confused the concepts of informed consent and shared decision-making all together; and a few providers viewed encouraging high-risk groups to conduct LDCT screening to be a part of shared decision-making. Some participants believed shared decision-making to be merely a procedural step to schedule a test appointment.

“I think shared decision-making means thoroughly informing those in high-risk groups about the pros and cons of a particular exam and ultimately letting them make the call.” M5 (a physician specialist, male, 25 years-old).
“When we suggest undergoing a medical examination, doctors might assume that this visit is a necessary step for patients to get a chance to be examined, not a step for shared decision-making. As a result, they may believe that there’s no necessity for patient education.” H13 (a high-risk individual, female, 53 years-old).

Theme 4: delicate doctor-patient bonds

Both health care providers and high-risk individuals emphasized that time constraints pose a significant barrier to shared decision-making. Some participants noted that doctors, who often express concerns about work-related burnout, were hesitant to provide comprehensive information about LDCT.

“I believe that doctor burnout contributes to their reluctance to discuss lung cancer screening with patients.” H9 (a high-risk individual, male, 57 years-old).

Furthermore, health care providers and participants encountered challenges with communication. Health care providers struggled to simplify complex information for easy understanding, while participants had difficulty clearly expressing their needs.

“Effective communication is essential for both doctors and patients. The doctor’s ability to convey information and the patient’s capacity to express their needs are crucial. Insufficient communication skills represent a challenge for both parties.” M6 (a physician specialist, male, 27 years-old).

Participants also mentioned that they were hesitant to express their thoughts to doctors whom they do not know well.

“Building trust is not a simple task. When patients and I have a strong connection and they trust us enough to share their true thoughts, it significantly reduces barriers to shared decision-making. On the other hand, some doctors who aren’t deeply connected with the community may struggle to gain patients’ trust, leading to communication challenges that hinder shared decision-making.” M2 (a nurse in grade A tertiary hospital, female, 41 years-old).

Others believe that the professional competence of doctors plays a pivotal role in shared decision-making in LCS. People often opt for doctors from tertiary hospitals who were perceived to have a higher level of professionalism, which is conducive to shared decision-making.

“Personally, I believe that the expertise of doctors in county-level hospitals may not be as advanced, which affects my level of trust in them. I tend to find doctors in top-tier tertiary hospitals to be more credible.” H12 (a high-risk individual, male, 52 years-old).

Decision support

Three categories related to the theme of decision support were identified: provide information throughout the LCS process, providing a shared decision-making coach, and provide decision tools.

Theme 1: provide information throughout the LCS process

Participants shared that they would like to know information about LDCT before and after undergoing the screening test. Desired information prior to screening included: eligibility criteria for LCS; benefits and risks of LDCT, the LDCT process itself, primary and secondary prevention of lung cancer, the cost of LDCT, potential emergencies and appropriate responses during LDCT, guidelines for Medicare reimbursement related to LDCT, and the medical visit steps. Most participants wanted information after the screening to include the interpretation and monitoring of LDCT results as well as the recommended frequency of LDCT.

Theme 2: providing a shared decision-making decision coach

Several participants said that it is necessary to enhance shared decision-making beliefs to better support the decision-making process for LCS, which is inherently a preference-sensitive decision.

“In China, shared decision-making isn’t commonly practiced. Many physicians here may not be familiar with the concept, even though it’s something they should consider adopting. Personally, I strongly believe in the importance of implementing shared decision-making.” H6 (a high-risk individual, male, 58 years-old).

High-risk individuals emphasize the importance of establishing a foundation for knowledge before engaging in shared decision-making. Participants advocated for a basic understanding of medical concepts, with decision counselors possessing specialized medical expertise.

“Before participating in shared decision-making, I’d like to gain some basic medical knowledge.” H4 (a high-risk individual, female, 53 years-old).

Due to time and energy constraints, clinicians found it challenging to engage in shared decision-making. However, the community doctors in our study stated that they had more time to communicate and share opinions and that their closer patient-provider relationships could facilitate the shared decision-making process in China.

“We only present the benefit and harm of LDCT briefly. We don’t have enough time to describe these in more detail. You know, lung cancer pathology and knowledge of imaging are too complex for high-risk individuals of lung cancer. For individuals who don’t have professional backgrounds, it is impossible for them to understand totally, what we can do is try to get them to understand as much as possible in a limited time.” M5 (a doctor in grade A tertiary hospital, male, 25 years-old).
“It’s important to involve community health providers in shared decision-making for a couple of reasons. Firstly, we tend to establish a strong rapport with patients, and they often trust us more compared to clinicians. Additionally, we have the advantage of spending more time communicating with patients, which makes us better suited to facilitate shared decision-making.” M9 (a general practitioner, male, 42 years-old).

Theme 3: providing decision tools

Participants expressed the need for decision tools and made several suggestions for decision tools to better cater to diverse groups. Decision tools are instruments that aid users in clarifying the congruence between their decisions and their individual values by presenting relevant options along with their associated benefits and potential drawbacks. Through the use of decision tools, users are assisted in arriving at clear, high-quality decisions.

The participants had several suggestions for providing decision tools. First, various information modalities such as videos, images, and written content should be integrated into tools to accommodate varying education levels and preferences. Second, tailored information that aligns with LCS decision-making is preferred. Third, a three-way interaction model involving patients, decision tools, and health care providers could enhance effectiveness. Fourth, medical knowledge should be presented in a comprehensible manner to improve accessibility. Additionally, access to more detailed information is necessary. Fifth, the time spent using decision tools should be less than 20 min to prevent impatience. Sixth, most participants emphasized addressing credibility concerns, through incorporating medical professionals into the tool’s development team, emphasizing authoritative sources, and involving experts from reputable hospitals. Finally, most participants acknowledged that value clarification exercises should be integrated to help users articulate their personal screening preferences to ensure a comprehensive approach to decision support.

Shared decision-making plays a crucial role in enhancing the understanding of LCS and LDCT in high-risk groups. Shared decision-making can also establish realistic expectations for health outcomes and ultimately improve decision-making for the best treatment or screening option [ 33 ]. This qualitative study provides insights into the decisional needs and necessary support for shared decision-making in LDCT screening, from the perspectives of health care providers and high-risk individuals in China. Specifically, LDCT screening decisions should evaluate the knowledge, availability of supportive resources, health care providers’ understanding of shared decision-making concepts, and quality of doctor-patient relationships. At present, both providers and screeners require decision support surrounding LDCT information and need shared decision-making coaching to effectively arrive at a decision. This study finding is valuable for shaping the design of future interventions that aim to facilitate decision-making and has the potential to increase the use of LDCT screening in Chinese society.

Our findings also contribute to the classification refinement of the ODSF. Regarding LCS knowledge, we have observed that high-risk groups not only lack specific knowledge of LCS, but also face challenges accessing relevant information and struggle with their capacity to distinguish accurate LCS information from misinformation. Previous multimodel public health interventions have focused on education related to specific LCS knowledge and ignored the need to access correct information, insufficiently addressing the needs of populations at high-risk of lung cancer [ 34 ]. Therefore, in addition to limited knowledge, limited access to information and lack of identification undermine the contributions of high-risk groups in shared decision-making.

In terms of support and resources, it is essential to consider not only conventional limitations such as financial and health system resources, but also the psychological well-being of high-risk populations. The proportion of smokers is greater among those at high-risk for lung cancer than among those at high-risk for other types of cancers (such as breast cancer and colorectal cancer) [ 35 ]. Being a smoker can affect the execution of shared decision-making due to perceived stigma, lung cancer fatalism, and heightened levels of worry and fear of contracting lung cancer [ 35 ]. Additionally, concerns about potential risks associated with LDCT serve as a barrier to the shared decision-making process with health care providers [ 9 ].

Our findings provide new insights into the core constructs of decisional needs, including awareness of shared decision-making and doctor-patient bonds. Additionally, shared decision-making awareness studies have demonstrated that bias can lead to differences in individual preferences, which can hinder the initiation of shared decision-making and result in higher levels of decision conflict [ 36 ]. Additionally, studies have shown that poor doctor-patient communication can lead to low-quality shared decision-making. For example, dismissive clinicians who dominate decision-making encounters, use negative verbal or nonverbal cues, or fail to respect patients’ concerns have been shown to act as barriers to shared decision-making for many patients [ 37 ]. Conversely, clinicians who strive to understand individual needs and preferences can foster a sense of partnership and facilitate their involvement in shared decision-making processes [ 38 ]. It has also been found that allocating limited time for consultations as well as poor communication skills results in ineffective shared decision-making [ 39 ]. Limitations in skill and time can impede the ability to be fully informed by health care providers, to process and reflect on the information received, and to engage in meaningful discussions between providers and individuals [ 37 ]. Furthermore, the presence of trust is identified as a facilitator of shared decision-making. Establishing a trusting relationship with health care providers encourages patients to feel more comfortable asking questions, sharing personal information, and discussing their concerns [ 39 ].

