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

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 3, Issue 2

Article No.: 37, Pages 1 - 24

https://doi.org/10.1145/3328908

Published: 21 June 2019 Publication History

Abstract

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.

References

[1]

Ali Alkahtani. 2015. Organizational citizenship behavior (OCB) and rewards. International Business Research 8, 4 (2015), 210.

[2]

Khaled Alsabti, Sanjay Ranka, and Vineet Singh. 1997. An efficient k-means clustering algorithm. (1997).

[3]

Nabil Alshurafa and Josiah Hester. 2017. Personalized medicine in the wearable era: translational barriers and call to action. In Proceedings of the First International Workshop on Human-centered Sensing, Networking, and Systems. ACM, 37--42.

Digital Library

[4]

G Anderson and C Viswesvaran. 1998. An update of the validity of personality scales in personnel selection: A meta-analysis of studies published after 1992. In 13th Annual Conference of the Society of Industrial and Organizational Psychology, Dallas.

[5]

Elizabeth J Austin, Ian J Deary, Gavin J Gibson, Murray J McGregor, and J Barry Dent. 1998. Individual response spread in self-report scales: Personality correlations and consequences. Personality and Individual Differences 24, 3 (1998), 421--438.

[6]

Murray R Barrick and Michael K Mount. 1991. The big five personality dimensions and job performance: a meta-analysis. Personnel psychology 44, 1 (1991), 1--26.

[7]

GÃŠrard Biau. 2012. Analysis of a random forests model. Journal of Machine Learning Research 13, Apr (2012), 1063--1095.

Digital Library

[8]

Walter C Borman and SM Motowidlo. 1993. Expanding the criterion domain to include elements of contextual performance. Personnel Selection in Organizations; San Francisco: Jossey-Bass (1993), 71.

[9]

Francesco Calabrese, Mi Diao, Giusy Di Lorenzo, Joseph Ferreira Jr, and Carlo Ratti. 2013. Understanding individual mobility patterns from urban sensing data: A mobile phone trace example. Transportation research part C: emerging technologies 26 (2013), 301--313.

[10]

John P Campbell, Jeffrey J McHenry, and Lauress L Wise. 1990. Modeling job performance in a population of jobs. Personnel psychology 43, 2 (1990), 313--575.

[11]

John P Campbell and Brenton M Wiernik. 2015. The modeling and assessment of work performance. Annu. Rev. Organ. Psychol. Organ. Behav. 2, 1 (2015), 47--74.

[12]

Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST) 2, 3 (2011), 27.

Digital Library

[13]

Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. ACM, 785--794.

Digital Library

[14]

Xiao-Ping Chen. 2005. Organizational citizenship behavior: A predictor of employee voluntary turnover. Handbook of organizational citizenship behavior (2005), 435--454.

[15]

Dan S Chiaburu, In-Sue Oh, Christopher M Berry, Ning Li, and Richard G Gardner. 2011. The five-factor model of personality traits and organizational citizenship behaviors: A meta-analysis. Journal of Applied Psychology 96, 6 (2011), 1140.

[16]

Gokul Chittaranjan, Jan Blom, and Daniel Gatica-Perez. 2011. Who's who with big-five: Analyzing and classifying personality traits with smartphones. In Wearable Computers (ISWC), 2011 15th Annual International Symposium on. IEEE, 29--36.

Digital Library

[17]

Jose M Cortina and Joseph N Luchman. 2012. Personnel selection and employee performance. Handbook of Psychology, Second Edition 12 (2012).

[18]

Reeshad S Dalal, Holly Lam, Howard M Weiss, Eric R Welch, and Charles L Hulin. 2009. A within-person approach to work behavior and performance: Concurrent and lagged citizenship-counterproductivity associations, and dynamic relationships with affect and overall job performance. Academy of Management Journal 52, 5 (2009), 1051--1066.

[19]

Trinh-Minh-Tri Do and Daniel Gatica-Perez. 2010. By their apps you shall understand them: mining large-scale patterns of mobile phone usage. In Proceedings of the 9th international conference on mobile and ubiquitous multimedia. ACM, 27.

Digital Library

[20]

Patrick D Dunlop and Kibeom Lee. 2004. Workplace deviance, organizational citizenship behavior, and business unit performance: The bad apples do spoil the whole barrel. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior 25, 1 (2004), 67--80.

