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Factors Affecting Impulse Buying Behavior of Consumers

In recent years, the study of consumer behavior has been marked by significant changes, mainly in decision-making process and consequently in the influences of purchase intention (Stankevich, 2017 ).

The markets are different and characterized by an increased competition, as well a constant innovation in products and services available and a greater number of companies in the same market. In this scenario it is essential to know the consumer well (Varadarajan, 2020 ). It is through the analysis of the factors that have a direct impact on consumer behavior that it is possible to innovate and meet their expectations. This research is essential for marketers to be able to improve their campaigns and reach the target audience more effectively (Ding et al., 2020 ).

Consumer behavior refers to the activities directly involved in obtaining products /services, so it includes the decision-making processes that precede and succeed these actions. Thus, it appears that the advertising message can cause a certain psychological influence that motivates individuals to desire and, consequently, buy a certain product/service (Wertenbroch et al., 2020 ).

Studies developed by Meena ( 2018 ) show that from a young age one begins to have a preference for one product/service over another, as we are confronted with various commercial stimuli that shape our choices. The sales promotion has become one of the most powerful tools to change the perception of buyers and has a significant impact on their purchase decision (Khan et al., 2019 ). Advertising has a great capacity to influence and persuade, and even the most innocuous, can cause changes in behavior that affect the consumer's purchase intention. Falebita et al. ( 2020 ) consider this influence predominantly positive, as shown by about 84.0% of the total number of articles reviewed in the study developed by these authors.

Kumar et al. ( 2020 ) add that psychological factors have a strong implication in the purchase decision, as we easily find people who, after having purchased a product/ service, wonder about the reason why they did it. It is essential to understand the mental triggers behind the purchase decision process, which is why consumer psychology is related to marketing strategies (Ding et al., 2020 ). It is not uncommon for the two areas to use the same models to explain consumer behavior and the reasons that trigger impulse purchases. Consumers are attracted by advertising and the messages it conveys, which is reflected in their behavior and purchase intentions (Varadarajan, 2020 ).

Impulse buying has been studied from several perspectives, namely: (i) rational processes; (ii) emotional resources; (iii) the cognitive currents arising from the theory of social judgment; (iv) persuasive communication; (v) and the effects of advertising on consumer behavior (Malter et al., 2020 ).

The causes of impulsive behavior are triggered by an irresistible force to buy and an inability to evaluate its consequences. Despite being aware of the negative effects of buying, there is an enormous desire to immediately satisfy your most pressing needs (Meena, 2018 ).

The importance of impulse buying in consumer behavior has been studied since the 1940's, since it represents between 40.0 and 80.0% of all purchases. This type of purchase obeys non-rational reasons that are characterized by the sudden appearance and the (in) satisfaction between the act of buying and the results obtained (Reisch and Zhao, 2017 ). Aragoncillo and Orús ( 2018 ) also refer that a considerable percentage of sales comes from purchases that are not planned and do not correspond to the intended products before entering the store.

According to Burton et al. ( 2018 ), impulse purchases occur when there is a sudden and strong emotional desire, which arises from a reactive behavior that is characterized by low cognitive control. This tendency to buy spontaneously and without reflection can be explained by the immediate gratification it provides to the buyer (Pradhan et al., 2018 ).

Impulsive shopping in addition to having an emotional content can be triggered by several factors, including: the store environment, life satisfaction, self-esteem, and the emotional state of the consumer at that time (Gogoi and Shillong, 2020 ). We believe that impulse purchases can be stimulated by an unexpected need, by a visual stimulus, a promotional campaign and/or by the decrease of the cognitive capacity to evaluate the advantages and disadvantages of that purchase.

The buying experience increasingly depends on the interaction between the person and the point of sale environment, but it is not just the atmosphere that stimulates the impulsive behavior of the consumer. The sensory and psychological factors associated with the type of products, the knowledge about them and brand loyalty, often end up overlapping the importance attributed to the physical environment (Platania et al., 2016 ).

The impulse buying causes an emotional lack of control generated by the conflict between the immediate reward and the negative consequences that the purchase can originate, which can trigger compulsive behaviors that can become chronic and pathological (Pandya and Pandya, 2020 ).