Currently, the use of shared decision-making in clinical practice is suboptimal in China [ 11 ]. Fortunately, our study provides potential mitigation strategies. First, the need for comprehensive decision tools that appeal to diverse groups of patients was emphasized by both high-risk groups and health providers. A decision tool can furnish information, facilitate patient-doctor dialog, and enhance therapeutic outcomes [ 33 ]. However, the availability of decision tools for LCS is limited and their applications are less than ideal, partly due to their failure to be tailored to personal needs. For instance, most LCS decision tools are presented as single-page materials or premade videos, which may not fully address participants’ needs. Our findings highlight the demand for personalized decision tools for LCS in China. Second, some participants suggested that decision counselors should not be limited solely to clinicians; community health care providers can also serve as counselors for decision-making. This aligns with the concept that shared decision-making requires multisectoral collaboration [ 40 ]. Community nurses in particular, share similar ethnic, linguistic, and geographic backgrounds with the residents they serve compared to other nurses. Consequently, they are more likely to encounter high-risk populations in the community [ 41 ]. Additionally, due to the nature of their work, they have more time to engage in shared decision-making discussions with high-risk groups. Research has revealed that community nurses, in their roles as coordinators, educators, researchers, navigators, and practitioners, can play multidimensional roles essential for leading successful LCS [ 42 ]. Hence, future research should actively promote the development of community nurses as counsellors for LCS to alleviate the burden on hospital-based physicians. Third, both health care providers and high-risk groups should receive education on shared decision-making. Our findings reveal that both sides still possess a vague understanding of shared decision-making, often conflating it with informed consent (patient-led) and paternalism (physician-led) models. Unlike in Western countries, humanistic medicine education in China is lacking, resulting in an inadequate grasp of patient-centered medical-ethical principles among health providers and patients [ 21 ]. Future interventions in China should emphasize humanistic medicine to establish the foundation of shared decision-making.

Our findings are rooted in Chinese culture, which, along with broader Asian cultural influences, places a significant emphasis on Confucianism and sociocultural values such as family support, care, and respect for familial hierarchy and authority [ 43 ]. Therefore, the insights provided by this paper may be applicable to other Asian countries. Despite the rapid development of SDM research in the West, the actual implementation of SDM in clinical practice is not as favorable [ 44 ]. One contributing factor is that highly developed patient decision aids often overly focus on standardized processes, deviating from a more humanistic approach that can be applied universally [ 44 ]. Moreover, the ongoing wave of globalization has resulted in increasingly multicultural societies, necessitating a broader scope of SDM coverage that includes individuals from diverse cultural backgrounds. Therefore, avoiding cultural stereotypes and actively inquiring about patients’ preferences become especially crucial. The results of our study contribute valuable insights into individual decisional needs and decision support from the perspectives of both individuals at high-risk for lung cancer and health care providers. These perspectives can assist patient decision aids in avoiding excessive standardization. Simultaneously, the perspective embedded in our findings is well-suited to accommodate the multicultural nature of Western countries. Future studies should seek to bridge the gap in SDM between Eastern and Western contexts.


There are several limitations in this study. First, since the high-risk lung cancer individuals in our study did not undergo LCS shared decision-making recently, their views on LCS shared decision-making may have been subject to recall bias. Second, all study participants were from Fujian Province, which is a southeastern province in China. It is possible that recruitment from a broader geographical area may have led to a wider range of perspectives and experiences and thus influenced the point at which data saturation was reached. Third, as a qualitative, in-depth interview study, generalizations of findings to a larger population are not possible. Future quantitative studies should explore decision-making experiences among a broad range of high-risk groups and health care providers in China to enhance data triangulation and thus, the credibility and reliability of the study’s findings.

Guiding high-risk groups toward well-informed choices regarding LCS represents a substantial gain toward advancing secondary prevention of lung cancer. This descriptive qualitative study offers valuable insights into decision-making regarding LDCT screening among Chinese high-risk groups and their health care providers. The findings from this study highlight the decisional needs and decision support for shared decision-making for LCS using the ODSF conceptual framework. Future studies should target intervention development to offer decision support by evaluating individuals’ decisional needs, enabling them to make choices confidently, and with minimal conflict and decisional regret. In addition, this study may also serve as a starting point for the development of more effective decision tools for LDCT screening.

Availability of data and materials

The de-identified datasets used and/or analysed during the current study available from the corresponding author on reasonable request.


Low-dose computed tomography

Lung cancer screening

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The authors are grateful to all the participants in this study.

This work was supported by the National Natural Science Foundation of China [grant number 72304068] and the General Project of Fujian Provincial Nature Science Foundation (grant number 2021J01133126).

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XJL had full access to all of the data and takes responsibility for the integrity of the data and the accuracy of the data analysis. FFW and YLL contributed to the study design, data collection, data analysis and interpretation, and writing of the manuscript. FL. and WSC contributed to the recruitment, data collection and interpretation, and writing of the manuscript. WTC contributed to the study design, coordination, interpretation, and writing of the manuscript. FFH contributed to the overall study design, interpretation, and writing of the manuscript. All authors approved the final version of the manuscript.

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The significance of metaverse platforms is growing in both research and practical applications. To utilize the chances and opportunities metaverse platforms offer, research and practice must understand how these platforms create value, which has not been adequately explored. Our research explores the characteristics of metaverse platforms that facilitate value creation for organizations in both B2B and B2C sectors. Employing a qualitative inductive approach, we conducted 15 interviews with decision-makers from international corporations active in the metaverse. We identified 26 metaverse platform characteristics, which we categorized into six dimensions based on the DeLone and McLean Information Systems success model. Subsequently, we provide examples to illustrate the application of these identified characteristics within metaverse platforms. This study contributes to the academic discourse by uncovering the characteristics that shape the competitive landscape of emerging metaverse platforms. Leveraging these characteristics may offer metaverse providers a competitive edge in attracting complementary organizations to their platforms.

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1 Introduction

Recently, companies have begun to invest in metaverse platforms. McKinsey and J.P. Morgan project the metaverse as a trillion-dollar opportunity (McKinsey, 2022 ; Moy & Gadgil, 2022 ), while Gartner predicts 30% of companies will serve customers via metaverse platforms by 2027 (Gartner, 2022 ). This culminates in JP Morgan’s assessment, emphasizing that “metaverses will likely infiltrate every sector in some way in the coming years” (Moy & Gadgil, 2022 ). Indeed, with annual spending over $54 billion, expenditure on metaverses nearly doubled that of music purchases in 2021 (Moy & Gadgil, 2022 ). Researchers attribute this growth to metaverses revolutionizing virtual societal and economic interactions (Di Pietro & Cresci, 2021 ). Specifically, the metaverse benefit from network effects, becoming more appealing as user and organizational participation increases (Gawer & Cusumano, 2014 ; Tingelhoff et al., 2024 ). Accordingly, metaverse users benefit from the presence of more participating organizations, and reciprocally, having many users renders the metaverse more attractive for organizations (Kim et al., 2016 ).

While research deems metaverses only as viable once many users and complementors participate, about 500 companies already joined a metaverse platform, which only amounts to about 0.00015% of all companies (Brimco, 2024 ; Newzoo, 2022 ). A major contributing circumstance to the discrepancy of predicted and actual predicted and actual corporate engagement in metaverses is the inconsistencies of how different metaverse platforms influence organizational value creation. While past studies have examined how organizations mitigate challenges when creating value on metaverse platforms (e.g., Schöbel & Tingelhoff, 2023 ), organizations find it challenging to join metaverses due to the diverse ways in which various metaverses support value creation. Ultimately, it has not been fully explored how organizations can utilize characteristics of metaverse platforms (e.g., personalization and immersion) in their offerings to maximize their value proposition. When selecting a metaverse platform, organizations must prioritize its characteristics carefully to guarantee it fits their strategy. This study aims to uncover which metaverse platform features aid or hinder organizational value creation, leading to the following research questions:

RQ1 : What platform characteristics influence an organization’s ability to create value on a metaverse platform?

RQ2 : How have these characteristics already been configured in existing metaverse platforms?