[21]

Hans Jurgen Eysenck. 2012. A model for intelligence. Springer Science & Business Media.

[22]

Xitao Fan, Brent C Miller, Kyung-Eun Park, Bryan W Winward, Mathew Christensen, Harold D Grotevant, and Robert H Tai. 2006. An exploratory study about inaccuracy and invalidity in adolescent self-report surveys. Field Methods 18, 3 (2006), 223--244.

[23]

Tom Fawcett. 2006. An introduction to ROC analysis. Pattern recognition letters 27, 8 (2006), 861--874.

Digital Library

[24]

Andrew Golub, Bruce D Johnson, and Eric Labouvie. 2000. On correcting biases in self-reports of age at first substance use with repeated cross-section analysis. Journal of Quantitative Criminology 16, 1 (2000), 45--68.

[25]

Mark A Griffin, Andrew Neal, and Sharon K Parker. 2007. A new model of work role performance: Positive behavior in uncertain and interdependent contexts. Academy of management journal 50, 2 (2007), 327--347.

[26]

Melissa L Gruys and Paul R Sackett. 2003. Investigating the dimensionality of counterproductive work behavior. International journal of selection and assessment 11, 1 (2003), 30--42.

[27]

Zahra Hajihashemi, Maria Yefimova, and Mihail Popescu. 2014. Detecting daily routines of older adults using sensor time series clustering. In Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. IEEE, 5912--5915.

[28]

Budde Henning, Voelcker-Rehage Claudia, Pietrabyk-Kendziorra Sascha, Ribeiro Pedro, and Tidow Gunter. 2008. Acute coordinative exercise improves attentional performance in adolescents. Neuroscience letters 441, 2 (2008), 219--223.

[29]

Daniel M Higgins, Jordan B Peterson, Robert O Pihl, and Alice GM Lee. 2007. Prefrontal cognitive ability, intelligence, Big Five personality, and the prediction of advanced academic and workplace performance. Journal of Personality and Social Psychology 93, 2 (2007), 298.

[30]

Pascal Huguet, Marie P Galvaing, Jean M Monteil, and Florence Dumas. 1999. Social presence effects in the Stroop task: further evidence for an attentional view of social facilitation. Journal of personality and social psychology 77, 5 (1999), 1011.

[31]

IARPA. 2018. IARPA MOSAIC Program. https://www.iarpa.gov/index.php/research-programs/mosaic. {Online; accessed November 10th, 2018}.

[32]

Gimbal Inc. 2018. Gimbal Beacons. https://gimbal.com/beacons/. {Online; accessed October 21st, 2018}.

[33]

Jeff W Johnson. 2001. The relative importance of task and contextual performance dimensions to supervisor judgments of overall performance. Journal of applied psychology 86, 5 (2001), 984.

[34]

Ludmila I Kuncheva, Christopher J Whitaker, Catherine A Shipp, and Robert PW Duin. 2003. Limits on the majority vote accuracy in classifier fusion. Pattern Analysis & Applications 6, 1 (2003), 22--31.

[35]

Huy Le, In-Sue Oh, Steven B Robbins, Remus Ilies, Ed Holland, and Paul Westrick. 2011. Too much of a good thing: Curvilinear relationships between personality traits and job performance. Journal of Applied Psychology 96, 1 (2011), 113.

[36]

Filip Lievens, James M Conway, and Wilfried De Corte. 2008. The relative importance of task, citizenship and counterproductive performance to job performance ratings: Do rater source and team-based culture matter? Journal of Occupational and Organizational Psychology 81, 1 (2008), 11--27.

[37]

Garmin Ltd. 2018. Activity Tracking. https://buy.garmin.com/en-US/US/p/567813. {Online; accessed October 21st, 2018}.

[38]

Anmol Madan, Sai T Moturu, David Lazer, and Alex Sandy Pentland. 2010. Social sensing: obesity, unhealthy eating and exercise in face-to-face networks. In Wireless Health 2010. ACM, 104--110.

Digital Library

[39]

Oppezzo Marily and Schwartz Daniel L. 2014. Give your ideas some legs: The positive effect of walking on creative thinking. Journal of Experimental Psychology: Learning, Memory, and Cognition 40, 4 (2014), 1142--1152.

