Customer perception, purchase intention and buying decision for branded products: measuring the role of price discounts

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  • Published: 13 March 2021
  • Volume 20 , pages 194–203, ( 2021 )

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purchase research article

  • Amit Dangi 1 ,
  • Chand P. Saini 1 ,
  • Vijay Singh 2 &
  • Jayant Hooda 3  

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The purpose of this paper is to explore the antecedents of customer perception and its effect on the purchase intention and finally on buying decision-making about branded products especially luxury products, finally the role of price discounts in converting intentions into buying decision. This research has been carried in NCR with a collection of primary data by including statements related to the customer perception, buying intentions regarding branded luxury products and one section of the questionnaire included statements of Price discounts and buying decisions. The study used Exploratory Factor Analysis, Structure Equation Modeling, and Mediation through AMOS 19 to analyze the data. Results explored four major determinants named Quality, Trust, Psychological, and Social which were considered to contribute to building the perception of any customer for branded products and creates the purchase intention which will finally be converted into buying decisions making. The price discount plays a role of partial mediation, where due to price discount available for luxury branded products the buying decision-making has been reduced significantly.

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Amit Dangi & Chand P. Saini

Department of Commerce, Indira Gandhi University, Meerpur, Rewari, India

Vijay Singh

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Dangi, A., Saini, C.P., Singh, V. et al. Customer perception, purchase intention and buying decision for branded products: measuring the role of price discounts. J Revenue Pricing Manag 20 , 194–203 (2021).

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Received : 08 December 2020

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Published : 13 March 2021

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The impact of online reviews on consumers’ purchasing decisions: evidence from an eye-tracking study.

Tao Chen

  • 1 School of Business, Ningbo University, Ningbo, China
  • 2 School of Business, Western Sydney University, Penrith, NSW, Australia

This study investigated the impact of online product reviews on consumers purchasing decisions by using eye-tracking. The research methodology involved (i) development of a conceptual framework of online product review and purchasing intention through the moderation role of gender and visual attention in comments, and (ii) empirical investigation into the region of interest (ROI) analysis of consumers fixation during the purchase decision process and behavioral analysis. The results showed that consumers’ attention to negative comments was significantly greater than that to positive comments, especially for female consumers. Furthermore, the study identified a significant correlation between the visual browsing behavior of consumers and their purchase intention. It also found that consumers were not able to identify false comments. The current study provides a deep understanding of the underlying mechanism of how online reviews influence shopping behavior, reveals the effect of gender on this effect for the first time and explains it from the perspective of attentional bias, which is essential for the theory of online consumer behavior. Specifically, the different effects of consumers’ attention to negative comments seem to be moderated through gender with female consumers’ attention to negative comments being significantly greater than to positive ones. These findings suggest that practitioners need to pay particular attention to negative comments and resolve them promptly through the customization of product/service information, taking into consideration consumer characteristics, including gender.


E-commerce has grown substantially over the past years and has become increasingly important in our daily life, especially under the influence of COVID-19 recently ( Hasanat et al., 2020 ). In terms of online shopping, consumers are increasingly inclined to obtain product information from reviews. Compared with the official product information provided by the sellers, reviews are provided by other consumers who have already purchased the product via online shopping websites ( Baek et al., 2012 ). Meanwhile, there is also an increasing trend for consumers to share their shopping experiences on the network platform ( Floh et al., 2013 ). In response to these trends, a large number of studies ( Floh et al., 2013 ; Lackermair et al., 2013 ; Kang et al., 2020 ; Chen and Ku, 2021 ) have investigated the effects of online reviews on purchasing intention. These studies have yielded strong evidence of the valence intensity of online reviews on purchasing intention. Lackermair et al. (2013) , for example, showed that reviews and ratings are an important source of information for consumers. Similarly, through investigating the effects of review source and product type, Bae and Lee (2011) concluded that a review from an online community is the most credible for consumers seeking information about an established product. Since reviews are comments from consumers’ perspectives and often describe their experience using the product, it is easier for other consumers to accept them, thus assisting their decision-making process ( Mudambi and Schuff, 2010 ).

A survey conducted by Zhong-Gang et al. (2015) reveals that nearly 60% of consumers browse online product reviews at least once a week and 93% of whom believe that these online reviews help them to improve the accuracy of purchase decisions, reduce the risk of loss and affect their shopping options. When it comes to e-consumers in commercial activities on B2B and B2C platforms, 82% of the consumers read product reviews before making shopping choices, and 60% of them refer to comments every week. Research shows that 93% of consumers say online reviews will affect shopping choices, indicating that most consumers have the habit of reading online reviews regularly and rely on the comments for their purchasing decisions ( Vimaladevi and Dhanabhakaym, 2012 ).

Consumer purchasing decision after reading online comments is a psychological process combining vision and information processing. As evident from the literature, much of the research has focused on the outcome and impact of online reviews affecting purchasing decisions but has shed less light on the underlying processes that influence customer perception ( Sen and Lerman, 2007 ; Zhang et al., 2010 ; Racherla and Friske, 2013 ). While some studies have attempted to investigate the underlying processes, including how people are influenced by information around the product/service using online reviews, there is limited research on the psychological process and information processing involved in purchasing decisions. The eye-tracking method has become popular in exploring and interpreting consumer decisions making behavior and cognitive processing ( Wang and Minor, 2008 ). However, there is very limited attention to how the emotional valence and the content of comments, especially those negative comments, influence consumers’ final decisions by adopting the eye-tracking method, including a gender comparison in consumption, and to whether consumers are suspicious of false comments.

Thus, the main purpose of this research is to investigate the impact of online reviews on consumers’ purchasing decisions, from the perspective of information processing by employing the eye-tracking method. A comprehensive literature review on key themes including online reviews, the impact of online reviews on purchasing decisions, and underlying processes including the level and credibility of product review information, and processing speed/effectiveness to drive customer perceptions on online reviews, was used to identify current research gaps and establish the rationale for this research. This study simulated a network shopping scenario and conducted an eye movement experiment to capture how product reviews affect consumers purchasing behavior by collecting eye movement indicators and their behavioral datum, in order to determine whether the value of the fixation dwell time and fixation count for negative comment areas is greater than that for positive comment area and to what extent the consumers are suspicious about false comments. Visual attention by both fixation dwell time and count is considered as part of moderating effect on the relationship between the valence of comment and purchase intention, and as the basis for accommodating underlying processes.

The paper is organized as follows. The next section presents literature reviews of relevant themes, including the role of online reviews and the application of eye movement experiments in online consumer decision research. Then, the hypotheses based on the relevant theories are presented. The research methodology including data collection methods is presented subsequently. This is followed by the presentation of data analysis, results, and discussion of key findings. Finally, the impact of academic practical research and the direction of future research are discussed, respectively.

Literature Review

Online product review.

Several studies have reported on the influence of online reviews, in particular on purchasing decisions in recent times ( Zhang et al., 2014 ; Zhong-Gang et al., 2015 ; Ruiz-Mafe et al., 2018 ; Von Helversen et al., 2018 ; Guo et al., 2020 ; Kang et al., 2020 ; Wu et al., 2021 ). These studies have reported on various aspects of online reviews on consumers’ behavior, including consideration of textual factors ( Ghose and Ipeirotiss, 2010 ), the effect of the level of detail in a product review, and the level of reviewer agreement with it on the credibility of a review, and consumers’ purchase intentions for search and experience products ( Jiménez and Mendoza, 2013 ). For example, by means of text mining, Ghose and Ipeirotiss (2010) concluded that the use of product reviews is influenced by textual features, such as subjectivity, informality, readability, and linguistic accuracy. Likewise, Boardman and Mccormick (2021) found that consumer attention and behavior differ across web pages throughout the shopping journey depending on its content, function, and consumer’s goal. Furthermore, Guo et al. (2020) showed that pleasant online customer reviews lead to a higher purchase likelihood compared to unpleasant ones. They also found that perceived credibility and perceived diagnosticity have a significant influence on purchase decisions, but only in the context of unpleasant online customer reviews. These studies suggest that online product reviews will influence consumer behavior but the overall effect will be influenced by many factors.

In addition, studies have considered broader online product information (OPI), comprising both online reviews and vendor-supplied product information (VSPI), and have reported on different attempts to understand the various ways in which OPI influences consumers. For example, Kang et al. (2020) showed that VSPI adoption affected online review adoption. Lately, Chen and Ku (2021) found a positive relationship between diversified online review websites as accelerators for online impulsive buying. Furthermore, some studies have reported on other aspects of online product reviews, including the impact of online reviews on product satisfaction ( Changchit and Klaus, 2020 ), relative effects of review credibility, and review relevance on overall online product review impact ( Mumuni et al., 2020 ), functions of reviewer’s gender, reputation and emotion on the credibility of negative online product reviews ( Craciun and Moore, 2019 ) and influence of vendor cues like the brand reputation on purchasing intention ( Kaur et al., 2017 ). Recently, an investigation into the impact of online review variance of new products on consumer adoption intentions showed that product newness and review variance interact to impinge on consumers’ adoption intentions ( Wu et al., 2021 ). In particular, indulgent consumers tend to prefer incrementally new products (INPs) with high variance reviews while restrained consumers are more likely to adopt new products (RNPs) with low variance.

Emotion Valence of Online Product Review and Purchase Intention

Although numerous studies have investigated factors that may influence the effects of online review on consumer behavior, few studies have focused on consumers’ perceptions, emotions, and cognition, such as perceived review helpfulness, ease of understanding, and perceived cognitive effort. This is because these studies are mainly based on traditional self-report-based methods, such as questionnaires, interviews, and so on, which are not well equipped to measure implicit emotion and cognitive factors objectively and accurately ( Plassmann et al., 2015 ). However, emotional factors are also recognized as important in purchase intention. For example, a study on the usefulness of online film reviews showed that positive emotional tendencies, longer sentences, the degree of a mix of the greater different emotional tendencies, and distinct expressions in critics had a significant positive effect on online comments ( Yuanyuan et al., 2009 ).

Yu et al. (2010) also demonstrated that the different emotional tendencies expressed in film reviews have a significant impact on the actual box office. This means that consumer reviews contain both positive and negative emotions. Generally, positive comments tend to prompt consumers to generate emotional trust, increase confidence and trust in the product and have a strong persuasive effect. On the contrary, negative comments can reduce the generation of emotional trust and hinder consumers’ buying intentions ( Archak et al., 2010 ). This can be explained by the rational behavior hypothesis, which holds that consumers will avoid risk in shopping as much as possible. Hence, when there is poor comment information presented, consumers tend to choose not to buy the product ( Mayzlin and Chevalier, 2003 ). Furthermore, consumers generally believe that negative information is more valuable than positive information when making a judgment ( Ahluwalia et al., 2000 ). For example, a single-star rating (criticism) tends to have a greater influence on consumers’ buying tendencies than that of a five-star rating (compliment), a phenomenon known as the negative deviation.

Since consumers can access and process information quickly through various means and consumers’ emotions influence product evaluation and purchasing intention, this research set out to investigate to what extent and how the emotional valence of online product review would influence their purchase intention. Therefore, the following hypothesis was proposed:

H1 : For hedonic products, consumer purchase intention after viewing positive emotion reviews is higher than that of negative emotion ones; On the other hand, for utilitarian products, it is believed that negative comments are more useful than positive ones and have a greater impact on consumers purchase intention by and large.

It is important to investigate Hypothesis one (H1) although it seems obvious. Many online merchants pay more attention to products with negative comments and make relevant improvements to them rather than those with positive comments. Goods with positive comments can promote online consumers’ purchase intention more than those with negative comments and will bring more profits to businesses.

Sen and Lerman (2007) found that compared with the utilitarian case, readers of negative hedonic product reviews are more likely to attribute the negative opinions expressed, to the reviewer’s internal (or non-product-related) reasons, and therefore, are less likely to find the negative reviews useful. However, in the utilitarian case, readers are more likely to attribute the reviewer’s negative opinions to external (or product-related) motivations, and therefore, find negative reviews more useful than positive reviews on average. Product type moderates the effect of review valence, Therefore, Hypothesis one is based on hedonic product types, such as fiction books.

Guo et al. (2020) found pleasant online customer reviews to lead to a higher purchase likelihood than unpleasant ones. This confirms hypothesis one from another side. The product selected in our experiment is a mobile phone, which is not only a utilitarian product but also a hedonic one. It can be used to make a phone call or watch videos, depending on the user’s demands.

Eye-Tracking, Online Product Review, and Purchase Intention

The eye-tracking method is commonly used in cognitive psychology research. Many researchers are calling for the use of neurobiological, neurocognitive, and physiological approaches to advance information system research ( Pavlou and Dimoka, 2010 ; Liu et al., 2011 ; Song et al., 2017 ). Several studies have been conducted to explore consumers’ online behavior by using eye-tracking. For example, using the eye-tracking method, Luan et al. (2016) found that when searching for products, customers’ attention to attribute-based evaluation is significantly longer than that of experience-based evaluation, while there is no significant difference for the experiential products. Moreover, their results indicated eye-tracking indexes, for example, fixation dwell time, could intuitively reflect consumers’ search behavior when they attend to the reviews. Also, Hong et al. (2017) confirmed that female consumers pay more attention to picture comments when they buy experience goods; when they buy searched products, they are more focused on the pure text comments. When the price and comment clues are consistent, consumers’ purchase rates significantly improve.

