Emotion is a response to the cognitive appraisal of stimuli from the environment and is an integral part of human behavior (Frayn et al., 2018). Consumers often engage emotions in decision-making processes (Thaler & Sunstein, 2008; Zaltman, 2003; Damasio, 1994), including food choices where emotions frequently guide behavior (Godet et al., 2022). Research has confirmed the link between emotional states and food choice and consumption (Ljubičić et al., 2023). Most studies in this area focus on the relationship between negative emotions and eating behaviors (Fuente Gonzales et al., 2022). However, some researchers point to a connection between positive emotions and food consumption (Devonport et al., 2019; Reichenberger et al., 2020; Ljubičić et al., 2023). Therefore, the term “emotional eating” is often used to describe increased food consumption as a reaction to emerging emotions, both positive and negative. Emotional eating affects both overweight and obese individuals, as well as those maintaining a normal weight. It has been found that the latter consume less food in response to emotions than overweight or obese individuals (Frayn et al., 2018). Emotional eating has also been proven to be positively associated with waist circumference, abdominal obesity, body mass index (BMI), and obesity according to the percentage of body fat (Betancourt-Núñez et al., 2022). It is important to emphasize that overweight and obesity have many secondary health consequences, such as cardiovascular diseases, diabetes, and an increased risk of certain cancers (Obesity and Overweight, n.d.). They also constitute a significant economic burden on society (Scarborough et al., 2011; Shimul et al., 2021).

Regarding negative emotions, their appearance triggers a range of physiological reactions that can either promote a lack of appetite or reduce food intake, or increase food consumption (Betancourt-Núñez et al., 2022). High stimulation arousal in response to negative emotions (e.g., fear, anger) may reduce consumption, while negative emotions felt at a moderate level may increase it (Reichenberger et al., 2020). This has been especially verified about stress. Although stress is most commonly viewed as a negative stimulus, it can occasionally be interpreted as a positive response that stimulates and encourages activity (Ljubičić et al., 2023). In situations of strong, sudden stress, norepinephrine inhibits appetite, while in chronic stress, cortisol stimulates it (Ljubičić et al., 2023). Empirical evidence has shown that eating habits are modified under the influence of stress (Borsellino et al., 2020).

Studies on emotional eating behaviors suggest that negative mood, sadness, tension, and emotional instability precede an increase in food consumption (Devonport et al., 2019). However, stress, boredom, and depression were the emotions most often identified as those affecting higher food intake (Fuente Gonzales et al., 2022). It has been reported that food consumption distracts a person from experiencing negative emotions (Betancourt-Núñez et al., 2022), and is also a way to fill the void that can arise in situations of sadness, depression, social isolation, or other stressful life events (Ljubičić et al., 2023). Negative emotions strongly associated with eating behaviors include anxiety, sadness, loneliness, worry, boredom, anger, stress, and depression (Fuente Gonzales et al., 2022). Depression is linked with stress, negative mood, loneliness, and social isolation, all of which contribute to emotional eating (Ljubičić et al., 2023). Additionally, tendencies to experience boredom, aggression, and anger are positively associated with emotional eating (Devonport et al., 2019).

Research indicates that positive emotions play a significant role in enhancing food intake (Reichenberger et al., 2020; Fuente Gonzales et al., 2022). It has been observed that positive moods are closely linked to socialization and food consumption. Specifically, individuals tend to experience greater enjoyment and prolong their mealtime when dining with familiar and amicable companions, leading to increased food consumption. Furthermore, past studies suggest that people often opt for healthier food choices when experiencing positive emotions (Li et al., 2021).

As indicated, both positive and negative emotions influence an increase in food consumption, yet negative emotions are one of the most important causes of excessive consumption and emotional eating (Reichenberger et al., 2020).

Studies have shown that emotions affect not only the increase in consumption, but also the type of food that consumers reach for under their influence (Devonport et al., 2019). Individuals who engage in emotional eating tend to snack more frequently (Rachmawati et al., 2019). Snacking is eating food, often without hunger, between main meals. Emotional eating is positively associated with higher consumption of tasty, high-energy, sweet, and high-fat snacks (Rachmawati et al., 2019). This pattern is observed in both men and women across different life stages (Fuente Gonzales et al., 2022), particularly regarding increased consumption of fast food, salty snacks, sweet high-fat foods, or high-energy foods such as cakes, biscuits, cookies, ice cream, chocolate and its derivatives, candies, and artificially sweetened beverages (Betancourt-Núñez et al., 2022). Stress and negative emotions often lead to the consumption of high-energy, nutrient-poor foods (Devonport et al., 2019). It has been proven that consuming tasty dishes (usually rich in sugar or fat) provides immediate pleasure and reward (positive affective responses), which can lessen the impact of negative emotions (Betancourt-Núñez et al., 2022). As studies show overweight or obese individuals prefer sweet-tasting snacks (e.g., cakes, cookies, biscuits) or sweet and milky drinks, while underweight individuals tend to choose cooked snacks, fruits, and dairy products (Rachmawati et al., 2019).

When it comes to how our emotions affect what we eat, it is interesting to note that posi-tive emotions can lead to both healthy and unhealthy eating habits. For instance, research by Moss et al. (2021) found that positive emotions tend to kickstart the consumption of nutritious foods like fruits. On the flip side, another study (Evers et al., 2013) revealed that positive emotions can also prompt indulgence in unhealthy snacks. Age plays a role too. Among chil-dren, positive emotions seem to correlate with more unhealthy snack consumption, while among young adults, negative emotions are more closely linked to such behavior (Moss et al., 2021). Parents also influence what children eat, especially in terms of sweet snacks. For instance, they might reward good behavior or achievements with sugary treats. Furthermore, snacks can serve as a way to manage behavior and emotions during interactions between chil-dren and their parents (Jansen et al., 2021).

Changes in food consumption behaviors due to fluctuations in emotional states may be induced by situations or events that go beyond an individual’s daily routine. The COVID-19 pandemic, for instance, may have altered consumers’ emotional states and thereby changed consumer behaviors (Borsellino et al., 2020). In the literature, food choices are recognized as dynamic and evolving throughout life; they are also considered quite stable and largely driven by habits, especially over shorter periods. Significant changes or turning points in food choice patterns are usually initiated by important life events. Research suggests that the COVID-19 pandemic and related restrictions affecting daily life have caused, at least temporarily, changes in the patterns of food purchasing and consumption for a large share of consumers (Jansen et al., 2021).

Some research findings (Ben Hassen et al., 2020; Ben Hassen et al., 2021) have indicated that some individuals reduced their consumption of unhealthy food (e.g., sweets, desserts, cookies, and biscuits) during the pandemic, adopting healthier eating habits, particularly seeking to strengthen their immune system. However, this trend varied across countries. For instance, in Italy, Denmark, Norway, and the United States, there was an increase in the consumption of highly processed, high-fat, or high-sugar foods (including chocolate, chips, and snacks). This was more common among women, who tended to eat more food, explained by the fact that women were more depressed, stressed, and restless, leading to emotional eating (Li et al., 2021). Emotions, as a predictor of the mental health of society, also account for changes in eating habits during the pandemic. Studies on the scale of negative emotions among the general population during the COVID-19 pandemic have reported the prevalence of stress at 29.6%, anxiety 31.9%, and depression 33.7% (Salari et al., 2020). Social isolation, uncertainty, and the potential adverse effects of illness significantly altered eating behaviors, increasing the scale of dysfunctional eating habits (such as binge eating, emotional eating, impulsive or compulsive eating) as well as the purchase of comfort foods (unhealthy junk foods).

Considering the role of emotions in changing the level of food consumption, including the propensity to reach for snacks, we resolved to analyze this issue in more detail. The aim of this article is to verify how different emotional states determine food consumption, including the desire to reach for snacks. The following research hypotheses were formulated:

H1: Individuals who increase their food consumption in response to negative emotions (NE) rate their financial situation worse than other sample segments (PE, WE, BE).

H2: Individuals who increase their food consumption in response to negative emotions (NE) have a negative health profile characterized by:

a.a poorer assessment of general health,
b.greater weight gain,
c.BMI above the norm,
d.less physical activity.

H3: The tendency to reach for snacks differs between sample segments depending on the emotional state experienced and the flavor of the snack:

a.During positive emotions, PE consumers are more likely to reach for salty snacks (chips, snacks, crackers, pretzels) than other segments (NE, WE, BE).
b.During positive emotions, PE consumers are more likely to reach for sweet snacks (cookies, chocolates, candies, ice cream, candy bars) than other segments (NE, WE, BE).
c.During negative emotions, NE consumers are more likely to reach for salty snacks (chips, snacks, crackers, pretzels) than other segments (PE, WE, BE).
d.During negative emotions, NE consumers are more likely to reach for sweet snacks (cookies, chocolates, candies, ice cream, candy bars) than other segments (PE, WE, BE).

Methodology

To achieve the set objectives, a nationwide study was conducted using the Computer-Assisted Web Interviewing (CAWI) method on a representative sample of 707 respondents who had purchased food. The samples were representative for Polish adults citizens according to gender, age, and place of residence. The research was conducted by a specialised research agency certified by ESOMAR (European Society for Opinion and Market Research). The structure of the research sample, based on selected socio-demographic characteristics as well as physical condition and health status, is presented in Table 1. The study was positively reviewed by the Research Ethics Committee for research involving human participants at the Poznań University of Economics and Business.

In constructing research tools, a method commonly used in consumer behavior sciences, the masked method (deception), was employed. This means that the actual purpose of the research was concealed from its participants (Brzeziński, 2004). Participants were asked to fill out a questionnaire consisting of fourteen questions in the main part and ten questions in the expanded metric part. The results presented in the article come from this study and are part of a larger research project concerning the emotional determinants of food consumption. The specific objectives of the study were as follows:

1.to analyze the consumer profile based on socio-demographic characteristics in the context of changes in consumption under the influence of emotions,

2.to identify factors differentiating the tendency to reach for snacks when experiencing emotions (positive and negative).

