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.

Nearly 60 years ago, de Solla Price (1965) proposed scientific methods for the study of science (Boyack et al., 2005). The basis in bibliometric methods is bibliographic data from scientific publication databases, through which structural images of areas, scientific fields, can be constructed. The procedure for bibliometric analysis can be encapsulated in the following steps (Donthu et al., 2021a): (1) Defining the objectives and scope of the bibliometric study, (2) Selecting bibliometric analysis techniques, (3) Collecting data for bibliometric analysis and (4) Performing the bibliometric analysis and presenting the results. The two main applications of bibliometric methods are outcome analysis and science mapping (Cobo et al., 2011). Results analysis aims to assess the research and publication results of individuals and institutions. Science mapping, on the other hand, aims to reveal the structure and dynamics of scientific areas. With information on structure and development, it is possible for a researcher to determine the realisation, development or changes in research on a particular topic (Zupic & Cater, 2015).

The use of bibliometric methods has allowed objectivity to be introduced into the evaluation of scientific literature (Garfield, 1979). This is made possible by applying quantitative rigour to the subjective evaluation of the literature. Among bibliometric analysis techniques, a distinction can be made between review, evaluative and relational techniques. Review techniques include structured literature reviews and meta-analyses oriented towards generating knowledge through frequency analysis. Evaluative techniques can be used to identify qualitative and quantitative indicators of research and to compare the scientific contributions of other researchers. They can also identify the number of articles published or the number of citations of individual publications, authors and journals. Relational techniques examine the relationships between data in publications, e.g. topics, methods, co-authorship. They can include the analysis of co-citation, co-occurrence of words, co-authorship of publications, bibliographic links, co-citation clustering or direct citations (Lenart-Gansiniec, 2021).

The primary technique used in bibliometric analysis is citation analysis. It involves measuring the number of citations a paper has received, which enables an overall assessment of its quality (Anderson, 2006). This approach follows the fact that citations are used to determine impact-if an article is frequently cited, it is considered important. This thesis is based on the assumption that authors cite documents that they consider important to their work (Zupic & Cater, 2015).1 Citations also reflect the degree of knowledge transfer and spread by other authors, representing other research centres (Ejdys, 2016). Citation analysis can provide information on the relative impact of publications, but at the same time its disadvantage is that it is not possible to identify networks of interconnectedness between scientists (Usdiken & Pasadeos, 1995)-which can be taken as a rationale for distinguishing other variants of citation analysis that differ in the scope of the data considered. These are (Klinkiewicz et al., 2012):

• Direct citation analysis-construction of a matrix with data reflecting the cases of direct citations of one author’s texts by others;
• Bibliographic coupling analysis-identification of publications that cite the same article, i.e. refer to the same sources of knowledge; this variant of analysis usually makes it possible to identify the most recent publications in a given citation network;
• Co-citation clustering analysis-an indication of which publications, sources of knowledge, are referred to in selected articles, which usually makes it possible to identify the oldest publications in a given citation network;
• Co-citation analysis-covering selected publications and the knowledge sources they cite.

When two items (e.g. documents, journals or authors) are cited in the reference list of the citing item, there is a co-citation relationship between them (Osareh, 1996). Small (1973) presented co-citation analysis to investigate the relationship and structure of academic fields. Subsequently, co-citation analysis has been widely used to reveal the relationship and structure of authors, articles and journals in academic fields. Co-citation analysis uses the number of co-citations to construct measures of similarity between documents, authors or journals. Co-citation is defined as the frequency with which two units are cited together. The basic premise of cocitation analysis is that the more times two items are cited together, the more likely it is that their content is related. Depending on the unit of analysis, a co-citation analysis can be carried out: of documents, authors or journals. It should be noted that co-citation is a dynamic measure. This is because the publication process is time-consuming, and thus the result of a co-citation analysis reflects the state of the field at a given point in the preparation of the publication, and not necessarily what it looks like now (Zupic & Cater, 2015). At a further stage, the data obtained in the cocitation analysis can be used to divide into clusters/clustering (co-citation clustering analysis, co-citation clustering). For this purpose, representative examples of literature items are selected as objects to be analysed, and then a physical division into clusters is made through the network analysis method. The structure and characteristics of a specific field are thus obtained.

A thorough description of the issues surrounding bibliometrics, the characteristics of the other bibliographic techniques indicated earlier and the procedure for bibliometric analysis are presented in publications such as: the studies of Donthu et al. (2021a), Zupic and Cater (2015) and Roemer and Borchardt (2015).

Interest in 'word of mouth’ (WOM, whisper marketing) began in the US in the 1940s in academic circles. Early definitions of WOM appeared in the work of Allport and Postamam (1946) and Zaraket (2020). As research on WOM developed, further definitions of the concept emerged. Examples include the work of Westbrook (1987), Buttle (1998), Stokes et al. (2002) and Litvin et al. (2008), among others. Most often, classic 'word of mouth’ is defined quite broadly as an informal exchange between consumers of positive or negative information about a product or service that is not commercial in nature (Zaraket, 2020). The mentioned transmission of information takes place within the sender’s circle of acquaintances, which implies its local reach. The transfer of knowledge about products or services is usually accompanied by the need for interaction or friendship (Linkiewicz, 2015). This is the result of the WOM mechanism, which is based on the creation of conversational capital (Cesvet et al., 2010; Gawrońska, 2013). It is only revealed in one situation when consumers want to talk to other people about a brand, product or service. The emerging desire to talk to others is justified in the literature by the following factors: (1) the psychological construct of human beings-the need to share opinions with others, which may have a basis in the operation of the instinct of self-preservation, (2) the desire to establish contacts with others and thus accumulate a kind of social capital, (3) economic reasons and (4) the desire to free oneself from the pressures/influence of advertising.