Eye-tracking method to explore and interpret consumers’ decision-making behavior and cognitive processing is primarily based on the eye-mind hypothesis proposed by Just and Carpenter (1992) . Just and Carpenter (1992) stated that when an individual is looking, he or she is currently perceiving, thinking about, or attending to something, and his or her cognitive processing can be identified by tracking eye movement. Several studies on consumers’ decision-making behavior have adopted the eye-tracking approach to quantify consumers’ visual attention, from various perspectives including determining how specific visual features of the shopping website influenced their attitudes and reflected their cognitive processes ( Renshaw et al., 2004 ), exploring gender differences in visual attention and shopping attitudes ( Hwang and Lee, 2018 ), investigating how employing human brands affects consumers decision quality ( Chae and Lee, 2013 ), consumer attention and different behavior depending on website content, functions and consumers goals ( Boardman and McCormick, 2019 ). Measuring the attention to the website and time spent on each purchasing task in different product categories shows that shoppers attend to more areas of the website for purposes of website exploration than for performing purchase tasks. The most complex and time-consuming task for shoppers is the assessment of purchase options ( Cortinas et al., 2019 ). Several studies have investigated fashion retail websites using the eye-tracking method and addressed various research questions, including how consumers interact with product presentation features and how consumers use smartphones for fashion shopping ( Tupikovskaja-Omovie and Tyler, 2021 ). Yet, these studies considered users without consideration of user categories, particularly gender. Since this research is to explore consumers’ decision-making behavior and the effects of gender on visual attention, the eye-tracking approach was employed as part of the overall approach of this research project. Based on existing studies, it could be that consumers may pay more attention to negative evaluations, will experience cognitive conflict when there are contradictory false comments presented, and will be unable to judge good or bad ( Cui et al., 2012 ). Therefore, the following hypothesis was proposed:

H2 : Consumers’ purchasing intention associated with online reviews is moderated/influenced by the level of visual attention.

To test the above hypothesis, the following two hypotheses were derived, taking into consideration positive and negative review comments from H1, and visual attention associated with fixation dwell time and fixation count.

H2a : When consumers intend to purchase a product, fixation dwell time and fixation count for negative comment areas are greater than those for positive comment areas.

Furthermore, when consumers browse fake comments, they are suspicious and actively seek out relevant information to identify the authenticity of the comments, which will result in more visual attention. Therefore, H2b was proposed:

H2b : Fixation dwell time and fixation count for fake comments are greater than those for authentic comments.

When considering the effect of gender on individual information processing, some differences were noted. For example, Meyers-Levy and Sternthal (1993) put forward the selectivity hypothesis, a theory of choice hypothesis, which implies that women gather all information possible, process it in an integrative manner, and make a comprehensive comparison before making a decision, while men tend to select only partial information to process and compare according to their existing knowledge—a heuristic and selective strategy. Furthermore, for an online product review, it was also reported that gender can easily lead consumers to different perceptions of the usefulness of online word-of-mouth. For example, Zhang et al. (2014) confirmed that a mixed comment has a mediating effect on the relationship between effective trust and purchasing decisions, which is stronger in women. This means that men and women may have different ways of processing information in the context of making purchasing decisions using online reviews. To test the above proposition, the following hypothesis was proposed:

H3 : Gender factors have a significant impact on the indicators of fixation dwell time and fixation count on the area of interest (AOI). Male purchasing practices differ from those of female consumers. Male consumers’ attention to positive comments is greater than that of female ones, they are more likely than female consumers to make purchase decisions easily.

Furthermore, according to the eye-mind hypothesis, eye movements can reflect people’s cognitive processes during their decision process ( Just and Carpenter, 1980 ). Moreover, neurocognitive studies have indicated that consumers’ cognitive processing can reflect the strategy of their purchase decision-making ( Rosa, 2015 ; Yang, 2015 ). Hence, the focus on the degree of attention to different polarities and the specific content of comments can lead consumers to make different purchasing decisions. Based on the key aspects outlined and discussed above, the following hypothesis was proposed:

H4 : Attention to consumers’ comments is positively correlated with consumers’ purchasing intentions: Consumers differ in the content of comments to which they gaze according to gender factors.

Thus, the framework of the current study is shown in Figure 1 .

Figure 1 . Conceptual framework of the study.

Materials and Methods

The research adopted an experimental approach using simulated lab environmental settings for collecting experimental data from a selected set of participants who have experience with online shopping. The setting of the task was based on guidelines for shopping provided on , which is the most famous and frequently used C2C platform in China. Each experiment was set with the guidelines provided and carried out for a set time. Both behavioral and eye movement data were collected during the experiment.


A total of 40 healthy participants (20 males and 20 females) with online shopping experiences were selected to participate in the experiment. The participants were screened to ensure normal or correct-to-normal vision, no color blindness or poor color perception, or other eye diseases. All participants provided their written consent before the experiment started. The study was approved by the Internal Review Board of the Academy of Neuroeconomics and Neuromanagement at Ningbo University and by the Declaration of Helsinki ( World Medical Association, 2014 ).

With standardization and small selection differences among individuals, search products can be objectively evaluated and easily compared, to effectively control the influence of individual preferences on the experimental results ( Huang et al., 2009 ). Therefore, this research focused on consumer electronics products, essential products in our life, as the experiment stimulus material. To be specific, as shown in Figure 2 , a simulated shopping scenario was presented to participants, with a product presentation designed in a way that products are shown on . Figure 2 includes two segments: One shows mobile phone information ( Figure 2A ) and the other shows comments ( Figure 2B ). Commodity description information in Figure 2A was collected from product introductions on , mainly presenting some parameter information about the product, such as memory size, pixels, and screen size. There was little difference in these parameters, so quality was basically at the same level across smartphones. Prices and brand information were hidden to ensure that reviews were the sole factor influencing consumer decision-making. Product review areas in Figure 2B are the AOI, presented as a double-column layout. Each panel included 10 (positive or negative) reviews taken from real online shopping evaluations, amounting to a total of 20 reviews for each product. To eliminate the impact of different locations of comments on experimental results, the positions of the positive and negative comment areas were exchanged, namely, 50% of the subjects had positive comments presented on the left and negative comments on the right, with the remaining 50% of the participants receiving the opposite set up.

Figure 2 . Commodity information and reviews. (A) Commodity information, (B) Commodity reviews. Screenshots of Alibaba shopfront reproduced with permission of Alibaba and Shenzhen Genuine Mobile Phone Store.

A total of 12,403 product reviews were crawled through and extracted from the two most popular online shopping platforms in China (e.g., and ) by using GooSeeker (2015) , a web crawler tool. The retrieved reviews were then further processed. At first, brand-related, price-related, transaction-related, and prestige-related contents were removed from comments. Then, the reviews were classified in terms of appearance, memory, running speed, logistics, and so on into two categories: positive reviews and negative reviews. Furthermore, the content of the reviews was refined to retain the original intention but to meet the requirements of the experiment. In short, reviews were modified to ensure brevity, comprehensibility, and equal length, so as to avoid causing cognitive difficulties or ambiguities in semantic understanding. In the end, 80 comments were selected for the experiment: 40 positive and 40 negative reviews (one of the negative comments was a fictitious comment, formulated for the needs of the experiment). To increase the number of experiments and the accuracy of the statistical results, four sets of mobile phone products were set up. There were eight pairs of pictures in total.

Before the experiment started, subjects were asked to read the experimental guide including an overview of the experiment, an introduction of the basic requirements and precautions in the test, and details of two practice trials that were conducted. When participants were cognizant of the experimental scenario, the formal experiment was ready to begin. Participants were required to adjust their bodies to a comfortable sitting position. The 9 points correction program was used for calibration before the experiment. Only those with a deviation angle of less than 1-degree angle could enter the formal eye movement experiment. In our eye-tracking experiment, whether the participant wears glasses or not was identified as a key issue. If the optical power of the participant’s glasses exceeds 200 degrees, due to the reflective effect of the lens, the eye movement instrument will cause great errors in the recording of eye movements. In order to ensure the accuracy of the data recorded by the eye tracker, the experimenter needs to test the power of each participant’s glasses and ensure that the degree of the participant’s glasses does not exceed 200 degrees before the experiment. After drift correction of eye movements, the formal experiment began. The following prompt was presented on the screen: “you will browse four similar mobile phone products; please make your purchase decision for each mobile phone.” Participants then had 8,000 ms to browse the product information. Next, they were allowed to look at the comments image as long as required, after which they were asked to press any key on the keyboard and answer the question “are you willing to buy this cell phone?.”

In this experiment, experimental materials were displayed on a 17-inch monitor with a resolution of 1,024 × 768 pixels. Participants’ eye movements were tracked and recorded by the Eyelink 1,000 desktop eye tracker which is a precise and accurate video-based eye tracker instrument, integrating with SR Research Experiment Builder, Data Viewer, and third-party software tools, with a sampling rate of 1,000 Hz. ( Hwang and Lee, 2018 ). Data processing was conducted by the matching Data Viewer analysis tool.

The experiment flow of each trial is shown in Figure 3 . Every subject was required to complete four trials, with mobile phone style information and comment content different and randomly presented in each trial. After the experiment, a brief interview was conducted to learn about participants’ browsing behavior when they purchased the phone and collected basic information via a matching questionnaire. The whole experiment took about 15 min.

Figure 3 . Experimental flow diagram. Screenshots of Alibaba shopfront reproduced with permission of Alibaba and Shenzhen Genuine Mobile Phone Store.

Data Analysis

Key measures of data collected from the eye-tracking experiment included fixation dwell time and fixation count. AOI is a focus area constructed according to experimental purposes and needs, where pertinent eye movement indicators are extracted. It can guarantee the precision of eye movement data, and successfully eliminate interference from other visual factors in the image. Product review areas are our AOIs, with positive comments (IA1) and negative comments (IA2) divided into two equal-sized rectangular areas.

Fixation can indicate the information acquisition process. Tracking eye fixation is the most efficient way to capture individual information from the external environment ( Hwang and Lee, 2018 ). In this study, fixation dwell time and fixation count were used to indicate users’ cognitive activity and visual attention ( Jacob and Karn, 2003 ). It can reflect the degree of digging into information and engaging in a specific situation. Generally, a more frequent fixation frequency indicates that the individual is more interested in the target resulting in the distribution of fixation points. Valuable and interesting comments attract users to pay more attention throughout the browsing process and focus on the AOIs for much longer. Since these two dependent variables (fixation dwell time and fixation count) comprised our measurement of the browsing process, comprehensive analysis can effectively measure consumers’ reactions to different review contents.

The findings are presented in each section including descriptive statistical analysis, analysis from the perspective of gender and review type using ANOVA, correlation analysis of purchasing decisions, and qualitative analysis of observations.

Descriptive Statistical Analysis

Fixation dwell time and fixation count were extracted in this study for each record. In this case, 160 valid data records were recorded from 40 participants. Each participant generated four records which corresponded to four combinations of two conditions (positive and negative) and two eye-tracking indices (fixation dwell time and fixation count). Each record represented a review comment. Table 1 shows pertinent means and standard deviations.

Table 1 . Results of mean and standard deviations.

It can be noted from the descriptive statistics for both fixation dwell time and fixation count that the mean of positive reviews was less than that of negative ones, suggesting that subjects spent more time on and had more interest in negative reviews. This tendency was more obvious in female subjects, indicating a role of gender.

Fixation results can be reported using a heat mapping plot to provide a more intuitive understanding. In a heat mapping plot, fixation data are displayed as different colors, which can manifest the degree of user fixation ( Wang et al., 2014 ). Red represents the highest level of fixation, followed by yellow and then green, and areas without color represent no fixation count. Figure 4 implies that participants spent more time and cognitive effort on negative reviews than positive ones, as evidenced by the wider red areas in the negative reviews. However, in order to determine whether this difference is statistically significant or not, further inferential statistical analyses were required.

Figure 4 . Heat map of review picture.

Repeated Measures From Gender and Review Type Perspectives—Analysis of Variance

The two independent variables for this experiment were the emotional tendency of the review and gender. A preliminary ANOVA analysis was performed, respectively, on fixation dwell time and fixation count values, with gender (man vs. woman) and review type (positive vs. negative) being the between-subjects independent variables in both cases.

A significant dominant effect of review type was found for both fixation dwell time ( p 1  < 0.001) and fixation count ( p 2  < 0.001; see Table 2 ). However, no significant dominant effect of gender was identified for either fixation dwell time ( p 1  = 0.234) or fixation count ( p 2  = 0.805). These results indicated that there were significant differences in eye movement indicators between positive and negative commentary areas, which confirms Hypothesis 2a. The interaction effect between gender and comment type was significant for both fixation dwell time ( p 1  = 0.002) and fixation count ( p 2  = 0.001). Therefore, a simple-effect analysis was carried out. The effects of different comment types with fixed gender factors and different gender with fixed comment type factors on those two dependent variables (fixation dwell time and fixation count) were investigated and the results are shown in Table 3 .

Table 2 . Results of ANOVA analysis.

Table 3 . Results of simple-effect analysis.

When the subject was female, comment type had a significant dominant effect for both fixation dwell time ( p 1  < 0.001) and fixation count ( p 2  < 0.001). This indicates that female users’ attention time and cognitive level on negative comments were greater than those on positive comments. However, the dominant effect of comment type was not significant ( p 1  = 0.336 > 0.05, p 2  = 0.43 > 0.05) for men, suggesting no difference in concern about the two types of comments for men.

Similarly, when scanning positive reviews, gender had a significant dominant effect ( p 1  = 0.003 < 0.05, p 2  = 0.025 < 0.05) on both fixation dwell time and fixation count, indicating that men exerted longer focus and deeper cognitive efforts to dig out positive reviews than women. In addition, the results for fixation count showed that gender had significant dominant effects ( p 1  = 0.18 > 0.05, p 2  = 0.01 < 0.05) when browsing negative reviews, suggesting that to some extent men pay significantly less cognitive attention to negative reviews than women, which is consistent with the conclusion that men’s attention to positive comments is greater than women’s. Although the dominant effect of gender was not significant ( p 1  = 0.234 > 0.05, p 2  = 0.805 > 0.05) in repeated measures ANOVA, there was an interaction effect with review type. For a specific type of comment, gender had significant influences, because the eye movement index between men and women was different. Thus, gender plays a moderating role in the impact of comments on consumers purchasing behavior.