Sohn and Ko ( 2021 ), argue that although all impulse purchases can be considered as unplanned, not all unplanned purchases can be considered impulsive. Unplanned purchases can occur, simply because the consumer needs to purchase a product, but for whatever reason has not been placed on the shopping list in advance. This suggests that unplanned purchases are not necessarily accompanied by the urgent desire that generally characterizes impulse purchases.

The impulse purchases arise from sensory experiences (e.g., store atmosphere, product layout), so purchases made in physical stores tend to be more impulsive than purchases made online. This type of shopping results from the stimulation of the five senses and the internet does not have this capacity, so that online shopping can be less encouraging of impulse purchases than shopping in physical stores (Moreira et al., 2017 ).

Researches developed by Aragoncillo and Orús ( 2018 ) reveal that 40.0% of consumers spend more money than planned, in physical stores compared to 25.0% in online purchases. This situation can be explained by the fact that consumers must wait for the product to be delivered when they buy online and this time interval may make impulse purchases unfeasible.

Following the logic of Platania et al. ( 2017 ) we consider that impulse buying takes socially accepted behavior to the extreme, which makes it difficult to distinguish between normal consumption and pathological consumption. As such, we believe that compulsive buying behavior does not depend only on a single variable, but rather on a combination of sociodemographic, emotional, sensory, genetic, psychological, social, and cultural factors. Personality traits also have an important role in impulse buying. Impulsive buyers have low levels of self-esteem, high levels of anxiety, depression and negative mood and a strong tendency to develop obsessive-compulsive disorders. However, it appears that the degree of uncertainty derived from the pandemic that hit the world and the consequent economic crisis, seems to have changed people's behavior toward a more planned and informed consumption (Sheth, 2020 ).

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

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.

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Open Access

Peer-reviewed

Research Article

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

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

* E-mail: [email protected]

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

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Roles Conceptualization, Methodology, Writing – review & editing

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

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

PLOS

  • Published: June 9, 2021
  • https://doi.org/10.1371/journal.pone.0253231
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Table 1

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

Citation: Schmidt S, Benke C, Pané-Farré CA (2021) Purchasing under threat: Changes in shopping patterns during the COVID-19 pandemic. PLoS ONE 16(6): e0253231. https://doi.org/10.1371/journal.pone.0253231

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

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

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

Data Availability: All files (data set, R code) are available from the data_UMR repository under the following URL: https://data.uni-marburg.de/handle/dataumr/110 .

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

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

Introduction

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

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

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

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

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

Purpose of the present study and hypotheses

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

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

Participants

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

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Predictors.

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

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

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

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

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

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

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

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

Outcome measures.

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

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

Data analysis

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

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

Change in purchasing frequency

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

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

https://doi.org/10.1371/journal.pone.0253231.g001

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

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

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https://doi.org/10.1371/journal.pone.0253231.t003

Change in purchasing quantity

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

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https://doi.org/10.1371/journal.pone.0253231.g002

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

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

Change in purchasing quantity for individual products

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

Perceived Threat of COVID-19

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

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

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

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

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

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

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

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

Supporting information

S1 appendix. german version of the online questionnaire..

https://doi.org/10.1371/journal.pone.0253231.s001

S2 Appendix. English version of the online questionnaire.

https://doi.org/10.1371/journal.pone.0253231.s002

S3 Appendix. Non-parametric data analysis.

https://doi.org/10.1371/journal.pone.0253231.s003

S1 Table. Bivariate correlations.

https://doi.org/10.1371/journal.pone.0253231.s004

S2 Table. Bivariate correlations for the full range scale.

https://doi.org/10.1371/journal.pone.0253231.s005

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

https://doi.org/10.1371/journal.pone.0253231.s006

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

https://doi.org/10.1371/journal.pone.0253231.s007

S5 Table. Multiple regression analysis for individual products.

https://doi.org/10.1371/journal.pone.0253231.s008

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

https://doi.org/10.1371/journal.pone.0253231.s009

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

https://doi.org/10.1371/journal.pone.0253231.s010

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Research papers: What influences price and purchase

  • Published: 28 August 2023
  • Volume 22 , pages 325–326, ( 2023 )

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One of the roles of research is to determine why something happens, in this issue of the Journal of Revenue and Pricing Management ; a series of papers explore the what’s, how’s, and why’s that influence the pricing and purchase decision of consumers across a range of different products and experiences.