To verify the research hypotheses, two types of snacks were distinguished: salty (chips, snacks, crackers, pretzels) and sweet (cookies, chocolates, candies, ice cream, candy bars).

A statistical cluster analysis (k-means) was performed to check if there were segments among the respondents showing increased food consumption depending on the emotions experienced (including the valence of emotions – negative/positive emotions). This data exploration method divided respondents into homogeneous groups based on their answers so that each group contained individuals whose responses were similar. The cluster analysis was based on responses to questions about the impact of the emotional states experienced by the respondent on the amount of food consumed. The EMAQ scale (Emotional Appetite Questionnaire) was used to determine the emotional attitude toward food consumption, which has been validated and then developed in many studies (Bilici et al., 2020; Bourdier et al., 2017; Geliebter & Aversa, 2003; Nolan et al., 2010). Positive emotional states such as being happy, relaxed, cheerful, enthusiastic, and self-satisfied were distinguished, as well as negative states like being sad, bored, bad, restless, frustrated, tired, depressed, and scared.

Basic descriptive statistics (mean, standard deviation) were calculated to compare the results for each group, considering the criterion of increased food consumption during positive/negative emotional experiences.

Appropriate statistical tests were conducted for the verification of the research hypotheses: one-way ANOVA for data with a normal distribution, and the non-parametric Kruskal-Wallis test for data without a normal distribution. Post-hoc tests were conducted where appropriate. The significance level was set at p=0.05. SPSS Statistics software was used to perform the analyses.

Results

Emotional determinats of increased consumption

Cluster analysis showed that the increase in food consumption by respondents differs depending on the emotional states they experience. K-means analysis allowed respondents to be classified into 4 segments (Table 2). The first of these are people who eat more depending on emotions, but irrespective of their valence (WE). In other words, experiencing both positive and negative emotions determines their increased consumption. The second and third segments were made up of people for whom increased consumption is already dependent on the valence of emotions: people who eat more in states of emotional tension (NE), which means that they declared increased food consumption under the influence of negative emotions, such as being sad or frustrated, and people who eat more during positive emotional experiences (PE), which means that they showed increased consumption under the influence of positive emotional states (e.g., being happy and/or relaxed). The last segment was made up of people who do not show increased consumption while experiencing emotions (BE).

For the vast majority of respondents (78%), feeling certain emotions contributes to an increase in food consumption. Almost half of the respondents (53%) reported increased consumption when in a state of emotional tension, especially when feeling sad (x̄=5.46), lonely (x̄=5.45), or frustrated (x̄=5.35). The NE segment is mostly composed of people with secondary education (43.5%) living in urban areas (63.2%, mainly in cities with a population of 20,000–50,000). These respondents also indicated that they have large households, with 60.3% having at least three members (26.7% – three members, 20.0% – four, 8.5% – five, and 5.1% – six+). Women more frequently reported increased consumption in response to negative emotions than men (54.4% women, 45.6% men).

Consumers who increase their food intake due to positive emotions represent 13% of the study participants. They report the highest increase when feeling happy (average x̄=7.76), cheerful (x̄=7.34), and relaxed (x̄=7.32). These consumers are primarily city residents with secondary education and are urban residents (64.5%, including cities of 20,000–50,000 and over 200,000 inhabitants). Almost 70% reported that their household consists of at least three members (21.5% – three members, 25.8% – four members, 12.9% – five members, and 9.7% – six+). Men are more likely than women to report increased consumption due to positive emotions (men: 54.8%, women: 45.2%).

For about one in every eight respondents (12%), the valence of emotions does not affect their food consumption; that is, they report increased consumption regardless of whether the emotions are positive or negative. The emotions associated with the highest increase in consumption include feeling depressed (negative emotion; mean x̄ =7.05), enthusiastic (positive emotion; x̄ =7.02), and lonely (negative emotion; x̄ =6.95). The WE segment mainly consists of individuals with secondary education (44.7%), living in both villages and cities (45.9% and 54.1%, respectively). A significant majority of these respondents have large households: 35.3% have three-person households, 24.7% four-person, 11.8% five-person, and 7.1% have households of six or more. The increase in consumption, independent of emotional valence, is reported equally by women (49.4%) and men (50.6%).

One in five participants (22%) reported that experiencing emotions, whether negative or positive, does not lead to increased food consumption. This segment is largely composed of individuals with secondary education (40.9%), residing in cities, predominantly those with populations between 20,000 to 50,000. Women (55.2%) more frequently reported that their consumption does not increase due to emotional states, as compared to men (44.8%).

Characteristics defining the health status and economic situation of the consumer as factors differentiating sample segments

To verify hypotheses H1 and H2, a one-way ANOVA analysis was conducted. The analysis showed that factors such as consumers’ subjective perception of their financial situation (H1; F=3.480; p=0.016), subjective assessment of overall health (H2a; F=3.356; p=0.019), and weight change during the pandemic (since March 2020) (H2b; F=7.086; p<0.001) differentiate respondents’ susceptibility to emotional influence on increased food consumption (Table 3, Figure 1).

The segment of individuals who increase consumption under the influence of negative emotions (NE) evaluated their financial situation as poor (mean x̄=2.24) and overall health state as low (x̄=2.46), with a frequent declaration of weight gain during the pandemic (x̄=2.15). Conversely, the segment influenced by positive emotions (PE) rated their financial situation (x̄=2.44) and health (x̄=2.63) higher, reporting weight loss or stability during the pandemic (x̄=1.97). Those influenced by emotions regardless of valence (WE) rated their financial and health situation slightly worse, with the highest weight gain reported during the pandemic (x̄=2.39). Consumers not influenced by emotions (BE) had a more positive view of their financial status (x̄=2.42) and the best health state (x̄=2.67), with no significant weight changes in the pandemic (x̄=2.06). One-way ANOVA indicated that other factors like BMI index or physical activity did not differentiate the segments significantly (respectively: H2c: F=1.914; p=0.126 and H2d: F=0.454; p=0.714).

Positive emotions and the desire to eat snacks (salty/sweet)

The study further examined whether belonging to a particular segment of the sample, meaning the relationship between experiencing emotions and their valence, correlates with an increase in overall food consumption and a propensity to eat snacks. This relationship was investigated regarding the emotional state felt (positive for H3ab, negative for H3cd) and the taste of the snacks (salty for H3ac, sweet for H3bd).

For those experiencing positive emotions, a notable difference was seen in the consumption of salty snacks among the segments. The PE segment reported often consuming salty snacks (mean x̄=2.58) when feeling positive emotions. They tend to eat salty snacks such as chips, snacks, crackers, and pretzels more than other segments. The WE group also frequently consumes salty snacks when experiencing positive emotions, though less so than the PE group (mean x̄=2.40). The BE and NE segments showed the least tendency to consume salty snacks when in a positive emotional state, with x̄=2.15 and x̄=2.13 respectively. The one-way ANOVA analysis revealed a statistically significant impact of emotional conditions on the overall consumption of the tendency to eat salty snacks when experiencing positive emotions (H=24.067; p<0.001), thereby confirming hypothesis H3a.

Similarly, the results shape the propensity for consuming sweet snacks while experiencing positive emotions. While feeling positive emotions, the PE segment declares a high desire to consume cookies, chocolates, candies, ice cream, or candy bars x̄=2.77. Their inclination towards eating sweet snacks is higher than other segments. While experiencing positive emotions, individuals belonging to the WE groups also frequently reach for sweet snacks (though less often than PE, x̄=2.65). Conversely, the NE and BE segments less frequently show a desire to consume cookies, chocolates, candies, ice cream, or candy bars, respectively: x̄=2.41 and x̄=2.37. To verify hypothesis H3b, a one-way ANOVA analysis was performed, which showed a statistically significant influence of emotional factors on reaching for sweet snacks while experiencing positive emotions (H=19.659; p<0.001). Hypothesis H3b was therefore confirmed.

Negative emotions and desire for snacks (salty/sweet)

Next, we examined whether the negative emotional state differentiates the propensity to reach for snacks (salty/sweet) among the specified sample segments (WE, NE, PE, BE).

Consumers in the PE and WE segments declare a high inclination to reach for salty snacks while experiencing negative emotions (respectively: x̄=2.47 and x̄=2.45) (Table 5). They admit to frequently consuming chips, snacks, crackers, and pretzels when in an emotionally tension state. The NE group showed a significantly lower inclination to consume these snacks. This segment declared rarely reaching for salty snacks (x̄=2.21). Experiencing negative emotions also affects the consumption of chips, snacks, crackers, and pretzels by BE individuals. They exhibited the lowest inclination to reach for salty snacks (x̄=2.14). To verify hypothesis H3c, a one-way ANOVA analysis was conducted, which showed a statistically significant influence of emotional conditions on reaching for salty snacks in an emotional tense state (F=3.743; p=0.11). Despite the statistically significant relationship between variables, hypothesis H3c was refuted, because it was the PE segment (rather than the NE segment) that exhibited the greatest desire for snack consumption.

In a state of emotional tension, consumers show a high inclination to reach for sweet snacks. The WE, PE, and NE groups often reach for cookies, chocolates, candies, ice cream, and candy bars, showing respective values of: x̄=2.66; x̄=2.63, and x̄=2.53. Consumers who did not declare an increase in consumption under the influence of emotions (BE) indicated that experiencing negative emotions rarely affects their consumption of sweet snacks (x̄=2.36). To check for differences between segments and the consumption of sweet snacks while experiencing negative emotions, a Kruskal-Wallis test was carried out, which did not show a statistically significant influence of emotional conditions on reaching for sweet snacks (F=3.743; p=0.11). Hypothesis H3d was therefore refuted.

