Differentiating Higher and Lower Job Performers in the Workplace Using Mobile Sensing

Mirjafari, S and Masaba, K and Grover, T and Wang, W and Audia, P and Campbell, Andrew T. and Chawla, N V and Swain, V D and Choudhury, M D and Dey, A K and D'Mello, S K and Gao, G and Gregg, J M and Jagannath, K and Jiang, K and Lin, S and Liu, Q and Mark, G and Martinez, G J and Mattingly, S M and Moskal, E and Mulukutla, R and Nepal, S and Nies, K and Reddy, M D and Robles-Granda, P and Saha, K and Sirigiri, A and Striegel, A (2019) Differentiating Higher and Lower Job Performers in the Workplace Using Mobile Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3 (2). pp. 1-24. ISSN 2474-9567

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Assessing performance in the workplace typically relies on subjective evaluations, such as, peer ratings, supervisor ratings and self assessments, which are manual, burdensome and potentially biased. We use objective mobile sensing data from phones, wearables and beacons to study workplace performance and offer new insights into behavioral patterns that distinguish higher and lower performers when considering roles in companies (i.e., supervisors and non-supervisors) and different types of companies (i.e., high tech and consultancy). We present initial results from an ongoing year-long study of N=554 information workers collected over a period ranging from 2-8.5 months. We train a gradient boosting classifier that can classify workers as higher or lower performers with AUROC of 0.83. Our work opens the way to new forms of passive objective assessment and feedback to workers to potentially provide week by week or quarter by quarter guidance in the workplace.

Item Type: Article
Additional Information: The research paper was published by the author with the affiliation of Dartmouth College, Hanover
Subjects: Entrepreneurship
Date Deposited: 03 Apr 2021 10:33
Last Modified: 10 Jul 2023 16:32
URI: https://eprints.exchange.isb.edu/id/eprint/1438

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