Book contents
- Technology and Measurement around the Globe
- Educational and Psychological Testing in a Global Context
- Technology and Measurement around the Globe
- Copyright page
- Contents
- Contributors
- Introduction
- Part I Foundations
- Chapter 1 Machine Learning Algorithms and Measurement
- Chapter 2 Toward Improved Workplace Measurement with Passive Sensing Technologies
- Chapter 3 Information Privacy
- Part II Global Perspectives on Key Methods/Topics
- Part III Regional Focus
- Index
- References
Chapter 2 - Toward Improved Workplace Measurement with Passive Sensing Technologies
Opportunities and Challenges
from Part I - Foundations
Published online by Cambridge University Press: 08 November 2023
Book contents
- Technology and Measurement around the Globe
- Educational and Psychological Testing in a Global Context
- Technology and Measurement around the Globe
- Copyright page
- Contents
- Contributors
- Introduction
- Part I Foundations
- Chapter 1 Machine Learning Algorithms and Measurement
- Chapter 2 Toward Improved Workplace Measurement with Passive Sensing Technologies
- Chapter 3 Information Privacy
- Part II Global Perspectives on Key Methods/Topics
- Part III Regional Focus
- Index
- References
Summary
Behavioral measurement is the hallmark of research in the field of computational social science. We are witnessing innovative as well as clever use of existing and novel, commercial, or research-grade “sensors” to measure various aspects of human behavior and well-being. Passive sensing, a version of measurement where data is gathered and tracked unobtrusively using pervasive and ubiquitous sensors, is increasingly recognized and utilized in organizational science research. This chapter presents an overview of where passive sensing has been successful in workplace measurement, ranging from assessing worker personality and productivity, to their well-being, and understanding the overall organizational pulse. A range of passive sensing infrastructures are described (e.g., smartphones, wearable devices, social media) and several machine-learning-based predictive approaches are noted in this body of research. The chapter then highlights outstanding challenges as this field matures, which include issues of limited generalizability in computational measurement of workplace behaviors, gaps and limitations of gold standard assessment, model simplicity and sophistication tradeoffs, and, importantly, privacy risks. The chapter concludes with recommendations on important areas that need further or altogether new investments, so as to fully realize the potential of passive sensing technologies in more accurate, actionable, and ethical workplace measurement.
Keywords
- Type
- Chapter
- Information
- Technology and Measurement around the Globe , pp. 46 - 106Publisher: Cambridge University PressPrint publication year: 2023
References
Abbas, A., Ali, M., Khan, M. U. S., & Khan, S. U. (2016). Personalized healthcare cloud services for disease risk assessment and wellness management using social media. Pervasive and Mobile Computing, 28, 81–99.CrossRefGoogle Scholar
Abdullah, S., Murnane, E. L., Matthews, M., Kay, M., Kientz, J. A., Gay, G., & Choudhury, T. (2016). Cognitive rhythms: Unobtrusive and continuous sensing of alertness using a mobile phone. In Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing (pp. 178–189).CrossRefGoogle Scholar
Abowd, D., Dey, A. K., Orr, R., & Brotherton, J. (1998). Context-awareness in wearable and ubiquitous computing. Virtual Reality, 3(3), 200–211.CrossRefGoogle Scholar
Abowd, G. D., Dey, A. K., Brown, P. J., Davies, N., Smith, M., & Steggles, P. (1999). Towards a better understanding of context and context-awareness. In International symposium on handheld and ubiquitous computing (pp. 304–307).CrossRefGoogle Scholar
Ajzen, I. (1987). Attitudes, traits, and actions: Dispositional prediction of behavior in personality and social psychology. In Berkowitz, L. (Ed.), Advances in experimental social psychology (Vol. 20, pp. 1–63). Elsevier.Google Scholar
Albalooshi, N., Mavridis, N., & Al-Qirim, N. (2012). A survey on social networks and organization development. In 2012 international conference on collaboration technologies and systems (CTS) (pp. 539–545).CrossRefGoogle Scholar
Allen, B. (2020). Your boss is watching you: Work-from-home boom leads to more surveillance. https://www.npr.org/2020/05/13/854014403/your-boss-is-watching-you-work-from-home-boom-leads-to-more-surveillanceGoogle Scholar
Amir, S., Coppersmith, G., Carvalho, P., Silva, M. J., & Wallace, B. C. (2017). Quantifying mental health from social media with neural user embeddings. arXiv preprint arXiv:1705.00335.Google Scholar
Anderson, G., & Viswesvaran, C. (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.Google Scholar
Andreu-Perez, J., Leff, D. R., Ip, H. M., & Yang, G.-Z. (2015). From wearable sensors to smart implants – toward pervasive and personalized healthcare. IEEE Transactions on Biomedical Engineering, 62(12), 2750–2762.CrossRefGoogle ScholarPubMed
Appel-Meulenbroek, R., Groenen, P., & Janssen, I. (2011). An end-user’s perspective on activity-based office concepts. Journal of Corporate Real Estate, 13(2), 122–135.CrossRefGoogle Scholar
Archambault, A., & Grudin, J. (2012). A longitudinal study of Facebook, LinkedIn, and Twitter use. In Proceedings of Chi 2012. Austin, TX.Google Scholar
Bagheri, G., Zarei, R., & Aeen, M. N. (2012). Organizational silence (basic concepts and its development factors). Ideal Type of Management, 1(1), 47–58.Google Scholar