Discussion

Unhealthy snacks, rich in energy, sugar, and salt, have a negative impact on consumers’ health, and their excessive consumption significantly contributes to the rise in overweight and obesity in society (Almoraie Karlsson et al., 2021). It seems that promoting healthy snacks with high nutritional content through education is important for improving health and reducing the risk of diseases, but it may be ineffective due to the emotional motivations for their consumption. The results of our study confirmed not only the emotional patterns of food consumption but also the emotional nature of snack consumption. Both positive and negative emotions shape food consumption, but negative ones have a greater impact, consistent with the findings of Saine and Zhao (2021). Cluster analysis revealed four patterns of emotional eating, three of which (WE, NE, and PE), covering over 78% of the respondents, indicate the influence of emotions (Figure 1). The most negative emotions influencing food consumption in all three segments were loneliness (ranking 2nd in WE and NE, and 3rd in PE) and sadness (ranking 3rd and 1st, respectively, in WE and NE). These emotions were often highlighted as negative consequences of the pandemic, leading to increased consumption of unhealthy snacks. This suggests that the negative impact of the pandemic may have been one of the factors increasing appetite and food consumption. Individuals experiencing negative emotions such as anxiety, depression, and pandemic-related stress were more likely to reach for snacks as a way of coping with these emotions. Thus consumption could serve as an emotional function and as a coping mechanism during challenging situations / facing difficulties.

Experiencing emotions leads to a greater craving for sweet snacks than for salty ones. The sweet taste of snacks is perceived as a way to soothe negative feelings and a source of pleasure. Wołosiak et al. (2016) suggest that consuming sugars and other sweet substances additionally provides pleasure due to the production of endorphins, often referred to as happiness hormones. Sweetness can act as a motivating factor for consumption, hence it is reasonable to assume that this attribute will play a crucial role in consumer communications. Associating a product with sweetness may translate into its being preferred and selected. Decoupling the sweet taste from snacks opens up new avenues for promoting more beneficial snacking options.

Linking specific categories of snacks to emotions being experienced may have serious consequences in terms of reinforcing unhealthy dietary behaviors. Emotions are treated as triggers for certain behaviors, which over time become habits. Snack consumption may be tied to habits acquired in childhood and youth, then perpetuated in adulthood and maturity, and passed on to subsequent generations. Using sweet snacks as a reward for good behavior or consolation for a sad child create a conditioned response, in which the young consumer learns such behavior and acquires a routine. Therefore, it is crucial to promote healthy dietary patterns among parents and children in emotional contexts, based on healthy snacks and shifting the narrative around food discourse (so that food serves neither as a reward, nor as a punishment).

Research has shown a strong link between emotions and snack consumption, revealing certain characteristic patterns associated with consumer profiles. We found that financial situation, health status, and weight change are associated with membership in distinct clusters (Figure 2). In our study, dissatisfaction with one’s financial situation, health status, and weight gain were predictors of belonging to the WE and NE groups, while a positive profile of these features translated into membership in the PE group.

The assessment of one’s financial situation may influence behaviors in response to stressful life situations. Individuals who rate their financial situation worse may be more prone to negative emotions related to financial problems. Food consumption could serve as a form of emotional compensation or coping mechanism for life difficulties. Additionally, lower socioeconomic status could limit access to other stress coping mechanisms, increasing the attractiveness of eating as a way to alleviate negative feelings.

Individuals who rated their financial situation worse may also be more prone to be overweight and face health problems associated with excessive food consumption. This can lead to more serious health problems such as obesity, diabetes, and heart disease. There is a need for targeted interventions to support individuals with low financial self-esteem in managing emotions and coping with stress in a healthier way than through excessive food consumption. Individuals with poorer financial self-esteem may also be more susceptible to compulsive food consumption as a way to cope with negative emotions.

Subjective assessment of one’s health status and weight gain significantly influences dietary behaviors in situations of negative emotions. Individuals who felt that their health had deteriorated during the pandemic more often reached for snacks as a way to cope with stress and anxiety related to illness. This suggests that worsening health may be a risk factor for increased consumption of unhealthy food during periods of emotional stress.

Moreover, individuals who experienced weight gain during the pandemic tended to reach for snacks as a form of emotional compensation. This behavior may be related to lower self-esteem and greater psychological burden associated with weight gain. Weight gain may be a risk factor for increased snack consumption as a coping mechanism for stress.

Understanding these relationships is crucial for developing effective nutritional and mental health strategies, especially during times of health and emotional crises. Further research should focus on identifying the mechanisms underlying these relationships and developing interventions aimed at improving mental health and dietary habits during challenging times, such as pandemics.

Research has shown a significant association between product positioning and visibility in stores, price promotions, and snack consumption (Luick et al., 2023; Piernas et al., 2022; Ravensbergen et al., 2015). While our study did not address this, examining how emotions are influenced at the point of purchase through product availability and presentation could be a valuable direction for future research.

One limitation of our study was its focus on a specific demographic group (adults) and the categorization of snacks limited solely to unhealthy food, characterized by a binary taste designation (sweet/salty). In future research, it would be valuable to include other groups such as children, and adolescents, and also consider a wider variety of types of snacks.

References

Almoraie Karlsson, J., Persson, L. O., Sjöström, L., & Sullivan, M. (2000). Psychometric properties and factor structure of the Three-Factor Eating Questionnaire (TFEQ) in obese men and women. Results from the Swedish Obese Subjects (SOS) study. International Journal of Obesity and Related Metabolic Disorders: Journal of the International Association for the Study of Obesity, 24(12), 1715–1725. https://doi.org/10.1038/sj.ijo.0801442

Ben Hassen, T., El Bilali, H.& Allahyari, M.S. (2020). Impact of COVID-19 on Food Behavior and Consumption in Qatar. Sustainability, 12(17), 6973. https:// doi.org/10.3390/su13158617

Ben Hassen, T., El Bilali, H., Allahyari, M.S., Karabašević, D., Radosavac, A., Berjan, S., Vaško, Ž., Radanov, P. & Obhodaš, I. (2021). Food Behavior Changes during the COVID-19 Pandemic: Statistical Analysis of Consumer Survey Data from Bosnia and Herzegovina. Sustainability, 13(15), 8617. https://doi.org/10.3390/ su13158617

Betancourt-Núñez, A., Torres-Castillo, N., Martínez-López, E., De Loera-Rodríguez, C.O., Durán-Barajas, E., Márquez-Sandoval, F., Bernal-Orozco, M.F., Garaulet, M. & Vizmanos, B. (2022). Emotional Eating and Dietary Patterns: Reflecting Food Choices in People with and without Abdominal Obesity. Nutrients, 14, 1371. https://doi.org/10.3390/nu14071

Bilici, S., Ayhan, B., Karabudak, E., & Koksal, E. (2020). Factors affecting emotional eating and eating palatable food in adults. Nutrition Research and Practice, 14(1), 70–75. https://doi.org/10.4162/nrp.2020.14.1.70

Borsellino, V., Kaliji, S.A. & Schimmenti, E. (2020). COVID-19 Drives Consumer Behaviour and Agro-Food Markets towards Healthier and More Sustainable Patterns. Sustainability, 12(20), 8366. https://doi.org/10.3390/su12208366

Bourdier, L., Lalanne, C., Morvan, Y., Kern, L., Romo, L., & Berthoz, S. (2017). Validation and Factor Structure of the French-Language Version of the Emotional Appetite Questionnaire (EMAQ). Frontiers in Psychology, 8, 442. https://doi.org/10.3389/fpsyg.2017.00442 Brzeziński, J. (red.). (2004). Metodologia badań psychologicznych – Wybór tekstów [Methodology of Psychological Research: Selection of Texts]. Poznań: Wydawnictwo Naukowe PWN.

Damasio, A. (1994). Descartes’ Error: Emotion, Reason, and the Human Brain. Avon Books.

Devonport, T.J., Nicholls, W. & Fullerton, C. (2019). A systematic review of the association between emotions and eating behaviour in normal and overweight adult populations. Journal of Health Psychology, 24(1), 3–24. https://doi.org/ 10.1177/1359105317697813

Evers, C., Adriaanse, M., de Ridder, D. T., & de Witt Huberts, J. C. (2013). Good mood food: Positive emotion as a neglected trigger for food intake. Appetite, 68, 1–7. https://doi.org/10.1016/j.appet.2013.04.007

Frayn, M., Livshits, S. & Knauper, B. (2018). Emotional eating and weight regulation: a qualitative study of compensatory behaviors and concerns. Journal of Eating Disorders, 6, 23. https://doi.org/10.1186/s40337-018-0210-6

Fuente González, C.E., Chávez-Servín, J.L., de la Torre-Carbot, K., Ronquillo González, D., de los Ángeles Aguilera Barreiro, M. & Ojeda Navarro, L.R. (2022). Relationship between Emotional Eating, Consumption of Hyperpalatable Energy-Dense Foods, and Indicators of Nutritional Status: A Systematic Review. Journal of Obesity, 2022, Article ID 4243868.

