types of research papers in social sciences

Research and Writing Guides

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Types of research papers

There are multiple different types of research papers. It is important to know which type of research paper is required for your assignment, as each type of research paper requires different preparation. Below is a list of the most common types of research papers.

➡️ Read more:  What is a research paper?

• Analytical research paper

In an analytical research paper you:

• pose a question
• collect relevant data from other researchers
• analyze their different viewpoints

You focus on the findings and conclusions of other researchers and then make a personal conclusion about the topic. It is important to stay neutral and not show your own negative or positive position on the matter.

• Argumentative or persuasive paper

The argumentative paper presents two sides of a controversial issue in one paper. It is aimed at getting the reader on the side of your point of view.

You should include and cite findings and arguments of different researchers on both sides of the issue, but then favor one side over the other and try to persuade the reader of your side. Your arguments should not be too emotional though, they still need to be supported with logical facts and statistical data.

Tip: Avoid expressing too much emotion in a persuasive paper.

• Definition paper

The definition paper solely describes facts or objective arguments without using any personal emotion or opinion of the author. Its only purpose is to provide information. You should include facts from a variety of sources, but leave those facts unanalyzed.

• Compare and contrast paper

Compare and contrast papers are used to analyze the difference between two:

Make sure to sufficiently describe both sides in the paper, and then move on to comparing and contrasting both thesis and supporting one.

• Cause and effect paper

Cause and effect papers are usually the first types of research papers that high school and college students write. They trace probable or expected results from a specific action and answer the main questions "Why?" and "What?", which reflect effects and causes.

In business and education fields, cause and effect papers will help trace a range of results that could arise from a particular action or situation.

• Interpretative paper

An interpretative paper requires you to use knowledge that you have gained from a particular case study, for example a legal situation in law studies. You need to write the paper based on an established theoretical framework and use valid supporting data to back up your statement and conclusion.

• Experimental research paper

This type of research paper basically describes a particular experiment in detail. It is common in fields like:

Experiments are aimed to explain a certain outcome or phenomenon with certain actions. You need to describe your experiment with supporting data and then analyze it sufficiently.

• Survey research paper

This research paper demands the conduction of a survey that includes asking questions to respondents. The conductor of the survey then collects all the information from the survey and analyzes it to present it in the research paper.

➡️ Ready to start your research paper? Take a look at our guide on how to start a research paper .

• Frequently Asked Questions about the different types of research papers

In an analytical research paper, you pose a question and then collect relevant data from other researchers to analyze their different viewpoints. You focus on the findings and conclusions of other researchers and then make a personal conclusion about the topic.

The definition paper solely describes facts or objective arguments without using any personal emotion or opinion of the author. Its only purpose is to provide information.

Cause and effect papers are usually the first types of research papers that high school and college students are confronted with. The answer questions like "Why?" and "What?", which reflect effects and causes. In business and education fields, cause and effect papers will help trace a range of results that could arise from a particular action or situation.

This type of research paper describes a particular experiment in detail. It is common in fields like biology, chemistry or physics. Experiments are aimed to explain a certain outcome or phenomenon with certain actions.

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SCS 224 (Campus) - Social Science Research Methods

Institutional review board.

Institutional review boards are committees formed to review and monitor biomedical and behavioral research with human subjects. All research involving human subjects must be approved by the IRB before research begins. Visit the SNHU IRB site to learn more, review the research submission process, and download the forms you'll need to get started.

• SNHU Institutional Review Board This link opens in a new window

SNHU Undergraduate Research

SNHU hosts an annual Undergraduate Research Day on campus to showcase research done by undergraduates during the year. Students select a mentor, submit a proposal by the deadline, and if accepted, conduct their research and present on the first Wednesday of April at Undergraduate Research Day. Students conducting research using human subjects are required to submit a proposal to the IRB (see box above) prior to submitting their proposal to UGR. 

This SCS224 class is encouraged to submit their research projects to SNHU Undergraduate Research Day. See the SNHU Undergraduate Research site for more information. Proposals may be submitted directly using the link below:

• SNHU UG Research Proposal link This link opens in a new window

Research Paper Assignment

The research paper should be a 10-page paper (5000 words minimum) based on the resources in your annotated bibliography.  If you have chosen a topic that is controversial, you need to include literature that represents all sides of the controversy.  The outline for the paper is adapted from  Trochim This link opens in a new window .   It is as follows:

I. Introduction

• Statement of the problem:  What is your research question? What is the general problem area?  Why is it important or significant
• Statement of constructs:  What are the theoretical concepts, themes, variables in your study?

II. The Literature Review

Literature citations and review:  The literature cited is from reputable and appropriate sources (e.g., professional journals, books and not Time, Newsweek, etc.) and you have a minimum of fifteen references. The literature is condensed in an intelligent fashion with only the most relevant information included. Citations are in the correct format (see APA guidelines).

III. Conclusion

The conclusions should summarize the key findings from your review of the literature.  Be sure to discuss if there are gaps in what we know about your topic based on the published literature.  Are there recommendations made for future research? 

IV. Appendix: What Proposed Methods would you use for a follow-up study?

Sampling section.

• What would be your sampling procedure?  The procedure for selecting units (e.g., subjects, records) for the study is described and is appropriate. Which sampling method is proposed and why. The population and sampling frame are described. If you are proposing an evaluation, the program participants are frequently self-selected (i.e., volunteers) and, if so, should be described as such.
• Sample description:  The sample should be described accurately and be appropriate.
• What are the external validity considerations?  Generalizability from the sample to the sampling frame and population is considered.

Design and Procedures section

• Description of procedures:  An overview of how the study would be conducted is included. The sequence of events is described and is appropriate to the design. Sufficient information is included so that the essential features of the study could be replicated by a reader.

V. References

All citations are included in the correct format (APA) and are appropriate for the study described. 

Beginning your research...

To get an overview of your topic and the potential issues it incorporates, you might begin by searching for your general overarching topic in the CREDO database. This will provide you with tertiary sources that explain issues, so you can glean search terms for your research.

1. Start with Credo Reference database

2. Next try the Multi-Search

The Multi-Search box on the library home page searches a portion of the library's overall resources all at once. It can be a good place to begin trying out search terms using key words from your topic and perusing the results. 

Search for books, articles, and more:

About this Search   |   Search Tips

3. Then try Social Science Resources OR Databases Specific to your Topic

On the homepage of this guide you will see a list of databases that are good places to find sources for your research topic. The content held in most of them is recognizable by the title, at least those that are for specific disciplines. 

If your topic involves disciplines outside the Social Sciences, go to the A-Z Database List (also accessible from the Quick Links box on the library home page) and click on the "All Subjects" box. Then scroll down to the appropriate discipline or topic to see a page of databases to choose from.

4. For the Appendix assignment...

Use the next page in this guide, Conducting Social Science Research , and the Methods Map in the SAGE Research Methods database to help you select the appropriate method to use to conduct the follow-up study proposed in your paper.

Still struggling? Ask a Librarian!

Email: [email protected] Chat: 24/7 with a librarian - button in upper right corner Make an appointment: 

• Karin Heffernan (Social Sciences Librarian)  

Qualtrics: Description & Help links

SNHU students have access to Qualtrics to construct and administer surveys. Below are some helpful links to learn how to use Qualtrics. Qualtrics is administered by the SNHU Office of Instructional Support. You may email with questions to  [email protected] . 

• SNHU Qualtrics page (Instructional Support) This link opens in a new window
• Qualtrics Support: Creating a Project This link opens in a new window
• << Previous: Homework #5 - Annotated Bibliography
• Next: Conducting Social Science Research >>

Organizing Your Social Sciences Research Paper: Types of Research Designs

• Purpose of Guide
• Writing a Research Proposal
• Design Flaws to Avoid
• Independent and Dependent Variables
• Narrowing a Topic Idea
• Broadening a Topic Idea
• The Research Problem/Question
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• The C.A.R.S. Model
• Background Information
• Theoretical Framework
• Citation Tracking
• Evaluating Sources
• Reading Research Effectively
• Primary Sources
• Secondary Sources
• What Is Scholarly vs. Popular?
• Is it Peer-Reviewed?
• Qualitative Methods
• Quantitative Methods
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism [linked guide]
• Annotated Bibliography
• Grading Someone Else's Paper

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. Note that your research problem determines the type of design you should use, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base . 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations far too early, before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing research designs in your paper can vary considerably, but any well-developed design will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the data which will be necessary for an adequate testing of the hypotheses and explain how such data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction and varies in length depending on the type of design you are using. However, you can get a sense of what to do by reviewing the literature of studies that have utilized the same research design. This can provide an outline to follow for your own paper.

NOTE : Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

• SAGE Videos This link opens in a new window The library provides access to the Education and the Media, Communication and Cultural Studies collections. SAGE Video collections were created for use in teaching and research across higher education and combine originally commissioned material with licensed videos. With over 500 videos, the Education collection offers a practical view into a full range of teaching settings and situations, from early years to educational leadership to support students needing to understand theory or how to apply it in practice collection. With nearly 600 videos, the Media collection offers academic viewpoints and real-life insights into practices studied in Communication and Media Studies courses such as film production, advertising, representation and journalism.
• Sage Navigator This link opens in a new window SAGE Navigator is the place to turn when you are searching for the key readings in a subject area. It is a social sciences literature review tool that guides users through the seminal literature from multiple publishers as selected by prominent academics. Main topics include Business & Management, Education, Politics & International Relations, Psychology and Sociology. Dates vary.
• SAGE Knowledge This link opens in a new window Sociology, social work, multicultural and gender studies,political science, psychology, anthropology, criminal justice, health & medicine.

Causal Design

Definition and Purpose

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.

What do these studies tell you ?

• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.

What these studies don't tell you ?

• Not all relationships are casual! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation ; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base . 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, r ather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101 . Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study . Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design, Application, Strengths and Weaknesses of Cross-Sectional Studies . Healthknowledge, 2009. Cross-Sectional Study . Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies . Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design , September 26, 2008. Explorable.com website.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs . School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research . Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design . Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research . Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research . Wikipedia.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study . Wikipedia.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research . Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

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Organizing Your Social Sciences Research Paper

• The Research Problem/Question
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials
• Bibliography

A research problem is a definite or clear expression [statement] about an area of concern, a condition to be improved upon, a difficulty to be eliminated, or a troubling question that exists in scholarly literature, in theory, or within existing practice that points to a need for meaningful understanding and deliberate investigation. A research problem does not state how to do something, offer a vague or broad proposition, or present a value question.

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Guba, Egon G., and Yvonna S. Lincoln. “Competing Paradigms in Qualitative Research.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, editors. (Thousand Oaks, CA: Sage, 1994), pp. 105-117.

Importance of...

The purpose of a problem statement is to:

• Introduce the reader to the importance of the topic being studied . The reader is oriented to the significance of the study.
• Anchors the research questions, hypotheses, or assumptions to follow . It offers a concise statement about the purpose of your paper.
• Place the topic into a particular context that defines the parameters of what is to be investigated.
• Provide the framework for reporting the results and indicates what is probably necessary to conduct the study and explain how the findings will present this information.

In the social sciences, the research problem establishes the means by which you must answer the "So What?" question. This declarative question refers to a research problem surviving the relevancy test [the quality of a measurement procedure that provides repeatability and accuracy]. Note that answering the "So What?" question requires a commitment on your part to not only show that you have reviewed the literature, but that you have thoroughly considered the significance of the research problem and its implications applied to creating new knowledge and understanding or informing practice.

To survive the "So What" question, problem statements should possess the following attributes:

• Clarity and precision [a well-written statement does not make sweeping generalizations and irresponsible pronouncements; it also does include unspecific determinates like "very" or "giant"],
• Demonstrate a researchable topic or issue [i.e., feasibility of conducting the study is based upon access to information that can be effectively acquired, gathered, interpreted, synthesized, and understood],
• Identification of what would be studied, while avoiding the use of value-laden words and terms,
• Identification of an overarching question or small set of questions accompanied by key factors or variables,
• Identification of key concepts and terms,
• Articulation of the study's conceptual boundaries or parameters or limitations,
• Some generalizability in regards to applicability and bringing results into general use,
• Conveyance of the study's importance, benefits, and justification [i.e., regardless of the type of research, it is important to demonstrate that the research is not trivial],
• Does not have unnecessary jargon or overly complex sentence constructions; and,
• Conveyance of more than the mere gathering of descriptive data providing only a snapshot of the issue or phenomenon under investigation.

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Brown, Perry J., Allen Dyer, and Ross S. Whaley. "Recreation Research—So What?" Journal of Leisure Research 5 (1973): 16-24; Castellanos, Susie. Critical Writing and Thinking. The Writing Center. Dean of the College. Brown University; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Selwyn, Neil. "‘So What?’…A Question that Every Journal Article Needs to Answer." Learning, Media, and Technology 39 (2014): 1-5.

Structure and Writing Style

I.  Types and Content

There are four general conceptualizations of a research problem in the social sciences:

• Casuist Research Problem -- this type of problem relates to the determination of right and wrong in questions of conduct or conscience by analyzing moral dilemmas through the application of general rules and the careful distinction of special cases.
• Difference Research Problem -- typically asks the question, “Is there a difference between two or more groups or treatments?” This type of problem statement is used when the researcher compares or contrasts two or more phenomena. This a common approach to defining a problem in the clinical social sciences or behavioral sciences.
• Descriptive Research Problem -- typically asks the question, "what is...?" with the underlying purpose to describe the significance of a situation, state, or existence of a specific phenomenon. This problem is often associated with revealing hidden or understudied issues.
• Relational Research Problem -- suggests a relationship of some sort between two or more variables to be investigated. The underlying purpose is to investigate specific qualities or characteristics that may be connected in some way.

A problem statement in the social sciences should contain :

• A lead-in that helps ensure the reader will maintain interest over the study,
• A declaration of originality [e.g., mentioning a knowledge void or a lack of clarity about a topic that will be revealed in the literature review of prior research],
• An indication of the central focus of the study [establishing the boundaries of analysis], and
• An explanation of the study's significance or the benefits to be derived from investigating the research problem.

NOTE :   A statement describing the research problem of your paper should not be viewed as a thesis statement that you may be familiar with from high school. Given the content listed above, a description of the research problem is usually a short paragraph in length.

II.  Sources of Problems for Investigation

The identification of a problem to study can be challenging, not because there's a lack of issues that could be investigated, but due to the challenge of formulating an academically relevant and researchable problem which is unique and does not simply duplicate the work of others. To facilitate how you might select a problem from which to build a research study, consider these sources of inspiration:

Deductions from Theory This relates to deductions made from social philosophy or generalizations embodied in life and in society that the researcher is familiar with. These deductions from human behavior are then placed within an empirical frame of reference through research. From a theory, the researcher can formulate a research problem or hypothesis stating the expected findings in certain empirical situations. The research asks the question: “What relationship between variables will be observed if theory aptly summarizes the state of affairs?” One can then design and carry out a systematic investigation to assess whether empirical data confirm or reject the hypothesis, and hence, the theory.

Interdisciplinary Perspectives Identifying a problem that forms the basis for a research study can come from academic movements and scholarship originating in disciplines outside of your primary area of study. This can be an intellectually stimulating exercise. A review of pertinent literature should include examining research from related disciplines that can reveal new avenues of exploration and analysis. An interdisciplinary approach to selecting a research problem offers an opportunity to construct a more comprehensive understanding of a very complex issue that any single discipline may be able to provide.

Interviewing Practitioners The identification of research problems about particular topics can arise from formal interviews or informal discussions with practitioners who provide insight into new directions for future research and how to make research findings more relevant to practice. Discussions with experts in the field, such as, teachers, social workers, health care providers, lawyers, business leaders, etc., offers the chance to identify practical, “real world” problems that may be understudied or ignored within academic circles. This approach also provides some practical knowledge which may help in the process of designing and conducting your study.

Personal Experience Don't undervalue your everyday experiences or encounters as worthwhile problems for investigation. Think critically about your own experiences and/or frustrations with an issue facing society or related to your community, your neighborhood, your family, or your personal life. This can be derived, for example, from deliberate observations of certain relationships for which there is no clear explanation or witnessing an event that appears harmful to a person or group or that is out of the ordinary.

Relevant Literature The selection of a research problem can be derived from a thorough review of pertinent research associated with your overall area of interest. This may reveal where gaps exist in understanding a topic or where an issue has been understudied. Research may be conducted to: 1) fill such gaps in knowledge; 2) evaluate if the methodologies employed in prior studies can be adapted to solve other problems; or, 3) determine if a similar study could be conducted in a different subject area or applied in a different context or to different study sample [i.e., different setting or different group of people]. Also, authors frequently conclude their studies by noting implications for further research; read the conclusion of pertinent studies because statements about further research can be a valuable source for identifying new problems to investigate. The fact that a researcher has identified a topic worthy of further exploration validates the fact it is worth pursuing.

III.  What Makes a Good Research Statement?

A good problem statement begins by introducing the broad area in which your research is centered, gradually leading the reader to the more specific issues you are investigating. The statement need not be lengthy, but a good research problem should incorporate the following features:

1.  Compelling Topic The problem chosen should be one that motivates you to address it but simple curiosity is not a good enough reason to pursue a research study because this does not indicate significance. The problem that you choose to explore must be important to you, but it must also be viewed as important by your readers and to a the larger academic and/or social community that could be impacted by the results of your study. 2.  Supports Multiple Perspectives The problem must be phrased in a way that avoids dichotomies and instead supports the generation and exploration of multiple perspectives. A general rule of thumb in the social sciences is that a good research problem is one that would generate a variety of viewpoints from a composite audience made up of reasonable people. 3.  Researchability This isn't a real word but it represents an important aspect of creating a good research statement. It seems a bit obvious, but you don't want to find yourself in the midst of investigating a complex research project and realize that you don't have enough prior research to draw from for your analysis. There's nothing inherently wrong with original research, but you must choose research problems that can be supported, in some way, by the resources available to you. If you are not sure if something is researchable, don't assume that it isn't if you don't find information right away--seek help from a librarian !

NOTE:   Do not confuse a research problem with a research topic. A topic is something to read and obtain information about, whereas a problem is something to be solved or framed as a question raised for inquiry, consideration, or solution, or explained as a source of perplexity, distress, or vexation. In short, a research topic is something to be understood; a research problem is something that needs to be investigated.

IV.  Asking Analytical Questions about the Research Problem

Research problems in the social and behavioral sciences are often analyzed around critical questions that must be investigated. These questions can be explicitly listed in the introduction [i.e., "This study addresses three research questions about women's psychological recovery from domestic abuse in multi-generational home settings..."], or, the questions are implied in the text as specific areas of study related to the research problem. Explicitly listing your research questions at the end of your introduction can help in designing a clear roadmap of what you plan to address in your study, whereas, implicitly integrating them into the text of the introduction allows you to create a more compelling narrative around the key issues under investigation. Either approach is appropriate.

