Interaction between cognitive process and social dynamics in shaping individual analyzing of online reviews. Part I.

While internal factors like review and reviewer characteristics have been extensively studied, the influence of external, particularly social factors, on online review processing remains relatively unexplored. This research bridges this gap by investigating how social pressures: social conformity, social validation, affects how individuals analyze online reviews. By combining the Elaboration Likelihood Model (ELM) and the Social Influence Theory (SIT), we propose a comprehensive framework to examine two key dimensions: Online review processing and the influence of social factors on this process. We conduct a study among skincare product consumers, revealing that social conformity, significantly influence reliance on peripheral cues (ratings, source credibility), however social validation does not exhibit a significant effect. Furthermore, by introducing expertise as a moderator, we observe that the direct impact of social factors becomes attenuated. This suggests that individuals with strong subject knowledge are less susceptible to the persuasive power of social cues when evaluating online review.

1 Introduction

Online reviews has become a significant factor in consumer purchase decisions (Cheung 2014). With the rise of the internet and social media, consumers can easily share their opinions and experiences about products and services with others (Hennig-Thurau 2004). Social media has shifted the power dynamic between brands and consumer (Kočišová 2023). Comparing it with traditional word of mouth, the effect of eWOM is expected to be greater due to its convenience, reach, source and speed of interactions (Augusto and Torres 2018). People are more likely to trust word-of-mouth recommendations than direct advertising; this is because they trust real user experiences more than controlled marketing messages (Idrawati and Mathaiyah 2023). It exerts a powerful influence that transcends product and service sales (Ye 2011; Li 2020), intention to by (Ismagilova 2020), shaping brand image (Park and Jeon 2018), and decision risks reduction (Ismagilova 2017). As a result, this has led to an abundant body of research on the impact of online review on consumer behavior.
However, relatively few studies have delved into the impact of personal factors on the process analyzing of such reviews (Varma 2023b). As human behavior is guided by not only subjective values or attitudes, but also by the perceived behavior of others (Cialdini and Goldstein 2004), this complexity leads to nuanced results of studies (Ismagilova, Dwivedi and Rana 2021), regarding the analysis, interpretation, and reaction to online reviews. This study aims to address this gap by investigating how these factors impact the cognitive processes involved in information analysis.

2 Literature review, framework and hypothesis

Electronic word of mouth is a positive or negative statement made by potential, current or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet (Hennig-Thurau 2004) Ismagilova et al. (2017) has proposed a comprehensive definition as „dynamic and continuous process of information exchange related to a brand, product, company, or service. This exchange is facilitated by former, current, and potential consumers on the Internet, accessible to a wide audience.“
People believe that online reviews are more credible and reliable than company websites created by marketing departments (Fernandes et al. 2022), for that reason, they increasingly rely on online reviews to guide their buying choices (Erkan and Evans 2016). However, the important volume of online reviews can present a challenge for consumers. Sifting through large amounts of information can be time-consuming and overwhelming, making it difficult to identify the most relevant and credible reviews. One key step in the process of analyzing online reviews is to identify the most credible reviews. Online review credibility refers to the extent to which consumers perceive online reviews as credible, true, factual (Varma et al. 2023a, Levy and Gvili 2015).
Several studies tried to address online review analysis by proposing theoretical models, in order to better understand this process and its impact to consumer behavior. They adopted for example: Information Adoption Model „IAM” (Gunawan and Huarng 2015; Tapanainen et al. 2021; Onofrei et al. 2022), Theory Reasoned Action „TRA” (Erkan and Evans 2016; Lee and Hong 2016; Rao et al. 2021). According to Ismagilova, Dwivedi and Rana (2021), the Elaboration Likelihood Model (ELM) is considered as the most suitable framework for explaining online review analysis. This model is supposed to be a hypothesis of dual-track information processing (central and peripheral), which affect consumer reactions and decisions (Abbasi et al. 2023).
This model has proven especially useful in the domain of marketing and advertising (Petty and Brinol 2013), and it has been widely adopted by marketing scholars to explain how the same online review can be perceived differently by consumer (Zheng 2021). For this reason, this research initially adopted this model.
According to ELM, receivers process Online review through the central route witch include analyzing argument quality(Chung and Thadani 2012; Varma et al. 2023), valence of review (Srivastava and Kalro 2019). For the peripheral route, the most cited variables to analyze are, source credibility (Ohanian 1990; Cheung and Kuan 2012), consistency (Zhang et al. 2014), and review rating (Cheung et al. 2009). The literature indicates that the influence of the ELM peripheral route factors (source credibility) on consumers is smaller than of central route factors (Cheng and Ho 2015).
As we mentioned previously, most of the research on online review credibility, and its impacts, has focused on the internal factors (e.g., review and reviewer related characteristics), ignoring social external factors (Varma et al. 2023a; Varma et al. 2023b), and the fact that ELM is not clear regarding situational context in which receiver process information (Le, Robinson and Dobele 2022). So we decided to integrate the Social Influence Theory (SIT) to our framework, as an extension of ELM, to explain how the context, social norms, affects online review processing, and contribute to the formation of attitudes toward online reviews.
The SIT was developed by (Kelman 2006), to explain how individuals’ thoughts, feelings, and behaviors are influenced by the presence and actions of others within social contexts. It suggests that changes in attitudes and behaviors occur due to the social influence via various mechanisms: compliance, identification, and internalization (Zhao, Stylianou and Zheng 2018).
The main raison for integrating this theory is that we presume that individuals, change their perceptions and analysis of online review, depending on the extent of influence of external social factors. we hypothesis that these external social factors can change the way individuals analyze online review, such as the weight they give to different characteristics of information (review and reviewer characteristics) and the criteria they use to evaluate online review.
Principal hypothesis:
Individuals who are more influenced by social factors, are more likely to process information superficially (peripheral route), thereby positively influencing their online review analysis behavior on social media platforms by increasing the importance attributed to overall evaluations.

