Tram Pham^a* , Sascha Steinmann^a and Birger Boutrup Jensen^a

^aAarhus University, School of Business and Social Sciences, Department of Management

*Corresponding author’s e-mail address: dttp@mgmt.au.d

Keywords: Marketing and consumer behavior (online review system, review system design, online retailing, systematic literature review).

Abstract:

Online product reviews play an important role in defining the social presence of retailers (Yin et al., 2017) as well as generating online store traffic (Mudambi and Schuff, 2010). Online reviews are also perceived as a source of information (Mudambi and Schuff, 2010) that gives consumers the power to make better purchasing decisions (Park et al., 2021). Online retailers that are perceived to have more credible and useful product reviews, are also perceived as offering greater value to customers (Chevalier and Mayzlin, 2006) and increasing sales of retailers (Floyd et al., 2014).

Zhu and Zhang (2010) argue that design of review system such as including displaying distribution or displaying variance of online ratings is a construct that affects consumers’ purchase decision. Previous research has also found that credibility and usefulness of reviews affect consumer purchase decision (Anderson and Simester, 2014). Anderson and Simester argue that a properly designed online review system might affect whether consumers perceive online reviews as credible.

There are two main types of review systems i.e., the third-party reviews and retailers’ or manufacturers’ internal online review system. Previous studies have not differentiated these two systems. According to source credibility theory (Hovland et al., 1953), how people adopt and process information and behave accordingly is strongly influenced by the trustworthiness of different sources. Therefore, we made an assumption that online product reviews from retailers’ websites have different effects on credibility and usefulness of reviews compared to the ones from third-party platforms. In this paper, we only focus on retailers’ internal review system.

We aim to address the two following research questions: (1) How have the impact of online product reviews on consumer behaviour and retailers’ performance been addressed in previous literature? and (2) How have the design features of different retailers’ review system been addressed in previous literature?

The PRISMA protocol is used as the guideline for this systematic review (Page et al., 2021a). The review was conducted through three steps according to the new PRISMA protocol 2020, i.e., identification, screening and inclusion (Page et al., 2021a). A literature search was conducted in twelve leading Marketing and Retailing journals using the time span from 2000 to 2021. At first, a total of 175 articles were identified. Three important inclusion criteria were used to access the eligibility of the papers. The first criterion is that the topic of the paper is consumer behaviour. The second criterion is that the papers cover an online review system that can be controlled by retailers. The last criterion is excluding all general platforms. After the screening stage, 92 papers were eligible for further review.

The analysis was split into two parts. First, the papers were allocated into two different categorizations with regard to impacts on retailers and impacts on consumers. For the second part, we attempted to make a synthesis of individual design features using the ten design features of an online review system developed by Dominik Gutt and colleagues (2019).

The preliminary findings show that the numbers of included articles have significantly increased from 2010 to 2021 with 4 articles published 2010 and 11 publications found in 2021. This field is highly dominated by quantitative research methods. Out of the 92 papers included, 4 papers are considered as conceptual papers using meta-analysis, and only two papers using qualitative research methods.

Several authors have addressed the importance of online product reviews on sales including Floyd et al. (2014), Zhu and Zhang (2010). However, the view on whether positive or negative reviews have an impact on sales is still inconclusive. Online product reviews also play an important role in defining the reputation and image of retailers (Ahmad and Guzman, 2021). By using useful and credible online product reviews, consumers can overcome the challenge of information asymmetry in online retailing (Yin et al., 2017), which could increase their willingness-to-pay. While examining the usefulness and credibility of online product reviews, consumers could suffer from various issues such as social influence (Risselada et al., 2018), reviewer ambiguity (Naylor et al., 2011) and fake and irrelevant reviews (Munzel, 2016).

To be perceived as useful, a large volume of reviews is needed (Ifie, 2020). The formation of online reviews suffers from a free-rider problem (Reimer and Benkenstein, 2016). By voluntarily contributing to the system, the whole community will be better-off, yet, it is difficult to realize as an individual (ibid.). In addition, if consumers want to contribute to the online review system, they will have to invest quite some time and effort (Reimer and Benkenstein, 2016). To overcome this problem, solutions such as offering monetary incentives (ibid.) or non-monetary incentives (Woolley and Sharif, 2021). How to apply these incentives in the design of an online review system is still a research gap that needs to be filled.

There is no paper in these 92 included papers talking about the design of review system as a whole. We also find that some design features have received much more attention compared to others. Among 92 papers included, 60 papers focus on adapted metrics (volume, variance and valence) while other design features such as options to rank/filter reviews or offering reviewers’ ranking are mostly neglected. Nonetheless, simple-designed review systems do not provide consumers enough information to help them diminish uncertainty and evaluate products in their decision-making process while multidimensional rating system can (Chen et al., 2018). It is necessary to consider to include further design features in the system apart from the original adapted metrics. Several research gaps have been identified in order to have a useful and credible review system such as the use of photos and videos in reviews, a “verified purchase” mechanism, providing monetary or non-monetary incentives for review formation, etc.

This review is the first research that has differentiated between the internal review systems from retailers with the ones from third-party platforms. The purpose of this paper is to synthesize relevant research papers in the field of Marketing and Retailing and identify research gaps.

References

Ahmad, F. and Guzmán, F. (2021), “Negative online reviews, brand equity and emotional contagion”, European Journal of Marketing, Vol. 55 No. 11, pp. 2825-2870.

Anderson, E.T. and Simester, D.I. (2014). “Reviews without a purchase: Low ratings, loyal customers, and deception”, Journal of Marketing Research, Vol. 51 No. 3, pp. 249-269.