This study seeks to contribute towards the research questions using qualitative data. We analyzed interviews with 15 decision-makers, all tasked with strategizing for corporations' engagements on metaverse platforms. We built on the DeLone and McLean Information Systems success model (D&M IS success model) (DeLone & McLean, 1992 , 2002 , 2003 ) to structure our data collection and analysis. From this, we identified 26 platform characteristics across six dimensions. Furthermore, we discovered these characteristics, such as accessibility and privacy, play different roles in metaverse platforms compared to traditional ones, a topic we expand on in our discussion. To substantiate our findings, we illustrate how these characteristics are divergently implemented in two existing metaverse platforms: Roblox and Decentraland.

Our study deepens the understanding of how organizations adopt metaverse platforms, laying groundwork for future research into corporate activities within these platforms. The study also broadens our knowledge of how emerging technologies' features support value-creation, beyond known platform mechanisms. Our results can help metaverse providers design more effective and appealing platforms. Moreover, by clarifying which platform features affect value creation, our study aids organizations in deciding how to offer their products or services on metaverse platforms. In summary, this study enables informed decisions about engaging with metaverse platforms for organizations.

This paper is structured as follows. The next section introduces foundational knowledge on metaverse platform ecosystems and outlines the structuring of their characteristics. Following that, we detail the methodology, then report and analyze the study's results. Subsequently, we exemplify our findings with case studies of two prominent metaverse platforms. In the sixth section, we explore the study's limitations and suggest directions for future research. The paper concludes with a summary of key insights.

2 Theoretical Background

2.1 the metaverse platform ecosystem and how it can create organizational value.

The metaverse is a multi-user virtual platform built on Web3 technology to revolutionize how people interact in any context (Di Pietro & Cresci, 2021 ). The malleable features of this emerging platform generate an immersive user experience, mimicking the real world sans physical constraints (Jaynes et al., 2003 ), thus enabling new and richer kinds of social and business interactions (e.g., through immersive 3D communication) (Bourlakis et al., 2009 ). By combining technologies like self-sovereign identity, virtual reality, and blockchain, these platforms evolve into parallel societies and economies. Though social media platforms (e.g., Facebook) or e-commerce platforms (e.g., Amazon) have already impacted human interactions and purchasing behaviors, this unique technology blend is unprecedented in virtual environments (Wang et al., 2021 ), enabling users to construct a parallel life with all its facets (i.e., work and leisure) in one platform for the first time.

The organizational structures around metaverse platforms classify them as an emergent platform ecosystem type (Schöbel & Leimeister, 2023 ). An ecosystem can be described as the underlying structure among partners, designed to enhance interactions and deliver a core value proposition (Adner, 2017 ). Generally, Typically, a platform is any product, service, or technology used by ecosystem actors to innovate and create complementary offerings (Gawer & Cusumano, 2014 ). It is typically integrated into ecosystems to help actors fulfill their value propositions (Gawer & Cusumano, 2014 ). For instance, the Apple App Store is integrated into the Apple ecosystem, including hardware and operating systems, enabling partners to create valuable apps and services.

Value is defined by an individual’s overall utility assessment based on their perception of input and output (Zeithaml, 1988 ). Two processual value components exist: use value and exchange value (Bowman & Ambrosini, 2000 ). Use value describes the recipient’s perception of a product or service based on their needs and values. Thus, use value stems from an organization's products and services, shaped by the recipient's personal and situational evaluation. Conversely, exchange value is what the recipient is willing to give in return for the use value. It can take other forms than monetary compensation, such as brand awareness or customer relationship, and is realized through the reciprocal transfer via the platform (Sanders & Simons, 2009 ).

Platform ecosystems comprise three major roles: platform leaders, complementors, and users. Platform leaders (or orchestrators) are the providers of the platform ecosystem (Gawer & Cusumano, 2002 ; Oliveira et al., 2019 ). They orchestrate the platform and are responsible for its regulation, maintenance, and adoption of standards within the ecosystem. Complementors provide products and services built for the platform, enhancing the ecosystem's core value. Lastly, users procure the complementors’ products and services via the platform. Usually, they access the platform and its complementary offering directly through a user interface. Consequently, in a metaverse platform ecosystem, value creation occurs through the reciprocal exchange of use and exchange values between complementors and users. Users receive products or services in the metaverse (use value) and are willing to compensate complementors with money and attention (exchange value). Accordingly, complementors are the source, users are the target, and the metaverse platform is the locus of value creation.

Some researchers contend that metaverse platform ecosystems challenge traditional role allocations (Schöbel & Leimeister, 2023 ). Notably, metaverse platforms represent early examples of decentralized governance models (Goldberg & Schär, 2023 ). Decentralized platforms are collectively owned by stakeholders rather than a single orchestrator. This collective ownership model is known as a Decentralized Autonomous Organization (DAO). On such a platform, users and complementors can impact decisions about platform governance, such as technical standards or the maximum amount of land created for users. This shifts the power balance among ecosystem participants, unlike in traditional platforms. In decentralized platforms, power is distributed; complementors and customers can propose and implement changes, not just the orchestrator (Goldberg & Schär, 2023 ). reducing the orchestrator's role also affects the direct relationships between customers and complementors (Yoo et al., 2023 ). While traditionally, both parties only interacted through the platform and, hence, the orchestrator, metaverses enable customers and complementors to interact directly. This is further emphasized by the fact that a metaverse does not require transaction intermediaries (e.g., banks). Specifically, the metaverse enables the direct exchanges of value items (Tapscott & Tapscott, 2017 ). This uniqueness to metaverse platforms potentially leads to simpler, more flexible, and efficient business processes by reducing communication complexities.

While many researchers contributed conceptualizing the metaverse, Schöbel et al. ( 2023 ) made the first effort to distinguish different types of metaverse platforms. Their taxonomy evaluates the technologies in a metaverse platform's infrastructure to highlight differences in value propositions. For example, they argue that a metaverse platform ecosystem like Decentraland focuses more on the creator economy and, hence, the creation and distribution of value items. In contrast, game-based platforms like Roblox focus on entertaining experiences that deepen user-brand relationships, alongside transactional value. This visualizes the paucity of platform types and business foci under the metaverse umbrella.

2.2 The DeLone and McLean IS Success Model

Identifying key success factors for organizational value creation is essential for insights that stay relevant in the rapidly changing metaverse platform ecosystems (Schöbel & Leimeister, 2023 ). In this context, past research employed value creation or value co-creation theory, predominantly focusing on aspects of value creation that are within an organization’s control. For instance, value creation theory typically centers on the consumer's perceived assessment, which organizations can influence by modifying their offerings or engaging in value co-creation with consumers. This aspect of how organizations can steer this process has been explored in existing literature, including in the context of the metaverse (e.g., Schöbel & Tingelhoff, 2023 ; Tingelhoff et al., 2024 ).

Conversely, our study examines platform characteristics that impact an organization's ability to create value. These external factors, often outside organizations' control, significantly influence value creation dynamics in technology-driven settings like metaverse platforms. In this context, the DeLone and McLean IS success model (D&M IS success model) (DeLone & McLean, 1992 , 2002 , 2003 ) has emerged as a cornerstone in the domain of Information Systems (Wang, 2008 ), enabling analysis of platform characteristics and their effects on organizational value creation. It provides a multifaceted perspective, deconstructing information systems into six key dimensions: information quality, system quality, service quality, usage intentions, user satisfaction, and net benefits.

Firstly, metaverse platforms are inherently complex digital ecosystems that thrive on exchanging information (Schöbel & Leimeister, 2023 ). The dimension of information quality is directly tied to the nature of data presented in metaverses, its accuracy, timeliness, and relevance. High-quality information will invariably influence user and organizational decision-making within a metaverse platform (Balica et al., 2022 ). System quality mirrors the technical prowess of a metaverse platform. As immersive environments, metaverse platforms demand high system performance, ease of navigation, and reliability. The better the system quality, the more seamless the user experience, thus attracting more complementors (Schöbel & Tingelhoff, 2023 ). Service quality in the metaverse context indicates the support structures in place. This could involve technical support, user guidelines, and assistance in content creation (Park & Kim, 2022 ). A high-quality service structure can significantly enhance the desirability of a metaverse platform for users and, in turn, organizations (Jo & Park, 2022 ).

The next dimensions, usage intentions and user satisfaction , arise from the interaction of the prior dimensions. Usage intentions resonate with how frequently users and organizations engage with the metaverse platform. A platform with a higher intent of usage becomes a hotspot for value creation and exchange, which is paramount in a networked environment like the metaverse (Ataman et al., 2023 ). Conversely, increased user satisfaction levels in a metaverse indicate that the platform effectively meets or surpasses the multifaceted expectations of users and organizations. When users are satisfied with their experiences, they are more likely to invest time, resources, and encourage peer participation, thus amplifying the platform's network effects and cementing its value-creation potential for organizations (Golf-Papez et al., 2022 ).