Geliebter, A. & Aversa, A. (2003). Emotional eating in overweight, normal weight, and underweight individuals. Eating Behaviors, 3(4), 341–347. https://doi.org/10.1016/ s1471-0153(02)00100-9

Godet, A., Fortier, A., Bannier, E. i in. (2022). Interactions between emotions and eating behaviors: Main issues, neuroimaging contributions, and innovative preventive or corrective strategies. Reviews in Endocrine and Metabolic Disorders, 23, 807–831. https://doi.org/10.1007/s11154-021-09700-x

Jansen, E., Thapaliya, G., Aghababian, A., Sadler, J., Smith, K., & Carnell, S. (2021). Parental stress, food parenting practices and child snack intake during the COVID-19 pandemic. Appetite, 161, 105119. https://doi.org/10.1016/j.appet.2021. 105119

Li, S., Kallas, Z., Rahmani, D. & Gil, J.M. (2021). Trends in Food Preferences and Sustainable Behavior during the COVID-19 Lockdown: Evidence from Spanish Consumers. Foods, 10, 1898. https://doi.org/ 10.3390/foods10081898

Ljubičić, M., Matek Sarić, M., Klarin, I., Rumbak, I., Colić Barić, I., Ranilović, J., Dželalija, B., Sarić, A., Nakić, D., Djekic, I., i in. (2023). Emotions and Food Consumption: Emotional Eating Behavior in a European Population. Foods, 12(4), 872. https://doi.org/10.3390/foods12040872

Luick, M., Pechey, R., Harmer, G., Bandy, L., Jebb, S. A., & Piernas, C. (2023). The impact of price promotions on confectionery and snacks on the energy content of shopping baskets: A randomised controlled trial in an experimental online supermarket. Appetite, 186, 106539. https://doi.org/10.1016/j.appet.2023.106539

Moss, R.H., Conner, M. & O’Connor, D.B. (2021). Exploring the effects of positive and negative emotions on eating behaviours in children and young adults, Psychology, Health & Medicine, 26(4), 457–466. https://doi.org/10.1080/13548506.2020.1761553

Nolan, L. J., Halperin, L. B., & Geliebter, A. (2010). Emotional Appetite Questionnaire. Construct validity and relationship with BMI. Appetite, 54(2), 314–319. https://doi.org/10.1016/j.appet.2009.12.004

Obesity and overweight. (n.d.). Retrieved 9 August 2023, from https://www.who.int/ news-room/fact-sheets/detail/obesity-and-overweight

Piernas, C., Harmer, G., & Jebb, S. A. (2022). Removing seasonal confectionery from prominent store locations and purchasing behaviour within a major UK supermarket: Evaluation of a nonrandomised controlled intervention study. PLoS Medicine, 19(3), e1003951. https://doi.org/10.1371/journal.pmed.1003951 Rachmawati, Y., Anantanyu, S. & Kusnandar, K. (2019). Emotional eating, snacking behavior and nutritional status among adolescents. International Journal of Public Health Science, 8, 413. https://doi.org/10.11591/ijphs.v8i4.20398

Ravensbergen, E. A. H., Waterlander, W. E., Kroeze, W., & Steenhuis, I. H. M. (2015). Healthy or Unhealthy on Sale? A cross-sectional study on the proportion of healthy and unhealthy foods promoted through flyer advertising by supermarkets in the Netherlands. BMC Public Health, 15, 470. https://doi.org/ 10.1186/s12889-015-1748-8

Reichenberger, J., Schnepper, R., Arend, A.K. & Blechert, J. (2020). Emotional eating in healthy individuals and patients with an eating disorder: evidence from psychometric, experimental and naturalistic studies. Proceedings of the Nutrition Society, 79(3), 290–299. https://doi.org/10.1017/S0029665120007004

Saine, R., & Zhao, M. (2021). The asymmetrical effects of emotional loneliness vs. Social loneliness on consumers’ food preferences. Food Quality and Preference, 87, 104040. https://doi.org/10.1016/j.foodqual.2020.104040

Salari, N., Hosseinian-Far, A., Jalali, R., Vaisi-Raygani, A., Rasoulpoor, S., Mohammadi, M., Rasoulpoor, S., & Khaledi-Paveh, B. (2020). Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: A systematic review and meta-analysis. Globalization and Health, 16(1), 57. https://doi.org/10.1186/s12992-020-00589-w

Scarborough, P., Bhatnagar, P., Wickramasinghe, K. K., Allender, S., Foster, C., & Rayner, M. (2011). The economic burden of ill health due to diet, physical inactivity, smoking, alcohol and obesity in the UK: An update to 2006-07 NHS costs. Journal of Public Health (Oxford, England), 33(4), 527–535. https://doi.org/10.1093/pubmed/fdr033

Shimul, A. S., Cheah, I., & Lou, A. J. (2021). Regulatory focus and junk food avoidance: The influence of health consciousness, perceived risk and message framing. Appetite, 166, 105428. https://doi.org/10.1016/j.appet.2021.105428

Thaler, R.H., & Sunstein, C.R. (2008). Nudge: Improving Decisions about Health, Wealth, and Happiness. Yale University Press.

Wołosiak, R., Szczepańska, J., Ciecierska, M., Derewiak, D., Drużyńska, B., Kowalska, J. & Majewska, E. (2016). Ocena zachowań konsumentów na rynku produktów słodyczy. Bromatologia i Chemia Toksykologiczna, 3, 676–680, https://www.ptfarm.pl/pub/File/Bromatologia/2016/Nr%203/BR%203_2016%20art%2089%20s%20676-680.pdf

Zaltman, G. (2003). How Customers Think: Essential Insigts into the Mind of the Market. Harvard Business School.

The modern consumer is a traveler navigating two distinct realms: the real world and the virtual world. The ability to move between these worlds makes it increasingly difficult today to gain the consumer’s attention (interest). This challenge is faced by retailers, who are looking for new methods to capture and hold the consumer’s attention, if only for a few seconds. Also, the consumer experience has heightened expectations, making consumers more demanding and expecting new emotions, as emotional-impulsive purchases account for an ever-larger share of their shopping carts.

Understanding and interpreting consumer behavior and the emotions driving it remains a critical objective in research. Researchers continue to seek an appropriate model able to at least partially elucidate what is going on in the consumer’s head during decision-making. Concurrently, advanced tools have been emerging to record or track human behavior, such as electroencephalography (EEG), eye tracking, and virtual reality (VR).

A notable gap therefore exists at the confluence of emotional consumer decision-making and the application of modern technologies for emotion measurement. This paper aims to bridge this gap by conducting a Structured Literature Review (SLR) using two leading academic databases: Web of Science and Scopus. We highlight a particular deficiency in the literature concerning the use of VR tools to study emotions in consumers of fast-moving consumer goods (FMCG).

Literature review

A prevalent tool used over the years for influencing consumer moods and emotions o has been merchandising, which has evolved from a form of merchandise display and planning store displays into comprehensive decor of the sales area (Laermans, 1993). However, verbal and visual stimulation of consumers proved to be insufficient, and so efforts expanded into the field of sensory experiences (Park et al., 2015; Parker, 2003). This has resulted in the emergence of two concepts in the literature today – Merchandising and Visual Merchandising (VM) –alongside the notion of shop atmosphere, related to the second concept. The distinction between these concepts – encompassing both the internal design of retail outlets and external attributes of the retailer’s offerings – has sometimes led to unnecessary confusion (Davies & Ward, 2005).

Merchandising encompasses the overall image of the store, including the architecture of the facility itself but also the interior display and retail brand communication. Within the framework of merchandising, studies have been undertaken on store layout (Levy & Weitz, 2001), fixturing (Donnellan, 1996), merchandise (Kerfoot et al., 2003), presentation techniques (Buchanan et al., 1999), color and packaging (Bruce & Cooper, 1997). Merchandising could be considered an umbrella term – designing places of purchase to enhance the consumer experience to convert potential customers into buyers, also often called the ‘silent selling technique’ (Bruce & Cooper 1997).

The application of Visual Merchandising (VM) is wide, as it is currently applied not only in stationary stores but also in e-commerce (Eroglu et al., 2003; Swanson & Everett, 2015). The goal of VM is to create sensory stimuli to stimulate purchase decisions (Nobbs et al., 2011), but also to attract the consumer to the store and provide an exceptional experience for the consumer and the store’s positioning (Nobbs et al., 2015). This positioning is especially important for any company operating in the online environment because it provides an opportunity to gain attention in the minds of the consumers, to stand out from other companies. This can be achieved by creating a set of special values for the consumer (Bist & Mehta, 2023). Some authors consider VM to involve the overall perception of the store and the impression it makes (HKim & Lee, 2017), while others see it as the strategic display of goods in the store, supported by point-of-sale materials and events in the area (Dash et al., 2019; Iberahim et al., 2018). In marketing terms, VM is seen as a marketing communication tool aimed at persuading consumers to buy (Fill, 2009) and generating long-term profitability (Dash et al., 2016; Iberahim et al., 2018).

Despite the growing prevalence of online shopping habits, yet in more than 88% of cases, consumers abandon their shopping cart (Wang et al., 2023). Understanding and analyzing consumer behavior, particularly the emotions involved in the purchasing process, remains a critical focus area. Research in this domain underscores that consumers are often more emotional than rational in their decision-making, highlighting the importance of continued exploration into the emotional aspects of consumer behavior.

The contemporary landscape of research on consumer behavior, especially consumer purchase decision-making in the 21st century, is not uniform or consistent. Various attempts have been made to categorize concepts, analyze information processing, study consumer loyalty and experience, and capture patterns in consumer thinking (Halkias, 2015; Ishak & Abd Ghani, 2013; Jain et al., 2017; Novak & Hoffman, 2009; Wheeler et al., 2005; Zaltman & Zaltman, 2008). An important aspect of consumer behavior research, which has continued since the 1980s, has been the analysis of emotions surrounding market decisions (Achar et al., 2016; Chitturi, 2009; G. R. Foxall, 2011; Hirschman & Stern, 1999; Laros & Steenkamp, 2005; Niedzielska, 2016; Richard et al., 2002; Williams et al., 2014).

