consumer purchase decision research paper

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Front Psychol

The Impact of Online Reviews on Consumers’ Purchasing Decisions: Evidence From an Eye-Tracking Study

1 School of Business, Ningbo University, Ningbo, China

Premaratne Samaranayake

2 School of Business, Western Sydney University, Penrith, NSW, Australia

XiongYing Cen

Yi-chen lan, associated data.

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

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.

Introduction

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 .

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 Taobao.com , 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.

Participants

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 Taobao.com . 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 Taobao.com , 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.

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., Taobao.com and JD.com ) 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.

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.

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.

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 .

Results of ANOVA analysis.

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 .

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 .

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.

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

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.

Acknowledgments

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.

• Ahluwalia R., Burnkrant R., Unnava H. (2000). Consumer response to negative publicity: the moderating role of commitment . J. Mark. Res. 37 , 203–214. doi: 10.2307/1558500 [ CrossRef ] [ Google Scholar ]
• Archak N., Ghose A., Ipeirotis P. (2010). Deriving the pricing power of product features by mining . Con. Rev. Manag. Sci. 57 , 1485–1509. doi: 10.1287/mnsc.1110.1370 [ CrossRef ] [ Google Scholar ]
• Bae S., Lee T. (2011). Product type and consumers’ perception of online consumer reviews . Electron. Mark. 21 , 255–266. doi: 10.1007/s12525-011-0072-0 [ CrossRef ] [ Google Scholar ]
• Baek H., Ahn J., Choi Y. (2012). Helpfulness of online consumer reviews: readers’ objectives and review cues . Int. J. Electron. Commer. 17 , 99–126. doi: 10.2753/jec1086-4415170204 [ CrossRef ] [ Google Scholar ]
• Boardman R., McCormick H. (2019). The impact of product presentation on decision making and purchasing . Qual. Mark. Res. Int. J. 22 , 365–380. doi: 10.1108/QMR-09-2017-0124 [ CrossRef ] [ Google Scholar ]
• Boardman R., Mccormick H. (2021). Attention and behaviour on fashion retail websites: an eye-tracking study . Inf. Technol. People . doi: 10.1108/ITP-08-2020-0580 [Epub ahead of print] [ CrossRef ] [ Google Scholar ]
• Chae S. W., Lee K. (2013). Exploring the effect of the human brand on consumers’ decision quality in online shopping: An eye-tracking approach . Online Inf. Rev. 37 , 83–100. doi: 10.1108/14684521311311649 [ CrossRef ] [ Google Scholar ]
• Changchit C., Klaus T. (2020). Determinants and impact of online reviews on product satisfaction . J. Internet Commer. 19 , 82–102. doi: 10.1080/15332861.2019.1672135 [ CrossRef ] [ Google Scholar ]
• Chen C. D., Ku E. C. (2021). Diversified online review websites as accelerators for online impulsive buying: the moderating effect of price dispersion . J. Internet Commer. 20 , 113–135. doi: 10.1080/15332861.2020.1868227 [ CrossRef ] [ Google Scholar ]
• Cortinas M., Cabeza R., Chocarro R., Villanueva A. (2019). Attention to online channels across the path to purchase: an eye-tracking study . Electron. Commer. Res. Appl. 36 :100864. doi: 10.1016/j.elerap.2019.100864 [ CrossRef ] [ Google Scholar ]
• Craciun G., Moore K. (2019). Credibility of negative online product reviews: reviewer gender, reputation and emotion effects . Comput. Hum. Behav. 97 , 104–115. doi: 10.1016/j.chb.2019.03.010 [ CrossRef ] [ Google Scholar ]
• Cui G., Lui H.-K., Guo X. (2012). The effect of online consumer reviews on new product sales. International . J. Elect. Com. 17 , 39–58. doi: 10.2753/jec1086-4415170102 [ CrossRef ] [ Google Scholar ]
• Floh A., Koller M., Zauner A. (2013). Taking a deeper look at online reviews: The asymmetric effect of valence intensity on shopping behaviour . J. Mark. Manag. 29 :646670, 646–670. doi: 10.1080/0267257X.2013.776620 [ CrossRef ] [ Google Scholar ]
• Garbarino E., Strahilevitz M. (2004). Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation . J. Bus. Res. 57 , 768–775. doi: 10.1016/S0148-2963(02)00363-6 [ CrossRef ] [ Google Scholar ]
• Ghose A., Ipeirotiss P. G. (2010). Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics . IEEE Trans. Knowl. Data Eng. 23 :188. doi: 10.1109/TKDE.2010.188 [ CrossRef ] [ Google Scholar ]
• GooSeeker (2015), E. coli . Available at: http://www.gooseeker.com/pro/product.html , (Accessed January 20, 2020).
• Guo J., Wang X., Wu Y. (2020). Positive emotion bias: role of emotional content from online customer reviews in purchase decisions . J. Retail. Consum. Serv. 52 :101891. doi: 10.1016/j.jretconser.2019.101891 [ CrossRef ] [ Google Scholar ]
• Hasanat M., Hoque A., Shikha F., Anwar M., Abdul Hamid A. B., Huam T. (2020). The impact of coronavirus (COVID-19) on E-Business in Malaysia . Asian J. Multidisc. Stud. 3 , 85–90. [ Google Scholar ]
• Hong H., Xu D., Wang G., Fan W. (2017). Understanding the determinants of online review helpfulness: a meta-analytic investigation . Decis. Support. Syst. 102 , 1–11. doi: 10.1016/j.dss.2017.06.007 [ CrossRef ] [ Google Scholar ]
• Huang P., Lurie N., Mitra S. (2009). Searching for experience on the web: an empirical examination of consumer behavior for search and experience goods . J. Mark. Am. Mark. Assoc. 73 , 55–69. doi: 10.2307/20619010 [ CrossRef ] [ Google Scholar ]
• Hwang Y. M., Lee K. C. (2018). Using an eye-tracking approach to explore gender differences in visual attention and shopping attitudes in an online shopping environment . Int. J. Human–Comp. Inter. 34 , 15–24. doi: 10.1080/10447318.2017.1314611 [ CrossRef ] [ Google Scholar ]
• Jacob R., Karn K. (2003). “ Eye tracking in human-computer interaction and usability research: ready to deliver the promises, ” in The mind’s eye North-Holland (New York: Elsevier; ), 573–605. [ Google Scholar ]
• Jiménez F. R., Mendoza N. A. (2013). Too popular to ignore: the influence of online reviews on purchase intentions of search and experience products . J. Interact. Mark. 27 , 226–235. doi: 10.1016/j.intmar.2013.04.004 [ CrossRef ] [ Google Scholar ]
• Just M., Carpenter P. (1980). A theory of reading: from eye fixations to comprehension . Psychol. Rev. 87 , 329–354. doi: 10.1037/0033-295X.87.4.329, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Just M., Carpenter P. (1992). A capacity theory of comprehension: individual differences in working memory . Psychol. Rev. 99 , 122–149. doi: 10.1037/0033-295x.99.1.122, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kang T. C., Hung S. Y., Huang A. H. (2020). The adoption of online product information: cognitive and affective evaluations . J. Internet Commer. 19 , 373–403. doi: 10.1080/15332861.2020.1816315 [ CrossRef ] [ Google Scholar ]
• Kanungo S., Jain V. (2012). Online shopping behaviour: moderating role of gender and product category . Int. J. Bus. Inform. Syst. 10 , 197–221. doi: 10.1504/ijbis.2012.047147 [ CrossRef ] [ Google Scholar ]
• Kaur S., Lal A. K., Bedi S. S. (2017). Do vendor cues influence purchase intention of online shoppers? An empirical study using SOR framework . J. Internet Commer. 16 , 343–363. doi: 10.1080/15332861.2017.1347861 [ CrossRef ] [ Google Scholar ]
• Lackermair G., Kailer D., Kanmaz K. (2013). Importance of online product reviews from a consumer’s perspective . Adv. Econ. Bus. 1 , 1–5. doi: 10.13189/aeb.2013.010101 [ CrossRef ] [ Google Scholar ]
• Liu H.-C., Lai M.-L., Chuang H.-H. (2011). Using eye-tracking technology to investigate the redundant effect of multimedia web pages on viewers’ cognitive processes . Comput. Hum. Behav. 27 , 2410–2417. doi: 10.1016/j.chb.2011.06.012 [ CrossRef ] [ Google Scholar ]
• Luan J., Yao Z., Zhao F., Liu H. (2016). Search product and experience product online reviews: an eye-tracking study on consumers’ review search behavior . Comput. Hum. Behav. 65 , 420–430. doi: 10.1016/j.chb.2016.08.037 [ CrossRef ] [ Google Scholar ]
• Mayzlin D., Chevalier J. (2003). The effect of word of mouth on sales: online book reviews . J. Mark. Res. 43 :409. doi: 10.2307/30162409 [ CrossRef ] [ Google Scholar ]
• Meyers-Levy J., Sternthal B. (1993). A two-factor explanation of assimilation and contrast effects . J. Mark. Res. 30 , 359–368. doi: 10.1177/002224379303000307 [ CrossRef ] [ Google Scholar ]
• Mudambi S., Schuff D. (2010). What makes a helpful online review? A study of customer reviews on Amazon.com . MIS Q. 34 , 185–200. doi: 10.1007/s10107-008-0244-7 [ CrossRef ] [ Google Scholar ]
• Mumuni A. G., O’Reilly K., MacMillan A., Cowley S., Kelley B. (2020). Online product review impact: the relative effects of review credibility and review relevance . J. Internet Commer. 19 , 153–191. doi: 10.1080/15332861.2019.1700740 [ CrossRef ] [ Google Scholar ]
• Pavlou P., Dimoka A. (2010). NeuroIS: the potential of cognitive neuroscience for information systems research . Inform. Sys. Res. Art. Adv. 19 , 153–191. doi: 10.1080/15332861.2019.1700740 [ CrossRef ] [ Google Scholar ]
• Plassmann H., Venkatraman V., Huettel S., Yoon C. (2015). Consumer neuroscience: applications, challenges, and possible solutions . J. Mark. Res. 52 , 427–435. doi: 10.1509/jmr.14.0048 [ CrossRef ] [ Google Scholar ]
• Racherla P., Friske W. (2013). Perceived “usefulness” of online consumer reviews: an exploratory investigation across three services categories . Electron. Commer. Res. Appl. 11 , 548–559. doi: 10.1016/j.elerap.2012.06.003 [ CrossRef ] [ Google Scholar ]
• Renshaw J. A., Finlay J. E., Tyfa D., Ward R. D. (2004). Understanding visual influence in graph design through temporal and spatial eye movement characteristics . Interact. Comput. 16 , 557–578. doi: 10.1016/j.intcom.2004.03.001 [ CrossRef ] [ Google Scholar ]
• Rosa P. J. (2015). What do your eyes say? Bridging eye movements to consumer behavior . Int. J. Psychol. Res. 15 , 1250–1256. doi: 10.1116/1.580598 [ CrossRef ] [ Google Scholar ]
• Ruiz-Mafe C., Chatzipanagiotou K., Curras-Perez R. (2018). The role of emotions and conflicting online reviews on consumers’ purchase intentions . J. Bus. Res. 89 , 336–344. doi: 10.1016/j.jbusres.2018.01.027 [ CrossRef ] [ Google Scholar ]
• Sen S., Lerman D. (2007). Why are you telling me this? An examination into negative consumer reviews on the web . J. Interact. Mark. 21 , 76–94. doi: 10.1002/dir.20090 [ CrossRef ] [ Google Scholar ]
• Song W., Park S., Ryu D. (2017). Information quality of online reviews in the presence of potentially fake reviews . Korean Eco. Rev. 33 , 5–34. [ Google Scholar ]
• Tupikovskaja-Omovie Z., Tyler D. (2021). Eye tracking technology to audit google analytics: analysing digital consumer shopping journey in fashion m-retail . Int. J. Inf. Manag. 59 :102294. doi: 10.1016/j.ijinfomgt.2020.102294 [ CrossRef ] [ Google Scholar ]
• Vimaladevi K., Dhanabhakaym M. (2012). A study on the effects of online consumer reviews on purchasing decision . Prestige In. J. Manag. 7 , 51–99. doi: 10.1504/IJIMA.2012.044958 [ CrossRef ] [ Google Scholar ]
• Von Helversen B., Abramczuk K., Kopeć W., Nielek R. (2018). Influence of consumer reviews on online purchasing decisions in older and younger adults . Decis. Support. Syst. 113 , 1–10. doi: 10.1016/j.dss.2018.05.006 [ CrossRef ] [ Google Scholar ]
• Wang Y., Minor M. (2008). Validity, reliability, and applicability of psychophysiological techniques in marketing research . Psychol. Mark. 25 , 197–232. doi: 10.1002/mar.20206 [ CrossRef ] [ Google Scholar ]
• Wang Q., Yang S., Cao Z., Liu M., Ma Q. (2014). An eye-tracking study of website complexity from cognitive load perspective . Decis. Support. Syst. 62 , 1–10. doi: 10.1016/j.dss.2014.02.007 [ CrossRef ] [ Google Scholar ]
• World Medical Association (2014). World medical association declaration of Helsinki: ethical principles for medical research involving human subjects . J. Am. Coll. Dent. 81 , 14–18. doi: 10.1111/j.1447-0756.2001.tb01222.x, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wu Y., Liu T., Teng L., Zhang H., Xie C. (2021). The impact of online review variance of new products on consumer adoption intentions . J. Bus. Res. 136 , 209–218. doi: 10.1016/J.JBUSRES.2021.07.014 [ CrossRef ] [ Google Scholar ]
• Wu Y., Ngai E., Pengkun W., Wu C. (2020). Fake online reviews: literature review, synthesis, and directions for future research . Decis. Support. Syst. 132 :113280. doi: 10.1016/j.dss.2020.113280 [ CrossRef ] [ Google Scholar ]
• Yang S. F. (2015). An eye-tracking study of the elaboration likelihood model in online shopping . Electron. Commer. Res. Appl. 14 , 233–240. doi: 10.1016/j.elerap.2014.11.007 [ CrossRef ] [ Google Scholar ]
• Yu X., Liu Y., Huang X., An A. (2010). Mining online reviews for predicting sales performance: a case study in the movie domain . IEEE Trans. Knowl. Data Eng. 24 , 720–734. doi: 10.1109/TKDE.2010.269 [ CrossRef ] [ Google Scholar ]
• Yuanyuan H., Peng Z., Yijun L., Qiang Y. J. M. R. (2009). An empirical study on the impact of online reviews sentimental orientation on sale based on movie panel data . Manag. Rev. 21 , 95–103. doi: 10.1007/978-3-642-00205-2_9 [ CrossRef ] [ Google Scholar ]
• Zhang K., Cheung C., Lee M. (2014). Examining the moderating effect of inconsistent reviews and its gender differences on consumers’ online shopping decision . Int. J. Inf. Manag. 34 , 89–98. doi: 10.1016/j.ijinfomgt.2013.12.001 [ CrossRef ] [ Google Scholar ]
• Zhang J., Craciun G., Shin D. (2010). When does electronic word-of-mouth matter? A study of consumer product reviews . J. Bus. Res. 63 , 1336–1341. doi: 10.1016/j.jbusres.2009.12.011 [ CrossRef ] [ Google Scholar ]
• Zhong-Gang Y., Xiao-Ya W., Economics S. O. J. S. E. (2015). Research progress and future prospect on online reviews and consumer behavior . Soft Science. 6 :20. doi: 10.3760/cma.j.cn112137-20200714-02111 [ CrossRef ] [ Google Scholar ]