The technological developments we have seen over the last 20–30 years have contributed to changes in the communication environment. The emergence and development of various new online communication channels has led to the emergence of the term electronic word of mouth (eWOM, online whisper marketing). It has been defined as 'any positive or negative message spoken by a potential, current or former customer about a product or company that is shared with many people and institutions on the Internet’ (Hennig-Thurau et al., 2004).

In the marketing field, it is believed that WOM-type communication can significantly influence a consumer’s purchasing decision. Therefore, WOM has been recognised as a highly reliable form of marketing information (Huang et al., 2011). Research results indicate that in addition to influencing the purchase decision (O’Reilly & Marx, 2011), WOM can also affect consumer choice (Richins, 1983), service change (v. Wangenheim & Bayón, 2004) or product/service perception (Sweeney et al., 2014). Whisper marketing also appears as an exogenous or endogenous variable in various estimated models. A review of such models is provided in the work of Kundu and Rajan (2016). Based on a review and analysis of 20 papers, they conclude that WOM strongly influences consumers’ behavioural attitudes. In the literature, we can also find papers on literature review and bibliometric analyses, as well as meta-analyses focussed on eWOM and WOM (e.g. Abbas et al., 2020; Bhaiswar et al., 2021; De Matos & Rossi, 2008; Donthu et al., 2021b; Huete-Alcocer, 2017).

Based on a review of publications on both bibliographic and WOM analyses, it has been assumed that the purpose of the article is to identify and present the most frequently cited works (along with their authors), as well as the publications most often referred to by authors publishing WOMrelated works.

Methodology

It was assumed that the purpose of bibliometric analysis of the concept of WOM is: (1) to identify which works are most often cited in a given research field (mandatory reading list); as well as (2) to identify which publications are most often referred to by published articles. Based on a search of the Web of Science (WoS) database, 8,332 publications were identified, 83% of which were articles (as at March 20, 2023). The above result was obtained by adopting the following publication search criterion: 'word of mouth’ OR 'wom’, included in the publication title and/or keywords and/or abstract. The search for publications was limited to the following categories (as defined by WoS): business, management, tourism and hospitality, communication, economics, social sciences, sociology and psychology. Another restriction applied was the indication of the types of publications to be included in the search: articles, post-conference materials, books, chapters in books and publications in so-called early access. The characteristics of the resulting set of documents are presented in Table 1.

The completeness of the individual fields in the database was then assessed to estimate the quality of the data obtained. For basic document information (i.e. author(s), publication title, etc.), the level of missing responses is at a very low level. The highest level of missingness was observed among the keywords given by the authors, amounting to 979 (i.e. 11%), but this was assessed as acceptable.

It will be possible to achieve the stated objectives by performing citation analysis, co-citation analysis and co-citation clustering analysis on the downloaded dataset. The indicated analyses were performed using VOSviewer (version 1.6.17) and Biblioshiny software (https://www.bibliome trix.org).

The authors of the VOSviewer program are Nees Jan van Eck and Ludo Waltman. The program is based on the visualisation of similarities (VOS) technique, where similarity between objects is used for visualisation (similar objects are located close to each other and less similar objects are located away from each other) (van Eck & Waltman, 2010). The results of the analyses are presented as a network, where each node represents an entity (e.g. article, author, country, institution, keyword, journal), whereby:

1. the size of a node indicates the occurrence of a given item (e.g. the number of times a keyword occurs),
2. the link between nodes represents the co-occurrence between items (e.g.
   keywords that co-occur or occur together),
3. link thickness indicates the occurrence of co-occurrence between items (the number of times that, for example, keywords co-occur or occur together) – the thicker the link between nodes, the more frequent the occurrence of co-occurrence,
4. the larger the node, the more frequent the occurrence of a given item, e.g. a keyword,
5. each colour represents a topic cluster, where the nodes and links in this cluster can be used to explain the coverage of the topic (cluster), the topics (nodes) and the relationships (links) between topics (nodes) manifested within this topic (cluster).

Bibliometrix (https://www.bibliometrix.org/home/) and its companion application Biblioshiny run in the R environment. Their aim is to provide a complete set of functions to support the overall bibliomteric analysis, together with the possibilities to visualise the results obtained and prepare a report with selected results of the analyses performed. A detailed description of the capabilities of this software is presented in the study of Aria & Cuccurullo (2017).

Results of Citation and Co-Citation Analysis

The first publication in the WOS database related to WOM was published in 1925. For the following years, the above topic did not arouse the interest of authors and researchers. The beginning of an increased number of publications can be observed in the 1980s, while a jump in the number of publications can be observed from the beginning of the 21st century. Last year, i.e. 2022, 833 new publications were recorded in the WoS database. Figure 1 shows the changes in the number of publications on whisper marketing from 1925 to the present.

The author with the most publications on whisper marketing is Rob Law with 52 items (as author or co-author), followed by Juran Kim, who has published 41 of them. When it comes to place of publication, it is the Journal of Business Research (320 items) and the Journal of Retailing and Consumer Services (209 items) that have the highest number of articles in this field. A detailed list of the 10 most published authors on a given topic as well as the 10 most frequent publication places is presented in Table 2.

The resulting picture is completed by graphing the average citations of published works. An analysis of the average number of citations in individual years reveals an increased interest in this area in particular years, as well as its variability. Recent years have been characterised by a decreasing interest in the addressed issues (Figure 2).