The number of questions you attempt to address should be based on the complexity of the problem you are investigating and what areas of inquiry you find most critical to study. Practical considerations, such as, the length of the paper you are writing or the availability of resources to analyze the issue can also factor in how many questions to ask. In general, however, there should be no more than four research questions underpinning a single research problem.

Given this, well-developed analytical questions can focus on any of the following:

• Highlights a genuine dilemma, area of ambiguity, or point of confusion about a topic open to interpretation by your readers;
• Yields an answer that is unexpected and not obvious rather than inevitable and self-evident;
• Provokes meaningful thought or discussion;
• Raises the visibility of the key ideas or concepts that may be understudied or hidden;
• Suggests the need for complex analysis or argument rather than a basic description or summary; and,
• Offers a specific path of inquiry that avoids eliciting generalizations about the problem.

NOTE:   Questions of how and why concerning a research problem often require more analysis than questions about who, what, where, and when. You should still ask yourself these latter questions, however. Thinking introspectively about the who, what, where, and when of a research problem can help ensure that you have thoroughly considered all aspects of the problem under investigation and helps define the scope of the study in relation to the problem.

V.  Mistakes to Avoid

Beware of circular reasoning! Do not state the research problem as simply the absence of the thing you are suggesting. For example, if you propose the following, "The problem in this community is that there is no hospital," this only leads to a research problem where:

• The need is for a hospital
• The objective is to create a hospital
• The method is to plan for building a hospital, and
• The evaluation is to measure if there is a hospital or not.

This is an example of a research problem that fails the "So What?" test . In this example, the problem does not reveal the relevance of why you are investigating the fact there is no hospital in the community [e.g., perhaps there's a hospital in the community ten miles away]; it does not elucidate the significance of why one should study the fact there is no hospital in the community [e.g., that hospital in the community ten miles away has no emergency room]; the research problem does not offer an intellectual pathway towards adding new knowledge or clarifying prior knowledge [e.g., the county in which there is no hospital already conducted a study about the need for a hospital, but it was conducted ten years ago]; and, the problem does not offer meaningful outcomes that lead to recommendations that can be generalized for other situations or that could suggest areas for further research [e.g., the challenges of building a new hospital serves as a case study for other communities].

Alvesson, Mats and Jörgen Sandberg. “Generating Research Questions Through Problematization.” Academy of Management Review 36 (April 2011): 247-271 ; Choosing and Refining Topics. Writing@CSU. Colorado State University; D'Souza, Victor S. "Use of Induction and Deduction in Research in Social Sciences: An Illustration." Journal of the Indian Law Institute 24 (1982): 655-661; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); How to Write a Research Question. The Writing Center. George Mason University; Invention: Developing a Thesis Statement. The Reading/Writing Center. Hunter College; Problem Statements PowerPoint Presentation. The Writing Lab and The OWL. Purdue University; Procter, Margaret. Using Thesis Statements. University College Writing Centre. University of Toronto; Trochim, William M.K. Problem Formulation. Research Methods Knowledge Base. 2006; Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Walk, Kerry. Asking an Analytical Question. [Class handout or worksheet]. Princeton University; White, Patrick. Developing Research Questions: A Guide for Social Scientists . New York: Palgrave McMillan, 2009.

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Different Types Of Research Papers

17 Aug 2021

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❔What Is A Research Paper?

☝️The Research Paper Includes

✒️Difficulties of Paper Writing

📑Research Papers Types

✍️10 Types of Research Papers

If you are studying in college or university, you must know how to write a research paper because it is an integral part of the curriculum. Typically, while writing these research papers, you will research various phenomena, including technical, scientific, or social, and then organize the collected information. It takes effort to do this kind of work. But with our tips and tricks, you can swiftly curate a research paper for your coursework. Read all the information and struggle no more to write research papers anymore.

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What Is a Research Paper?

A research paper is a piece of  scientific research work  and related to research. There are many research papers, but the basic parameters of each are similar as they are based on research. Experiments are designed to advance and expand knowledge, test theories, create rules that appear in nature and society, make generalizations, and confirm projects.

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What Does a Research Paper Consist of?

The whole text is based on your opinion piece, experience, and thoughts that you write using the analysis of the topic you knew before and the data you acquired. Students typically dislike these kinds of papers. It involves extensive research, report writing, and data analysis. It's an essential kind of paper since it helps students understand how and where to conduct proper research and gives them valuable skills.

What Makes Research Paper Writing Difficult?

Students who come across this type of academic writing for the first time may need help. The approach is complex because of several difficulties, and most people get stuck at the beginning while selecting appropriate research paper topics . Many students can't write research articles independently and need help writing the work. Fortunately, there is an easy solution for the latter problem, and you can contact any professional agency to " write my research paper " for assistance. For some of you, it might be the best option.

Let's learn about all the different types of research papers.

Main Types of Research Papers

There are a few different kinds of research papers. Each requires a specific method, which you should note as you prepare to write. To better comprehend each form of research paper and the variations among them, we will go through each one with you step-by-step.

• Argumentative
• Compare and contrast
• Cause and effect
• Interpretive Essay
• Experimental
• Problem-Solution

A research paper can be a complex and time-consuming task, and many students struggle with it. To make the process easier, one can make use of an academic writing service . Such services can help students to gather reliable sources, structure their paper properly, and ensure that their research paper meets the highest academic standards.

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10 Types of Research Papers - In Detail

Here are ten major types of research papers. Let us read about research paper types in detail, which will help you learn the different formats and help you identify them the next time you see research papers.

Argumentative Research Paper

While writing an argumentative research paper, you should focus on grasping the reader's attention to the arguments relevant to your main issue. You have to support objective statements for your point of view with evidence from primary and secondary sources. Despite appearing straightforward at first glance, it relies on the assignment the teacher gives the class. You must adhere to several standard writing rules for your argumentative paper to have the correct format.

The argumentative paper presents two different viewpoints on a controversial issue in one report. It strives to persuade the viewer to agree with your stance. The author can portray a unique perspective to get the readers to stick to their side of the story.

Analytical Research Paper

The term "analytical research paper" might initially sound confusing, but you won't need to worry once you grasp what this kind of coursework entails. It is not a daunting task to pull off! You can begin working on this paper with a research question and further research the topic.

A research paper that offers an informal study of a particular topic or idea is known as an analytical research paper. For instance, deforestation is your primary subject. Because there are numerous topics to cover, such a theme is excessively broad. Consequently, critical thought and compelling arguments are needed for the analytical paper!

Definition Research Paper

You must present only the most basic, objective arguments and facts in your definition paper, avoiding using your personal opinion. That is the type of content that is most informative. The information in a definition paper comes from various sources, whereas academic papers only present the results of other studies.

In a definition paper, the author's only purpose is to provide information without emotional persuasion or a personal conclusion. There is no space for personal emotion while writing a definition paper. You should sufficiently describe your original research and analysis in the personal conclusion of the paper.

Compare and Contrast Research Paper

Compare and contrast paper is a piece of work used to analyze the distinctions between two viewpoints, subjects, authors, viewpoints, leadership styles, or other criteria. It is a typical assignment for literature, philosophy, and social science. Irrespective of the subject, it typically follows the same format. Also, we can assist with any style of philosophy paper .

In a compare and contrast paper, subjects are typically briefly discussed or defined, and the article primarily concentrates on giving examples of comparison and contrast that support the author's viewpoint. This type of research paper has a thesis statement in the introduction, a body containing a comparison, and a conclusion. Contrast papers generally examine two or three topics. A contrast paper compares multiple points, so deciding on a focus based on the similarities and differences in the issues is imperative.

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Cause and Effect Research Paper

In cause and effect paper compositions, the reader must be able to follow a logical sequence that tracks the anticipated outcomes of an action. This approach applies to both the corporate and academic worlds. The cause-and-effect paper method abstracts the expected results and identifies several potential effects depending on the specific conditions.

Reports Research Paper

The report paper drafts the case for a research situation. These documents comprise a breakdown summary, statistical data, the problem, the primary issue's identification, and recommendations. It is an organized and thorough overview of a few case study issues. You put logical facts and collected data in a short report in research papers.

Interpretive Essay Research Paper

Interpretive or Interpretative paper is typically assigned in social science and literature classes. Students must apply the knowledge they learned from this assignment's specific case study situation. Poems, artwork, studies, psychology, education fields, and business materials are some examples. To compose a text for this interpretive essay, one must draw on established theoretical knowledge and sources to support the thesis and conclusions.

While preparing an interpretative paper, you should use an established theoretical framework and supporting data to support your research. This type of research paper is used to share ideas and options of your finding and other researchers on a specific topic. Once you have an established theoretical framework for your essay, you can write an interpretive paper with the knowledge gained from a particular case study.

Survey Research Paper

While writing a survey research paper, a quantitative method is used for evaluating sources and gathering data from a group of respondents. When people answer questions in a survey, the data is collected for analysis. The researcher must conduct the research, analyze the information, examine the results, and make conclusions in this report. Staying neutral and avoiding taking a side or expressing an opinion is critical.

This type of research paper is frequently used in sociology, business, marketing, law studies, and health care. From its title, it is evident that respondents are questioned or surveyed regarding a particular subject. You can collect relevant data to create a survey paper by conducting surveys. Students learn the subject entirely and identify gaps in the existing information by creating a survey paper.

Experimental Research Paper

An experimental Research Paper is a type of research paper that must collect relevant data from a particular experiment done by researchers. These experiments are conducted to come to a certain outcome. This paper presents valid supporting data and a particular experiment research report. The supporting data can be from experiments done by different researchers. Analyzing all the information and short reports made during investigations makes it easier to come to a certain outcome.

These research papers are often written for sociology, psychology, chemistry, and biology research papers . The researcher should stay neutral and summarize their experiment in this report and provide relevant data and experimental analyses.

Problem-Solution Research Paper

The goal of a problem-solution research paper is to identify solutions to particular issues. The researcher presents the problem, evaluates the facts, and identifies potential answers. Then, it shows how effective those solutions are through examples, specifics, statistical information, etc.

The first step in writing a problem-solution essay is to create an outline. This form of persuasive writing identifies the issue, proposes a set of solutions, further chooses a preferred course of action, and justifies why it is the best course of action to do.

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Above we provided the ten main types of research papers. Also, you may get a brief description of each of them and their different viewpoints. We hope that this information was helpful to you and will make it easier for you to complete your upcoming research projects. Maybe a particular case study, a book report, or future research. These kinds of projects are crucial to your overall academic performance! That could seem like a difficult task, but it is not.

Still, there are many details to consider, and in addition to adhering to the specifications of a specific project type, you must also follow the correct format and structure. Hence, if you still believe it would be difficult for you to complete this assignment independently, we advise contacting a term paper writing service to handle the task for you.  

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Social Science Research Paper

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Can human studies, e.g., sociology and psychology, be properly scientific? Since the mid-nineteenth century, philosophers and social scientists have debated this question more or less continuously. It has periodically assumed considerable urgency, indeed become a focus of attention within the disciplines, as new influences on these subjects were felt or as new models of science itself were developed. Indeed, the complexity and potential obscurity of the issue is already evident. There is an implied equation, ‘human studies are are not science,’ in at least two variables. There has been considerable debate, at least since the 1850s, about what kinds of role human studies might play. Likewise, what is required for a discipline to be scientific has also been a matter of continuing controversy. As Peter Manicas put it (1987, p. 168), ‘neither ‘‘science’’ nor, a fortiori, the branches of the social sciences are ‘‘natural kinds’’.’ Notice, furthermore, that the variables are not entirely independent of one another. What human studies practitioners have aspired to has itself been influenced by what they think about the desirability and the possibility of making their enterprises genuinely scientific. Similarly, though perhaps to a lesser extent, what has counted as scientific has reflected the actual practices of, inter alia, students of sociology and psychology. To approach this question, a suitably sophisticated taxonomy of possibilities is therefore required.

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Academic writing, editing, proofreading, and problem solving services, 1. some taxonomic distinctions and a definition.

If human studies are to count as scientific, then what it takes to make a science must first be considered.

1.1 Modality

First, empirical and normative approaches to this question need to be distinguished—roughly, ‘What are the sciences like?’ and ‘What should the sciences be like?’ Of course, these two questions cannot be treated in isolation, especially in view of the widely accepted maxim that ‘Ought implies can.’ This maxim means, in this instance, that those subjects whose scientific status is being assessed cannot sensibly be required to attain some excellence unless, of course, paradigms of scientific achievement have themselves attained this excellence. With these points in mind, the question about human studies can be understood in this way: given what human studies are like, do they or can they conceivably measure up to whatever normative standards of excellence the best scientific disciplines do measure up to?

Of course, there are other issues about what it takes to make a science. The question of focus is perhaps the most important. Science and nonscience can be distinguished either in terms of their products or in terms of their processes. This fundamental distinction divides pre-and post-Kuhnian conceptions of science. Whereas before Thomas Kuhn’s epochal work The Structure of Scientific REvolutions (1970), science was distinguished from nonscience by the kinds of statements the two discourses produced, or, more importantly, by the status of these statements relative to epistemic standards of confirmation, generality, or objectivity, after this work, science was increasingly distinguished from nonscience in terms of the activities of its practitioners. Obviously, it will make a difference which of these criteria is deemed more appropriate. On the product conception, it might count against the scientific pretensions of sociology and psychology, for instance, that the more robust results in these subjects lack the kind of generality that robust results typically display in paradigm, ‘hard’ sciences such as physics and chemistry. But perhaps this lack of generality is irrelevant if a process criterion is adopted. Perhaps sociologists and psychologists obtain their less general findings by the same technical means and methods that natural scientists use in producing more general or more stable results.

Finally, there is the issue of scope. Roughly speaking, wide and narrow understandings of the sciences can be distinguished. On the narrow reading, it is the concrete and specific particularities of the paradigm sciences that provide criteria for judging other candidate sciences. Typically, the paradigm sciences are laboratory based, involve heavy reliance on statistical techniques of data analysis, and produce results in the form of highly confirmed differential equations relating measurable variables. On the narrow criterion, then, to count as a science, any other discipline must also exhibit these specific features. On the other hand, a wide approach to the issue requires of candidate sciences merely that they exhibit some abstract and high-level virtue which is shared by paradigm scientific achievements—that they permit the attainment of objectivity, for instance. A wide approach to the issue is probably indicated; at the concrete and specific level of technique and theory, there is enough variation within the paradigm sciences to rule out any narrow approach.

In considering whether human studies are scientific, an approach which is normative, process oriented, and wide in its scope should therefore be adopted. The question will be, in other words, whether it is possible that human studies be conducted in such a way that they might achieve whatever levels of objectivity in their procedures as are attainable in the paradigm sciences. (The issue is phrased in this way to permit the rather complicated judgment (likely to be favored by ‘postmodernists’) that human studies can achieve the same degree of objectivity as paradigm sciences, but that neither can achieve the kind of ‘objectivity’ that was previously thought to be characteristic of paradigm, natural sciences such as physics.)

1.4 Objectivity

To assess the scientific status of disciplines such as sociology and psychology, the objectivity of their procedures must, on this account, be assessed. But what is objectivity? Of course, there may be no more determinacy on this matter than on some others which have already been alluded to. Just as ‘science’ does not designate any natural kind, neither does ‘objectivity’; it will be a matter of persistent debate what counts as objective. Some preliminary observations can nevertheless be offered.

First of all, transcendental and nontranscendental accounts of objectivity can be distinguished. In the transcendental account, objectivity in investigative activities has been attained when and only when all that is specific to the situation in which they are undertaken, including the personalities of the investigators, has been eliminated from them. Individuals who are otherwise dissimilar must, on this account, conduct themselves in some common way when they examine the objects of their investigations. (When scientists have managed to specify methods for the replication of results, some approximation to this ideal may have been achieved.) They know how to tell others who differ from them how to negate the effects of these differences and thus achieve the same results as they have. Objectivity, on this account, consists of transcending local circumstances by eliminating all factors which vary from one situation to another.

Of course, philosophers and others working under the rubric of ‘postmodernism’ have widely repudiated this ideal. True transcendence of local circumstances is not possible, it is not desirable, and, indeed, it is not even necessary for a form of objectivity (that is worthy of being pursued) to be attained. Objectivity requires not that local differences be eliminated, but, rather, that they be exploited in order to discover, where this is possible, a point of convergence of otherwise very different perspectives. According to this account, objectivity is attained when an individual is able to affirm from his or her own perspective a system of propositions that can likewise be affirmed from the otherwise different perspectives of such other individuals whose agreement matters (see Rorty 1989).

2. Why The Issue Matters

Why should it matter whether human studies are or are not scientific, especially if there is little agreement about the concepts in terms of which the issue is defined. Discussing the so-called ‘interpretive turn’ in human studies, Geertz (1993, p. 8) put the point this way: ‘Whether this [development] is making the social sciences less scientific or humanistic study more so (or, as I believe, altering our view, never very stable anyway, of what counts as science) is not altogether clear and perhaps not altogether important.’ There are, in fact, at least three reasons why it matters whether human studies are considered scientific.

2.1 Institutionally

The scientism of its public culture, i.e., the privilege that is granted to scientific disciplines and their methods and results, is one prominent characteristic of modernity. Science has immense, though perhaps no longer undisputed, cognitive authority, and plays an influential role in defining as well as creating the world which human beings inhabit. If human studies succeed in gaining recognition as scientific subjects, then they too could hope to exercise this kind of authority. Foucault, typically, puts the matter the other way around (1980, p. 85): ‘What types of knowledge do you want to disqualify in the very instant of your demand: ‘‘Is it a science?’’ Which speaking, discoursing subjects of experience and knowledge do you want to ‘‘diminish’’ when you say: ‘‘I who conduct this discourse am conducting a scientific discourse, and I am a scientist?’’ Which theoretical-political a ant garde do you want to enthrone in order to isolate it from all the discontinuous forms of knowledge that circulate about it?’

2.2 Heuristically

Whether sociologists and psychologists see themselves and are seen by others as scientists will, of course, influence the conduct of their enquiries and the kinds of products they aspire to deliver. As Geertz (1993, p. 8) put it, the issue is ‘who borrows what from whom.’ Do human studies borrow techniques of observation, experimentation, and data manipulation from ‘hard’ sciences such as chemistry and biology, or do they, instead, look to other disciplines for their techniques or their standards of objectivity?