[40]

Gloria Mark, Shamsi Iqbal, Mary Czerwinski, and Paul Johns. 2014. Capturing the mood: facebook and face-to-face encounters in the workplace. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing. ACM, 1082--1094.

Digital Library

[41]

Gloria Mark, Shamsi T Iqbal, Mary Czerwinski, Paul Johns, and Akane Sano. 2016. Neurotics can't focus: An in situ study of online multitasking in the workplace. In Proceedings of the 2016 CHI conference on human factors in computing systems. ACM, 1739--1744.

Digital Library

[42]

Gloria Mark, Melissa Niiya, Stephanie Reich, et al. 2016. Sleep debt in student life: Online attention focus, Facebook, and mood. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 5517--5528.

Digital Library

[43]

Aleksandar Matic, Venet Osmani, and Oscar Mayora-Ibarra. 2014. Mobile monitoring of formal and informal social interactions at workplace. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication. ACM, 1035--1044.

Digital Library

[44]

Stephen M Mattingly, Julie M Gregg, Pino Audia, Ayse Elvan Bayraktaroglu, Andrew T Campbell, Nitesh V Chawla, Vedant Das Swain, Munmun De Choudhury, Sidney K D'Mello, Anind K Dey, et al. 2019. The Tesserae Project: Large-Scale, Longitudinal, In Situ, Multimodal Sensing of Information Workers. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, CS11.

[45]

Melissa Mazmanian, Wanda J Orlikowski, and JoAnne Yates. 2013. The autonomy paradox: The implications of mobile email devices for knowledge professionals. Organization science 24, 5 (2013), 1337--1357.

[46]

Sue Falter Mennino, Beth A Rubin, and April Brayfield. 2005. Home-to-job and job-to-home spillover: The impact of company policies and workplace culture. The Sociological Quarterly 46, 1 (2005), 107--135.

[47]

Thomas WH Ng and Daniel C Feldman. 2008. The relationship of age to ten dimensions of job performance. Journal of applied psychology 93, 2 (2008), 392.

[48]

NSF. 2018. NSF Future of Work Program. https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505528. {Online; accessed November 10th, 2018}.

[49]

Dennis W Organ. 1988. Organizational citizenship behavior: The good soldier syndrome. Lexington Books/DC Heath and Com.

[50]

Dennis W Organ. 1997. Organizational citizenship behavior: It's construct clean-up time. Human performance 10, 2 (1997), 85--97.

[51]

Dennis W Organ and Andreas Lingl. 1995. Personality, satisfaction, and organizational citizenship behavior. The journal of social psychology 135, 3 (1995), 339--350.

[52]

Chao-Ying Joanne Peng, Kuk Lida Lee, and Gary M Ingersoll. 2002. An introduction to logistic regression analysis and reporting. The journal of educational research 96, 1 (2002), 3--14.

[53]

Nathan P Podsakoff, Steven W Whiting, Philip M Podsakoff, and Brian D Blume. 2009. Individual-and organizational-level consequences of organizational citizenship behaviors: A meta-analysis. Journal of applied Psychology 94, 1 (2009), 122.

[54]

Philip M Podsakoff and Scott B MacKenzie. 1997. Impact of organizational citizenship behavior on organizational performance: A review and suggestion for future research. Human performance 10, 2 (1997), 133--151.

[55]

Philip M. Podsakoff and Dennis W. Organ. 1986. Self-Reports in Organizational Research: Problems and Prospects. Journal of Management 12, 4 (1986), 531--544. arXiv:https://doi.org/10.1177/014920638601200408

[56]

Sebastiaan Rothmann and Elize P Coetzer. 2003. The big five personality dimensions and job performance. SA Journal of Industrial Psychology 29, 1 (2003), 68--74.

[57]

Maria Rotundo and Paul R Sackett. 2002. The relative importance of task, citizenship, and counterproductive performance to global ratings of job performance: A policy-capturing approach. Journal of applied psychology 87, 1 (2002), 66.

[58]

Paul R Sackett. 2002. The structure of counterproductive work behaviors: Dimensionality and relationships with facets of job performance. International journal of selection and assessment 10, 1-2 (2002), 5--11.

[59]

Jesus F Salgado. 1998. Big Five personality dimensions and job performance in army and civil occupations: A European perspective. Human Performance 11, 2-3 (1998), 271--288.

