I Like My Anonymity: An Empirical Investigation of the Effect of Multidimensional Review Text and Role Anonymity on Helpfulness of Employer Reviews

Srikanth Parameswaran (), Pubali Mukherjee () and Rohit Valecha ()
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Srikanth Parameswaran: Binghamton University, SUNY
Pubali Mukherjee: Binghamton University, SUNY
Rohit Valecha: University of Texas at San Antonio

Information Systems Frontiers, 2023, vol. 25, issue 2, No 21, 853-870

Abstract: Abstract Employer review sites have grown popular over the last few years, with 86 percent of job seekers referring to reviews on these sites before applying to job positions. Though the antecedents of review helpfulness have been studied in various contexts, it has received limited attention in the employee review context. These sites provide review text in multiple dimensions, such as pros and cons. Besides, to solicit unbiased reviews, these sites allow an option of keeping reviewer information anonymous. Rooted in the diagnosticity perspective, we investigate review helpfulness focusing on the role of review text in multiple dimensions and the anonymity of the reviewers. We use a publicly available Glassdoor dataset to model review helpfulness using a Tobit regression. The results show that the review length in multiple dimensions of review text and anonymity positively impact review helpfulness. Moreover, anonymity positively moderates the review length in the cons section. As a post-hoc analysis, we perform topic modeling to gain better insights on the review text in multiple dimensions and anonymity. The post-hoc analyses show that non-anonymous reviewers discuss firm reputation in the pros section, which anonymous reviewers do not. In the cons section, non-anonymous reviewers discuss politics, unfair and unethical treatment, and prospects of the employer, while anonymous reviewers discuss incompetency of the leadership. This research has important practical implications for online review sites’ design and crafting guidelines and policies for employees writing reviews.

Keywords: Helpfulness; Electronic word of mouth; Online employer reviews; Anonymity; Topic models; Multidimensional review text (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10796-022-10268-3

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