Indeed, this issue is part of a long-standing debate. In the late nineteenth century, the crucial questions were about the Geisteswissenschaften—are they concerned with understanding or with explanation? Can they articulate general psychic and/or social laws or must they, instead, confine themselves to the accurate description and contextualization of concrete and specific incidents (Windelband’s nomothetic vs. idiographic conceptions)? In the middle of the twentieth century, the key issue was the cognitive status of social scientific generalizations and, indeed, their very possibility in the face of innumerable obstacles and points of difference from paradigm ‘hard’ sciences. At the end of the twentieth century, the question is whether interpretation, with all its vicissitudes, constitutes a more central method for human studies than law based explanation, and, indeed, whether interpretation itself is possible in a sufficiently ‘objective’ manner to permit the judgment that human studies are, perhaps in some extended sense, scientific.

2.3 Morally

How human studies are conceptualized may have profound moral significance. This point is dependent on the previous two. Suppose human studies are modeled on the ‘hard’ sciences and that success in doing so is conspicuous enough so that these subjects gain the kind of cognitive authority that some of their practitioners have long aspired to. In this case, ideas about the human situation that are moral and cultural foundations of our longest-standing and most deeply embedded traditions are at risk of being undermined. Putnam (1978, p. 63) put it this way: ‘[S]uppose our functional organization became transparent to us. Suppose we had a theory of it, and we could actually use this theory in a significant class of cases. What would happen to us? … Would it be possible even to think of oneself as a person? … [T]he development of that sort of knowledge of ourselves and each other would modify our natures in ways that we cannot predict at all. Every institution we now have: arts, politics, religion—even science—would be changed beyond all recognition.’

2.4 Some Positions

Whether human studies can be scientific is therefore too important a question simply to ignore. And, of course, it has not been ignored. Philosophers, social scientists, and others have, in fact, taken a number of positions on this question. Before proceeding with more substantive matters, a threefold primary distinction among these positions should be considered. In the middle of the twentieth century, the most common position, though not so designated until later, was naturalism. According to this doctrine, there is nothing methodologically or theoretically distinctive about human studies in relation to paradigm, natural sciences. In particular, social and natural scientists legitimately share both aspirations (to discover and confirm general laws covering specific phenomena) and techniques (operationalization of variables, controlled experimentation, and statistical analysis of its results).

Both earlier and later than this period, however, a very common, perhaps even the most common position was interpretivism. According to this doctrine, human studies are distinguished from paradigm, ‘hard’ sciences both in their aspirations and in their techniques. Social scientists should not try to emulate the paradigm sciences narrowly construed (see Sect. 1.3); they should strive, instead, to interpret human social and psychic phenomena, and should employ the techniques of hermeneutics, broadly construed, to achieve this.

Finally, towards the end of the twentieth century, though also but to lesser degrees at earlier periods, ‘scepticism’ about the social sciences has been an increasingly influential position. On this account, laws of human being cannot be discovered, nor can properly objective interpretations of what human beings do be provided. Although the results of sociological or psychological research are often presented in the form of statements of knowledge, they are not entitled to this status, and must be understood in some other, less exhaulted way.

Sceptical positions may be either local or global. Global scepticism, often associated with the work of Jacques Derrida, repudiates all claims to determinate and methodologically adequate knowledge of human phenomena. Local scepticism adopts such a dismissive attitude only to certain claims, e.g., those which have been derived in some specific way or from some specific social position. (For instance, Marxists might be skeptical in this local sense about the claims produced by mainstream, and hence in their view ‘bourgeois’ sociology or psychology. Similarly, feminists are often skeptical about the claims of mainstream and hence in their view ‘patriarchal’ social science.)

3. Impediments To Human Studies Being Scientific

That the legitimacy of human studies has so long been a matter of discussion is prima facie evidence that the investigation of human phenomena is not straightforward. What, then, are some of the reasons why it might not be possible to have a genuinely scientific approach to human social and psychic phenomena?

3.1 Preinterpretation

Theorists otherwise as diverse as Peter Winch, R. G. Collingwood, and Alfred Schutz have pointed, in distinguishing human studies from science per se, to the fact that students of human phenomena work, whereas paradigm scientists do not work, with materials that already have meaning and significance (see, e.g., Winch 1958, p. 87). There are strong and weak versions of this point. On the weak version, students of human phenomena must always rely, methodologically, on the objects of their research in ways in which paradigm scientists need not. Saying what is going on is preliminary to saying why it is happening, and hence to any scientific explanation. But what is going on, socially or psychically, must be understood, in Geertz’s phrase, ‘from the native’s point of view.’ How the subject understands what is going on determines the description of what is going on. On the strong version, once scientists have characterized ‘the native’s point of view,’ they have discovered all that they need to or can know about the objects of their investigation. In either case, what paradigm scientists do and what human studies practitioners do is very different. The latter seem forced to rely on the subjective perspectives of the objects of their research.

3.2 Instability

There are three, inter-related factors which make it unlikely that sociologists and psychologists will be able to develop stable, objective accounts of the phenomena they take an interest in.

First, there is the historicity of both the subjects and the objects of human studies research. When Gadamer (1975, p. 204) asks ‘Is not the fact that consciousness is historically conditioned inevitably an insuperable barrier to reaching perfect fulfilment in historical knowledge?’ he articulates this fundamental difficulty: neither scientist nor subject ‘stands still,’ and hence what the one should say about the other changes as they themselves change. Paradigm, natural sciences, on the other hand, typically deal with statements of ‘timeless’ generality.

Second, in human studies relations of representation are subject to reflexivity in a way that is not common in the sciences per se. What practitioners say about human phenomena influences how their subjects enact or react to these phenomena, and hence often under- mines the claim, necessary if objectivity is sought, that scientists have accurately represented the behavior of their subjects. (Practitioners have, instead, caused or persuaded subjects to act so that their behavior conforms to the practitioners’ descriptions of it.) To mid-twentieth century philosophers, this possibility was best known under the heading ‘reflexive predictions’ (self-defeating and self-confirming ‘prophecies’), but the crucial points have since been generalized extensively. Of course, as Lyotard (1984, p. 57) rightly points out, reflexivity must not be understood in too one-sided a way. It is not just that sociologists and psychologists can create the objects of their investigations; it is, as well, that these objects can, strategically, resist the characterizations which practitioners attempt to fit them with. This too is an impediment to the achievement of objectivity as it is usually conceived.

Finally, there is radical indeterminacy about the objects of investigation. This point is an ontological not an epistemological one. It is not that, in some or perhaps many cases, scientists cannot tell when they have characterized some phenomenon accurately. It is, rather, that there may be no sense, in these cases, to the very idea of accurate characterization. Typically, this means that there are, in the human domain, no fixed objects with well-defined characteristics for scientists to (try to) represent. In its more familiar forms, this point is, of course, an obvious development of Dilthey’s observations about the ‘hermeneutic circle.’ It has been much emphasized by Derrida (1978) and others of his school. Specifically, if interpretation takes place against and in terms of a background that is not ‘given’ but must itself be an object of interpretation, then every interpretation has the form, not of an objective (if possibly incorrect) description, but, rather, of a (performative) proposal to see the situation in one of the many ways in which it could (as) legitimately be seen. There is on this account insufficient interpretive stability to warrant attributions of objectivity.

3.3 Complexity

The economist Hayek (1978, 26f) has long emphasized the peculiar kind of complexity which human studies practitioners must try to deal with. Such ‘organized complexity’ resists reduction either to deterministic or to statistical models that might be appropriated from the ‘hard’ sciences. The behavior of a social aggregate often cannot, in other words, be reduced to the sum or product of the behavior of its individual parts, nor can it be adequately understood by characterizing its statistical properties (as the behavior of an aggregate of gas molecules might be understood, for instance). Notice, furthermore, that there may be moral and prudential (as well as practical) reasons for refusing to reduce this complexity in ways standard in the ‘hard’ sciences, i.e., by ‘experimentalizing’ it. So, while scientists might be able to ‘standardize’ individual agents in an experimental setting so that their interactions there can be understood deterministically or statistically, there are moral objections to exporting these conditions to extra-experimental settings. Indeed, there may be prudential reasons for not using these techniques of standardization in ‘naturalistic’ settings—as is implied, of course, by Hayek’s own analysis of the market. In particular, adequate functioning of a complexly organized whole may depend precisely on its individual elements not being standardized. In either case, there are thus limits to the objectivity (actually the ‘ecological validity’) of the results that might be obtained in laboratory settings.

3.4 Interim Conclusion

All these impediments manifest themselves, of course, in a simple fact: there is, especially compared with paradigm ‘hard’ sciences, a conspicuous lack of progress in human studies. In particular, multiple and opposed paradigms persist in human studies, especially in any area of investigation which is likely to be of broadly ‘political’ interest and concern. As Geertz (1993, p. 4) has said, ‘calls for ‘‘a general theory’’ of just about anything social sound increasingly hollow, and claims to have one megalomanic. Whether this is because it is too soon to hope for unified science or too late to believe in it is, I suppose, debatable. But it has never seemed further away, harder to imagine, or less certainly desirable than it does now.’

4. Alternative Conceptions Of Human Studies

It may not make sense to compare sociology and psychology to the ‘hard’ sciences. How, then, are they to be conceptualized … and hence institutionalized? There are a number of possibilities. (Manicas’s cautionary note still applies, of course; if ‘science’ isn’t a natural kind, then no more is ‘literature’ or any of the other disciplines with which human studies might be compared.)

4.1 Literature

If human studies cannot properly be compared to the ‘hard’ sciences, perhaps a comparison with literature and other forms of potentially edifying narrative or practical discourses is more appropriate. This, certainly, is the opinion of a wide range of late-twentieth century theorists, including Geertz, Charles Taylor, Gadamer, and others. Geertz, for instance, advocates (1993, p. 6) that we ‘turn from trying to explain social phenomena by weaving them into grand textures of cause and effect to trying to explain them by placing them in local frames of awareness … .’ Of course, it might seem, to practitioners, that such a heuristic reorientation (see Sect. 2.2) would reduce the likelihood that human studies might have significant institutional influence (see Sect. 2.1). However, it needs to be borne in mind that the institutional influence of sociology and psychology is rather patchy (this is made clear in utilization studies), and that literature still forms the basis for highly influential mass-media representations and analyses of the human situation.

Another related possibility is this. While sociology and psychology do not or cannot provide an objective account of human phenomena, they can and do provide a variety of potentially important perspectives on these phenomena, and thus enable human beings to orient themselves more productively to them, especially by giving them frameworks for understanding these phenomena. In this vein, Rorty (1982, p. 247) draws a contrast between scientistic goals of prediction and control, on the one hand, and, on the other, practical goals of developing ‘descriptions which help one decide what to do.’ Since deciding what to do depends on values and other normative elements, and since there is widespread and apparently irreducible diversity in relation to these elements, the kind of ‘practical knowledge’ that nonscientistic forms of psychology and sociology might provide will not, of course, satisfy the most demanding requirements of objectivity.

4.2 Critique

There is a subtle difference, but an important one between the view that human studies offer edifying perspectives on or frameworks for the interpretation of human phenomena (see Sect. 4.1) and the view that they offer a basis for critique and the advocacy of change. In the latter half of the twentieth century, critical theorists (e.g., Habermas), genealogists (e.g., Foucault), feminists, and advocates for other marginalized groups (see Seidman 1994) proposed and in some cases made progress in achieving a ‘takeover’ of human studies activities and institutions. Across this spectrum techniques which have some claim to being ‘antiscientific’ (Foucault 1980, p. 83) are widely employed. In particular, practitioners of critique shift the focus from showing how what is, socially, is an intelligible consequence of its causal antecedents to showing how what is, socially, represents the highly contingent realization of practices and structures to which there are clear, achievable, and desirable alternatives. As Foucault says (Rabinow 1984, p. 46): ‘[T]his critique will be genealogical in the sense that it will not deduce from the form of what we are, what is impossible for us to do and to know; but it will separate out, from the contingency that has made us what we are, the possibility of no longer being, doing, thinking, what we are, do, or think. It is not seeking to make possible a metaphysics that has finally become a science; it is seeking to give new impetus, as far and wide as possible, to the undefined work of freedom.’ It is not, then, a matter of objective representation, but, rather, of politically effective analysis of the possibilities for change. (This is, of course, an extension of Karl Marx’s 11th thesis on Feuerbach: ‘Philosophers have only interpreted the world in various ways; the point is to change it.’)

4.3 Disciplines Or Ideologies

There are (see Sect. 3.3) moral and prudential disincentives to investigating human phenomena experimentally and, a fortiori, to ‘exporting’ into ‘naturalistic’ settings those conditions in which objective, reproducible experimental effects have been obtained. This has not, of course, stopped such things from happening and, indeed, it is the main conclusion of Foucault and others of his school that human studies have played a key role in this process, and, in particular, that they have contributed to the production of a ‘disciplinary society’ in which individuals are governed via their consent, i.e., through the ways in which they are made into ‘subjects.’ Although Jurgen Habermas disputed much that Foucault wrote and did, he was at one with him on this point. Habermas (1994, p. 54) says, for instance: ‘It is no accident that these sciences, especially clinical psychology, but also pedagogy, sociology, political science, and cultural anthropology, can, as it were, frictionlessly intermesh in the overall technology of power that finds its architectural expression in the closed institution. They are translated into therapies and social techniques, and so form the most effective medium of the new, disciplinary violence that dominates modernity.’

That human studies are ‘disciplines’ is one way of capturing their importance and their negative impact on the lives of ordinary people; that they have functioned ideologically is another way of making this point. Perhaps the most important issue here is the way in which human studies have been recruited by other powerful institutions to serve the specifically political interests of those institutions while seeming to provide a nonpolitical analysis, a value-neutral ac- count of contemporary social conditions. The idea of expertise has played an important role in this development. Insofar as social and psychic problems can be modeled on purely technical problems, solutions can be offered and consumed on a purely technical basis, and the reality of their dependence on fundamentally political matters can remain inaccessible to consciousness.

5. Conclusion

Should human studies plausibly aspire to scientific status? This remains questionable, as it has long been. Can they realistically aspire to such a status? This too is questionable. Perhaps Rorty (see Sect. 4.1) is right to suggest that human studies have aspired to too much, that their two main goals are disjoint and hence cannot both be achieved under the same methodological or institutional auspices. In this case, practitioners would perhaps be wise to disentangle their various objectives, and adjust their ‘aspiration levels’ (Simon 1996, p. 30) to more realistic goals. Or perhaps an even more radical adjustment of aspirations and disciplinary cultures is called for. Certainly Toulmin thinks so. Toulmin (1992, p. 193) says: ‘The task is not to build new, more comprehensive systems of theory with universal and timeless relevance, but to limit the scope of even the best-framed theories, and fight the intellectual reductionism that became entrenched during the ascendancy of rationalism … [thus reinstating] respect for the pragmatic methods appropriate in dealing with concrete human problems.’ On this account, human studies are to be understood more on the model of ad hoc tools for a variety of perhaps incommensurable human purposes, and not, as in their ascendency in the mid-twentieth century, as sciences with all the cognitive and institutional entitlements which are associated with that sometimes honorific term.

Bibliography:

• Derrida J 1978 Writing and Diff University of Chicago Press, Chicago
• Foucault M 1980 Power Knowledge. Pantheon, New York
• Gadamer H-G 1975 Truth and Method. Seabury Press, New York
• Geertz C 1993 Local Knowledge. Fontana Press, London
• Habermas J 1994 The critique of reason as an unmasking of the human sciences. In: Kelly M 1994 Critique and Power. MIT Press, Cambridge, MA
• Hayek F A 1978 New Studies in Philosophy, Politics, Economics and the History of Ideas. Routledge and Kegan Paul, London
• Kuhn T S 1970 The Structure of Scientific Revolutions. University of Chicago Press, Chicago
• Lyotard J-F 1984 The Postmodern Condition. University of Minnesota Press, Minneapolis, MN
• Manicas P T 1987 A History and Philosophy of the Social Sciences. Blackwell, Oxford, UK
• Putnam H 1978 Meaning and the Moral Sciences. Routledge and Kegan Paul, London
• Rabinow P 1984 The Foucault Reader. Penguin, London
• Rorty R 1982 Consequences of Pragmatism. University of Minnesota Press, Minneapolis, MN
• Rorty R 1989 Contingency, Irony, and Solidarity. Cambridge University Press, Cambridge, UK
• Seidman S 1994 Contested Knowledge. Blackwell, Oxford, UK
• Simon H A 1996 The Sciences of the Artificial. MIT Press, Cambridge, MA
• Toulmin S 1992 Cosmopolis. University of Chicago Press, Chicago
• Winch P 1958 The Idea of a Social Science. Routledge and Kegan Paul, London

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Social Sciences Research Paper Writing Tips and Topic Ideas

Note: Only 'PhD' academic level option is available for Dissertation

30 Oct 2019

Social sciences are a broad field of academia that studies humans, their relationships, the environment, and the society they form. Being an umbrella term, social studies encompass such disciplines as anthropology, archeology, history, geography, law, economics, politics, linguistics, sociology, and psychology . Whichever discipline you specialize in, an academic essay assigned by your teacher will require critical thinking, thorough understanding of the subject, as well as good planning and writing skills. An interesting and offbeat topic of research can become a key to scoring a high grade; however, many students find the process of choosing an essay topic to be especially challenging.

Further, you’ll find some practical tips on how to write a perfect essay, recommendations on choosing the right format, a social science research paper example of a basic outline, and a list of excellent social studies topics.

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Tips on Choosing the Best Social Sciences Topics for Research Paper

Unless your teacher provides you with a list of approved topics to choose from, you are free to pick one yourself. Writing experts advise opting for a topic that interests you most. The process of investigating on a topical issue and writing well-rounded ground research on it can be both satisfying and rewarding. If it’s difficult for you to come up with good ideas, you can draw some inspiration from the recent scientific publications or journals found in your school library or on the online databases.

To begin with, you can look for the interesting issues related to the following contemporary topics that currently have special relevance in our society:

• Gender roles
• Privacy and identity theft
• Social networks
• Sub-cultures
• Cultural appropriation
• Intercultural or interpersonal communication
• Family issues
• Poverty gap

Before you start writing, it’s essential to do quick background research to ensure you can find enough information to refer to in your paper. Always choose only official and relevant sources, and most recent research data. Switch to another topic if the issue of your first choice hasn’t been investigated enough by the other researchers.

Social Science Research Paper Sample of an Outline

Just as research papers on many other disciplines, an essay on social science has a standard structure. Its outline will include:

• Introduction (a part which introduces the readers to the issue and includes a thesis statement);
• The main body (which provides the key arguments backed up with relevant data);
• Conclusion (which summarizes the statements of the paper clearly and intelligently);
• Bibliography/Reference page .