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.

Design/methodology/approach

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.

Originality/value

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. https://doi.org/10.1108/PRR-08-2017-0034

Emerald Publishing Limited

Copyright © 2018, Jalal Rajeh Hanaysha.

Published in the PSU Research Review: An International Journal . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

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 ).

Methodology

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.

Implications

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.

Research framework

Structural model

Discriminant validity

Results of hypotheses

Measurements of constructs

Agrawal , D. ( 1996 ), “ Effect of brand loyalty on advertising and trade promotions: a game theoretic analysis with empirical evidence ”, Marketing Science , Vol. 15 No. 1 , pp. 86 - 108 .

Albus , H. ( 2012 ), “ The effects of corporate social responsibility on service recovery evaluations in casual dining restaurants”, Master’s dissertation , University of Central Florida Orlando , FL .

Ali , F. , Omar , R. and Amin , M. ( 2013 ), “ An examination of the relationships between physical environment, perceived value, image and behavioural intentions: a SEM approach towards Malaysian resort hotels ”, Journal of Hotel and Tourism Management , Vol. 27 No. 2 , pp. 9 - 26 .

Amofah , O. , Gyamfi , I. and Tutu , C.O. ( 2016 ), “ The influence of service marketing mix on customer choice of repeat purchase of restaurant in Kumasi, Ghana ”, European Journal of Business and Management , Vol. 8 No. 11 , pp. 102 - 112 .

Andreti , J. , Zhafira , N.H. , Akmal , S.S. and Kumar , S. ( 2013 ), “ The analysis of product, price, place, promotion and service quality on customers’ buying decision of convenience store: a survey of young adult in Bekasi, West Java, Indonesia ”, International Journal of Advances in Management and Economics , Vol. 2 No. 6 , pp. 72 - 78 .

Astuti , R. , Silalahi , R.L.R. and Wijaya , G.D.P. ( 2015 ), “ Marketing strategy based on marketing mix influence on purchasing decisions of Malang apples consumers at giant Olympic garden mall (MOG), Malang city, East Java province, Indonesia ”, Agriculture and Agricultural Science Procedia , Vol. 3 , pp. 67 - 71 .

Bagnoli , M. and Watts , S.G. ( 2003 ), “ Selling to socially responsible consumers: competition and the private provision of public goods ”, Journal of Economics & Management Strategy , Vol. 12 No. 3 , pp. 419 - 445 .

Bagozzi , R.P. , Yi , Y. and Phillips , L.W. ( 1991 ), “ Assessing construct validity in organizational research ”, Administrative Science Quarterly , Vol. 63 , pp. 421 - 458 .

Baker , J. , Parasuraman , A. , Grewal , D. and Voss , G.B. ( 2002 ), “ The influence of multiple store environment cues on perceived merchandise value and patronage intentions ”, Journal of Marketing , Vol. 66 No. 2 , pp. 120 - 141 .

Bakırtaş , H. ( 2013 ), “ Impact of sales promotion on purchase decision of consumers: an application in tourism sector ”, Journal of Human Sciences , Vol. 10 No. 1 , pp. 676 - 694 .

Basil , G. , Etuk , E. and Ebitu , E.T. ( 2013 ), “ The marketing mix element as determinants of consumer’s choice of made-in-Nigeria shoes in Cross River state ”, European Journal of Business and Management , Vol. 5 No. 6 , pp. 141 - 147 .

Belk , R.W. ( 1975 ), “ Situational variables and consumer behaviour ”, Journal of Consumer Research , Vol. 2 No. 3 , pp. 157 - 164 .

Bhattacharya , C.B. and Sen , S. ( 2004 ), “ Doing better at doing good: when, why, and how consumers respond to corporate social initiatives ”, California Management Review , Vol. 47 No. 1 , pp. 9 - 24 .

Biswas , A. , Bhowmick , S. , Guha , A. and Grewal , D. ( 2013 ), “ Consumer evaluations of sale prices: role of the subtraction principle ”, Journal of Marketing , Vol. 77 , No. 4 , pp. 49 - 66 .

Blackwell , R.D. , Miniard , P.W. and Engel , J.F. ( 2001 ), Consumer Behavior , 9th ed. , South-Western Thomas Learning , Mason, OH .

Blythe , S.E. ( 2005 ), “ Digital signature law of the United Nations, European Union, United Kingdom and United States: promotion of growth in E-commerce with enhanced security ”, Richmond Journal of Law and Technology , Vol. 11 No. 2 , pp. 6 - 8 .

