Skip to main content

Advertisement

Log in

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

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Aghakhani, N., Oh, O., Gregg, D. G., & Karimi, J. (2021). Online review consistency matters: An elaboration likelihood model perspective. Information Systems Frontiers, 23, 1287–1301. https://doi.org/10.1007/s10796-020-10030-7

    Article  Google Scholar 

  • Agnihotri, A., & Bhattacharya, S. (2016). Online review helpfulness: Role of qualitative factors. Psychology & Marketing, 33(11), 1006–1017.

    Article  Google Scholar 

  • Ahluwalia, R. (2002). How prevalent is the negativity effect in consumer environments? Journal of Consumer Research, 29(2), 270–279.

    Article  Google Scholar 

  • Akanfe, O., Valecha, R., & Rao, H. R. (2020). Design of a Compliance Index for Privacy Policies: A Study of Mobile Wallet and Remittance Services. IEEE Transactions on Engineering Management, 1-13https://doi.org/10.1109/TEM.2020.3015222

  • Albadi, N., Kurdi, M., & Mishra, S. (2019). Hateful people or hateful bots? Detection and characterization of bots spreading religious hatred in Arabic social media. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1–25.

    Article  Google Scholar 

  • Archak, N., Ghose, A., & Ipeirotis, P. G. (2011). Deriving the pricing power of product features by mining consumer reviews. Management Science, 57(8), 1485–1509.

    Article  Google Scholar 

  • Askalidis, G., Kim, S. J., & Malthouse, E. C. (2017). Understanding and overcoming biases in online review systems. Decision Support Systems, 97, 23–30.

    Article  Google Scholar 

  • Baek, H., Ahn, J., & Choi, Y. (2012). Helpfulness of online consumer reviews: Readers’ objectives and review cues. International Journal of Electronic Commerce, 17(2), 99–126.

    Article  Google Scholar 

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

    Google Scholar 

  • Cao, Q., Duan, W., & Gan, Q. (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50(2), 511–521.

    Article  Google Scholar 

  • Carpentier, M., & Van Hoye, G. (2021). Managing organizational attractiveness after a negative employer review: Company response strategies and review consensus. European Journal of Work and Organizational Psychology, 30(2), 274–291.

    Article  Google Scholar 

  • Chang, W.-L., & Chen, Y.-P. (2019). Way too sentimental? a credible model for online reviews. Information Systems Frontiers, 21, 453–468. https://doi.org/10.1007/s10796-017-9757-z

    Article  Google Scholar 

  • Chen, P.-Y., Hong, Y., & Liu, Y. (2018). The value of multidimensional rating systems: Evidence from a natural experiment and randomized experiments. Management Science, 64(10), 4629–4647.

    Article  Google Scholar 

  • Cheung, C.M.-Y., Sia, C.-L., & Kuan, K. K. (2012). Is this review believable? A study of factors affecting the credibility of online consumer reviews from an ELM perspective. Journal of the Association for Information Systems, 13(8), 2.

    Article  Google Scholar 

  • Chua, A. Y., & Banerjee, S. (2015). Understanding review helpfulness as a function of reviewer reputation, review rating, and review depth. Journal of the Association for Information Science and Technology, 66(2), 354–362.

    Article  Google Scholar 

  • Dabirian, A., Kietzmann, J., & Diba, H. (2017). A great place to work!? Understanding Crowdsourced Employer Branding. Business Horizons, 60(2), 197–205.

    Article  Google Scholar 

  • Davis, J. M., & Agrawal, D. (2018). Understanding the role of interpersonal identification in online review evaluation: An information processing perspective. International Journal of Information Management, 38(1), 140–149.

    Article  Google Scholar 

  • Evertz, L., Kollitz, R., & Süß, S. (2019). Electronic word-of-mouth via employer review sites–the effects on organizational attraction. The International Journal of Human Resource Management, 3428–3457.