Sometimes your teacher may require to include some more parts such as a literature review (to present the works integral to your paper), methodology (to explain the methods you use for investigation), findings (the results of your own research if you’ve conducted one), and discussion (overview of ideas brought up by other researchers).

Using the right format is another crucial thing to consider. Research papers on social studies most often stick to APA or ASA formatting rules. The correct style of citation will surely increase your chances of getting the highest grade.

The List of Noteworthy Topics for Social Sciences Research Papers

Here you’ll find an extensive list of research paper topics, divided into groups on various social studies disciplines.

• The advantages and drawbacks of globalization
• What are the pros and cons of international treaties?
• The fundamental principles of IMF work
• Is the statement that attractive people have higher salaries correct?

Political science

• How biased is the present-day US media?
• The principles of polls work;
• What are the best ways of checking facts?
• The most popular conspiracy theories of the 20 th century
• The importance of germs discovery and its impact on the household
• The role of working women in WWII
• The inventions of the 20 th century that brought ground changes to school work
• Key patterns in local architecture

Law & justice

• The biggest socially irresponsible corporations
• What are the most effective methods to prevent juvenile delinquency?
• Why is absolute justice impossible?
• How can criminal law be applied to cybercrimes?

Anthropology

• Marriage rituals in different parts of the world
• The role of beliefs in supernatural and magic in world cultures
• Storytelling and its role in various cultures
• The development and role of education around the globe
• The ethical issues of adopting children from third world countries
• Various methods of population control across the world
• Which system of education is better: the government or private?
• The influence of social networks on people’s perception of truth
• The impact of the Internet and television on the teens’ progress in studies
• The influence of the social networks on health and fitness of children
• The rates of competitiveness in boys’ and girls’ sports teams
• The present day and future of bullying

Sometimes things don’t go as planned, and the process of writing a research paper on social science turns out more complicated than you expected it to be. At some point, you may start wondering: “Can someone write my research paper for me?” If you have difficulties coming up with a good research topic, can’t find enough relevant information or simply are at risk of missing a deadline, you should consider applying to a professional research paper writing service . A team of trained writers working in such a company will assist you in all stages of preparing an essay – from suggesting an excellent subject of research to outlining, writing, editing, and formatting your paper. Don’t miss your chance of getting the top grade with some help from a reliable online company.

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How to use ChatGPT to do research for papers, presentations, studies, and more

ChatGPT is often thought of as a tool that will replace human work on tasks such as writing papers for students or professionals. But ChatGPT can also be used to support human work, and research is an excellent example. 

Whether you're working on a research paper for school or doing market research for your job, initiating the research process and finding the correct sources can be challenging and time-consuming. 

Also:  5 handy AI tools for school that students, teachers, and parents can use, too

ChatGPT and other AI chatbots can help by curtailing the amount of time spent finding sources, allowing you to jump more quickly to the actual reading and research portion of your work.

Picking the right chatbot 

Before we get started, it's important to understand the limitations of using ChatGPT . Because ChatGPT is not connected to the internet, it will not be able to give you access to information or resources after 2021, and it will also not be able to provide you with a direct link to the source of the information. 

Also :  The best AI chatbots: ChatGPT and other noteworthy alternatives

Being able to ask a chatbot to provide you with links for the topic you are interested in is very valuable. If you'd like to do that, I recommend using a chatbot connected to the internet, such as Bing Chat , Claude , ChatGPT Plus , or Perplexity . 

This how-to guide will use ChatGPT as an example of how prompts can be used, but the principles are the same for whichever chatbot you choose.

1. Brainstorm

When you're assigned research papers, the general topic area is generally assigned, but you'll be required to identify the exact topic you want to pick for your paper or research. ChatGPT can help with the brainstorming process by suggesting ideas or even tweaking your own. 

Also:  How ChatGPT (and other AI chatbots) can help you write an essay

For this sample research paper, I will use the general topic of "Monumental technological inventions that caused pivotal changes in history." If I didn't have a specific idea to write about, I would tell ChatGPT the general theme of the assignment with as much detail as possible and ask it for some proposals. 

My prompt: I have to write a research paper on "Monumental technological inventions that caused pivotal changes in history." It needs to be ten pages long and source five different primary sources. Can you help me think of a specific topic? 

As seen by the screenshot (below), ChatGPT produced 10 viable topics, including "The Printing Press and the Spread of Knowledge", "The Internet and the Digital Age", "The Telegraph and the Communication Revolution", and more. 

Also:  How to use the new Bing (and how it's different from ChatGPT)

You can then follow up with ChatGPT to ask for further information. You can even tweak these topics with an angle you like more, and continue the feedback loop until you have a topic you are settled on. 

2. Generate an outline

Once you have selected a topic, you can ask ChatGPT to generate an outline, including as much detail for your assignment as possible. For this example, I used the first topic that ChatGPT suggested in the previous step.

My prompt: Can you give me an outline for a research paper that is ten pages long and needs to use five primary sources on this topic, "The Printing Press and the Spread of Knowledge"? 

ChatGPT generated a 13-point outline that carefully described the areas I should touch on in my paper, as seen in the photo (above). You can then use this outline to structure your paper and use the points to find sources, using ChatGPT as delineated below. 

3. Tell ChatGPT your topic and ask for sources

Now that you have a topic and outline established, you can ask ChatGPT about the topic of your project and ask it to deliver sources for you.

My prompt: Can you give me sources for a ten-page long paper on this topic, "The Printing Press and the Spread of Knowledge"?

ChatGPT outputs a list of five primary and five secondary sources that you can include in your paper. Remember, because ChatGPT can't give you internet links, you will need to seek out the specific resources on your own, whether that's Googling or visiting your school library. 

Also:  How to use Stable Diffusion AI to create amazing images

When I asked Bing Chat the same question, it provided sources with clickable links that you can use to access the material you need quicker. For that reason, I would use Bing Chat for this step. 

4. Describe a specific idea and ask for sources

Instead of describing the whole topic, you can also use a chatbot to find sources for a specific aspect of your paper.

Also:  How (and why) to subscribe to ChatGPT Plus

For example, I asked ChatGPT for sources for a specific bullet in the paper outline that it generated above. 

My prompt: Can you give me sources for the social and intellectual climate of when the printing press was generated?

As in the prior example, ChatGPT generated five primary and five secondary resources for the topic. 

Using this feature for smaller chunks of your essay is a good alternative because it gives you more options on sources and provides tailored insight that you can use to carefully craft your piece. 

5. Ask for examples of a specific incident

I use this prompt a lot in my workflow because I can sometimes remember that something specific happened, but can't pinpoint what it was or when it happened. 

This tool can also be used when you need to find a specific example to support your topic. 

Also:  How to use ChatGPT to write an essay

In both cases, you can ask ChatGPT to help you identify a specific event or time period, and incorporate those details in your article. 

In our essay example, if I wanted to include a rebuttal and delineate a time when implementing technology had negative impacts, but couldn't think of an incident on my own, I could ask ChatGPT to help me identify one.

My prompt: What was a time in history when implementing technology backfired on society and had negative impacts?

Within seconds, ChatGPT generated 10 examples of incidents that I could weave into the research as a rebuttal. 

6. Generate citations

Creating a page of the works you cited, although valuable and necessary for integrity, is a pain. Now, you can ask ChatGPT to generate citations for you by simply dropping the link or the title of the work, and asking it to create a citation in the style of your paper. 

Also:  How to make ChatGPT provide sources and citations

I asked ChatGPT to generate a citation for this article for ZDNET. As seen by the photo (above), the tool asked me to include the access date and the style for the citation, and then quickly generated a complete citation for the piece.

ChatGPT generated: 

Great, here's the MLA citation for the web link "How to Use ChatGPT to Write an Essay" from ZDNET, accessed on September 15: "How to Use ChatGPT to Write an Essay." ZDNET, https://www.zdnet.com/article/how-to-use-chatgpt-to-write-an-essay/. Accessed 15 Sept. 2023.

If you used something other than a website as a source, such as a book or textbook, you can still ask ChatGPT to provide a citation. The only difference is that you might have to input some information manually. 

Artificial Intelligence

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Back to school? How ChatGPT can help you with your essay writing

This paper is in the following e-collection/theme issue:

Published on 26.9.2023 in Vol 25 (2023)

Factors Influencing the Acceptability, Acceptance, and Adoption of Conversational Agents in Health Care: Integrative Review

Authors of this article:

• Maximilian Wutz, MA   ; 
• Marius Hermes, MSc   ; 
• Vera Winter, PhD   ; 
• Juliane Köberlein-Neu, PhD  

Center for Health Economics and Health Services Research, Schumpeter School of Business and Economics, University of Wuppertal, Wuppertal, Germany

Corresponding Author:

Maximilian Wutz, MA

Center for Health Economics and Health Services Research

Schumpeter School of Business and Economics

University of Wuppertal

Rainer-Gruenter-Str 21

Wuppertal, 42119

Phone: 49 202 439 1381

Email: [email protected]

Background: Conversational agents (CAs), also known as chatbots, are digital dialog systems that enable people to have a text-based, speech-based, or nonverbal conversation with a computer or another machine based on natural language via an interface. The use of CAs offers new opportunities and various benefits for health care. However, they are not yet ubiquitous in daily practice. Nevertheless, research regarding the implementation of CAs in health care has grown tremendously in recent years.

Objective: This review aims to present a synthesis of the factors that facilitate or hinder the implementation of CAs from the perspectives of patients and health care professionals. Specifically, it focuses on the early implementation outcomes of acceptability, acceptance, and adoption as cornerstones of later implementation success.

Methods: We performed an integrative review. To identify relevant literature, a broad literature search was conducted in June 2021 with no date limits and using all fields in PubMed, Cochrane Library, Web of Science, LIVIVO, and PsycINFO. To keep the review current, another search was conducted in March 2022. To identify as many eligible primary sources as possible, we used a snowballing approach by searching reference lists and conducted a hand search. Factors influencing the acceptability, acceptance, and adoption of CAs in health care were coded through parallel deductive and inductive approaches, which were informed by current technology acceptance and adoption models. Finally, the factors were synthesized in a thematic map.

Results: Overall, 76 studies were included in this review. We identified influencing factors related to 4 core Unified Theory of Acceptance and Use of Technology (UTAUT) and Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) factors (performance expectancy, effort expectancy, facilitating conditions, and hedonic motivation), with most studies underlining the relevance of performance and effort expectancy. To meet the particularities of the health care context, we redefined the UTAUT2 factors social influence, habit, and price value. We identified 6 other influencing factors: perceived risk, trust, anthropomorphism, health issue, working alliance, and user characteristics. Overall, we identified 10 factors influencing acceptability, acceptance, and adoption among health care professionals (performance expectancy, effort expectancy, facilitating conditions, social influence, price value, perceived risk, trust, anthropomorphism, working alliance, and user characteristics) and 13 factors influencing acceptability, acceptance, and adoption among patients (additionally hedonic motivation, habit, and health issue).

Conclusions: This review shows manifold factors influencing the acceptability, acceptance, and adoption of CAs in health care. Knowledge of these factors is fundamental for implementation planning. Therefore, the findings of this review can serve as a basis for future studies to develop appropriate implementation strategies. Furthermore, this review provides an empirical test of current technology acceptance and adoption models and identifies areas where additional research is necessary.

Trial Registration: PROSPERO CRD42022343690; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=343690

Introduction

Health care services worldwide face significant challenges from increasing demand on the one hand and an increasing lack of availability and accessibility on the other hand, accompanied by rising health care costs [ 1 ]. The current COVID-19 pandemic has also affected health care delivery and has highlighted the need for alternative approaches that can overcome geographic, temporal, and organizational barriers to providing comprehensive high-quality care [ 2 ].

A promising way to overcome these barriers is technological progress, which is driven in particular by increasing digitization and advances in the field of artificial intelligence (AI). One promising technology is conversational agents (CAs), also known as chatbots [ 3 , 4 ]. On the basis of previous definitions, we define CAs as digital dialog systems that enable people to have text-based, speech-based, or nonverbal conversations with a computer or another machine based on natural language. The related concepts and variants of CAs are provided in Multimedia Appendix 1 [ 5 - 38 ].

The use of CAs offers new opportunities and various benefits for health care. Current research points to their ability to improve the accessibility of health care services and medical knowledge and to foster patient-centered care while reducing health care costs. Furthermore, their ability to communicate in multiple languages has been discussed [ 1 , 39 ]. This technology can support health care professionals in their daily work and thus reduce their burden [ 29 ]. Numerous studies have demonstrated the effectiveness and efficiency of using CAs in health care, such as in supporting diagnostic decision-making [ 40 ] and cognitive behavioral therapy for psychiatric and somatic disorders [ 41 - 43 ]. In this regard, CAs support effective, acceptable, and practical health care comparable with that provided by human physicians [ 44 , 45 ]. Owing to the nonjudgmental nature and impartiality of CAs, studies postulate that the systems may even be better suited than health care professionals to meet the needs of patients in some areas [ 29 ].

Achieving acceptability, acceptance, and adoption is challenging for new technologies, as the user’s journey to technology acceptability, acceptance, and adoption is complex and nonlinear [ 46 , 47 ]. The success of an innovation depends on its use by end users, that is, its acceptability, acceptance, and adoption. Acceptability is understood as a person’s perception of a technology before its use. Acceptance , by contrast, is a person’s perception of a technology after its initial use [ 46 ]. Adoption refers to a multistage process that explains a person’s choice to use an innovation. It involves a decision-making process that begins with the perception of the technology and ends with the confirmation of the adoption decision or achievement of permanent use [ 46 - 48 ]. The users of the technology must, therefore, be at the center of the digitization process because without including their values and interests in the acceptability, acceptance, and adoption processes, an innovation cannot be successful [ 46 , 48 - 52 ]. Therefore, it is necessary to determine the factors that affect these processes. Such a broad knowledge base of influencing factors will enable the development of effective strategies for the implementation of new technologies and will serve as a starting point for tailoring new technologies in a user-centric manner, which is crucial for sustainable use [ 46 ]. Research regarding the acceptability, acceptance, and adoption of CAs in health care has gained interest tremendously in recent years and has become a significant field.

It is not only private users and patients who are crucial stakeholders within the innovation process of CAs but also staff in health care organizations. In particular, the attitudes and beliefs of staff are crucial for the introduction of CAs to medical institutions because the establishment of new technologies often fails not because of the nature of the systems but because of the employees [ 50 ]. One of the most common reasons for the failure of innovations is insufficient knowledge about the acceptability, acceptance, and adoption processes at the time of introduction [ 53 ].

Several studies have indicated that despite the benefits of CAs, there is insufficient acceptability, acceptance, and adoption among those who can most benefit from this technology, namely people with health issues [ 54 ]. In addition, this technology is usually associated with poor adoption by physicians [ 39 ]. To date, several studies have investigated the factors influencing the user acceptability, acceptance, and adoption of CAs in health care. Some factors, such as performance expectations, effort expectations, trust, and facilitating conditions, have already been determined [ 55 - 57 ]. However, a complete overview of the factors influencing the acceptability, acceptance, and adoption of CAs in health care does not yet exist.

This study presents an overview of the facilitating and hindering factors that influence the acceptability, acceptance, and adoption of CAs from the perspectives of patients and health care professionals. Both groups are considered separately to assess whether the influencing factors differ and to derive recommendations for how CAs in the health care system must be designed so that they are used by both patients and health care professionals. Furthermore, CAs can be sustainably integrated into care only if health care professionals are convinced of their benefits and prescribe or recommend them to patients. From the perspective of health care professionals, it is crucial to differentiate between providers’ perception of the use of CAs for patients and their perception of the use of CAs for supporting their own work. On the basis of the identified influencing factors, which were derived from previous technology acceptance and adoption research, a comprehensive thematic map was developed, providing a visualization of the factors that determine the acceptability, acceptance, and adoption of CAs in health care. This up-to-date literature review that shows the factors influencing the acceptability, acceptance, and adoption of health care CAs will enable the design of effective strategies for the implementation and establishment of the technology and user-centered design of the systems, which will lead to sustainable use. Furthermore, the review can serve as a guide for developers, as it shows how the technology should be designed so that it is accepted and adopted by the target group.

These objectives lead to the following research question: what are the factors influencing the acceptability, acceptance, and adoption of CAs in health care from the perspectives of patients and health care professionals?

Technology Acceptance and Adoption Models

Numerous studies have demonstrated that technology acceptance and adoption models are suitable for investigating the factors influencing technology acceptance and adoption in the health care sector [ 58 , 59 ]. These models attempt to explain the adoption process and use of new technologies and share a basic conceptual framework. This framework explains how individual attitudes affect the intention to use and, ultimately, actual use of new technologies. Researchers from a wide range of disciplines have developed various user acceptance and adoption models for understanding acceptability, acceptance, and adoption from the perspective of individuals or organizations. Table 1 shows the important models and theories of individual acceptance and adoption and their respective determinants.

Whereas previous models could explain between 17% and 53% of an individual’s intention to use a technology, the Unified Theory of Acceptance and Use of Technology (UTAUT) can explain approximately 70% of the variance in an employee’s behavioral intention and up to 50% of the variance in technology use in the organizational context. Moreover, the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) can explain approximately 74% of the variance in stated behavioral intentions and approximately 53% of the variance in technology use in the consumer context [ 51 , 52 ]. Furthermore, both models have been successfully used in the health care sector on several occasions [ 55 , 56 , 58 , 59 ]. Thus, these 2 models serve as the initial models for this review and are described in more detail below.

The UTAUT emerged from a review and synthesis of the 8 most prominent user acceptance models identified in a literature review by Venkatesh et al [ 51 ]. The reformulated model includes 4 core determinants, namely performance expectancy, effort expectancy, social influence, and facilitating conditions, which directly determine behavioral intention and use behavior. Unlike previous models, the UTAUT also includes 4 moderators (gender, age, experience, and voluntariness of use) that have a moderating influence on the 4 core determinants [ 51 ]. In 2012, Venkatesh et al [ 52 ] modified the UTAUT for the consumer technology acceptance and use context to form the UTAUT2. Whereas the UTAUT focuses on predicting the intention to use and actual use of a technology primarily in the organizational context, additional constructs and relationships were identified for the UTAUT2 to predict the intentions to use and actual use of a technology in the consumer context. In particular, the determinants hedonic motivation, price value, and habit were added, and the moderating factor voluntariness of use was removed. Therefore, in the UTAUT2, 7 determinants are moderated by 3 factors [ 52 ].

a TRA: Theory of Reasoned Action.

b TAM: Technology Acceptance Model.

c TPB: Theory of Planned Behavior.

d MPCU: Model of PC Utilization.

e C-TAM-TPB: Combined Technology Acceptance Model and Theory of Planned Behavior.

f IDT: Innovation Diffusion Theory.

g MM: Motivation Model.

h SCT: Social Cognitive Theory.

i TAM 2: Technology Acceptance Model 2.

j UTAUT: Unified Theory of Acceptance and Use of Technology.

k UTAUT2: Unified Theory of Acceptance and Use of Technology 2.