Of particular importance in studying the impact of emotions on consumer behavior is behavioral economics, a science that combines economic and psychological aspects (Hursh, 2014; Reed et al., 2013; Zalega, 2015). Behavioral economics uses scientific research on human, social, cognitive, and emotional factors to better understand the economic decisions of individuals (Achar et al., 2016; G. Foxall, 2017; Mruk, 2017; Williams et al., 2014). Behavioral economics research on consumer behavior has highlighted a number of contentious issues, such as irrationality (Arcidiacono, 2011; Banyte et al., 2016; Matušínská & Zapletalová, 2021; Trevisan, 2016), unpredictability (Gabriel & Lang, 2006; Richardson Bareham, 2004; Valecha et al., 2018), and emotionality (Bell, 2011; Williams et al., 2014) in consumer decision-making (Babin & Harris, 2023).

Emotions can be defined as a significant state of agitation of the mind. They can appear suddenly, combined with somatic arousal and reaching high intensity, but can also be transient. From a psychological point of view, emotions encompass a set of changes involving physiological arousal, sensations, cognitive processes, and behavioral reactions, occurring in response to a situation that the individual perceives as important (Alsharif et al., 2021; Foxall, 2011; Gurgu et al., 2020; Hirschman & Stern, 1999; Izard, 1991; Laros & Steenkamp, 2005; Reisenzein, 2007; Williams et al., 2014).

The emotions that accompany consumers in their shopping and purchasing decisions can also result from, be shaped by, or be stimulated or mitigated by, the impact of other direct and indirect determinants (Das & Varshneya, 2017; Le et al., 2020; Mullen & Johnson, 2013; Szymańska, 2017; Verduyn et al., 2012). Understanding these influences is crucial, especially when considering the dimensions and categories of emotional perception.

One key dimension used to categorize emotions is known as Valence (Kruszewska, 2018; Rasmussen & Berntsen, 2009; Waszkiewicz-Raviv et al., 2018), which refers to the intrinsic degree of attractiveness of an event phenomenon or object, making it possible to characterize and categorize emotions (Gorbatkow, 2002). Emotions of the same valence have a similar effect on consumer judgments and choices (Gaczek, 2016; Kim & Gupta, 2012; Li et al., 2021; Patrzałek, 2016).

Another dimension used to describe emotions is Arousal (Robbins & Everitt, 1995), which denotes a state of increased physiological activity. Emotional arousal can manifest as both positive and negative states, including feelings such as fear, anger, curiosity, and love, which drive individuals to act, often impulsively (Thayer, 1990). The intensity of stimulation directly correlates with the level of arousal; stronger stimuli lead to greater arousal (Eysenck, 2012; Groeppel-Klein, 2005; Reisenzein, 1994; Robbins & Everitt, 1995).

There are numerous models in the literature that combine different dimensions of emotions. One such model is Russel’s circumplex model of affect (Russell, 1980). Emotions in this model are viewed in terms of both valence and arousal, with four regions represented on a rectangular coordinate system: enthusiasm, anxiety, satisfaction, and depression. The model includes 28 descriptors describing emotional states (Olson et al., 2014; Thayer, 1990; Thayer & McNally, 1992).

The complex nature of emotions complicates predicting consumer decisions. Therefore, research on emotions, particularly through the use of modern tracking tools, is vital for gaining deeper insights into consumer behavior.

The purpose of this study was to explore the links between the concepts of emotion and Virtual Reality (VR) based on a bibliometric survey conducted using two databases. The research question posed in the study was: What are the links between the concepts of consumer emotions and Virtual Reality?

The article is structured as follows: this introduction outlines the purpose and relevance of the problem under study; the next section reviews the relevant concepts (emotions, decision-making, merchandising) based on the literature on the subject; the research section then presents the research procedure along with the tools; the following sections of the paper present the results of the analysis, accompanied by a discussion of the findings and their implications.

Research methodology

The methodology utilized in the investigation is illustrated in Figure 1.

In this article, bibliometrics is defined as a set of statistical and mathematical methods used to analyze scientific literature. This bibliometric study, using a Structured Literature Review (SLR), included two databases: Web of Science (WoS) and Scopus. Details of the quantitative content of these databases is listed in Table 1. It is worth noting that Google Scholar was excluded from further analysis (for the reason given in Table 1).

It should, of course, be taken into account that this bibliometric analysis was performed based on two databases, which may limit the set of sources analyzed. On the other hand, we did utilize two of the most popular and high-scoring databases. WoS and Scopus are the preferred databases for conducting Systematic Literature Reviews (SLRs) due to their high coverage of scientific articles, high data quality, availability of advanced search tools, citation indexing, support for meta-analysis and recognition in the scientific community, which provides broad access to reliable data, facilitates analysis and adds credibility to research.

Table 2 presents the comprehensive query components, along with the outcomes derived from two databases investigated (WoS and Scopus).

It should, of course, be taken into account that this bibliometric analysis was performed based on two databases, which may limit the set of sources analyzed. On the other hand, we did utilize two of the most popular and high-scoring databases. WoS and Scopus are the preferred databases for conducting Systematic Literature Reviews (SLRs) due to their high coverage of scientific articles, high data quality, availability of advanced search tools, citation indexing, support for meta-analysis and recognition in the scientific community, which provides broad access to reliable data, facilitates analysis and adds credibility to research.

Table 2 presents the comprehensive query components, along with the outcomes derived from two databases investigated (WoS and Scopus).

Results

The files used in the bibliometric analysis were separately imported into VOSviewer software for Scopus and WoS databases. Graphical representations of the results are shown in Figure 2 and Figure 4. Regarding the WoS database, the title and abstract fields were chosen for extracting data, and the full counting method was employed. In the case of the Scopus analysis, after the format files were uploaded, the title field was selected as the field from which data would be extracted and the full counting method was chosen. The subsequent stage involved specifying the frequency of occurrences for a given term (keyword). Table 3 shows the results for the queries used.

Regarding the Scopus database, the criterion of a minimum of 2 occurrences for each keyword was employed, leading to 501 terms and 58 instances that met the specified threshold. However, the final selection comprised 35 unique terms after duplicate keywords were eliminated. The outcomes from Scopus encompassed 23 items, constituting 42 connections that could be categorized into 6 groups. A graphical depiction of these findings is illustrated in Figure 2.

These can be identified as group 1 (red color) – virtual reality in the supermarket, group 2 (green color) – analysis/methodology, group 3 (dark blue color) – user experience UX, group 4 (yellow color) – survey, group 5 (purple color) – virtual reality, group 6 (light blue color) – EEG. The resulting categorization is an unprecedented categorization for this type of study; there is no combination of consumer emotions in the virtual store.

Figure 3 shows the distribution of publications by year for the Scopus database.

Until 2013, the number of publications remained at 3 or below. In 2014 there was a surge, reaching the number of 9 publications, and then in the following year, the number dropped to 3 per year and remained so until 2017. From 2018 to the present a significant increase was observed, reaching in excess of 15 publications per year.

In the case of the WoS base, the condition of a minimum of 4 occurrences for each keyword was implemented, resulting in 3868 terms and 363 instances meeting the specified threshold. However, the final selection included 218 unique terms. Figure 4 showcases the outcomes from WoS, featuring 218 distinct items forming 3433 connections that can be categorized into 7 groups.

These can be identified as group 1 (red color) – 2D/3D models and interfaces, group 2 (green color) – Eye tracking analysis in a virtual environment, group 3 (dark blue color) – cognitive load, group 4 (yellow color) – marketing, group 5 (purple color) – immersive technique, group 6 (light blue color) – neuroscience, group 7 (orange color) – prototype. As before, this database also lacks a combination of research related to consumer emotions in virtual reality.

Figure 5 shows the year-by-year distribution of the number of publications for the Web of Science (WoS) database.

Until 2017, the number of publications remained below 10 per year. The following years saw a significant increase, exceeding the level of 15, and this trend has continued into the current year. An exceptional jump in what is new was recorded in 2022, where the number of publications reached 41, the largest increase compared to previous years.

The differences in the selection of parameters for the two databases are the result of hving to meet the set conditions, where selecting at least 4 expressions for the Scopus database results in 10 expressions. Such a situation would limit the analysis to two groups: the first on virtual reality with emotions and the second group on research. To obtain a more diverse set of results and to account for a greater variety of topics, a more flexible approach to parameter selection was needed.

Disscussion

The fact that consumers function in two worlds, virtual and real, is making it increasingly difficult to attract their attention and even more difficult to understand their emotions. On the other hand, consumers are becoming more aware and tech-savvy, and so searching for information and comparing options using new technologies has become much simpler and faster. These shifts in consumer behavior pose significant challenges for today’s retailers, who have to decide in which world they engage with consumers. For example, recent studies have shown that consumers often buy online and pick up or return goods offline (buy-online-and-return-in-store (BORS)) (Nageswaran et al., 2019; Xie et al., 2023).

The retailer’s choice of operating environment influences their choice of merchandising tools and techniques to stimulate consumer emotions. Today, it is already known that a well-planned storefront or website can spur purchasing decisions. Although there are many studies on the impact of Merchandising or Visual Merchandising on consumer purchase decisions, it is still not entirely clear what ultimately determines a particular purchase decision. This is also shown by qualitative research, in which consumers themselves are unable to identify the specific factors swaying their decisions.