Bardzell, S. (2010). Feminist HCI: Taking stock and outlining an agenda for design. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1301–1310).CrossRefGoogle Scholar
Barley, S. R., Meyer, G. W., & Gash, D. C. (1988). Cultures of culture: Academics, practitioners and the pragmatics of normative control. Administrative Science Quarterly, 33(1), 24–60.CrossRefGoogle Scholar
Barnett, R. C. (1994). Home-to-work spillover revisited: A study of full-time employed women in dual-earner couples. Journal of Marriage and the Family, 56(3), 647–656.CrossRefGoogle Scholar
Barrick, M. R., & Mount, M. K. (1991). The big five personality dimensions and job performance: A meta-analysis. Personnel Psychology, 44(1), 1–26.CrossRefGoogle Scholar
Barthelemy, C. (2019). Workplace engagement program: A prototype for maximizing adaptation to activity-based working. Workplace Engagement Program Report. https://capstone.extension.harvard.edu/files/capstone/files/barthelemy_hdcapstone_report.pdfGoogle Scholar
Baruch, Y., & Holtom, B. C. (2008). Survey response rate levels and trends in organizational research. Human Relations, 61(8), 1139–1160.CrossRefGoogle Scholar
Bin Morshed, M., Saha, K., Li, R., D’Mello, S. K., De Choudhury, M., Abowd, G. D., & Plötz, T. (2019). Prediction of mood instability with passive sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(3).Google Scholar
Biran, O., & Cotton, C. (2017). Explanation and justification in machine learning: A survey. In Ijcai-17 workshop on explainable ai (xai) (Vol. 8, pp. 8–13).Google Scholar
Bone, D., Goodwin, M. S., Black, M. P., Lee, C.-C., Audhkhasi, K., & Narayanan, S. (2015). Applying machine learning to facilitate autism diagnostics: Pitfalls and promises. Journal of Autism and Developmental Disorders, 45(5), 1121–1136.CrossRefGoogle Scholar
Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679.CrossRefGoogle Scholar
Brown, C., Efstratiou, C., Leontiadis, I., Quercia, D., Mascolo, C., Scott, J., & Key, P. (2014). The architecture of innovation: Tracking face-to-face interactions with ubicomp technologies. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing (pp. 811–822).CrossRefGoogle Scholar
Budde, H., Voelcker-Rehage, C., Pietraßyk-Kendziorra, S., Ribeiro, P., & Tidow, G. (2008). Acute co-ordinative exercise improves attentional performance in adolescents. Neuroscience Letters, 441(2), 219–223.CrossRefGoogle Scholar
Buschek, D., Bisinger, B., & Alt, F. (2018). Researchime: A mobile keyboard application for studying free typing behaviour in the wild. In Proceedings of the 2018 CHI conference on human factors in computing systems (pp. 1–14).CrossRefGoogle Scholar
Cable, D. M., & Judge, T. A. (1996). Person–organization fit, job choice decisions, and organizational entry. Organizational Behavior and Human Decision Processes, 67(3), 294–311.CrossRefGoogle Scholar
Caceres, R., & Friday, A. (2011). Ubicomp systems at 20: Progress, opportunities, and challenges. IEEE Pervasive Computing, 11(1), 14–21.CrossRefGoogle Scholar
Calabrese, F., Diao, M., Di Lorenzo, G., Ferreira, Jr, , J., & Ratti, C. (2013). Understanding individual mobility patterns from urban sensing data: A mobile phone trace example. Transportation Research Part C: Emerging Technologies, 26, 301–313.CrossRefGoogle Scholar
Campbell, A. T., Eisenman, S. B., Lane, N. D., Miluzzo, E., Peterson, R. A., Lu, H., … Ahn, G.-S. (2008). The rise of people-centric sensing. IEEE Internet Computing.CrossRefGoogle Scholar
Castano, L. M., & Flatau, A. B. (2014). Smart fabric sensors and e-textile technologies: A review. Smart Materials and Structures, 23(5), 053001.CrossRefGoogle Scholar
Cerwall, P., Jonsson, P., Möller, R., Bävertoft, S., Carson, S., Godor, I., et al. (2015). On the pulse of the networked society. Ericsson Mobility Report. https://www.ericsson.com/en/reports-and-papers/mobility-report/reports/june-2023Google Scholar
Cetto, A., Klier, J., Klier, M., Richter, A., & Wiesneth, K. (2016). The blessing of giving: Knowledge sharing and knowledge seeking in enterprise social networks. In Ecis (p. ResearchPaper64).Google Scholar
Chamberlain, A. (2015). Does company culture pay off? analyzing stock performance of ‘best places to work’ companies (Tech. Rep.). Glassdoor Research Report, March 2015.Google Scholar
Chan, L., Swain, V. D., Kelley, C., de Barbaro, K., Abowd, G. D., & Wilcox, L. (2018). Students’ experiences with ecological momentary assessment tools to report on emotional well-being. IMWUT.CrossRefGoogle Scholar
Chancellor, S., & Counts, S. (2018). Measuring employment demand using internet search data. In Proceedings of the 2018 CHI conference on human factors in computing systems (p. 122).CrossRefGoogle Scholar
Chancellor, S., & De Choudhury, M. (2020). Methods in predictive techniques for mental health status on social media: A critical review. NPJ Digital Medicine, 3(1), 1–11.CrossRefGoogle ScholarPubMed
Chawla, N. V., & Davis, D. A. (2013). Bringing big data to personalized healthcare: A patient-centered framework. Journal of General Internal Medicine, 28(3), 660–665.CrossRefGoogle ScholarPubMed
Chen, Z., Lin, M., Chen, F., Lane, N. D., Cardone, G., Wang, R., … Campbell, A. T. (2013). Unobtrusive sleep monitoring using smartphones. In 2013 7th international conference on pervasive computing technologies for healthcare and workshops (pp. 145–152).CrossRefGoogle Scholar
Chiaburu, D. S., Oh, I.-S., Berry, C. M., Li, N., & Gardner, R. G. (2011). The five-factor model of personality traits and organizational citizenship behaviors: A meta-analysis. Journal of Applied Psychology, 96(6), 1140–1166.CrossRefGoogle ScholarPubMed