An integrative review (IR) was chosen to answer the research question. This approach allows the inclusion of studies with diverse methodologies (ie, experimental and nonexperimental research) [ 67 , 68 ] and can precisely represent the state of the current research literature [ 69 ]. IRs are the most comprehensive methodological approach to reviews [ 70 ] and have many benefits, including identifying gaps in the current research and the need for future studies, evaluating the strength of the scientific evidence, identifying a conceptual or theoretical framework [ 69 ], and analyzing methodological issues of a particular topic [ 71 ]. Furthermore, the varied sampling frame of IRs in conjunction with the multiplicity of its purpose has the potential to generate a comprehensive understanding of problems related to health care [ 68 ].

To ensure methodological rigor, we used Cooper’s [ 67 ] 5-stage IR method modified by Whittemore and Knafl [ 68 ]. This five-step approach includes (1) problem identification, (2) data collection, (3) data evaluation (quality appraisal), (4) data analysis and interpretation (data extraction), and (5) presentation of results. At the same time, we used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist as a guide for preparing the IR, which is available in Multimedia Appendix 2 [ 72 ].

This review has been registered on PROSPERO (CRD42022343690).

Information Sources and Search Strategy

To identify the relevant literature for this review, a broad literature search was conducted in June 2021 with no date limits and using all fields in 5 databases (PubMed, Cochrane Library, Web of Science, LIVIVO, and PsycINFO). To keep the IR current, a second search was conducted in March 2022. The search terms were derived from the guiding research question. The following 3 keyword groups were set: “conversational agent,” “acceptability, acceptance, and adoption,” and “influencing factor.” Various synonyms within these keyword groups were generated from the Medical Subject Headings terms of the 5 databases, and further synonyms were derived through a web-based search and from previously published literature discussing CAs. Finally, we used an extensive list of 43 search terms ( Multimedia Appendix 3 ). The search strategy was cross-checked with the Guideline Statement for Electronic Search Strategies [ 73 ]. In addition, a preliminary search was conducted in each database to ensure the appropriateness and relevance of the adopted keywords because different digital databases use search engines with different requirements.

Eligibility Criteria

The PRISMA selection process was used to review publications for inclusion [ 74 ]. All studies were assessed against a set of predetermined inclusion and exclusion criteria, which were defined and guided by the research question and purpose of the IR. Studies were included in the IR if they met the following criteria: (1) the language was English or German; (2) the papers were primary studies (3) published until December 31, 2021, and (4) described the acceptability, acceptance, and adoption of CAs and their influencing factors (5) in health care; and (6) the studies adopted a quantitative, qualitative, or mixed methods design ( Table 2 ).

a N/A: not applicable.

b CA: conversational agent.

Selection and Data Collection Process

Screening of studies for inclusion was independently performed by 2 authors (MW and MH) in 2 stages: title and abstract review and full paper review. There were 5 disagreements between the 2 reviewers. The discrepancies were discussed between the authors and resolved through consensus.

Data Extraction and Outcomes

Data analysis was conducted via the 4-phase process described by Whittemore and Knafl [ 68 ]. During the initial phase (data reduction), we extracted the following information from the studies: the perspectives (of patients and health care professionals) on acceptability, acceptance, and adoption; the wording used in terms of acceptability, acceptance, and adoption of the technology; methodology; theory; study area; number of participants; year; country; and the influencing factors. In addition, it was noted whether acceptability, acceptance, or adoption was part of the research question. In the second phase (data display), we converted the extracted data from the individual sources into a table matrix. During the third phase (data comparison), the factors were analyzed in more detail, paraphrased, and assigned to superordinate categories based on the relationships between them and their underlying meaning.

Synthesis of Results

The influencing factors were coded through parallel deductive and inductive approaches. In the deductive approach, we searched for statements reflecting the factors proposed by the UTAUT or UTAUT2. During the inductive coding, we developed categories that were not included in the UTAUT or UTAUT2. To verify the identification and classification of the influencing factors by the first reviewer (MW), a second independent reviewer (MH) checked the identification and classification of the influential factors in 10% (8/76) of the included studies that were randomly selected. There was only 1 disagreement between the 2 reviewers. The disagreement was resolved after a short conversation, and the identification and assignment of the first author was followed. The final phase (conclusion drawing and verification) comprised interpreting the information derived from the previous stages [ 68 ].

Risk of Bias Assessment

All the retrieved papers were subjected to a quality assessment. The Mixed Methods Assessment Tool version 2018 was used because it is a critical appraisal tool for reviews that include qualitative, quantitative, and mixed methods studies [ 75 ].

One researcher (MW) rated all the identified studies, and a second researcher (JK-N) independently rated 10% (8/76) of the identified studies that were randomly selected. For 1 study, the researchers provided slightly different ratings of quality. However, this difference was quickly resolved and was judged to be sufficiently minor to not question the viability of the other ratings.

Study Selection

Figure 1 illustrates the flow diagram of the database searches and study screenings.

The first database search yielded 602 studies, and the second database search yielded 303 studies. After duplicates were removed, the titles and abstracts of 532 studies were screened, of which 195 were included in the full-text screening.

Finally, 72 studies were identified through a systematic search. According to Whittemore and Knafl [ 68 ], complementary searches are essential for an IR to identify the maximum number of eligible primary sources. Therefore, we used a snowballing approach by searching the reference lists of the eligible studies. In addition, we conducted a hand search. Through the snowballing approach we were able to identify 1 more study and by hand search another 3 studies. A total of 76 studies were included in the review.

Risk of Bias

The results of the appraisal are available in Multimedia Appendix 4 [ 2 , 10 , 12 - 29 , 35 - 38 , 42 , 54 - 57 , 76 - 122 ]. Overall, the quality of the included studies was high. Because the aim of this IR is to provide a comprehensive account of the factors influencing the acceptability, acceptance, and adoption of CAs in health care, the authors decided to include all studies.

The critical appraisal of the papers revealed a minor risk of bias in 11 (14%) of the 76 publications.

Study Characteristics

The characteristics of the included studies are summarized in Multimedia Appendix 5 [ 2 , 10 , 12 - 29 , 35 - 38 , 42 , 54 - 57 , 76 - 122 ].

Of the 76 included studies, 69 (91%) exclusively focused on acceptability, acceptance, or adoption among patients, and 3 (4%) focused solely on acceptability, acceptance, or adoption among health care professionals. The remaining 4 (5%) of the 76 studies (the studies by Dupuy et al [ 22 ], Kowatsch et al [ 76 ], LeRouge et al [ 26 ], and Potts et al [ 77 ]) explored and described the influencing factors from both the patient and health care professional perspectives.

The 76 included papers were published between 2005 and 2021, with most (n=62, 82%) published from 2019 to 2021. The studies originated from 19 countries. Most studies were conducted in the United States (29/76, 38%) and the United Kingdom (13/76, 17%). The sample sizes of the studies ranged from 4 to 16.519. Of the 76 studies, concerning the study design, 21 (28%) studies had a qualitative design, 17 (22%) had a quantitative nonrandomized design, 15 (20%) had a quantitative randomized controlled design, 14 (18%) had a mixed methods design, and 9 (12%) had a quantitative descriptive design.

Furthermore, the included studies were mostly pilot studies with short intervention periods (mostly between 2 and 4 weeks). Among the 76 studies, there were only 1 (1%) long-term study conducted for >12 months [ 19 ] and 2 (3%) studies with a timeframe of >6 months [ 78 , 79 ]. Moreover, the studies were mostly laboratory studies conducted in a controlled environment; only 14 (18%) of the 76 papers used and tested CAs in real-world conditions [ 25 , 28 , 37 , 56 , 76 , 78 - 86 ]. Only the studies by Sillice et al [ 19 ], Baptista et al [ 78 ], and Fan et al [ 79 ] were long-term studies under real-world conditions.

The reviewed studies displayed a wide variation in the wording used in relation to the acceptability, acceptance, and adoption of technology. Of the 76 studies, 36 (47%) used the term “acceptability” exclusively, 12 (16%) used the term “acceptance,” and 11 (14%) used the term “adoption.” In addition, of the 76 studies, 8 (11%) studies used both “acceptance” and “adoption congruently,” 7 (9%) used both “acceptance” and “acceptability,” and 1 (1%) used both “acceptability” and “adoption.” Similarly, 1 (1%) study used all 3 terms, (“acceptability,” “acceptance,” and “adoption,”) congruently in their descriptions. By contrast, 3 (4%) studies used none of the terms explicitly but only described the users’ perceptions [ 35 , 87 , 88 ]. These studies were assigned to the term “adoption” [ 47 ]. It was also noted that none of the included studies defined the terms used. Overall, the included studies described a high level of acceptability, acceptance, and adoption of CAs in health care.

Furthermore, only in 10 (13%) of the 76 studies, “acceptability,” “acceptance,” “adoption,” or a synonym was part of the research question or primary research objective [ 13 , 21 , 22 , 27 , 55 , 56 , 76 , 83 , 89 , 90 ]. In 44 (58%) of the 76 studies, “acceptability,” “acceptance,” “adoption,” or a synonym was part of the secondary objectives. Of the 8 studies with an established model to measure the acceptability, acceptance, and adoption of health CAs, 6 (75%) referred to the Technology Acceptance Model [ 10 , 86 , 90 - 93 ], and 2 (25%) referred to the UTAUT2 [ 55 , 56 ].

The CAs of the included studies targeted various health domains, as shown in Textbox 1 . The textbox shows which health domain was supported by a CA and where it was used within a common medical care pathway. The most CAs dealt with mental health issues and covered the complete care path.

• Mental health [ 23 , 88 ]
• Family health history [ 10 , 20 , 94 ]
• Pregnancy care [ 88 , 95 ]
• Health adviser and promoter [ 81 , 91 ]
• Vaccination [ 96 ]
• Sexual health advice [ 57 , 97 ]
• Health care for children [ 98 ]
• Healthy lifestyle behavior [ 28 , 99 ]
• Exercise and sun protection [ 19 ]
• Tuberculosis [ 92 ]
• Physical activity [ 100 ]
• Cancer [ 90 ]
• Diabetes [ 101 ]
• Self-diagnosis [ 55 , 79 ]
• Mental health [ 36 ]
• COVID-19 [ 102 ]
• Mental health [ 16 , 21 , 24 , 42 , 56 , 77 , 80 , 84 , 103 - 109 ]
• Pregnancy care [ 110 ]
• Genetic counseling [ 111 ]
• Chronic pain [ 82 ]
• Diabetes [ 17 , 78 ]
• Sleeping concerns [ 25 ]
• Heart disease [ 18 , 83 , 112 ]
• Adiposities [ 26 ]
• Smoking cessation [ 113 ]
• Substance misuse [ 93 , 114 ]
• Sickle cell disease [ 115 ]
• Drug information and risk minimization measures by physicians [ 116 ]

Rehabilitation

• Physical therapy [ 76 ]
• Mental health [ 38 , 88 , 117 ]
• Cancer [ 117 , 118 ]
• Pregnancy care [ 88 ]
• Dementia [ 37 ]
• Home care [ 22 ]
• Care of older people [ 13 , 15 , 27 , 85 - 87 , 119 , 120 ]
• General use of CAs in health care [ 29 , 35 , 54 , 89 , 121 ]
• General use of CAs in relation to COVID-19 [ 2 ]

Influencing Factors

Among the 76 papers, the 73 (96%) papers dealing with acceptability, acceptance, and adoption among patients included 354 mentions of 13 distinct influencing factors. The 7 (9%) of the 73 studies dealing with the acceptability, acceptance, and adoption among health care professionals referred to different health care professional groups. In addition to physicians [ 26 , 29 , 116 ], the studies investigated the acceptability, acceptance, and adoption of CAs among physiotherapists [ 76 ], mental health professionals [ 77 ], and (home) care providers [ 22 , 85 ]. In the analysis of the data and description of the results, it was important to distinguish between the perception of health care professionals of the use of CAs for patients and their perception of the use of CAs to support their daily work.

Figure 2 summarizes the factors, along with their subthemes, that explain the acceptability, acceptance, and adoption of CAs among patients and health care professionals. For a clearer presentation of the influencing factors, different shades of gray are used in the thematic map (influencing factors and subthemes mentioned by both patients and health care professionals are shaded in light gray, those mentioned by patients only are shaded in white, and those mentioned by providers only are shaded in dark gray).

For better comprehensibility within the presentation of results, no distinction was made among the outcomes acceptability, acceptance, and adoption. The original terms from the included primary studies regarding acceptability, acceptance, and adoption can be seen in Multimedia Appendix 6 [ 2 , 10 , 12 - 28 , 35 - 38 , 42 , 51 , 52 , 54 - 57 , 76 - 84 , 86 - 115 , 117 - 124 ] and Multimedia Appendix 7 [ 22 , 26 , 29 , 51 , 52 , 56 , 76 , 77 , 85 , 116 , 123 , 124 ] for patients and health care professionals, respectively. In addition, Multimedia Appendices 6 and 7 include a numerical listing of the influencing factors.

UTAUT and UTAUT2 Factors

Performance expectancy.

Performance expectancy refers to the degree to which individuals believe that using a technology will provide them with benefits in performing certain activities [ 51 , 52 ]. According to Venkatesh et al [ 51 ], performance expectancy captures the relative advantage [ 125 ] and perceived usefulness [ 49 ] of the target technology. In 68 (93%) of the 73 studies among patients, performance expectancy was the most frequently identified and researched factor influencing the acceptability, acceptance, and adoption of CAs among patients. A total of 63 (86%) of the 73 studies found that CAs were used by patients when they were perceived as useful and helped them improve their health and quality of life. However, the results from the included studies also suggested that many users rated the performance expectancy of CAs as low because they felt that the technology was not yet sophisticated enough to address complex health issues or detect symptoms of less common health conditions or diseases [ 54 - 56 , 79 , 97 , 103 , 104 ]. Some studies (2/73, 3%) highlighted that AI at this stage is far too limited and simplistic to be truly effective in many complex health cases [ 54 , 56 ]. Therefore, CAs were more preferred for general questions and interactions with physicians for specific questions [ 13 , 27 , 54 , 79 , 97 ]. The immaturity of the technology was also reflected in the fact that a large number of studies (21/73, 29%) pointed to technical problems with CAs that significantly affected the performance expectancy, acceptability, acceptance, and adoption of the systems. Owing to technical problems, patients did not use the systems or ended the process prematurely [ 12 , 13 , 15 , 20 , 21 , 26 , 28 , 37 , 80 , 83 , 90 , 93 , 95 , 96 , 100 , 103 , 104 , 113 , 115 , 118 , 119 ]. Moreover, some patients found CAs unhelpful and found talking to a machine disturbing [ 80 , 87 , 97 , 107 , 115 , 117 , 120 ].

In addition, some studies (21/73, 29%) reported that patients perceived certain unique advantages of AI-performed therapy over human-performed therapy [ 13 , 17 , 19 , 21 , 27 , 54 , 56 , 76 - 79 , 82 , 90 , 92 - 95 , 97 , 102 , 107 , 111 ]. Above all, anonymity in the interactions with CAs and the nonjudgmental nature of CAs were strong motivators for their acceptability, acceptance, and adoption and motivated people to use a health CA. Therefore, patients were more willing to share personal, embarrassing, and uncomfortable information with a CA than with a human. Some patients reported that they experienced judgment and blame for their conditions from real people [ 19 , 21 , 42 , 54 , 56 , 77 - 79 , 90 , 93 - 95 , 97 , 102 , 107 , 108 , 115 ]. Furthermore, patients liked the convenience of a CA-based therapy, which is not possible in a traditional human therapy. They appreciated the ubiquitous availability of CAs and the facts that they have no time pressure in their requests, there is no waiting time, they do not disturb the physicians and waste their time unnecessarily, they can repeat questions or ask uninformed questions, and they can repeat or replay the conversation as often as they want. In addition, patients valued receiving personalized medical treatment or advice through CAs because it is exclusively about them and there are no interruptions from other patients. Moreover, that a CA never makes a patient feel alone and always motivates them to improve their health was perceived as pleasant. Some users felt that well-designed CAs can be more accurate and logical than physicians [ 12 , 13 , 17 , 19 , 21 , 26 - 28 , 54 , 56 , 76 , 77 , 82 , 88 , 90 , 92 - 95 , 97 , 107 , 111 ]. Furthermore, patients perceived CAs to be faster, more anonymous, and more informative than information pipelines and search engines. Nevertheless, there were concerns that health CAs could affect the overall quality of health care by replacing experienced professionals [ 54 ].

With mentions in all of the 7 included studies on health care professionals, performance expectancy was also the most frequently identified and researched factor influencing the acceptability, acceptance, and adoption of CAs among health care professionals. Health CAs were described by health care professionals as important, useful, and promising [ 22 , 26 , 29 , 76 , 77 , 85 , 116 ].

Some studies (5/7, 71%) showed that health care professionals expected patients’ use of CAs to significantly improve health care. By using the systems, patients can better manage their health, access to care can be improved, travel times to medical facilities can be reduced, and unnecessary treatment visits can be avoided. Furthermore, health care professionals anticipated that patients would give more information to the CA owing to the anonymity in the interactions. Significant facilitation and benefits from establishing CAs were observed primarily in the areas of scheduling appointments, finding medical facilities, medication reminders, treatment adherence, providing treatment instructions, and requesting health care. In addition, benefits were also expected in physical therapy. In particular, the freedom of time and space for patients when using a CA and the real-time feedback provided during home exercises were seen as major advantages [ 26 , 29 , 76 , 77 , 85 ].

In addition to the benefits that the introduction of CAs will bring to patients, health care professionals expected that the systems could be of great help in their daily work and would make it much easier. Health care professionals assumed that the use of health CAs would free up time that the providers could then use to provide higher-quality and more individualized care to the remaining patients. The main requirement that health care professionals placed on CAs was that they quickly provide accurate medical information [ 22 , 29 , 77 , 116 ].