2.1 Impact of social conformity on review processing

Social conformity is the act of changing one’s personal opinion when challenged by a contradicting group majority. Asch’s work highlighted the powerful influence of social factors on individual decision-making and demonstrated the tendency of people to conform to group opinions, even when those opinions are clearly wrong (Asch 1951). For instance, (Tsao at al. 2015) have found that, for conformists people, a large quantity of negative online review is more damaging to their booking intentions; for non-conformists, a large quantity of positive online review is more likely to increase their booking intentions. Moreover, (Zhu et al. 2014; Huberman 2012) found that individuals are more likely to change their online purchase decisions under social pressure, and align with the majority after reading opposing reviews, even when they have predetermined personal preferences. Social conformity to online review, can be assimilated to , informational conformity, which is the process of adjusting to other people’s choices after receiving important information (Badawi et al. 2021), so it’s a function of majority size, nature of the task, but also, self-confidence and certain personality traits (Wijenayake et al. 2020).
Therefore, this concept, can be also viewed as a personality trait where individuals have willingness or tendency to follow others ideas, values, and and behavior (Mehrabian and Stefl 1995), this approach recognizes that people differ in their susceptibility to social conformity. From this perspective, people with high level of susceptibility to conformity (conformity tendency) are more liked to follow the opinions, values, and actions of others and are more likely to follow their behaviors (Zhu et al. 2009). For that reason, online review is used by marketers to spread positive information related to their products, so that it can increase consumer conformity (Ngarmwongnoi et al. 2020). This means that, some people are more likely to be swayed by the majority, while others prioritize independent thinking. The thoughtful effort might be more pronounced in individuals who resist conformity and engage in deeper evaluation despite social pressure. In our case of study, we state that higher levels of social conformity may lead individuals to prioritize shortcuts (peripheral route) to evaluate online review, rather than deep cognitive process of evaluation.
H01: Higher levels of social conformity tendency, will lead individuals to adopt the peripheral route when processing (online review).

2.2 Impact of social validation on review processing

Another explanation can be found in the principle of social validation, also known as social proof. It is based on the recognition that individuals often judge the appropriateness of their actions by what others do (Cialdini 1984). It is a psychological phenomenon where individuals seek approval or affirmation from their social environment. When consulting online reviews, the need for social validation can lead individuals to conform to reviewers comments. They may be influenced by the number of approvals, likes, or positive comments associated with a product or service, and might alter their own perceptions to align with the majority in order to feel accepted or validated within the online community. (Zhao, Stylianou and Zheng 2018). The impact of social validation need, is greater in situations where a person is unsure of appropriate response (Guandagno 2013).
H02: Higher levels of social validation will lead individuals to adopt the peripheral route when processing (online review).

2.3 Moderated effect of expertise of the reader

Ears do not hear the same way; online review messages are not processed identically due to receiver characteristics especially there degree of knowledge (expertise). The expertise of online review receivers is built from their knowledge of the product and their understanding of the surrounding context (Le, Robinson and Dobele 2022). Expertise in the ELM aligns with deeper information processing; it is associated with the ability to process information. Due to his expertise and knowledge, consumers have enough cognitive resources to perform this kind of information processing (Park and Kim 2008). As a result, ELM proposes that consumers with low expertise tend to favor peripheral cues, such as the number of arguments presented, as they require less cognitive effort. Conversely, individuals with high expertise are more likely to engage in the central route, expending mental effort to analyze the quality of arguments themselves (Luo et al. 2014; Petty et al. 1983).
H03: Expertise moderates the impact of social factors on online review processing, attenuating reliance on peripheral cues.
So we suggest that when individuals have high expertise in a particular subject, products, or areas, they are less likely to be swayed by social factors and rely on simpler cues (peripheral cues) when processing online review. Instead, they focus on deeper cues like content quality, factual accuracy, and alignment with their own knowledge and experience.