  • Fisher, C. D., Ilgen, D. R., & Hoyer, W. D. (1979). Source credibility, information favorability, and job offer acceptance. Academy of Management Journal, 22(1), 94–103.

    Article  Google Scholar 

  • Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291–313.

    Article  Google Scholar 

  • Gao, B., Hu, N., & Bose, I. (2017). Follow the herd or be myself? An analysis of consistency in behavior of reviewers and helpfulness of their reviews. Decision Support Systems, 95, 1–11.

    Article  Google Scholar 

  • Ghose, A., & Ipeirotis, P. G. (2006). Designing ranking systems for consumer reviews: The impact of review subjectivity on product sales and review quality. Proceedings of the 16th annual workshop on information technology and systems,

  • Ghose, A., & Ipeirotis, P. G. (2010). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23(10), 1498–1512.

    Article  Google Scholar 

  • Glassdoor. (2019). A Guide to the Ultimate Candidate Experience. Glassdoor. Retrieved 1–17–2022 from https://www.glassdoor.com/employers/blog/a-guide-to-the-ultimate-candidate-experience/

  • Guiso, L., Sapienza, P., & Zingales, L. (2015). The value of corporate culture. Journal of Financial Economics, 117(1), 60–76.

    Article  Google Scholar 

  • Guo, B., & Zhou, S. (2017). What makes population perception of review helpfulness: An information processing perspective. Electronic Commerce Research, 17(4), 585–608.

    Article  Google Scholar 

  • Han, M. (2021a). Examining the effect of reviewer expertise and personality on reviewer satisfaction: an empirical study of TripAdvisor. Computers in Human Behavior, 114, 106567.

    Article  Google Scholar 

  • Han, M. (2021b). How does mobile device usage influence review helpfulness through consumer evaluation? Evidence from TripAdvisor. Decision Support Systems, 113682.

  • Herr, P. M., Kardes, F. R., & Kim, J. (1991). Effects of word-of-mouth and product-attribute information on persuasion: An accessibility-diagnosticity perspective. Journal of Consumer Research, 17(4), 454–462.

    Article  Google Scholar 

  • Hong, H., Xu, D., Wang, G. A., & Fan, W. (2017). Understanding the determinants of online review helpfulness: A meta-analytic investigation. Decision Support Systems, 102, 1–11.

    Article  Google Scholar 

  • Hu, N., Liu, L., & Zhang, J. J. (2008). Do online reviews affect product sales? The role of reviewer characteristics and temporal effects. Information Technology and Management, 9(3), 201–214.

    Article  Google Scholar 

  • Hu, Y.-H., & Chen, K. (2016). Predicting hotel review helpfulness: The impact of review visibility, and interaction between hotel stars and review ratings. International Journal of Information Management, 36(6), 929–944.

    Article  Google Scholar 

  • Huang, N., Burtch, G., Hong, Y., & Polman, E. (2016). Effects of multiple psychological distances on construal and consumer evaluation: A field study of online reviews. Journal of Consumer Psychology, 26(4), 474–482.

    Article  Google Scholar 

  • Ivens, S., Schaarschmidt, M., & Könsgen, R. (2021). When employees speak as they like: Bad mouthing in social media. Corporate Reputation Review, 24, 1–13. https://doi.org/10.1057/s41299-019-00086-w

    Article  Google Scholar 

  • Jung, Y., & Suh, Y. (2019). Mining the voice of employees: A text mining approach to identifying and analyzing job satisfaction factors from online employee reviews. Decision Support Systems, 123, 113074.

    Article  Google Scholar 

  • Kanar, A. M., Collins, C. J., & Bell, B. S. (2010). A comparison of the effects of positive and negative information on job seekers’ organizational attraction and attribute recall. Human Performance, 23(3), 193–212.

    Article  Google Scholar 

  • Kong, D., Yang, J., Duan, H., & Yang, S. (2020). Helpfulness and economic impact of multidimensional rating systems: Perspective of functional and hedonic characteristics. Journal of Consumer Behaviour, 19(1), 80–95.