Birgelen , M. , Wetzels , M. and de Ruyter , K. ( 1997 ), “ Commitment in service relationships: An empirical test of its antecedents and consequence ”, paper presented at the EMAC Annual Conference , University of Warwick , 20-23 May , pp. 1255 - 1271 .

Brodie , R.J. , Ilic , A. , Juric , B. and Hollebeek , L. ( 2013 ), “ Consumer engagement in a virtual Brand community: an exploratory analysis ”, Journal of Business Research , Vol. 66 No. 1 , pp. 105 - 114 .

Bulearca , M. and Bulearca , S. ( 2010 ), “ Twitter: A viable marketing tool for SMEs ”, Journal of Global Business & Management Research , Vol. 2 No. 4 , pp. 296 - 309 .

Chaipradermsak , T. ( 2007 ), “ The influential factors on consumers’ purchasing decision in Bangkok pet retailing business”, MBA Thesis , Shinawatra University .

Chappuis , B. Gaffey , B. and Parvizi , P. ( 2011 ), “ Are your customers becoming digital junkies ”, McKinsey Quarterly , available at: www.mckinsey.com/business-functions/marketing-and-sales/our-insights/are-your-customers-becoming-digital-junkies (accessed 8 July 2017 ).

Chebat , J.C. and Michon , R. ( 2003 ), “ Impact of ambient odors on mall shoppers’ emotions, cognition, and spending: a test of competitive causal theories ”, Journal of Business Research , Vol. 56 No. 7 , pp. 529 - 539 .

Cheng , M.S. , Cripps , H. and Chen , C.H. ( 2006 ), “ The relationships between perceived quality, perceived value, and purchase intentions – a study in internet marketing ”, Australian and New Zealand Marketing Academy Conference , Brisbane , pp. 1 - 7 , available at: http://eprints.utar.edu.my/1921/1/Microsoft_Word_-_Factors_influencing_consumer’s_willingness_to_purchase_PLBs.pdf

Cui , B. , Yang , K. and Chou , T. ( 2016 ), “ Analyzing the impact of price promotion strategies on manufacturer sales performance ”, Journal of Service Science and Management , Vol. 9 No. 2 , pp. 182 - 187 .

Darko , E. ( 2012 ), “ The influence of sales promotion on consumer buying behaviour in the telecom industry; the case of Vodafone Ghana”, MBA dissertation , Institute of Distance Learning, Kwame Nkrumah University of Science and Technology .

de Medeiros , J.F. , Ribeiro , J.L.D. and Cortimiglia , M.N. ( 2016 ), “ Influence of perceived value on purchasing decisions of green products in Brazil ”, Journal of Cleaner Production , Vol. 110 , pp. 158 - 169 .

de Pablos , P.O. ( 2016 ), “ Managerial strategies and solutions for business success in Asia ”, Business Science Reference , University of Oviedo .

Demirgünescedil , B.K. ( 2015 ), “ Relative importance of perceived value, satisfaction and perceived risk on willingness to pay more ”, International Review of Management and Marketing , Vol. 5 No. 4 , pp. 211 - 220 .

Dube , L. and Renaghan , L. ( 2000 ), “ Creating visible customer value ”, Cornell Hotel and Restaurant Administration Quarterly , Vol. 41 No. 1 , pp. 62 - 72 .

Duffett , R.G. ( 2015 ), “ Facebook advertising’s influence on intention-to-purchase and purchase amongst millennials ”, Internet Research , Vol. 25 No. 4 , pp. 498 - 526 .

Eleboda , S.S. ( 2017 ), “ Sales promotion as a strategy in service marketing: exploring the believability dilemma and consumer purchase decision ”, American Journal of Marketing Research , Vol. 3 No. 2 , pp. 8 - 15 .

Elg , U. and Hultman , J. ( 2016 ), “ CSR: retailer activities vs consumer buying decisions ”, International Journal of Retail & Distribution Management , Vol. 44 No. 6 , pp. 640 - 657 .

El-Garaihy , W.H. , Mobarak , A.K.M. and Albahussain , S.A. ( 2014 ), “ Measuring the impact of corporate social responsibility practices on competitive advantage: a mediation role of reputation and customer satisfaction ”, International Journal of Business and Management , Vol. 9 No. 5 , pp. 109 - 124 .

Ehrenberg , A. and Sharp , B. ( 2000 ), “ Managerial uses of descriptive marketing models-the case of the Dirichlet”, Doctoral dissertation , Griffith University .

Erdoğmuş , ĐE. and Cicek , M. ( 2012 ), “ The impact of social media marketing on Brand loyalty ”, Procedia-Social and Behavioral Sciences , Vol. 58 , pp. 1353 - 1360 .

Erkollar , A. and Oberer , B.J. ( 2012 ), “ Responsible business: the European Union is driving forward the European strategies on corporate social responsibility ”, Journal of EU Research in Business , Vol. 2012 , pp. 1 - 15 .

Ghosh , D. and Gurunathan , L. ( 2014 ), “ Linking perceived corporate social responsibility and intention to quit: the mediating role of job embeddedness ”, Vision: The Journal of Business Perspective , Vol. 18 No. 3 , pp. 175 - 183 .

Godes , D. and Mayzlin , D. ( 2009 ), “ Firm-created word-of-mouth communication: Evidence from a field test ”, Marketing Science , Vol. 28 No. 4 , pp. 721 - 739 .

Green , T. and Peloza , J. ( 2011 ), “ How does corporate social responsibility create value for consumers? ”, Journal of Consumer Marketing , Vol. 28 No. 1 , pp. 48 - 56 .

Guchait , P. Anner , M. and Wu , L. ( 2011 ), “ Customer perceptions of corporate social responsibility of service firms: Impact on customer attitudes and behavioral intentions ”, available at: http://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1034&context=gradconf_hospitality (accessed 5 January 2016 ).

Hair , J.F. , Jr , Black , W.C. , Jr , Babin , B.J. , Anderson , R.E. and Tatham , R.L. ( 2010 ), Multivariate Data Analysis , 7th ed. , Prentice Hall , Upper Saddle River, NJ .

Han , H. and Ryu , K. ( 2009 ), “ The roles of the physical environment, price perception, and customer satisfaction in determining customer loyalty in the family restaurant industry ”, Journal of Hospitality and Tourism Research , Vol. 33 No. 4 , pp. 487 - 510 .

Hanaysha , J. ( 2016 ), “ The importance of social media advertisements in enhancing Brand equity: a study on fast food restaurant industry in Malaysia ”, Business and Economic Research , Vol. 6 No. 1 , pp. 46 - 51 .

Handelman , J.M. and Arnold , S.J. ( 1999 ), “ The role of marketing actions with a social dimension: appeals to the institutional environment ”, Journal of Marketing , Vol. 63 No. 3 , pp. 33 - 48 .

Hasan , S. , Wahid , M. , Syafi , M. and Basalamah , I. ( 2016 ), “ Marketing mix effect on the decision of customers in the matter in choosing the home purchase loans in pt.Bni 46 griya bni (persero) tbk branch Makassar ”, International Journal of Business and Management Invention , Vol. 5 No. 6 , pp. 20 - 30 .

Hassan , L. , Shaw , D. , Shiu , E. , Walsh , G. and Parry , S. ( 2013 ), “ Uncertainty in ethical consumer choice: A conceptual model ”, Journal of Consumer Behaviour , Vol. 12 No. 3 , pp. 182 - 193 .

Hill , R.P. and Langan , R. ( 2014 ), Handbook of Research on Marketing and Corporate Social Responsibility , Edward Elgar Publishing , Northampton, MA .

Hosseini , Z. , Jayashree , S. and Malarvizhi , C. ( 2014 ), “ Store image and its effect on customer perception of retail stores ”, Asian Social Science , Vol. 10 No. 21 , pp. 223 - 235 .

Hudson , S. , Huang , L. , Roth , M.S. and Madden , T.J. ( 2016 ), “ The influence of social media interactions on consumer-brand relationships: a three-country study of brand perceptions and marketing behaviors ”, International Journal of Research in Marketing , Vol. 33 No. 1 , pp. 27 - 41 .

Hussain , R. and Ali , M. ( 2015 ), “ Effect of store atmosphere on consumer purchase intention ”, International Journal of Marketing Studies , Vol. 7 No. 2 , pp. 35 - 43 .

Kaplan , A.M. and Haenlein , M. ( 2010 ), “ Users of the world, unite! the challenges and opportunities of social media ”, Business Horizons , Vol. 53 No. 1 , pp. 59 - 68 .

Khatib , F. ( 2016 ), “ The impact of social media characteristics on purchase decision empirical study of Saudi customers in Aseer region ”, International Journal of Business and Social Science , Vol. 7 No. 4 , pp. 41 - 50 .

Kilcullen , M. and Kooistra , J.O. ( 1999 ), “ At least do no harm: sources on the changing role of business ethics and corporate social responsibility ”, Reference Services Review , Vol. 27 No. 2 , pp. 158 - 178 .

Köksal , Y. ( 2014 ), “ The efficiency of monetary and non-monetary sales promotions on brand preference: a case of Albanian GSM companies ”, Journal of the School of Business Administration, Istanbul University , Vol. 43 No. 2 , pp. 319 - 331 .

Kotler , P. and Keller , K.L. ( 2012 ), Marketing Management , 14th ed. , Pearson Education Limited , Upper Saddle River, NJ .

Kotler , P. and Lee , N. ( 2005 ), Corporate Social Responsibility: Doing the Most Good for Your Company and Your Cause , John Wiley & Sons , Hoboken, NJ .

Kotler , P. , Wong , V. , Saunders , J. and Armstrong , G. ( 2004 ), Principles of Marketing , 4th ed. , Prentice Hall , NJ .

Kumar , I. , Garg , R. and Rahman , Z. ( 2010 ), “ Influence of retail atmospherics on customer value in an emerging market condition ”, Great Lakes Herald , Vol. 4 No. 1 , pp. 1 - 13 .