Figure 1: Conceptual framework
Source: Authors

3 Research methodology

This study adopts an explanatory approach to test the impact of social influence on consumers’ processing of online review. Explanatory research excels at identifying causal relationships (Rahi 2017), which is crucial for understanding how social factors influence how individuals evaluate online reviews . In this kind of research (explanatory) the data are quantitative and almost always require the use of a statistical test to establish the validity of the relationships (Sue and Ritter 2012).
The sampling method employed in this study involved a two-step process. Firstly, a large Facebook group dedicated to skincare products was identified. The choice of skincare product sector is due to many reasons:
1) it’s a sector experiencing significant growth of 3.6 from 2022 to 2031 according to (Allied Market Research 2023), making it a relevant and timely area to explore consumer behavior.
2) consumers in the skincare sector heavily rely on online reviews (Joshi et al. 2022; Putri and Wandobori 2016) on social media (Alamar et al. 2023) to make informed purchasing decisions.
3) This type of groups are considered as active online communities. These groups typically involve frequent discussions, product recommendations, and question-and-answer sessions, creating an active environment with continuous user engagement.
Secondly, a convenience sampling method was employed to select participants from within this Facebook group. Convenience sampling involves selecting participants who are readily available and willing to participate, making it a cost-effective and time-efficient method for data collection. While convenience sampling may not ensure the generalizability of findings to the entire population (Andrade 2020), it was chosen due to limited resources, allowing for data collection within budget and time constraints.
Data collection was conducted over a three-month period (September, October, November 2023), through an online, self-administered questionnaire developed using Google Form. To enhance data relevance, our online survey within a skincare Facebook group began with a screener question: „Do you buy skincare products for personal use?”. This ensured only participants actively involved in the skincare market participated, strengthening the connection to our research on social pressures and online review analysis.
The questionnaire was structured in three main sections (dependent/independent variables and the moderated variable). Each sub-section focused on a distinct topic related to a variable from our research framework. The instruments utilized with each variable were taken from previous research and subsequently adapted to align with the specific research context.

Table 1: Construct variables and items sources
Source: Authors

A structural equation analysis using SPPS AMOS will be used to examine the relationships between these variables. Due to the inherent latent nature of social conformity and social validation in our research framework, SEM offers a powerful combination of confirmatory factor analysis like identifying underlying psychological traits and path analysis (exploring causal relationships) within a single model (Fan et al. 2016). This allows for a more comprehensive understanding of how these latent variables influence online review processing. By investigating these interactions within the specific context of skincare, this research aims to offer valuable insights into the intricate mechanisms influencing consumer decision-making in the digital age.

End of part I.

