Predicting Job Performance Using Mobile Sensing

Autor:	Andrew T. Campbell, Koustuv Saha, Nitesh V. Chawla, Aaron Striegel, Gonzalo J. Martinez, Subigya Nepal, Pino G. Audia, Hessam Bagherinezhad, Anind K. Dey, Shayan Mirjafari, Mikio Obuchi
Rok vydání:	2021
Předmět:	Ubiquitous computing ComputingMilieux_THECOMPUTINGPROFESSION Computer science Wearable computer Behavioral pattern Computer Science Applications Screen time Computational Theory and Mathematics Job performance Phone Human–computer interaction Mobile sensing Mobile device Software
Zdroj:	IEEE Pervasive Computing. 20:43-51
ISSN:	1558-2590 1536-1268
DOI:	10.1109/mprv.2021.3118570
Popis:	We hypothesize that behavioral patterns of people are reflected in how they interact with their mobile devices and that continuous sensor data passively collected from their phones and wearables can infer their job performance. Specifically, we study day-today job performance (improvement, no change, decline) of N=298 information workers using mobile sensing data and offer data-driven insights into what data patterns may lead to a high-performing day. Through analyzing workers' mobile sensing data, we predict their performance on a handful of job performance questionnaires with an F-1 score of 75%. In addition, through numerical analysis of the model, we get insights into how individuals must change their behavior so that the model predicts improvements in their job performance. For instance, one worker may benefit if they put their phone down and reduce their screen time, while another worker may benefit from getting more sleep.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b56aaeac067afd5f55cbb4e1d1defa0b https://doi.org/10.1109/mprv.2021.3118570 Zobrazit plný text záznamu