TY - JOUR
T1 - Differentiating Higher and Lower Job Performers in the Workplace Using Mobile Sensing
AU - Mirjafari, Shayan
AU - Masaba, Kizito
AU - Grover, Ted
AU - Wang, Weichen
AU - Audia, Pino
AU - Campbell, Andrew t.
AU - Chawla, Nitesh v.
AU - Swain, Vedant das
AU - Choudhury, Munmun de
AU - Dey, Anind k.
AU - D'mello, Sidney k.
AU - Gao, Ge
AU - Gregg, Julie m.
AU - Jagannath, Krithika
AU - Jiang, Kaifeng
AU - Lin, Suwen
AU - Liu, Qiang
AU - Mark, Gloria
AU - Martinez, Gonzalo j.
AU - Mattingly, Stephen m.
AU - Moskal, Edward
AU - Mulukutla, Raghu
AU - Nepal, Subigya
AU - Nies, Kari
AU - Reddy, Manikanta d.
AU - Robles-Granda, Pablo
AU - Saha, Koustuv
AU - Sirigiri, Anusha
AU - Striegel, Aaron
PY - 2019/6/21
Y1 - 2019/6/21
N2 - 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.
AB - 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.
U2 - 10.1145/3328908
DO - 10.1145/3328908
M3 - Conference article
SN - 2474-9567
VL - 3
SP - 1
EP - 24
JO - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
JF - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
IS - 2
ER -