    Article  Google Scholar 

  • Könsgen, R., Schaarschmidt, M., Ivens, S., & Munzel, A. (2018). Finding meaning in contradiction on employee review sites—effects of discrepant online reviews on job application intentions. Journal of Interactive Marketing, 43, 165–177.

    Article  Google Scholar 

  • Korfiatis, N., García-Bariocanal, E., & Sánchez-Alonso, S. (2012). Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content. Electronic Commerce Research and Applications, 11(3), 205–217.

    Article  Google Scholar 

  • Kuan, K. K., Hui, K.-L., Prasarnphanich, P., & Lai, H.-Y. (2015). What makes a review voted? An empirical investigation of review voting in online review systems. Journal of the Association for Information Systems, 16(1), 1.

    Article  Google Scholar 

  • Kusumasondjaja, S., Shanka, T., & Marchegiani, C. (2012). Credibility of online reviews and initial trust: The roles of reviewer’s identity and review valence. Journal of Vacation Marketing, 18(3), 185–195.

    Article  Google Scholar 

  • Kwok, L., & Xie, K. L. (2016). Factors contributing to the helpfulness of online hotel reviews: Does manager response play a role? International Journal of Contemporary Hospitality Management, 28(10), 2156–2177.

    Article  Google Scholar 

  • Lee, H. A., Law, R., & Murphy, J. (2011). Helpful reviewers in TripAdvisor, an online travel community. Journal of Travel & Tourism Marketing, 28(7), 675–688.

    Article  Google Scholar 

  • Lee, J., & Kang, J. (2017). A study on job satisfaction factors in retention and turnover groups using dominance analysis and LDA topic modeling with employee reviews on Glassdoor. com Thirty Eighth International Conference on Information Systems, South Korea.

  • Li, H., Zhang, Z., Meng, F., & Janakiraman, R. (2017). Is peer evaluation of consumer online reviews socially embedded?–An examination combining reviewer’s social network and social identity. International Journal of Hospitality Management, 67, 143–153.

    Article  Google Scholar 

  • Li, J., Xu, X., & Ngai, E. W. (2021). Does certainty tone matter? Effects of review certainty, reviewer characteristics, and organizational niche width on review usefulness. Information & Management, 58(8), 103549.

    Article  Google Scholar 

  • Liu, Y., Chen, P.-Y., & Hong, Y. (2014). Value of multi-dimensional rating systems: An information transfer view. 35th International Conference on Information Systems 2014, Auckland.

  • Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140–151.

    Article  Google Scholar 

  • Loria, S. (2020). textblob Documentation. Retrieved 1–17–2022 from https://buildmedia.readthedocs.org/media/pdf/textblob/latest/textblob.pdf

  • Luo, N., Zhou, Y., & Shon, J. (2016). Employee Satisfaction and Corporate Performance: Mining Employee Reviews on Glassdoor. com. 37th International Conference on Information Systems, Dublin.

  • Merriam-Webster. (2021). Merriam-Webster Online Dictionary: Sentiment. Retrieved 12–29–2021 from https://www.merriam-webster.com/dictionary/sentiment

  • Mousavizadeh, M., Koohikamali, M., Salehan, M., & Kim, D. J. (2020). An investigation of peripheral and central cues of online customer review voting and helpfulness through the lens of elaboration likelihood model. Information Systems Frontiers. https://doi.org/10.1007/s10796-020-10069-6

    Article  Google Scholar 

  • Mudambi, S. M., & Schuff, D. (2010). Research note: What makes a helpful online review? A study of customer reviews on Amazon. com. MIS Quarterly, 34(1), 185–200.

    Article  Google Scholar 

  • Munzel, A. (2016). Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus. Journal of Retailing and Consumer Services, 32, 96–108.

    Article  Google Scholar 

  • OSHA. (2021). Whistleblower Laws Enforced by OSHA. U.S. Department of Labor. Retrieved 12–21–2021 from https://www.whistleblowers.gov/about-us

  • Pan, Y., & Zhang, J. Q. (2011). Born unequal: A study of the helpfulness of user-generated product reviews. Journal of Retailing, 87(4), 598–612.