Visuals at the point of sale can evoke emotions, both positive and negative, and thus influence the consumer’s ultimate behavior. As a result, consumer emotions in Virtual Reality can not only determine the choice of a product or brand, making the final purchase decision, but also how long the consumer will stay at the point of sale, or what distance he will travel to find the product that is the “right” one in his or her opinion (Achar et al., 2016; Alsharif et al., 2021; Ceccacci et al., 2018; Chitturi, 2009; Dawson et al., 1990; East et al., 1994; Gaur et al., 2014; Guo et al., 2020; Hansen & Christensen, 2007; Hui et al., 2013; Larson et al., 2005; McDonald, 1994; Mostafa & Kasamani, 2020; Petrosky-Nadeau et al., 2016; Pluta-Olearnik & Szulga, 2022; Spanjaard et al., 2014; Syaekhoni et al., 2018; XWang et al., 2019) The practical application of emotion research in Virtual Reality to analyze consumer behavior in the market may encompass a variety of aspects, such as emotional states and choices, extreme emotions in shopping, emotional evaluations of stimuli, the universality of emotions in consumer behavior, culture versus consumer expression of emotions, the functions of mood and emotions in consumer decisions, impulsive purchases, and advertising as a source of consumer emotions (Amin Ul Haq & Abbasi, 2016; Babin & Harris, 2023; Cruz et al., 2016; Curtis et al., 2017; de Mooij, 2019; East et al., 1994; Furnham & Milner, 2013; Gerrig et al., 2015; Geuens et al., 2011; Grigorios et al., 2022; Hamelin et al., 2017; Hansen & Christensen, 2007; Laros & Steenkamp, 2005; Olney et al., 1991; Otamendi & Sutil Martín, 2020; Poels & Dewitte, 2019; Rodgers & Thorson, 2012; Schiffman et al., 2013; Soscia, 2013; Vainikka, 2015; Virvilaitė et al., 2011; Watson & Spence, 2007; Weinberg & Gottwald, 1982; Williams et al., 2014; Yi & Jai, 2020).

In our bibliometric study, 213 results were obtained from the Scopus database and 206 results from the WoS database. The empirical findings suggest that the notions of Virtual Reality and emotions are extensively described in the literature, albeit predominately as separate issues.

Our focus solely on two major databases may have resulted in our overlooking certain areas of the literature that may be present in other, less popular databases. Consequently, our conclusions based solely on these two databases might be incomplete or contain some gaps in the literature, potentially distorts the outcomes. This underscores the need for further research that integrates these topics with each other and with related issues.

Conclusions

Consumer emotions are profoundly important for understanding consumer behavior. This study has provided an in-depth bibliometric analysis of the intersection between consumer emotions and Virtual Reality (VR) within the context of merchandising, utilizing data from two major databases, Web of Science (WoS) and Scopus. Our investigation revealed significant insights into how these domains are treated in the academic literature, highlighting both the extensive coverage and the fragmentation of the field. The study employed a systematic literature review (SLR) approach, ensuring a structured and comprehensive examination of the available literature. The use of VOSviewer for bibliometric mapping proved effective in visualizing the relationships and gaps within the research field.

The analysis identified a substantial body of literature addressing consumer emotions and VR, but these topics are predominantly treated as separate entities. There is a paucity of integrated studies that examine the combined impact of VR on consumer emotions and decision-making processes. The study noted a significant increase in publications related to VR and consumer emotions over the past decade. This trend reflects growing academic and practical interest in understanding how VR can influence consumer behavior and emotional responses.

The bibliometric mapping identified several distinct clusters of research within the dataset. For Scopus, these included themes like VR in supermarkets, user experience (UX), and EEG studies, while the WoS database highlighted clusters around 2D/3D models, eye-tracking analysis, and cognitive load. These clusters indicate focused areas of study but also suggest opportunities for cross-pollination of ideas across these domains.

Future research should aim to bridge the gap between studies on consumer emotions and VR. There is a need for more integrated approaches that examine how VR environments can be designed to evoke specific emotional responses and influence purchasing decisions. While this study focused on WoS and Scopus, incorporating additional databases could provide a more comprehensive view of the literature and uncover niche areas that may have been overlooked. Leveraging insights from behavioral economics, psychology, and marketing could enrich the understanding of how VR impacts consumer emotions. Collaborative studies across these disciplines could yield more comprehensive insights. Retailers and marketers can use the findings to enhance VR-based merchandising strategies, aiming to create immersive experiences that elicit desired emotional responses and drive consumer engagement and sales.

In conclusion, while the current literature provides a robust foundation, there is substantial scope for further research to explore the synergistic effects of VR and consumer emotions. Such efforts will not only advance academic knowledge but also offer practical insights for enhancing consumer experiences in virtual retail environments. The analysis conducted indicates the need for further research in the field of emotions in Virtual Reality. A review of the literature in terms of emotions shows how important a role they play in the decision-making process. This area is not fully explored and requires constant up-to-date research, indicating the great potential of the phenomenon.

The results may also have certain practical implications. They can be used by institutions or organizations and business practitioners (e.g., managers). The findings can serve as a guideline for the creation of virtual sales venues and further exploration of the impact of emotions on consumer purchase decisions. At the same time, we acknowledge that the analysis of two databases is a limitation, but it is a subject of interest and ongoing research.

References

Achar, C., So, J., Agrawal, N., & Duhachek, A. (2016). What we feel and why we buy: The influence of emotions on consumer decision-making. Current Opinion in Psychology, 10, 166–170. https://doi.org/10.1016/j.copsyc.2016.01.009

Alsharif, A. H., Md Salleh, N. Z., & Baharun, R. (2021). To Better Understand the Role of Emotional Processes in Decision-Making. International Journal of Academic Research in Economics and Management Sciences, 10(2), 49–67. https://doi.org/10.6007/IJAREMS/v10-i2/9883

Amin Ul Haq, M., & Abbasi, S. (2016). Indirect Impact of Hedonic Consumption and Emotions on Impulse Purchase Behavior: A Double Mediation Model. Journal of Management Sciences, 3, 108–122. https://doi.org/10.20547/jms.2014. 1603202

Arcidiacono, D. (2011). Consumer rationality in a multidisciplinary perspective. The Journal of Socio-Economics, 40(5), 516–522. https://doi.org/10.1016/j.socec. 2011.01.003

Babin, B. J., & Harris, E. G. (2023). CB Consumer Behaviour (3rd ed.). Cengage Canada.

Banyte, J., Rutelione, A., Gadeikiene, A., & Belkeviciute, J. (2016). Expression of Irrationality in Consumer Behaviour: Aspect of Price Perception. Engineering Economics, 27(3), Article 3. https://doi.org/10.5755/j01.ee.27.3.14318

Bell, H. (2011). A Contemporary Framework for Emotions in Consumer Decision-Making: Moving Beyond Traditional Models (SSRN Scholarly Paper 2340213). https://papers.ssrn.com/abstract=2340213

Biercewicz, K., & Sulich, A. (2022). The ecological games in environmental education: A bibliometric analysis. In E. Ardieli (Ed.). Proceedings of the 5th International Scientific Conference Development and Administration of Border Areas of the Czech Republic and Poland – Support for Sustainable Development, RASPO 2022. Technical University of Ostrava, 2022, pp. 74–81.

Bist, S. S., & Mehta, N. (2023). Positioning through visual merchandising: Can multi-brand outlets do it? Asia Pacific Management Review, 28(3), 267–275. https://doi.org/10.1016/j.apmrv.2022.10.004

Bruce, M., & Cooper, R. (1997). Marketing and Design Management (1st ed.). Thomson Learning.

Buchanan, L., Simmons, C. J., & Bickart, B. A. (1999). Brand Equity Dilution: Retailer Display and Context Brand Effects. Journal of Marketing Research, 36(3), 345–355. https://doi.org/10.1177/002224379903600304

Ceccacci, S., Generosi, A., Giraldi, L., & Mengoni, M. (2018). An Emotion Recognition System for monitoring Shopping Experience. Proceedings of the 11th Pervasive Technologies Related to Assistive Environments Conference, 102–103. https://doi.org/10.1145/3197768.3201518

Chitturi, R. (2009). Emotions by Design: A Consumer Perspective. International Journal of Design, 3(2), 7–17.

Cruz, C. M. L., Medeiros, J. F. D., Hermes, L. C. R., Marcon, A., & Marcon, É. (2016). Neuromarketing and the advances in the consumer behaviour studies: A systematic review of the literature. International Journal of Business and Globalisation, 17(3), 330–351. https://doi.org/10.1504/IJBG.2016.078842

Curtis, T., Arnaud, A., & Waguespack, B. P. (2017). Advertising Effect on Consumer Emotions, Judgements, and Purchase Intent. Asian Journal of Business Research, 7(2), 57. https://doi.org/10.14707/ajbr.170037

Das, G., & Varshneya, G. (2017). Consumer emotions: Determinants and outcomes in a shopping mall. Journal of Retailing and Consumer Services, 38, 177–185. https://doi.org/10.1016/j.jretconser.2017.06.008

Dash, B., Swain, D., & Swain, D. (2016). A Pragmatic Delineation on Cache Bypass Algorithm in Last-Level Cache (LLC). In H. S. Behera & D. P. Mohapatra (Eds.), Computational Intelligence in Data Mining – Volume 2 (vol. 411, pp. 37–45). Springer India. https://doi.org/10.1007/978-81-322-2731-1_4

Dash, M., Sharma, K., & Lakhani, A. (2018). Impulse purchase and impulse non-purchase in apparel retail outlets in shopping malls. International Journal of Marketing and Business Communication, 7(2), 9–13. https://doi.org/10.13140/ RG.2.2.12576.94723

Davies, B. J., & Ward, P. (2005). Exploring the connections between visual merchandising and retail branding: An application of facet theory. International Journal of Retail & Distribution Management, 33(7), 505–513. https://doi.org/10.1108/09590550510605578

Dawson, S., Bloch, P., & Ridgway, N. (1990). Shopping Motives, Emotional States, and Retail Outcomes. Journal of Retailing, 58, 34–57.

de Mooij, M. (2019). Consumer Behavior and Culture: Consequences for Global Marketing and Advertising. Sage Publications.

Donnellan, J. (1996). Merchandise Buying and Management. Fairchild Publications.