Influencing price

Nowadays, one of the most important factors influencing the customers' behavior of buying sports products is the price and the components that affect it. Sepehrian et al. ( 2022 ) raise the issue about price transparency on perception of fair price and satisfaction with the purchase of sports products. Their research showed that price transparency has a positive and significant effect on the perception of fair price and customer satisfaction with the purchase. In addition, the structural equation modeling showed that the perception of fair price plays a mediating role in the relationship between price transparency and purchase satisfaction.

Airfares are affected by a variety of factors, but it is less clear which factors are the key determinants and how they interact. Gu ( 2022 ) introduces a widely used, machine learning-based pricing tool to investigate the airline market segmentation and dynamic price discrimination problems. The empirical results suggest that purchasing time, city distance, market structure, market size, and seat availability are the five most important pricing factors in order. One of the activities that can grab customers' attention and rise sales for convenience stores is promotional pricing strategy. Phumchusri et al. ( 2022 )'s study examines the effects of promotional pricing and other factors on sales. The results provide guidelines for retailers to measure their pricing strategy.

Perceived price and value

In today’s hotel industry, ‘book now, pay later’ practice is widespread. However, despite the prevalence of this phenomenon, the psychological and behavioral influences of ‘book now, pay later’ transactions on consumers have received almost no research attention. Jang et al. ( 2022 ) examined how deferred payments influence consumers’ perceived price, perceived risks, and purchase intention according to the time of booking in the leisure travel context. The results found the significant interaction effects between delays in payments and temporal distance on purchase intention and perceived risks. It showed that the effects of delays in payments on purchase intention and perceived risks varied across different temporal distance levels. The study revealed that temporal distance indeed appeared to act as a moderator of the relative effects of delays in payments. The research contributes to the hospitality literature by particularly focusing on the ‘book now, pay later’ option and investigating its dynamic impacts on consumers’ perceptions and behavioral intent. Pay-What-You-Want (PWYW) is a pricing strategy increasingly applied in many different industries, both profitable and non-profitable. Vizuete-Luciano et al. ( 2022 ) identify influential cited works in PWYW research, determine the current status, and indicate the extent to which influential works have shaped the field.

Jablonowski and MacEachern ( 2022 ) investigate the role that reputation plays in the determination of transaction costs and contracting outcomes in the offshore oil and gas drilling rig market. The analysis specifies a novel economic model of drilling rig contracting and estimates the econometric models on an original dataset to test the hypotheses about the factors that determine the contracting outcomes.

Gu, C. 2022. Market segmentation and dynamic price discrimination in the U.S. airline industry. Journal of Revenue and Pricing Management . https://doi.org/10.1057/s41272-022-00407-5 .

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Jablonowski, C.J., and D.P. MacEachern. 2022. Transaction costs and the value of a good reputation: Evidence from the offshore drilling rig market. Journal of Revenue and Pricing Management . https://doi.org/10.1057/s41272-022-00413-7 .

Jang, Y., L. Miao, and C.-C. Chen. 2022. Book now, pay later: The effects of delays in payments and temporal distance on consumers’ perceptions and purchase intention. Journal of Revenue and Pricing Management . https://doi.org/10.1057/s41272-022-00416-4 .

Phumchusri, N., W. Kosawanitchakarn, S. Chawanapranee, and S. Srimook. 2022. Evaluating promotional pricing effectiveness using convenience store daily sales data. Journal of Revenue and Pricing Management . https://doi.org/10.1057/s41272-022-00415-5 .

Sepehrian, A.H., Z.S. Mirzazadeh, and M.S. Aghaei Shahri. 2022. Effect of price transparency on the perception of fair price and satisfaction with the purchase of sports products. Journal of Revenue and Pricing Management . https://doi.org/10.1057/s41272-022-00391-w .

Vizuete-Luciano, E., O. Güzel, and J.M. Merigó. 2022. Bibliometric research of the Pay-What-You-Want Topic. Journal of Revenue and Pricing Management . https://doi.org/10.1057/s41272-022-00414-6 .