Another summary obtained was the ranking of the most cited works and their authors. The ranking can be made on the basis of citations from the entire database (so-called global citations, global citations) as well as from the generated set of documents (so-called local citations). The difference between the results in the two sets and the position may be a result of the fact that for many documents a large proportion of the global citations may come from other disciplines. In the analysed set in the first and second cases, the most cited paper is the article by Chevalier, J. A., and Mayzlin, D., from 2006, entitled: The effect of word of mouth on sales: Online book reviews. In it, the authors present the results of their research on the importance of consumer reviews for book sales at two online bookstores, i.e. Amazon.com and BarnesandNoble.com.

They conclude that customer WOM has a causal effect on consumer buying behaviour at both online bookstores i.e. an improvement in book reviews leads to an increase in relative sales at that site. Furthermore, they note that the impact of onestar (negative) reviews is greater than the impact of five-star (positive) reviews. The second most cited paper in the entire database is an article written by a team of authors (Van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P. and Verhoef, P. C.) in 2010, entitled Customer engagement behavior: Theoretical foundations and research directions and published in Journal of service research. In the article, the authors focussed on 'customer engagement behaviours’ (CEBs), which they defined as expressions of customer behaviour towards a brand or company, beyond purchase, driven by motivation. According to the authors, CEBs encompass a wide range of behaviours, including WOM-type activity, recommendations, helping other customers, blogging, writing reviews and even engaging in legal activities.

In the case of so-called local citations, the second most cited item is a 2004 article by Godes and Mayzlin (2004), entitled Using online conversations to study word-of-mouth communication and published in the journal 'Marketing science’. The authors of the article highlighted significant challenges in measuring WOM. A summary of the most frequently cited papers, so-called global citations and local citations, is presented in Table 3.

Co-citation analysis makes it possible to determine the frequency with which two items are cited together, i.e. a co-citation relationship is formed between them. In the case of the set of publications analysed, the 10 most frequently co-cited items of literature are presented in Figure 3. This allows the identification of publications (selected using citation thresholds) considered important by the researchers citing them (Zupic & Cater, 2015).

The first is an article by Fornell and Larcker (1981) entitled 'Evaluating Structural Equation Models with Unobservable Variables and Measurement Error’ published in 1981 in the Journal of Marketing Research. It addresses the issue of evaluating the quality of the resulting structural model. In the case of the set of publications under discussion, the appearance of this item is due to the use of this method for modelling the phenomenon under study.

The same situation will also apply to keyword analysis, where there will also be words associated with structural modelling. Another item is an article published by four authors (Hennig-Thurau, T., Gwinner, K. P., Walsh, G. and Gremler, D. D.) in 2004 and entitled: Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet?. The authors, basing themselves on the results of research on virtual communities and the traditional WOM literature, developed a typology of motives for opinion articulation by consumers online. Using an online sample of 2,000 consumers, they generated information on the structure and importance of consumers’ motives for articulating opinions online. The results indicate that the desire for social interaction, the desire for economic incentives, concern for other consumers and the opportunity to increase one’s self-esteem are the main factors leading to eWOM behaviour. The third most frequently co-cited item is the aforementioned publication by Chevalier and Mayzlin (2006), entitled: The effect of word of mouth on sales: Online book reviews.

At a further stage, the identified literature items were divided into clusters/clustering. The division was carried out using the VOSviewer software. As there were more than 200,000 citations in the sample, it is not possible to perform a co-citation analysis for the whole sample. McCain (1990) suggested that a cut-off point could be established to select the most influential papers. Therefore, items that were cited at least 200 times were selected for this study. Figure 4 highlights the most relevant co-cited pairs. The size of the node represents the normalised number of citations received by the articles and the thickness of the line represents the strength of the cocitation relationships. The line and proximity between two articles identify the co-citation relationships between them. The colour of the node indicates the cluster with which the article is associated. Each node was labelled by the first author and the year of the article’s publication. In the figure shown, there are 112 nodes, 6,260 links and 3 clusters. Due to the large number of papers in each cluster, only leading papers will be presented.

The first largest set (red) contains 52 items. The central part is taken up by a publication by Fornell and Larcker (1981).2 For example, it is co-cited with another item from the previously presented list of publications (i.e. Podsakoff et al., 2003) 509 times. Another item from this set is a paper by Anderson and Gerbing (1988) on structural modelling. Given the examples cited, it can be assumed that this set can be identified with the widely understood methodology of WOM research.

The second largest set (green) contains 33 items. The article by Chevalier and Mazylin (2006) plays an important role in this set. The total strength of the links of this node is 9,521. Other relevant items in this collection are the papers by: Godes and Mayzlin (2004) (411 shared citations with the Chevalier and Mazylin item), Liu (2006) (435 shared citations) or Zhu et al. (2015) (317 shared citations). The theme of the works in this set can be generalised to the statement-the use of WOM. The last set (blue colour) has 27 items. In this case, the central role is played by the article by Hennig-Thuran Hennig — Thurau (2004). The total strength of the links of this node is 9,907. For example, this item together with the work of Trusov et al. (2009) were co-cited 159 times, and with the work of Brown et al. (1987) a similar number of times, i.e. 160 times. In the case of this set, it can be concluded that the topics of the papers are related to the use of networked WOM (eWOM). It should also be noted that there are also co-citations between items in each set. Examples of pairs of works are presented in Table 4.