Among health care professionals, technical problems with CAs had a significant impact on perceived usefulness, acceptability, acceptance, and adoption. However, health care professionals were aware that CAs are still at an experimental stage and, therefore, not yet mature enough to take on more complex tasks. However, some health care workers did not consider CAs to be useful for health care and were skeptical about whether the systems could improve the quality of care and facilitate their daily work [ 29 , 76 , 116 ].

Effort Expectancy

Effort expectancy is defined as the degree of ease associated with the use of a technology [ 51 , 52 ]. It can relate to the ease of use for consumers or clients [ 52 ] as well as the ease of use for employees or providers [ 51 ]. In 82% (n=60) of the 73 studies that addressed the patient perspective, the effort expectancy of CAs was described as a significant factor influencing the acceptability, acceptance, and adoption of CAs. In this regard, it was crucial for patients that the CA responds in a pleasant, light-hearted, user-friendly, and interactive manner; that the interface is simple and easy to use; that the CA is easily accessible; and that the explanations are easy to understand. It was perceived as particularly advantageous that the CA provides all types of health care through 1 device [ 2 , 10 , 12 , 13 , 15 - 26 , 28 , 36 , 37 , 42 , 55 , 56 , 76 - 78 , 82 , 86 - 88 , 90 - 101 , 103 - 105 , 107 - 115 , 118 - 122 ]. However, other patients found CA applications too complicated, the technology too fast or too slow, or the input into a digital system too time consuming [ 2 , 20 , 23 , 42 , 55 , 79 , 81 , 95 , 96 , 103 - 105 , 110 ]. Overall, it became apparent that users’ requirements for the usability of CAs vary widely. Some patients wished that each user could personalize (eg, with regard to speed and skills) the CA themselves. CAs that were customizable were highly appreciated by patients [ 12 , 19 , 27 , 28 , 76 , 79 , 82 , 93 , 99 , 101 , 109 , 111 , 113 , 118 , 120 ]. In addition, the usability of CAs was found to affect the usefulness of the systems [ 92 ].

Another aspect of usability concerned the restriction on user input during conversations. In most CA applications, the user can only respond with a list of response options instead of a free-text input. However, patients would like to formulate their answers freely so that they can describe the problems as accurately as possible [ 15 , 56 , 82 , 97 , 109 , 115 , 120 ]. In addition, some studies (5/73, 7%) found that patients preferred to talk to the CA rather than chat with the system through text [ 15 , 78 , 87 , 94 , 121 ]. These patients wanted the CA to talk, as interactions with physicians also occur via oral conversations. Moreover, it was pointed out that the patient needs to be able to multitask in a text-based conversation, as questions need to be read and answered simultaneously, and attention must be focused on the CA [ 87 ]. In the study by Easton et al [ 24 ], the participants were able to participate in the development of the CA and its features and preferred to be able to choose between voice and text communication.

Effort expectancy was mentioned in 4 (57%) of the 7 studies among health care professionals as an important factor for the acceptability, acceptance, and adoption of CAs among health care professionals. Health care professionals wanted an accessible and easy-to-use CA that offers easy-to-read information [ 22 , 76 , 77 , 116 ]. CAs were considered easier to use by physicians than the currently available databases for health care professionals [ 116 ].

Facilitating Conditions

Facilitating conditions are defined as an individual’s perception of the resources and support available to execute and use a system [ 51 , 52 ]. Facilitating conditions also represent an important factor for the acceptability, acceptance, and adoption of CAs among patients and were mentioned in 18 (25%) of 73 studies among patients. It was frequently pointed out that it is crucial to have the necessary resources to use a CA, such as a cell phone or computer, and to be able to obtain help from others when needed. Thus, higher acceptability, acceptance, and adoption of CAs in health care have been demonstrated when patients possess such resources and support [ 2 , 12 , 15 , 19 , 55 , 57 , 92 , 99 , 120 ]. Furthermore, the studies pointed out that a reliable internet connection is usually required to use CAs. Some patients had concerns about the internet connection being interrupted or not having any internet access [ 26 , 27 , 77 , 92 ]. Therefore, patients considered it important to continue traditional treatment methods in addition to the CA application, as this approach gives those who do not have reliable internet or smartphone access and those who are reluctant to use CAs the opportunity to receive medical care [ 111 ].

The compatibility of the CA with its environment was also important for patients. Compatibility in this context can be defined as the perception that the CA is well integrated into the user’s (health) environment. Especially in the health care context, the compatibility of the CA with the existing health care environment could influence the perception of the usefulness of the technology. Patients wanted a health care CA to be multimodal and accessible through various consumer devices that they already have, such as computers, tablets, cell phones, and televisions. In addition, the system should have the ability to interact with other digital services and home devices, such as calendars, smart home technology, and existing medical devices or applications [ 21 , 24 , 26 , 28 , 55 , 113 , 120 ].

Another important factor for patients was the perceived access to the health care system [ 55 , 97 ]. This can be defined as the availability of health care services. Some patients reported that they had quick access to physicians and that this discouraged them from using CAs. By contrast, long distances or the unavailability of health care services or physicians can lead patients to use CAs. Perceived access to the health care system may be limited by local, financial, or institutional factors and largely determines perceptions of the usefulness of CAs [ 55 , 97 ].

For high acceptability, acceptance, and adoption of CAs among health care professionals, the systems should be easy to integrate into daily practice, whether before, during, or after a patient’s treatment. Moreover, the technology should be easily connected and combined with other devices [ 85 , 116 ]. However, a lack of internet access in medical facilities was a clear barrier to the acceptability, acceptance, adoption, and installation of CAs [ 85 ].

For the use of CAs by patients, health care professionals considered it crucial for the technology to be multimodal and accessible via multiple devices. A lack of internet access was also seen by health care professionals as a challenge and barrier to the use of CAs. Some suggested embedding the CA in a stand-alone program that does not require constant internet access. Thus, the patient would only need to go on the web at certain intervals to update the system [ 26 , 77 ].

Hedonic Motivation

Hedonic motivation refers to “the fun or pleasure derived from using a technology” [ 52 ]. In 29% (n=21) of the 73 studies among patients, patients wanted CAs to have a self-fulfilling value (instrumental value) for them in addition to health benefits, that is, to be hedonic in nature. Thus, the enjoyment of using health CAs is also a crucial factor influencing their acceptability, acceptance, and adoption. Some studies (3/73, 4%) suggested that a lack of fun makes CAs boring to use and, therefore, decreases their acceptability, acceptance, and adoption [ 23 , 76 , 78 ].

However, Laumer et al [ 55 ] stated that hedonic motivation is not an important factor influencing the acceptability, acceptance, and adoption of CAs in health care. They argued that hedonic motivation is important when a CA serves entertainment purposes but is irrelevant when a CA serves a more serious purpose, such as in the health care domain.

Hedonic motivation was not found to be an influencing factor for acceptability, acceptance, and adoption among health care professionals in the included studies.

Social Influence

Social influence describes the extent to which an individual perceives that important others (eg, family and friends) believe that the individual should use a particular technology [ 51 , 52 ]. In 11% (n=8) of the 73 studies among patients, social influence was suggested to be an important factor for the acceptability, acceptance, and adoption of CAs among patients. Furthermore, 1 (1%) study found that social influence could affect the performance expectancy of a CA [ 92 ]. The results of our analysis showed that the definition of social influence according to Venkatesh et al [ 51 , 52 ] was not sufficient for the application of CAs in the health care sector. Not only was the request or expectation of a certain behavior important but also the recommendation and experience of a person whom the individual trusts [ 55 ]. The collected studies showed that patients value the recommendations and experiences of trusted people and would accept and use a CA simply based on testimonials from their social environment [ 22 , 26 , 55 , 56 , 92 , 111 ].

Social influence was also described by 29% (n=2) of the 7 studies among health care professionals as an important factor for the acceptability, acceptance, and adoption of CAs by health care professionals. Some health care professionals were convinced of the benefits of CAs in health care and would, therefore, recommend the technology to their colleagues. Furthermore, some studies (2/7, 29%) were able to establish a positive correlation between the perceptions of the CA by health care professionals and patients. If a patient perceived a CA as acceptable and useful, this was accompanied by a positive assessment of the CA by health care professionals [ 22 , 29 ].

According to the previous descriptions, the definition of social influence by Venkatesh et al [ 51 , 52 ] must be extended to include recommendations and experiences of trusted persons to fully describe the social influence on the acceptability, acceptance, and adoption of CAs in health care. In terms of the application of CAs in the health sector, social influence should, therefore, be defined as follows: social influence refers to the extent to which a person perceives that significant others (eg, family and friends) believe that the person should use a particular technology or to which the person’s perception is influenced by others’ attitudes toward the use of, intention to use, and actual use of the new technology.

Price Value

Price value is defined as “consumers’ cognitive trade-off between the perceived benefits of the applications and the monetary cost for using them” [ 52 ]. Value for money was described in 7 (10%) of the 73 studies among patients as an important factor influencing the acceptability, acceptance, and adoption of CAs among patients. It was found that the price value represents not only the cost-benefit trade-off but also a comparison of the cost of using a CA with the cost of other health services, such as visiting a physician [ 55 , 56 ].

Health care professionals weighed the perceived benefits of CAs against the financial costs for them and patients. Overall, the systems were seen as a cost-effective extension of health care that can improve its quality [ 29 ]. Thus, price value also influences health care professionals’ acceptability, acceptance, and adoption of CAs.

According to Laumer et al [ 55 ], the comparison between cost and alternative options could be a decisive factor in the acceptability, acceptance, and adoption of CAs, especially in countries with poor insurance coverage or high costs for the use of health care services. In countries with statutory health insurance, such as Germany, patients do not have to pay much for health services. In other countries, such as the United States, patients can incur significant costs depending on their insurance status. Therefore, it is important to compare not only the direct costs of a CA application with the benefits achieved but also the cost-benefit ratio of a CA with that of other health care services [ 55 ]. Thus, in terms of the application of CAs in the health sector, the definition of Venkatesh et al [ 52 ] should be expanded as follows: price value is consumers’ cognitive trade-off between the perceived benefits of the applications and the financial costs of using them as well as the trade-off between the cost of using a CA and the cost of using other health services.

Habit refers to “the extent to which people tend to perform behaviors automatically because of learning” [ 52 ]. None of the identified studies mentioned habit as defined by Venkatesh et al [ 52 ]. However, 1 (1%) of the 73 studies among patients reported that patients would use CAs in health care if they had the habit of using CAs in other areas of their lives [ 55 ]. The definition of habit for current health care CA use must, therefore, be expanded to include the extent to which people tend to perform a behavior of interest automatically because they are used to performing a certain action that is close to the behavior of interest [ 55 ]. Habit should thus be defined as follows: habit describes the extent to which people tend to perform a behavior of interest automatically because of learning and because they are used to performing a certain action that is close to the behavior of interest.

Habit was not found to be an influencing factor for acceptability, acceptance, and adoption among health care professionals in the included studies.

Additional Factors

Perceived risk.

Perceived risk refers to users’ perceived uncertainty of the possible negative consequences of using health CAs [ 56 ]. The perceived risk in relation to the acceptability, acceptance, and adoption of CAs among patients was reported in 23 (32%) of 73 studies among patients as an important influencing factor and could be divided into the subtopics of perceived data privacy risk and perceived security risk.

A significant barrier to the acceptability, acceptance, and adoption of health CAs was patients’ concern about potential data privacy risks [ 2 , 14 - 16 , 21 , 27 , 28 , 54 - 57 , 86 , 97 , 107 , 110 , 111 , 118 - 120 ]. Users often lacked confidence in CAs’ privacy policies and data-sharing practices and in the ability (or inability) of these systems to maintain confidentiality so that their sensitive health-related information was protected from potential hacking or data leakage [ 14 , 54 , 56 ]. In particular, the access of other data on the end device (eg, photographs, call logs, or location data) by the CA was viewed critically by patients. In addition, the rate of acceptability, acceptance, and adoption of CAs decreased if they were accessible via third-party services, such as Facebook (Meta Platforms, Inc), as there was a fear that the data would be passed on to third parties [ 56 ]. Especially with regard to health issues, data protection was particularly important for users, as the data can be extremely sensitive [ 14 , 54 - 56 ]. Because automated agent-assisted therapy, unlike conventional therapy, offers the prospect of patient anonymity, users expected their data and identity to be protected [ 56 ]. Furthermore, privacy concerns were found to lower the performance expectancy for CAs in addition to acceptability, acceptance, and adoption [ 55 ]. To alleviate privacy concerns, patients wanted the security of the CA to be made clearer and the CA to be offered via a trusted tool. In addition, access to data should always be password protected [ 14 , 27 , 111 ].

Moreover, there were concerns about the risk to user safety and well-being when using CAs in health care. Many patients were unsure about the quality and accuracy of the health information provided by CAs and feared that their use could lead to misdiagnosis. Furthermore, some studies (10/73, 14%) also highlighted criticism about how CAs could put users at risk when used for health issues. Misunderstandings could occur between a CA and its user, who may not be able to accurately describe their health problem or symptoms. In addition, the use of CAs may exacerbate the health problem instead of curing it. Finally, the use of CAs could also lead to increased loneliness and isolation, as it encourages users to seek help from a device rather than from a fellow human [ 2 , 21 , 27 , 28 , 54 , 56 , 77 , 88 , 110 ]. There were also concerns that patients would be disadvantaged or penalized if they did not use the CA offered [ 111 ].

However, in 1 (1%) of the 73 studies among patients, patients had no concerns about data security or an individual security risk when using a CA. There were no privacy concerns, as patients felt that sharing and entering data was commonplace in today’s society. In addition, they felt safe using a CA and, therefore, would not be concerned about being harmed by it [ 24 ].

Regarding health care professionals, 5 (71%) of the 7 studies among health care professionals stated that the risk associated with the use of CAs was an important factor in their acceptability, acceptance, and adoption. Their worries could be divided into the subtopics of perceived safety risk for patients, perceived risk for health care professionals, and perceived privacy risk.

Health care professionals feared safety risks from the use of CAs, both for patients and for themselves. They were concerned that CAs could compromise the quality of health care. They were also worried that patients would abuse CAs, incorrectly self-diagnose, and not properly understand the diagnoses displayed. They were also concerned that the systems could indirectly affect the safety and well-being of users by not knowing all the personal factors or not being able to properly clarify issues owing to inaccurate medical information [ 29 ]. Furthermore, many health care professionals believed that CAs would play an important role in health care in the future and feared that the systems could replace human workers [ 29 , 116 ]. In addition, there were significant concerns about whether sensitive health-related information was protected from potential hacking or data loss when using CAs [ 76 , 85 , 116 ].

In 49% (n=36) of the 73 studies among patients, trust was identified as another important factor in the acceptability, acceptance, and adoption of CAs among patients. Trust can be defined as “a psychological state comprising the intention to accept vulnerability based upon positive expectations of the intentions or behavior of another” [ 123 ]. In terms of the acceptability, acceptance, and adoption of health CAs among patients, trust is not a monolithic concept and should be differentiated into “trust in the provider” and “trust in the technology” [ 126 ].

For health CAs, patients’ trust in the provider was a critical factor and played a crucial role in whether the patients would ultimately accept, adopt, and use the CAs [ 27 , 55 - 57 , 102 , 107 , 111 ].

To have confidence in the technology, patients had to be able to rely on the CA’s capabilities. The reliability, professional competence, and functionality of the system were particularly crucial in this regard. In health care, this means that the CA correctly diagnoses the disease and that the information comes from a credible and evidence-based source. For patients, the comparison between the existing relationship of trust with physicians and the relationship with a health care CA was particularly important. Therefore, it was crucial that patients establish a relationship of trust with a CA similar to that with a physician [ 13 , 18 , 21 - 23 , 25 , 27 , 35 , 54 - 56 , 77 - 79 , 81 , 82 , 88 , 91 , 93 , 94 , 104 , 107 , 110 - 112 , 120 ].

In addition, trust was shown to influence other factors such as perceived risk, effort expectancy, performance expectancy, and hedonic motivation [ 25 , 36 , 55 , 56 , 102 ]. The relationship between the 2 aspects of trust clearly showed that higher trust in the provider also increases initial trust in the CA [ 55 ]. Philip et al [ 36 ] assumed that credibility is the strongest dimension in terms of patient engagement.

In 5 (71%) of the 7 studies among health care professionals, trust was found to be an important factor for the acceptability, acceptance, and adoption of CAs among health care professionals. Health care professionals trusted neither the technology, assuming that CAs could not correctly assess health problems and situations, nor the patients, with whom they associated the use of CAs with frequent self-diagnosis and lack of understanding of the results delivered [ 29 , 77 , 85 , 116 ]. For many health care professionals, mutual trust could only be built through face-to-face encounters. With a CA’s constant monitoring of a patient, they feared a negative impact on the trust relationship between them and the patient [ 76 ]. Again, this indicates that trust is not a monolithic concept. With regard to health care professionals, trust should be divided into the categories of “trust in the technology,” “trust in the provider,” and “trust in the patients.”

Anthropomorphism

Anthropomorphism can be described as the assignment of human-like attributes or traits to nonhuman agents or objects such as robots, computers, or animals. CAs are often attributed human-like characteristics owing to their unique ability to converse in natural language [ 124 ]. Anthropomorphism was shown in 67% (n=49) of the 73 studies among patients to be a critical influence on the acceptability, acceptance, and adoption of these systems among patients. According to Nadarzynski et al [ 54 ], the lack of human presence is one of the main limitations to using CAs. For health CAs, anthropomorphism can be divided into the subthemes of empathy, intelligence level, personality, and visual features.

For patients, it was important that health care CAs have empathic qualities. In this regard, they wanted CAs to be humorous, caring, friendly, empathetic, warm, honest, supportive, and compassionate. In addition, one of the points they liked most about the technology was that the CA is always there when needed and always listens to them. Many patients would, with increased use, even call the CA a friend [ 14 - 17 , 19 , 23 - 26 , 35 , 38 , 42 , 54 , 56 , 77 , 78 , 82 , 87 , 88 , 93 , 94 , 103 - 105 , 108 , 109 , 112 , 113 , 115 , 120 ]. However, some patients were concerned about a lack of empathy and the CAs’ possible inability to understand emotional issues [ 2 , 54 , 97 ]. Therefore, they perceived the system as nonemotional, rude, or unsympathetic and imagined the conversation as cold and inhuman [ 54 , 56 , 76 , 95 ]. However, it has already been demonstrated that the empathic abilities of health care CAs can be comparable with those of a real person [ 16 , 104 ].