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Yeoman, I. Research papers: What influences price and purchase. J Revenue Pricing Manag 22 , 325–326 (2023). https://doi.org/10.1057/s41272-023-00442-w

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ORIGINAL RESEARCH article

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

Tao Chen

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

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

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 .

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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.

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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.

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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.

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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.

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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 .

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Table 2 . Results of ANOVA analysis.

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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 .

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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 .

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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.

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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.

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

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

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

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

*Correspondence: XiongYing Cen, [email protected]

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

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The Asahi Shimbun

Reuse shop thrives as buying used articles the new norm

By FUMINA OKA/ Staff Writer

February 16, 2024 at 07:00 JST

Photo/Illutration

YAO, Osaka Prefecture--A male clerk appeared bewildered as he appraised used products that customers had brought in at a worktable behind the purchasing counter.

“My head is swimming with question marks all over,” he said.

The staffer was handling a set of six dolls at the Super 2nd Street outlet in Yao, Osaka Prefecture, where used articles are purchased and resold, on a recent day in early December.

The set included approximately 30-centimeter-tall “Peko” dolls representing Fujiya Co.’s popular mascot character.

One wore Peko’s hallmark coveralls, whereas another was dressed, like a high school girl, in a blazer and a beige checkered skirt. The dolls each came in a transparent bag but carried no explanatory notes.

The worker carefully studied the soles of the shoes, the insides of the clothing and other parts to find clues as to when the dolls were released, which company released them and whether they are popular with collectors.

The staffer found that the dolls were not particularly rare, so he priced them at 250 yen ($1.68) each.

‘LOCAL CONSUMPTION OF LOCAL PRODUCTS’

Eligible for valuation at the Yao outlet are a wide range of articles, including clothes, musical instruments and furniture, which do not fall under legal trading restrictions. Unlike new articles, the used products have no suggested retail prices.

The outlet’s workers turn to an in-house database for records of the previous purchases of similar products, which, however, are not always available.

Values may change with demand of the time, such as when pieces of furniture or musical instruments of the sort that has appeared in an anime suddenly gain popularity and appreciate.

To stay abreast of the trends, the staffers study flea market apps and auction websites to gather information on a daily basis, officials said.

On display for sale at the outlet are more than 100,000 pieces of used articles, ranging from a stationery item worth only 50 yen to a luxury brand watch carrying a price tag of 449,900 yen.

Some 90 percent of those articles were bought at this shop of the 2nd Street chain.

“What we are doing is precisely ‘local consumption of local products,’” said Ryo Nakagawa, a chain representative.

The reuse market has been growing rapidly.

Figures compiled by Recycling Tsushin, a trade newspaper covering the reuse industry, show the market value grew from 1.1 trillion yen in 2009, when statistics were first available, to 2.9 trillion yen in 2022.

The newspaper has projected the figure will rise to about 4 trillion yen in 2030.

2nd Street Corp., the Nagoya-based operator of the reuse shop chain, now has more than 800 outlets across the nation, including the one in Yao, more than double the corresponding figure from a decade ago.

CONSUMERS EMBRACING REUSING GOODS

In the backdrop is a change in consumer awareness.

A 38-year-old man, who had come to the Yao outlet to resell baby gates to the shop, said he brings disused articles here about twice a year to coincide with a general cleaning of his home.

“I previously didn’t believe they would ever fetch anything,” the man said. “But we now have this outlet nearby and it would be a lot of work to dump them.”

He had bought the baby gates at 40,000 to 50,000 yen when he had a child five years ago. They were now resold for 1,500 yen.

A woman in her 60s, who was trying on a pair of black pumps at the outlet, said that mass consumption was the order of the day in her youth, which coincided with Japan’s asset-inflated economic growth of the late 1980s.

“I now feel that reuse is so important,” she said, adding that she has acquired the habit of sorting things out when she plans to dispose of them into items that can be resold and those that should be trashed. 

“The phrase ‘What a waste!’ previously meant, ‘Why are you going to dispose of these items?’” said Yasuo Kogure, president of Uridoki Inc., which operates a website on which users can compare suggested purchase prices for used articles. “The connotation is now changing to, ‘Why don't you find out the value of what you have?’”

