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Most businesses understand that they must attract star performers — and compete fiercely for them — to thrive in the marketplace. What they struggle with is how to do it well. The perennial challenge of finding the right people and matching them with the right roles has become even more complex now that AI and robotics are rapidly changing jobs and in-demand technical skills are in short supply. While most organizations still rely on traditional hiring methods such as résumé screenings, job interviews, and psychometric tests, a new generation of assessment tools is quickly gaining traction and, we argue, making talent identification more precise and less biased.
Certain things have remained constant and are unlikely to change anytime soon. When sizing up candidates, managers try to predict job performance while assessing cultural fit and capacity to grow. Studies show that managers look for three basic traits: ability, which includes technical expertise and learning potential; likability, or people skills; and drive, which amounts to ambition and work ethic.1
What we need from talent identification tools and methods — old or new — has also stayed the same. To assess their effectiveness, we must look for a strong correlation between candidates’ scores and subsequent job performance. This may sound obvious, but we’ve found in our work with recruiters and hiring managers that many of them use tools based instead on ease and familiarity — and rarely correlate them to results.
Emerging assessment methods can be grouped into three broad categories: gamified assessments, digital interviews, and candidate data mining. What they have in common is their ability to detect new talent signals (that is, new indicators of performance potential).2 Here we’ll explain how each of these methods work and their strengths and limitations.
A new breed of psychometric tests for recruitment focuses on enhancing the candidate experience. These tools apply gamelike features, such as real-time feedback, interactive and immersive scenarios, and shorter modules, which make the test taking more enjoyable. The catch is that users’ choices and behaviors are mined by computer-generated algorithms to identify suitability for a given role.
For example, HireVue’s MindX employs gamified cognitive-ability tests by asking users to play sleek games — think Nintendo’s Brain Age — that predict IQ.
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1. R. Hogan, T. Chamorro-Premuzic, and R.B. Kaiser, “Employability and Career Success: Bridging the Gap Between Theory and Reality,” Industrial and Organizational Psychology 6, no. 1 (March 2013): 3-16.
2. T. Chamorro-Premuzic, D. Winsborough, R.A. Sherman, and R. Hogan, “New Talent Signals: Shiny New Objects or a Brave New World?” Industrial and Organizational Psychology 9, no. 3 (September 2016): 621-640.
3. J. Bersin, “HR Technology Disruptions for 2018: Productivity, Design, and Intelligence Reign,” Bersin by Deloitte, 2017.
4. N. Perveen, N. Ahmad, M. Abdul Qadoos Bilal Khan, R. Khalid, and S. Qadri, “Facial Expression Recognition Through Machine Learning,” International Journal of Scientific and Technology Research 5, no. 4 (March 2016): 91-97.
5. C.P. Latha and M.M. Priya, “A Review on Deep Learning Algorithms for Speech and Facial Emotion Recognition,” International Journal of Control Theory and Applications 9, no. 24 (January 2016): 183-204.
6.G. Park, H.A. Schwartz, J.C. Eichstaedt, M.L. Kern, M. Kosinski, D.J. Stillwell, L.H. Ungar, and M.E.P. Seligman, “Automatic Personality Assessment Through Social Media Language,” Journal of Personality and Social Psychology 108, no. 6 (June 2015): 934-952.
7. G. Farnadi, G. Sitaraman, S. Sushmita, F. Celli, M. Kosinski, D. Stillwell, S. Davalos, M.F. Moens, and M. De Cock, “Computational Personality Recognition in Social Media,” User Modeling and User-Adapted Interaction 26, no. 2-3 (June 2016).
8. L.M. Hough, F.L. Oswald, and R.E. Ployhart, “Determinants, Detection, and Amelioration of Adverse Impact in Personnel Selection Procedures: Issues, Evidence, and Lessons Learned,” International Journal of Selection and Assessment 9, no. 1-2 (March 2001): 152-194.