Summarisation

'Word of mouth’ as a communication tool has evolved quite significantly over the past few decades. Originally, the transmission of information by WOM was limited to local coverage i.e. it was restricted only to the closest people in the circle of the sender of the message. The sender was usually the consumer, treated as an impartial source of information. The technological developments that have taken place over the past 30 years, especially computer and mobile technologies, have caused significant changes in this form of communication. The information transmitted has acquired a global reach; the consumer can establish direct communication with the entrepreneur. Unfortunately, a revealing problem-and one that is becoming increasingly important for consumer protection-is the appearance of communications from unknown sources, which indicates a greater likelihood of insincerity and potential manipulation of consumer behaviour.

The objectives identified in the research methodology section resulted in the use of only data on publications and their authors as part of the citation and co-citation analysis. In the citation analysis, some of the measures related to the use of authors’ affiliations of papers and information related to their country of origin were omitted. On the other hand, in the case of co-citation analysis, for example, the places of publication of individual works were omitted. This does not change the fact that the collected information (Table 5) allowed the procurement of answers to the set objectives.

The first stated objective concerned the most frequently cited publications and their authors in the field of WOM. The most cited publication in the field of social sciences is the work of Chevalier and Mayzlin (2006). This is followed by the works of Van Doorn et al. (2010) and Dellarocas (2003). When the citations are restricted to the generated set of publications only, the ranking is as follows: Chevalier and Mayzlin (2006), Godes and Mayzlin (2004) and Liv (2006). It should be noted that the publications indicated were published relatively long ago and the youngest paper was published in 2016. This does not change the fact that the publications indicated on both lists form a list of works one should consult when studying the issue of WOM. The second objective indicated was to identify the publications most often referred to by authors writing papers on WOM. These are: Fornell and Larcker (1981), Hennig-Thurau et al. (2004) and Chevalier and Mayzlin (2006).

Endnotes

¹ It should be noted that purely quantitative citation analysis has been widely criticised. It is based on the view that citations should not be treated equally (Zhang et al., 2013). In practice, citations may arise for various reasons and serve different functions (Zhang et al., 2013; Jha et al., 2017). Giving all citations equal value ignores the numerous potential functions they perform for the citing authors (Zhu et al., 2015).
² The total link strength of a given node is the sum of the link strength of that node to all other nodes.