[60]

Riccardo Sartori, Arianna Costantini, Andrea Ceschi, and Andrea Scalco. 2017. Not only correlations: a different approach for investigating the relationship between the Big Five personality traits and job performance based on workers and employees' perception. Quality & Quantity 51, 6 (2017), 2507--2519.

[61]

Florian Schaule, Jan Ole Johanssen, Bernd Bruegge, and Vivian Loftness. 2018. Employing Consumer Wearables to Detect Office Workers' Cognitive Load for Interruption Management. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1 (2018), 32.

Digital Library

[62]

Suzanne C Segerstrom and Lise Solberg Nes. 2007. Heart rate variability reflects self-regulatory strength, effort, and fatigue. Psychological science 18, 3 (2007), 275--281.

[63]

Vinod Sharma, Kunal Mankodiya, Fernando De La Torre, Ada Zhang, Neal Ryan, Thanh GN Ton, Rajeev Gandhi, and Samay Jain. 2014. SPARK: personalized parkinson disease interventions through synergy between a smartphone and a smartwatch. In International Conference of Design, User Experience, and Usability. Springer, 103--114.

[64]

D.J. Sheskin. 2003. Handbook of Parametric and Nonparametric Statistical Procedures: Third Edition. CRC Press. https://books.google.com/books?id=ZvDLBQAAQBAJ

Digital Library

[65]

Sabine Sonnentag, Judith Volmer, and Anne Spychala. 2008. Job performance. The Sage handbook of organizational behavior 1 (2008), 427--447.

[66]

Robert P Tett, Douglas N Jackson, and Mitchell Rothstein. 1991. Personality measures as predictors of job performance: A meta-analytic review. Personnel psychology 44, 4 (1991), 703--742.

[67]

Adam G. Thomas, Andrea Dennis, Nancy B. Rawlings, Charlotte J. Stagg, Lucy Matthews, Martyn Morris, Shannon H. Kolind, Sean Foxley, Mark Jenkinson, Thomas E. Nichols, Helen Dawes, Peter A. Bandettini, and Heidi Johansen-Berg. 2016. Multi-modal characterization of rapid anterior hippocampal volume increase associated with aerobic exercise. NeuroImage 131 (2016), 162--170. Effects of Physical and Cognitive activity on brain structure and function.

[68]

Catherine Tong, Gabriella M Harari, Angela Chieh, Otmane Bellahsen, Matthieu Vegreville, Eva Roitmann, and Nicholas D Lane. 2018. Inference of Big-Five Personality Using Large-scale Networked Mobile and Appliance Data. In Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services. ACM, 530--530.

Digital Library

[69]

Hans PA Van Dongen, Naomi L Rogers, and David F Dinges. 2003. Sleep debt: Theoretical and empirical issues. Sleep and Biological Rhythms 1, 1 (2003), 5--13.

[70]

Chockalingam Viswesvaran and Deniz S Ones. 2000. Perspectives on models of job performance. International Journal of Selection and Assessment 8, 4 (2000), 216--226.

[71]

Matthew P. Walker. 2009. The Role of Slow Wave Sleep in Memory Processing. Journal of clinical sleep medicine: JCSM: official publication of the American Academy of Sleep Medicine 5, 2 (2009), 20--26.

[72]

Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T Campbell. 2014. StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing. ACM, 3--14.

Digital Library

[73]

Rui Wang, Gabriella Harari, Peilin Hao, Xia Zhou, and Andrew T Campbell. 2015. SmartGPA: how smartphones can assess and predict academic performance of college students. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. ACM, 295--306.

Digital Library

[74]

Rui Wang, Weichen Wang, Alex daSilva, Jeremy F Huckins, William M Kelley, Todd F Heatherton, and Andrew T Campbell. 2018. Tracking depression dynamics in college students using mobile phone and wearable sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1 (2018), 43.

Digital Library

[75]

Weichen Wang, Gabriella M Harari, Rui Wang, Sandrine R Müller, Shayan Mirjafari, Kizito Masaba, and Andrew T Campbell. 2018. Sensing Behavioral Change over Time: Using Within-Person Variability Features from Mobile Sensing to Predict Personality Traits. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 3 (2018), 141.

