Social influence bias in online reviews


(This academic blog post is based on Muchnik, L., Aral, S., & Taylor, S. J. (2013). Social influence bias: A randomized experiment. Science, 341(6146), 647-651.)

During our course we learn that there are four functions of customer value creation: recommend & develop products, compose & co-brand products, sell products & digital distribution and P2P support & product evaluations. In this post I want to focus on the fourth function, namely product evaluations.

When consumers make an online purchase decisions, they tend to rely on online reviews generated by other consumers. Consumers regard them as more persuasive than traditional advertisement from marketers and companies, and reports from third party consumer report companies. This is because online reviews focus more on experience than on technical specifications (Lu et al., 2014). Industry reports state that 61% of consumers consult online reviews before making a new purchase (Cheung et al., 2012).

So we know that consumers base their buying decision on online reviews. Muchnik et al. (2013) research if online reviews accurately represent individual opinions about the quality of a product or service. They suspect that social influence create irrational herding effects, where users follow the decisions of prior users. This can lead to suboptimal decisions and a thereby disrupt the wisdom of the crowds. If that is the case, it means that online reviews could easily be manipulated and disturb our decision behaviour.

To research the social influence bias on individual rating behaviour Muchnik et al. (2013) did a large-scale randomized experiment in a news aggregation web site. They find that negative social influence were corrected by other users by giving a positive rating, so there is no significant herding effect there. However, they did find evidence for herding effects by positive social influence. Positive social influence increased the likelihood of giving a positive rating by 32%. Overall, this increased the final ratings by 25% on average.

An important theoretical contribution of this article is that it confirms prior hypotheses on a tendency towards positive ratings, which makes these results more generalizable. This applies to all different kinds of users (e.g. frequent or infrequent voters) that could be distinguished in the experiment. Future research will need to research about the mechanisms that drive individual and aggregate ratings.

Managerial implications can be interesting for companies who want to use reviews as a marketing tool. If they can up vote positive reviews it can lead to herding effects and thereby positively increase sales. Taking the findings of this article in mind, would you be more critical about online reviews? Or are they too important for your decision making process?

References

Cheung, C. M. K., & Thadani, D. R. (2012). The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decision Support Systems, 54(1), 461-470. Available: http://dx.doi.org/10.1016/j.dss.2012.06.008

Lu, X., Li, Y., Zhang, Z., & Rai, B. (2014). CONSUMER LEARNING EMBEDDED IN ELECTRONIC WORD OF MOUTH. Journal of Electronic Commerce Research, 15(4).

Muchnik, L., Aral, S., & Taylor, S. J. (2013). Social influence bias: A randomized experiment. Science, 341(6146), 647-651

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