Lee , S. and Jeong , M. ( 2012 ), “ Effects of e-servicescape on consumers’ flow experiences ”, Journal of Hospitality and Tourism Technology , Vol. 3 No. 1 , pp. 47 - 59 .

Luo , X. and Bhattacharya , C.B. ( 2006 ), “ Corporate social responsibility, customer satisfaction, and market value ”, Journal of Marketing , Vol. 70 No. 4 , pp. 1 - 18 .

McDaniel , C. and Gates , R.H. ( 1998 ), Marketing Research Essentials , South-Western College , OH .

Mahmood , R. and Khan , S.M. ( 2014 ), “ Impact of service marketing mixes on customer perception: a study on Eastern Bank Limited, Bangladesh ”, European Journal of Business and Management , Vol. 6 No. 34 , pp. 164 - 167 .

Mangold , W.G. and Faulds , D.J. ( 2009 ), “ Social media: the new hybrid element of the promotion mix ”, Business Horizons , Vol. 52 No. 4 , pp. 357 - 365 .

Mohammad , J.A.M. and Yusoff , M.S.B. ( 2017 ), “ Psychometric properties of the mentor behaviour scale in a sample of Malaysian medical students ”, Journal of Taibah University Medical Sciences , Vol. 10 No. 2 , pp. 159 - 168 .

Mullin , R. and Cummins , J. ( 2010 ), Sales Promotion: How to Create, Implement and Integrate Campaigns That Really Work , 4th ed. , Kogan Page Publishers .

Neha , S. and Manoj , V. ( 2013 ), “ Impact of sales promotion tools on consumer’s purchase decision towards white good (refrigerator) at durg and Bhilai region of CG, India ”, Research Journal of Management Sciences , Vol. 2 No. 7 , pp. 10 - 14 .

Newman , A.J. and Patel , D. ( 2004 ), “ The marketing directions of two fashion retailers ”, European Journal of Marketing , Vol. 38 No. 7 , pp. 770 - 789 .

Nochai , R. and Nochai , T. ( 2011 ), “ The influence of sale promotion factors on purchase decisions: a case study of portable PCs in Thailand ”, International Proceedings of Economics Development & Research , p. 130 .

Oh , J. , Fiorito , S.S. , Cho , H. and Hofacker , C.F. ( 2008 ), “ Effects of design factors on store image and expectation of merchandise quality in web-based stores ”, Journal of Retailing and Consumer Services , Vol. 15 No. 4 , pp. 237 - 249 .

Pakseresht , A. ( 2010 ), “ Brand equity and corporate responsibility: a review of brand valuation methods ”, available at: http://stud.epsilon.slu.se/1932/1/pakseresht_a_101019.pdf (accessed 20 January 2016 ).

Puncheva-Michelotti , P. and Michelotti , M. ( 2010 ), “ The role of the stakeholder perspective in measuring corporate reputation ”, Marketing Intelligence & Planning , Vol. 28 No. 3 , pp. 249 - 274 .

Qualman , E. ( 2013 ), Socialnomics: How Social Media Transforms the Way We Live and Do Business , John Wiley & Sons , Hoboken, NJ .

Reibstein , D.J. ( 1985 ), Marketing, Concepts, Strategies, and Decisions , Prentice Hall , NJ .

Reich , A.Z. , Xu , Y.H. and McCleary , K.W. ( 2010 ), “ The influence of social responsibility image relative to product and service quality on brand loyalty: an exploratory study of quick-service restaurants ”, Hospitality Review , Vol. 28 No. 1 , pp. 20 - 50 .

Richardson , P. , Jain , A.K. and Dick , A. ( 1996 ), “ The influence of store aesthetics on evaluation of private label brands ”, Journal of Product & Brand Management , Vol. 5 No. 1 , pp. 19 - 28 .

Rodrigues , P. and Borges , A.P. ( 2015 ), “ Corporate social responsibility and its impact in consumer decision-making ”, Social Responsibility Journal , Vol. 11 No. 4 , pp. 690 - 701 .

Santini , F.D.O. , Sampaio , C.H. , Perin , M.G. and Vieira , V.A. ( 2015 ), “ An analysis of the influence of discount sales promotion in consumer buying intent and the moderating effects of attractiveness ”, Revista de Administração (São Paulo) , Vol. 50 No. 4 , pp. 416 - 431 .

Schiffman , L.G. and Kanuk , L.L. ( 2007 ), Consumer Behavior , 9th ed. , Prentice-Hall , NJ .

Schivinski , B. and Dabrowski , D. ( 2014 ), “ The effect of social media communication on consumer perceptions of brands ”, Journal of Marketing Communications , Vol. 22 No. 2 , pp. 1 - 26 .

Sen , S. and Bhattacharya , C.B. ( 2001 ), “ Does doing good always lead to doing better? Consumer reactions to corporate social responsibility ”, Journal of Marketing Research , Vol. 38 No. 2 , pp. 225 - 243 .

Severi , E. , Ling , K.C. and Nasermoadeli , A. ( 2014 ), “ The impacts of electronic word of mouth on brand equity in the context of social media ”, International Journal of Business and Management , Vol. 9 No. 8 , pp. 84 - 96 .

Shamsher , R. ( 2015 ), “ Store image and its impact on consumer behaviour ”, Elk Asia Pacific Journal of Marketing and Retail Management , Vol. 7 No. 2 , pp. 1 - 27 .

Shareef , M.A. , Kumar , U. and Kumar , V. ( 2008 ), “ Role of different electronic-commerce (EC) quality factors on purchase decision: a developing country perspective ”, International Journal of Electronic Marketing and Retailing , Vol. 2 No. 2 , pp. 92 - 113 .

Shrestha , A. ( 2015 ), “ Effects of sales promotion on purchasing decision of customer: a case study of Baskin Robbins ice–cream franchise Thailand ”, Master’s thesis , Bangkok University .

Simonson , I. , Carmon , Z. and O’curry , S. ( 1994 ), “ Experimental evidence on the negative effect of product features and sales promotions on brand choice ”, Marketing Science , Vol. 13 No. 1 , pp. 23 - 40 .

Soewandi , M. ( 2015 ), “ The impact of social media communication forms on Brand equity dimensions and consumer purchase intention ”, iBuss Management , Vol. 3 No. 2 , pp. 204 - 213 .

Stanton , W.J. , Etzel , M.J. and Walker , B.J. ( 2007 ), Marketing , 14th ed. , McGraw-Hill , Irwin .

Tabachnick , B.G. and Fidell , L.S. ( 2001 ), Using Multivariate Statistics , Allyn and Bacon , Boston, MA .

Thang , D.C.L. and Tan , B.L.B. ( 2003 ), “ Linking consumer perception to preference of retail stores: an empirical assessment of the multi-attributes of store image ”, Journal of Retailing and Consumer Services , Vol. 10 No. 4 , pp. 193 - 200 .

Tong , C. and Wong , A. ( 2014 ), “ The influences of corporate social responsibility to customer repurchases intentions, customer word-of-mouth intentions and customer perceived food quality of fast-food restaurants in Hong Kong and the mediating effects of corporate reputation ”, British Journal of Economics, Management & Trade , Vol. 4 No. 11 , pp. 1655 - 1678 .

Torres , A. , Bijmolt , T.H. , Tribó , J.A. and Verhoef , P. ( 2012 ), “ Generating global Brand equity through corporate social responsibility to key stakeholders ”, International Journal of Research in Marketing , Vol. 29 No. 1 , pp. 13 - 24 .

Villarejo-Ramos , A.F. and Sánchez-Franco , M.J. ( 2005 ), “ The impact of marketing communication and price promotion on brand equity ”, Journal of Brand Management , Vol. 12 No. 6 , pp. 431 - 444 .

Yee , C.J. and San , N.C. ( 2011 ), “ Consumers’ perceived quality, perceived value and perceived risk towards purchase decision on automobile ”, American Journal of Economics and Business Administration , Vol. 3 No. 1 , pp. 47 - 57 .

Yeshin , T. ( 2006 ), Sales Promotion , Cengage Learning EMEA .

Yogesh , F. and Yesha , M. ( 2014 ), “ Effect of social media on purchase decision ”, Pacific Business Review International , Vol. 6 No. 11 , pp. 45 - 50 .

Zeithaml , V.A. ( 1988 ), “ Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence ”, The Journal of Marketing , Vol. 52 No. 3 , pp. 2 - 22 .

Zoellner , F. and Schaefers , T. ( 2015 ), “ The impact of price promotion types on sales and brand perception of premium products ”, Developments in Marketing Science: Proceedings of the Academy of Marketing Science in Ideas in Marketing: Finding the New and Polishing the Old, Springer , Cham , pp. 528 - 528 .

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.

Related articles

We’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

The past, present, and future of consumer research

• Published: 13 June 2020
• Volume 31 , pages 137–149, ( 2020 )

Cite this article

• Maayan S. Malter   ORCID: orcid.org/0000-0003-0383-7925 1 ,
• Morris B. Holbrook 1 ,
• Barbara E. Kahn 2 ,
• Jeffrey R. Parker 3 &
• Donald R. Lehmann 1  

45k Accesses

37 Citations

3 Altmetric

Explore all metrics

In this article, we document the evolution of research trends (concepts, methods, and aims) within the field of consumer behavior, from the time of its early development to the present day, as a multidisciplinary area of research within marketing. We describe current changes in retailing and real-world consumption and offer suggestions on how to use observations of consumption phenomena to generate new and interesting consumer behavior research questions. Consumption continues to change with technological advancements and shifts in consumers’ values and goals. We cannot know the exact shape of things to come, but we polled a sample of leading scholars and summarize their predictions on where the field may be headed in the next twenty years.

Similar content being viewed by others

Consumer Behavior Research Methods

Consumer dynamics: theories, methods, and emerging directions

Understanding effect sizes in consumer psychology

Avoid common mistakes on your manuscript.