Chittaranjan, G., Blom, J., & Gatica-Perez, D. (2011). Who’s who with big-five: Analyzing and classifying personality traits with smartphones. In 2011 15th annual international symposium on wearable computers (pp. 29–36).CrossRefGoogle Scholar
Chittaranjan, G., Blom, J., & Gatica-Perez, D. (2013). Mining large-scale smartphone data for personality studies. Personal and Ubiquitous Computing, 17(3), 433–450.CrossRefGoogle Scholar
Christin, D. (2016). Privacy in mobile participatory sensing: Current trends and future challenges. Journal of Systems and Software, 116, 57–68.CrossRefGoogle Scholar
Ciman, M., & Wac, K. (2016). Individuals’ stress assessment using human-smartphone interaction analysis. IEEE Transactions on Affective Computing, 9(1), 51–65.CrossRefGoogle Scholar
Como, R., Hambley, L., & Domene, J. (2021). An exploration of work-life wellness and remote work during and beyond Covid-19. Canadian Journal of Career Development, 20(1), 46–56.Google Scholar
Cooke, R. A., & Rousseau, D. M. (1988). Behavioral norms and expectations: A quantitative approach to the assessment of organizational culture. Group & Organization Studies, 13(3), 245–273.CrossRefGoogle Scholar
Cooke, R. A., & Szumal, J. L. (2000). Using the organizational culture inventory to understand the operating cultures of organizations. In Ashkanasy, N. M., Wilderom, C. P. M., & Peterson, M. F. (Eds.), Handbook of organizational culture and climate (Vol. 4, pp. 1032–1045). Sage.Google Scholar
Coppersmith, G., Harman, C., & Dredze, M. (2014). Measuring post traumatic stress disorder in Twitter. In International AAAI conference on web and social media.CrossRefGoogle Scholar
Council, J. R. (1993). Context effects in personality research. Current Directions in Psychological Science, 2(2), 31–34.CrossRefGoogle Scholar
Cunningham, J. A., & Whalley, J. (2020). Internet of things: Promises and complexities. In Cunningham, J. A. & Whalley, J. (Eds.), The internet of things entrepreneurial ecosystems (pp. 1–11). Springer.CrossRefGoogle Scholar
Das Swain, V., Chen, V., Mishra, S., Mattingly, S. M., Abowd, G. D., & De Choudhury, M. (2022). Semantic gap in predicting mental wellbeing through passive sensing. In CHI conference on human factors in computing systems (pp. 1–16).Google Scholar
Das Swain, V., Kwon, H., Saket, B., Bin Morshed, M., Tran, K., Patel, D., … Abowd, G. D. (2020). Leveraging wifi network logs to infer social interactions: A case study of academic performance and student behavior. arXiv:2005.11228 [cs.HC].Google Scholar
Das Swain, V., Reddy, M. D., Nies, K. A., Tay, L., De Choudhury, M., & Abowd, G. D. (2019). Birds of a feather clock together: A study of person–organization fit through latent activity routines. Proceedings of ACM Human-Computer Interaction (CSCW).Google Scholar
Das Swain, V., Saha, K., Rajvanshy, H., Sirigiri, A., Gregg, J. M., Lin, S., … De Choudhury, M. (2019). A multisensor person-centered approach to understand the role of daily activities in job performance with organizational personas. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(4), 1–27.CrossRefGoogle Scholar
Das Swain, V., Saha, K., Reddy, M. D., Rajvanshy, H., Abowd, G. D., & De Choudhury, M. (2020). Modeling organizational culture with workplace experiences shared on glassdoor. In ACM CHI conference on human factors in computing systems.Google Scholar
De Choudhury, M., & Counts, S. (2013). Understanding affect in the workplace via social media. In Proceedings of the 2013 conference on computer supported cooperative work (pp. 303–316).CrossRefGoogle Scholar
De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. In ICWSM.Google Scholar
Dey, A. K. (2001). Understanding and using context. Personal and Ubiquitous Computing, 5(1), 4–7.CrossRefGoogle Scholar
Dey, A. K. (2018). Context-aware computing. In Krumm, J. (Ed.), Ubiquitous computing fundamentals (pp. 335–366). Chapman & Hall/CRC.Google Scholar
DiMicco, J., Millen, D. R., Geyer, W., Dugan, C., Brownholtz, B., & Muller, M. (2008). Motivations for social networking at work. In Proceedings of the 2008 ACM conference on computer supported cooperative work (pp. 711–720).CrossRefGoogle Scholar
Di Stefano, G., Gino, F., Pisano, G. P., & Staats, B. R. (2016). Making experience count: The role of reflection in individual learning. Harvard Business School NOM Unit Working Paper (14-093).Google Scholar
Dollinger, S. J., Urban, K. K., & James, T. A. (2004). Creativity and openness: Further validation of two creative product measures. Creativity Research Journal, 16(1), 35–47.CrossRefGoogle Scholar
Dunn, W., Brown, C., & McGuigan, A. (1994). The ecology of human performance: A framework for considering the effect of context. American Journal of Occupational Therapy, 48(7), 595–607.CrossRefGoogle ScholarPubMed
Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3–4), 211–407.CrossRefGoogle Scholar
D’Mello, S. K., Tay, L., & Southwell, R. (2022). Psychological measurement in the information age: Machine-learned computational models. Current Directions in Psychological Science, 31(1), 76–87.CrossRefGoogle Scholar
Eagle, N., & Pentland, A. (2006). Reality mining: Sensing complex social systems. Personal and Ubiquitous Computing, 10(4), 255–268.CrossRefGoogle Scholar
Eagle, N., & Pentland, A. S. (2009). Eigenbehaviors: Identifying structure in routine. Behavioral Ecology and Sociobiology, 63(7), 1057–1066.CrossRefGoogle Scholar
Ehrlich, K., & Shami, N. S. (2010). Microblogging inside and outside the workplace. In ICWSM.CrossRefGoogle Scholar
Ernala, S. K., Birnbaum, M. L., Candan, K. A., Rizvi, A. F., Sterling, W. A., Kane, J. M., & De Choudhury, M. (2019). Methodological gaps in predicting mental health states from social media: Triangulating diagnostic signals. In Proceedings of the 2019 CHI conference on human factors in computing systems (pp. 1–16).CrossRefGoogle Scholar