Correlation Analysis of Purchase Decision

Integrating eye movement and behavioral data, whether participants’ focus on positive or negative reviews is linked to their final purchasing decisions were explored. Combined with the participants’ purchase decision results, the areas with large fixation dwell time and concerns of consumers in the picture were screened out. The frequency statistics are shown in Table 4 .

Table 4 . Frequency statistics of purchasing decisions.

The correlation analysis between the type of comment and the decision data shows that users’ attention level on positive and negative comments was significantly correlated with the purchase decision ( p  = 0.006 < 0.05). Thus, Hypothesis H4 is supported. As shown in Table 4 above, 114 records paid more attention to negative reviews, and 70% of the participants chose not to buy mobile phones. Also, in the 101 records of not buying, 80% of the subjects paid more attention to negative comments and chose not to buy mobile phones, while more than 50% of the subjects who were more interested in positive reviews chose to buy mobile phones. These experimental results are consistent with Hypothesis H1. They suggest that consumers purchasing decisions were based on the preliminary information they gathered and were concerned about, from which we can deduce customers’ final decision results from their visual behavior. Thus, the eye movement experiment analysis in this paper has practical significance.

Furthermore, a significant correlation ( p  = 0.007 < 0.05) was found between the comments area attracting more interest and purchase decisions for women, while no significant correlation was found for men ( p  = 0.195 > 0.05). This finding is consistent with the previous conclusion that men’s attention to positive and negative comments is not significantly different. Similarly, this also explains the moderating effect of gender. This result can be explained further by the subsequent interview of each participant after the experiment was completed. It was noted from the interviews that most of the male subjects claimed that they were more concerned about the hardware parameters of the phone provided in the product information picture. Depending on whether it met expectations, their purchasing decisions were formed, and mobile phone reviews were taken as secondary references that could not completely change their minds.

Figure 5 shows an example of the relationship between visual behavior randomly selected from female participants and the correlative decision-making behavior. The English translation of words that appeared in Figure 5 is shown in Figure 4 .

Figure 5 . Fixation count distribution.

The subjects’ fixation dwell time and fixation count for negative reviews were significantly greater than those for positive ones. Focusing on the screen and running smoothly, the female participant decided not to purchase this product. This leads to the conclusion that this subject thought a lot about the phone screen quality and running speed while selecting a mobile phone. When other consumers expressed negative criticism about these features, the female participant tended to give up buying them.

Furthermore, combined with the result of each subject’s gaze distribution map and AOI heat map, it was found that different subjects paid attention to different features of mobile phones. Subjects all had clear concerns about some features of the product. The top five mobile phone features that subjects were concerned about are listed in Table 5 . Contrary to expectations, factors, such as appearance and logistics, were no longer a priority. Consequently, the reasons why participants chose to buy or not to buy mobile phones can be inferred from the gazing distribution map recorded in the product review picture. Therefore we can provide suggestions on how to improve the design of mobile phone products for businesses according to the features that users are more concerned about.

Table 5 . Top 5 features of mobile phones.

Fictitious Comments Recognition Analysis

The authenticity of reviews is an important factor affecting the helpfulness of online reviews. To enhance the reputation and ratings of online stores, in the Chinese e-commerce market, more and more sellers are employing a network “water army”—a group of people who praise the shop and add many fake comments without buying any goods from the store. Combined with online comments, eye movement fixation, and information extraction theory, Song et al. (2017) found that fake praise significantly affects consumers’ judgment of the authenticity of reviews, thereby affecting consumers’ purchase intention. These fictitious comments glutted in the purchasers’ real ones are easy to mislead customers. Hence, this experiment was designed to randomly insert a fictitious comment into the remaining 79 real comments without notifying the participants in advance, to test whether potential buyers could identify the false comments and find out their impact on consumers’ purchase decisions.

The analysis of the eye movement data from 40 product review pictures containing this false commentary found that only several subjects’ visual trajectories were back and forth in this comment, and most participants exhibited no differences relative to other comments, indicating that the vast majority of users did not identify the lack of authenticity of this comment. Moreover, when asked whether they had taken note of this hidden false comment in interviews, almost 96% of the participants answered they had not. Thus, Hypothesis H2b is not supported.

This result explains why network “water armies” are so popular in China, as the consumer cannot distinguish false comments. Thus, it is necessary to standardize the e-commerce market, establish an online comment authenticity automatic identification information system, and crack down on illegal acts of employing network troops to disseminate fraudulent information.

Discussion and Conclusion

In the e-commerce market, online comments facilitate online shopping for consumers; in turn, consumers are increasingly dependent on review information to judge the quality of products and make a buying decision. Consequently, studies on the influence of online reviews on consumers’ behavior have important theoretical significance and practical implications. Using traditional empirical methodologies, such as self-report surveys, it is difficult to elucidate the effects of some variables, such as review choosing preference because they are associated with automatic or subconscious cognitive processing. In this paper, the eye-tracking experiment as a methodology was employed to test congruity hypotheses of product reviews and explore consumers’ online review search behavior by incorporating the moderating effect of gender.

Hypotheses testing results indicate that the emotional valence of online reviews has a significant influence on fixation dwell time and fixation count of AOI, suggesting that consumers exert more cognitive attention and effort on negative reviews than on positive ones. This finding is consistent with Ahluwalia et al.’s (2000) observation that negative information is more valuable than positive information when making a judgment. Specifically, consumers use comments from other users to avoid possible risks from information asymmetry ( Hong et al., 2017 ) due to the untouchability of online shopping. These findings provide the information processing evidence that customers are inclined to acquire more information for deeper thinking and to make a comparison when negative comments appear which could more likely result in choosing not to buy the product to reduce their risk. In addition, in real online shopping, consumers are accustomed to giving positive reviews as long as any dissatisfaction in the shopping process is within their tolerance limits. Furthermore, some e-sellers may be forging fake praise ( Wu et al., 2020 ). The above two phenomena exaggerate the word-of-mouth effect of negative comments, resulting in their greater effect in contrast to positive reviews; hence, consumers pay more attention to negative reviews. Thus, Hypothesis H2a is supported. However, when limited fake criticism was mixed in with a large amount of normal commentary, the subject’s eye movements did not change significantly, indicating that little cognitive conflict was produced. Consumers could not identify fake comments. Therefore, H2b is not supported.

Although the dominant effect of gender was not significant on the indicators of the fixation dwell time and fixation count, a significant interaction effect between user gender and review polarity was observed, suggesting that consumers’ gender can regulate their comment-browsing behavior. Therefore, H3 is partly supported. For female consumers, attention to negative comments was significantly greater than positive ones. Men’s attention was more homogeneous, and men paid more attention to positive comments than women. This is attributed to the fact that men and women have different risk perceptions of online shopping ( Garbarino and Strahilevitz, 2004 ). As reported in previous studies, men tend to focus more on specific, concrete information, such as the technical features of mobile phones, as the basis for their purchase decision. They have a weaker perception of the risks of online shopping than women. Women would be worried more about the various shopping risks and be more easily affected by others’ evaluations. Specifically, women considered all aspects of the available information, including the attributes of the product itself and other post-use evaluations. They tended to believe that the more comprehensive the information they considered, the lower the risk they faced of a failed purchase ( Garbarino and Strahilevitz, 2004 ; Kanungo and Jain, 2012 ). Therefore, women hope to reduce the risk of loss by drawing on as much overall information as possible because they are more likely to focus on negative reviews.

The main finding from the fixation count distribution is that consumers’ visual attention is mainly focused on reviews containing the following five mobile phone characteristics: running smoothly, battery life, fever condition of phones, pixels, and after-sales service. Considering the behavior results, when they pay more attention to negative comments, consumers tend to give up buying mobile phones. When they pay more attention to positive comments, consumers often choose to buy. Consequently, there is a significant correlation between visual attention and behavioral decision results. Thus, H4 is supported. Consumers’ decision-making intention can be reflected in the visual browsing process. In brief, the results of the eye movement experiment can be used as a basis for sellers not only to formulate marketing strategies but also to prove the feasibility and strictness of applying the eye movement tracking method to the study of consumer decision-making behavior.

Theoretical Implications

This study has focused on how online reviews affect consumer purchasing decisions by employing eye-tracking. The results contribute to the literature on consumer behavior and provide practical implications for the development of e-business markets. This study has several theoretical contributions. Firstly, it contributes to the literature related to online review valence in online shopping by tracking the visual information acquisition process underlying consumers’ purchase decisions. Although several studies have been conducted to examine the effect of online review valence, very limited research has been conducted to investigate the underlying mechanisms. Our study advances this research area by proposing visual processing models of reviews information. The findings provide useful information and guidelines on the underlying mechanism of how online reviews influence consumers’ online shopping behavior, which is essential for the theory of online consumer behavior.

Secondly, the current study offers a deeper understanding of the relationships between online review valence and gender difference by uncovering the moderating role of gender. Although previous studies have found the effect of review valence on online consumer behavior, the current study first reveals the effect of gender on this effect and explains it from the perspective of attention bias.

Finally, the current study investigated the effect of online reviews on consumer behavior from both eye-tracking and behavioral self-reports, the results are consistent with each other, which increased the credibility of the current results and also provides strong evidence of whether and how online reviews influence consumer behavior.

Implications for Practice

This study also has implications for practice. According to the analysis of experimental results and findings presented above, it is recommended that online merchants should pay particular attention to negative comments and resolve them promptly through careful analysis of negative comments and customization of product information according to consumer characteristics including gender factors. Based on the findings that consumers cannot identify false comments, it is very important to establish an online review screening system that could automatically screen untrue content in product reviews, and create a safer, reliable, and better online shopping environment for consumers.

Limitations and Future Research

Although the research makes some contributions to both theoretical and empirical literature, it still has some limitations. In the case of experiments, the number of positive and negative reviews of each mobile phone was limited to 10 positive and 10 negative reviews (20 in total) due to the size restrictions on the product review picture. The number of comments could be considered relatively small. Efforts should be made in the future to develop a dynamic experimental design where participants can flip the page automatically to increase the number of comments. Also, the research was conducted to study the impact of reviews on consumers’ purchase decisions by hiding the brand of the products. The results would be different if the brand of the products is exposed since consumers might be moderated through brand preferences and brand loyalty, which could be taken into account in future research projects.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author Contributions

TC conceived and designed this study. TC, PS, and MQ wrote the first draft of the manuscript. TC, XC, and MQ designed and performed related experiments, material preparation, data collection, and analysis. TC, PS, XC, and Y-CL revised the manuscript. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The authors wish to thank the Editor-in-Chief, Associate Editor, reviewers and typesetters for their highly constructive comments. The authors would like to thank Jia Jin and Hao Ding for assistance in experimental data collection and Jun Lei for the text-polishing of this paper. The authors thank all the researchers who graciously shared their findings with us which allowed this eye-tracking study to be more comprehensive than it would have been without their help.

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Keywords: online reviews, eye-tracking, consumers purchasing decisions, emotion valence, gender

Citation: Chen T, Samaranayake P, Cen X, Qi M and Lan Y-C (2022) The Impact of Online Reviews on Consumers’ Purchasing Decisions: Evidence From an Eye-Tracking Study. Front. Psychol . 13:865702. doi: 10.3389/fpsyg.2022.865702

Received: 30 January 2022; Accepted: 02 May 2022; Published: 08 June 2022.

Reviewed by:

Copyright © 2022 Chen, Samaranayake, Cen, Qi and Lan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: XiongYing Cen, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.


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Research: How Price Changes Influence Consumers’ Buying Decisions

  • Ioannis Evangelidis
  • Manissa Gunadi

purchase research article

Online shopping platforms allow people to see changes in a product’s price over time — offering opportunities for buyers and sellers alike.

Whether on retailers’ own platforms or through third-party price tracking services, today’s consumers often have access to detailed information regarding changes in a product’s price over time. But how does this visibility influence their purchasing decisions? Through a series of studies, the authors found that buyers are more likely to buy now if they see a single large price decrease or a series of smaller price increases, because they’ll assume that the price will go up if they wait. Conversely, they’re more likely to hold off on buying if they see a single large price increase or a series of smaller decreases, because they’ll assume the price will fall. As such, they argue that sellers should consider this effect when pricing their products, while buyers should recognize and question this natural tendency — to expect price streaks to continue and single large changes to reverse — before acting on it.

Whether you’re looking to buy a plane ticket or a pair of socks, more and more online shopping platforms now offer consumers a detailed look into products’ historical prices. But how does this information influence buying decisions?

  • IE Ioannis Evangelidis is an associate professor of marketing at ESADE Business School, Ramon Llull University, in Barcelona, Spain. His research focuses on how consumers make decisions, particularly how their purchase behavior can be influenced by changes in the decision environment.
  • MG Manissa Gunadi is an assistant professor of marketing at EADA Business School in Barcelona, Spain. In her research, Manissa primarily investigates how different forms of numerical information influence consumers’ judgments, decision-making, and behavior.

Partner Center

An examination of the factors affecting consumer’s purchase decision in the Malaysian retail market

PSU Research Review

ISSN : 2399-1747

Article publication date: 13 February 2018

Issue publication date: 12 April 2018

The purpose of this paper is to examine the effects of corporate social responsibility, social media marketing, sales promotion, store environment and perceived value on a purchase decision in the retail sector.