References

1. Abbas, A. F., Jusoh, A. B., Mas’od, A., & Ali, J. (2020). Bibliometric analysis of global research trends on electronic word of mouth using Scopus database. Journal of Critical Reviews, 7(16), 405–412.
2. Allport, G. W., & Postman, L. (1946). An analysis of rumor. Public Opinion Quarterly, 10(4), 501–517.
3. Anderson, M. H. (2006). How can we know what we think until we see what we said? A citation and citation context analysis of Karl Weick’s the social psychology of organizing. Organization Studies, 27(11), 1675–1692.
4. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411.
5. Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.
6. Bhaiswar, R., Meenakshi, N., & Chawla, D. (2021). Evolution of electronic word of mouth: A systematic literature review using bibliometric analysis of 20 years (2000–2020). FIIB Business Review, 10(3), 215–231.
7. Boyack, K., Klavans, R., & Börner, K. (2005). Mapping the backbone of science. Scientometrics, 64, 351–374.
8. Brodie, R. J., Ilic, A., Juric, B., & Hollebeek, L. (2013). Consumer engagement in a virtual brand community: An exploratory analysis. Journal of Business Research, 66(1), 105–114.
9. Brown, J. J., & Reingen, P. H. (1987). Social ties and word-of-mouth referral behavior. Journal of Consumer Research, 14, 350–362.
10. Buttle, F. A. (1998). Word of mouth: Understanding and managing referral marketing. Journal of Strategic Marketing, 6(3), 241–254.
11. Cesvet, B., Babinski, T., & Alper, E. (2010). Kapitał konwersacyjny: jak tworzyć rzeczy, o których inni będą chcieli rozmawiać. Oficyna a Wolters Kluwer Business.
12. Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345–354.
13. Chu, S. C., & Kim, Y. (2011). Determinants of consumer engagement in electronic wordofmouth (eWOM) in social networking sites. International Journal of Advertising, 30(1), 47–75.
14. Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5(1), 146–166.
15. De Matos, C. A., & Rossi, C. A. V. (2008). Word-of-mouth communications in marketing: A meta-analytic review of the antecedents and moderators. Journal of the Academy of Marketing Science, 36, 578–596.
16. de Solla Price, D. J. (1965). Networks of scientific papers. Science, 149(3683), 510–515.
17. Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407–1424.
18. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021a). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296.
19. Donthu, N., Kumar, S., Pandey, N., Pandey, N., & Mishra, A. (2021b). Mapping the electronic word-of-mouth (eWOM) research: A systematic review and bibliometric analysis. Journal of Business Research, 135, 758–773.
20. Ejdys, J. (2016). Problematyka społecznej odpowiedzialności biznesu jako obiekt naukowych zainteresowań — wyniki analizy bibliometrycznej. Przegląd Organizacji, 4(915), 36–44.
21. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
22. Garfield, E. (1979). Is citation analysis a legitimate evaluation tool? Scientometrics, 1(4), 359–375.
23. Gawrońska, M. (2013). Marketing szeptany jako nowoczesna forma promocji. Rynek, Społeczeństwo, Kultura, 2(6), 30–35.
24. Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4), 545–560.
25. Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38–52.
26. Herr, P. M., Kardes, F. R., & Kim, J. (1991). Effects of word-of-mouth and productattribute information on persuasion: An accessibility-diagnosticity perspective. Journal of Consumer Research, 17(4), 454–462.
27. Huang, M., Cai, F., Tsang, A. S. L., & Zhou, N. (2011). Making your online voice loud: The critical role of WOM information. European Journal of Marketing, 45(7/8), 1277–1297.
28. Huete-Alcocer, N. (2017). A literature review of word of mouth and electronic word of mouth: Implications for consumer behavior. Frontiers in Psychology, 8:1256.
29. Jha, R., Jbara, A.-A., Qazvinian, V., & Radev, D. R. (2017). NLP-driven citation analysis for scientometrics. Natural Language Engineering, 23(1), 93–130.
30. Klincewicz, K., Mijal, M., & Żemigała, M. (2012). Bibliometria w zarządzaniu technologiami i badaniami naukowymi. Ministerstwo Nauki i Szkolnictwa Wyższego.
31. Kundu, S., & Rajan, C. R. (2016). Word of mouth: A literature review. International Journal of Economics and Management Sciences, 6, 467.
32. Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96.
33. Lenart-Gansiniec, R. (2021). Systematyczny przegląd literatury w naukach społecznych. Przewodnik dla studentów, doktorantów i nie tylko. Wydawnictwo Naukowe SCHOLAR, Warszawa, Poland.
34. Linkiewicz, A. (2015). Ewolucja marketingu szeptanego jako narzędzia komunikacji marketingowej w świetle rozwoju technologii informacyjnych. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 406, 346–357.
35. Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008). Electronic word-of-mouth in hospitality and tourism management. Tourism Management, 29(3), 458–468.
36. Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74–89.
37. McCain, K. W. (1990). Mapping scholars in intellectual space: A technical overview. Journal of the American Society for Information Science, 41(6), 433–443.
38. Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M. J. (2000). Self-service technologies: Understanding customer satisfaction with technology-based service encounters. Journal of Marketing, 64(3), 50–64.
39. Mudambi, S. M., & Schuff, D. (2010). Research note: What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Quarterly, 34(1), 185–200.
40. O’Reilly, K., & Marx, S. (2011). How young, technical consumers assess online WOM credibility. Qualitative Market Research: An International Journal, 14 (4), 330–359.
41. Osareh, F. (1996). Bibliometrics, citation analysis and co-citation analysis: A review of literature I. Libri, 46(3), 227–225 149–158.
42. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879.
43. Richins, M. L. (1983). Negative word-of-mouth by dissatisfied consumers: A pilot study. Journal of Marketing, 47(1), 68–78.
44. Roemer, R. C., & Borchardt, R. (2015). Meaningful metrics: A 21st century librarian’s guide to bibliometrics, altmetrics, and research impact. American Library Association.
45. Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.
46. Stokes, D., Syed, S. A., & Lomax, W. (2002). Shaping up word of mouth marketing strategy: The case of an independent health club. Journal of Research in Marketing and Entrepreneurship, 4(2), 119–133.
47. Sweeney, J., Soutar, G., & Mazzarol, T. (2014). Factors enhancing word-of-mouth influence: Positive and negative service-related messages. European Journal of Marketing, 48(1/2), 336–359.
48. Trusov, M., Bucklin, R. E., & Pauwels, K. (2009). Effects of word-of-mouth versus traditional marketing: Findings from an internet social networking site. Journal of Marketing, 73(5), 90–102.
49. Usdiken, B., & Pasadeos, Y. (1995). Organizational analysis in North-America and Europe — A comparison of co-citation networks. Organization Studies, 16(3), 503–526.
50. v. Wangenheim, F., & Bayón, T. (2004). The effect of word of mouth on services switching: Measurement and moderating variables. European Journal of Marketing, 38(9/10), 1173–1185.
51. Van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C. (2010). Customer engagement behavior: Theoretical foundations and research directions. Journal of Service Research, 13(3), 253–266.
52. Westbrook, R. A. (1987). Product/consumption — based affective responses and postpurchase processes. Journal of Marketing Research, 24(3), 258–270.
53. Zaraket, S. (2020). Consumer behaviour and social network sites: The impact of negative word of mouth. Routledge.
54. Zhang, G., Ding, Y., & Milojević, S. (2013). Citation content analysis (CCA): A framework for syntactic and semantic analysis of citation content. Journal of the American Society for Information Science and Technology, 64(7), 1490–1503.
55. Zhu, X., Turney, P., Lemire, D., & Vellino, A. (2015). Measuring academic influence: Not all citations are equal. Journal of the Association for Information Science and Technology, 66(2), 408–427.
56. Zhu, F., & Zhang (Michael), X. (2010). Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing, 74(2), 133–14.
57. Zupic, I., & Cater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472.
58. van Eck, N. J., Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538

According to Fullerton and Punj (1998), customer misbehaviour is a paradox of contemporary consumer culture manifested in the fact that its positive features trigger negative ones. Customer misbehaviour has become an intrinsic part of contemporary consumer behaviour and is sustained by the same factors that define the essence of consumer culture. This indicates that inappropriate customer behaviour is a result of the inadvertent stimulation of consumption ideologies by the marketing activities of companies (Fullerton and Punj, 2004).