Flea market apps and other similar tools have taken root, which has rendered it convenient to look up the prices of used articles and has turned people’s eyes toward the value of daily goods, Kogure added.

A survey taken in 2022 by the Environment Ministry showed that 30 percent of the respondents said they had bought at least one used item during the past year. Younger people were more active in purchasing used products, the study found.

PURCHASES MADE WITH CASH FROM RESALE

Consumers have also come to weigh new and used articles on an equal footing and choose either option depending on the occasion.

“What matters in shopping is how you strike a balance,” said a 49-year-old man, who bought a pair of skis for his 10-year-old child at the Yao shop. “I choose to buy some articles new and others secondhand.”

The man said he usually buys his child's playthings secondhand because the child is growing fast and has changing interests. The skis set him back about 1,500 yen on the day he was interviewed.

“A pair of new skis would have cost at least 10,000 yen,” he said. “The money I’ve saved can be spent on another skiing trip.”

An 18-year-old woman, who was looking for a down jacket from a famous brand, said that, when she wants to buy something, she first looks for it on flea market apps and then searches for it at used-product shops and, finally, at outlets selling new products.

“I take into account various factors, such as price differences and the conditions of used products, to decide where I will buy what I want,” the woman said.

A new way of thinking, in addition, has emerged.

A 2023 survey by the Mercari research institute, run by flea market app operator Mercari Inc., showed that 26 percent of the respondents said they had bought something on the assumption that they could afford to buy it only if they resold their belongings.

The corresponding ratio was about 40 percent among members of Generation Z, defined in the study as those aged from 18 through 24.

“The act of buying something with the anticipated cash from reselling something has been common when it comes to cars and houses,” said Hikaru Yamamoto, a Keio University professor of business and commerce, who supervised the survey. “That practice has now spread to daily goods as well. Part of the consumer market has come to view consumable goods as assets.”

The Yao outlet remained crowded at 9 p.m., including with young customers stopping by on their way home from work.

A 23-year-old man who was checking out at the cashier's desk, who said he is a native of China, was seen holding a compact platform cart that had been purchased by the shop only a day earlier. The man said he was planning a self house move.

“This cart is just the right size and appears easy to handle,” he said. “I will use this one to carry a refrigerator on.”

So went another day at the secondhand outlet, with disused articles being purchased for fresh use by somebody else.

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The New School Lists Its Greenwich Village Townhouse for $20 Million

Under increasing pressure to offset a looming budget deficit, the school is selling the renovated 19th-century brick townhouse on West 11th Street.

A light-filled living room and foyer in a townhouse.

By Vivian Marino

Vivian Marino writes about prominent real estate listings and sales.

The New School’s Greenwich Village townhouse, an elegant 19th-century brick building that has served as a residence for university presidents and a venue for school functions for the last four decades, is now on the market.

The asking price is $20 million, according to the listing brokers, Nikki Field and Mara Flash Blum of Sotheby’s International Realty. Annual property taxes will be determined at the closing, they said.

The school recently decided to sell the property, known as the Scatturo House, located just around the corner from the campus at 21 West 11th Street, amid growing pressure from faculty and students to find ways to offset a looming budget deficit . The townhouse is currently occupied by the interim president, Donna Shalala, a former member of Congress and secretary of health and human services under President Bill Clinton.

Four other university presidents have lived at the townhouse since the New School purchased it for $990,000 in 1984 from the children’s science book authors Herman and Nina Schneider . (Public records show a $740,000 mortgage was taken out and repaid in 1995.)

The five-story structure — 20 feet wide with a classic front stoop and window boxes for flowers — encompasses 4,038 square feet, with four bedrooms, five and a half bathrooms and a library/media room that could be converted into an additional bedroom. There is a total of 900 square feet of outdoor space, including a roomy terrace off the primary bedroom and a landscaped and irrigated rear garden with trees, assorted plants and perennial flowers — a popular backdrop for school events.

“These events include student, faculty and staff receptions and community gatherings,” Amy Malsin, a spokeswoman for the university, said in an email.

The home underwent a major transformation in 2000 and had additional upgrades in recent years, according to Ms. Malsin. It was dedicated in 2003 to Philip J. Scatturo, a former chancellor and trustee of the school.