A quantitative research methodology was used and the data were collected from 278 customers of retail stores in Malaysia. The collected data were analysed using SPSS 19 and structural equation modelling on AMOS.

The findings showed that corporate social responsibility has significant positive effects on a purchase decision, whereas sales promotion has a negative effect on purchase decision. The outcomes of this study also indicated that store environment has a significant positive effect on consumers’ purchase decisions. Contrary to expectations, the findings revealed that the effect of social media marketing on purchase decision is insignificant. Finally, the results showed that perceived value has a significant positive effect on a purchase decision.


The findings of this study contribute to an understanding of the importance of the selected factors in affecting a consumer’s purchase decision in the retail industry.

Purchase decision

Sales promotion, perceived value, social media marketing, store environment.

Hanaysha, J.R. (2018), "An examination of the factors affecting consumer’s purchase decision in the Malaysian retail market", PSU Research Review , Vol. 2 No. 1, pp. 7-23.

Emerald Publishing Limited

Copyright © 2018, Jalal Rajeh Hanaysha.

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In today’s continuously changing and dynamic business environment, it has become necessary for retail managers to clearly understand and foresee how different types of consumers behave when buying different products and services to fulfil their needs. Thus, to establish a competitive advantage in the marketplace, several retailers have focused on creating favourable images about their brands in the minds of consumers to influence their purchase behaviour ( Shamsher, 2015 ). Consumer behaviour emphasizes on understanding the purchase decision process of individual consumers and how they utilize their existing resources such as time, money and effort to get a product or service ( Schiffman and Kanuk, 2007 ). Hence, retail managers should have knowledge about consumers’ characteristics and preferences as they play an important role in forming purchase decisions. This information could enable them to foster their competitiveness and ensure their long-term survival.

The consumer decision-making process can be described as the phases that consumers go through in making a final purchase decision. The task of a marketer is to focus on the whole purchasing process instead of emphasizing solely on a purchase decision, because consumers experience different phases before reaching a conclusion ( Basil et al. , 2013 ). Understanding buyer behaviour is not easy because several factors can influence consumer behaviour before making a purchase decision. In some cases, consumers tend to spend less time in thinking about purchasing either low- or high-value products, because they consider that fulfilling their needs is more important. This has urged marketing managers to adopt strategies that motivate consumers to purchase their offerings by creating an effective marketing plan. Previous studies reported that corporate social responsibility ( Elg and Hultman, 2016 ) and social media marketing ( Duffett, 2015 ) play significant roles in influencing a consumer’s purchase behaviour and attitude towards a brand. Other scholars also considered store atmosphere ( Hosseini et al. , 2014 ), perceived value and sales promotion ( Andreti et al. , 2013 ) as important predictors of consumer behaviour and brand choice.

However, although previous studies emphasized on both corporate social responsibility and social media marketing in influencing consumer behaviour, only few scholars examined their effects on purchase decision in retail industry settings, particularly in Malaysia. Furthermore, limited studies have explored the role of store environment and sales promotion in forming purchase decision. In other words, considerable research has already been done on examining consumers’ purchase decisions in various business sectors, but there is no mutual agreement towards the factors conditioning consumers’ purchase decision. Thus, this paper is designed to examine the effects of corporate social responsibility, social media marketing, store environment, perceived value and sales promotion on consumers’ purchase decision with empirical data from department stores in Malaysia. The next sections present a brief review on past literature and methodological approach used in data collection; finally, the conclusion and recommendations for this study are established based on the findings.

Literature review

Purchase decision involves a sequence of choices formed by a consumer before making a purchase which starts once he/she has a willingness to fulfil a need. The consumer should reach a decision with regard to the place of purchasing, the desired brand, model, purchase quantity, time to buy, amount of money to be spent and the method of payment. These decisions can be influenced by marketers by providing information about their products or services that may inform consumer’s assessment process. Schiffman and Kanuk (2007) stated that consumers normally search for information relevant about a specified consumption-related need from their past experiences before looking for external sources of information. In other words, past purchase experience is regarded as an internal source of information that a consumer relies on before making a decision. In addition, several consumers’ decisions are most likely to be formed by integrating past purchase experience as well as marketing programs and non-commercial information sources ( Schiffman and Kanuk, 2007 ). Past literature also stressed that consumers usually attempt to minimize the risk in their purchase decisions ( Chaipradermsak, 2007 ).

Blackwell et al. (2001) reported that to comprehend consumers’ purchasing decisions, marketing managers should understand their consumption process and the benefits of organizational products and services in their perceptions. The authors also added that when consumers intend to buy certain products, they pass through numerous phases which would influence their purchase decision process and post-purchase behaviour. The first phase represents the problem recognition wherein consumers intend to satisfy their needs and wants. The role of marketers in this phase emerges while using advertisements, personal selling and packaging to arouse the recognition of desired needs or wants. In the second phase, consumers begin to seek information from either internal sources (usually from their past experiences) about the products or outside sources, for example, friends, family, relatives, neighbours, annual reports, publications, sales persons, social media or packaging label. Finally, consumers evaluate the alternatives and select from brands that best suit them and satisfy their needs.

Corporate social responsibility

Corporate social responsibility has been conceptualized in the literature by a number of scholars. However, there is no consensus on its definition and measurement despite the significant amount of research on this topic. According to Kilcullen and Kooistra (1999 , p. 158), corporate social responsibility can be conceptualized as “the degree of moral obligation that may be ascribed to corporations beyond their simple obedience to the laws of the state.” Similarly, Kotler and Lee (2005) expressed the concept of corporate social responsibility as an organization’s commitment to enhance the welfare of a society through voluntarily business activities and support from its resources. Erkollar and Oberer (2012) also illustrated that the majority of scholars view corporate social responsibility as a term through which organizations incorporate social and environmental aspects or considerations into their business processes and in their dealings with various stakeholders. Corporate social responsibility is usually used as a tactical tool for creating a positive brand image and attracting a larger number of customers ( Reich et al. , 2010 ).

In today’s business environment that is characterized by strong rivalry, corporate social responsibility is regarded as an important strategy for assisting businesses to maintain their competitive strengths ( Luo and Bhattacharya, 2006 ). Albus (2012) reported that corporate social responsibility represents an important strategic marketing tool than can be employed to develop a positive brand image. Corporate social responsibility is a key strategy that organizations should exploit, not only for the purposes of uplifting profit margins, but also due to the necessity to protect the environment. For example, organizations can be involved in social responsibility activities, such as treating business stakeholders (customers, vendors and staff) well. Pakseresht (2010) reported that several brands can be distinguished based on how they behave under the observation of business stakeholders. Consequently, the investment in corporate social responsibility programs enables a brand to foster its competitive advantage and improve its performance in the long term ( El-Garaihy et al. , 2014 ; Ghosh and Gurunathan, 2014 ).

Corporate social responsibility has a positive effect on purchase decision.

Social media is an important marketing communication tool to reach and interact with customers at minimal cost and at different times of the day. Effective management and implementation of social media marketing is one of the key objectives and interests of several brands ( Hanaysha, 2016 ). Successful brands have become aware of the power of social media marketing in today’s interactive marketplace for building and maintaining customer relationships, as well as communicating and interacting with larger numbers of customers ( Bulearca and Bulearca, 2010 ). Kaplan and Haenlein (2010) conceptualized social media as an internet-based program that provides a platform for consumers to express their own opinions, share information and past experiences using different social networks, blogs and other content areas. The efficiency of social media has empowered the marketers and customers with fast interaction and communication processes to enhance customer service, increase brand awareness and build strong customer–brand relationships. Using social media tools, consumers will have the chance to express their opinions to a larger number of individuals and also find the desired information quickly without incurring much cost ( Severi et al. , 2014 ).

Social media channels have appeared as the foremost convenient digital communication media through which several consumers can learn, share information and directly interact with business stakeholders ( Chappuis et al. , 2011 ; Qualman, 2013 ). With the existence of social media, business marketers will have the opportunity to interact with their existing and potential customers using two-way communications to obtain rich and valuable insights quickly and at lower costs. Marketers have also realized the additional values of social media channels through easier collaborations with brand referrals and quality of information sharing ( Hudson et al. , 2016 ). In addition, social media has enabled consumers to easily share important information about products or services offered by certain brands with their peers ( Erdoğmuş and Cicek, 2012 ; Mangold and Faulds, 2009 ). Such exchanges have provided companies with several advantages represented by cost-effectiveness, increased brand awareness, improved brand recognition, higher customer loyalty and greater profit margins.

Effective implementation of marketing programs on social media can enable organizations to create beneficial relationships with their customers by increasing customer satisfaction ( Hanaysha, 2016 ) and commitment as well as generating positive word of mouth. Through the continuous development and wide-ranging applications of several social media channels, many businesses considered this way of communication to be a noteworthy prospect. They have also started looking for the best ways of using social media for sustaining their businesses, creating healthier relationships with their consumers, marketing their products and services and developing reputable images for their brands to the public. To stay competitive in today’s challenging business environments, it requires firms to put prime emphasis on social media as a marketing strategy. Global companies employ several experts and consultants in social media to gain better recommendations on the contents and features of their advertisements before sharing them on social media to maximize the efficiency of the marketing program ( Erdoğmuş and Cicek, 2012 ).Moreover, customers regard social media communication as a tool to engage with various brands any time.

Social media marketing has a positive effect on purchase decision.

The importance of constructing an appealing physical environment has attained considerable attention from several scholars and business managers due to its power in attracting and satisfying customers ( Ali et al. , 2013 ; Han and Ryu, 2009 ). In retail stores, the atmospheric environment is considered as a key competitive tactic employed by retailers to stimulate consumer behaviour and increase sales volumes ( Chebat and Michon, 2003 ). The attributes of atmospheric environment focus on several stimuli such as colour, music, scene, layout and space, as they have been considered to be important clues for consumers ( Oh et al. , 2008 ). Lee and Jeong (2012) described physical environment as an environment that is shaped through overall layout, colour, design, decoration, surroundings and aesthetics. Particularly, the atmospheric environment in a store includes various stimuli such as ambience, colour, sound, scent, taste, layout and space, which are important clues for buyers. Prior research also established that physical environment enables a service provider to differentiate itself from rivals and influence customer’s choice ( Mahmood and Khan, 2014 ).

Assessing consumers’ perceptions of the characteristics of a store’s environment may form certain brand associations in their minds, enhance their perception of brand value and elevate buying intentions by minimizing cost and time, as well as the efforts in acquiring potential customers ( Kumar et al. , 2010 ). According to Mahmood and Khan (2014) , the physical environment allows service providers to distinguish their brands from those of competitors and influence consumers’ purchase decisions. Prior literature showed that store environment had a positive impact on consumer purchase behaviour. For instance, Belk (1975) found that the physical environment of a retail store influenced consumer’s buying behaviour. Likewise, creating an attractive store atmosphere was stressed in the past studies as a key strategic factor that many retailers consider to stimulate consumer behaviour and improve their performance ( Chebat and Michon, 2003 ). Further support can be found in the study by Richardson et al. (1996) who revealed that store atmosphere enhances the consumers’ perceptions toward the service and product quality of the department store. Similarly, Newman and Patel (2004) indicated that store environment plays an important role in affecting consumer choice.

Store environment has a positive effect on purchase decision.

In the theoretical literature, promotion is regarded as a key element of marketing mix that aims to inform, encourage and remind the target market about a product of service offer in an attempt to influence the consumers’ feelings, perceptions or purchasing decisions ( Stanton et al. , 2007 ). In other words, promotion programs are used by organizations with the purpose of communicating the benefits of certain products or services to a group of potential and existing customers ( Reibstein, 1985 ). Sales promotion is widely accepted as an important component in marketing campaigns for inspiring and stimulating quicker and effective response (comprising purchase quantity and speed) to the sales of particular products or services. According to Kotler and Keller (2012) , sales promotion represents a strong incentive tool for attracting consumers and increasing sales volumes. Agrawal (1996) conceptualized sales promotions as an aggressive strategy used by many brands to attract profitable customers and avoid issues of switching to other competitors. Thus, sales promotions are adopted by brands to motivate customers’ purchases and reward fast responses ( Kotler et al. , 2004 ). Other benefits of sales promotion can be achieved by attracting the attention of consumers and influencing their purchase decisions.

In the previous studies, it can be observed that price promotion is one of the main strategies frequently used by a number of marketing managers to exploit their sales and performance ( Zoellner and Schaefers, 2015 ). Essentially, promotional sales that can be grasped through several approaches such as customer coupons, displays and price reductions are usually used in diverse retail stores around the world. Price promotions as explained by Mullin and Cummins (2010) can comprise numerous forms such as buy one and then get the other one free, extra packs and money-off coupons. In the early 1990s, several retailers used price promotions to influence consumers who have price sensitivity by presenting to them the discounts on various product items. Generally, retail managers apply promotion strategies as incentives for obtaining a greater number of consumers and uplift their sales revenues ( Cui et al. , 2016 ). Currently, consumers deemed to be price sensitive tend to have high awareness towards the promotional deals and look for them frequently ( Yeshin, 2006 ).

Sales promotion has a positive effect on purchase decision.

Perceived value has a positive effect on purchase decision.

Based on the above literature review and existing research gaps between the selected variables, the framework for this research is presented as follows ( Figure 1 ).


This research aimed to examine the predictors of a purchase decision in the retail industry. Therefore, the data was collected using a survey method from 278 customers of several department stores in East Coast Malaysia. The selection of a quantitative approach to conduct this research was considered appropriate to involve as many participants as possible and obtain larger number of responses. Additionally, a quantitative survey methodology is the researchers’ best choice when the targeted population comprises a larger number of individuals without requiring special skills to fill in the questionnaire. McDaniel and Gates (1998) illustrated that the quantitative survey enables researchers to conduct statistical analysis and generalize the results in a given context. To minimize the response bias and sampling error, the respondents were briefed about the purpose of the study and assured that their answers will be kept confidential.

