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

Shayan Mirjafari, Kizito Masaba, Ted Grover, Weichen Wang, Pino Audia, Andrew t. Campbell, Nitesh v. Chawla, Vedant das Swain, Munmun de Choudhury, Anind k. Dey, Sidney k. D'mello, Ge Gao, Julie m. Gregg, Krithika Jagannath, Kaifeng Jiang, Suwen Lin, Qiang Liu, Gloria Mark, Gonzalo j. Martinez, Stephen m. MattinglyEdward Moskal, Raghu Mulukutla, Subigya Nepal, Kari Nies, Manikanta d. Reddy, Pablo Robles-Granda, Koustuv Saha, Anusha Sirigiri, Aaron Striegel

Research output: Contribution to journalConference articlepeer-review


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.
Original languageEnglish (US)
Pages (from-to)1-24
JournalProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Issue number2
StatePublished - Jun 21 2019
Externally publishedYes


Dive into the research topics of 'Differentiating Higher and Lower Job Performers in the Workplace Using Mobile Sensing'. Together they form a unique fingerprint.

Cite this