Among the various changes to the building, Ms. Blum said, were a new kitchen with radiant-heat tiled floors, central air-conditioning, new windows and bathrooms, and the addition of a dramatic two-story glass wall and door panels that overlook and open to the garden.

“The house has been upgraded and renovated, but it still maintains its charm and history,” Ms. Blum said, noting that some original architectural details remain, like the marble fireplace mantels, painted pillars, and the staircase with mahogany balustrades. “The bones of the house are beautiful,” she added.

Designed in a Greek Revival style with Federal architecture influences, the home is situated in the Greenwich Village Historic District , between Fifth Avenue and Avenue of the Americas. It was built in 1838, initially with two and a half stories , as part of a row of houses on West 11th Street, and several decades later was modernized and expanded.

“West 11th is a very sought-after and highly regarded block,” Ms. Field said of the location. “Some people refer to it as the billionaires’ row downtown.”

The main entrance to the townhouse is through a foyer on the parlor floor that opens to a large living room, where there is a working wood-burning fireplace, one of two in the home.

  • Eitan Gamliely for Sotheby’s International Realty

Beyond the living room is the formal dining room, overlooking the garden, and a powder room.

The eat-in kitchen is downstairs, on the garden level, which has a separate exterior entrance through a wrought-iron gate, as well as access to the garden in the back through glass doors. The kitchen is equipped with granite countertops, high-end appliances, a center island with a breakfast bar, and a large pantry. The garden level also has a mudroom, areas for storage and laundry, and a bathroom.

The primary bedroom suite is on the third floor and features two bathrooms, two large walk-in closets and a terrace. Across from the bedroom is a library/media room with a fireplace and built-in shelves. Upstairs are two more bedrooms and a shared bathroom, and at the top is a guest suite with a bathroom and an exercise area.

“My favorite room is the living room,” Ms. Shalala said through the school spokeswoman, adding that she has especially enjoyed reading the newspaper there in front of the fireplace.

Throughout the house are hardwood floors and extra-large windows that let in plenty of sunlight, and displayed in many of the rooms is a rotating collection of artwork from the university.

“It’s like a living, breathing gallery,” Ms. Field said.

“The new owners will have plenty of opportunities to display their own collection,” Ms. Blum added.

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

    This article aims to explore the key factors on e-commerce adoption from elements of social psychology, such as attitude, subjective norms, perceived behavioral control, ease of use and perceived usefulness, introducing the study of non-traditional elements like buying impulse, compatibility, and self-efficacy in online stores, contrasting relationships in a cross-cultural environment.

  2. Trust and Consumers' Purchase Intention in a Social Commerce Platform

    In the past 10 years, research on social commerce has grown exponentially, which reflects the widespread adoption of social commerce tactics and methods (Doha et al., 2019).From traditional marketplaces to e-commerce platforms to social commerce (SC), the widespread usage of social media and Web 2.0 apps has contributed to a better business phenomenon (Yahia et al., 2018).

  3. Factors affecting repurchase intentions in retail shopping: An

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

  4. How online reviews affect purchase intention: A meta-analysis across

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

  5. Factors affecting green purchase behavior: A systematic literature

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

  6. The Impact of Online Reviews on Consumers' Purchasing Decisions

    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 ...

  7. Purchase decision-making within professional consumer services

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

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

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

  9. A Scoping Review of the Effect of Content Marketing on Online Consumer

    Research article. First published online April 23, 2022. A Scoping Review of the Effect of Content Marketing on Online Consumer Behavior. ... Consumer behavior (or buyer behavior) is a popular field of study on how consumers decide to buy products or services (Solomon & Lowrey, 2018). However, consumer behavior is never constant, it changes ...

  10. What prompts consumers to purchase online? A machine ...

    With e-commerce emerging as a prominent mode of purchasing, there is a pressing need for businesses across the globe to understand online consumer purchase behavior and, in particular, their purchase intention. Information on purchase behavior provides valuable insights for designing marketing activities to reach wider target audiences, promote greater customer involvement, and achieve higher ...

  11. What Influences Consumers' Intention to Purchase Innovative Products

    First, it investigates the marketing management of innovative products, which is an overlooked area. Through a detailed explanation of the innovative product situation, the study helps enrich the research on purchase intention. Second, this study explores the impact of risk propensity on consumers' purchase intention.