1 Introduction

Beginning in the late 1950s, business schools shifted from descriptive and practitioner-focused studies to more theoretically driven and academically rigorous research (Dahl et al. 1959 ). As the field expanded from an applied form of economics to embrace theories and methodologies from psychology, sociology, anthropology, and statistics, there was an increased emphasis on understanding the thoughts, desires, and experiences of individual consumers. For academic marketing, this meant that research not only focused on the decisions and strategies of marketing managers but also on the decisions and thought processes on the other side of the market—customers.

Since then, the academic study of consumer behavior has evolved and incorporated concepts and methods, not only from marketing at large but also from related social science disciplines, and from the ever-changing landscape of real-world consumption behavior. Its position as an area of study within a larger discipline that comprises researchers from diverse theoretical backgrounds and methodological training has stirred debates over its identity. One article describes consumer behavior as a multidisciplinary subdiscipline of marketing “characterized by the study of people operating in a consumer role involving acquisition, consumption, and disposition of marketplace products, services, and experiences” (MacInnis and Folkes 2009 , p. 900).

This article reviews the evolution of the field of consumer behavior over the past half century, describes its current status, and predicts how it may evolve over the next twenty years. Our review is by no means a comprehensive history of the field (see Schumann et al. 2008 ; Rapp and Hill 2015 ; Wang et al. 2015 ; Wilkie and Moore 2003 , to name a few) but rather focuses on a few key thematic developments. Though we observe many major shifts during this period, certain questions and debates have persisted: Does consumer behavior research need to be relevant to marketing managers or is there intrinsic value from studying the consumer as a project pursued for its own sake? What counts as consumption: only consumption from traditional marketplace transactions or also consumption in a broader sense of non-marketplace interactions? Which are the most appropriate theoretical traditions and methodological tools for addressing questions in consumer behavior research?

2 A brief history of consumer research over the past sixty years—1960 to 2020

In 1969, the Association for Consumer Research was founded and a yearly conference to share marketing research specifically from the consumer’s perspective was instituted. This event marked the culmination of the growing interest in the topic by formalizing it as an area of research within marketing (consumer psychology had become a formalized branch of psychology within the APA in 1960). So, what was consumer behavior before 1969? Scanning current consumer-behavior doctoral seminar syllabi reveals few works predating 1969, with most of those coming from psychology and economics, namely Herbert Simon’s A Behavioral Model of Rational Choice (1955), Abraham Maslow’s A Theory of Human Motivation (1943), and Ernest Dichter’s Handbook of Consumer Motivations (1964). In short, research that illuminated and informed our understanding of consumer behavior prior to 1969 rarely focused on marketing-specific topics, much less consumers or consumption (Dichter’s handbook being a notable exception). Yet, these works were crucial to the rise of consumer behavior research because, in the decades after 1969, there was a shift within academic marketing to thinking about research from a behavioral or decision science perspective (Wilkie and Moore 2003 ). The following section details some ways in which this shift occurred. We draw on a framework proposed by the philosopher Larry Laudan ( 1986 ), who distinguished among three inter-related aspects of scientific inquiry—namely, concepts (the relevant ideas, theories, hypotheses, and constructs); methods (the techniques employed to test and validate these concepts); and aims (the purposes or goals that motivate the investigation).

2.1 Key concepts in the late - 1960s

During the late-1960s, we tended to view the buyer as a computer-like machine for processing information according to various formal rules that embody economic rationality to form a preference for one or another option in order to arrive at a purchase decision. This view tended to manifest itself in a couple of conspicuous ways. The first was a model of buyer behavior introduced by John Howard in 1963 in the second edition of his marketing textbook and quickly adopted by virtually every theorist working in our field—including, Howard and Sheth (of course), Engel-Kollat-&-Blackwell, Franco Nicosia, Alan Andreasen, Jim Bettman, and Joel Cohen. Howard’s great innovation—which he based on a scheme that he had found in the work of Plato (namely, the linkages among Cognition, Affect, and Conation)—took the form of a boxes-and-arrows formulation heavily influenced by the approach to organizational behavior theory that Howard (University of Pittsburgh) had picked up from Herbert Simon (Carnegie Melon University). The model represented a chain of events

where I = inputs of information (from advertising, word-of-mouth, brand features, etc.); C = cognitions (beliefs or perceptions about a brand); A = Affect (liking or preference for the brand); B = behavior (purchase of the brand); and S = satisfaction (post-purchase evaluation of the brand that feeds back onto earlier stages of the sequence, according to a learning model in which reinforced behavior tends to be repeated). This formulation lay at the heart of Howard’s work, which he updated, elaborated on, and streamlined over the remainder of his career. Importantly, it informed virtually every buyer-behavior model that blossomed forth during the last half of the twentieth century.

To represent the link between cognitions and affect, buyer-behavior researchers used various forms of the multi-attribute attitude model (MAAM), originally proposed by psychologists such as Fishbein and Rosenberg as part of what Fishbein and Ajzen ( 1975 ) called the theory of reasoned action. Under MAAM, cognitions (beliefs about brand attributes) are weighted by their importance and summed to create an explanation or prediction of affect (liking for a brand or preference for one brand versus another), which in turn determines behavior (choice of a brand or intention to purchase a brand). This took the work of economist Kelvin Lancaster (with whom Howard interacted), which assumed attitude was based on objective attributes, and extended it to include subjective ones (Lancaster 1966 ; Ratchford 1975 ). Overall, the set of concepts that prevailed in the late-1960s assumed the buyer exhibited economic rationality and acted as a computer-like information-processing machine when making purchase decisions.

2.2 Favored methods in the late-1960s

The methods favored during the late-1960s tended to be almost exclusively neo-positivistic in nature. That is, buyer-behavior research adopted the kinds of methodological rigor that we associate with the physical sciences and the hypothetico-deductive approaches advocated by the neo-positivistic philosophers of science.

Thus, the accepted approaches tended to be either experimental or survey based. For example, numerous laboratory studies tested variations of the MAAM and focused on questions about how to measure beliefs, how to weight the beliefs, how to combine the weighted beliefs, and so forth (e.g., Beckwith and Lehmann 1973 ). Here again, these assumed a rational economic decision-maker who processed information something like a computer.

Seeking rigor, buyer-behavior studies tended to be quantitative in their analyses, employing multivariate statistics, structural equation models, multidimensional scaling, conjoint analysis, and other mathematically sophisticated techniques. For example, various attempts to test the ICABS formulation developed simultaneous (now called structural) equation models such as those deployed by Farley and Ring ( 1970 , 1974 ) to test the Howard and Sheth ( 1969 ) model and by Beckwith and Lehmann ( 1973 ) to measure halo effects.

2.3 Aims in the late-1960s

During this time period, buyer-behavior research was still considered a subdivision of marketing research, the purpose of which was to provide insights useful to marketing managers in making strategic decisions. Essentially, every paper concluded with a section on “Implications for Marketing Managers.” Authors who failed to conform to this expectation could generally count on having their work rejected by leading journals such as the Journal of Marketing Research ( JMR ) and the Journal of Marketing ( JM ).

2.4 Summary—the three R’s in the late-1960s

Starting in the late-1960s to the early-1980s, virtually every buyer-behavior researcher followed the traditional approach to concepts, methods, and aims, now encapsulated under what we might call the three R’s —namely, rationality , rigor , and relevance . However, as we transitioned into the 1980s and beyond, that changed as some (though by no means all) consumer researchers began to expand their approaches and to evolve different perspectives.

2.5 Concepts after 1980

In some circles, the traditional emphasis on the buyer’s rationality—that is, a view of the buyer as a rational-economic, decision-oriented, information-processing, computer-like machine for making choices—began to evolve in at least two primary ways.

First, behavioral economics (originally studied in marketing under the label Behavioral Decision Theory)—developed in psychology by Kahneman and Tversky, in economics by Thaler, and applied in marketing by a number of forward-thinking theorists (e.g., Eric Johnson, Jim Bettman, John Payne, Itamar Simonson, Jay Russo, Joel Huber, and more recently, Dan Ariely)—challenged the rationality of consumers as decision-makers. It was shown that numerous commonly used decision heuristics depart from rational choice and are exceptions to the traditional assumptions of economic rationality. This trend shed light on understanding consumer financial decision-making (Prelec and Loewenstein 1998 ; Gourville 1998 ; Lynch Jr 2011 ) and how to develop “nudges” to help consumers make better decisions for their personal finances (summarized in Johnson et al. 2012 ).

Second, the emerging experiential view (anticipated by Alderson, Levy, and others; developed by Holbrook and Hirschman, and embellished by Schmitt, Pine, and Gilmore, and countless followers) regarded consumers as flesh-and-blood human beings (rather than as information-processing computer-like machines), focused on hedonic aspects of consumption, and expanded the concepts embodied by ICABS (Table 1 ).

2.6 Methods after 1980

The two burgeoning areas of research—behavioral economics and experiential theories—differed in their methodological approaches. The former relied on controlled randomized experiments with a focus on decision strategies and behavioral outcomes. For example, experiments tested the process by which consumers evaluate options using information display boards and “Mouselab” matrices of aspects and attributes (Payne et al. 1988 ). This school of thought also focused on behavioral dependent measures, such as choice (Huber et al. 1982 ; Simonson 1989 ; Iyengar and Lepper 2000 ).

The latter was influenced by post-positivistic philosophers of science—such as Thomas Kuhn, Paul Feyerabend, and Richard Rorty—and approaches expanded to include various qualitative techniques (interpretive, ethnographic, humanistic, and even introspective methods) not previously prominent in the field of consumer research. These included:

Interpretive approaches —such as those drawing on semiotics and hermeneutics—in an effort to gain a richer understanding of the symbolic meanings involved in consumption experiences;

Ethnographic approaches — borrowed from cultural anthropology—such as those illustrated by the influential Consumer Behavior Odyssey (Belk et al. 1989 ) and its discoveries about phenomena related to sacred aspects of consumption or the deep meanings of collections and other possessions;

Humanistic approaches —such as those borrowed from cultural studies or from literary criticism and more recently gathered together under the general heading of consumer culture theory ( CCT );

Introspective or autoethnographic approaches —such as those associated with a method called subjective personal introspection ( SPI ) that various consumer researchers like Sidney Levy and Steve Gould have pursued to gain insights based on their own private lives.