Digital Library

[76]

Steven W Whiting, Philip M Podsakoff, and Jason R Pierce. 2008. Effects of task performance, helping, voice, and organizational loyalty on performance appraisal ratings. Journal of Applied Psychology 93, 1 (2008), 125.

[77]

Larry J Williams and Stella E Anderson. 1991. Job satisfaction and organizational commitment as predictors of organizational citizenship and in-role behaviors. Journal of management 17, 3 (1991), 601--617.

[78]

Henry H Wilmer, Lauren E Sherman, and Jason M Chein. 2017. Smartphones and cognition: A review of research exploring the links between mobile technology habits and cognitive functioning. Frontiers in psychology 8 (2017), 605.

[79]

Nicholas Jing Yuan, Fuzheng Zhang, Defu Lian, Kai Zheng, Siyu Yu, and Xing Xie. 2013. We know how you live: exploring the spectrum of urban lifestyles. In Proceedings of the first ACM conference on Online social networks. ACM, 3--14.

Digital Library

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Yan RLiu XDutcher JTumminia MVillalba DCohen SCreswell JCreswell KMankoff JDey ADoryab A(2024)Identifying Links Between Productivity and Biobehavioral Rhythms Modeled From Multimodal Sensor Streams: Exploratory Quantitative StudyJMIR AI10.2196/471943(e47194)Online publication date: 18-Apr-2024
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Index Terms

Differentiating Higher and Lower Job Performers in the Workplace Using Mobile Sensing
1. Human-centered computing
  1. Ubiquitous and mobile computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 3, Issue 2

June 2019

802 pages

EISSN:2474-9567

DOI:10.1145/3341982

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Copyright © 2019 ACM.

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Publication History

Published: 21 June 2019

Accepted: 01 April 2019

Revised: 01 February 2019

Received: 01 November 2018

Published in IMWUT Volume 3, Issue 2

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https://doi.org/10.2196/47194
Choi AOoi ALottridge D(2024)Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature ReviewJMIR mHealth and uHealth10.2196/4068912(e40689)Online publication date: 23-May-2024
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Affiliations

Shayan Mirjafari

Dartmouth College, Hanover, NH, USA

Kizito Masaba

Dartmouth College, Hanover, NH, USA

Ted Grover

University of California at Irvine, Irvine, CA, USA

Weichen Wang

Dartmouth College, Hanover, NH, USA

Pino Audia

Dartmouth College, Hanover, NH, USA

Andrew T. Campbell

Dartmouth College, Hanover, NH, USA

Nitesh V. Chawla

University of Notre Dame, Notre Dame, IN, USA

Vedant Das Swain

Georgia Institute of Technology, Atlanta, GA, USA

Munmun De Choudhury

Georgia Institute of Technology, USA, Atlanta, GA

Anind K. Dey

University of Washington, Seattle, WA, USA

Sidney K. D'Mello

University of Colorado at Boulder, Boulder, CO, USA

Ge Gao

University of California at Irvine, Irvine, CA, USA

Julie M. Gregg

University of Colorado at Boulder, Boulder, CO, USA

Krithika Jagannath

University of California at Irvine, Irvine, CA, USA

Kaifeng Jiang

Ohio State University, Columbus, OH, USA

Suwen Lin

University of Notre Dame, Notre Dame, IN, USA

Qiang Liu

University of Texas at Austin, Austin, TX, USA

Gloria Mark

University of California at Irvine, Irvine, CA, USA

Gonzalo J. Martinez

University of Notre Dame, Notre Dame, IN, USA

Stephen M. Mattingly

University of Notre Dame, Notre Dame, IN, USA

Edward Moskal

University of Notre Dame, Notre Dame, IN, USA

Raghu Mulukutla

Carnegie Mellon University, Pittsburgh, PA, USA

Subigya Nepal

Dartmouth College, Hanover, NH, USA

Kari Nies

University of California at Irvine, Irvine, CA, USA

Manikanta D. Reddy

Georgia Institute of Technology, Atlanta, GA, USA

Pablo Robles-Granda

University of Notre Dame, Notre Dame, IN, USA

Koustuv Saha

Georgia Institute of Technology, Atlanta, GA, USA

Anusha Sirigiri

Dartmouth College, Hanover, NH, USA

Aaron Striegel

University of Notre Dame, Notre Dame, IN, USA

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