Disapproved customer behaviour is known by various terminology in the literature, such as Jaycustomers, Aberrant consumer behaviour, Consumer misbehaviour, Deviant consumer behaviour, Opportunistic consumer behaviour, Unethical consumer behaviour), Customer dysfunctional behaviour or Pathological consumer behaviour (Smyczek, Grybś-Kabocik, Matysiewicz, & Tetla, 2017). A review of their definition reveals that the authors highlight different aspects of such behaviour and use issues related to ethics, pathology or deviancy of clients in their definitions (Smyczek et al., 2017). A survey of the definitions of the terms indicated makes it possible to conclude that there are no fundamental differences between the perceptions of such behaviour, and the nomenclature proposed can be regarded as similar and complementary (Błoński, 2021). For the purposes of this article, the author adopts the name dysfunctional customer behaviour as a neutral term compared with those mentioned above (Harris & Reynolds, 2003). Fisk et al. (2010), based on past research findings, have identified various categories of such behaviour. In addition to theft, vandalism or verbal abuse, they point out that such actions may have diverse motives (financial and non-financial), may be impulsive or planned, of varying frequency, as well as overt or covert in nature (Fisk et al., 2010, p. 420).

The aim of this article is to identify, based on publications in the field of 'dysfunctional customer behaviour’, the most frequently cited objects that are important to the researchers citing them, as well as to introduce the topics and their relationships that represent the conceptual space of 'dysfunctional customer behaviour’. The indicated objective will be realised on the basis of selected bibliometric analyses. The selected analyses (citation analysis, co-citation analysis and word co-occurrence analysis) are performed on the basis of data obtained from the Web of Science (WoS) and Scopus¹ databases.

Bibliometric Analysis

Bibliometric analysis consists of the use of various data relating to scientific publications and the citations given in these publications, to assess the performance of scientific activity and observe the development of science. Appropriately conducted bibliometric research makes it possible to obtain a comprehensive overview; identify gaps in knowledge; find new ideas for research and locate one’s intended contribution to a particular field (Donthu, Kumar, Mukherjee, Pandey, & Lim, 2021, p. 285). The bibliometric analysis procedure can be structured as follows (Donthu et al., 2021, p. 291):

1. Defining the objectives and scope of the bibliometric research;
2. Selection of bibliometric analysis techniques;
3. Collection of data for bibliometric analysis;
4. Carrying out a bibliometric analysis and presenting the results.

Among the techniques available, distinction can be made between review, evaluation and relational bibliometric analysis techniques. Review techniques include structured literature reviews and meta-analyses oriented towards generating knowledge through frequency analysis. Evaluative techniques can be used to identify qualitative and quantitative indicators of research and to compare the scientific contributions of other researchers. They also allow identification of the number of items published or the number of citations of individual publications, authors and journals. Relational techniques examine the relationships between data found in publications, for example, topics, methods, co-authorship. Among the relational techniques are analysis of co-citation, co-occurrence of words, co-authorship of publications, bibliographic links, clustering of co-citations or direct citations (Lenart-Gansiniec, 2021, pp. 174–175).

Most bibliometric research publications include an analysis of citations in a given research area. It is presented in the form of the most frequently cited studies, authors or journals in the area under study. This approach is based on the fact that citations are used to determine impact-if an article is frequently cited, it is considered important. This thesis is based on the assumption that authors cite papers that they consider important to their work (Zupic & Cater, 2015). Citations also reflect the degree of knowledge transfer and dissemination by other authors, representing other scientific centres (Ejdys, 2016). Citation analysis can provide information on the relative impact of publications; however, at the same time its downside is that it cannot identify networks of interconnections between scientists (Usdiken & Pasadeos, 1995).

Co-citation analysis allows the use of a number of co-citations to construct measures of similarity between documents, authors or journals. Co-citation is defined as the frequency with which two entities are cited together. The basic premise of co-citation analysis is that the more times two objects are cited together, the more likely their content is related. Depending on the unit, analysis can be carried out on co-citation of documents, authors or journals. It should be noted that co-citation is a dynamic measure. This is because the publication process is time-consuming, so the result of a co-citation analysis reflects the state of the field at a given point in the preparation of the publication, and not necessarily what it looks like at the time of analysis (Zupic & Cater, 2015).

Co-word analysis (Callon, Courtial, Turner, & Bauin, 1983) is a content analysis technique that uses words in documents to establish relationships and build a conceptual structure within a particular domain. The idea behind this method is that if words frequently co-occur in documents, it indicates that the concepts behind the words are closely related. This is a method that relies on the actual content of documents to construct a measure of similarity, while others link documents indirectly through citations or co-authorship. The analysis can be carried out at the level of various elements (areas) of the text: titles, abstracts, keywords, the actual text of the publication or on the basis of various combinations of these elements. The result of the analysis is a network of topics and their relationships, which represent the conceptual space of the domain.

The indicated analyses will be performed using the VOSviewer program (version 1.6.17), whose authors are Nees Jan van Eck and Ludo Waltman. The program is based on the visualisation of similarities (VOS) technique, where similarity between objects is used for visualisation (similar objects are located close to each other, and less similar objects are located away from each other) (van Eck & Waltman, 2007). The results of the analysis are presented in the form of a network, where each node represents an individual unit (e.g., article, author, country, institution, keyword, journal), whereby:

1. the size of the node indicates the occurrence of the unit (e.g., the number of times the keyword occurs);
2. the link between nodes represents co-occurrence between the units (e.g., keywords that co-occur or occur together);
3. the link thickness signals the occurrence of co-occurrence between the units (the number of times that, e.g. keywords co-occur or occur together)-the thicker the link between nodes, the more frequent the occurrence of co-occurrence;
4. the larger the node, the more frequent the occurrence of a given unit, e.g. a keyword;
5. and where each colour represents a topic cluster, where the nodes and links in that cluster can be used to explain the coverage of the topic (cluster) with topics (nodes) and relationships (links) between topics (nodes) manifested within that topic (cluster).

