The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
New Talent Signals: Shiny New Objects or a Brave New World?
Open accessAbstract
Almost 20 years after McKinsey introduced the idea of a war for talent, technology is disrupting the talent identification industry. From smartphone profiling apps to workplace big data, the digital revolution has produced a wide range of new tools for making quick and cheap inferences about human potential and predicting future work performance. However, academic industrial–organizational (I-O) psychologists appear to be mostly spectators. Indeed, there is little scientific research on innovative assessment methods, leaving human resources (HR) practitioners with no credible evidence to evaluate the utility of such tools. To this end, this article provides an overview of new talent identification tools, using traditional workplace assessment methods as the organizing framework for classifying and evaluating new tools, which are largely technologically enhanced versions of traditional methods. We highlight some opportunities and challenges for I-O psychology practitioners interested in exploring and improving these innovations.
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The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods
Tausczik, Y. R., & Pennebaker, J. W. (2010) · Journal of Language and Social Psychology · doi:10.1177/0261927x09351676 · Cited by 5,774
Schmidt, F. L., & Hunter, J. E. (1998) · Psychological Bulletin · doi:10.1037//0033-2909.124.2.262 · Cited by 4,180
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E. P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073791 · Cited by 1,700
Hogan, B. (2010) · Bulletin of Science, Technology & Society · doi:10.1177/0270467610385893 · Cited by 1,318 · cited 1×
“That said, social media is as deceptive as any other form of communication (B. Hogan, 2010); employers and recruiters are right to regard it as a rich source of information about candidates’ talent—if they can get past the noise and make accurate inferences.”
BackgroundFacebook Profiles Reflect Actual Personality, Not Self-Idealization
Back, M. D., Stopfer, J. M., Vazire, S., Gaddis, S., Schmukle, S. C., Egloff, B., & Gosling, S. D. (2010) · Psychological Science · doi:10.1177/0956797609360756 · Cited by 1,169
Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers
Yarkoni, T. (2010) · Journal of Research in Personality · doi:10.1016/j.jrp.2010.04.001 · Cited by 637
Personality Psychology and Economics
Almlund, M., Duckworth, A. L., Heckman, J., & Kautz, T. (2011) · National Bureau of Economic Research · doi:10.3386/w16822 · Cited by 540
Reversing the extraverted leadership advantage: The role of employee proactivity
Grant, A. M., Gino, F., & Hofmann, D. A. (2011) · Academy of Management Journal · doi:10.5465/amj.2011.61968043 · Cited by 530
The Pareto principle in organizational decision making
Craft, R. C., & Leake, C. (2002) · Management Decision · doi:10.1108/00251740210437699 · Cited by 166
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Mitigating bias in algorithmic hiring: evaluating claims and practices
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372828 · Cited by 845
Köchling, A., & Wehner, M. C. (2020) · Business Research · doi:10.1007/s40685-020-00134-w · Cited by 511
Ethics of AI-enabled recruiting and selection: a review and research agenda
Hunkenschroer, A. L., & Luetge, C. (2022) · Journal of Business Ethics · doi:10.1007/s10551-022-05049-6 · Cited by 447
Woods, S. A., Ahmed, S., Nikolaou, I., Costa, A. C., & Anderson, N. R. (2020) · European Journal of Work and Organizational Psychology · doi:10.1080/1359432x.2019.1681401 · Cited by 218
Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2019) · arXiv · Cited by 176
Metadata
- Authors
- Chamorro-Premuzic, T., Winsborough, D., Sherman, R. A., & Hogan, R.
- Venue
- Industrial and Organizational Psychology
- Publisher
- Cambridge University Press (CUP)
- Published
- 2016-05-11
- Volume
- 9
- Issue
- 3
- Pages
- 621-640
- ISSN
- 1754-9426 (ISSN-L)
- 1754-9434
- OpenAlex
- W2363129255
- Cited by
- 215
- References
- 9
- FWCI
- 30.18
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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