Estrin, D. (2014). Small data, where n=me. Communications of the ACM, 57(4), 32–34.CrossRefGoogle Scholar
Feng, T., & Narayanan, S. S. (2020). Modeling behavioral consistency in large-scale wearable recordings of human bio-behavioral signals. In ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 1011–1015).CrossRefGoogle Scholar
Ferreira, D., Kostakos, V., & Dey, A. K. (2015). Aware: Mobile context instrumentation framework. Frontiers in ICT, 2, 6.CrossRefGoogle Scholar
Fiesler, C., & Proferes, N. (2018). “Participant” perceptions of Twitter research ethics. Social Media + Society.CrossRefGoogle Scholar
Foster, K. R., Koprowski, R., & Skufca, J. D. (2014). Machine learning, medical diagnosis, and biomedical engineering research-commentary. Biomedical Engineering Online, 13(1), 94.CrossRefGoogle ScholarPubMed
Froehlich, J., Chen, M. Y., Consolvo, S., Harrison, B., & Landay, J. A. (2007). Myexperience: A system for in situ tracing and capturing of user feedback on mobile phones. In Proceedings of the 5th international conference on mobile systems, applications and services (pp. 57–70).CrossRefGoogle Scholar
Furnham, A., & Zacherl, M. (1986). Personality and job satisfaction. Personality and Individual Differences, 7(4), 453–459.CrossRefGoogle Scholar
Giddens, L., Leidner, D., & Gonzalez, E. (2017). The role of fitbits in corporate wellness programs: Does step count matter? In Proceedings of the 50th Hawaii International Conference on System Sciences.CrossRefGoogle Scholar
Glaser, S. R., Zamanou, S., & Hacker, K. (1987). Measuring and interpreting organizational culture. Management Communication Quarterly, 1(2), 173–198.CrossRefGoogle Scholar
Goffman, E. (1959). The presentation of self in everyday life. University of Edinburgh Social Sciences Research Centre.Google Scholar
Goldberg, A., Srivastava, S. B., Manian, V. G., Monroe, W., & Potts, C. (2016). Fitting in or standing out? The tradeoffs of structural and cultural embeddedness. American Sociological Review, 81(6), 1190–1222.CrossRefGoogle Scholar
Golder, S. A., & Macy, M. W. (2011). Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science, 333(6051), 1878–1881.CrossRefGoogle ScholarPubMed
Golestani, A., Masli, M., Shami, N. S., Jones, J., Menon, A., & Mondal, J. (2018). Real-time prediction of employee engagement using social media and text mining. In 2018 17th IEEE international conference on machine learning and applications (ICMLA) (pp. 1383–1387).CrossRefGoogle Scholar
Golub, A., Johnson, B. D., & Labouvie, E. (2000). On correcting biases in self-reports of age at first substance use with repeated cross-section analysis. Journal of Quantitative Criminology, 16(1), 45–68.CrossRefGoogle ScholarPubMed
Groves, R. M., & Peytcheva, E. (2008). The impact of nonresponse rates on nonresponse bias: A meta-analysis. Public Opinion Quarterly, 72(2), 167–189.CrossRefGoogle Scholar
Grünerbl, A., Muaremi, A., Osmani, V., Bahle, G., Oehler, S., Tröster, G., … Lukowicz, P. (2015). Smartphone-based recognition of states and state changes in bipolar disorder patients. IEEE JBHI.CrossRefGoogle Scholar
Guy, I., Ronen, I., Zwerdling, N., Zuyev-Grabovitch, I., & Jacovi, M. (2016). What is your organization ‘like’?: A study of liking activity in the enterprise. In Proceedings of the 2016 CHI conference on human factors in computing systems.CrossRefGoogle Scholar
Hanna, B., Kee, K. F., & Robertson, B. W. (2017). Positive impacts of social media at work: Job satisfaction, job calling, and Facebook use among co-workers. In Shs web of conferences (Vol. 33, p. 00012).CrossRefGoogle Scholar
Harari, G. M., Lane, N. D., Wang, R., Crosier, B. S., Campbell, A. T., & Gosling, S. D. (2016). Using smartphones to collect behavioral data in psychological science: Opportunities, practical considerations, and challenges. Perspectives on Psychological Science, 11(6), 838–854.CrossRefGoogle Scholar
Harari, G. M., Müller, S. R., Aung, M. S., & Rentfrow, P. J. (2017). Smartphone sensing methods for studying behavior in everyday life. Current Opinion in Behavioral Sciences, 18, 83–90.CrossRefGoogle Scholar
Harari, G. M., Vaid, S. S., Müller, S. R., Stachl, C., Marrero, Z., Schoedel, R., … Gosling, S. D. (2020). Personality sensing for theory development and assessment in the digital age. European Journal of Personality, 34(5), 649–669.CrossRefGoogle Scholar
Heilman, M. E. (2012). Gender stereotypes and workplace bias. Research in Organizational Behavior, 32, 113–135.CrossRefGoogle Scholar
Hellhammer, J., & Schubert, M. (2012). The physiological response to trier social stress test relates to subjective measures of stress during but not before or after the test. Psychoneuroendocrinology, 37(1), 119–124.CrossRefGoogle ScholarPubMed
Hickman, L., Saha, K., De Choudhury, M., & Tay, L. (2019). Automated tracking of components of job satisfaction via text mining of twitter data. In Ml symposium, SIOP.Google Scholar
Hofstede, G. (2001). Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations. Sage Publications.Google Scholar
Hofstede, G., Neuijen, B., Ohayv, D. D., & Sanders, G. (1990). Measuring organizational cultures: A qualitative and quantitative study across twenty cases. Administrative Science Quarterly, 35(2), 286–316.CrossRefGoogle Scholar
Hovsepian, K., al’Absi, M., Ertin, E., Kamarck, T., Nakajima, M., & Kumar, S. (2015). cstress: Towards a gold standard for continuous stress assessment in the mobile environment. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing (pp. 493–504).CrossRefGoogle Scholar