Before starting the data collection process, the questionnaire was designed based on several measurement items for the constructs. Purchase decision was measured using a five-item scale adapted from the study of Shareef et al. (2008) . Furthermore, the measurement scale of corporate social responsibility was adapted from Tong and Wong (2014) . To measure social media marketing, five items were taken from the study by Schivinski and Dabrowski (2014) . In addition, the items used to measure store environment were taken from the study by Hussain and Ali (2015) . To measure sales promotion, a total of four items were taken from Villarejo-Ramos and Sánchez-Franco (2005) and modified to fit the context of this study. Finally, perceived value was measured using four items taken from Puncheva-Michelotti and Michelotti (2010) . All of the items were measured on a five-point Likert scale which ranges from strongly disagree to strongly agree.

Analysis of results

Out of the 384 sets of questionnaires distributed to visitors of department stores in East Coast Malaysia, only 278 responses were received from the participants. While analysing the demographic characteristics, it was found that 54.7 per cent of the respondents were women and men represented 45.3 per cent. The respondents’ profile also showed that most of the participants held a bachelor degree certificate. Additionally, the respondents were classified based on monthly income and it was found that 48 participants (17.2 per cent) received an average income of less than RM 500 per month, while 15 participants (5.4 per cent) obtained a monthly income between RM 501 and RM 1000. A total of 44 responses (52 per cent) were represented by the participants with an average income of RM 1,001 to RM 4,000. Those whose monthly income ranged from RM 4001 and above accounted for 71 (25.4 per cent) responses. Furthermore, the reliability assumptions were established on all constructs and the results revealed that the value of Cronbach’s alpha for the measurement scales of constructs exceeded the cut-off point of 0.70. Therefore, the reliability assumptions are fulfilled ( Appendix ).

For testing the hypotheses of this study, structural equation modelling method was used and the procedure was carried out using AMOS 18. At first, the measurement model comprising all measurement items of the constructs was drawn to calculate confirmatory factor analysis. The results indicated that the factor loadings for remaining items of each construct exceeded 0.50; therefore, convergent validity was achieved. Then, the structural model with the residual items was estimated. According to Hair et al. (2010) , the hypotheses can be tested when the fit indices in the structural model fall in the accepted range. Overall, the findings as shown in Figure 2 indicate that the structural model for this study maintained a reasonable fit with the data with the chi-square value being 376.333 1( p = 0.000); values of other criteria (GFI = 0.841, AGFI = 0.792, df = 230, TLI = 0.909, CFI = 0.924 and RMSEA = 0.063) attained the acceptable cut-off point based on the suggestions of Hair et al. (2010) .

To check the normal distribution of the data set, multicollinearity was calculated using AMOS 18 for all variables. According to Tabachnick and Fidell (2001) , multicollinearity issues exist when the relationship between any two distinct variables is 0.90 or more. As shown in Table I , the relationship between any two different variables is less than 0.90; thus, there is no sign of multicollinearity issues in the current data set. Furthermore, the discriminant validity among the constructs was verified by computing the average variance extracted (AVE) and correlation values between each pair of constructs. As cited by de Pablos (2016) , Bagozzi et al. (1991) reported that discriminant validity is achieved when the correlation values between pairs of constructs are less than 1.00. This was further advocated by Mohammad and Yusoff (2017) who stated that discriminant validity exists when the correlation values between pairs constructs are below 0.95. Overall, the output confirmed the existence of discriminant validity among the constructs.

After achieving an acceptable fit for the structural model and fulfilling the reliability and validity assumptions, the hypotheses in this study were verified. The results presented in Table II show that corporate social responsibility has a significant positive effect on purchase decision ( β = 0.188, C.R. = 1.803, p < 0.10); hence, H1 is accepted. Contrary to expectations, the results showed that social media marketing has an insignificant effect on purchase decision ( β = −0.165, C.R. = –1.536, p > 0.05); therefore, H2 is rejected. Moreover, the analysis confirmed that store environment has a significant positive effect on purchase decision ( β = 0.351, C.R. = 2.637, p < 0.05); consequently, H3 is accepted. The results also indicated that sales promotion ( β = −0.158, C.R. = −2.035, p < 0.05) has a significant positive effect on purchase decision; thus, H4 is rejected. Finally, the findings of this paper showed that perceived value has a significant positive effect on purchase decision ( β = 0.593, C.R. = 4.142, p < 0.05), which implied that that H5 is validated. Overall, these factors explain 72 per cent of the total variance in purchase decision.

Discussion and conclusion

This study aimed to examine the effects of corporate social responsibility, social media marketing, sales promotion, store environment and perceived value on purchase decision in the retail industry. The findings revealed that corporate social responsibility has a significant positive effect on purchase decision and this is in line with previous researches ( Elg and Hultman, 2016 ; Green and Peloza, 2011 ). Hassan et al. (2013) stated that if individuals feel that a brand has social responsibility towards them and the society, they will prefer to select its products/services. Similarly, Handelman and Arnold (1999) found that marketing activities which are socially responsible influence consumers’ evaluation of a brand and enhance their willingness to purchase its offerings. The second purpose of this paper was to test the link between social media marketing and purchase decision. Contrary to expectations, the results showed that the effect of social media marketing on a consumer’s purchase decision is insignificant. The insignificant result could be attributed to the lack of or inefficient marketing activities among the selected retail stores through social media. Additionally, negative word of mouth through social media sites could lead to negative perceptions among consumers, which may hinder their purchase intentions. Overall, social media sites can be a strong platform for building brand awareness, but its effect on purchase decision may not be strong enough in the retail context.

The findings of this study also showed that the store environment has a significant positive impact on purchase decision. The result was supported by many scholars ( Amofah et al. , 2016 ; Hasan et al. , 2016 ) who confirmed that the store environment plays an important role in affecting consumer purchase behaviour. Mahmood and Khan (2014) indicated that the store environment enables a brand to distinguish itself from competitors, thus leading to favourable customer’s choice. Therefore, store environment is an important means through which retailers can influence consumers’ behaviour and their purchase decisions. Furthermore, the results revealed that sales promotion has a negative effect on purchase decision. Eleboda (2017) also confirmed that sales promotion had a negative impact on consumer purchase decision. The result was supported by Santini et al. (2015) who stated that much discount leads to a state of discomfort among consumer, which will ultimately causes a sense of caution highlighted earlier, associating negatively with the hedonic features. Furthermore, Simonson et al. (1994) confirmed that sales promotion had a negative impact on brands. Similar views were shared by Shrestha (2015) who revealed that sales promotion does not have any effect on brand building and may lead to declining impacts for the brand, especially those which are well established. Thus, this study concludes that sales promotions could have a negative effect on consumers’ perceptions towards brand quality as lower priced items tend to have low quality.

Finally, the outcomes of this research confirmed that perceived value has a significant positive effect on purchase decision. The results were supported by a number of researchers ( Astuti, Silalahi, and Wijaya, 2015 ; Bakırtaş, 2013 ; Cheng et al. , 2006 ; Nochai and Nochai, 2011 ) who reported that perceived value plays a significant role in affecting purchase decision. Demirgünescedil (2015) also reported that perceived value plays an important role in affecting consumers’ purchase decisions. This means that marketing programs associated with added values reinforce consumers’ purchases and improve organizational profitability. Consequently, retailers are recommended to cultivate their customer value to attain greater competitive advantages in the presence of competitive marketplace environment. This study also suggests that retailers should focus on communicating their product values to customers and compare their prices with those competitors and observe how they influence consumers’ purchase decisions.

This study has some limitations which would provide directions for future research. Firstly, the main focus of the study was restricted to department stores and involved only consumers. Therefore, future studies can extend the scope by collecting the data at different areas in the country and include several staff of department stores to get better insights into the important factors in retail sector. Secondly, the data were gathered through quantitative survey using structured questions; thus, future studies can involve other research methodologies to confirm the findings. Additionally, the sample size used in this study may not be enough to represent the population. Thus, future studies are recommended to rely on larger sample sizes and in different industry contexts. Future studies may also examine other marketing factors, such as cultural factors and reference groups to gain further insights about their role in affecting consumers’ purchase decision in the retail sector. Finally, only five independent variables were examined in this study; hence, future research can consider other factors that can influence consumers’ purchase decision in the Malaysian retail sector such as service quality and store image.


The examination of the direct effects of corporate social responsibility, social media marketing, store environment, sales promotion and perceived value on purchase decision in the retail industry provides a theoretical contribution to the existing literature in this field. This study is one of the few research studies which attempted to examine the causal link between these variables. Particularly, the findings have theoretical significance by providing empirical evidence with regard to the relationships between the stated factors and purchase decision. Furthermore, there are useful practical implications for the business practitioners of retail stores. Managers can benefit from the results of this research to achieve better recognition and sustainable competitive advantage. The findings of this study also indicate that managers should understand the implications with respect to social media marketing in the Malaysian context; although this variable was found to be insignificant in affecting purchase decision in the retail context, it may yield different outcomes in future research.

purchase research article

Research framework

purchase research article

Structural model

Discriminant validity

Results of hypotheses

Measurements of constructs

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Corresponding author

About the author.

Jalal Rajeh Hanaysha is currently a Senior Lecturer at DRB-HICOM University of Automotive Malaysia. He obtained his PhD majoring in Management from Universiti Utara Malaysia, Malaysia, in 2015, as well as an MSc (Management) from Universiti Utara Malaysia in 2011. He also received a Bachelor’s degree in Marketing from Arab American University – Jenin, Palestine in 2008. To date, he has published more than 45 research articles in international journals and conferences. He has also received several awards for best research papers being presented at local and international conferences. His research interests include business management and marketing, in particular branding, consumer behaviour, social media marketing, CSR, business and product innovation, human resource practices, and business strategy.

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Open Access


Research Article

Purchasing under threat: Changes in shopping patterns during the COVID-19 pandemic

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

* E-mail: [email protected]

Affiliation Department of Psychology, Clinical Psychology, Experimental Psychopathology, and Psychotherapy, Philipps University Marburg, Marburg, Germany

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Roles Conceptualization, Methodology, Writing – review & editing

Affiliations Department of Psychology, Clinical Psychology, Experimental Psychopathology, and Psychotherapy, Philipps University Marburg, Marburg, Germany, Center for Mind, Brain and Behavior (CMBB), Philipps University Marburg and Justus Liebig University Giessen, Gießen, Germany

  • Sebastian Schmidt, 
  • Christoph Benke, 
  • Christiane A. Pané-Farré


  • Published: June 9, 2021
  • Peer Review
  • Reader Comments

Table 1

The spreading of COVID-19 has led to panic buying all over the world. In this study, we applied an animal model framework to elucidate changes in human purchasing behavior under COVID-19 pandemic conditions. Purchasing behavior and potential predictors were assessed in an online questionnaire format ( N = 813). Multiple regression analyses were used to evaluate the role of individually Perceived Threat of COVID-19 , anxiety related personality traits (trait-anxiety, intolerance of uncertainty) and the role of media exposure in predicting quantity and frequency of purchasing behavior. High levels of Perceived Threat of COVID-19 were associated significantly with a reported reduction in purchasing frequency ( b = -.24, p < .001) and an increase in the quantity of products bought per purchase ( b = .22, p < .001). These results are comparable to observed changes in foraging behavior in rodents under threat conditions. Higher levels of intolerance of uncertainty ( b = .19, p < .001) and high extend of media exposure ( b = .27, p < .001) were positively associated with Perceived Threat of COVID-19 and an increase in purchasing quantity. This study contributes to our understanding of aberrated human purchasing behavior and aims to link findings from animal research to human behavior beyond experimental investigations.

Citation: Schmidt S, Benke C, Pané-Farré CA (2021) Purchasing under threat: Changes in shopping patterns during the COVID-19 pandemic. PLoS ONE 16(6): e0253231.

Editor: Marta Andreatta, Erasmus University Rotterdam: Erasmus Universiteit Rotterdam, GERMANY

Received: January 14, 2021; Accepted: May 31, 2021; Published: June 9, 2021

Copyright: © 2021 Schmidt et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All files (data set, R code) are available from the data_UMR repository under the following URL: .

Funding: The authors received no specific funding for this work.

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


The spreading of the coronavirus disease (COVID-19) has led to worldwide stockpiling of food and hygiene products which caused temporally shortages [ 1 ]. In early March 2020, when the number of daily COVID-19 infections reached its peak in Germany [ 2 ], the German Federal Statistical Office recorded an enormous increase in sales of goods of sanitary and daily needs [ 3 ]: e.g., early in March 2020, a 150% increase for pasta, 153% for soap, and 751% for disinfectants. Similar changes in shopping behavior were recorded in the USA [ 4 ] and the UK [ 5 ]. At the same time, studies indicated an increase in fear and worries related to the virus [ 6 , 7 ].

The modulation of foraging behavior by threat has extensively been studied in the animal model [ 8 ]. In the natural environment, animals need to ensure a sufficient calorie intake while trying to avoid predatory attack. To parallel the natural habitat, animal studies use a safe nest area that must be left to obtain food. To evaluate threat related changes in foraging, the animals are confronted with a threat stimulus in the foraging area, such as the smell of a predator [ 9 ] or an electric shock [ 8 ]. In response to such threat encounter animals show an increase in risk assessment behaviors, e.g., attentive head-scanning [ 10 ], an inhibition of appetitive behavior [ 11 ], an increased latency in the procurement of food pellets [ 12 ] as well as a reduction in number of meals accompanied by an increase of the size of portions to maintain caloric intake [ 8 ].