These qualitative approaches tended not to appear in the more traditional journals such as the Journal of Marketing , Journal of Marketing Research , or Marketing Science . However, newer journals such as Consumption, Markets, & Culture and Marketing Theory began to publish papers that drew on the various interpretive, ethnographic, humanistic, or introspective methods.

2.7 Aims after 1980

In 1974, consumer research finally got its own journal with the launch of the Journal of Consumer Research ( JCR ). The early editors of JCR —especially Bob Ferber, Hal Kassarjian, and Jim Bettman—held a rather divergent attitude about the importance or even the desirability of managerial relevance as a key goal of consumer studies. Under their influence, some researchers began to believe that consumer behavior is a phenomenon worthy of study in its own right—purely for the purpose of understanding it better. The journal incorporated articles from an array of methodologies: quantitative (both secondary data analysis and experimental techniques) and qualitative. The “right” balance between theoretical insight and substantive relevance—which are not in inherent conflict—is a matter of debate to this day and will likely continue to be debated well into the future.

2.8 Summary—the three I’s after 1980

In sum, beginning in the early-1980s, consumer research branched out. Much of the work in consumer studies remained within the earlier tradition of the three R’s—that is, rationality (an information-processing decision-oriented buyer), rigor (neo-positivistic experimental designs and quantitative techniques), and relevance (usefulness to marketing managers). Nonetheless, many studies embraced enlarged views of the three major aspects that might be called the three I’s —that is, irrationality (broadened perspectives that incorporate illogical, heuristic, experiential, or hedonic aspects of consumption), interpretation (various qualitative or “postmodern” approaches), and intrinsic motivation (the joy of pursuing a managerially irrelevant consumer study purely for the sake of satisfying one’s own curiosity, without concern for whether it does or does not help a marketing practitioner make a bigger profit).

3 The present—the consumer behavior field today

3.1 present concepts.

In recent years, technological changes have significantly influenced the nature of consumption as the customer journey has transitioned to include more interaction on digital platforms that complements interaction in physical stores. This shift poses a major conceptual challenge in understanding if and how these technological changes affect consumption. Does the medium through which consumption occurs fundamentally alter the psychological and social processes identified in earlier research? In addition, this shift allows us to collect more data at different stages of the customer journey, which further allows us to analyze behavior in ways that were not previously available.

Revisiting the ICABS framework, many of the previous concepts are still present, but we are now addressing them through a lens of technological change (Table 2 )

. In recent years, a number of concepts (e.g., identity, beliefs/lay theories, affect as information, self-control, time, psychological ownership, search for meaning and happiness, social belonging, creativity, and status) have emerged as integral factors that influence and are influenced by consumption. To better understand these concepts, a number of influential theories from social psychology have been adopted into consumer behavior research. Self-construal (Markus and Kitayama 1991 ), regulatory focus (Higgins 1998 ), construal level (Trope and Liberman 2010 ), and goal systems (Kruglanski et al. 2002 ) all provide social-cognition frameworks through which consumer behavior researchers study the psychological processes behind consumer behavior. This “adoption” of social psychological theories into consumer behavior is a symbiotic relationship that further enhances the theories. Tory Higgins happily stated that he learned more about his own theories from the work of marketing academics (he cited Angela Lee and Michel Pham) in further testing and extending them.

3.2 Present Methods

Not only have technological advancements changed the nature of consumption but they have also significantly influenced the methods used in consumer research by adding both new sources of data and improved analytical tools (Ding et al. 2020 ). Researchers continue to use traditional methods from psychology in empirical research (scale development, laboratory experiments, quantitative analyses, etc.) and interpretive approaches in qualitative research. Additionally, online experiments using participants from panels such as Amazon Mechanical Turk and Prolific have become commonplace in the last decade. While they raise concerns about the quality of the data and about the external validity of the results, these online experiments have greatly increased the speed and decreased the cost of collecting data, so researchers continue to use them, albeit with some caution. Reminiscent of the discussion in the 1970s and 1980s about the use of student subjects, the projectability of the online responses and of an increasingly conditioned “professional” group of online respondents (MTurkers) is a major concern.

Technology has also changed research methodology. Currently, there is a large increase in the use of secondary data thanks to the availability of Big Data about online and offline behavior. Methods in computer science have advanced our ability to analyze large corpuses of unstructured data (text, voice, visual images) in an efficient and rigorous way and, thus, to tap into a wealth of nuanced thoughts, feelings, and behaviors heretofore only accessible to qualitative researchers through laboriously conducted content analyses. There are also new neuro-marketing techniques like eye-tracking, fMRI’s, body arousal measures (e.g., heart rate, sweat), and emotion detectors that allow us to measure automatic responses. Lastly, there has been an increase in large-scale field experiments that can be run in online B2C marketplaces.

3.3 Present Aims

Along with a focus on real-world observations and data, there is a renewed emphasis on managerial relevance. Countless conference addresses and editorials in JCR , JCP , and other journals have emphasized the importance of making consumer research useful outside of academia—that is, to help companies, policy makers, and consumers. For instance, understanding how the “new” consumer interacts over time with other consumers and companies in the current marketplace is a key area for future research. As global and social concerns become more salient in all aspects of life, issues of long-term sustainability, social equality, and ethical business practices have also become more central research topics. Fortunately, despite this emphasis on relevance, theoretical contributions and novel ideas are still highly valued. An appropriate balance of theory and practice has become the holy grail of consumer research.

The effects of the current trends in real-world consumption will increase in magnitude with time as more consumers are digitally native. Therefore, a better understanding of current consumer behavior can give us insights and help predict how it will continue to evolve in the years to come.

4 The future—the consumer behavior field in 2040

The other papers use 2030 as a target year but we asked our survey respondents to make predictions for 2040 and thus we have a different future target year.

Niels Bohr once said, “Prediction is very difficult, especially if it’s about the future.” Indeed, it would be a fool’s errand for a single person to hazard a guess about the state of the consumer behavior field twenty years from now. Therefore, predictions from 34 active consumer researchers were collected to address this task. Here, we briefly summarize those predictions.

4.1 Future Concepts

While few respondents proffered guesses regarding specific concepts that would be of interest twenty years from now, many suggested broad topics and trends they expected to see in the field. Expectations for topics could largely be grouped into three main areas. Many suspected that we will be examining essentially the same core topics, perhaps at a finer-grained level, from different perspectives or in ways that we currently cannot utilize due to methodological limitations (more on methods below). A second contingent predicted that much research would center on the impending crises the world faces today, most mentioning environmental and social issues (the COVID-19 pandemic had not yet begun when these predictions were collected and, unsurprisingly, was not anticipated by any of our respondents). The last group, citing the widely expected profound impact of AI on consumers’ lives, argued that AI and other technology-related topics will be dominant subjects in consumer research circa 2040.

While the topic of technology is likely to be focal in the field, our current expectations for the impact of technology on consumers’ lives are narrower than it should be. Rather than merely offering innumerable conveniences and experiences, it seems likely that technology will begin to be integrated into consumers’ thoughts, identities, and personal relationships—probably sooner than we collectively expect. The integration of machines into humans’ bodies and lives will present the field with an expanding list of research questions that do not exist today. For example, how will the concepts of the self, identity, privacy, and goal pursuit change when web-connected technology seamlessly integrates with human consciousness and cognition? Major questions will also need to be answered regarding philosophy of mind, ethics, and social inequality. We suspect that the impact of technology on consumers and consumer research will be far broader than most consumer-behavior researchers anticipate.

As for broader trends within consumer research, there were two camps: (1) those who expect (or hope) that dominant theories (both current and yet to be developed) will become more integrated and comprehensive and (2) those who expect theoretical contributions to become smaller and smaller, to the point of becoming trivial. Both groups felt that current researchers are filling smaller cracks than before, but disagreed on how this would ultimately be resolved.

4.2 Future Methods

As was the case with concepts, respondents’ expectations regarding consumer-research methodologies in 2030 can also be divided into three broad baskets. Unsurprisingly, many indicated that we would be using many technologies not currently available or in wide use. Perhaps more surprising was that most cited the use of technology such as AI, machine-learning algorithms, and robots in designing—as opposed to executing or analyzing—experiments. (Some did point to the use of technologies such as virtual reality in the actual execution of experiments.) The second camp indicated that a focus on reliable and replicable results (discussed further below) will encourage a greater tendency for pre-registering studies, more use of “Big Data,” and a demand for more studies per paper (versus more papers per topic, which some believe is a more fruitful direction). Finally, the third lot indicated that “real data” would be in high demand, thereby necessitating the use of incentive-compatible, consequential dependent variables and a greater prevalence of field studies in consumer research.

As a result, young scholars would benefit from developing a “toolkit” of methodologies for collecting and analyzing the abundant new data of interest to the field. This includes (but is not limited to) a deep understanding of designing and implementing field studies (Gerber and Green 2012 ), data analysis software (R, Python, etc.), text mining and analysis (Humphreys and Wang 2018 ), and analytical tools for other unstructured forms of data such as image and sound. The replication crisis in experimental research means that future scholars will also need to take a more critical approach to validity (internal, external, construct), statistical power, and significance in their work.

4.3 Future Aims

While there was an air of existential concern about the future of the field, most agreed that the trend will be toward increasing the relevance and reliability of consumer research. Specifically, echoing calls from journals and thought leaders, the respondents felt that papers will need to offer more actionable implications for consumers, managers, or policy makers. However, few thought that this increased focus would come at the expense of theoretical insights, suggesting a more demanding overall standard for consumer research in 2040. Likewise, most felt that methodological transparency, open access to data and materials, and study pre-registration will become the norm as the field seeks to allay concerns about the reliability and meaningfulness of its research findings.