Howard, M. C., & Hoffman, M. E. (2018). Variable-centered, person-centered, and person-specific approaches: Where theory meets the method. Organizational Research Methods, 21(4), 846–876.CrossRefGoogle Scholar
Insel, T. R. (2017). Digital phenotyping: Technology for a new science of behavior. JAMA, 318(13), 1215–1216.CrossRefGoogle ScholarPubMed
Jacovi, M., Guy, I., Kremer-Davidson, S., Porat, S., & Aizenbud-Reshef, N. (2014). The perception of others: Inferring reputation from social media in the enterprise. In Proceedings of the 17th ACM conference on computer supported cooperative work & social computing (pp. 756–766).CrossRefGoogle Scholar
Jain, S. H., Powers, B. W., Hawkins, J. B., & Brownstein, J. S. (2015). The digital phenotype. Nature Biotechnology, 33(5), 462–463.CrossRefGoogle ScholarPubMed
Jebelli, H., Hwang, S., & Lee, S. (2018). EEG signal-processing framework to obtain high-quality brain waves from an off-the-shelf wearable EEG device. Journal of Computing in Civil Engineering, 32(1), 04017070.CrossRefGoogle Scholar
Jhaver, S., Cranshaw, J., & Counts, S. (2019). Measuring professional skill development in U.S. cities using internet search queries. In ICWSM.Google Scholar
Jouhki, J., Lauk, E., Penttinen, M., Sormanen, N., & Uskali, T. (2016). Facebook’s emotional contagion experiment as a challenge to research ethics. Media and Communication, 4(4), 75–85.CrossRefGoogle Scholar
Judge, T. A., & Zapata, C. P. (2015). The person–situation debate revisited: Effect of situation strength and trait activation on the validity of the big five personality traits in predicting job performance. Academy of Management Journal, 58(4), 1149–1179.CrossRefGoogle Scholar
Kamdar, D., & Van Dyne, L. (2007). The joint effects of personality and workplace social exchange relationships in predicting task performance and citizenship performance. Journal of Applied Psychology, 92(5), 1286–1298.CrossRefGoogle ScholarPubMed
Khakurel, J., Melkas, H., & Porras, J. (2018). Tapping into the wearable device revolution in the work environment: A systematic review. Information Technology & People, 31(3).CrossRefGoogle Scholar
King, C. E., & Sarrafzadeh, M. (2018). A survey of smartwatches in remote health monitoring. Journal of Healthcare Informatics Research, 2(1), 1–24.CrossRefGoogle ScholarPubMed
Klasnja, P., Consolvo, S., McDonald, D. W., Landay, J. A., & Pratt, W. (2009). Using mobile & personal sensing technologies to support health behavior change in everyday life: Lessons learned. In Amia annual symposium proceedings (Vol. 2009, p. 338).Google Scholar
Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788– 8790.CrossRefGoogle ScholarPubMed
Kreuter, F., Haas, G.-C., Keusch, F., Bähr, S., & Trappmann, M. (2020). Collecting survey and smartphone sensor data with an app: Opportunities and challenges around privacy and informed consent. Social Science Computer Review, 38(5), 533–549.CrossRefGoogle Scholar
Krumpal, I. (2013). Determinants of social desirability bias in sensitive surveys: A literature review. Quality & Quantity, 47, 2025–2047.CrossRefGoogle Scholar
Lampert, R. (2015). ECG signatures of psychological stress. Journal of Electrocardiology, 48(6), 1000–1005.CrossRefGoogle ScholarPubMed
Langheinrich, M. (2009). Privacy in ubiquitous computing. In Krumm, J. (Ed.), Ubiquitous computing (pp. 104–174). Chapman & Hall/CRC.Google Scholar
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of google flu: Traps in big data analysis. Science, 343(6176), 1203–1205.CrossRefGoogle ScholarPubMed
Lazer, D., & Radford, J. (2017). Data ex machina: Introduction to big data. Annual Review of Sociology, 43, 19–39.CrossRefGoogle Scholar
Lee, H., Kim, S., Couper, M. P., & Woo, Y. (2019). Experimental comparison of PC web, smartphone web, and telephone surveys in the new technology era. Social Science Computer Review, 37(2), 234–247.CrossRefGoogle Scholar
Lee, J., & Kang, J. (2017). A study on job satisfaction factors in retention and turnover groups using dominance analysis and LDA topic modeling with employee reviews on glassdoor.com.Google Scholar
Lee, J., Lam, M., & Chiu, C. (2019). Clara: Design of a new system for passive sensing of depression, stress and anxiety in the workplace. In International symposium on pervasive computing paradigms for mental health (pp. 12–28).CrossRefGoogle Scholar
Leesman. (2018). The rise and rise of activity based working. https://www.leesmanindex.com/The Rise and Rise of Activity Based Working Research book.pdfGoogle Scholar
Li, I., Dey, A., & Forlizzi, J. (2010). A stage-based model of personal informatics systems. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 557–566).CrossRefGoogle Scholar
Li, S., Zhou, X., Dong, Y., & Li, J. (2020). Flexible self-repairing materials for wearable sensing applications: Elastomers and hydrogels. Macromolecular Rapid Communications, 41(23), 2000444.CrossRefGoogle ScholarPubMed
Lindberg, A., Berente, N., Gaskin, J., Lyytinen, K., & Yoo, Y. (2013). Computational approaches for analyzing latent social structures in open source organizing. In International conference on information systems.Google Scholar
Ling, R., & Horst, H. A. (2011). Mobile communication in the global south (Vol. 13) (No. 3). Sage Publications.CrossRefGoogle Scholar
Liu, J., Wang, Y., Chen, Y., Yang, J., Chen, X., & Cheng, J. (2015). Tracking vital signs during sleep leveraging off-the-shelf wifi. In Proceedings of the 16th ACM international symposium on mobile ad hoc networking and computing (pp. 267–276).CrossRefGoogle Scholar