A recent study investigated factors influencing stockpiling during the COVID-19 pandemic. Increased COVID-related worry (e.g., “I will become very ill.”; “I will not have access to food.”) was associated with stockpiling of more products indicating that negative affect like worries and anxiety influence shopping behavior [ 13 ].

In parallel to a predatory attack which constitutes a threat during natural foraging, the possibility of an infection with COVID-19 constitutes a threat in a human purchasing situation under pandemic conditions. In line with the described animal and human findings, we hypothesized that individually perceived threat resulting from possible COVID-19 infection will predict changes in human purchasing behavior under the current pandemic. Human purchasing is not only limited to food items. Increased selling rates were also reported for hygiene products such as disinfectant and toilet paper. Therefore, it seemed reasonable to consider purchasing of these necessities as a part of human foraging. Based on findings from animal research [ 8 ], we expected that perceived Threat of COVID-19 will lead to (1) a reduction in purchasing frequency and (2) an increase in purchasing quantity per purchase.

Additionally, we were interested in the influence of other factors known to influence feelings of anxiety that thus might be associated with threat perception of COVID-19 and changes in purchasing behavior. It has been demonstrated that psychological vulnerability factors such as trait-anxiety (i.e., the tendency to experience anxiety and perceive situations as threatening) and intolerance of uncertainty (i.e., the tendency of an individual to experience possible negative future events as unacceptable and threatening) increase the risk to fearfully respond to potentially negative or uncertain stimuli, events or situations such as those arising during the current pandemic. Both psychological factors have been linked with occurrence of anxiety-related disorders [ 14 – 16 ]. Initial evidence from the current COVID-19 pandemic revealed that trait-anxiety and intolerance of uncertainty are associated with higher levels of threat perception and fear of the coronavirus [ 17 , 18 ]. Another relevant factor that has been discussed to increase fear and threat perception of COVID-19 via transmission of threat information is the level of exposure to media. Studies from current COVID-19 pandemic higlight the role of increased media exposure on elevated anxiety and stress responses as well as increased fear of COVID-19 under the COVID-19 pandemic [ 17 , 19 ]. In the present study, we tested whether Perceived Threat of COVID-19 explains changes in purchasing behavior beyond these factors.

Purpose of the present study and hypotheses

Understanding the causes for changes in consumers purchasing behavior under the COVID-19 pandemic is of high relevance for governments and policymakers, e.g., to avoid panic buying which in turn may cause shortage of important goods. As stated in a perspective article by Van Bavel and colleagues [ 20 ] there are several research topics relevant to the COVID-19 pandemic which have to be addressed by social and behavioral sciences. Fear is a central emotional response during a pandemic which shapes information processing (e.g., risk perception) and behavior (e.g., shopping behavior). Based on a theoretical framework derived from rodent foraging behavior under threat, we examined the role of perceived threat originating from the present COVID-19 pandemic situation in predicting changes in purchasing patterns of groceries and hygiene products in an online questionnaire study. Our main hypotheses are that higher levels of Perceived Threat of COVID-19 would be (1) associated with a reduction of purchasing frequency and (2) an increase in purchasing quantity per purchase. We also expected a positive correlation between Perceived Threat of COVID-19 and an increase in purchasing quantity for individual products. In face of the known relevance of trait-anxiety and intolerance of uncertainty as risk factors for anxiety disorders and depression, we hypothesized that these constructs would be positively related to Perceived Threat of COVID-19 . Additionally, we expected individuals with high vulnerability to develop anxiety disorders (high trait-anxiety, high intolerance of uncertainty) to show a decrease in shopping frequency and an increase in purchasing quantity per purchase. Besides we hypothesized that being part of a risk group for a severe course of an infection with COVID-19 or having regular contact with a high-risk person would be associated with higher levels of Perceived Threat of COVID-19 and changes in shopping behavior as described above. A high extend of media exposure was also hypothesized to be positively associated with Perceived Threat of COVID-19 and changes in purchasing patterns (increased purchasing quantity while reducing shopping frequency).

The study was conducted from April 23 rd to May 18 th , 2020. In this time window the total amount of confirmed COVID-19 cases in Germany had reached 175.896. The implementation of public health measures by the German federal states started in March 2020 (e.g., prohibition to meet with others in public places, closure on non-essential shops, or closure of kindergartens or daycare institutions [ 21 ] while risk communication increased in the media, e.g., daily report of case numbers or information that infection with COVID-19 may cause a life-threatening disease and recommendations on how to avoid infection [ 19 ]. In effect, for March 2020 massive increases in sales figures were reported [ 3 ]. We asked participants to retrospectively rate their purchasing behavior for this month. The online questionnaire was realized using SoSci Survey [ 22 ] and was published on (see supplementary information for a German ( S1 Appendix ) and an English version ( S2 Appendix ) of the questionnaire).


In total 1074 individuals completed the online questionnaire and gave an answer to every question. Participants who did not finish the questionnaire were excluded. Data analysis was further limited to those participants for whom buying groceries constituted an actual risk of COVID-19 infection at time of assessment, i.e., we excluded participants who had already gone through a COVID-19 infection ( n = 3), or did not actually visit any stores during the assessment period due to either being in quarantine ( n = 30) or exclusively shopping online ( n = 49). To achieve a valid assessment of purchasing behavior changes from pre-pandemic to pandemic, we also excluded participants who did not make their own purchases (because, e.g., the partner did) before ( n = 65) and during the pandemic ( n = 150) leaving a final sample of 813 respondents (78% female). Participants were aged between 18 and 79 ( M = 42.42, SD = 15.00) (see Table 1 for descriptive statistics). The survey was advertised via the central e-mail system of Philipps University Marburg and on social media platforms. In order to motivate as many people as possible to participate in the online study, the raffle of three food delivery vouchers worth € 39.99 each was announced. The study was approved by the ethics committee of the Department of Psychology at the Phillips University of Marburg. Participants were informed that participation is voluntary and can be cancelled at any time without giving reasons, and that data will be stored anonymously. Written informed consent was obtained on the first page of the online questionnaire.


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Perceived Threat of COVID-19 . Perceived Threat of COVID-19 was measured using six semantic differential seven-point rating scales. The six items were introduced with “ The novel coronavirus is for me . . .. ” following two oppositely poled adjectives, (“concerning” vs. “not concerning”, “frightening” vs. “not frightening”, “something I am thinking about all the time” vs. “something I am not thinking about all the time”, “something I feel helpless about” vs. “something I can actively do something about”, “burdensome” vs. “not burdensome”, “close” vs. “far away”). These items were taken from the COSMO Snapshot Monitoring study conducted by the University of Erfurt [ 7 ]. The internal consistency of this scale was good (Cronbach´s α = .86). A principle component analysis indicated a one-dimensional construct, so we used the mean score of all six items as an indicator of perceived threat.

Intolerance of Uncertainty Scale . We used the 12-item short version of the Intolerance of Uncertainty Scale which maps the tendency of an individual to experience possible negative future events as unacceptable and threatening (e.g., “Unforeseen events upset me greatly.”) and is associated with worry, state-anxiety and related to anxiety pathologies [ 23 ]. The reported internal consistency of the short version is Cronbach´s α = .91. The internal consistency in this sample was good (Cronbach´s α = .87). A German validation study reported similar results (Cronbach´s α = .90) and reported intolerance of uncertainty to be predictive for worrying [ 24 ].

State Trait Anxiety Inventory . We used the trait portion of the State Trait Anxiety Inventory (A-Trait, e.g., “I worry too much over something that really doesn´t matter.”) which consists of 20 items. The internal consistency in this sample reached an excellent value of Cronbach´s α = .94. The reported Cronbach´s α for the A-Trait lies between .86 - .95 [ 25 ], for the German version Cronbach´s α = .90 [ 26 ].

Risk Perception . Participants assessed the likelihood of being infected with COVID-19 while shopping on a continuous scale ranging from 0% (“very unlikely”) to 100% (“very likely”).

Extend of media exposure . We asked participants to indicate how often they gather information about the COVID-19 pandemic on a four-point Likert scale (1 = “never”, 2 = “less than once a day”, 3 = “once a day”, 4 = “several times a day”).

Risk Group . Based on a standardized description ( “There is an increased risk of a severe course of COVID-19 disease for persons aged 50 years or older , smokers , persons with existing heart or lung diseases , chronic liver disease , diabetes mellitus , cancer or a weakened immune system . ” ) participants indicated if they (in person) belong to a risk group for a severe course of COVID-19 or if they have regular contact to a person (e.g., household member) belonging to such a risk group (coding: 0 = “no”, 1 = “yes”).

Social Desirability Bias . The Scale for Detecting Test Manipulation through Faking Good and Social Desirability Bias consists of seven five-level Likert items [ 27 ]. We used the individual scores to control for socially desirable reporting biases.

Demographic Variables . Participants reported their age in years, sex (coding: 0 = “female”, 1 = “male”), in which federal state they live, their highest level of education (1 = “no degree”, 2 = “primary education”, 3 = “secondary school diploma”, 4 = “high school graduation”, 5 = “university degree”) and their household size (number of persons living in a household).

Outcome measures.

Purchasing Behavior . Participants indicated the change in purchasing frequency and change in purchasing quantity for the month March 2020 relative to January 2020. We used January 2020 as a reference because at that point the German government did not consider the coronavirus to be a risk for Germany [ 28 ], no infection control measures were implemented yet [ 29 ] and no changes in purchasing behavior were observed compared to the usual level [ 3 ]. Participants were able to indicate the full range of change in purchasing frequency on a seven-point rating scale: Compared to January 2020 , before the outbreak of the Corona pandemic in Germany , how often did you go shopping in March 2020 ? (options: -3 = “much less frequently”, -2 = “less frequently”, -1 = “little less frequently”, 0 = “just as often”, 1 = “little more often”, 2 = “more often”, 3 = “much more often”). In correspondence, change in purchasing quantity was assessed using the following item: Compared to January 2020 , before the outbreak of the Corona pandemic in Germany , how much (quantity) did you buy per purchase in March 2020 ? (options: -3 = “much less”, -2 = “less”, -1 = “a little less”, 0 = “just as much”, 1 = “a little more”, 2 = “more”, 3 = “much more”).

Purchasing Quantity for individual products . For a more differentiated analysis we asked respondents to rate the purchasing quantity for individual products for March 2020 relative to January 2020. The following products were rated: toilet paper, soap, disinfectants, canned food, noodles/rice and fresh products (e.g., cheese, meat). There was the additional option to choose “do not usually buy this product”.

Data analysis

Purchasing Frequency and Purchasing Quantity were analyzed in separate multiple regressions controlled for gender, age, education, household size, and social desirability bias. In a next step, we entered all COVID-19 related variables (being part of a risk group, extend of media exposure to inform about COVID-19, risk perception of getting an infection, Perceived Threat of COVID-19 ) and anxiety related personality traits (intolerance of uncertainty, trait-anxiety) as a predictor of interest and examined its specific effect above the baseline model. In a final set of analyses, we entered all significant variables in one model and compared their effects. The same model was used to analyze the change in purchasing quantity for individual products.

Since Perceived Threat of COVID-19 was our main predictor of interest, we conducted an additional multiple regression analysis with the same baseline model as explained above and included the additional factors (e.g., sex, age, intolerance of uncertainty) to examine their specific predictive value for Perceived Threat of COVID-19 . For the ease of interpretation all continuous variables were z-standardized before entered into the model. We checked for multicollinearity using the variance inflation factor (VIF). All VIFs were smaller than two and thus considered unproblematic [ 30 ]. Since the dependent variables (purchasing frequency and purchasing quantity) were not normally distributed, we decided to additionally report confidence intervals (95% CI) based on bootstrapping [ 31 ] to bypass the assumptions for multiple linear regression. 2000 samples were generated to obtain an empirical distribution (using the from the R package “boot”). Note that these results were highly comparable to the results of the parametric test. Additionally, we report non-parametric analyses (e.g., ordinal logistic regression) for the main findings as supplementary information (see S3 Appendix ), again showing highly comparable results. All analyses were conducted with R [ 32 ].

Change in purchasing frequency

The distribution of participants’ rating of change in purchasing frequency (see Fig 1 ) shows that 32.1% of study participants indicated that they went shopping for groceries as often in March as they did in January. 57.3% of the participants indicated that they went shopping less often and 10.6% indicated that they went shopping for groceries more often in March as compared to January. Overall, a one-sample t -test revealed a significant decrease in purchasing frequency from January to March ( M = -0.86, SD = 1.35), t (812) = -18.274, p < .001, d = 0.64.


N = 813. Note that categories “less” and “more” each comprise three gradations of the original scale (see section Outcome Measures ).

Regression models were calculated for the full range scale (see S3 Table ) and, for clarity of hypothesis testing regarding a decrease in foraging frequency, excluding those 10.6% of participants who report an increase in purchasing frequency (see text below). Bivariate correlations between all variables are presented in S1 and S2 Tables (full range scale) in the supporting information. In the baseline model (see Table 2 ) sex and educational level were the only significant predictors for purchasing frequency. Female sex was associated with a decrease in purchasing frequency in March 2020 compared to January 2020, b = 0.32, t (672) = 3.46, p = .001, 95% CI [.13, .49]. Higher education was associated with a reduction of purchasing frequency, b = -.13, t (672) = 3.49, p = .001, 95% CI [-.21, -.06]. Adding Perceived Threat of COVID-19 to the model revealed that higher subjective threat was associated with a decrease in purchasing frequency, b = -.30, t (671) = 8.21, p < .001, 95% CI [-.39, -.23]. Intolerance of uncertainty and trait-anxiety revealed suppression effects, b = -.08, t (671) = 2.12, p = .035, 95% CI [-.16, -.01] respectively b = -.10, t (671) = 2.57, p = .010, 95% CI [-.18, -.03] (see S1 Table for correlations). The perception for being at high risk for infection with COVID-19 during shopping was associated with a decrease in purchasing frequency, b = -.19, t (671) = 5.03, p < .001, 95% CI [-.26, -.10]. Adding media exposure significantly improved the model, b = -.18, t (671) = 4.62, p < .001, 95% CI [-.25, -.10]. Belonging to a risk group was not a significant predictor of change in purchasing frequency ( b = -.17, t (671) = 1.92, p = .056, 95% CI [-.35, .01]) nor was having regular contact with a risk person ( b = -.11, t (671) = 1.46, p = .145, 95% CI [-.26, .05]).


