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

Author

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  • Srikanth Parameswaran

    (Binghamton University, SUNY)

  • Pubali Mukherjee

    (Binghamton University, SUNY)

  • Rohit Valecha

    (University of Texas at San Antonio)

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.

Suggested Citation

  • Srikanth Parameswaran & Pubali Mukherjee & Rohit Valecha, 2023. "I Like My Anonymity: An Empirical Investigation of the Effect of Multidimensional Review Text and Role Anonymity on Helpfulness of Employer Reviews," Information Systems Frontiers, Springer, vol. 25(2), pages 853-870, April.
    • Handle: RePEc:spr:infosf:v:25:y:2023:i:2:d:10.1007_s10796-022-10268-3
    DOI: 10.1007/s10796-022-10268-3
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    1. Park, Cheol & Lee, Thae Min, 2009. "Information direction, website reputation and eWOM effect: A moderating role of product type," Journal of Business Research, Elsevier, vol. 62(1), pages 61-67, January.
    2. Andreas Munzel, 2016. "Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus," Post-Print hal-02423574, HAL.
    3. Munzel, Andreas, 2016. "Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus," Journal of Retailing and Consumer Services, Elsevier, vol. 32(C), pages 96-108.
    4. Liu, Zhiwei & Park, Sangwon, 2015. "What makes a useful online review? Implication for travel product websites," Tourism Management, Elsevier, vol. 47(C), pages 140-151.
    5. Ahluwalia, Rohini, 2002. "How Prevalent Is the Negativity Effect in Consumer Environments?," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 29(2), pages 270-279, September.
    6. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    7. Herr, Paul M & Kardes, Frank R & Kim, John, 1991. "Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(4), pages 454-462, March.
    8. Stefan Ivens & Mario Schaarschmidt & Raoul Könsgen, 2021. "When Employees Speak as They Like: Bad Mouthing in Social Media," Corporate Reputation Review, Palgrave Macmillan, vol. 24(1), pages 1-13, February.
    9. Alton Y.K. Chua & Snehasish Banerjee, 2015. "Understanding review helpfulness as a function of reviewer reputation, review rating, and review depth," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(2), pages 354-362, February.
    10. Davis, Joshua M. & Agrawal, Deepti, 2018. "Understanding the role of interpersonal identification in online review evaluation: An information processing perspective," International Journal of Information Management, Elsevier, vol. 38(1), pages 140-149.
    11. Hu, Ya-Han & Chen, Kuanchin, 2016. "Predicting hotel review helpfulness: The impact of review visibility, and interaction between hotel stars and review ratings," International Journal of Information Management, Elsevier, vol. 36(6), pages 929-944.
    12. Guiso, Luigi & Sapienza, Paola & Zingales, Luigi, 2015. "The value of corporate culture," Journal of Financial Economics, Elsevier, vol. 117(1), pages 60-76.
    13. Navid Aghakhani & Onook Oh & Dawn G. Gregg & Jahangir Karimi, 2021. "Online Review Consistency Matters: An Elaboration Likelihood Model Perspective," Information Systems Frontiers, Springer, vol. 23(5), pages 1287-1301, September.
    14. Pei-Yu Chen & Yili Hong & Ying Liu, 2018. "The Value of Multidimensional Rating Systems: Evidence from a Natural Experiment and Randomized Experiments," Management Science, INFORMS, vol. 64(10), pages 4629-4647, October.
    15. Raoul Könsgen & Mario Schaarschmidt & Stefan Ivens & Andreas Munzel, 2018. "Finding Meaning in Contradiction on Employee Review Sites — Effects of Discrepant Online Reviews on Job Application Intentions," Post-Print halshs-01897830, HAL.
    16. Wei-Lun Chang & Yi-Pei Chen, 2019. "Way too sentimental? a credible model for online reviews," Information Systems Frontiers, Springer, vol. 21(2), pages 453-468, April.
    17. Srivastava, Vartika & Kalro, Arti D., 2019. "Enhancing the Helpfulness of Online Consumer Reviews: The Role of Latent (Content) Factors," Journal of Interactive Marketing, Elsevier, vol. 48(C), pages 33-50.
    18. Pitt, Christine S. & Botha, Elsamari & Ferreira, João J. & Kietzmann, Jan, 2018. "Employee brand engagement on social media: Managing optimism and commonality," Business Horizons, Elsevier, vol. 61(4), pages 635-642.
    19. Dabirian, Amir & Kietzmann, Jan & Diba, Hoda, 2017. "A great place to work!? Understanding crowdsourced employer branding," Business Horizons, Elsevier, vol. 60(2), pages 197-205.
    20. Bin Guo & Shasha Zhou, 2017. "What makes population perception of review helpfulness: an information processing perspective," Electronic Commerce Research, Springer, vol. 17(4), pages 585-608, December.
    21. Raoul Könsgen & Mario Schaarschmidt & Stefan Ivens & Andreas Munzel, 2018. "Finding Meaning in Contradiction on Employee Review Sites — Effects of Discrepant Online Reviews on Job Application Intentions," Post-Print hal-02957523, HAL.
    22. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    23. Pan, Yue & Zhang, Jason Q., 2011. "Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews," Journal of Retailing, Elsevier, vol. 87(4), pages 598-612.
    24. Könsgen, Raoul & Schaarschmidt, Mario & Ivens, Stefan & Munzel, Andreas, 2018. "Finding Meaning in Contradiction on Employee Review Sites — Effects of Discrepant Online Reviews on Job Application Intentions," Journal of Interactive Marketing, Elsevier, vol. 43(C), pages 165-177.
    25. Andreas Munzel, 2016. "Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus," Post-Print halshs-01522497, HAL.
    26. Wang, Xia & Yu, Chunling & Wei, Yujie, 2012. "Social Media Peer Communication and Impacts on Purchase Intentions: A Consumer Socialization Framework," Journal of Interactive Marketing, Elsevier, vol. 26(4), pages 198-208.
    27. Park, Sangwon & Nicolau, Juan L., 2015. "Asymmetric effects of online consumer reviews," Annals of Tourism Research, Elsevier, vol. 50(C), pages 67-83.
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