  12. The goods on consumer behavior

    People are more willing to go into debt for experiential purchases than for material purchases, according to research by Eesha Sharma, PhD, an associate professor of business administration at Dartmouth's Tuck School of Business (Journal of Consumer Research, Vol. 44, No. 5, 2018). This seems to be because experiences are often time-dependent ...

  13. Full article: The effect of digital marketing transformation trends on

    Based on the theory of planned behavior and the theory of consumer behavior, this study aims to explain the effects digital marketing forms on customer attitudes toward digital marketing and purchase intention. Research sample with a size of 210 potential customers, from the important people in the B2B purchasing chain at the enterprise.

  14. Full article: Understanding online purchase intention: the mediating

    In the research of Barata and Coelho (Citation 2021), hedonic motivation (entertainment) has been empirically proven as one of the variables which explain behavioral intention to buy a premium account of music streaming services. This factor was also a key predictor of purchase intention in the research of Alalwan (Citation 2018). Therefore, we ...

  15. Factors Affecting Impulse Buying Behavior of Consumers

    Advertising has a great capacity to influence and persuade, and even the most innocuous, can cause changes in behavior that affect the consumer's purchase intention. Falebita et al. consider this influence predominantly positive, as shown by about 84.0% of the total number of articles reviewed in the study developed by these authors.

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

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

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

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

  18. A Literature Review On Purchase Intention Factors In E-Commerce

    Purchase Intention is a tendency or desire that arises from consumers to buy certain products or services (Yoo et al., 2000). Purchase intention is a plan that exists in a person to buy a product ...

  19. Full article: The impact of online shopping attributes on customer

    1. Introduction. E-commerce growth has grown exponentially in recent years. An e-commerce transaction starts when the seller advertises products on a website, and customers show acceptance, evaluate the products' features, prices, and delivery options, buy products of interest, and then check out (Ribadu & Rahman, Citation 2019).Tailoring these products to specific markets and targeted ...

  20. Research papers: What influences price and purchase

    Jang et al. ( 2022) examined how deferred payments influence consumers' perceived price, perceived risks, and purchase intention according to the time of booking in the leisure travel context. The results found the significant interaction effects between delays in payments and temporal distance on purchase intention and perceived risks.

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  22. 5 Best Sites to Buy Research Papers Online

    If you're looking to purchase research papers from WritePaperForMe, the minimum rate per page starts at $6.99. 4. EssayPro: Best Bidding Platform.

  23. Frontiers

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

  24. Elevance, SCAN deals highlight health insurer acquisition scrutiny

    Elevance wanted to buy Blue Cross and Blue Shield of Louisiana, and The SCAN Group wanted CareOregon. Both deals collapsed this week. ... Long Covid research funding at NIH gets a nearly 50% boost

  25. Vendor offering citations for purchase is latest bad actor in ...

    Some of the citing publications didn't even reference the researcher's work in the main text of the article; the citation had simply been added to the reference list at the end. The team also noticed that one of the authors had received many citations from papers hosted by an account on Research Gate, a social networking site for scientists.

  26. The impact of consumer perceived value on repeat purchase intention

    Secondly, considering the large sample size of the data in this article (43,752 review texts of 1,273 listings were obtained in total), finally, we chose to construct the perceived value indicators based on the LDA topic model. ... Future research could measure the repeat purchase behavior of consumers by mining the actual occupancy information ...

  27. Up 18% in 2024, Is ASML Stock a Buy?

    Fool.com contributor Parkev Tatevosian answers if ASML (ASML 0.58%) stock is a buy for long term investors. *Stock prices used were the afternoon prices of Feb. 6, 2024. The video was published on ...

  28. Reuse shop thrives as buying used articles the new norm

    A 2023 survey by the Mercari research institute, run by flea market app operator Mercari Inc., showed that 26 percent of the respondents said they had bought something on the assumption that they ...

  29. The New School Lists Its Greenwich Village Townhouse for $20 Million

    The five-story structure — 20 feet wide with a classic front stoop and window boxes for flowers — encompasses 4,038 square feet, with four bedrooms, five and a half bathrooms and a library ...