In many cases, CAs were attributed human-like personality traits by users [ 2 , 15 , 24 , 26 , 56 , 77 , 78 , 86 , 87 , 90 , 93 , 99 , 104 , 105 , 109 , 120 ]. A light-hearted, fun, and friendly personality was valued [ 56 , 78 , 93 , 105 ]. In addition, an authoritarian personality was not desired in a health care context [ 35 , 78 ]. Similar to how an authoritarian health care professional would be less accepted by patients, if the CA was perceived as an authority figure, the system was less accepted by patients [ 78 ]. However, some patients had a negative perception of the CA’s personality. The fact that interacting with a CA feels like interacting with a real person caused them anxiety. Therefore, the systems were perceived by these individuals as creepy, scary, and strange [ 56 , 104 ].

Visual features (appearance), for example, an avatar, were decisive in terms of the acceptability, acceptance, and adoption of a CA application [ 14 , 17 , 19 , 23 , 24 , 26 , 35 , 38 , 76 - 78 , 80 , 83 , 87 , 90 , 93 , 94 , 96 , 97 , 103 , 108 , 111 , 120 ]. However, there should be a match between the appearance of the CA and the expectations of the users. Whereas some patients preferred a serious human appearance to discuss important health issues, others preferred a funny character. Some preferred an avatar of a specific gender or age. Overall, it appeared that patients’ requirements for an avatar varied widely. Therefore, it was suggested that patients should be able to configure the appearance of the CA themselves [ 13 , 14 , 19 , 23 , 24 , 26 , 38 , 76 , 78 , 87 , 90 , 94 , 108 , 111 , 120 ]. It was also shown that the appearance of an avatar has a crucial impact on whether the CA appears credible and intelligent [ 14 , 90 ]. Moreover, a visual representation of the system increased the perceived usefulness and enjoyment of the technology [ 26 ].

The intelligence level of a CA was reflected in its conversational responsiveness and ability to understand user input. A significant problem with CAs was their lack of intelligibility owing to limited vocabulary, accuracy of speech recognition, or error management of word input or output. In many cases, the inputs were not understood. Systems often needed to be asked more than one question to process the input. In addition, CAs’ responses were reported to be unnatural, impersonal, cold, limited, and repetitive, or arbitrary, scripted responses were given. Because system intelligence was considered important by patients, it has a critical impact on the acceptability, acceptance, and adoption of CAs in health care [ 12 , 14 , 15 , 17 , 19 - 21 , 23 , 26 , 35 , 37 , 38 , 42 , 54 , 56 , 77 - 81 , 87 , 88 , 90 , 93 , 95 - 97 , 100 , 101 , 103 - 106 , 108 , 113 , 120 ]. Furthermore, 1 (1%) of the 73 studies found that the lack of system intelligence has a negative impact on intention to use, usefulness, and trust [ 37 ]. In addition, the perfection of natural communication through congruence between verbal and nonverbal communication was crucial to the acceptability, acceptance, and adoption of the CA. Nonverbal cues, such as facial expressions, gestures, posture, and body movements, had a major impact on guided communication, as many individuals inferred the outcome and social meaning of the conversation from nonverbal behavior. Therefore, a CA should also be able to provide and understand nonverbal cues and respond appropriately [ 24 , 78 , 94 , 120 ].

Attribution of human characteristics to a CA was mentioned in 5 (71%) of the 7 studies among health care professionals and is, therefore, also a critical factor for acceptability, acceptance, and adoption among health care professionals. Health care professionals ranked the intelligence of CAs as very important [ 29 , 77 , 85 , 116 ]. The prevailing lack of comprehension by CAs was also a severe impediment to the acceptability, acceptance, and adoption of the systems among health care professionals [ 116 ]. In addition, health care professionals believed that CAs lack the intelligence and knowledge to accurately assess patients’ health concerns and fully address their needs [ 29 ].

Health care professionals expressed great enthusiasm for the use of avatars in health care treatment, as they could serve as a motivator for patients. It was crucial for them that the user can customize and personalize the avatar [ 26 ]. For their own use of the systems, health care professionals wanted the CAs to have a neutral and professional appearance that they could customize [ 116 ]. Furthermore, health care professionals considered it important for the CA to have empathic properties. However, they believed that mutual empathy could only occur in face-to-face encounters and that CAs are currently unable to understand and represent emotions [ 26 , 29 ].

Health Issue

Of the 73 studies among patients, 14 (19%) demonstrated that the acceptability, acceptance, and adoption of CAs in health care were also influenced by the severity and type of the health issue. The severity and type of a disease can be defined as the extent of impairment of physical, mental, and social well-being due to physical dysfunction and the reasons for the physical dysfunction. Mild health problems were found to increase the acceptability, acceptance, and adoption rate of CAs; however, for more serious problems, patients were less willing to use a CA and preferred to be treated or advised by a human [ 54 , 57 , 79 , 83 , 89 , 97 , 107 ]. Nevertheless, CAs were perceived as more helpful and credible by patients with more severe diseases than those with less severe diseases [ 25 , 80 , 102 ]. In addition to the severity of the disease, the type of disease could have a decisive effect on the acceptability, acceptance, and adoption of CAs among patients. In particular, patients who were afraid or embarrassed about their illness or symptoms tended to direct their inquiries to CAs owing to the anonymous and nonjudgmental nature of the interactions [ 13 , 54 , 79 , 97 ]. In addition, some studies (3/73, 4%) found that CAs could be a viable treatment method for stigmatized health problems [ 14 , 79 , 89 ]. However, other studies (2/73, 3%) found no effect of the severity and type of the health issue on the acceptability, acceptance, and adoption of CAs [ 2 , 36 ].

The severity and type of the health issue were not found to be influencing factors for the acceptability, acceptance, and adoption of CAs among health care professionals in the included studies.

Working Alliance

The therapeutic relationship that exists between a patient and a physician was also found to be crucial for the acceptability, acceptance, and adoption of CAs among patients in 27% (n=20) of the 73 studies among patients. Therefore, this relationship should also exist between users and the technology [ 13 , 15 , 16 , 23 , 26 , 54 , 76 , 78 , 79 , 82 , 84 , 87 , 88 , 94 , 105 , 112 - 114 ]. The working alliance in this context can be defined as a therapeutic relationship between a user and a health CA to jointly achieve the desired (treatment) goal. Establishing a good relationship between a CA and the user was essential to encourage continued use of the technology and an important prerequisite for building a therapeutic alliance that benefits the patient [ 23 ]. Moreover, this bond was a motivating factor for patients to continue interacting with the CA [ 15 ]. A therapeutic alliance was found to be the result of the empathy, care, and trust that health care professionals demonstrated toward patients [ 16 , 54 ]. To build a patient-CA relationship, recall of past interactions with users and some variability in the systems’ verbal and nonverbal behaviors were critical elements [ 15 ]. Patients could only build a relationship with a CA if it was human like. If no relationship could be established with the technology, patients did not value its opinion and would not follow its advice [ 78 ].

Of the 7 studies among health care professionals, 2 (29%) also described the therapeutic relationship that exists between a patient and a physician as crucial to the acceptability, acceptance, and adoption of CAs by health care professionals. Health care professionals were concerned that the increasing use of CAs would make patients feel less and insufficiently connected to health care professionals [ 29 ]. There was skepticism about whether CAs could help build a strong working alliance between patients and health care professionals. Health care professionals also questioned whether a relationship could be established between a CA and a patient, believing that a working alliance could only be established through face-to-face encounters [ 76 ].

User Characteristics

Of the 73 studies among patients, 40% (n=29) identified multiple user-related factors influencing the acceptability, acceptance, and adoption of CAs among patients. These included the UTAUT2 factor user experience with the technology [ 52 ] and demographic factors such as age, gender, origin, and level of education.

Experience is defined as “the passage of time from the initial use of a technology by an individual” [ 52 ]. In the beginning, the patient was in an exploratory phase with the CA as a new technology, trying out the functions and not really knowing how to handle the device. After some time, the patient mastered the CA and knew exactly how to handle the device and use it specifically to improve their health. This experience made health care through a CA very efficient [ 16 , 19 , 37 , 93 , 104 , 106 , 114 , 121 ]. Moreover, increasing use made the interaction with the CA more familiar, which affected both the trust relationship and the therapeutic relationship between a patient and a CA [ 15 , 16 , 19 , 79 , 106 ]. Thus, temporal use has a decisive influence on the acceptability, acceptance, and adoption of health care CAs.

Furthermore, our review revealed that the given definition of experience is not sufficient for the use of CAs in health care, as, in addition to the time of use, patients’ experience with health care IT support and CAs in general [ 2 , 13 , 27 , 55 , 97 ], as well as their individual technology knowledge, influenced the acceptability, acceptance, and adoption of CAs. Thus, the systems were less accepted and less widely adopted by individuals with low or moderate IT knowledge [ 15 , 24 , 27 , 54 , 57 , 86 , 87 , 92 , 97 , 102 , 111 , 121 ]. In addition, 1 (1%) of the 73 studies among patients found that patients who searched the internet more frequently for health information had more fun interacting with CAs and attributed more human-like characteristics to the systems [ 2 ]. Experience could also be identified as a user-related factor influencing health care professionals’ acceptability, acceptance, and adoption of CAs in health care. Among health care professionals, acceptability, acceptance, and adoption were influenced by their experience with health care IT support and CAs in general, as well as their individual technology knowledge [ 85 ]. Therefore, the definition of experience was extended with regard to the acceptability, acceptance, and adoption of CAs in health care as follows: experience is defined as the time that elapses since a person first uses a technology as well as their experience using similar technologies and their resulting individual knowledge.

In addition to experience, the demographic factors age [ 2 , 25 , 35 - 37 , 83 , 84 , 92 , 111 ], gender [ 35 , 88 ], origin [ 2 , 83 , 84 , 102 , 114 , 120 ], and level of education [ 2 , 36 , 114 ] influenced the acceptability, acceptance, and adoption of health CAs among patients. It was shown that older age was associated with greater use of CAs among patients and that older patients were more engaged and satisfied with the system than younger patients [ 2 , 36 , 37 , 83 , 84 ]. By contrast, 1 (1%) of the 73 studies among patients showed that patients aged <30 years enjoyed interacting with a CA more than those aged >30 years [ 2 ]. Furthermore, male patients perceived CAs to be more useful in the health context than female patients [ 88 ]. In addition, patients who were less educated rated CAs as more useful than patients who were well educated [ 36 , 114 ]. It was also showed that Black patients used CAs less than patients of other races with otherwise similar characteristics [ 83 ]. Furthermore, people of Asian descent perceived CAs as more useful [ 84 , 114 ].

However, it should be noted that some studies (9/73, 12%) failed to identify any influence of user-related factors on acceptability, acceptance, and adoption among patients [ 2 , 17 , 22 , 23 , 25 , 36 , 54 , 86 , 108 ].

Demographic factors such as age, gender, origin, and education level were not identified as factors influencing acceptability, acceptance, and adoption among health care professionals in the included studies.

Principal Findings

The objective of this IR was to identify the factors that influence the acceptability, acceptance, and adoption of CAs among patients and health care professionals. We identified 13 factors that influence the acceptability, acceptance, and adoption of CAs among patients and 10 factors that influence the acceptability, acceptance, and adoption of CAs among health care professionals.

We found that performance expectancy and effort expectancy are the most studied factors influencing the acceptability, acceptance, and adoption of CAs in health care. The findings are consistent with the literature on human-computer interaction (HCI). Perceived ease of use and perceived usefulness are described as key factors in predicting the use of technologies in general and CAs in particular [ 49 , 127 ]. Overall, both health care professionals and patients clearly recognize the benefits of CAs in health care, which has already been shown in a number of studies [ 41 , 128 ]. In addition, studies demonstrated that the health care provided by a CA is comparable with that provided by human physicians [ 44 , 45 ]. Thus, the systems represent a cost-effective alternative to the classic therapy option with the same benefits [ 39 , 95 ].

One of the most interesting findings of the analysis was that, in addition to performance expectancy and effort expectancy, anthropomorphism, trust, perceived risk, and working alliance have been identified as having a decisive influence on the acceptability, acceptance, and adoption of CAs in health care and have not previously been considered in UTAUT or UTAUT2.

In accordance with the literature on the theory of anthropomorphism [ 124 ], this IR found that patients attribute human-like characteristics to health CAs and try to interact with them as if the systems were human. HCI research has also found that individuals interact with internet-based agents as if they were humans, even when they know that they are computer programs [ 129 ]. In addition, previous work has shown that anthropomorphism has a positive effect in terms of continued use and increased satisfaction with the technology [ 124 ]. Moreover, previous work on CAs has already indicated that perceived anthropomorphism can influence CA acceptability, acceptance, and adoption [ 130 ].

Nevertheless, it was found that the perceived anthropomorphism could also trigger fear and discomfort in some patients. They perceived the CA as creepy, scary, and strange. In the HCI literature, this is known as the “uncanny valley effect.” The uncanny valley theory states that a technology that appears almost human can evoke negative affective reactions in users [ 131 ]. The findings obtained are also consistent with the results of other studies on this topic. Although some studies have reported positive effects of anthropomorphic CAs [ 124 ], others have shown that anthropomorphism can lead to frustration, confusion, and even a sense of eeriness [ 132 ]. Another criticism of anthropomorphism is that users can be deceived into thinking that they are interacting with a real person instead of a system [ 16 ]. Therefore, a CA should always be labeled as a machine.

Furthermore, our results support previous literature on trust by showing that trust is not a monolithic concept but must be differentiated into “trust in the provider” and “trust in the technology” from the patient’s perspective. Whereas trust in the provider refers to patients’ beliefs about the provider’s benevolence, integrity, and competence, trust in the technology refers to patients’ beliefs about the system’s benevolence, functionality, helpfulness, and reliability [ 126 , 133 ]. With regard to health care professionals, it was found that they also have little trust in their patients to use the CA correctly and to interpret the given information correctly. Thus, our results show that trust is represented by the categories “trust in the technology,” “trust in the provider,” and “trust in the patient” from the perspective of the health care professionals. To increase the trustworthiness of CAs, it is suggested in relation to research on AI-driven intelligent systems that responses be presented in a meaningful, understandable, and trustworthy format. In addition, users should be provided with a variety of system-related information, including data on the reliability and performance of the system and the source used for the response output. This should enable users to better understand the information displayed and its origin and then decide whether to trust the technology’s recommendation [ 134 ]. It is further suggested that credibility can be demonstrated to users through expert vocabulary and appropriate presentation [ 133 ]. Nonmedical studies have also demonstrated that credibility in the form of systems’ functionality, capability, reliability, and benevolence can predict the acceptability, acceptance, and adoption of wearable technologies such as CAs [ 135 ]. Furthermore, it was shown that for trust building, the user should have a positive impression of the technology. These impressions are influenced by static and dynamic features. Static features include the appearance of the system, and dynamic features include the verbal and nonverbal behaviors of the system [ 136 ]. Moreover, in line with the literature on trust, the results show that building trust in automated systems is a major challenge for developers [ 137 ]. Furthermore, it is assumed that patients will use CAs only if they trust them. These explanations show that trust is one of the key factors influencing CA acceptability, acceptance, and adoption [ 36 ]. Therefore, it is suggested that trust in health CAs should always be systematically assessed before deployment. However, standardized and validated scales to measure trust are lacking, especially in medicine [ 138 ].

Another key barrier to the acceptability, acceptance, and adoption of health CAs is the perceived risk of the technology, stemming from the uncertainty around the protection of personal data and the risk to users’ lives and well-being. Concern about data privacy and the fear of misuse of sensitive information are key barriers to the acceptability, acceptance, and adoption as well as to the widespread use of digital health applications [ 139 ]. Studies have shown that privacy concerns can be addressed by the automatic transfer of data from an electronic health record and the regular addition of information by health care professionals. In addition, concerns may be addressed by explaining the measures succinctly and presenting them in layperson’s terms [ 140 ].

The literature on digital health applications also frequently discussed whether patient safety is compromised [ 141 ] and who is responsible if the CA misdiagnoses someone [ 4 ]. The results show that health care professionals fear a safety risk not only for patients but also for themselves. They fear that CAs will play such an important role in the future that they could replace human workers and compromise the quality of health care. We believe that this fear is one of the key barriers to the acceptability, acceptance, and adoption of CAs by health care professionals. In line with the literature, our results clearly show that the development of CAs is still in the early stages, is rudimentary, and thus does not jeopardize jobs [ 139 ]. Patients are more willing to share confidential information with a CA than with a health care professional because of its anonymous and nonjudgmental nature of the interactions. However, the preferred use of the systems is for minor illnesses. For more serious conditions, patients prefer to seek advice and treatment from a physician [ 44 , 45 ]. Thus, the use of CAs is purely supportive and does not jeopardize employment. This should be clearly communicated to increase the acceptability, acceptance, and adoption of the technology among health care professionals.

A therapeutic relationship is crucial for the success of a treatment [ 142 ]. Such a relationship is the result of empathy, care, and trust and can significantly improve the benefits of a health interaction [ 143 ]. Empathy is the most important factor in building a working relationship [ 144 ]. We were able to identify the factors of empathy, care, and trust as crucial for the acceptability, acceptance, and adoption of CAs in health care. It has already been demonstrated in some studies that a working alliance can be formed between a CA and a user [ 4 , 33 , 34 , 43 ]. For establishing and maintaining a relationship between a patient and a CA, memory of past interactions and variability in verbal and nonverbal responses are crucial elements. This finding is consistent with previous research and shows that, for the correct application of relational behaviors, it is necessary to talk about the past and the future [ 145 ] and the time spent apart [ 146 ]. Bickmore et al [ 15 ] suggested designing health CAs such that interactions are initially relatively distant and professional but gradually become more personal, social, and familiar over time. In addition, systems should have a sense of humor as well as empathy and talk to the user about the present relationship to maintain it [ 34 ].

Another crucial barrier to the acceptability, acceptance, and adoption of CAs is their lack of comprehensibility and limited communication capabilities. The literature showed that language skills are a major problem and should be urgently improved [ 3 ]. Owing to language limitations, CAs currently use predetermined response options because, unlike free-text entry, they can ensure data validity and accuracy and minimize speech recognition errors. This approach is particularly important in a health-related context, as the multiple-choice input modality avoids potentially dangerous effects of misunderstandings due to ambiguous utterances about medical topics in unrestricted text and speech input. At the same time, it clearly communicates to users how they should respond to the system’s output and ensures that the system can understand and process input with high accuracy. It also enables the CA to be more easily accepted and used by people with different computer and language skills [ 16 , 147 ]. However, our analysis shows that many patients did not want a user input restriction while communicating with systems. Instead of choosing between predefined answers, they would like to be able to answer with a free-text entry to describe their health complaints as precisely as possible.