4.4 Summary - Future research questions and directions

Despite some well-justified pessimism, the future of consumer research is as bright as ever. As we revised this paper amidst the COVID-19 pandemic, it was clear that many aspects of marketplace behavior, consumption, and life in general will change as a result of this unprecedented global crisis. Given this, and the radical technological, social, and environmental changes that loom on the horizon, consumer researchers will have a treasure trove of topics to tackle in the next ten years, many of which will carry profound substantive importance. While research approaches will evolve, the core goals will remain consistent—namely, to generate theoretically insightful, empirically supported, and substantively impactful research (Table 3 ).

5 Conclusion

At any given moment in time, the focal concepts, methods, and aims of consumer-behavior scholarship reflect both the prior development of the field and trends in the larger scientific community. However, despite shifting trends, the core of the field has remained constant—namely, to understand the motivations, thought processes, and experiences of individuals as they consume goods, services, information, and other offerings, and to use these insights to develop interventions to improve both marketing strategy for firms and consumer welfare for individuals and groups. Amidst the excitement of new technologies, social trends, and consumption experiences, it is important to look back and remind ourselves of the insights the field has already generated. Effectively integrating these past findings with new observations and fresh research will help the field advance our understanding of consumer behavior.

Beckwith, N. E., & Lehmann, D. R. (1973). The importance of differential weights in multiple attribute models of consumer attitude. Journal of Marketing Research, 10 (2), 141–145.

Article   Google Scholar  

Belk, R. W., Wallendorf, M., & Sherry Jr., J. F. (1989). The sacred and the profane in consumer behavior: theodicy on the odyssey. Journal of Consumer Research, 16 (1), 1–38.

Dahl, R. A., Haire, M., & Lazarsfeld, P. F. (1959). Social science research on business: product and potential . New York: Columbia University Press.

Ding, Y., DeSarbo, W. S., Hanssens, D. M., Jedidi, K., Lynch Jr., J. G., & Lehmann, D. R. (2020). The past, present, and future of measurements and methods in marketing analysis. Marketing Letters, https://doi.org/10.1007/s11002-020-09527-7 .

Farley, J. U., & Ring, L. W. (1970). An empirical test of the Howard-Sheth model of buyer behavior. Journal of Marketing Research, 7 (4), 427–438.

Farley, J. U., & Ring, L. W. (1974). “Empirical” specification of a buyer behavior model. Journal of Marketing Research, 11 (1), 89–96.

Google Scholar  

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: an introduction to theory and research . Reading: Addison-Wesley.

Gerber, A. S., & Green, D. P. (2012). Field experiments: design, analysis, and interpretation . New York: WW Norton.

Gourville, J. T. (1998). Pennies-a-day: the effect of temporal reframing on transaction evaluation. Journal of Consumer Research, 24 (4), 395–408.

Higgins, E. T. (1998). Promotion and prevention: regulatory focus as a motivational principle. Advances in Experimental Social Psychology, 30 , 1–46.

Howard, J. A., & Sheth, J. (1969). The theory of buyer behavior . New York: Wiley.

Huber, J., Payne, J. W., & Puto, C. (1982). Adding asymmetrically dominated alternatives: violations of regularity and the similarity hypothesis. Journal of Consumer Research, 9 (1), 90–98.

Humphreys, A., & Wang, R. J. H. (2018). Automated text analysis for consumer research. Journal of Consumer Research, 44 (6), 1274–1306.

Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating: can one desire too much of a good thing? Journal of Personality and Social Psychology, 79 (6), 995–1006.

Johnson, E. J., Shu, S. B., Dellaert, B. G., Fox, C., Goldstein, D. G., Häubl, G., et al. (2012). Beyond nudges: tools of a choice architecture. Marketing Letters, 23 (2), 487–504.

Kruglanski, A. W., Shah, J. Y., Fishbach, A., Friedman, R., Chun, W. Y., & Sleeth-Keppler, D. (2002). A theory of goal systems. Advances in Experimental Social Psychology, 34 , 311–378.

Lancaster, K. (1966). A new theory of consumer behavior. Journal of Political Economy, 74 , 132–157.

Laudan, L. (1986). Methodology’s prospects. In PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association, 1986 (2), 347–354.

Lynch Jr., J. G. (2011). Introduction to the journal of marketing research special interdisciplinary issue on consumer financial decision making. Journal of Marketing Research, 48 (SPL) Siv–Sviii.

MacInnis, D. J., & Folkes, V. S. (2009). The disciplinary status of consumer behavior: a sociology of science perspective on key controversies. Journal of Consumer Research, 36 (6), 899–914.

Markus, H. R., & Kitayama, S. (1991). Culture and the self: implications for cognition, emotion, and motivation. Psychological Review, 98 (2), 224–253.

Payne, J. W., Bettman, J. R., & Johnson, E. J. (1988). Adaptive strategy selection in decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14 (3), 534–552.

Prelec, D., & Loewenstein, G. (1998). The red and the black: mental accounting of savings and debt. Marketing Science, 17 (1), 4–28.

Rapp, J. M., & Hill, R. P. (2015). Lordy, Lordy, look who’s 40! The Journal of Consumer Research reaches a milestone. Journal of Consumer Research, 42 (1), 19–29.

Ratchford, B. T. (1975). The new economic theory of consumer behavior: an interpretive essay. Journal of Consumer Research, 2 (2), 65–75.

Schumann, D. W., Haugtvedt, C. P., & Davidson, E. (2008). History of consumer psychology. In Haugtvedt, C. P., Herr, P. M., & Kardes, F. R. eds.  Handbook of consumer psychology (pp. 3-28). New York: Erlbaum.

Simonson, I. (1989). Choice based on reasons: the case of attraction and compromise effects. Journal of Consumer Research, 16 (2), 158–174.

Trope, Y., & Liberman, N. (2010). Construal-level theory of psychological distance. Psychological Review, 117 (2), 440–463.

Wang, X., Bendle, N. T., Mai, F., & Cotte, J. (2015). The journal of consumer research at 40: a historical analysis. Journal of Consumer Research, 42 (1), 5–18.

Wilkie, W. L., & Moore, E. S. (2003). Scholarly research in marketing: exploring the “4 eras” of thought development. Journal of Public Policy & Marketing, 22 (2), 116–146.

Download references

Author information

Authors and affiliations.

Columbia Business School, Columbia University, New York, NY, USA

Maayan S. Malter, Morris B. Holbrook & Donald R. Lehmann

The Wharton School, University of Pennsylvania, Philadelphia, PA, USA

Barbara E. Kahn

Department of Marketing, University of Illinois at Chicago, Chicago, IL, USA

Jeffrey R. Parker

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Maayan S. Malter .

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Malter, M.S., Holbrook, M.B., Kahn, B.E. et al. The past, present, and future of consumer research. Mark Lett 31 , 137–149 (2020). https://doi.org/10.1007/s11002-020-09526-8

Download citation

Published : 13 June 2020

Issue Date : September 2020

DOI : https://doi.org/10.1007/s11002-020-09526-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Consumer behavior
• Information processing
• Judgement and decision-making
• Consumer culture theory
• Find a journal
• Publish with us
• Track your research

ORIGINAL RESEARCH article

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

• 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.

Introduction

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 Taobao.com , 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.

Participants

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 Taobao.com . 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 Taobao.com , 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., Taobao.com and JD.com ) 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.

Acknowledgments

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.

Ahluwalia, R., Burnkrant, R., and Unnava, H. (2000). Consumer response to negative publicity: the moderating role of commitment. J. Mark. Res. 37, 203–214. doi: 10.2307/1558500

CrossRef Full Text | Google Scholar

Archak, N., Ghose, A., and Ipeirotis, P. (2010). Deriving the pricing power of product features by mining. Con. Rev. Manag. Sci. 57, 1485–1509. doi: 10.1287/mnsc.1110.1370

Bae, S., and Lee, T. (2011). Product type and consumers’ perception of online consumer reviews. Electron. Mark. 21, 255–266. doi: 10.1007/s12525-011-0072-0

Baek, H., Ahn, J., and Choi, Y. (2012). Helpfulness of online consumer reviews: readers’ objectives and review cues. Int. J. Electron. Commer. 17, 99–126. doi: 10.2753/jec1086-4415170204

Boardman, R., and McCormick, H. (2019). The impact of product presentation on decision making and purchasing. Qual. Mark. Res. Int. J. 22, 365–380. doi: 10.1108/QMR-09-2017-0124

Boardman, R., and Mccormick, H. (2021). Attention and behaviour on fashion retail websites: an eye-tracking study. Inf. Technol. People . doi: 10.1108/ITP-08-2020-0580 [Epub ahead of print]

Chae, S. W., and Lee, K. (2013). Exploring the effect of the human brand on consumers’ decision quality in online shopping: An eye-tracking approach. Online Inf. Rev. 37, 83–100. doi: 10.1108/14684521311311649

Changchit, C., and Klaus, T. (2020). Determinants and impact of online reviews on product satisfaction. J. Internet Commer. 19, 82–102. doi: 10.1080/15332861.2019.1672135

Chen, C. D., and Ku, E. C. (2021). Diversified online review websites as accelerators for online impulsive buying: the moderating effect of price dispersion. J. Internet Commer. 20, 113–135. doi: 10.1080/15332861.2020.1868227

Cortinas, M., Cabeza, R., Chocarro, R., and Villanueva, A. (2019). Attention to online channels across the path to purchase: an eye-tracking study. Electron. Commer. Res. Appl. 36:100864. doi: 10.1016/j.elerap.2019.100864

Craciun, G., and Moore, K. (2019). Credibility of negative online product reviews: reviewer gender, reputation and emotion effects. Comput. Hum. Behav. 97, 104–115. doi: 10.1016/j.chb.2019.03.010

Cui, G., Lui, H.-K., and Guo, X. (2012). The effect of online consumer reviews on new product sales. International. J. Elect. Com. 17, 39–58. doi: 10.2753/jec1086-4415170102

Floh, A., Koller, M., and Zauner, A. (2013). Taking a deeper look at online reviews: The asymmetric effect of valence intensity on shopping behaviour. J. Mark. Manag. 29:646670, 646–670. doi: 10.1080/0267257X.2013.776620

Garbarino, E., and Strahilevitz, M. (2004). Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation. J. Bus. Res. 57, 768–775. doi: 10.1016/S0148-2963(02)00363-6

Ghose, A., and Ipeirotiss, P. G. (2010). Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics. IEEE Trans. Knowl. Data Eng. 23:188. doi: 10.1109/TKDE.2010.188

GooSeeker (2015), E. coli . Available at: http://www.gooseeker.com/pro/product.html , (Accessed January 20, 2020).