Finally, to check whether Perceived Threat of COVID-19 , risk perception and media exposure explained specific variance above and beyond the baseline model, all three predictors were entered in one block after the baseline model (see Table 3 ). The analysis revealed that Perceived Threat of COVID-19 ( b = -.24, t (667) = 5.60, p < .001, 95% CI [-.33, -.15]), risk perception ( b = -.10, t (667) = 2.63, p = .006, 95% CI [-.18, -.02]) and media exposure ( b = -.11, t (667) = 2.78, p = .009, 95% CI [-.19, -.03]) added incremental variance to the baseline model. The overall model explained 12.9% of the variance in change in purchasing frequency, F (10, 667) = 11.07, p < .001.


Change in purchasing quantity

The distribution of participants’ rating of change in purchasing quantity (see Fig 2 ) shows that 45.5% of the participants indicated that they bought the same number of products per purchase in March as in January 2020. 8.6% of the sample indicated that they bought less products per purchase and 45.9% indicated that they bought more products per purchase in March as compared to January. A one-sample t -test confirmed a significant increase in purchasing quantity ( M = 0.58, SD = 1.12), t (812) = 14.673, p < .001, d = 0.51.


Regression models were calculated for the full range scale (see S3 Table ) and, for clarity of hypothesis testing regarding an increase in purchasing quantity, excluding those 8.6% of participants who report a decrease in purchasing quantity (see text below). The baseline model (see Table 2 ) revealed that female sex ( b = -.19, t (672) = 2.05, p = .040, 95% CI [-.32, .02]), younger age ( b = -.09, t (667) = 2.38, p = .018, 95% CI [-.17, -.02]) and higher education ( b = .11, t (667) = 2.95, p = .003, 95% CI [.03, .19]) were associated with an increase in purchasing quantity. As expected, there was a positive association between Perceived Threat of COVID-19 and change in purchasing quantity, b = .29, t (671) = 7.86, p < .001, 95% CI [.22, .37]. Subjects who felt more threatened by COVID-19 increased their quantity of bought products per purchase. Intolerance of uncertainty and trait-anxiety explained significant variance and were both positively associated with changes in purchasing quantity, b = .11, t (671) = 2.70, p = .007, 95% CI [.02, .18] respectively b = .10, t (671) = 2.42, p = .016, 95% CI [.01, .18]. Higher risk perception for an infection during shopping was associated with an increase in purchased quantity, b = .13, t (671) = 3.72, p < .001, 95% CI [.11, .27]. People who indicated to inform themselves more frequently about COVID-19 (extend of media exposure) showed in increase in purchasing quantity, b = .19, t (671) = 5.20, p < .001, 95% CI [.12, .26]. Belonging to a risk group was not a significant predictor of change in purchasing quantity ( b = .03, t (671) = .39, p = .700, 95% CI [-.15, .20]) nor was having regular contact with a risk person ( b = -.01, t (671) = .07, p = .943, 95% CI [-.16, .13] ) .

Finally, all significant predictors were added to the baseline model (see Table 3 ). As observed for change in purchasing frequency, Perceived Threat of COVID-19 ( b = .22, t (667) = 5.02, p < .001, 95% CI [.13, .30]), the extend of media exposure ( b = .12, t( 667 ) = 3.10, p = .002, 95% CI [.05, .20]) and the perceived risk of getting infected while shopping (risk perception; b = .11, t (667) = 2.90, p = .004, 95% CI [.03, .20]) remained significant when adding all five variables together to the baseline model. The overall model explained 12.3% of the variance in change in purchasing quantity, F (10, 667) = 10.51, p < .001. Note that Perceived Threat of COVID-19 ( b = .08, p = .026) remained significant predictor for purchasing quantity when controlling for purchasing frequency (see S4 Table ).

Change in purchasing quantity for individual products

We analyzed the change of purchasing quantity for the individual products by entering all variables in the model. For an easier interpretation, we calculated the mean of change in purchasing quantity for “hygiene products”, i.e., toilet paper, soap, and disinfectants. In the same manner ratings for pasta/rice and canned food were aggregated to form the variable “non-perishable food”. For clarity of data interpretation, participants indicating that they bought less of a product were excluded from the analysis (3.3% for non-perishable food, 1.1% for hygiene products and 8.2% for fresh food). Perceived Threat of COVID-19 (see S5 Table ) was associated with an increase in purchasing quantity for non-perishable food ( b = .21, t (777) = 5.56, p < .001, 95% CI [.14, .29]), hygiene products ( b = .17, t (789) = 4.32, p < .001, 95% CI [.09, .25]), and fresh food ( b = .10, t (737) = 2.29, p = .028, 95% CI [.01, .18]). Risk Perception explained additional variance only for non-perishable food ( b = .11, t (777) = 2.94, p = .003, 95% CI [.04, .18]). High intolerance of uncertainty was associated with an increase in purchasing of non-perishable food ( b = .10, t (777) = 2.77, p = .006, 95% CI [.03, .17]), hygiene products ( b = .14, t (789) = 3.83, p < .001, 95% CI [.07, .21]), and fresh food ( b = .09, t (737) = 2.21, p = .028, 95% CI [.01, .16]). The extend of media exposure increased explained variance for non-perishable food ( b = .11, t (777) = 3.00, p = .003, 95% CI [.04, .18]) and hygiene products ( b = .13, t (789) = 3.47, p < .001, 95% CI [.05, .20]). Having regular contact to a risk person was associated with an increase in purchasing of non-perishable food ( b = .17, t (777) = 2.33, p = .020, 95% CI [.03, .31]). Belonging to a risk group oneself also was associated positively with an increase in purchasing of non-perishable food ( r = .102) but did not remain significant in the multiple regression analysis. The results for the full range scale are reported as supplementary information ( S6 Table ).

Perceived Threat of COVID-19

Since Perceived Threat of COVID-19 was our main predictor of interest, we conducted an additional multiple regression analysis (see S7 Table for all predictors) on Perceived Threat of COVID-19 . We entered the baseline model and all variables to the model that revealed a significant bivariate correlation with Perceived Threat of COVID-19 (see S2 Table ) to analyze which variables add specific variance to Perceived Threat of COVID-19 . Female subjects indicated higher Perceived Threat of COVID-19 , b = -.33, t (803) = 4.46, p < .001, 95% CI [-.47, -.18]. Age showed a negative association with Perceived Threat of COVID-19 , b = -.09, t(803) = 2.66, p = .008, 95% CI [-.15, -.02]. Educational level was positively related with Perceived Threat of COVID-19 , b = .13, t (803) = 4.48, p < .001, 95% CI [.08, .19]. Trait-anxiety ( b = .21, t (803) = 5.44, p < .001, 95% CI [.13, .29]) and risk perception ( b = .26, t (803) = 8.30, p < .001, 95% CI [.20, .32]) were positively related with Perceived Threat of COVID-19 and added specific variance to the model. Besides, higher frequency of information gathering (media exposure) was positively associated with Perceived Threat of COVID-19 , b = .27, t (803) = 8.57, p < .001, 95% CI [.21, .33]. The model explained 28.3% of the variance of Perceived Threat of COVID-19 . Note that due to the high correlation between trait-anxiety and intolerance of uncertainty ( r = . 61 ), intolerance of uncertainty did not reach significance ( p = .050). Intolerance of uncertainty added incremental variance when trait-anxiety was removed from the model, b = .19, t (804) = 5.98, p < .001, 95% CI [.13, .25].

The COVID-19 pandemic affected purchasing behavior all over the world. For future pandemics or a new flaring up of the COVID-19 infections it is important to understand relevant factors that influence panic buying. The aim of the study therefore was to investigate the role of Perceived Threat of COVID-19 and anxiety related measures on purchasing behavior. So far, studies investigating the influence of threat and anxiety on changes in purchasing behavior are scarce (e.g., Garbe and colleagues who have investigated the role of threat on purchasing of toilet paper [ 33 ] and Bentall and colleagues who also used a foraging framework [ 34 ]). In the present study, we investigated the role of Perceived Threat of COVID-19 and anxiety on purchasing behavior on a more general level and for different individual products.

The current study provides the following main findings: First and in line with our hypotheses, we found that the extend of Perceived Threat of COVID-19 is a significant predictor for changes in purchasing behavior, i.e., high threat was associated with a tendency to buy larger quantities per purchase and a reduction in purchasing frequency in March 2020 as compared to January 2020. Second, high intolerance of uncertainty was associated with an increase in purchasing quantity but not purchasing frequency (but significant suppression effect); trait-anxiety, which was highly correlated with intolerance of uncertainty, revealed a similar pattern, although there was a significant but small correlation with purchasing frequency ( r = -.08). Third, participants indicating a high extend of information gathering about COVID-19 tended to buy larger quantities and reduced purchasing frequency in March as compared to January 2020. Contrary to our expectations, being part of a risk group for a severe course of a COVID-19 infection or having contact to a person being part of such a group was not predictive for changes in purchasing behavior. All reported effects were controlled for gender, age, educational level, household size and a social desirability bias. Entering all significant predictors in one model revealed that Perceived Threat of COVID-19 was the best predictor for change in purchasing frequency as well as for change in purchasing quantity. For change in purchasing frequency Perceived Threat of COVID-19 , the extend of media exposure and participants’ risk perception of getting infected with COVID-19 while shopping were the only predictors that remained significant. The overall analysis for change in purchasing quantity revealed the same pattern of significant effects.

The observed purchasing pattern in our study shows resemblance to the strategic behavior seen in rodents. After the experience of an electrical shock in a foraging area, animals modified their foraging behavior to reduce the possibility of experiencing an aversive event by reducing the number of entrances to the foraging area while increasing meal size [ 8 ]. According to the threat imminence model, there are three defensive modes, each associated with a specific set of behaviors [ 35 ]. The mode activated depends on predatory imminence, i.e., the probability to encounter a predator. The pre-encounter mode is the first mode in the threat continuum and is activated when entering an area indicating some predatory potential. This mode is associated with meal pattern reorganization or protective nest maintenance which can be observed in animals. Our study provides evidence that humans also show similar adaptions in the face of the threat of a virus: buying larger quantities reduces the number of visits to stores necessary to maintain food supply and thus reduces the risk of an infection in the store. Importantly, the observed pattern of purchasing behavior was also predicted by the participant´s risk perception of being infected while shopping, which was correlated positively with Perceived Threat of COVID-19 ( r = .36). These findings suggest that the subjective assessment of infection risk is associated with feelings of threat and influences purchasing behavior. Similar results were observed in another online survey which also used an animal foraging framework to explain changes in purchasing behavior [ 34 ]. In this study, perceived probability of getting an infection was positively associated with increased purchasing quantity. In contrast to the present study, the authors emphasized on threat due to scarcity which is not covered in our study. The moderate correlation between risk perception and Perceived Threat of COVID-19 as found in our study suggests that additional factors–as for instance threat of scarcity–might explain additional variance in perceived threat. According to Bentall and colleagues [ 34 ], perceived risk of infection is a factor influencing scarcity vulnerability. Future studies ought to include threat due to scarcity to test whether Perceived Threat of COVID-19 remains a meaningful predictor for changes in purchasing behavior after controlling for threat due to scarcity. Unlike the rodents in the experiments by Fanselow and colleagues [ 8 ], participants did not experience an aversive event (e.g., electrical shock). Experimental studies (instructed fear paradigms) show that next to direct experience, fear and anxiety can be acquired also by informational transmission [ 36 , 37 ]. Since the outbreak of COVID-19, information about the virus and current numbers of new infections are reported on a daily basis. As reported elsewhere regular media exposure is a predictor of fear of the coronavirus [ 17 ]. In line with these findings, our analyses revealed that a greater extend of media exposure was associated with a higher level of Perceived Threat of COVID-19 suggesting its possible role as a form of verbal instruction of threat during the corona pandemic. At the same time, media exposure was associated with an increase in purchasing quantity and a decrease in purchasing frequency. Another study, using structural equation models, revealed that cyberchondria—that is, excessive information gathering about COVID-19 combined with feelings of frustration and anxiety—is positively associated with the intention to make unusual purchases [ 38 ]. A qualitative study on contents on twitter about toilet paper hoarding found out that nearly half of the analyzed tweets expressed negative feelings toward panic buying [ 39 ]. The authors hypothesize that this might lead to emotional distress, depression and anxiety-driven panic buying (see also [ 40 ]).

To get a more differentiated view, we also assessed the change in purchasing quantity for individual products. Our study extends the results reported by Garbe and colleagues [ 33 ] who investigated the role of perceived threat by COVID-19 and personality traits on purchasing of toilet paper. The authors found that high perceived threat by COVID-19 and high levels of emotionality predicted the amount of stockpiled toilet paper. In line with this finding, our data revealed that Perceived Threat of COVID-19 was positively associated with an increase in purchasing quantity for non-perishable food (canned foods, pasta/rice) and hygiene products (soap, toilet paper, disinfectants). Unexpectedly, high threat was also associated with an increase in the purchasing of fresh products, although this model showed the least variance explanation (see S5 Table ). Next to Perceived Threat of COVID-19 , intolerance of uncertainty added incremental variance for all product categories indicating that anxiety as a personality trait drives changes in purchasing behavior under threat.