Literatúra/List of References

1. Abbasi, Z. A. et al., 2023. Investigating the impact of social media images’ value, consumer engagement, and involvement on eWOM of a tourism destination: A transmittal mediation approach. In: Journal of Retailing and Consumer Services. 2023, 71. ISSN 0969-6989. Available at: <https://doi.org/10.1016/j.jretconser.2022.103231>
2. Alamar, M. A. et al., 2023. Impact of social media on choosing skin care and cosmetic products among females in Saudi Arabia. In: Cureus. 2023, 15(12). ISSN 2168-8184. Available at: <https://doi.org/7759/cureus.49922>
3. Andrade, C., 2020. The inconvenient truth about convenience and purposive samples. In: Indian Journal of Psychological Medicine. 2020, 43(1). ISSN 0975-1564. Available at: <https://doi.org/1177/025371762097700>
4. Asch, S., 1951. Groups, leadership and men. In: H. Guetzkow (Ed.), Effects of group pressure upon the modification and distortion of judjments. Oxford: Carnegie Press, 1951, p. 177-190.
5. Augusto, M. and Torres, P., 2018. Effects of brand attitude and eWOM on consumers’ willingness to pay in the banking industry: mediating role of consumer-brand identification and brnad equity. In: Journal of Retailing and Consumer Services. 2018, 42, 1-10. ISSN 0969-6989.
6. Badawi, B. et al., 2021. Consumer conformity, social ties and EWOM in digital marketing. In: Ingénierie des Systèmes d’Information. 2021, 569-576. ISSN 1247-0317.
7. Chakraborty, U., 2019. Perceived credibility of online hotel reviews and its impact on hotel booking intentions. In: International Journal of Contemporary Hospitality. 2019, 31(9), 3465-3483. ISSN 1757-1049.
8. Cheng, Y. H. and Ho, H. Y., 2015. Social influence’s impact on reader perceptions of online reviews. In: Journal of Business Research. 2015, 68(4), 883-887. ISSN 1905-355X.
9. Cheng, X. et al., 2021. The paradox of word-of-mouth in social commerce: Exploring the juxtaposed impacts of source credibility and information quality on SWOM spreading. In: Information & Management. 2021, 58(7), 103505. ISSN 0262-2769.
10. Cheung, C. Y., Sia, C. L. and Kuan, K., 2012. Is this review believable? A study of factors affecting the credibility of online consumer reviews from an ELM perspective. In: Journal of the association information system. 2012, 13(8), 618-635. ISSN 1536-9323.
11. Cheung, R., 2014. The influence of electronic word-of-mouth on information adoption in onlne customer communities. In: Global economic review. 2014, 43(1), 42-57. ISSN 1226-508X.
12. Cheung, M. Y. et al., 2009. Credibility of electronicword-of-mouth: Informational and normative determinants of online consumer recommendations. In: International Journal of Electronic Commerce. 2009, 13(4), 9-38. ISSN 1086-4415.
13. Cheung, C. and Lee, M., 2012. What drives consumers to spread electronic word of mouth in online consumer-opinion platforms. In: Decision Support Systems. 2012, 53(1), 218-225. ISSN 0167-9236.
14. Chung, C. M. K. and Thadani, D. R., 2012. The impact of electronic word-of-mouthcommunication: A literature analysis and integrative model. In: Decision Support Systems. 2012, 54(1), 461-470. ISSN 0167-9236. Available at: <https://doi.org/10.1016/j.dss.2012.06.008>
15. Cialdini, R., 1984. Influence: The psychology of persuasion. Harper Collins, 1984. ISSN 978-0-06-189990-4.
16. Cialdini, R. B. and Goldstein, N. J., 2004. Social influence: Dompliance and conformity. In: Annual Review Psychology. 2004, 55, 591-621. ISSN 0066-4308. Available at: <https://doi.org/1146/annurev.psych.55.090902.142015>
17. De Canniére, M. H. et al., 2010. Relationship quality and purchase intention and behavior: The moderating impact of relationship strength. In: Journal of Business and Psychology. 2010, 25, 87-98. ISSN 0889-3268.
18. Eastman, J. K. et al., 2022. Luxury not for the masses: Measuring inconspicuous luxury motivations. In: Journal of business research. 2022, 145, 509-523. ISSN 2079-8296.
19. Erkan, I. and Evans, C., 2016. The influence of eWOM in social media on consumers’ purchaseintentions: An extended approach to information adoption. In: Computers in Human Behavior. 2016, 61, 47-55. ISSN 0747-5632. Available at: <https://doi.org/10.1016/j.chb.2016.03.003>
20. Fan, Y. et al., 2016. Applications of structural equation modeling (SEM) in ecological studies: an updated review. In: Ecological Processes. 2016, 5(19). ISSN 2192-1709. Available at: <https://doi.org/10.1186/s13717-023-00477-2>
21. Fang, Y. H., 2014. Beyond the credibility of electronic word of mouth: Exploring eWOM adoption on social networking sites from affective and curiosity perspectives. In: International Journal of Electronic Commerce. 2014, 18(3), 67-102. ISSN 1086-4415. Available at: <https://doi.org/10.2753/JEC1086-4415180303>
22. Fernandes, S. et al., 2022. Measuring the impact of online reviews on consumer purchase decisions – A scale development study. In: Journal of Retailing and Consumer Services. 2022, 68. ISSN 0969-6989. Available at: <https://doi.org/10.1016/j.jretconser.2022.103066>
23. Filieri, R., 2016. What makes an online consumer review trustworthy. In: Annals of Tourism Research. 2016, 58, 46-64. ISSN 0160-7383. Available at: <https://doi.org/10.1016/j.annals.2015.12.019>
24. Fisher, R. J. and Katz, J., 2000. Social desirability bias and the validity of self-reported values. In: Psychology and Marketing. 2000, 17(2), 105-120. ISSN 0742-6046.