East, R., Lomax, W., Willson, G., & Harris, P. (1994). Decision Making and Habit in Shopping Times. European Journal of Marketing, 28(4), 56–71. https://doi.org/10.1108/03090569410061196

Eroglu, S. A., Machleit, K. A., & Davis, L. M. (2003). Empirical testing of a model of online store atmospherics and shopper responses. Psychology & Marketing, 20(2), 139–150. https://doi.org/10.1002/mar.10064

Eysenck, M. (2012). Attention and Arousal: Cognition and Performance. Springer Science & Business Media.

Fill, C. (2009). Marketing communications: Interactivity, communities and content. Pearson Education.

Foxall, G. (2017). Behavioral Economics in Consumer Behavior Analysis. The Behavior Analyst, 40(1) 15–28. https://doi.org/10.1007/s40614-017-0127-4

Foxall, G. R. (2011). Brain, Emotion, and Contingency in the Explanation of Consumer Behaviour. In G. P. Hodgkinson & J. K. Ford (Eds.), International review of industrial and organizational psychology (pp. 47–91). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119992592.ch2

Furnham, A., & Milner, R. (2013). The impact of mood on customer behavior: Staff mood and environmental factors. Journal of Retailing and Consumer Services, 20(6), 634–641. https://doi.org/10.1016/j.jretconser.2013.06.001

Gabriel, Y., & Lang, T. (2006). The Unmanageable Consumer. SAGE.

Gaczek, P. (2016). Emocje czy rozum? Co kształtuje decyzje zakupowe nabywców? [Emotions or Reason? What Shapes Consumer Purchase Decisions]. In K. Pająk (Ed.) Innowacyjność wyzwaniem dla współczesnej gospodarki (pp. 251–262). [in Polish].

Gaur, S. S., Herjanto, H., & Makkar, M. (2014). Review of emotions research in marketing, 2002–2013. Journal of Retailing and Consumer Services, 21(6), 917–923. https://doi.org/10.1016/j.jretconser.2014.08.009

Gerrig, R. J., Zimbardo, P. G., Campbell, A. J., Cumming, S. R., & Wilkes, F. J. (2015). Psychology and Life. Pearson Higher Education AU.

Geuens, M., De Pelsmacker, P., & Faseur, T. (2011). Emotional advertising: Revisiting the role of product category. Journal of Business Research, 64(4), 418–426. https://doi.org/10.1016/j.jbusres.2010.03.001

Gorbatkow, A. A. (2002). Zależność relacji między pozytywnymi i negatywnymi emocjami od poziomu rozwoju podmiotowego jednostki [Efficiency as a Moderator of Correlations Between Positive and Negative Emotions]. Czasopismo Psychologiczne, 8(1), 49–64. [in Polish].

Grigorios, L., Magrizos, S., Kostopoulos, I., Drossos, D., & Santos, D. (2022). Overt and covert customer data collection in online personalized advertising: The role of user emotions. Journal of Business Research, 141, 308–320. https://doi.org/10.1016/j.jbusres.2021.12.025

Groeppel-Klein, A. (2005). Arousal and consumer in-store behavior. Brain Research Bulletin, 67(5), 428–437. https://doi.org/10.1016/j.brainresbull.2005.06.012

Guo, J., Wang, X., & Wu, Y. (2020). Positive emotion bias: Role of emotional content from online customer reviews in purchase decisions. Journal of Retailing and Consumer Services, 52, 101891. https://doi.org/10.1016/j.jretconser.2019.101891

Gurgu, E., Gurgu, I.-A., & Tonis, R. B. M. (2020). Neuromarketing for a better understanding of consumer needs and emotions. Independent Journal of Management & Production, 11(1), Article 1. https://doi.org/10.14807/ijmp. v11i1.993

Halkias, G. (2015). Mental representation of brands: A schema-based approach to consumers’ organization of market knowledge. Journal of Product & Brand Management, 24(5), 438–448. https://doi.org/10.1108/JPBM-02-2015-0818

Hamelin, N., Moujahid, O. E., & Thaichon, P. (2017). Emotion and advertising effectiveness: A novel facial expression analysis approach. Journal of Retailing and Consumer Services, 36, 103–111. https://doi.org/10.1016/j.jretconser.2017. 01.001

Hansen, F., & Christensen, S. R. (2007). Emotions, Advertising and Consumer Choice. Copenhagen Business School Press DK.

Hirschman, E. C., & Stern, B. B. (1999). The Roles of Emotion in Consumer Research. ACR North American Advances, NA-26. https://www.acrwebsite.org/volumes/ 8216/volumes/v26/NA-26/full

Hui, S. K., Inman, J. J., Huang, Y., & Suher, J. (2013). The Effect of In-Store Travel Distance on Unplanned Spending: Applications to Mobile Promotion Strategies. Journal of Marketing, 77(2), 1–16. https://doi.org/10.1509/jm.11.0436

Hursh, S. R. (2014). Behavioral economics and the analysis of consumption and choice. In The Wiley Blackwell handbook of operant and classical conditioning (pp. 275–305). Wiley Blackwell. https://doi.org/10.1002/9781118468135.ch12

Iberahim, H., Zureena, Z. N. A., Adila, R. A. S. R. N., & Quraisyiah, R. S. (2018). Determinants of Customer Impulse Buying Behavior at Product Specialist Fashion Retail Stores. Journal of Economic & Management Perspectives, 12(1), 538–544. https://www.proquest.com/docview/2266300723/abstract/ BF2E862FE0F34B36PQ/1

Ishak, F., & Abd Ghani, N. H. (2013). A review of the literature on brand loyalty and customer loyalty. Conference on Business Management Research (pp. 186–198). https://repo.uum.edu.my/id/eprint/16316/

Izard, C. E. (1991). The Psychology of Emotions. Springer Science & Business Media.

Jain, R., Aagja, J., & Bagdare, S. (2017). Customer experience – a review and research agenda. Journal of Service Theory and Practice, 27(3), 642–662. https://doi.org/10.1108/JSTP-03-2015-0064

Kim, H. S., & Lee, J. H. (2017). Neuro-scientific Approach to Fashion Visual Merchandising –Comparison of Brain Activation to Positive/Negative VM in Fashion Store Using fNIRS. Journal of the Korean Society of Clothing and Textiles, 41(02), 254–265. https://doi.org/10.5850/JKSCT.2017.41.2.254

Kim, J., & Gupta, P. (2012). Emotional expressions in online user reviews: How they influence consumers’ product evaluations. Journal of Business Research, 65(7), 985–992. https://doi.org/10.1016/j.jbusres.2011.04.013

Kruszewska, A. (2018). Emocje, uczucia i społeczne zachowania emocjonalne dzieci na progu szkolnym: Z teorii i badań [Emotions, Feelings, and Social Emotional Behaviors of Children at the School Threshold: From Theory and Research]. Akka. [in Polish].

Laermans, R. (1993). Learning to Consume: Early Department Stores and the Shaping of the Modern Consumer Culture (1860–1914). Theory, Culture & Society, 10(4), 79–102. https://doi.org/10.1177/026327693010004005

Laros, F. J. M., & Steenkamp, J.-B. E. M. (2005). Emotions in consumer behavior: A hierarchical approach. Journal of Business Research, 58(10), 1437–1445. https://doi.org/10.1016/j.jbusres.2003.09.013

Larson, J. S., Bradlow, E. T., & Fader, P. S. (2005). An exploratory look at supermarket shopping paths. International Journal of Research in Marketing, 22(4), 395–414. https://doi.org/10.1016/j.ijresmar.2005.09.005

Le, D., Pratt, M., Wang, Y., Scott, N., & Lohmann, G. (2020). How to win the consumer’s heart? Exploring appraisal determinants of consumer pre-consumption emotions. International Journal of Hospitality Management, 88, 102542. https://doi.org/10.1016/j.ijhm.2020.102542

Levy, M., & Weitz, B. A. (2001). Retailing Management. Irwin/McGraw-Hill.

Li, H., Qi, R., Liu, H., Meng, F., & Zhang, Z. (2021). Can time soften your opinion? The influence of consumer experience valence and review device type on restaurant evaluation. International Journal of Hospitality Management, 92, 102729. https://doi.org/10.1016/j.ijhm.2020.102729

Matušínská, K., & Zapletalová, Š. (2021). Rational and emotional aspects of consumer behaviour. Forum Scientiae Oeconomia, 9(2), Article 2. https://doi.org/10.23762/ FSO_VOL9_NO2_5

McDonald, W. J. (1994). Time use in shopping: The role of personal characteristics. Journal of Retailing, 70(4), 345–365. https://doi.org/10.1016/0022-4359(94)90004-3

Mostafa, R. B., & Kasamani, T. (2020). Brand experience and brand loyalty: Is it a matter of emotions? Asia Pacific Journal of Marketing and Logistics, 33(4), 1033–1051. https://doi.org/10.1108/APJML-11-2019-0669

Mruk, H. (2017). Zachowania konsumentów w świetle ekonomii behawioralnej [Consumer behavior in the light of behavioral economics]. Studia Ekonomiczne, 312, 82–95. [in Polish].

Mullen, B., & Johnson, C. (2013). The Psychology of Consumer Behavior. Psychology Press.

Nageswaran, L., Cho, S.-H., & Scheller-Wolf, A. A. (2019). Consumer Return Policies in Omnichannel Operations (SSRN Scholarly Paper 3046206). https://doi.org/10.2139/ssrn.3046206

Niedzielska, A. (2016). Emotions on Marketing in the Light of Theoretical Deliberations and Results of Qualitative Research. Marketing i Zarządzanie, 45, 327–337. https://doi.org/10.18276/miz.2016.45-29

Nobbs, K., Foong, K. M., & Baker, J. (2015). An exploration of fashion visual merchandising and its role as a brand positioning device. Journal of Global Fashion Marketing, 6(1), 4–19. https://doi.org/10.1080/20932685.2014.971491

Nobbs, K., McColl, J., & Shearer, L. (2011). An exploratory study into the strategic significance of visual merchandising: The case of vintage fashion retailing. In M. Pedroni (Ed.) From Production to Consumption: The Cultural Industry of Fashion. Inter-Disciplinary Press.