Lastly, the dimension of net benefits encapsulates the tangible and intangible outcomes that organizations derive from their engagement with an information system. When organizations can measure the positive impact of their involvement in a metaverse platform—whether in terms of revenue, brand recognition, skills acquisition, or social connections—it reinforces their commitment to the platform and ensures sustained engagement (Polyviou & Pappas, 2022 ). This sustained engagement, powered by recognized net benefits, can indicate the platform's long-term value-creation potential for organizations (Periyasami & Periyasamy, 2022 ).

While the D&M IS success model is tried and tested across various IS contexts, its inherent adaptability makes it particularly apt for metaverse platforms. The model’s validity at both individual and organizational levels (Petter et al., 2008 ) makes it a versatile tool for understanding an emergent and complex environment like the metaverse. Furthermore, previous validations of this model in diverse IS environments, from enterprise systems to e-commerce platforms (Wang, 2008 ), have shown its robustness and adaptability (Ahlan, 2014 ; Al-Kofahi et al., 2020 ). Applying it to the metaverse, an amalgamation of various IS types, seems like a logical progression.

The essence of the D&M IS success model is its capability to elucidate key dimensions that contribute to its capability to support complementors to create and deliver value through an information system (Wang, 2008 ). Yet, the interplay of these dimensions over time and the progressive stages of adoption need to be considered for a more comprehensive understanding. This aspect is vital as it bridges the D&M IS success model with real-world technological adoption behaviors and patterns, making it more contextually relevant, especially for evolving digital realms like the metaverse. By integrating process steps, one can understand not just what supports value creation (as indicated by the D&M IS success model) but also how and when these characteristics manifest and influence organizational value creation over the adoption lifecycle. To expand theories for a temporal interplay of characteristics, Ahlan ( 2014 ) proposed three process steps: system creation, system use, and system impact. This hierarchical order of influence characteristics can be applied to the D&M IS success model, where information, system, and service quality correspond to the system creation, usage intention and user satisfaction to system usage, and net benefits to system impact. The combined model is depicted in Fig.  1 .

figure 1

Research Model, based on DeLone and McLean ( 1992 , 2002 , 2003 ) and Ahlan ( 2014 )

The initial phase of any technological endeavor involves its conceptualization, development, and implementation (Weber et al., 2023 ). This phase is particularly crucial within the metaverse context as it lays the foundation for the user experience. Information, system, and service quality become essential metrics at this juncture. The quality and relevance of information guide the design and functionalities of the metaverse platform (Bayraktar et al., 2023 ). System quality ensures the platform’s technical soundness and scalability, which is vital to handling the dynamic nature of the metaverse’s ever-evolving virtual landscapes and foundational technologies (Peukert et al., 2022 ). Service quality, conversely, pertains to the support mechanisms, ensuring that complementors have a smooth onboarding process and immediate resolution to any technical hitches (Xi et al., 2023 ). A robust system creation phase, bolstered by these quality metrics, sets the tone for subsequent adoption and usage.

Once a system has been created and launched, its success is predominantly gauged by its acceptance and the extent of its use. Here, usage intention is a precursor, indicating initial interest and potential adoption rates (Jeong & Kim, 2023 ). However, for the system to embed itself into the daily routines of users and organizations, satisfaction becomes pivotal (Xi et al., 2023 ). As users engage with the metaverse platform, their experiences, which are shaped by immersive interactions, realistic representations, and the fulfillment of intended purposes, dictate their satisfaction levels. Satisfied users not only continue their engagement but also promote organic growth through positive word-of-mouth and peer recommendations (Mladenović et al., 2023 ).

For metaverse platforms, the system impact, as denoted by net benefits, encapsulates how effectively platform characteristics support organizational value creation (Polyviou & Pappas, 2022 ). This could manifest in diverse ways, from driving innovation in product or service offerings, facilitating unique customer engagement models, to fostering new revenue streams or enhancing brand visibility within the virtual realms (Hadi et al., 2023 ). Moreover, it might also encompass intangible benefits such as enhanced collaborative potentials, access to new market segments, or the ability to test and iterate offerings in risk-mitigated virtual scenarios (Yoo et al., 2023 ). Thus, system impact, in this context, underscores whether the metaverse platform meets the technical and experiential needs of its complementors and provides a conducive environment for organizations to harness its potential and realize tangible value.

In summary, the adapted D&M IS success model provides an exhaustive framework to unpack, understand, and measure how metaverse platform characteristics influence organizational value creation. By mapping its six dimensions to the specific characteristics of metaverse platforms along Ahlan ( 2014 )’s process steps, this study aims to clarify the characteristics that influence organizational value creation in this burgeoning digital frontier.

3 Methodology

3.1 ensuring the quality of qualitative data.

This study investigates how metaverse platform characteristics impact organizational value creation. The phenomenon of the metaverse is relatively new, and less is known about how organizations create value on metaverse platforms. To gain exploratory insights into organizational value creation, we adopted a qualitative research method (Draper, 2004 ).

To ensure our qualitative data's validity and reliability, we adhered to Lincoln and Guba ( 1985 )’s criteria: credibility, transferability, dependability, and confirmability. Credibility refers to the data's internal validity and its alignment with reality (Merriam & Grenier, 2019 ). We selected our interviewees based on three criteria. First, we considered whether interviewees had a holistic overview of organizational value creation. This includes individuals who are not only involved in strategic decision-making but also with a broad understanding of how various organizational units contribute to value creation. Second, as we explored metaverse platforms, our participants needed substantial experience in the metaverse sector. This ensures that our data comes from individuals who are not only familiar with the concept but are actively engaged in its application and development within their organizations. Third, we focused on participants whose organizations actively offer products or services on metaverse platforms (e.g., Roblox or Decentraland). This practical involvement ensures our insights are grounded in real-world experiences and challenges. To guarantee a similar level of abstraction and comparability of our findings, we selected interview partners based on similar levels of responsibility regarding the metaverse. Furthermore, to increase credibility and mitigate possible biases in the data collection and analysis (Valenzuela & Shrivastava, 2002 ), the first three authors of the paper coded the data independently. Specifically, we employed the qualitative inductive approach described by Gioia et al. ( 2013 ), which is designed to enhance qualitative rigor in conducting and presenting inductive research. This approach is particularly suitable for inductively developing grounded theory, offering rich and detailed theoretical descriptions, which, in our case, pertains to the value creation on metaverse platforms. In line with the method proposed by Gioia et al. ( 2013 ), we constructed first-order concepts, second-order themes, and aggregate dimensions, with the latter reflecting the theoretical constructs.

To ensure intercoder reliability, the coders critically discussed their initial coding results until the first-order codes were sufficiently reviewed, and a second iteration could start that led to a detailed description of our second-order constructs. During this process, the coders aimed for the codes to be on a comparable level of abstraction and still reflect the individual experiences of each informant. Our analysis was rooted in a hermeneutic approach, emphasizing the shared understanding of the nature of metaverse platforms. Following the insights of Paterson and Higgs ( 2005 ), we embraced hermeneutics for its dialogical nature, which in our research manifested through the interviews conducted between experts and the interviewer.

To improve the transferability—whether the results of one study can be transferred to different settings with different participants—of our results, we have provided sufficient contextual information about each informant and their interview in Appendix 1. Dependability describes the extent to which the findings of a study can be replicated (Merriam & Grenier, 2019 ). We noticed a high degree of concept/coding saturation (95% of codes surfaced) after the 11th interview, which aligns with previous studies on the method (Guest et al., 2006 ) and the topic at hand (Schöbel & Tingelhoff, 2023 ). Finally, confirmability mainly concerns objectivity and addressing potential research biases that can result, for instance, from inherent values and beliefs but also the mere presence or timing of follow-up questions (Valenzuela & Shrivastava, 2002 ). We addressed confirmability by independently executing tasks simultaneously and comparing results (e.g., for data coding and interpretation). Further, all the researchers were at least aware of their potential biases and influences, which we critically, openly, and proactively addressed during the planning and execution of this research.

3.2 Data Collection

In our qualitative study, we interviewed 15 metaverse decision-makers from multilateral organizations in both B2B and B2C markets. These participants represent the mission and vision of an organization; they have the highest responsibility for their organization’s metaverse strategy, were familiar with key metaverse platforms, and understood how different organizational units contribute to value creation. In other words, we interviewed CEOs and senior leaders who are deeply familiar with their organizations and can insightfully discuss how metaverse platforms align with their business models to create value. Interviewees explicitly consented to their interviews being recorded and their personal data being published, both before and after the interviews. This procedure adhered to ethical research standards, ensuring transparency and reliability in data collection.