    Article  Google Scholar 

  • Park, C., & Lee, T. M. (2009). Information direction, website reputation and eWOM effect: A moderating role of product type. Journal of Business Research, 62(1), 61–67.

    Article  Google Scholar 

  • Park, D.-H., & Kim, S. (2008). The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews. Electronic Commerce Research and Applications, 7(4), 399–410.

    Article  Google Scholar 

  • Park, D.-H., & Lee, J. (2008). eWOM overload and its effect on consumer behavioral intention depending on consumer involvement. Electronic Commerce Research and Applications, 7(4), 386–398.

    Article  Google Scholar 

  • Park, H., Jiang, S., Lee, O.-K.D., & Chang, Y. (2021). Exploring the Attractiveness of Service Robots in the Hospitality Industry: Analysis of Online Reviews. Information Systems Frontiers. https://doi.org/10.1007/s10796-021-10207-8

    Article  Google Scholar 

  • Park, S., & Nicolau, J. L. (2015). Asymmetric effects of online consumer reviews. Annals of Tourism Research, 50, 67–83.

    Article  Google Scholar 

  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., & Dubourg, V. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.

    Google Scholar 

  • Pennebaker, J. W., Booth, R. J., Boyd, R. L., & Francis, M. E. (2015a). Linguistic Inquiry and Word Count: LIWC2015. In Austin, TX: Pennebaker Conglomerates (www.LIWC.net).

  • Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015b). The development and psychometric properties of LIWC2015.

  • Pentina, I., Bailey, A. A., & Zhang, L. (2018). Exploring effects of source similarity, message valence, and receiver regulatory focus on yelp review persuasiveness and purchase intentions. Journal of Marketing Communications, 24(2), 125–145.

    Article  Google Scholar 

  • Pitt, C. S., Botha, E., Ferreira, J. J., & Kietzmann, J. (2018). Employee brand engagement on social media: Managing optimism and commonality. Business Horizons, 61(4), 635–642.

    Article  Google Scholar 

  • Qiu, L., Pang, J., & Lim, K. H. (2012). Effects of conflicting aggregated rating on eWOM review credibility and diagnosticity: The moderating role of review valence. Decision Support Systems, 54(1), 631–643.

    Article  Google Scholar 

  • Racherla, P., & Friske, W. (2012). Perceived ‘usefulness’ of online consumer reviews: An exploratory investigation across three services categories. Electronic Commerce Research and Applications, 11(6), 548–559.

    Article  Google Scholar 

  • Rains, S. A. (2007a). The anonymity effect: The influence of anonymity on perceptions of sources and information on health websites. Journal of Applied Communication Research, 35(2), 197–214.

    Article  Google Scholar 

  • Rains, S. A. (2007b). The impact of anonymity on perceptions of source credibility and influence in computer-mediated group communication: A test of two competing hypotheses. Communication Research, 34(1), 100–125.

    Article  Google Scholar 

  • Rietsche, R., Frei, D., Stöckli, E., & Söllner, M. (2019). Not all Reviews are Equal-a Literature Review on Online Review Helpfulness. European Conference on Information Systems, Stockholm-Uppsala, Sweden.

  • Röder, M., Both, A., & Hinneburg, A. (2015). Exploring the space of topic coherence measures. Proceedings of the eighth ACM international conference on Web search and data mining, Shanghai, China.

  • Sainju, B., Hartwell, C., & Edwards, J. (2021). Job satisfaction and employee turnover determinants in Fortune 50 companies: Insights from employee reviews from Indeed. com. Decision Support Systems, 113582.

  • Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30–40.

    Article  Google Scholar 

  • Sheng, J. (2019). Asset pricing in the information age:Employee expectations and stock returns. https://ssrn.com/abstract=3321275

  • Shin, S., Chung, N., Xiang, Z., & Koo, C. (2019). Assessing the impact of textual content concreteness on helpfulness in online travel reviews. Journal of Travel Research, 58(4), 579–593.