Novak, T. P., & Hoffman, D. L. (2009). The Fit of Thinking Style and Situation: New Measures of Situation-Specific Experiential and Rational Cognition. Journal of Consumer Research, 36(1), 56–72. https://doi.org/10.1086/596026

Olney, T. J., Holbrook, M. B., & Batra, R. (1991). Consumer Responses to Advertising: The Effects of Ad Content, Emotions, and Attitude toward the Ad on Viewing Time. Journal of Consumer Research, 17(4), 440–453. https://doi.org/10.1086/208569

Olson, D., Russell, C. S., & Sprenkle, D. H. (2014). Circumplex Model: Systemic Assessment and Treatment of Families. Routledge.

Otamendi, F., & Sutil Martín, D. L. (2020). The Emotional Effectiveness of Advertisement. Frontiers in Psychology, 11. https://www.frontiersin.org/ articles/10.3389/fpsyg.2020.02088

Park, H. H., Jeon, J. O., & Sullivan, P. (2015). How does visual merchandising in fashion retail stores affect consumers’ brand attitude and purchase intention? The International Review of Retail, Distribution and Consumer Research, 25(1), 87–104. https://doi.org/10.1080/09593969.2014.918048

Parker, K. W. (2003). Sign Consumption in the 19th-Century Department Store: An Examination of Visual Merchandising in the Grand Emporiums (1846–1900). Journal of Sociology, 39(4), 353–371. https://doi.org/10.1177/0004869003394003

Patrzałek, W. (Ed.). (2016). Konsumpcja jako forma komunikacji społecznej: Nowe paradygmaty i konteksty badawcze [Consumption as a Form of Social Communication: New Paradigms and Research Contexts]. Wydawnictwo Uniwersytetu Ekonomicznego. [in Polish].

Petrosky-Nadeau, N., Wasmer, E., & Zeng, S. (2016). Shopping time. Economics Letters, 143, 52–60. https://doi.org/10.1016/j.econlet.2016.02.003

Pluta-Olearnik, M., & Szulga, P. (2022). The Importance of Emotions in Consumer Purchase Decisions – A Neuromarketing Approach. Marketing of Scientific and Research Organizations, 44(2), 87–104. https://doi.org/10.2478/minib-2022-0010

Poels, K., & Dewitte, S. (2019). The role of emotions in advertising: A call to action. Journal of Advertising, 48(1), 81–90. https://doi.org/10.1080/00913367.2019. 1579688

Rasmussen, A. S., & Berntsen, D. (2009). Emotional valence and the functions of autobiographical memories: Positive and negative memories serve different functions. Memory & Cognition, 37(4), 477–492. https://doi.org/10.3758/MC. 37.4.477

Reed, D. D., Niileksela, C. R., & Kaplan, B. A. (2013). Behavioral economics: A tutorial for behavior analysis in practice. Behavior Analysis in Practice, 6(1), 34–54.

Reisenzein, R. (1994). Pleasure-arousal theory and the intensity of emotions. Journal of Personality and Social Psychology, 67(3), 525–539. https://doi.org/10.1037/ 0022-3514.67.3.525

Reisenzein, R. (2007). What is a definition of emotion? And are emotions mental-behavioral processes? Social Science Information, 46(3), 424–428. https://doi.org/10.1177/05390184070460030110

Richard, B., Zynep, G.-C., & Joseph, P. (2002). The Social Psychology Of Consumer Behaviour. McGraw-Hill Education (UK).

Richardson Bareham, J. (2004). Can consumers be predicted or are they unmanageable? International Journal of Contemporary Hospitality Management, 16(3), 159–165. https://doi.org/10.1108/09596110410531140

Robbins, T. W., & Everitt, B. J. (1995). Arousal systems and attention. In The cognitive neurosciences (pp. 703–720). The MIT Press.

Rodgers, S., & Thorson, E. (2012). Advertising Theory. Routledge.

Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178. https://doi.org/10.1037/h0077714

Schiffman, L., O’Cass, A., Paladino, A., & Carlson, J. (2013). Consumer Behaviour. Pearson Higher Education AU.

Soscia, I. (2013). Emotions and Consumption Behaviour. Edward Elgar Publishing.

Spanjaard, D., Young, L., & Freeman, L. (2014). Emotions in supermarket brand choice: A multi-method approach. Qualitative Market Research: An International Journal, 17(3), 209–224. https://doi.org/10.1108/QMR-10-2012-0049

Swanson, K. K., & Everett, J. C. (2015). Promotion in the merchandising environment. Bloomsbury Publishing USA.

Syaekhoni, M. A., Lee, C., & Kwon, Y. S. (2018). Analyzing customer behavior from shopping path data using operation edit distance. Applied Intelligence, 48(8), 1912–1932. https://doi.org/10.1007/s10489-016-0839-2

Szymańska, K. (2017). Determinanty zachowań klientów w miejscu sprzedaży – wstępne wyniki badań [Determinants of Customer Behavior at the Point of Sale – Preliminary Research Results]. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 489, 416–426. [in Polish].

Thayer, R. E. (1990). The Biopsychology of Mood and Arousal. Oxford University Press.

Thayer, R. E., & McNally, R. J. (1992). The Biopsychology of Mood and Arousal. Cognitive and Behavioral Neurology, 5(1), 65.

Trevisan, E. (2016). The Irrational Consumer: Applying Behavioural Economics to Your Business Strategy. Routledge.

Vainikka, B. (2015). Psychological Factors Influencing Consumer Behaviour. (Bachelor’s thesis). Centria ammattikorkeakoulu (Keski-Pohjanmaan ammattikorkeakoulu). http://www.theseus.fi/handle/10024/96405

Valecha, H., Varma, A., Khare, I., Sachdeva, A., & Goyal, M. (2018). Prediction of Consumer Behaviour using Random Forest Algorithm. 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), 1–6. https://doi.org/10.1109/UPCON.2018.8597070

Verduyn, P., Mechelen, I. V., & Frederix, E. (2012). Determinants of the shape of emotion intensity profiles. Cognition and Emotion, 26(8), 1486–1495. https://doi.org/10.1080/02699931.2012.662152

Virvilaitė, R., Saladienė, V., & Žvinklytė, J. (2011). The Impact of external and internal stimuli on impulsive purchasing. Ekonomika Ir Vadyba, 16, 1329–1336.

Wang, S., Cheah, J.-H., & Lim, X.-J. (2023). Online shopping cart abandonment: A review and research agenda. International Journal of Consumer Studies, 47(2), 453–473. https://doi.org/10.1111/ijcs.12876

Wang, X., Guo, J., Wu, Y., & Liu, N. (2019). Emotion as signal of product quality: Its effect on purchase decision based on online customer reviews. Internet Research, 30(2), 463–485. https://doi.org/10.1108/INTR-09-2018-0415

Waszkiewicz-Raviv, A., Jasiewicz, J., & Jupowicz-Ginalska, A. (2018). Walencja emocjonalna online. Kompetencje cyfrowe a pomiar reakcji emocjonalnych w świetle badań jakościowych i biometrycznych [Emotional Valence Online: Digital Competencies and Measurement of Emotional Reactions in the Light of Qualitative and Biometric Research]. Annales Universitatis Paedagogicae Cracoviensis. Studia de Cultura, 4(10), 114–124. [in Polish]. https://doi.org/10.24917/20837275.10.4.9

Watson, L., & Spence, M. T. (2007). Causes and consequences of emotions on consumer behaviour: A review and integrative cognitive appraisal theory. European Journal of Marketing, 41(5/6), 487–511. https://doi.org/10.1108/ 03090560710737570

Weinberg, P., & Gottwald, W. (1982). Impulsive consumer buying as a result of emotions. Journal of Business Research, 10(1), 43–57. https://doi.org/10.1016/ 0148-2963(82)90016-9

Wheeler, S. C., Petty, R. E., & Bizer, G. Y. (2005). Self-Schema Matching and Attitude Change: Situational and Dispositional Determinants of Message Elaboration. Journal of Consumer Research, 31(4), 787–797. https://doi.org/10.1086/426613

Williams, P., Hung, I. W., Mukhopadhyay, A., Pieters, R., Zhou, X., Wildschut, T., Sedikides, C., Shi, K., Feng, C., Mogilner, C., Aaker, J., Kamvar, S. D., Di Muro, F., & Murray, K. B. (2014). Emotions and Consumer Behavior. Journal of Consumer Research, 40(5), viii–xi. https://doi.org/10.1086/674429

Xie, C., Chiang, C.-Y., Xu, X., & Gong, Y. (2023). The impact of buy-online- and-return-in-store channel integration on online and offline behavioral intentions: The role of offline store. Journal of Retailing and Consumer Services, 72, 103227. https://doi.org/10.1016/j.jretconser.2022.103227

Yi, S., & Jai, T. (2020). Impacts of consumers’ beliefs, desires and emotions on their impulse buying behavior: Application of an integrated model of belief-desire theory of emotion. Journal of Hospitality Marketing & Management, 29(6), 662–681. https://doi.org/10.1080/19368623.2020.1692267

Zalega, T. (2015). Ekonomia behawioralna jako nowy nurt ekonomii – Zarys problematyki [Behavioral Economics as a New Trend in Economics – An Outline of the Problem]. Studia i Materiały / Wydział Zarządzania. Uniwersytet Warszawski, 1, 7–22. [in Polish]. https://doi.org/10.7172/1733-9758.2015.18.1

Zaltman, G., & Zaltman, L. H. (2008). Marketing Metaphoria: What Deep Metaphors Reveal about the Minds of Consumers. Harvard Business Press.