Liu, S., Hachen, D., Lizardo, O., Poellabauer, C., Striegel, A., & Milenkoviĉ, T. (2018). Network analysis of the nethealth data: Exploring co-evolution of individuals’ social network positions and physical activities. Applied Network Science, 3(1), 1–26.CrossRefGoogle ScholarPubMed
Liu, Y., & Du, S. (2018). Psychological stress level detection based on electrodermal activity. Behavioural Brain Research, 341, 50–53.CrossRefGoogle ScholarPubMed
Lowe, G. S., Schellenberg, G., & Shannon, H. S. (2003). Correlates of employees’ perceptions of a healthy work environment. American Journal of Health Promotion, 17(6), 390–399.CrossRefGoogle ScholarPubMed
Lu, H., Pan, W., Lane, N. D., Choudhury, T., & Campbell, A. T. (2009). Soundsense: Scalable sound sensing for people-centric applications on mobile phones. In Proceedings of the 7th international conference on mobile systems, applications, and services (pp. 165–178).CrossRefGoogle Scholar
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., … Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67.CrossRefGoogle ScholarPubMed
Luo, N., Zhou, Y., & Shon, J. (2016). Employee satisfaction and corporate performance: Mining employee reviews on glassdoor.com.Google Scholar
Madan, A., Moturu, S. T., Lazer, D., & Pentland, A. (2010). Social sensing: Obesity, unhealthy eating and exercise in face-to-face networks. In Wireless health 2010 (pp. 104–110).CrossRefGoogle Scholar
Madden, T. J., Ellen, P. S., & Ajzen, I. (1992). A comparison of the theory of planned behavior and the theory of reasoned action. Personality and Social Psychology Bulletin, 18(1), 3–9.CrossRefGoogle Scholar
Mallinckrodt, C. H., Sanger, T. M., Dubé, S., DeBrota, D. J., Molenberghs, G., Carroll, R. J., … Tollefson, G. D. (2003). Assessing and interpreting treatment effects in longitudinal clinical trials with missing data. Biological Psychiatry, 53(8), 754–760.CrossRefGoogle ScholarPubMed
Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P., & Dewhurst, M. (2017). A future that works: AI, automation, employment, and productivity. McKinsey Global Institute Research, Tech. Rep, 60, 1–135.Google Scholar
Mariani, B., Jiménez, M. C., Vingerhoets, F. J., & Aminian, K. (2012). On-shoe wearable sensors for gait and turning assessment of patients with Parkinson’s disease. IEEE Transactions on Biomedical Engineering, 60(1), 155–158.CrossRefGoogle Scholar
Mark, G. (2015). Multitasking in the digital age. Synthesis Lectures on Human-Centered Informatics, 8(3), 1–113.CrossRefGoogle Scholar
Mark, G., Czerwinski, M., Iqbal, S., & Johns, P. (2016). Workplace indicators of mood: Behavioral and cognitive correlates of mood among information workers. In Proceedings of the 6th international conference on digital health (pp. 29–36).CrossRefGoogle Scholar
Mark, G., Iqbal, S. T., Czerwinski, M., Johns, P., Sano, A., & Lutchyn, Y. (2016). Email duration, batching and self-interruption: Patterns of email use on productivity and stress. In Proceedings of the 2016 CHI conference on human factors in computing systems (pp. 1717–1728).CrossRefGoogle Scholar
Martinez, G. J., Mattingly, S. M., Young, J., Faust, L., Dey, A. K., Campbell, A. T., De Choudhury, M., Mirjafari, S., Nepal, S. K., Robles-Granda, P., Saha, K., & Striegel, A. D. (2020). Improved sleep detection through the fusion of phone agent and wearable data streams. In Wristsense 2020.CrossRefGoogle Scholar
Mathur, A., Van den Broeck, M., Vanderhulst, G., Mashhadi, A., & Kawsar, F. (2015). Tiny habits in the giant enterprise: Understanding the dynamics of a quantified workplace. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing (pp. 577–588).CrossRefGoogle Scholar
Mattingly, S. M., Gregg, J. M., Audia, P., Bayraktaroglu, A. E., Campbell, A. T., Chawla, N. V., … Dey, A. K. (2019). The Tesserae project: Large-scale, longitudinal, in situ, multimodal sensing of information workers. CHI conference on human factors in computing systems.CrossRefGoogle Scholar
McLaren, H. J., Wong, K. R., Nguyen, K. N., & Mahamadachchi, K. N. D. (2020). Covid-19 and women’s triple burden: Vignettes from Sri Lanka, Malaysia, Vietnam and Australia. Social Sciences, 9(5), Article 87.CrossRefGoogle Scholar
Mehl, M. R. (2017). The electronically activated recorder (ear): A method for the naturalistic observation of daily social behavior. Current Directions in Psychological Science, 26(2), 184–190.CrossRefGoogle Scholar
Mehrotra, A., & Musolesi, M. (2018). Using autoencoders to automatically extract mobility features for predicting depressive states. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(3), 127.CrossRefGoogle Scholar
Mehrotra, A., Pejovic, V., & Musolesi, M. (2014). Sensocial: A middleware for integrating online social networks and mobile sensing data streams. In Proceedings of the 15th international middleware conference (pp. 205–216).CrossRefGoogle Scholar
Meyer, B., Burtscher, M. J., Jonas, K., Feese, S., Arnrich, B., Tröster, G., & Schermuly, C. C. (2016). What good leaders actually do: Micro-level leadership behaviour, leader evaluations, and team decision quality. European Journal of Work and Organizational Psychology, 25(6), 773–789.CrossRefGoogle Scholar
Miluzzo, E., Lane, N. D., Eisenman, S. B., & Campbell, A. T. (2007). Cenceme – injecting sensing presence into social networking applications. In European conference on smart sensing and context (pp. 1–28).CrossRefGoogle Scholar
Mirjafari, S., Masaba, K., Grover, T., Wang, W., Audia, P., Campbell, A. T., … Mark, G. (2019). Differentiating higher and lower job performers in the workplace using mobile sensing. Proc. ACM IMWUT, 3(2), Article 37.Google Scholar
Mitra, T., Muller, M., Shami, N. S., Golestani, A., & Masli, M. (n.d.). Spread of employee engagement in a large organizational network: A longitudinal analysis. PACM HCI(CSCW).Google Scholar