25. Gawronski, B. and Creighton, L. A., 2013. Dual process theory. In: D. Carlston, The Oxford handbook for social cognition. Oxford University Press, 2013, p. 282. ISBN 9780199984688.
26. Good, M. C. and Hyman, M. R., 2020. Fear of missing out’: antecedents and influence on purchase likelihood. In: Journal of Marketing Theory and Practice. 2020, 28(3), 330-341. ISSN 1069-6679. Available at: <https://doi.org/1080/10696679.2020.1766359>
27. Granovetter, M. S., 1973. The strength of weak ties. In: American Journal of Sociology. 1973, 78(6), 1360-1380. ISSN 0002-9602.
28. Guandagno, R., 2013. Social Influence Online: The Six Principles in Action. In C. Liberman, Social influence online: The six principles in action from casing persuasive communication. Kandall Hunt Publishing, 2013. ISBN 978-1-4652-1753-0.
29. Gunawan, D. D. and Huarng, K. H., 2015. Viral effects of social network and media on consumers’purchase intention. In: Journal of Business Research. 2015, 2237-2241. ISSN 0148-2963.
30. Hagiladi, N. and Plaut, P. O., 2021. Social networks’ group tie strength and travel behavior. In: Journal of transport Geograpghy. 2021, 93 103079. ISSN 0966-6923.
31. Hennig-Thurau, T. et al., 2004. Electronic word of mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet. In: Journal of Interactive Marketing. 18(1), 38-52. ISSN 1094-9968.
32. Hong, S. and Pittman, M., 2020. eWOM anatomy of online product reviews: Interaction effects of review number, valence, and star ratings on perceived credibility. In: International Journal of Advertising. 2020, 39(7), 892-920. ISSN 0265-0487.
33. Hsu, C. L. et al., 2017. Exploring purchase intention of green skincare products using the theory ofplanned behavior: Testing the moderating effects of country of origin and. In: Journal of Retailing and Consumer Services. 2017, 34, 145-152. ISSN 0969-6989.
34. Huberman, B. and Zhu, H., 2012. To switch or not to switch: Understanding social influence in online choices. In: American Behavior Scientist. 58(10), 1329-1344. ISSN 0002-7642. Available at: <https://doi.org/10.1177/0002764214527089>
35. Hung, S. W., Chang, C. W. and Chen, S. Y., 2023. Beyond a bunch of reviews: The quality and quantity of electronic word of mouth. In: Information & Management. 2023, 60(3), 103777. ISSN 0930-5181.
36. Hussain, S. et al., 2023. Fear of missing out and compulsive buying behavior: The moderating role of mindfulness. In: Journal of Retailing and Consumer Services. 2023, 75, 103512. ISSN 0969-6989.
37. Idrawati, Y. P. C. P. and Mathaiyah, S., 2023. eWOM via the TikTok application and its influence on the purchase intention of somethinc products. In: Asia Pacific Management Review. 2023, 28(2), 174-184. ISSN 0954-2957.
38. Ismagilova, E., et al., 2020. The effect of electronic word of mouth communications on intention to buy: a meta-analysis. In: Information Systems Frontiers. 2020, 22, 1203-1226. ISSN 1387-3326.
39. Ismagilova, E., Dwivedi, Y. K. and Rana, N., 2021. The use of elaboration likelihood model in eWOM research: Literature review and weight-analysis. In: Dennehy, D. et al.: Responsible AI and analytics for an ethical and inclusive digitized society. Springer, 2021, pp. 506-516. ISSN 0302-9743. Available at: <https://doi.org/1007/978-3-030-85447-8_41>
40. Ismagilova, E. et al., 2017. Electronic word of moth (eWOM) in the marketing context. A state of art analysis and future directions. Springer Cham, 2017. ISBN 978-3-319-52458-0. Available at: <https://doi.org/1007/978-3-319-52459-7>
41. Joshi, M. C. et al., 2022. The impact of social media on skin care: A narrative review. In: Lviv Clinical Bulletin. 2022, 85-96. ISSN 2306-4269. Available at: <https://doi.org/10.25040/lkv2022.01-02.085>
42. Kai, Y. at al., 2021. Social learning? Conformity? Or comparison? An empirical study on the impact of peer effects on Chinese seniors’ intention to purchase travel insurance. In: Tourism Management Perspectives. 2021, 38, 100809. ISSN 2211-9744.
43. Kelman, H., 2006. Interests, relationships, identities: Three central issues for individuals and groups in negotiating their social environment. In: Annual Review Psychology. 2006, 57, 1-26. ISSN 1545-2085.
44. Kim, S. et al., 2018. The influence of eWOM communications: An application of online social network framework. In: Computers in Human Behavior. 2018, 80, 243-254. ISSN 0747-5632.
45. Kinasih, V. T. T. et al., 2023. The influence of brand image, celbrity endorser, and onlie customer review on purchasing decision on skintific beauty products with fear of missing out FOMO as madiating role. In: Journal Ekonomi. 2023, ISSN 2252-5564.
46. Kočišová, L. et al., 2023. The role of marketing metrics in social media: A comprehensive analysis. In: Marketing Science & Inspirations. 2023, 18(2), 40-49. ISSN 1338-7944. Available at: <https://doi.org/10.46286/msi.2023.18.2.4>
47. Krah, B. A. and Miklošík, A., 2022. Creating communities: A way of entering foreign markets in 2022. Marketing Science & Inspirations. 2022, 17(1), 38-50. Available at: <https://doi.org/10.46286/msi.2022.17.1.4>
48. Ladhari, R. and Michaud, M., 2015. eWOM effects on hotel booking intentions, attitudes, trust and website perceptions. In: International Journal of Hospitality Management. 2015, 46, 36-45. ISSN 0278-4319.