We refer to individual participants using the abbreviation IP (interview partner) and their number (1–15). We used a structured interview guide to ensure interviewees fully understood the platform characteristics being studied. The guide featured open-ended, concise questions, supplemented with follow-up probes to resolve ambiguities. Additionally, we provided contextual information and examples where necessary to aid comprehension. We began by asking participants about their demographics. This was followed by general questions about the metaverse (how they defined it, for instance). We then focused on which platform characteristics they deemed crucial for strategic decisions regarding their metaverse offerings. We explicitly referenced the three process steps of system creation, system use, and system impact. In addition to discussing the impact on their organizations, we invited opinions on future metaverse platform developments and their relevance to the interviewee’s organization.

Interviews were held online (Lo Iacono et al., 2016 ) and conducted in the interviewees' native languages when possible (Harzing & Maznevski, 2002 ). To address the potential for translation-induced alterations, we employed a rigorous process. First, we conducted 66% of the interviews in the native languages of both interviewees and interviewers, reducing the necessity for translation. The remaining interviews were conducted in English. For interviews needing translation, a bilingual, topic-knowledgeable co-author handled the task. A second bilingual co-author reviewed and verified the transcripts against the audio recordings for translation accuracy. The interviews lasted between 26 and 56 min (mean: 34). In one interview (IP 5), technical difficulties obstructed the recording. The interviewer—in this case, the second author— immediately after the interview reconstructed the interview details from notes and memory.

We based our coding, for which we used the Atlas.ti22 software, on the transcripts (Gioia et al., 2013 ). The first three authors coded simultaneously and independently. According to Gioia et al. ( 2013 ), the first-order coding was an open coding mechanism that remained faithful to the words of each interviewee. Ending up with a high number of codes prompted us to summarize those codes where the informants addressed the same topic but with different terminology. Since the initial open coding was executed independently, ongoing researcher discussions shaped the final constructs. After identifying platform characteristics, we assessed their relative importance. To this end, we presented the ordered variables to our interview partners for an ordinal rating of relative strategic importance. To substantiate our findings, we showcased the highest-rated characteristics using two practical examples. Roblox and Decentraland serve as leading examples of early metaverse platforms (Schöbel et al., 2023 ). As current recipients of immense funding and publicity from practitioners (Kamin, 2021 ; Wang & Ho, 2022 ), they are ideal subjects for further investigation.

In line with our research goals, we identified 26 platform characteristics and categorized them according to the six dimensions of the D&M IS success model. Additionally, our metaverse experts ranked these characteristics based on their importance and relevance to organizational value creation in metaverse environments. Figure 2 reports the characteristics per dimension ranked by their average importance (with 1 indicating the highest importance). However, the rankings reflect the experts' average views. Though indicative of relative importance, they are not meant as precise statistical measures. Given this, we advise that small differences in rankings, particularly those within 0.1 or 0.2, may be negligible.

In subsequent sections, we detail our empirical findings and analyze each of the six success dimensions.

figure 2

Adapted Research Model (including the average ranks by IPs)

4.1 System Creation: System Quality, Information Quality, and Service Quality

The system quality dimension encompassed the most platform characteristics. In this dimension, interview partners (IPs 1, 2, 4, 8, 9, 12, 13, 14, and 15) ranked accessibility —ease of access for complementors and users—as the most crucial. The interviewees specifically mentioned that platforms must be accessible through various devices, including tablets, smartphones, and desktop PCs (IPs 1, 2, and 9). IP 9 elaborates:

It is all about being available across the devices. The more devices you have available for the platform, the easier it is to enter. I mean, as we all know, traffic is coming from mobile and desktop. […] So, you know, if your platform is only compatible with PCs, you’re missing out on a big portion of the market. Footnote 1

However, accessibility encompasses not only device compatibility but also the ease of navigating the platform's functions. “One should be able to move around and execute actions without having to read a one-hour manual,” argued IP8 when asked about the necessary functionalities regarding the system quality of a metaverse platform. Similarly, IP 14 emphasized that operability is currently more important than functionality.

Interviewees (IPs 4, 12, 14, and 15) also deemed an integrated economic system essential. The interviewees highlighted the necessity of integrated payment systems (IP 12) and their ability to create new user experiences (IP 15). This includes a currency that offers voting rights on decentralized platforms. Further, economic systema enable new functionalities, such as play-to-earn, where users are rewarded with items or tokens of monetary value. In this way, a platform can ensure its most active users receive a higher voting share. Additionally, new business models—for instance, transferring goods from the virtual to the physical world and vice versa—require digital ownership structures that metaverse platforms must provide (IP 14).

Across all dimensions, platform stability emerged as the most frequently mentioned characteristic (IPs 2, 3, 4, 6, 7, 9, 11, 12, 14, and 15) . Interviewees expressed concerns that platform bugs, like a faulty login page, could harm their brand image (IPs 4, 6, and 9). While some worried that these “bugs lead to users exiting the metaverse altogether” (IP11), others cautioned against entry barriers: IP12 warned that “[t]he metaverse-guest’s fear about crossing the threshold into the virtual immersion must be addressed for them to use it at all.”

Information quality represents the second dimension in the D&M IS success model. Content quality emerged as the primary concern in the interviews (IPs 3, 4, 6, 7, 8, 10, 12, 13, 14, and 15). Organizations worry about losing potential customers if the platform's overall user experience and other complementors' offerings are unsatisfactory. “So, you know, if the content is not attractive,” IP 14 explained, “people will just hop onto it, take a look, and probably never come back. If you don’t engage your participants, that becomes an issue.” Specifically, IP14 worries that a bad experience on one platform could deter customers from using any metaverse platform. Compared to other platforms, this concern is particularly pertinent for metaverse platforms given their current stage of development. Additionally, companies consider the connotation of the content produced on a platform. For instance, companies may hesitate to join game-based metaverses due to concerns that users could associate the game's characteristics with the company. IP12 specified this concern: “We try hard to avoid becoming a Disneyland. This is not just a game and fun. It is totally okay if it is around fun; that is not wrong. […] Even though a platform might have entertainment aspects, we also want to be able to educate our customers in a serious manner.”

Personalization (IPs 1, 8, 11, and 14) is another integral metaverse platform characteristic. This encompasses two facets of customization. First, organizations aim to align their virtual presence with their brand identity: “Nobody wants to access a generic platform with this great template-world. No, everybody wants their own logos, their own trees, their own information channels” (IP 11). In addition to platform design flexibility, organizations also aim to offer personalized products to their customers. This introduces several technological challenges, as discussed by Duan et al. ( 2021 ). To provide personalized features effectively, orchestrators must balance individual customization with maintaining a cohesive design across the platform. This creates tension among stakeholders, as personalization challenges the standardization and regulations needed for a unified look. IP11 cited Meta’s AI builder as a notable example of addressing these challenges:

The amount that individualization and customization are wanted by the customers is extremely effortful. […] There is now this AI builder by Meta. It’s a prototype based on voice recognition. So, Mark Zuckerberg is standing there [on the metaverse platform] as an avatar and says: ‘Hey, I need an island,’ and then an island appears. And then he says, ‘I want trees,’ and a palm tree appears. […] And this is how you could build customized yet standardized objects for your customers in a few minutes.

Service quality marks the last dimension within the system creation phase. Here, aspects of privacy and security (IPs 1, 3, 6, 11, 12, 13, and 14) emerged as the focal platform characteristic. This focus stems from the unique types of data collected by metaverse platforms. With advancements enabling the collection of micro-movements and face-tracking data, organizations can now access unprecedented types of personal data. Aware of these risks, interviewees unanimously agreed that “if it [a metaverse platform] is not built safely, it won’t be successful” (IP 6). In other words, our interviewees require security and privacy concepts to develop a metaverse platform “into a safe space” (IP 13). Nevertheless, organizations often require sensitive data to effectively serve their customers. For instance, a hotel company testing its designs in the metaverse necessitates personal data on user movements within the platform. Consequently, organizations are concerned that customer reluctance to share data on metaverse platforms, coupled with their cautious approach to data collection, may restrict product sales (IPs 1 and 14). This creates tension between the need for privacy and safety versus the drive for product offerings and sales, often prioritizing the latter at the expense of the former. Therefore, striking the correct balance becomes crucial for complementors, given its significant influence on a platform's operational capabilities.