Google Scholar

Guo, J., Wang, X., and Wu, Y. (2020). Positive emotion bias: role of emotional content from online customer reviews in purchase decisions. J. Retail. Consum. Serv. 52:101891. doi: 10.1016/j.jretconser.2019.101891

Hasanat, M., Hoque, A., Shikha, F., Anwar, M., Abdul Hamid, A. B., and Huam, T. (2020). The impact of coronavirus (COVID-19) on E-Business in Malaysia. Asian J. Multidisc. Stud. 3, 85–90.

Hong, H., Xu, D., Wang, G., and Fan, W. (2017). Understanding the determinants of online review helpfulness: a meta-analytic investigation. Decis. Support. Syst. 102, 1–11. doi: 10.1016/j.dss.2017.06.007

Huang, P., Lurie, N., and Mitra, S. (2009). Searching for experience on the web: an empirical examination of consumer behavior for search and experience goods. J. Mark. Am. Mark. Assoc. 73, 55–69. doi: 10.2307/20619010

Hwang, Y. M., and Lee, K. C. (2018). Using an eye-tracking approach to explore gender differences in visual attention and shopping attitudes in an online shopping environment. Int. J. Human–Comp. Inter. 34, 15–24. doi: 10.1080/10447318.2017.1314611

Jacob, R., and Karn, K. (2003). “Eye tracking in human-computer interaction and usability research: ready to deliver the promises,” in The mind’s eye North-Holland (New York: Elsevier), 573–605.

Jiménez, F. R., and Mendoza, N. A. (2013). Too popular to ignore: the influence of online reviews on purchase intentions of search and experience products. J. Interact. Mark. 27, 226–235. doi: 10.1016/j.intmar.2013.04.004

Just, M., and Carpenter, P. (1980). A theory of reading: from eye fixations to comprehension. Psychol. Rev. 87, 329–354. doi: 10.1037/0033-295X.87.4.329

PubMed Abstract | CrossRef Full Text | Google Scholar

Just, M., and Carpenter, P. (1992). A capacity theory of comprehension: individual differences in working memory. Psychol. Rev. 99, 122–149. doi: 10.1037/0033-295x.99.1.122

Kang, T. C., Hung, S. Y., and Huang, A. H. (2020). The adoption of online product information: cognitive and affective evaluations. J. Internet Commer. 19, 373–403. doi: 10.1080/15332861.2020.1816315

Kanungo, S., and Jain, V. (2012). Online shopping behaviour: moderating role of gender and product category. Int. J. Bus. Inform. Syst. 10, 197–221. doi: 10.1504/ijbis.2012.047147

Kaur, S., Lal, A. K., and Bedi, S. S. (2017). Do vendor cues influence purchase intention of online shoppers? An empirical study using SOR framework. J. Internet Commer. 16, 343–363. doi: 10.1080/15332861.2017.1347861

Lackermair, G., Kailer, D., and Kanmaz, K. (2013). Importance of online product reviews from a consumer’s perspective. Adv. Econ. Bus. 1, 1–5. doi: 10.13189/aeb.2013.010101

Liu, H.-C., Lai, M.-L., and Chuang, H.-H. (2011). Using eye-tracking technology to investigate the redundant effect of multimedia web pages on viewers’ cognitive processes. Comput. Hum. Behav. 27, 2410–2417. doi: 10.1016/j.chb.2011.06.012

Luan, J., Yao, Z., Zhao, F., and Liu, H. (2016). Search product and experience product online reviews: an eye-tracking study on consumers’ review search behavior. Comput. Hum. Behav. 65, 420–430. doi: 10.1016/j.chb.2016.08.037

Mayzlin, D., and Chevalier, J. (2003). The effect of word of mouth on sales: online book reviews. J. Mark. Res. 43:409. doi: 10.2307/30162409

Meyers-Levy, J., and Sternthal, B. (1993). A two-factor explanation of assimilation and contrast effects. J. Mark. Res. 30, 359–368. doi: 10.1177/002224379303000307

Mudambi, S., and Schuff, D. (2010). What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Q. 34, 185–200. doi: 10.1007/s10107-008-0244-7

Mumuni, A. G., O’Reilly, K., MacMillan, A., Cowley, S., and Kelley, B. (2020). Online product review impact: the relative effects of review credibility and review relevance. J. Internet Commer. 19, 153–191. doi: 10.1080/15332861.2019.1700740

Pavlou, P., and Dimoka, A. (2010). NeuroIS: the potential of cognitive neuroscience for information systems research. Inform. Sys. Res. Art. Adv. 19, 153–191. doi: 10.1080/15332861.2019.1700740

Plassmann, H., Venkatraman, V., Huettel, S., and Yoon, C. (2015). Consumer neuroscience: applications, challenges, and possible solutions. J. Mark. Res. 52, 427–435. doi: 10.1509/jmr.14.0048

Racherla, P., and Friske, W. (2013). Perceived “usefulness” of online consumer reviews: an exploratory investigation across three services categories. Electron. Commer. Res. Appl. 11, 548–559. doi: 10.1016/j.elerap.2012.06.003

Renshaw, J. A., Finlay, J. E., Tyfa, D., and Ward, R. D. (2004). Understanding visual influence in graph design through temporal and spatial eye movement characteristics. Interact. Comput. 16, 557–578. doi: 10.1016/j.intcom.2004.03.001

Rosa, P. J. (2015). What do your eyes say? Bridging eye movements to consumer behavior. Int. J. Psychol. Res. 15, 1250–1256. doi: 10.1116/1.580598

Ruiz-Mafe, C., Chatzipanagiotou, K., and Curras-Perez, R. (2018). The role of emotions and conflicting online reviews on consumers’ purchase intentions. J. Bus. Res. 89, 336–344. doi: 10.1016/j.jbusres.2018.01.027

Sen, S., and Lerman, D. (2007). Why are you telling me this? An examination into negative consumer reviews on the web. J. Interact. Mark. 21, 76–94. doi: 10.1002/dir.20090

Song, W., Park, S., and Ryu, D. (2017). Information quality of online reviews in the presence of potentially fake reviews. Korean Eco. Rev. 33, 5–34.

Tupikovskaja-Omovie, Z., and Tyler, D. (2021). Eye tracking technology to audit google analytics: analysing digital consumer shopping journey in fashion m-retail. Int. J. Inf. Manag. 59:102294. doi: 10.1016/j.ijinfomgt.2020.102294

Vimaladevi, K., and Dhanabhakaym, M. (2012). A study on the effects of online consumer reviews on purchasing decision. Prestige In. J. Manag. 7, 51–99. doi: 10.1504/IJIMA.2012.044958

Von Helversen, B., Abramczuk, K., Kopeć, W., and Nielek, R. (2018). Influence of consumer reviews on online purchasing decisions in older and younger adults. Decis. Support. Syst. 113, 1–10. doi: 10.1016/j.dss.2018.05.006

Wang, Y., and Minor, M. (2008). Validity, reliability, and applicability of psychophysiological techniques in marketing research. Psychol. Mark. 25, 197–232. doi: 10.1002/mar.20206

Wang, Q., Yang, S., Cao, Z., Liu, M., and Ma, Q. (2014). An eye-tracking study of website complexity from cognitive load perspective. Decis. Support. Syst. 62, 1–10. doi: 10.1016/j.dss.2014.02.007

World Medical Association (2014). World medical association declaration of Helsinki: ethical principles for medical research involving human subjects. J. Am. Coll. Dent. 81, 14–18. doi: 10.1111/j.1447-0756.2001.tb01222.x

Wu, Y., Liu, T., Teng, L., Zhang, H., and Xie, C. (2021). The impact of online review variance of new products on consumer adoption intentions. J. Bus. Res. 136, 209–218. doi: 10.1016/J.JBUSRES.2021.07.014

Wu, Y., Ngai, E., Pengkun, W., and Wu, C. (2020). Fake online reviews: literature review, synthesis, and directions for future research. Decis. Support. Syst. 132:113280. doi: 10.1016/j.dss.2020.113280

Yang, S. F. (2015). An eye-tracking study of the elaboration likelihood model in online shopping. Electron. Commer. Res. Appl. 14, 233–240. doi: 10.1016/j.elerap.2014.11.007

Yu, X., Liu, Y., Huang, X., and An, A. (2010). Mining online reviews for predicting sales performance: a case study in the movie domain. IEEE Trans. Knowl. Data Eng. 24, 720–734. doi: 10.1109/TKDE.2010.269

Yuanyuan, H., Peng, Z., Yijun, L., and Qiang, Y. J. M. R. (2009). An empirical study on the impact of online reviews sentimental orientation on sale based on movie panel data. Manag. Rev. 21, 95–103. doi: 10.1007/978-3-642-00205-2_9

Zhang, K., Cheung, C., and Lee, M. (2014). Examining the moderating effect of inconsistent reviews and its gender differences on consumers’ online shopping decision. Int. J. Inf. Manag. 34, 89–98. doi: 10.1016/j.ijinfomgt.2013.12.001

Zhang, J., Craciun, G., and Shin, D. (2010). When does electronic word-of-mouth matter? A study of consumer product reviews. J. Bus. Res. 63, 1336–1341. doi: 10.1016/j.jbusres.2009.12.011

Zhong-Gang, Y., Xiao-Ya, W., and Economics, S. O. J. S. E. (2015). Research progress and future prospect on online reviews and consumer behavior. Soft Science. 6:20. doi: 10.3760/cma.j.cn112137-20200714-02111

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.