49. Laursen, B. and Veenstra, R., 2021. Toward understanding the functions of peer influence: A summary and synthesis of recent empirical research. In: Journal of Research on Adolescence. 2021, 31(4), 889-907. ISSN 1050-8392.
50. Le, T. D, Robinson, L. and Dobele, A. R., 2022. eWOM processing from receiver perspective: Conceptualising the relationships. In: International journal of consumer studies. 2022, 36(4), 440-454. ISSN 1470-6423.
51. Lee, J. and Hong, I. B., 2016. Predicting positive user responses to social media advertising: The roles of emotional appeal, informativeness, and creativity. In: International Journal of Information Management. 2016, 36(3), 360-373. ISSN 0268-4012.
52. Lee, J. E. at al., 2019. Understanding purchase intention of university students towards skin care products. In: PSU Research Review. 2019, 3(3), 161-178. ISSN 2399-1747.
53. Lee, M. K. O. and Cheung, C. M. K., 2012. What drives consumers to spread electronic word of mouth in online. In: Decision Support Systems. 2012, 53(1), 218-225. ISSN 0167-9236.
54. Lee, S. H. at al., 2010. The role of network centrality in the flow of consumer influence. In: Journal of Consumer Psychology. 2010, 20(1), 66-77. ISSN 1057-7408.
55. Levy, S. and Gvili, Y., 2015. How credible is e-word of mouth across digital-marketing: channels? The roles of social capital, information richness, and interactivity. In: Journal of Advertising Research. 2015, 55(1), 95-109. ISSN 0021-8499.
56. Li, C., 2013. Persuasive messages on information system acceptance: A theoretical extension of elaboration likelihood model and social influence theory. In: Computer in human behavior. 2013, 29(1), 264-275. ISSN 0747-5632.
57. Li, K. et al., 2020. Exploring the influence of online reviews and motivating factors on sales: A meta-analytic study and the moderating role of product category. In: Journal of Retailing and Consumer Services. 2020, 55, 102107. ISSN 0969-6989.
58. Luo, C. and al., 2014. The effects of individualism–collectivism cultural orientation oneWOM information. In: International Journal of Information Management. 2014, 34(4), 446-456. ISSN 0268-4012.
59. Maheshwari, S., 2019. When is a star not always star? When it´s an online review? 2019. [online]. [cit. 2024-01-05]. Available at: <https://www.nytimes.com/2019/11/28/business/online-reviews-fake.html>
60. Mehrabian, A. and Stefl, C. A., 1995. Basic temperament components of loneliness, shyness, and conformity. In: Social Behavior and Personality. 1995, 23(3), 253-264. ISSN 0301-2212.
61. Miremadi, A. and Haghayegh, M., 2022. The competitive advantage of EWOM in digital marketing. In: European Journal of Business and Management Research. 2022, 7(2), 258-269. ISSN 2507-1076.
62. Neumann, D., 2020. Fear of missing out. The international encyclopedia of media psychology. John Wiley & Sons, 2020, 1-9. ISBN 9781119011071.
63. Ngarmwongnoi, C. at al., 2020. The implications of eWOM adoption on the consumer journay. In: Journal of Consumer Marketing. 2020, 37(7), 749-759. ISSN 0736-3761.
64. Nieto-Garcia, M., Munoz-Gallego, P. and González-Benito, O., 2017. Tourists’ willingness to pay for an accommodation: The effect ofeWOM and internal reference price. In: International Journal of Hospitality Management. 2017, 62, 66-77. ISSN 0278-4319.
65. O’Reilly, K. et al., 2016. Extending our understanding of eWOM impact: The role of source credibility and message relevance. In: Journal of internet commerce. 2016, 15(2), 77-96. ISSN 1533-2861.
66. Ohanian, R., 1990. Construction and validation of a scale to measure celebrity endorsers’ perceived expertise, trustworthiness, and attractiveness. In: Journal of Advertising. 1990, 19(3), 39-52. ISSN 0091-3367.
67. Onofrei, G. et al., 2022. Social media interactions, purchase intention, and behavioural engagement: The mediating role of source and content factors. In: Journal of Business Research. 2022, 142, 100-112. ISSN 0148-2963.
68. O’Reilly, C. and Chatman, J., 1986. Organizational commitment and psychological attachment: The effects of compliance, identification, and internalization on prosocial behavior. In: Journal of Applied Psychology. 1986, 71(3), 493. ISSN 0021-9010.
69. Park, D. H. and Kim, S., 2008. The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews. In: Electronic Commerce Research and Applications. 2008, 7(4), 399-410. ISSN 1567-4223.
70. Park, H. H. and Jeon, J. O., 2018. The impact of mixed eWOM sequence on brand attitude change: Cross-cultural differences. In: International Marketing Review. 2018, 35(3). ISSN 0265-1335.
71. Petty, R., 1986. The elaboration likellihood model of persuasion. In: Advances in Experimental Social Psychology. 1986, 19, 124-206. ISSN 0065-2601.
72. Petty, R. E. and Brinol, P., 2013. The elaboration likelihood model. In: Dillard, J. P, and Shen, L., The SAGE handbook of persuasion. 2013, p. 240. ISBN 9781847875143.
73. Petty, R. E. et al., 1983. Central and peripheral routes to advertising effectiveness: the moderating role of involvement. In: Journal of Consumer Research. 1983, 134-146. ISSN 0093-5301.
74. Petty, R. E. and Cacioppo, J. T., 1981. Attitude and persuasion: Classic and contemporary approaches. Dubuque, IA: Wm. C. Brown, 1981. ISBN 0-697-06551-0.
75. Pihlaja, J. et al., 2017. From electronic WOM to social eWOM: Bridging the trust deficit. In: Journal of Marketing Theory and Practice. 2017, 25(4), 340-356. ISSN 1069-6679.