Results of Analyses

A total of 177 publications were identified based on searches on the WoS and Scopus databases. Seventy-seven publications in the WoS database and 100 publications in the Scopus database were identified (a detailed distribution of the number of publications by year is presented in Figure 1). In the next step, the two obtained collections were merged, verified and compared. Thanks to this, it was checked for repeated records. In the end, 74 publications in this field were obtained and analysed. The size of the publications collection is lower than that indicated in the literature². However, due to the niche nature of the topic under analysis and the associated number of publications, it can be assumed that the indicated collection size should be sufficient to analyse issues related to the citation and co-occurrence of words.

The analysis performed made it possible to distinguish the most frequently cited publications and thus the relative importance of a given article in the analysed area (see Figure 2 for details). These are as follows:

• Harris, L.C., & Reynolds, K.L. (2003). The Consequences of Dysfunctional Customer Behavior.
• Harris, L.C., & Reynolds, K.L. (2004). Jaycustomer behavior: An exploration of types and motives in the hospitality industry.
• Fullerton, R.A., & Punj, G. (2004). Repercussions of promoting an ideology of consumption: Consumer misbehavior.
• Albers-Miller, N.D. (1999). Consumer misbehavior: Why people buy illicit goods.

The first publication mentioned (Harris & Reynolds, 2003) provides a breakdown and description of the consequences borne by employees in direct contact with dysfunctional customers. The authors distinguished four sets of such consequences:

1. Long-term psychological;
2. Short-term emotional;
3. Behavioural;
4. Physical effects.

In the second publication, the same authors (Harris & Reynolds, 2004) focused on the motives of dysfunctional behaviour (financial or nonfinancial) and the actions taken by dysfunctional customers (overt or covert) which made it possible to create a typology of customer groups. These include Compensation letter writers; Undesirable customers; Property abusers; Service workers; Vindictive customers; Oral abusers; Physical abusers; and Sexual predators. The third most frequently cited position is Fullerton and Punj (2004). In this article, the authors presented a classification of bad customer behaviour. They distinguished the following behaviours:

1. Consumer misbehaviour directed against a marketer’s employees;
2. Consumer misbehaviour directed against other consumers in the exchange setting;
3. Consumer misbehaviour directed against a marketer’s merchandise and services;
4. Consumer misbehavior directed against a marketer’s financial assets;
5. Consumer misbehaviour directed against a marketer’s physical or electronic premises.

The last article (Albers-Miller, 1999), contains the results of a study on the reasons for consumers’ voluntary purchases of products that are stolen, contraband or counterfeit. The results of the study allowed the authors to conclude as follows:

• Some respondents are able to rationalise the decision to buy illegally;
• Some respondents treated counterfeit goods indiscriminately; others were strongly inclined to buy stolen products;
• Those not inclined to engage in illegal behaviour were discouraged by the level of perceived risk;
• Those inclined to engage in illegal behaviour were less willing to purchase when fear of criminal reprisals increased for the specific type of illegal behaviour they were considering.

If during the citation analysis, in addition to the year of citing publication, we include the year of the cited publication, it will enable us to determine the changing position of individual articles over time. The indicated items were mainly cited in the first decade of the 21st century, now there are more and more items published in the second decade of this century. In addition, analysis of the citation age reveals the time it takes for publications to find their way into the circulation of scientific information³ (Figure 3).

The results obtained in this area indicate that a minimum of a year or more elapses between publication and the appearance of citations, which is a consequence of the narrow subject matter covered by a small number of authors.

A continuation of the above issues is co-citation analysis, which assumes that the more times two objects are cited together, the more likely their content is related. The results of the co-citation analysis performed are contained in Figure 4.

The result of the analysis is essentially a confirmation of the citation results presented earlier. As a general rule of thumb, two highly co-cited papers are also highly cited individually (Jarneving, 2005). This makes it possible to identify publications (selected using co-citation thresholds) considered as important by the researchers citing them (Zupic & Cater, 2015). In the case of the topic of dysfunctional customer behaviour, 12 such items can be identified. The names of authors and titles of these
publications by year of publication are presented in Table 1.

The final analysis of co-occurrence of words carried out and the attempt to identify clusters allowed us to tentatively identify nine clusters relating to the topic of dysfunctional customer behaviour. The sub-areas that emerged are related to the various concepts presented above that relate to the phenomenon under analysis as well as the issue of consumer behaviour and ethics (Figure 5).

Therefore, a correction was made further and all terms referring to the analysed behaviours were replaced with one, that is, dysfunctional customer behaviour. This resulted in a network of relationships, where the essential nodes are dysfunctional customer behaviour, consumer
behaviour, ethics, crime and theft as one of the symptoms of the analysed behaviours⁴ (Figure 6).

Summary

Market transformations occur not only under the influence of changes in the environment, that is, among the players and things that make up a given market, but also under the influence of the flow of time. It causes evolutionary or revolutionary changes in customer behaviour. Such changes can take a desirable, positive form and be accepted by other market participants. The market also reveals changes that are contrary to accepted social norms, which are the result not only of changes in the market but also of the individual characteristics of customers. Such changes are variously defined by authors interested in this area of customer behaviour. For the purposes of the above publication, they have been referred to as 'dysfunctional customer behaviour’. The first publications in this area appeared in the second half of the 1980s. As time went by, more researchers presented their publications on this issue.

The identified collection of publications in this area allowed conducting selected bibliometric analyses. The presented results of the analyses made it possible to identify the group of most frequently cited publications, to isolate those publications that are important to the citing researchers, and to approximate the topics and their relationships that represent the conceptual space of 'dysfunctional customer behaviour.’ The author is aware of the existing limitations of the analysis carried out. They are mainly due to the following: restriction to the selected bibliographic databases (WoS and SCOPUS) and the linguistic limitation of searching the databases only for English-language texts. At a further stage of the research process, the bibliometric analysis should be supplemented by a qualitative and substantive evaluation of the content of articles of interest to the researcher.

