https://orcid.org/0000-0002-4581-8469
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ABSTRACT
Social technologies on online review platforms enable social interactions among users, such as establishing following relationships and commenting on others’ posts. Although it is well recognized that more socially engaged reviewers tend to be more active and generate content of higher quality, our knowledge about the impact of social interactions on peer evaluations of reviews is limited. To address this issue, we use a unique dataset from a major online review platform and find that, ceteris paribus, reviews posted by more socially engaged users receive more helpfulness votes than those posted by less socially engaged users. Similarly, users tend to vote more for reviews written by their mutual followers than for those written by nonfollowers. In addition, we find that less socially engaged users review a broader range of products and services but are less likely to stay on a platform, which may further contribute to the inflation of peer evaluations (toward online reviews). Our study provides unique empirical evidence regarding the influence of social interactions on review evaluations. Furthermore, we caution researchers and practitioners against utilizing review helpfulness scores as a sole measure for review quality and diagnosticity.
Disclosure statement
No potential conflict of interest was reported by the authors.
Supplementary Information
Supplemental data for this article can be accessed online at https://doi.org/10.1080/07421222.2023.2267323.
Notes
1. One may argue that vote casting could be considered a form of social interaction as well. However, review writers on focal platforms cannot access the identity of users who vote on their reviews. As a result, it is unlikely that they reciprocate based on the voting behavior of others. Hence, we do not include past vote casting behavior as an independent variable of interest (i.e., as a form of social interactions).
2. It is possible to argue that an increase in observation opportunities can also result in negative performance ratings. However, previous empirical evidence strongly indicates that observation ease positively impacts performance ratings, likely since negative events are more likely to arise from negative ratings than from positive ratings [23]. Therefore, we follow the literature and argue for the positive impact of observation opportunities on review evaluation.
3. .1 in Online Supplemental Appendix 1.1 reports the correlation coefficients among the continuous variables. We note that the variance inflation factors (VIFs) for our empirical models range from 1.38 to 1.66. This is significantly below the threshold of 10, which is generally considered the threshold of multicollinearity concerns. Therefore, it is reasonably safe to assume that multicollinearity would not be a significant issue in our regressions.
4. Note that the new set of data does not contain the review reading log. As a result, we cannot use this new dataset for our analyses in the current section, since the variable ViewRcvd cannot be constructed.
5. Our results of Gini coefficients, the regression of being socially engaged on rating deviation, and survival analysis in the next subsection are robust to different threshold definitions of “socially engaged users,” as evidenced by a 75 percent quantile (i.e., 2 comments) and 90 percent quantile (i.e., 5 comments). Our results are available upon request.
Additional information
Funding
Notes on contributors
Yinan Yu
Yinan Yu ([email protected]) is an Assistant Professor in the Division of Management Information Systems at the Michael F. Price College of Business, University of Oklahoma. She researches digital platforms, social media, user generated content, multi-channel management, mobile commerce, business analytics, and economics of information systems. Her work has been published in MIS Quarterly.
Warut Khern-am-nuai
Warut Khern-am-nuai ([email protected]) is an Associate Professor of Information Systems at the Desautels Faculty of Management, McGill University. He received his PhD in Management Information Systems from the Krannert School of Management, Purdue University. His research interests include platforms for online marketplaces, predictive analytics, and management information security. His work has appeared in premier academic journals, including Management Science, Information Systems Research, and MIS Quarterly, among others.
Alain Pinsonneault
Alain Pinsonneault ([email protected]) is Distinguished James McGill Professor and the Imasco Chair of Information Systems in the Desautels Faculty of Management at McGill University. He has been appointed to the Order of Québec and is Fellow of the Royal Society of Canada and of the Association for Information Systems. His research interests include the organizational and individual impacts of information technology (IT), user adaptation, social networks, business model in the digital economy, e-health, e-integration, strategic alignment of IT, and the business value of IT. His research has appeared in numerous journals, including Management Science, Information Systems Research, Journal of Management Information Systems, MIS Quarterly, and Organization Science. He has served on the editorial boards of the four latter journals.
Zaiyan Wei
Zaiyan Wei ([email protected]) is an Associate Professor of Management in the Daniels School of Business at Purdue University. He also serves as an academic committee member of Xinyuan FinTech Research Center at Tsinghua University, China. He holds a PhD in Economics. Dr. Wei’s research focuses on the economics of information systems with respect to FinTech lending, platform economics, and social media and networks. His papers have been published in leading academic journals such as Management Science, Information Systems Research, MIS Quarterly, and Production and Operations Management.