Mohr, D. C., Burns, M. N., Schueller, S. M., Clarke, G., & Klinkman, M. (2013). Behavioral intervention technologies: Evidence review and recommendations for future research in mental health. General Hospital Psychiatry, 35(4), 332–338.CrossRefGoogle ScholarPubMed
Mohr, D. C., Zhang, M., & Schueller, S. M. (2017). Personal sensing: Understanding mental health using ubiquitous sensors and machine learning. Annual Review of Clinical Psychology, 13, 23–47.CrossRefGoogle ScholarPubMed
Molenberghs, G., Thijs, H., Jansen, I., Beunckens, C., Kenward, M. G., Mallinckrodt, C., & Carroll, R. J. (2004). Analyzing incomplete longitudinal clinical trial data. Biostatistics, 5(3), 445–464.CrossRefGoogle ScholarPubMed
Moniz, A. (2015). Inferring employees’ social media perceptions of goal-setting corporate cultures and the link to firm value. Unpublished Working Paper.Google Scholar
Montanari, A., Mascolo, C., Sailer, K., & Nawaz, S. (2017). Detecting emerging activity-based working traits through wearable technology. Proc. IMWUT.CrossRefGoogle Scholar
Moran, T. P., & Dourish, P. (2001). Introduction to this special issue on context-aware computing. Human–Computer Interaction, 16(2–4), 87–95.CrossRefGoogle Scholar
Morrison, E. W., & Milliken, F. J. (2000). Organizational silence: A barrier to change and development in a pluralistic world. Academy of Management Review, 25(4), 706–725.CrossRefGoogle Scholar
Morshed, M. B., Saha, K., Li, R., D’Mello, S. K., De Choudhury, M., Abowd, G. D., & Plötz, T. (2019). Prediction of mood instability with passive sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(3), Article 75.CrossRefGoogle Scholar
Muaremi, A., Arnrich, B., & Tröster, G. (2013). Towards measuring stress with smartphones and wearable devices during workday and sleep. BioNanoScience, 3(2), 172–183.CrossRefGoogle ScholarPubMed
Muller, M., Shami, N. S., Guha, S., Masli, M., Geyer, W., & Wild, A. (2016). Influences of peers, friends, and managers on employee engagement. In Proceedings of the 19th international conference on supporting group work (pp. 131–136).CrossRefGoogle Scholar
Mundnich, K., Booth, B. M., l’Hommedieu, M., Feng, T., Girault, B., L’Hommedieu, J., … Narayanan, S. (2020). Tiles-2018, a longitudinal physiologic and behavioral data set of hospital workers. Scientific Data, 7(1), 1–26.CrossRefGoogle ScholarPubMed
Nadarajan, A., Somandepalli, K., & Narayanan, S. S. (2019). Speaker agnostic foreground speech detection from audio recordings in workplace settings from wearable recorders. In ICASSP 2019–2019 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 6765–6769).CrossRefGoogle Scholar
Nepal, S., Martinez, G. J., Mirjafari, S., Mattingly, S., Swain, V. D., Striegel, A., … Campbell, A. T. (2021). Assessing the impact of commuting on workplace performance using mobile sensing. IEEE Pervasive Computing, 20(4), 52–60.CrossRefGoogle Scholar
Nepal, S., Martinez, G. J., Mirjafari, S., Saha, K., Swain, V. D., Xu, X., … Campbell, A. T. (2022). A survey of passive sensing in the workplace. arXiv preprint arXiv:2201.03074.Google Scholar
Nguyen, D. D., Nguyen, L., & Sila, V. (2019). Does corporate culture affect bank risk-taking? Evidence from loan-level data. British Journal of Management, 30(1), 106–133.CrossRefGoogle Scholar
Olguin, D. O., & Pentland, A. S. (2008). Social sensors for automatic data collection. AMCIS 2008 Proceedings, 171.Google Scholar
Olguin, D. O., Waber, B. N., Kim, T., Mohan, A., Ara, K., & Pentland, A. (2009). Sensible organizations: Technology and methodology for automatically measuring organizational behavior. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(1), 43–55.CrossRefGoogle Scholar
Olguin-Olguin, D. (2011). Sensor-based organizational design and engineering (Unpublished doctoral dissertation). Massachusetts Institute of Technology.Google Scholar
Olmedo-Aguirre, J. O., Reyes-Campos, J., Alor-Hernández, G., Machorro-Cano, I., Rodriguez-Mazahua, L., & Sánchez-Cervantes, J. L. (2022). Remote healthcare for elderly people using wearables: A review. Biosensors, 12(2), 73.CrossRefGoogle ScholarPubMed
Olson, G. M., & Olson, J. S. (2000). Distance matters. Human–Computer Interaction, 15(2–3), 139–178.CrossRefGoogle Scholar
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2019). Social data: Biases, methodological pitfalls, and ethical boundaries. Frontiers in Big Data, 2, 13.CrossRefGoogle ScholarPubMed
Ones, D. S., Viswesvaran, C., & Dilchert, S. (2005). Personality at work: Raising awareness and correcting misconceptions. Human Performance, 18(4), 389–404.CrossRefGoogle Scholar
O’Reilly, C. (1989). Corporations, culture, and commitment: Motivation and social control in organizations. California Management Review, 31(4), 9–25.CrossRefGoogle Scholar
O’Reilly III, C. A., Chatman, J., & Caldwell, D. F. (1991). People and organizational culture: A profile comparison approach to assessing person-organization fit. Academy of Management Journal, 34(3), 487–516.CrossRefGoogle Scholar
Oswald, D., Sherratt, F., & Smith, S. (2014). Handling the hawthorne effect: The challenges surrounding a participant observer. Review of Social Studies, 1(1), 53–73.CrossRefGoogle Scholar
Pappalardo, L., Simini, F., Rinzivillo, S., Pedreschi, D., Giannotti, F., & Barabási, A.-L. (2015). Returners and explorers dichotomy in human mobility. Nature Communications, 6(1), 1–8.CrossRefGoogle ScholarPubMed
Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., … Seligman, M. E. (2015). Automatic personality assessment through social media language. Journal of Personality and Social Psychology, 108(6), 934–952.CrossRefGoogle ScholarPubMed
