Online shopping has grown dramatically in recent years. According to a survey conducted by Pew Research Center in June 2000, only 22% of Americans had made a purchase online. When the center conducted the same survey in 2016, it found that eight in ten Americans were then online shoppers (Smith, 2020). Especially due to the COVID-19 lockdown (and people’s fear of catching the virus) and ensuing disruptions, more and more people have switched from in-person to online shopping. As they have, product reviews and ratings have perhaps taken on more importance than ever before in guiding consumers’ choices. This presents a problem.
Most of us will readily agree that online ratings systems have provided us with useful guidance in determining the best products to suit our particular needs. However, the little-appreciated truth is that online ratings have significantly hindered people’s ability to think critically. A research article published in Harvard Business Review shows that “for every one-star increase that a business gets on Yelp, a review website, there was a 5-9% increase in revenue” (Donaker, 2019). This information indicates what common sense tells us: reviews have power over consumer choices. And people are often willing to make purchases based on reviews without interrogating their sources or otherwise doing their own research to ensure the quality of the products and services they spend their money on.
The conformity theory neatly captures the influence of online reviews. That theory references an individual’s tendency to change their beliefs and opinions to match a broader group's opinions (Yu, 2013). Solomon Asch, a pioneering social psychologist, studied conformity and other social processes. A particularly telling study of his investigated how a disagreement between a majority group and an individual could influence a person’s beliefs and opinions. In the experiment, small groups of people were asked to say out loud which comparison line (A, B, or C) was most similar to an objective line. The answer was objectively obvious. Unbeknownst to participants, the groups actually entailed only one (true) participant, the other members being confederates. The actors answered first and uniformly offered an obviously wrong answer. Asch theorized that if the true participant gave the same answer as the actors, it was likely that peer pressure played a major role in her decision.
The results demonstrated that 75% of participants abided by the actor's decisions at least once across trials (Asch, 1956). The percentage was even higher (90%) in social psychologist Muzafer Sherif's original conformity experiments (Sherif, 1935), in which the answer to the experimenter’s prompts were ambiguous. From these two experiments, it is clear that individuals’ responses are easily affected or supplanted by the majority’s answer, interfering with individuals’ forming (or at least maintaining) their own judgments. Zhang (2014) reaches similar findings, concluding that the perceived quantity of reviews–indicating a majority’s opinions–strongly influences individual purchasing decisions. In other words, the total number of other users’ comments is positively correlated with behavior intentions (Zhang, 2014). Zhang further proposed that the more interactions a comment has, the more value and trust people will invest in the comments. The conformity theory is easily linked to Zhang’s findings and the online rating system in that online reviews are roughly equivalent to people stating their opinions out loud about a product.
Therefore, in line with the conformity theory proposed by Asch, when most reviews are positive, a reader is likely to believe the product is worthy, even if she initially was skeptical or held a different belief. Sinan Aral, a network scientist at the Massachusetts Institute of Technology (MIT), further verified that the conformity effect applied to the rating system in his own experimental work. He designed a study in which comments on an online reviews website randomly received positive or negative votes. Over one month, Aral found that "comments that received fake positive votes from the researchers were 32% more likely to receive more positive votes compared with a control" (Muchnik, 2013). This result may in part be due to the fact that people lacked adequate information on the products, such that their own opinions were merely intuition and therefore weak, leaving them more likely to follow the majority. Regardless, the study provides strong evidence of the conformity theory’s applicability in online shopping behavior. (Apart from verifying the conformity theory, Aral’s work also strongly backs up the argument that people's values are easily swayed by ratings.) The concerning implication of all of the aforementioned scholars’ work is that people do not have the ability or will to form their own judgments, even if they consider themselves rational thinkers.
We can clearly see through the above discussion that even though e-commerce reviews are ostensibly a helpful tool in guiding consumers’ purchasing decisions, there can be a negative side to those reviews if consumers are unaware of the tool’s fallibilities. In other words, online reviews are double-edged swords. Given the above discussion, it is imperative that regulations and policies be implemented to stop companies from creating illusions with respect to their products, and otherwise manipulating people to gain an unfair advantage. In particular, fake reviews need to be addressed; since they are more easily disguised as real ones nowadays, companies and governmental regulators must be candid in setting up solutions to prevent such comments’ undue influence. While fake reviews might always exist in some capacity, creating policies to monitor and mitigate their effect would nonetheless likely decrease the extent to which consumers are manipulated and maintain fair markets for all competitors.
Asch, S. (1956). Studies of Independence and Conformity: I. A Minority of One Against a Unanimous Majority. District of Columbia: American Psychological Association, Psychological Monographs: General and Applied. Retrieved from Psychological Experiments Online database.
Donaker, G. (2019, October 22). Designing Better Online Review Systems. Harvard Business Review. Retrieved March 17, 2022, from https://hbr.org/2019/11/designing-better-online-review-systems
Muchnik, Aral, S., & Taylor, S. J. (2013). Social Influence Bias: A Randomized Experiment. Science (American Association for the Advancement of Science), 341(6146), 647–651. https://doi.org/10.1126/science.1240466
Sherif, M. (1935) A study of some social factors in perception. Archives of Psychology, no. 187.
Smith, A., & Anderson, M. (2020, May 30). Online shopping and E-Commerce. Pew Research Center: Internet, Science & Tech. Retrieved March 17, 2022, from https://www.pewresearch.org/internet/2016/12/19/online-shopping-and-e-commerce/
Yu, R., & Sun, S. (2013). To conform or not to conform: spontaneous conformity diminishes the sensitivity to monetary outcomes. PloS one, 8(5), e64530. https://doi.org/10.1371/journal.pone.0064530
Zhang, Zhao, S. J., Cheung, C. M. ., & Lee, M. K. . (2014). Examining the influence of online reviews on consumers’ decision-making: A heuristic–systematic model. Decision Support Systems, 67, 78–89. https://doi.org/10.1016/j.dss.2014.08.005