76. Przybylski, A. K. et al., 2013. Motivational, emotional, and behavioral correlates of fear of missing out. In: Computers in Human Behavior. 2013, 29(4), 1841-1848. ISSN 0747-5632.
77. Putri, L. and Wandobori, H., 2016. Factors influencing cosmetics purchase intention in indonesia based on online review. In: International Conference on Ethics of Business, Economics, and Social Science. 2016, pp. 255-263. ISSN 2528-617X.
78. Rahi, S., 2017. Research design and methods: A systematic review of research paradigms, sampling issues and instruments development. In: International Journal of Economics and Management Sciences. ISSN 3046-9279. Available at: <https://doi.org/10.4172/2162-6359.1000403>
79. Rao, K. S. at al., 2021. Examining eWOM-purchase intention link in Facebook brand fan pages: Trust beliefs, value co-creation and brand image as mediators. In: IIMB Management Review. 2021, 33(4), 309-321. ISSN 0970-3896.
80. Sparks, B. A, and Browning, V., 2011. The impact of online reviews on hotel booking intentions and perception of trust. In: Tourisme Management. 2011, 32(6), 1310-1323. ISSN 0261-5177.
81. Srivastava, V. and Kalro, A. D., 2019. Enhancing the helpfulness of online consumer reviews: the role latent (content) factors. In: Journal of interactional Marketing. 2019, 48, 33-50. ISSN 1094-9968.
82. Steffes, E., and Burgee, L., 2009. Social ties and online word of mouth. In: Internet Research. 2009, 19(1), 42-59. ISSN 1066-2243.
83. Sue, V. M. and Ritter, L. A., 2012. Conducting online surveys. SAGE Publications, 2012. ISBN 9781412992251.
84. Sukumaran, A., Vezich, S., McHugh, M. and Nass, C., 2011. Normative influences on thoughtful online participation. Proceedings of the SIGCHI conference on human factors in computing systems. New York, 2011, pp. 3401-3410.
85. Tapanainen, T. et al., 2021. Impacts of online word-of-mouth and personalities on intention to choose. In: Computers in Human Behavior. 2021, 116, 106656. ISSN 0747-5632.
86. Taylor, C. R., 2018. The new era of electronic word of mouth (eWOM): Be more chill over rules the critics. In: International Journal of Advertising. 2018, 37(6), 849-851. ISSN 0265-0487.
87. Tsao, W. C. et al., 2015. Compliance with eWOM: The influence of hotel reviews on booking intention from the perspective of consumer conformity. In: International Journal of Hospitality Management. 2015, 46, 99-111. ISSN 0278-4319.
88. Varma, D. et al., 2023b. Understanding the impact of eWOM communication through the lens ofinformation adoption model: A meta-analytic structural equation modeling perspective. In: Computers in Human Behavior. 2023, 143, 107710. ISSN 0747-5632.
89. Varma, D. et al., 2023a. A meta-analysis of antecedents and consequences of eWOM credibility: Investigation of moderating role of culture and platform type. In: Journal of Business Research. 2023, 154, 113292. ISSN 0148-2963.
90. Varma, S. and Yadav, N., 2021. Past, present, and future of electronic word of mouth (EWOM). In: Journal of interactive marketing. 2021, 53, 111-128. ISSN 1094-9968.
91. Vermeulen, I. E. and Seegers, D., 2009. Tried and tested: the impact of online hotelreviews on consumer consideration. In: Tourisme Management. 2009, 30(1), 123-127. ISSN 0261-5177.
92. Wang, X. et al., 2012. Social media peer communication and impacts on purchase intentions: A consumer socialization framework. In: Journal of Interactive Marketing. 2012, 26(4), 198-208. ISSN 1094-9968.
93. Wang, J. J. et al., 2018. Understanding the effects of eWOM social ties on purchase intentions: A moderated mediation investigation. In: Electronic Commerce Research and Applications. 2018, 28, 54-62. ISSN 1567-4223.
94. Wijenayake, S. et al., 2020. Impact of contextual and personal determinants on online social conformity. In: Computers in Human Behavior. 28, 106302. ISSN 0747-5632.
95. Ye, Q. et al., 2011. The influence of user-generated content on traveler behavior: an empirical investigation on the effects of eword- of-mouth to hotel online bookings. In: Computers in Human Behavior. 2011, 27(2), 634-639. ISSN 0747-5632.
96. Zhang, K. Z. et al., 2014. Examining the influence of online reviews on consumers’ decision-making: A heuristic–systematic model. In: Decision Support Systems. 2014, 67, 78-89. ISSN 0167-9236.
97. Zhao, K., Stylianou, A. C. and Zheng. Y., 2018. Sources and impacts of social influence from online anonymous user reviews. In: Information & Management. 2018, 55(1), 16-30. ISSN 0930-5181.
98. Zhao, M. and Xie, J., 2010. Effects of social and temporal distance on consumers’responses to peer recommendations. In: Journal of Marketing Research. 2010, 48(3). ISSN 0022-2437.
99. Zheng, L., 2021. The classification of online consumer reviews: A systematic literature review and integrative framework. In: Journal of Business Research. 2021, 135, 226-251. ISSN 0148-2963.
100. Zhu, H. et al., 2014. To switch or not to switch: Understanding social influence in online choices. In: American Behavioral Scientist. 2014, 58(10), 2257–2266. ISSN 1552-3381.
101. Zhu, R. et al., 2009. The effect of conformity tendency on pedestrians’ road-crossing intentions in China: An application of the theory of planned behavior. In: Accident Analysis & Prevention. 2009, 41(3), 491-497. ISSN 0001-4575.

