Abstract
This study investigates the association between Dark Triad traits (DTT), narcissism, Machiavellianism, and psychopathy, and employee review generation and consumption on Glassdoor. Using 533,007 reviews of S&P 500 companies, we applied the Linguistic Inquiry and Word Count method to infer DTT-linked language markers. Results show small but statistically reliable negative associations between narcissism and psychopathy and both review rating and perceived helpfulness. In contrast, Machiavellianism shows a small negative link to review ratings but a positive link to helpfulness. Confidence intervals and incremental fit statistics confirm the modest, context-dependent nature of these effects. Theoretically, the findings link trait-based organizational psychology with communication perspectives on online disinhibition and cue-reduced contexts, showing how antagonistic tendencies can surface in discursive evaluations outside the workplace. The study also advances a behavioral–linguistic approach to measuring personality at scale, complementing traditional self-report methods. Managerially, the results suggest that personality-linked patterns in employee reviews exist but operate alongside situational and platform factors, emphasizing the importance of context when interpreting online employer reputation signals.
Data availability
To enable verification of our methods and findings, we provide a de-identified, review-level analytic dataset via the Open Science Framework (OSF). During peer review, the data and replication materials are accessible through the following view-only link: https://osf.io/9pmzf/overview?view_only=dc569e5854554734b31950cebe6bb5ad. The shared dataset includes: (i) review outcomes (review rating; helpful-vote count and ln[helpful]); (ii) LIWC category outputs used to operationalize Dark Triad language markers; (iii) constructed indices for narcissism, Machiavellianism, and psychopathy (with formulas reported in Table 1); and (iv) all control variables (six job-dimension ratings; ln[word count]; ln[review age]), as well as month and year indicators, an anonymized firm identifier, and the code required to reproduce company fixed effects. The original raw review texts constitute third-party user-generated content hosted by Glassdoor. Because Glassdoor’s Terms of Use restrict the redistribution of verbatim review content, we do not publicly repost the original texts. Instead, we provide all derived linguistic outputs, analytic variables, complete replication scripts, and documentation necessary to reproduce the reported models and results.
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Acknowledgements
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Dr. Yousaf and Dr. Kim wrote the main manuscript, and Dr. Hyun revised and supervised the work. Dr. Yousaf developed the conceptual framework, while Dr. Kim identified the appropriate methodology. Dr. Kim and Dr. Hyun conducted the empirical analysis for the paper. Dr. Yousaf and Dr. Hyun carried out the editing. All authors reviewed the manuscript.
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Ethics approval
This study used secondary data consisting of employee reviews and associated metadata (for example, star ratings and helpful-vote counts) that were publicly accessible on Glassdoor at the time of collection. The research involved no direct interaction with human participants, no intervention or manipulation, and no collection of direct personal identifiers. On this basis, the study was not submitted for formal institutional ethics review because it was designed as a secondary analysis of publicly available online text and metadata and posed no more than minimal risk to individuals. Nevertheless, the study was conducted in line with widely used research-ethics principles for internet-mediated research in the social sciences and humanities, including proportionality, data minimization, and protection against re-identification. Privacy safeguards included analyzing and reporting findings in aggregate, avoiding any attempt to identify or contact reviewers, and not reproducing verbatim review text in ways that could reasonably enable traceability to specific individuals.
Informed consent
Because the dataset comprised reviews and metadata that were already publicly posted on Glassdoor prior to the research and because the authors had no direct contact with human participants, informed consent was not sought. This approach is consistent with the use of secondary analysis of publicly accessible materials where there is no interaction with individuals, no intervention, and no collection of direct identifiers, and where risk is minimized through appropriate safeguards. To further reduce privacy risk, we did not attempt to infer identities, we present results at an aggregated level, and any shared materials for transparency or replication are de-identified and limited to derived measures (for example, linguistic features and anonymized firm identifiers) rather than redistribution of verbatim review texts.
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Appendix
Appendix
Appendix Table 1 presents the standardized coefficients (Beta) for all models reported in Table 3. The inclusion of standardized coefficients enables a direct comparison of the relative strength of each predictor variable’s effect, eliminating the influence of differing measurement scales. For instance, in the Review Generation models (Models 1–3), culture values consistently show the largest positive standardized effect (β = 0.28), followed by career opportunities (β = 0.21) and senior management (β = 0.21). In contrast, the DDT display negative associations, with narcissism exerting the largest negative impact (β = –0.03), followed by Machiavellianism (β = –0.02) and psychopathy (β = –0.01). For the Review Consumption models (Models 1a–3a), the Beta values highlight that review rating, compensation and benefits, and word count are among the most influential predictors. These results underscore that even when standardized coefficients are numerically small, they can still support the theoretical significance of the proposed relationships.
Appendix Table 1. Standardized Coefficients Estimated in Table 3
Constructs | Review Generation | Review Consumption | ||||
|---|---|---|---|---|---|---|
Model (1) Beta | Model (2) Beta | Model (3) Beta | Model (1a) Beta | Model (2a) Beta | Model (3a) Beta | |
Narcissism | -0.031 | -0.031 | -0.029 | -0.258 | -0.253 | -0.262 |
Machiavellianism | -0.020 | -0.020 | -0.020 | -0.028 | -0.028 | -0.015 |
Psychopathy | -0.011 | -0.011 | -0.010 | 0.010 | 0.010 | 0.024 |
Work and life balance | 0.131 | 0.131 | 0.131 | -0.027 | -0.027 | -0.009 |
Diversity inclusion | 0.034 | 0.034 | 0.034 | -0.010 | 0.007 | -0.001 |
Career opportunities | 0.215 | 0.215 | 0.217 | -0.028 | -0.001 | 0.008 |
Compensation & benefits | 0.127 | 0.127 | 0.123 | -0.059 | -0.055 | -0.026 |
Senior management | 0.207 | 0.207 | 0.206 | 0.114 | 0.118 | 0.085 |
Culture values | 0.284 | 0.283 | 0.279 | -0.026 | -0.012 | -0.028 |
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Yousaf, S., Hyun, S. & Kim, J.M. Do employees with dark personality traits review their jobs unfavorably? Textual content analysis of online employee reviews. Humanit Soc Sci Commun (2026). https://doi.org/10.1057/s41599-026-06592-7
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DOI: https://doi.org/10.1057/s41599-026-06592-7