4.2 System Use: Usage Intention and User Satisfaction

Usage intention determines the target users of a platform and their manner of engagement. Naturally, organizations aim to reach their target audience upon entering a metaverse platform (IPs 1, 6, 10, 11, 12, and 14). IP 10 explains that large corporations might easily financially engage with any major platform, whereas small and medium enterprises (SMEs) must carefully choose platforms where their selected target audience is most active. For instance, while Roblox’s age demographic is age 13 to 25, the users of Meta’s Horizon are potentially older (IP 6). Additionally, complementors seek platforms with an active user base (IPs 3, 4, 6, 7, 8, 9, and 15). Platforms are appealing if “used by active users on a daily basis” (IP 4), as complementors aim to “engage and interact with users to enter a common dialogue and exchange” (IP 7). In other words, complementors view the metaverse as a venue for initiating interactions with current and potential customers. In this context, the metaverse acts as an additional channel for organizations to actively engage with their target audience (Hadi et al., 2023 ). Some companies aim to educate customers and create leads in the metaverse (IP 12), others focus on product-centric community creation to foster customer loyalty (IPs 3 and 6). This is consistent with the metaverse's focus on social interaction and immersion (Schöbel et al., 2023 ).

The user satisfaction dimension relates to the benefits users perceive from a platform. This perception shapes how users view the complementors on the platform. As a result, organizations favor platforms that cater to user needs. In this regard, the most critical platform characteristic concerns usability and user experience (IPs 1, 3, 4, 7, 8, 9, 10, 12, 13, and 14). This aspect is closely linked with system availability, a key part of the system quality dimension. It emphasizes a user-friendly experience throughout the application phase and the entire user journey (IP 7). IP 8 desires platforms that include “elements to positively surprise the user,” a point further elaborated by IP 4:

“Then another point would be the platform usage, this is all about how to access this platform and send the user out to discover the world and the many, many leads of the platform. How could we even make the consumer journey way easier to access the metaverse? […] How could we make this […] extend reality? How could we remove all the initial steps to make it like a natural, accessible environment?”

IP 4's concerns focus on how a metaverse platform's unique features are practically implemented. More specifically, metaverses are meant to extend reality by adding features for business and leisure (Bourlakis et al., 2009 ). For instance, making crypto wallet sign-up optional could increase platform accessibility. However, this could restrict the functionality of the platform's economic system, as seen in Decentraland's guest login feature. Therefore, platforms and complementors must find compromises to balance this tension.

Most interviewees agreed that a metaverse platform must offer added value to its users, essentially making it useful (IPs 1, 2, 3, 4, 6, 7, 8, 12, and 14). A metaverse platform “needs to provide an inherent added value to its users to convince [complementors] to have potential” (IP8). This implies that a metaverse platform must offer value to users even without the contributions of complementors. Here, value means any desirable outcome users perceive from using the platform. This challenges Bowman and Ambrosini ( 2000 )’ value theory that positions complementors as the exclusive source of use value in traditional ecosystems. Nonetheless, complementors prefer metaverse platforms that are already part of a functional, value-adding ecosystem (IPs 1, 4, 7, and 8).

The rationale behind these characteristics lies in the competitive dynamics of early-stage platform markets. IP 3 noted that platforms offering the highest perceived value will outlast others, becoming attractive investments for complementors. Although complementors are crucial to a digital platform's value proposition, they approach metaverse platforms with skepticism, demanding evidence of past performance. Complementors aim to minimize investment and brand reputation risks by insisting on metaverse platforms demonstrating success (i.e., adding user value) before participating. Such requirements pose significant challenges for emerging platforms. Larger orchestrators like Meta (formerly Facebook) and Microsoft benefit from established credibility, whereas smaller, entrepreneurial platforms face stricter scrutiny, complicating the development of strong complementor ecosystems.

4.3 System Impact: Net Benefits

Net benefits describe the positive outcomes expected by complementors from their participation in a platform. Unlike previous dimensions, which focus on the metaverse's structure and customer relations, this dimension concentrates on the direct interaction between complementors and the platform. Foremost, complementors value community building (IPs 3, 4, 6, and 14). Our experts view the metaverse as enhancing bidirectional relationships between brands and communities, beyond what is typical on social media. IP3 highlighted this advantage, noting it “allowing our fans to give feedback to us.” More specifically, companies can nurture their communities while profiting from their responses. Product development is a field where this potential is already visible (see customer engagement ). As an example, Starwood Hotels first constructed their properties in the metaverse world of “Second Life” (Gates ( n.d. )). Subsequently, they invited customers to experience the space in 3D and offer feedback. Only after several iterations did Starwood begin real-world construction. Moreover, complementors can benefit from community building in various other use cases. For instance, the energy drink brand Prime engages its audience through the metaverse and NFTs (see customer engagement ), fostering community feelings and boosting beverage consumption (see increased sales ). Thus, community building serves as a steppingstone, not just an end goal. This aligns with previous studies on community building and social media (e.g., Guo et al., 2016 ; Sledgianowski & Kulviwat, 2008 ).

Brand building (IPs 3, 6, 7, 10, and 14) involves marketing activities with enduring effects. IP 14, among others, highlighted marketing as the most viable current business model outcome in the metaverse. Interviewees see metaverses as crucial for educating customers about a brand (IPs 3, 6, 7, 14). As with every online marketing, the metaverse can be used to engage customers with the core principles of one’s brand (IP 14), to provide information and experiences about products (IPs 7 and 10), and to create strong emotional bonds, such as customer loyalty (IP 6). Yet, metaverses go beyond traditional functions by offering real-life brand experiences. For instance, in the metaverse, customers can test-drive a BMW (BMW, 2023 ), feel the difference of Nike shoes (Nike, 2023 ), or experience Givenchy's brand values at a virtual pool party (Smith, 2022 ). Immersing users to the point where fiction and reality blur (Lee et al., 2022 ) allows for deeper and more impactful brand experiences (Hadi et al., 2023 ). Therefore, interviewees stressed the importance of a metaverse platform's brand-building tools in choosing the right metaverse.

Although many interviewees (IPs 1, 2, 3, 6, 7, 8, 10, and 15) mentioned increased sales as a significant platform benefit, it received the lowest ranking across all dimensions, falling into the 80th percentile. This finding was surprising, given the portrayal of the metaverse as a prime venue for trading, evidenced by trends in NFTs and virtual real estate. However, our interviewees either did not see its revenue potential (yet) or appreciated its other unique opportunities, disregarding financial benefit. For instance, IP3 asserted that “there is no revenue potential out of this [metaverse],” contrasting with IP2's statement that “we just don’t want to burn money.” Ultimately, all interviewees agreed that sales increase was among the least important net benefits. However, metaverse usage becomes much more apparent when comparing the results of B2B companies to B2C companies. In our sample, all B2C interview partners used metaverse platforms for community or brand-building, indicating that consumer brands predominantly utilize these platforms to foster their existing assets (mainly customer base and brand equity). This emphasizes that, for these companies, the metaverse represents not a quick profit avenue but a long-term, sustainable investment. Conversely, B2B organizations show no clear tendencies. While five interviewees (IPs 6, 8, 11, 13, and 14) evaluated community and brand-building as the most important, two indicated reaching new customers and customer engagement as their highest priorities (IPs 3 and 2, respectively). However, none ranked increased sales first. This is in line with previous research on organizational value creation in metaverses. For instance, according to Schöbel and Tingelhoff ( 2023 ), organizations lack the foundational knowledge to understand a metaverse platform’s opportunities and challenges, thus hindering efficient decision-making in the B2B sector. Alternatively, the diversity in organizational needs within the B2B sector might explain the varied preferences. Nonetheless, the unanimous lack of priority for increased sales in both B2B and B2C groups highlights the metaverse's broader benefits, offering extensive marketing and customer interaction possibilities beyond mere e-commerce.

5 Discussion of Results: Illustrating How to Transfer Platform Characteristics

Building on earlier discussions about crucial metaverse platform characteristics for complementors, certain orchestrators are now incorporating features that align with these metaverse attributes. Decentraland and Roblox are two leading examples of early metaverse platforms. With 42 million daily active users globally, Roblox stands as one of the most popular metaverse platforms (Gollmer, 2022 ). Conversely, Decentraland has approximately 8,000 daily users, according to internal Decentraland Foundation data (Decentraland, 2022 ). Despite smaller user numbers, Decentraland remains a leading name among decentralized metaverse platforms (Brooke, 2022 ). Furthermore, Decentraland (IPs 2, 3, 4, 6, 7, 8, 9, and 15) and Roblox (IPs 3, 4, 6, and 13) were the most frequently mentioned metaverse platforms by our interviewees, referencing these platforms continuously as metaverse examples.