    Article  Google Scholar 

  • Siering, M., Muntermann, J., & Rajagopalan, B. (2018). Explaining and predicting online review helpfulness: The role of content and reviewer-related signals. Decision Support Systems, 108, 1–12.

    Article  Google Scholar 

  • Sionek, A. (2019). Data Jobs Listings - Glassdoor. https://doi.org/10.34740/KAGGLE/DS/418397

  • Skowronski, J. J., & Carlston, D. E. (1989). Negativity and extremity biases in impression formation: A review of explanations. Psychological Bulletin, 105(1), 131.

    Article  Google Scholar 

  • Srivastava, V., & Kalro, A. D. (2019). Enhancing the helpfulness of online consumer reviews: The role of latent (content) factors. Journal of Interactive Marketing, 48, 33–50.

    Article  Google Scholar 

  • Sun, X., Han, M., & Feng, J. (2019). Helpfulness of online reviews: Examining review informativeness and classification thresholds by search products and experience products. Decision Support Systems, 124, 113099.

    Article  Google Scholar 

  • Tausczik, Y. R., & Pennebaker, J. W. (2010). The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. Journal of Language and Social Psychology, 29(1), 24–54.

    Article  Google Scholar 

  • Uhl, M. W. (2011). Explaining US consumer behavior with news sentiment. ACM Transactions on Management Information Systems (TMIS), 2(2), 1–18.

    Article  Google Scholar 

  • Van Hoye, G. (2013). Word of Mouth as a Recruitment Source: An Integrative Model. In D. M. Cable & K. Y. T. Yu (Eds.), The Oxford Handbook of Recruitment. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199756094.013.023

  • Van Hoye, G., & Lievens, F. (2007a). Investigating web-based recruitment sources: Employee testimonials vs word-of-mouse. International Journal of Selection and Assessment, 15(4), 372–382.

    Article  Google Scholar 

  • Van Hoye, G., & Lievens, F. (2007b). Social Influences on Organizational Attractiveness: Investigating If and When Word of Mouth Matters. Journal of Applied Social Psychology, 37(9), 2024–2047.

    Article  Google Scholar 

  • Van Hoye, G., & Lievens, F. (2009). Tapping the grapevine: A closer look at word-of-mouth as a recruitment source. Journal of Applied Psychology, 94(2), 341.

    Article  Google Scholar 

  • Van Hoye, G., Weijters, B., Lievens, F., & Stockman, S. (2016). Social influences in recruitment: When is word-of-mouth most effective? International Journal of Selection and Assessment, 24(1), 42–53.

    Article  Google Scholar 

  • Wang, X., Yu, C., & Wei, Y. (2012). Social media peer communication and impacts on purchase intentions: A consumer socialization framework. Journal of Interactive Marketing, 26(4), 198–208.

    Article  Google Scholar 

  • Wu, P. F. (2013). In search of negativity bias: An empirical study of perceived helpfulness of online reviews. Psychology & Marketing, 30(11), 971–984.

    Article  Google Scholar 

  • Yin, D., Bond, S. D., & Zhang, H. (2014). Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. MIS Quarterly, 38(2), 539–560.

    Article  Google Scholar 

  • Zhou, S., & Guo, B. (2017). The order effect on online review helpfulness: A social influence perspective. Decision Support Systems, 93, 77–87.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Srikanth Parameswaran.

Ethics declarations

Conflict of Interest

The authors have no potential conflicts of interest concerning this article's research, authorship, and publication.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Parameswaran, S., Mukherjee, P. & Valecha, R. I Like My Anonymity: An Empirical Investigation of the Effect of Multidimensional Review Text and Role Anonymity on Helpfulness of Employer Reviews. Inf Syst Front 25, 853–870 (2023). https://doi.org/10.1007/s10796-022-10268-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-022-10268-3

Keywords

Navigation