Furthermore, most patients preferred voice-based communication with a CA over text-based communication. The preferred method of communication of CAs was also discussed controversially in the literature. Even within the definition of CAs, there is no consensus on the preferred mode of communication. The advantages of text-based communication are, for example, that text can be indexed, searched, and translated and that it can be easily corrected or improved after completion. Proponents of acoustic communication are of the opinion that speech is more natural and faster than text. In addition, the use of speech can enhance the perceived personality of a CA. Furthermore, systems that allow acoustic communication can also be used by patients with low or no literacy skills [ 40 , 148 ]. Moreover, we found that nonverbal communication also has a decisive influence on the acceptability, acceptance, and adoption of the systems. Nonverbal cues such as facial expressions, gestures, posture, and body movements have a significant impact on guided communication, as they convey empathy, thereby strengthening the therapeutic alliance and trust relationship between patients and CAs [ 30 , 33 , 34 , 43 ]. The 55-38-7 rule proposed by Mehrabian and Ferris [ 31 ] shows the importance of nonverbal communication and behavior. Communication can be improved only through a combination of verbal and nonverbal behaviors [ 30 ]. We believe that all types of communication will be important in health care in the future. Whether written or oral communication is advantageous will depend on the situation in which the CA is used. For example, whereas an oral dialog with a CA may be easier for a human who is severely injured or paraplegic or a human who is illiterate, a written conversation may be beneficial for a prescription transfer or a patient with speech impairment.

One of the main criticisms of CAs in the literature is that they would not be able to develop empathy, recognize users’ emotional states, or tailor their responses to them. A lack of empathy can affect the use of CAs in the health care sector [ 143 ]. To increase the acceptability, acceptance, and adoption, as well as effectiveness, of CAs among patients, it is, therefore, important that the systems have the same interpersonal and social characteristics as health care professionals. In addition, empathic responses help create a trusting relationship between the technology and the user, which guarantees continuous and long-term use of the system and increases the benefits for patients [ 16 ]. Consistent with the broader literature, our results show that CAs can be empathic [ 16 , 33 , 43 ]. Some studies even showed that the empathic abilities of CAs can be compared with those of a real person [ 149 ]. In health care, empathy as part of anthropomorphism is critical for the success of CAs [ 38 ]. It was found that the visualization of a CA in the form of an avatar makes it more credible, comfortable, sympathetic, and useful than a CA without an avatar [ 33 ].

The COVID-19 pandemic has had a significant impact on normal health care delivery and has demonstrated the urgent need for alternative approaches that can overcome geographic, temporal, and organizational barriers. The pandemic resulted in limited access to outpatient clinics, and the high rate of infection posed significant challenges to medical facilities, which affected the delivery of health services [ 2 , 110 ]. This situation has clearly demonstrated that the short-term unavailability of health services can occur even when rapid access to services is basically guaranteed. In this regard, technological systems such as CAs are a good alternative for the continued provision of quality care. It has been shown that CAs can improve and facilitate access to health care [ 150 ]. In addition, there are concerns about what happens once the internet connection is lost or individuals do not have the necessary resources such as a smartphone or internet access [ 57 ]. Services that can be accessed only through technology may lead to a digital divide and inequity in health care. This would limit access to health services, potentially for the very people who need the services most. For example, digital searching for health information is uncommon among older adults and other underserved groups. However, it should be noted that digital technologies expand the availability of health information and resources to many individuals and improve the quality of care [ 57 , 151 ]. Therefore, we propose that health care providers always offer traditional access to health care services alongside technology to provide quality care for everyone and prevent a 2-tier society. Solutions should also be sought to improve the access to digital resources such as the internet that are necessary to access emerging health technologies.

Consistent with Ling et al [ 152 ], we found that user-related factors influence the acceptability, acceptance, and adoption of CAs. These include the demographic factors age, gender, origin, and education level as well as the UTAUT2 factor user experience with the technology. Regarding the factors age, gender, and education level, we found different results within the analyzed studies as to whether they influence the acceptability, acceptance, and adoption of health CAs among patients. Other studies on this topic also provided different findings. Although some studies demonstrated the presence of these factors, other studies were unable to do so [ 135 , 147 ]. Furthermore, origin was found to influence the acceptability, acceptance, and adoption of CAs. However, overall, this is an understudied area. Little is known about ethical differences in technology acceptability, acceptance, and adoption. However, in line with previous research, our results show that it is crucial to tailor the technology to the target population and its cultural characteristics [ 83 ].

Moreover, it was found that the identified influencing factors influence each other and cannot always be clearly separated. At the same time, our results indicate that the importance of an influencing factor also depends on the purpose of the CA used and the health domain concerned. Multimedia Appendix 8 provides a summary of influencing factors by health domains and health categories (ie, aggregated domains). Whereas in the categories “mental health” and “specific diseases,” anthropomorphism is the most important factor in addition to performance and effort expectancy, credibility and the severity and type of health issue are crucial for CAs as general health advisers and promoters. In the category “pregnancy care and healthcare for children,” by contrast, hedonic motivation is the key influencing variable along with performance and effort expectancy. The importance of the individual determinants based on the purpose of a CA application is, therefore, understandable. However, owing to the small number of studies per health domain and category, this can only be generalized to a limited extent. The mutual influence and not-always-clear separation of the influencing factors as well as their variability and importance depending on the health care domain make the research on the acceptability, acceptance, and adoption of CAs in health care so extensive.

Strengths and Limitations

As with all studies, this IR has some limitations. One potential limitation is related to the search strategy. It is possible that not all studies on the topic were found despite our comprehensive search strategy, as studies may have discussed the acceptability, acceptance, or adoption of CAs and the influencing factors but used different terms than those we found. In addition, this review included studies published only in English and German, and this approach may have excluded relevant evidence published in other languages. Furthermore, the IR included only primary studies that had already been published, which also excluded relevant studies such as gray literature.

For quality appraisal, we followed the guidelines for rapid reviews [ 153 , 154 ]. A rapid review is a form of knowledge synthesis in which components of the systematic review process are simplified or omitted to produce evidence-based information in a timely manner [ 155 ]. As a result, the screening of the studies for the quality assessment of the papers was fully performed by only 1 researcher. A second researcher assessed only 10% (8/76) of the studies. Nevertheless, the expedited process may have introduced biases in quality assessment.

In addition, as the original studies did not consistently define and describe whether they analyzed acceptability, acceptance, or adoption, it was impossible for us to differentiate between these 3 outcomes in our synthesis. Hence, we cannot provide an answer to the question of whether some factors have been researched more frequently or are more influential for one of the outcomes than for the others.

Finally, the findings regarding acceptability, acceptance, and adoption among health care professionals are almost impossible to generalize, as we could only find and evaluate 7 studies on this topic. Owing to the rapid increase in the research literature on this topic, it is possible that new findings already emerged during the preparation and publication of our results and that the review, therefore, no longer reflects the current state of research.

Despite these potential limitations, this IR has several strengths. To our knowledge, this is the first review to provide a comprehensive picture of the acceptability, acceptance, and adoption of CAs and their influencing factors in health care. We described the factors influencing the acceptability, acceptance, and adoption of CAs in health care from the perspectives of patients and health care professionals and created a thematic map that clearly summarizes the findings. Furthermore, the IR follows the same scientific rigor as primary research in that we used Cooper’s [ 67 ] 5-step IR method modified by Whittemore and Knafl [ 68 ] for its construction. The review was developed, conducted, and reported in accordance with the PRISMA selection process, which allowed us to produce a high-quality review [ 74 ]. A total of 5 well-known and frequently used databases in the field of health were searched to retrieve as many studies as possible. The keywords for the search terms used for this purpose were derived from the main research question. Synonyms for the identified keywords were generated using the Medical Subject Headings terms of the 5 databases, a web-based search, and previously published literature on CAs. Freehand searching and forward-backward reference list checks allowed us to identify additional literature missed by the database search and minimize the risk of publication bias. As no restrictions were made with regard to study design, study setting, and country of publication, this review can be considered comprehensive.

Implication and Future Directions

This IR provides the first comprehensive overview of the acceptability, acceptance, and adoption of CAs in health care and their influencing factors from the perspectives of patients and health care professionals. From the results, it is clear that the acceptability, acceptance, and adoption of CAs from the perspective of health care professionals are significantly underresearched. Other reviews have also found that few studies on CAs have focused on health care professionals [ 40 ]. Therefore, future research should urgently explore the acceptability, acceptance, and adoption of CAs among this user group. For this purpose, a survey could be designed based on our theoretical model to confirm the identified influencing factors and determine new ones. Furthermore, health care professionals should also test currently available CAs and provide feedback. In particular, it is crucial to explore the acceptability, acceptance, and adoption of CAs and their influencing factors from physicians’ point of view. Moreover, physicians will only recommend or prescribe CAs if they accept and adopt the technology and are convinced of its benefits. In addition, we demonstrated that health care professionals’ opinions about CAs significantly influence patients. With knowledge about acceptability, acceptance, and adoption among health care professionals, CAs could be sustainably established in health care.

We succeeded in creating a comprehensive thematic map of the factors influencing the acceptability, acceptance, and adoption of CAs. However, the influence of the identified factors on acceptability, acceptance, and adoption as well as the interrelationship among them was not quantitatively validated. Future studies could build on the theoretical model and examine the relative influence of the factors on acceptability, acceptance, and adoption and the dynamics among the factors. Furthermore, the results show that the influence of facilitators and barriers depends on the intended use of a CA and the health domain in which it is used. However, nothing about the strength and importance of the identified factors was mentioned in the analyzed studies. Therefore, future research should also investigate the importance of the individual factors and their interactions with each other for individual areas of care.

We found that CAs have been tested almost exclusively in controlled environments that do not simulate the realistic interactions in clinical practice. It has already been demonstrated that the environment in which the interaction occurs influences technological acceptability, acceptance, and adoption [ 127 , 156 ]. The broader literature also criticized the fact that, to date, most studies have examined the use of CAs in controlled environments rather than in real-world contexts [ 148 , 157 ]. Moreover, most studies into the acceptability, acceptance, and adoption of CAs in health care are short-term studies. However, it has been shown that factors such as habit only develop when the technology is used over a longer period. Therefore, we consider it necessary that CAs be increasingly tested in real environments and over the long term in the future.

Consistent with the current review of Camile et al [ 46 ], we noted that, in relation to the technology, researchers attach different meanings to the terms “acceptability,” “acceptance,” and “adoption” and often use them synonymously without referring to established models and definitions from the literature. None of the included studies defined the terms used appropriately or distinguished them from each other. The definitions are often misunderstood, or researchers establish their own definitions. The inconsistent use of the terms “acceptability,” “acceptance,” and “adoption” makes it immensely difficult to compare the results of these studies. For future research, we, therefore, consider it necessary to follow the definitions and established models from the literature on acceptability, acceptance, and adoption, which clearly show the differences among the terms, to achieve consistency, which will allow comparisons across studies and the development of targeted implementation strategies.

Conclusions

In this review, we identified 13 factors that influence the acceptability, acceptance, and adoption of CAs among patients and 10 factors that influence the acceptability, acceptance, and adoption of CAs among health care professionals. On the basis of the identified influencing factors shown individually for acceptability, acceptance, and adoption, a comprehensive thematic map that explains the acceptability, acceptance, and adoption of CAs in health care was created. Overall, a high level of acceptability, acceptance, and adoption of CAs in health care was observed. This review shows the variety and complexity of influencing factors. Thus, it presents a comprehensive set of factors that can be implemented, improved, or steered to increase the acceptability, acceptance, and adoption of CAs in health care.

To the best of our knowledge, this IR extends the literature by providing the first overview of the research on the acceptability, acceptance, and adoption of CAs in health care. The findings of this review can, therefore, serve as the groundwork for future implementation studies of CAs in health care. Future research should focus on exploring acceptability, acceptance, and adoption from the perspective of health care professionals. Furthermore, it is crucial to test already developed CAs under real conditions and through long-term studies.

Acknowledgments

The authors acknowledge support from the Open Access Publication Fund of the University of Wuppertal.

Data Availability

All data generated or analyzed during this study are included in this published manuscript and its supplementary files.

Conflicts of Interest

None declared.

Definition, synonyms, and variants of conversational agents.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

Search terms.

Quality appraisal.

Characteristics of the included studies.

Numerical listing of the influencing factors for patients.

Numerical listing of the influencing factors for health care professionals.

Influencing factors by health domains and health categories.

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Abbreviations

Edited by T de Azevedo Cardoso, A Mavragani; submitted 15.02.23; peer-reviewed by C Bérubé, L Kremer; comments to author 20.04.23; revised version received 10.05.23; accepted 10.07.23; published 26.09.23

©Maximilian Wutz, Marius Hermes, Vera Winter, Juliane Köberlein-Neu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 26.09.2023.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

New 'inverse vaccine' could wipe out autoimmune diseases, but more research is needed

An "inverse vaccine," which selectively suppresses the immune system, treated multiple sclerosis in mice. But how well could this new approach work in people?

Scientists have created a new type of vaccine that instead of activating the immune system, selectively suppresses it. The so-called inverse vaccine, which has only been tested in mice so far, could one day be used to treat autoimmune diseases , in which the immune system attacks the body, the researchers say. 

The vaccine was given to mice with a condition similar to multiple sclerosis , an autoimmune disease in which myelin sheaths, or the insulating coats around nerves in the brain and spinal cord, are systematically destroyed. The treatment reversed symptoms of the disease and restored the function of nerve cells. The findings were described in a study published Sept. 7 in the journal Nature Biomedical Engineering .  

The vaccine essentially works by getting the immune system to recognize nerves as "safe," rather than as foreign invaders that should be attacked. The method hasn't been tested in humans, but experts told Live Science that the results are exciting. 

Related: In a 1st, scientists use designer immune cells to send an autoimmune disease into remission

"The idea of inducing tolerance in autoimmunity has been around for a while," Nick Jones , an associate professor of biomedical sciences at Swansea University in Wales who was not involved in the research, told Live Science in an email. But although the concept isn't new, this study is exciting because it showed this approach worked to alleviate, at least temporarily, autoimmune disease, he added.

Normally, immune cells called T cells protect the body from invaders like viruses and diseased cells, such as those in cancerous tumors. They identify which cells to attack by binding to specific antigens, or molecules, that typically appear on the outside of a virus or cell. However, in autoimmune disease, T cells mistakenly target healthy cells in the body by going after "autoantigens," molecules found only on those normal cells.

How do you get the body to stop attacking itself? You teach it to leave those autoantigens alone — and the body has a way of teaching this tolerance.

This teaching is done by a special group of cells in the liver that present antigens to T cells and tell them that they're safe; the liver has these special cells because, while filtering blood, it must differentiate between dangerous foreign antigens (from bacteria) and safe ones (from cells from one's self and food). In the new study, the researchers hijacked this process to mark the body's cells as "safe" from T-cell attack. 

They induced a form of multiple sclerosis in mice, which caused T cells to attack a specific antigen found in myelin. To stop the attack, they then tagged this antigen with a special sugar, and those sugar-tagged antigens got ferried to the liver, where the tolerance-teaching cells picked them up. The liver cells then reprogrammed T cells to leave myelin alone as well as protect it, essentially removing myelin from the immune system's "hit list." 

Related: The virus behind 'mono' might trigger multiple sclerosis in some

Inverse vaccines like these are exciting for a number of reasons, experts told Live Science. 

Firstly, the vaccines would suppress one cell type in the immune system, unlike many standard therapies that exert their effects more broadly. "Most immune therapies for autoimmune diseases act in a general way and don't just target the disease-inducing T cells," Lucy Walker , a professor of immune regulation at University College London who was not involved in the research, told Live Science in an email. "Ideally, we'd want suppression to act in an antigen-specific way, so only the pathogenic T cells are targeted and others are left free to function." This means you could avoid side effects, such as the increased risk of infection associated with using standard immune-suppressing therapies, such as methotrexate . 

Vaccines also stimulate the formation of immunological memory , or the body's ability to remember infections so that it can better respond the next time it encounters the same invading microbes. "Current therapies for autoimmunity are really sort of broad immune suppressants and they work while you're taking them but when you stop taking them, they stop working," study senior author Jeffrey Hubbell , a  professor of tissue engineering at the University of Chicago, told Live Science. "The idea with the vaccine is that you develop memory of that therapy." 

However, although the results of the new study are promising, more work needs to be done to develop this technology into a treatment that can be feasibly used in humans, Walker said. For instance, the protective effects shown in the study only lasted a few weeks, so it is unclear how long they could last, especially in people. 

Another potential issue is that the immune system could regain its memory of the target antigen, which may mean a booster dose would be needed, as is the case for many regular vaccines. Hubbell said that this is something that clinical studies will have to investigate. 

Success in animal models also doesn't always translate to humans. 

— Teen's year-long case of depression and seizures caused by brain-injuring autoimmune disease

— COVID-19 linked to 40% increase in autoimmune disease risk in huge study

— Woman who spontaneously vomited up to 30 times a day likely had rogue antibodies

"It's unlikely that a single approach will work in all humans with a particular disease because these diseases have more variation in the human population — in part, because people are genetically very different from each other, including for genes that are important in the immune system, so they respond differently," Dr. David Fox , a professor of internal medicine at the University of Michigan who was not involved in the research, told Live Science. 

Another tricky issue is that for each autoimmune disease, scientists will have to identify the specific autoantigen that the body is primed to attack, which Jones said could involve an "extensive amount of research." For some autoimmune conditions, such as psoriasis , there isn't a consensus on what the autoantigen is , Fox said, and in multiple sclerosis, for example, there are several autoantigens that are known to be targeted by the body's immune system. This may make it difficult to measure the benefit of treatment in humans, he said.

Nonetheless, this approach of using sugar-modified antigens to dampen an autoimmune response has already been shown to be both safe and effective in early clinical trials for celiac disease — an autoimmune condition that injures the small intestines when those affected eat gluten. A second trial is also currently assessing the safety of the approach for patients with multiple sclerosis . 

"It's a really exciting area of research," Walker said, although it's lagging behind other types of immunotherapy, such as Teplizumab, which was recently approved by the U.S. Food and Drug Administration to delay the onset of type 1 diabetes . Regardless, "I do think it's a promising area for the future," Walker said.

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Emily is a health news writer based in London, United Kingdom. She holds a bachelor's degree in biology from Durham University and a master's degree in clinical and therapeutic neuroscience from Oxford University. She has worked in science communication, medical writing and as a local news reporter while undertaking journalism training. In 2018, she was named one of MHP Communications' 30 journalists to watch under 30. ( [email protected] ) 

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