Kľúčové slová/Key words

online review processing, social conformity, social validation, expertise, Elaboration Likelihood Model (ELM), Social Influence Theory (SIT)
spracovanie online recenzií, sociálna konformita, sociálna validácia, odbornosť, model pravdepodobnosti vypracovania (ELM), teória sociálneho vplyvu (SIT)

JEL klasifikácia/JEL Classification

M31

Résumé

Interakcia medzi kognitívnym procesom a sociálnou dynamikou pri formovaní individuálnej analýzy online recenzií. Časť I.
Zatiaľ čo interné faktory, ako sú recenzie a charakteristiky recenzentov, boli podrobne preskúmané, vplyv externých, najmä sociálnych faktorov na spracovanie online recenzií zostáva pomerne málo známy. Tento výskum preklenuje túto medzeru tým, že skúma, ako sociálne tlaky: sociálna konformita, sociálna validácia, ovplyvňujú spôsob, akým jednotlivci analyzujú online recenzie. Kombináciou modelu pravdepodobnosti vypracovania (ELM) a teórie sociálneho vplyvu (SIT) navrhujeme komplexný rámec na skúmanie dvoch kľúčových dimenzií: Spracovanie online recenzií a vplyv sociálnych faktorov na tento proces. Uskutočnili sme štúdiu medzi spotrebiteľmi výrobkov starostlivosti o pleť, pričom sme odhalili, že sociálna konformita, významne ovplyvňuje spoliehanie sa na periférne podnety (hodnotenia, dôveryhodnosť zdroja), avšak sociálna validácia nevykazuje významný vplyv. Okrem toho zapojením expertízy moderátora pozorujeme, že priamy vplyv sociálnych faktorov sa oslabuje. To naznačuje, že jednotlivci so silnými znalosťami daného predmetu sú pri hodnotení online recenzií menej náchylní na presvedčovaciu silu sociálnych podnetov.

Recenzované/Reviewed

27. February 2024 / 14. April 2024