Studies indicate that both platforms possess the technological capabilities essential for metaverse classification, such as immersion or creator economy aspects (Schöbel et al., 2023 ). For instance, Decentraland encourages creativity by allowing users to own and develop land parcels. Each platform offers a comprehensive visualization of its virtual world. Collaborating with these platforms allows organizations to bypass the need for hiring game designers or creating immersive worlds from scratch. Thus, both platforms serve as accessible entry points into the metaverse. The key distinction lies in Roblox's centralized structure versus Decentraland's decentralized governance. The Roblox Corporation, a US-based public software company, controls significant updates to Roblox. In contrast, Decentraland operates as a DAO, where ownership and decision-making powers are distributed among users and complementors via the virtual currency, MANA. This model enables stakeholders to vote on governance issues and how the treasury is allocated (Brooke, 2022 ). We will use these two platforms as practical examples to illustrate our interview findings.

5.1 Illustration of System Creation

Both platforms’ user experiences are quite diverging. Roblox stands out for its accessibility , supporting web browsers, mobile devices, and Xbox consoles, aligning with our interviewees' preferences. Additionally, Roblox offers both 2D and immersive 3D experiences via virtual reality (VR) headsets. Despite its maturity, Roblox has yet to incorporate augmented reality (AR) technology (Shin, 2022 ; Yang et al., 2022a , b ). Metaverses aim to blend virtual and physical realities, enabling seamless information exchange between the two (Marabelli & Newell, 2022 ). AR offers significant advantages by overlaying virtual content onto the real world, intertwining both realities. Body sensors can further enhance user immersion by integrating physical movements into the virtual experience (Park & Kim, 2022 ). Despite our interviewees' emphasis on accessibility, metaverse platforms infrequently implement these immersive features (Park & Kim, 2022 ).

Although Roblox boasts an open and accessible design, it restricts users with a compulsory sign-in process that demands personal information, including date of birth. Additionally, signing in necessitates users' acceptance of Roblox’s terms of use and privacy policy. This requirement raises privacy and security concerns due to the submission of private data for platform access (IP 1, 3, 6, 11, 12, 13, and 14). Past research highlights the critical role of data security measures like privacy guidelines, stressing the need for user empowerment in decisions regarding data collection and storage (Guidi & Michienzi, 2022 ; Ning et al., 2021 ). Figure  3 shows Roblox’s access page.

figure 3

Roblox’s Web Access

Decentraland features a distinct economic system, a priority reflected in its user account setup. Unlike other platforms, Decentraland registration requires a third-party crypto wallet, such as MetaMask, instead of a platform-specific account. This shows that Decentraland, unlike Roblox, is not interested in being the proprietor of its users’ data. A crypto wallet is essential for owning and trading virtual goods and currencies, a fundamental aspect of metaverse platforms (Di Pietro & Cresci, 2021 ; Oliver et al., 2010 ; Tayal et al., 2022 ; Vidal-Tomás, 2022 ). Using a crypto wallets as accounts, Decentraland’s users can execute peer-to-peer economic transactions without intermediaries. This aligns with our interview results, where interviewees highlighted the need for integrated payment systems (IP 12) as enabler for unique user experiences (IP 15).

User-generated content (UGC) is a defining characteristic of metaverse platforms. UGC empowers users to enrich the platform with their creations, ranging from services and products to various content. While some researchers argue that all content must be user-generated in a metaverse (Ayiter, 2012 ), others believe metaverses should primarily enable user creativity (Dionisio et al., 2013 ) to facilitate UGC (Oliver et al., 2010 ). For instance, Roblox incorporates gaming elements (Getchell et al., 2010 ) and actively supports users in crafting their games from the ground up (Metcalf, 2022 ). The Roblox engine allows users to seamlessly transition between diverse game genres, like puzzles and sports —this is, in part, related to the metaverse vision of persistency and interoperability between different platforms. Additionally, Roblox offers resources for users to learn programming, further empowering them to design their games. Roblox's open creation platform has led to the development of over 32 million unique virtual experiences, indicating its focus on freedom of creativity . Greater creative freedom for content creators fosters more innovation in the design of experiences (Orgaz et al., 2012 ).

Similarly, creativity extends to the creation and sale of digital goods (Boughzala et al., 2012 ; Kim, 2021 ). Roblox, along with its users and complementors, can create and sell these digital items. Roblox thus empowers its users to transact and monetize their content, a vital characteristic of metaverse platforms (Popescu et al., 2022 ; Tayal et al., 2022 ). Yet, Roblox conducts all transactions with its proprietary currency, Robux, which can be purchased via the platform using traditional payment methods, such as credit cards. Studies indicate that transactions in FIAT currency benefit users by eliminating currency conversion and lock-in mechanisms (Gadalla et al., 2013 ; Hwang & Lee, 2022 ; Papagiannidis et al., 2008 ; Vidal-Tomás, 2022 ; Yang et al., 2022a , b ). Despite the emphasized importance of interoperability by research (Di Pietro & Cresci, 2021 ; Kim, 2021 ) and our interviewees (IPs 1, 7, and 14), Roblox lacks this feature. Although purchases on Roblox are persistent (Braud et al., 2021 ; Falchuk et al., 2018 ), they are not transferable to other platforms (Wang et al., 2021 ). Figure 4  shows the Roblox Avatar shop.

figure 4

Roblox’s Avatar Shop

Decentraland also prioritizes personalization . For instance, the platform offers extensive character customization options available even to guest users. Beyond many free features, Decentraland also offers paid personalization options, such as skins or collectibles. This is an essential feature of metaverse platforms, as scholars agree on the importance of designing avatars resembling the actual appearance of the user (e.g., Dionisio et al., 2013 ; Gadalla et al., 2013 ; Hwang & Lee, 2022 ; Shin, 2022 ). However, some researchers describe real-time 3D scans as the pinnacle of avatar customization (Schöbel et al., 2023 ). Photorealistic depictions of users offer significant advantages. This approach mitigates safety concerns related to anonymity and potential irresponsible behavior in virtual spaces (Falchuk et al., 2018 ; Guidi & Michienzi, 2022 ; Sykownik et al., 2022 ). Yet, achieving photorealism introduces several technological challenges. Implementing face scans requires infrastructure like depth sensors, while photorealistic rendering demands high computing power and significant data storage, both of which are already current bottlenecks in expanding virtual worlds. Decentraland's approach to character customization, depicted in Fig.  5 , strikes a balance between photorealism and anonymized character appearances.

figure 5

Decentraland’s Character Customization Page

Transitioning between locations on both metaverse platforms resulted in significant loading times. During our Decentraland test, specifically when creating the character, the platform crashed, erasing all progress (see Fig.  6 ). While platform stability was mentioned by almost every interviewee (IPs 2, 3, 4, 6, 7, 9, 11, 12, 14, and 15), it cannot always be guaranteed. These issues stem largely from the high demands for computational power and data transfer (Choi & Kim, 2017 ). The integration of technologies like VR, decentralized ledgers, photorealism, and platform interconnectivity means metaverses will eventually generate more data than current storage capacities can handle (Schöbel et al., 2023 ). Therefore, “hosting and handling a metaverse platform will require significantly more effort than organizing traditional two-sided market platforms” (Schöbel et al., 2023 , p. 8). These challenges can already be observed in the platforms’ governing decisions. To manage computational and data-sharing loads, both platforms cap the number of users per server, as computational power, rendering, and data traffic requirements increase exponentially with each additional user. Imposing these limitations, however, contradicts fundamental metaverse principles like unrestricted user movement (Jaynes et al., 2003 ; Owens et al., 2011 ) and independence (Davis et al., 2009 ; Khansulivong et al., 2022 ). Further, social interactions are a cornerstone of the metaverse concept (Davis et al., 2009 ; Wang et al., 2021 ). The present state of technology and governance in metaverse platforms indicates that a fully realized metaverse remains a distant goal (Peukert et al., 2022 ; Schöbel et al., 2023 ).

figure 6

Error Message while using Decentraland

5.2 Illustration of System Usage

Past research has identified trust as a critical component for the functionality of an ecosystem (Lang et al., 2019 ; Tawaststjerna & Olander, 2021 ). Stakeholders rely on trustful interactions among ecosystem actors, particularly in e-commerce and social media platforms (Bonina & Eaton, 2020 ). E-commerce platforms serve as centers for financial transactions. Customers and complementors must trust these platforms to accurately and error-free conduct their transactions. Given that many transactions on these platforms are one-time events, complementors face challenges in building trust through rapport with customers. Hence, e-commerce platforms (such as Amazon) act as trusted intermediaries (Friedrich et al., 2019 ; Molla & Licker, 2001 ; Torkzadeh & Dhillon, 2002 ). Users prepay for products, trusting in timely delivery, whereas complementors trust the platform to compensate them for sales. Thus, trust among its actors is essential for an e-commerce platform's proper function (Friedrich et al., 2019 ). Similarly, social media platforms manage valuable data. Unlike e-commerce platforms, social media platforms facilitate the sharing of personal information, like preferences and opinions, rather than financial transactions. On social media, users express their identities by customizing profiles, engaging with content, and sharing their posts (Krasnova et al., 2017 ; Lin & Lu, 2011 ). The importance of trust in social media was underscored when Twitter sold verification checkmarks without vetting accounts. As users trusted the check would verify an individual’s or company’s authenticity, many took announcements from fake accounts as serious news. This led to global stock market turmoil, with some companies losing billions in market value due to misinformation (Mac et al., 2022 ). Experts concluded that Twitter, as an information mediator, “undermine[s] the original purpose […] – to help users know they can trust information being shared” (Duffy, 2022 ). This instance highlights the crucial need for trust within social media ecosystems (Sledgianowski & Kulviwat, 2008 ).