Endnotes

¹ The article is part of a broader research project in which members of the research team, which includes the author, aimed to study dysfunctional human behavior as a consumer and employee.
² According to van Eck, Waltman (2010), VOSviewer should be used to visualize data volumes consisting of a minimum of 100 objects.
³ Citation age is calculated by comparing the date of the citing publication with the date of the cited publication.
In other words, it is the difference between the year of the citing publication and the year of the cited publication.
⁴ The set of dysfunctional customer behaviors most often includes theft, lying, forging documents, vulgar or aggressive behavior toward salespeople or other customers, or abuse of alcohol or drug substances.

References

1. Albers-Miller, N. D. (1999). Consumer misbehavior: Why people buy illicit goods. Journal of Consumer Marketing, 16(3), 273–287. https://doi.org/10.1108/07363769910271504
2. Błoński, K. (2021). Dysfunctional customer behavior-A review of research findings. Acta Scientiarum Polonorum. Oeconomia, 20(2), 5–12.
3. Callon, M., Courtial, J.-P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191–235. doi:10.1177/053901883022002003
4. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296.
5. Ejdys, J. (2016). Problematyka społecznej odpowiedzialności biznesu jako obiekt naukowych zainteresowań-wyniki analizy bibliometrycznej. Przegląd Organizacji, 4, 36–44. doi:10.33141/po.2016.04.06
6. Fisk, R., Grove, S., Harris, L. C., Keeffe, D. A., Russell/Bennett, R., Daunt, K. L., & Wirtz, J. (2010). Customers behaving badly: A state of the art review, research agenda and implications for practitioners. Journal of Services Marketing, 24(6), 417–429.
doi:10.1108/08876041011072537
7. Fullerton, R. A., & Punj, G. (1993). Choosing to misbehave: A structural model of aberrant consumer behavior. Advances in Consumer Research 20(1), 570–574, https://www.acrwebsite.org/volumes/7515/volumes/v20/NA-20
8. Fullerton, R. A., & Punj, G. (2004). Repercussions of promoting an ideology of consumption: Consumer misbehavior. Journal of Business Research, 57(11), 1239–1249. doi:10.1016/S0148-2963(02)00455-1
9. Harris, L. C., & Reynolds, K. L. (2003). The consequences of dysfunctional customer behavior. Journal of Service Research, 6(2), 144–161. doi:10.1177/1094670503257044 10. Harris, L. C., & Reynolds, K. L. (2004). Jaycustomer behavior: an exploration of types and motives in the hospitality industry. Journal of Services Marketing, 18(5), 339–357.
11. Jarneving, B. (2005). A comparison of two bibliometric methods for mapping of the research front. Scientometrics, 65(2), 245–263. doi:10.1007/s11192-005-0270-7 12. Lenart-Gansiniec, R. (2021). Systematyczny przegląd literatury w naukach społecznych.
Przewodnik dla studentów, doktorantów i nie tylko. Warszawa: Wydawnictwo Naukowe SCHOLAR.
13. Mazar, N., Amir, O., & Ariely, D. (2008). The dishonesty of honest people: A theory of self-concept maintenance. Journal of Marketing Research, 45(6), 633–644.
14. Mitchell, V. W., Balabanis, G., Schlegelmilch, B. B., & Cornwell, T. B. (2009). Measuring unethical consumer behavior across four countries. Journal of Business Ethics, 88(2), 395–412.
15. Muncy, J. A., & Vitell, S. J. (1992). Consumer ethics: An investigation of the ethical beliefs of the final consumer. Journal of Business Research. 24(4), 297–311.
https://doi.org/10.1016/0148-2963(92)90036-B
16. Reynolds, K. L., & Harris, L. C. (2009). Dysfunctional customer behavior severity: An empirical examination. Journal of Retailing, 85(3), 321–335.
17. Smyczek, S., Grybś_Kabocik, M., Matysiewicz, J., & Tetla, A. (2017). Patalogie w zachowaniach konsumentów na rynku. Placet. Warszawa.
18. Usdiken, B., & Pasadeos, Y. (1995). Organizational analysis in North-America and Europe-A comparison of cocitation networks. Organization Studies, 16(3), 503–526. doi:10.1177/017084069501600306
19. van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. doi:10.1007/s11192-009-0146-3 20. Vitell, S. J., & Muncy, J. (1992). Consumer ethics: An empirical investigation of factors influencing ethical judgments of the final consumer. Journal of Business Ethics, 11(8), 585–597.
21. Wirtz, J., & McColl-Kennedy, J. R. (2010). Opportunistic customer claiming during service recovery. Journal of the Academy of Marketing Science, 38(5), 654–675.
22. Zupic, I., & Cater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), pp. 429–472.
23. Fullerton, R. A., Punj, G. (1998). The unintended consequences of the culture of consumption: An historical-theoretical analysis of consumer misbehavior. Consumption Markets & Culture 1(4), 393–423, https://doi.org/10.1080/10253866.1998.9670308 24. Eck, N. J. V., & Waltman, L. (2007). VOS: A new method for visualizing similarities between objects. In Advances in data analysis (pp. 299–306). Springer, Berlin, Heidelberg.
25. Wirtz, J., & Kum, D. (2004). Consumer cheating on service guarantees. Journal of the Academy of Marketing Science, 32(2), 159–175.