Although only included as control variable, we found out that female sex was associated with a decrease in shopping frequency. This result could be interpreted as a more cautious behavior in female compared to male individuals. A study investigating the role of messaging and gender on intentions to wear a face covering under COVID-19 pandemic revealed that woman more than men intend to wear a face covering [ 41 ]. A mediating factor was the subjective likelihood to get the disease, supporting our post-hoc hypothesis that women behave more cautious under the COVID-19 pandemic. Note that in our data female sex was associated with higher levels of Perceived Threat of COVID-19 .

A limitation of this study is the retrospective rating of purchasing behavior in March 2020 which could be affected by memory biases. Longitudinal data would be important to see if subjective ratings of pre- and post-pandemic purchasing behavior differ and are associated with changes in perceived threat. The explained variance for change in purchasing frequency and purchasing quantity was rather small, indicating that additional factors were associated with a change in purchasing behavior. Recent studies indicate that e.g., right political affiliation [ 13 , 34 ], the extend of engaging in social distancing [ 13 ], and higher levels of paranoia [ 34 ] are associated with more stockpiling. Due to the correlational nature of this study no claims about causality can be made. Therefore, we cannot rule out that the found correlations between purchasing behavior and Perceived Threat of COVID-19 are coincidental although data from the German Federal Statistical Office suggests that there was indeed an unusual increase in sales figures in March compared to the mean of August 2019 to January 2020 [ 3 ]. More experimental studies should try to translate findings from animal experiments to human behavior to test whether certain behaviors are associated with different threat levels as reported in a study by Bach and colleagues [ 42 ]. Such studies could provide further evidence that foraging behavior is a relevant behavioral component of anxiety and fear in humans. Another limitation is the sex bias observed in the current study (78% of respondents were female) and the high proportion of high educated respondents which reduces generalizability although we controlled for sex and educational level. Two major strengths of this study can be mentioned: First, the derivation of hypotheses based on an animal model contributes to link findings from animal literature to human behavior. Second, this study collected purchasing behavior, anxiety ratings and Perceived Threat of COVID-19 around the peak of the COVID-19 pandemic in Germany and thus provides unique data about behavior and related predictors under an extreme event.

In conclusion perceived Perceived Threat of COVID-19 influences purchasing behavior in a twofold way: high levels of threat are associated with an increase in purchasing quantity and a reduction in purchasing frequency. The positive relation between Perceived Threat of COVID-19 and an increase of purchasing quantity was confirmed for individual products, too. Next to the Perceived Threat of COVID-19 , intolerance of uncertainty and the level of perceived risk for an infection during shopping also were significant predictors for purchasing behavior (quantity and frequency). While intolerance of uncertainty might be a relative stable personality trait, a reduction of risk perception could help to mitigate maladaptive changes in purchasing behavior like panic buying. Our data suggests that the extend of media exposure is associated with feeling of threat and change in purchasing pattern. This highlights the importance of appropriate risk communication. Information about effective protection measures while shopping could reduce high risk perception of being infected during shopping and might help to prevent panic buying. Additionally, recommendations about the amount of information gathering in media could have beneficial effects (e.g., informing only once per day to reduce negative effects).

Supporting information

S1 appendix. german version of the online questionnaire..

S2 Appendix. English version of the online questionnaire.

S3 Appendix. Non-parametric data analysis.

S1 Table. Bivariate correlations.

S2 Table. Bivariate correlations for the full range scale.

S3 Table. Multiple regression analysis for the full range scale.

S4 Table. Multiple regression analysis controlling for change in purchasing frequency.

S5 Table. Multiple regression analysis for individual products.

S6 Table. Multiple regression analysis for individual products for the full range scale.

S7 Table. Multiple regression analysis for Perceived Threat of COVID-19.

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International organizations and research methods: an introduction

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Timon Forster, International organizations and research methods: an introduction, International Affairs , Volume 100, Issue 3, May 2024, Pages 1303–1304,

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Research methods are predominantly viewed as techniques that enable academics to collect new data, test hypotheses, advance scholarly debates and produce knowledge. International organizations and research methods challenges readers to think about methods as performative tools as well. Accordingly, the application of any method generates a distinct representation of the social world in which international institutions operate. From this vantage point, the choice of method itself is a function of the researchers’ academic training and background. The editors, Fanny Badache, Leah R. Kimber and Lucile Maertens, therefore call for a ‘deliberate and reflexive stance in the research process’ (p. 4). By treating methods as technical and performative tools, this edited volume showcases an extensive range of approaches that illuminate both the well-trodden paths and the uncharted territory of international organizations.

Badache, Kimber and Maertens, alongside their 59 contributors, have written a gentle methodological introduction to the study of international organizations. The book consists of five parts dedicated to different steps of the research process: observing, interviewing, documenting, measuring and combining. Each chapter describes a particular method (or set of methods) and its relevance, offering a brief how-to guide and discussing common challenges. Moreover, the book features 26 boxes that focus on more specific methodological tools or tricks, and it includes five interludes to take stock of the pertinent debates in the field. Thus, the book invites readers to approach international organizations in new ways. For instance, one could perceive the headquarters of the International Monetary Fund and the World Bank on 19th Street in Washington DC as artefacts (Box o); an exhibition celebrating the United Nations’ 80th anniversary suddenly lends itself to branding analysis (Box n) or to composing collages (chapter 29); and when a former high-level official publishes a memoir, an opportunity to conduct prosopography emerges (chapter 26).

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    According to the research goal, we made the following selection criteria when collecting related articles: (1) its research topic is related to online reviews; (2) it is an empirical study; and (3) it must probe the association between online reviews and purchase intention.

  4. Consumer Attitude and their Purchase Intention: A Review of Literature

    articles were scrutinized on pre-set parameters, while 25 of them that are relevant research papers presented here. The factors considered include social media, traditional media, Word of mouth, message ... The online purchase environment is characterised and defined by rise in e-commerce industry. Meanwhile, social media in recent times has ...

  5. Consumers purchase intention in live-streaming e-commerce: A

    The rapid development of live-streaming e-commerce has driven billions of sales revenues and made customers' purchase intention a life-and-death issue for sellers. This study examines the influencing factors of customers' purchase intention from a value perspective by adopting and extending the Theory of Consumption Values (TCV). We also incorporated streamer popularity as a moderating ...

  6. Measuring the impact of online reviews on consumer purchase decisions

    1. Introduction. In October 2020, research by Wall Street Journal revealed surprising factual statistics every business would want to know and the importance of online reviews (The Wall Street Journal, 2020).Firms need to capitalize on their understanding of online reviews as online shoppers consider online reviews as channels of getting product information while making purchase decisions (Fu ...

  7. Frontiers

    The insights obtained from our research extend current knowledge as regards determinants of consumer attitudes and intentions toward online purchases, consumers' perceived shopping risk and repurchase behavior when buying products online, and perceived consumer online trust and purchase decisions. Limited research has considered consumers ...

  8. Full article: Meta-analytic review of online purchase intention

    The meta-analytic review in this article is focused on comprehending the study factors related to online purchasing intention. As a statistical technique for blending the findings of multiple investigations, meta-analysis is an excellent tool to compile and analyse a considerable body of data systematically and impartially (Amos et al., 2014 ).

  9. Purchase decision-making within professional consumer services:

    The article proposes a theoretical framework incorporating the typical characteristics of professional services as a decision-making context, specified in a set of propositions regarding the relative influence of the parties on the purchase decision. Practical and research implications are also presented.

  10. The Influence of Brand Social Interaction on Purchase Intention: A

    Our research attempts to study the role of BSI factors in the formation of social capital from the perspective of social capital, and to dig deeper into the influence of social capital on consumers' purchase intentions. In this research, we take (Guo et al., 2017) social capital framework as the overall theoretical framework to establish the ...

  11. Customer perception, purchase intention and buying decision ...

    The purpose of this paper is to explore the antecedents of customer perception and its effect on the purchase intention and finally on buying decision-making about branded products especially luxury products, finally the role of price discounts in converting intentions into buying decision. This research has been carried in NCR with a collection of primary data by including statements related ...

  12. Frontiers

    The remainder of this article is structured as follows: Section 2 is devoted to conceptual basis and research assumptions; Section 3 presents the research design; Section 4 is the empirical analysis; Section 5 concludes the paper. Conceptual Basis and Research Assumptions Consumer Purchase Behavior Changes During the COVID-19 Pandemic

  13. Factors affecting green purchase behavior: A systematic literature

    A detailed synopsis of research papers focusing on GPI and GPB shows that these studies were conducted globally, and most studies examined GPB at a general level. Few studies have undertaken research at a particular industry and specific product level. The different industry and product categories covered by previous research are discussed next.

  14. Frontiers

    1 School of Business, Ningbo University, Ningbo, China; 2 School of Business, Western Sydney University, Penrith, NSW, Australia; This study investigated the impact of online product reviews on consumers purchasing decisions by using eye-tracking. The research methodology involved (i) development of a conceptual framework of online product review and purchasing intention through the moderation ...

  15. Research: How Price Changes Influence Consumers' Buying Decisions

    In her research, Manissa primarily investigates how different forms of numerical information influence consumers' judgments, decision-making, and behavior. Whether on retailers' own platforms ...

  16. Sustainable Luxury and Consumer Purchase Intention: A Systematic

    Table 1 shows the distribution of research articles between 2000 and 2021. Notably, according to the author's criteria, the first journal articles related to sustainable luxury and consumer PI appeared in 2013. Between 2000 and 2012, no peer-reviewed articles were found in the Scopus and WoS databases that focused on sustainable luxury and PI.

  17. Impact of Pricing and Product Information on Consumer Buying Behavior

    To test these hypotheses, research was conducted on university students in China. Questionnaire-based convenience sampling was conducted on 500 students for data collection using online and offline sources. A total of 367 (73%) students responded, and 17 questionnaires were rejected due to missing information. ... When I buy products, I like to ...

  18. An examination of the factors affecting consumer's purchase decision in

    The purpose of this paper is to examine the effects of corporate social responsibility, social media marketing, sales promotion, store environment and perceived value on a purchase decision in the retail sector.,A quantitative research methodology was used and the data were collected from 278 customers of retail stores in Malaysia.

  19. Purchase intention and purchase behavior online: A cross-cultural

    2. Literature review and research hypotheses. Given this study is oriented to find the differences between the purchase intention formation between two countries, it is important to review the literature about national culture, which has been a valuable tool to explain the differences that underlie the behavior of the individual based on culture.

  20. Purchasing under threat: Changes in shopping patterns during the ...

    The spreading of COVID-19 has led to panic buying all over the world. In this study, we applied an animal model framework to elucidate changes in human purchasing behavior under COVID-19 pandemic conditions. Purchasing behavior and potential predictors were assessed in an online questionnaire format (N = 813). Multiple regression analyses were used to evaluate the role of individually ...

  21. Factors affecting young customers' smartphone purchase intention during

    Research shows that around 35% of the total people penetrate smartphone, of them, 94% search for local information via their phone, 91% researching products through phone and 42% made a purchase through it (Wong, 2014). Product appearances and packages have an essential role in influencing the consumer purchase intention when they are at the ...

  22. International organizations and research methods: an introduction

    International organizations and research methods challenges readers to think about methods as performative tools as well. Accordingly, the application of any method generates a distinct representation of the social world in which international institutions operate. ... View Article Abstract & Purchase Options. For full access to this pdf, sign ...

  23. Organic Electrode Materials for Energy Storage and Conversion

    The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at with additional details about the score and the social media presence for the given article. ... Purchase this article for 48 hours $48.00 Add to cart Purchase this ...

  24. ACM Research, Inc. (ACMR) Q1 2024 Earnings Call Transcript

    ACM Research, Inc. (NASDAQ:ACMR) Q1 2024 Earnings Conference Call May 8, 2024 8:00 AM ETCompany Participants. Steven Pelayo - MD, The Blueshirt Group David Wang - CEO Mark McKechnie - CFO Lisa ...

  25. All You Need to Know About Sprout Social (SPT) Rating Upgrade to Buy

    Investors might want to bet on Sprout Social (SPT Quick Quote SPT - Free Report) , as it has been recently upgraded to a Zacks Rank #2 (Buy).This upgrade primarily reflects an upward trend in ...

  26. (PDF) Purchase intention and purchase behavior online: A ...

    The intention to purchase not only refers to the decision to buy a product but also serves as a strong indicator of a person's buying behavior in the future [33]. This encompasses value perception ...

  27. Research article Factors affecting repurchase intentions in retail

    The proposed research model of this study (depicted in Figure 1) examines the direct impact of the six antecedents described above, on five aspects of consumer behavior (customer experience, in-shop emotions, perceived value, customer satisfaction, customer loyalty).These five factors are hypothesized to mediate the impact of the six antecedents (or independent factors) on the repurchase ...

  28. The 3 Best Nasdaq Stocks to Buy in May 2024

    Jump into these best Nasdaq stocks during the spring season. CyberArk Software (): CyberArk should benefit from rising concerns tied to unauthorized access.; ACM Research (): ACM Research is an ...

  29. 3 Reasons to Buy a CD in May

    CD yields are very competitive right now -- the best ones are offering 5.00% or more. You can make a short-term commitment (a year or less) and get the best rates. Rates may decline soon, as the ...