The metaverse stands out as a platform that integrates functionalities from e-commerce, social media, collaboration, and education into a single environment (Tingelhoff et al., 2024 ). Consequently, it is designed to support both financial transactions and the exchange of private information. Therefore, trust may play an even more critical role for ecosystem actors in the metaverse than in other digital environments (Badruddoja et al., 2022 ; Wang et al., 2022 ). Existing research consistently demonstrates how user experience influences trust in digital ecosystems (Seckler et al., 2015 ) and automated systems (Yang et al., 2017 ).

At its heart, the metaverse is focused on delivering a distinctive user experience (Tingelhoff et al., 2024 ). Immersion and automation technologies make user experience even more crucial in the metaverse compared to other digital platforms. Our interview partners mirrored this. For instance, IP4 emphasized the importance of removing obstacles to enhance the accessibility and enjoyment of the virtual world. Moreover, IP9 identified user experience as essential for the success of both the platform and its complementors. IP8 summarized: “The deciding factor is clearly customer experience, meaning that a user can develop a positive feeling on the metaverse platform and then leave it with a smile on their face.” This can be further supported by anecdotal evidence from our research team. When the previously described error message occurred while testing Decentraland (see Fig.  6 ), our first author exclaimed: “If the platform isn't stable enough to create an avatar, how can I trust it with my financial transactions?”.

5.3 Illustration of System Impact

Given the vast range of integration options Roblox and Decentraland provide for complementors, their business models and resulting net benefits can significantly vary. Even within a single platform, complementors often pursue varied objectives through their participation. For instance, Adidas launched an NFT collection to potentially attract new customers and boost sales (Bain, 2023 ),, whereas Nike concentrated on brand building . On Roblox, Nike created Nikeland, a virtual world where users engage in games themed around Nike shoes and interact with one another. Players navigate the map using features like sprinting or hoverboards, underscoring sportiness and innovation—qualities Nike aims to embody (Marr, 2022 ).

Nike additionally focuses on community building . Specifically, it nurtures a sense of community by blending collaborative and competitive dynamics. Competitive features are key in metaverses, engaging users, encouraging social interaction, and presenting challenges to tackle (Martins et al., 2022 ; Quintín et al., 2016 ). Nikeland features a leaderboard, allowing users to continuously compare achievements with peers. Users can also team up for challenges, promoting user collaboration (Martins et al., 2022 ). These game elements are proven to influence brand image within the metaverse (Oliver et al., 2010 ).

Beyond subconsciously building brand associations (Kim et al., 2022 ; Wagner et al., 2009 ), Nike actively educates customers on its brand values during gameplay. According to our interviewees (e.g., IP 12) and existing research (e.g., Tingelhoff et al., 2024 ), metaverse platforms deeply engage and educate customers through immersive content (Lee et al., 2022 ). Specifically, in Nikeland, the content is centered on Nike-related content. For instance, players interact with a guide named “Nike Coach.” Wearing a Nike shirt, the coach is presented as an authority on health and sports. The coach assigns quests and offers advice on increasing physical activity. Through this, Nike seeks to solidify its reputation as a sports authority. Additionally, the coach explicitly conveys Nike's values and goals, educating customers. Experts (Hazan et al., 2022 ) and researchers (Dwivedi et al., 2022 ) have underscored the metaverse's unique ability to blend gaming with e-commerce in that manner. Figure  7 depicts a screenshot of Nikeland with the previously discussed elements highlighted.

figure 7

Playing games in Nikeland, Nike’s virtual world on the Roblox platform

We caution against assuming that the net benefits for complementors can be definitively assessed from an external perspective. Nevertheless, Decentraland and Roblox demonstrate consideration for several identified characteristics conducive to complementors' value creation. Both platforms also exhibit limitations within certain dimensions, which, from our observation, detract from their overall performance. This may stem from the platforms' evolving nature, underscoring the imperative for ongoing enhancement. Moreover, the presence of the variables from our analysis in both platforms is irrespective of their governance structure . This reinforces the view that the organizational structure of a metaverse platform is not the primary distinguishing factor for complementors. Our interview findings, illustrated in Fig.  2 , support this observation.

6 Contributions and Implications

Our study offers several contributions to researchers and practitioners. Theoretically, our study enriches understanding of ecosystem dynamics in metaverse platforms. Specifically, we explored the role and relationships of complementors within metaverse platforms. We applied the D&M IS success model, a well-established framework, to examine the platform-business model fit across different contexts. From its six dimensions, we pinpointed 26 characteristics specific to metaverse platforms. These characteristics significantly impact complementors' value creation in metaverse environments. They progress the conceptualization of the of metaverse’s capabilities and the tensions with which complementors must deal within the metaverse ecosystem. Consequently, our study encourages researchers to explore the metaverse's nature, meaning, and ecosystem players further.

From a practitioner’s perspective, our study guides both orchestrators and complementors. For metaverse decision-makers, our findings highlight essential platform characteristics vital for effective value creation. This understanding can lead to complementors aligning with platforms that resonate with their business models and, conversely, enable orchestrators to refine their platforms to support organizational value creation better. The implications are dual: improving the alignment between business models and platforms, and fostering platform evolution to better facilitate value creation. Ultimately, these insights could promote stronger business cases in metaverse platforms, accelerating adoption among businesses and consumers.

7 Limitations and Future Research

Our study’s limitations provide grounds for future research. First, a consensus on defining the metaverse remains elusive. While conceptual papers discuss the metaverse's nature (e.g., Hadi et al., 2023 ), others like Peukert et al. ( 2022 ) highlight its ongoing evolution, complicating its definition. While this study aims to contribute to the technological design choices during its development, we also want to highlight the need to replicate our findings in the future to determine their validity over time. Second, our sample spanned experts from the B2B and B2C contexts and several industries, resulting in a reasonably general framework. Future research could replicate this study with industry-specific experts to explore how metaverse applications can be customized to meet distinct industry needs. Third, most of our interviewees were from Europe, with only one from Hong Kong and another from the USA. Therefore, cultural differences might yield varied findings. Given the concentration of metaverse developments in North America and Asia, conducting studies in these regions could uncover additional insights. Finally, our interviewees worked at companies already involved in metaverses or actively considering them, which could have biased our results. Future research should engage experts holding critical views on the metaverse to contrast their perspectives with our findings.

8 Conclusion

This study aimed to highlight key characteristics of metaverse platforms crucial for complementors seeking to maximize their value creation. Our research draws on platform ecosystem and value creation theories to provide a foundational understanding of how complementors generate value on metaverse platforms. We interviewed 15 metaverse decision-makers across various organizations, identifying 26 characteristics of metaverse platforms that impact complementors' ability to create value. We structured these characteristics according to the six dimensions of the DeLone and McLean IS success model and exemplified them through Decentraland and Roblox.

The journey to fully comprehend metaverse platforms is ongoing. This study clarifies the design characteristics of metaverse platforms and introduces a framework to aid complementors in choosing suitable platforms. Additionally, it empowers researchers and practitioners to design new metaverse platforms purposefully. Ultimately, our research seeks to lay the groundwork for future inquiries in this domain.

Data Availability

All data supporting the findings of this study are available within the paper and its Appendix. Any further data are available from the corresponding author upon reasonable request.

All quotes were translated to English for this paper.

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For this research, Fabian Tingelhoff received funding from the Konrad-Adenauer-Foundation (KAS).

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