Babić Rosario, A., Sotgiu, F., De Valck, K. and Bijmolt, T.H. (2016), “The effect of electronic word of mouth on sales: A meta-analytic review of platform, product, and metric factors”, Journal of Marketing Research, Vol. 53 No. 3, pp. 297-318.

Berger, J., Sorensen, A.T. and Rasmussen, S.J. (2010), “Positive effects of negative publicity: When negative reviews increase sales”, Marketing science, Vol. 29 No. 5, pp. 815-827.

Chen, P.Y., Hong, Y. and Liu, Y. (2018), “The value of multidimensional rating systems: Evidence from a natural experiment and randomized experiments”, Management Science, Vol. 64 No. 10, pp. 4629-4647.

Chen, Y. and Xie, J. (2005), “Third-party product review and firm marketing strategy”, Marketing science, Vol. 24 No. 2, pp. 218-240.

Chen, Y., Liu, Y. and Zhang, J. (2012), “When do third-party product reviews affect firm value and what can firms do? The case of media critics and professional movie reviews”, Journal of Marketing, Vol. 76 No. 2, pp. 116-134.

Chevalier, J.A. and Mayzlin, D. (2006), “The effect of word of mouth on sales: Online book reviews”, Journal of marketing research, Vol. 43 No. 3, pp. 345-354.

Floyd, K., Freling, R., Alhoqail, S., Cho, H.Y. and Freling, T. (2014), “How online product reviews affect retail sales: A meta-analysis”, Journal of Retailing, Vol. 90 No. 2, pp. 217-232.

Gutt, D., Neumann, J., Zimmermann, S., Kundisch, D. and Chen, J. (2019), “Design of review systems–A strategic instrument to shape online reviewing behavior and economic outcomes”, The Journal of Strategic Information Systems, Vol. 28 No. 2, pp. 104-117.

Ho-Dac, N.N., Carson, S.J. and Moore, W.L. (2013), “The effects of positive and negative online customer reviews: do brand strength and category maturity matter?”, Journal of marketing, Vol. 77 No. 6, pp. 37-53.

Hovland, C.I., Janis, I.L. and Kelley, H.H. (1953), Communication and persuasion, Yale University Press, New Haven and London.

Ifie, K. (2020), “Excellent product… But too early to say: Consumer reactions to tentative product reviews”, Journal of Interactive Marketing, Vol. 52, pp. 35-51.

Ismagilova, E., Slade, E., Rana, N.P. and Dwivedi, Y.K. (2020), “The effect of characteristics of source credibility on consumer behaviour: A meta-analysis”, Journal of Retailing and Consumer Services, Vol. 53, p. 101736.

Luca, M. and Zervas, G. (2016), “Fake it till you make it: Reputation, competition, and Yelp review fraud”, Management Science, Vol. 62 No. 12, pp. 3412-3427.

Mudambi, S.M. and Schuff, D. (2010), “Research note: What makes a helpful online review? A study of customer reviews on Amazon.com”, MIS quarterly, Vol. 34 No. 1, pp. 185-200.

Munzel, A. (2016), “Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus”, Journal of Retailing and Consumer Services, Vol. 32, pp. 96-108.

Naylor, R.W., Lamberton, C.P. and Norton, D.A. (2011), “Seeing ourselves in others: Reviewer ambiguity, egocentric anchoring, and persuasion”, Journal of Marketing Research, Vol. 48 No. 3, pp. 617-631.

Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E. and Chou, R. (2021a), “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews”, International Journal of Surgery, Vol. 88, p. 105906.

Page, M.J., Moher, D., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E. and Chou, R. (2021b), “PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews”, Bmj, Vol. 372.

Park, S., Shin, W. and Xie, J. (2021), “The fateful first consumer review”, Marketing Science, Vol. 40 No. 3, pp. 481-507.

Reimer, T. and Benkenstein, M. (2016), “Altruistic eWOM marketing: More than an alternative to monetary incentives”, Journal of Retailing and Consumer Services, Vol. 31, pp. 323-333.

Risselada, H., de Vries, L. and Verstappen, M. (2018), “The impact of social influence on the perceived helpfulness of online consumer reviews”, European Journal of Marketing, Vol. 52 No. 3/4, pp. 619-636.

Tang, T., Fang, E. and Wang, F. (2014), “Is neutral really neutral? The effects of neutral user-generated content on product sales”, Journal of Marketing, Vol. 78 No. 4, pp. 41-58.

Van Laer, T., Edson Escalas, J., Ludwig, S. and Van Den Hende, E.A. (2019), “What happens in Vegas stays on TripAdvisor? A theory and technique to understand narrativity in consumer reviews”, Journal of Consumer Research, Vol. 46 No. 2, pp. 267-285.

Woolley, K. and Sharif, M.A. (2021), “Incentives increase relative positivity of review content and enjoyment of review writing”, Journal of Marketing Research, Vol. 58 No. 3, pp. 539-558.

Yin, D., Bond, S.D. and Zhang, H. (2017), “Keep your cool or let it out: Nonlinear effects of expressed arousal on perceptions of consumer reviews”, Journal of Marketing Research, Vol. 54 No. 3, pp. 447-463.

You, Y., Vadakkepatt, G.G. and Joshi, A.M. (2015), “A meta-analysis of electronic word-of-mouth elasticity”, Journal of Marketing, Vol. 79 No. 2, pp. 19-39.

Zhu, F. and Zhang, X. (2010), “Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics”, Journal of marketing, Vol. 74 No. 2, pp. 133-148.