Pasch, S. (2018). Corporate culture and industry-fit: A text mining approach. In IZA conference.Google Scholar
Plötz, T. (2021). Applying machine learning for sensor data analysis in interactive systems: Common pitfalls of pragmatic use and ways to avoid them. ACM Computing Surveys (CSUR), 54(6), 1–25.CrossRefGoogle Scholar
Poushter, J., Bishop, C., & Chwe, H. (2018). Social media use continues to rise in developing countries but plateaus across developed ones. Pew Research Center, 22, 2–19.Google Scholar
Prabhakaran, V., Neralwala, H., Rambow, O., & Diab, M. T. (2012). Annotations for power relations on email threads. In Lrec (pp. 806–811).Google Scholar
Prajogo, D. I., & McDermott, C. M. (2011). The relationship between multidimensional organizational culture and performance. International Journal of Operations & Production Management, 31(7), 712–735.CrossRefGoogle Scholar
Purta, R., Mattingly, S., Song, L., Lizardo, O., Hachen, D., Poellabauer, C., & Striegel, A. (2016). Experiences measuring sleep and physical activity patterns across a large college cohort with fitbits. In Proceedings of the 2016 ACM international symposium on wearable computers (pp. 28–35).CrossRefGoogle Scholar
Quante, M., Wang, R., Weng, J., Kaplan, E. R., Rueschman, M., Taveras, E. M., … Redline, S. (2019). Seasonal and weather variation of sleep and physical activity in 12–14-year-old children. Behavioral Sleep Medicine, 17(4), 398–410.CrossRefGoogle ScholarPubMed
Quinn, R. E., & Rohrbaugh, J. (1983). A spatial model of effectiveness criteria: Towards a competing values approach to organizational analysis. Management Science, 29(3), 363–377.CrossRefGoogle Scholar
Reeves, B., Ram, N., Robinson, T. N., Cummings, J. J., Giles, C. L., Pan, J., … Yeykelis, L. (2021). Screenomics: A framework to capture and analyze personal life experiences and the ways that technology shapes them. Human–Computer Interaction, 36(2), 150–201.CrossRefGoogle Scholar
Robertson, B. W., & Kee, K. F. (2017). Social media at work: The roles of job satisfaction, employment status, and Facebook use with co-workers. Computers in Human Behavior, 70, 191–196.CrossRefGoogle Scholar
Robles-Granda, P., Lin, S., Wu, X., D’Mello, S., Martinez, G. J., Saha, K., … Chawla, N. V. (2020). Jointly predicting job performance, personality, cognitive ability, affect, and well-being. arXiv preprint arXiv:2006.08364.Google Scholar
Rooksby, J., Morrison, A., & Murray-Rust, D. (2019). Student perspectives on digital phenotyping: The acceptability of using smartphone data to assess mental health. In Proceedings of the 2019 CHI conference on human factors in computing systems (p. 425).CrossRefGoogle Scholar
Rudovic, O., Lee, J., Dai, M., Schuller, B., & Picard, R. W. (2018). Personalized machine learning for robot perception of affect and engagement in autism therapy. Science Robotics, 3(19).CrossRefGoogle ScholarPubMed
Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178.CrossRefGoogle Scholar
Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063–1064.CrossRefGoogle ScholarPubMed
Saha, K., Bayraktaroglu, A. E., Campbell, A. T., Chawla, N. V., De Choudhury, M., D’Mello, S. K., … Yoo, D. W. (2019). Social media as a passive sensor in longitudinal studies of human behavior and wellbeing. In Chi ext. abstracts.CrossRefGoogle Scholar
Saha, K., Chan, L., De Barbaro, K., Abowd, G. D., & De Choudhury, M. (2017). Inferring mood instability on social media by leveraging ecological momentary assessments. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(3), 95.CrossRefGoogle Scholar
Saha, K., Grover, T., Mattingly, S. M., Swain, V. D., Gupta, P., Martinez, G. J., … De Choudhury, M. (2021). Person-centered predictions of psychological constructs with social media contextualized by multimodal sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(1), 1–32.CrossRefGoogle Scholar
Saha, K., Reddy, M. D., Mattingly, S., Moskal, E., Sirigiri, A., & De Choudhury, M. (2019). Libra: On LinkedIn based role ambiguity and its relationship with wellbeing and job performance. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1–30.Google Scholar
Saha, K., Yousuf, A., Hickman, L., Gupta, P., Tay, L., & De Choudhury, M. (2021). A social media study on demographic differences in perceived job satisfaction. Proceedings of the ACM on Human-Computer Interaction (CSCW).CrossRefGoogle Scholar
Salganik, M. J., Lundberg, I., Kindel, A. T., Ahearn, C. E., Al-Ghoneim, K., Almaatouq, A., … McLanahan, S. (2020). Measuring the predictability of life outcomes with a scientific mass collaboration. Proceedings of the National Academy of Sciences, 117(15), 8398–8403.CrossRefGoogle ScholarPubMed
Sano, A., Johns, P., & Czerwinski, M. (2017). Designing opportune stress intervention delivery timing using multi-modal data. In 2017 seventh international conference on affective computing and intelligent interaction (ACII) (pp. 346–353).CrossRefGoogle Scholar
Sano, A., Phillips, A. J., Amy, Z. Y., McHill, A. W., Taylor, S., Jaques, N., … Picard, R. W. (2015). Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones. In 2015 IEEE 12th international conference on wearable and implantable body sensor networks (BSN) (pp. 1–6).CrossRefGoogle Scholar
Sano, A., & Picard, R. W. (2013). Stress recognition using wearable sensors and mobile phones. In ACII.CrossRefGoogle Scholar
Sansone, C., Morf, C. C., & Panter, A. T. (2003). The Sage handbook of methods in social psychology. Sage.Google Scholar
Schaule, F., Johanssen, J. O., Bruegge, B., & Loftness, V. (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), 1–20.CrossRefGoogle Scholar
Schmid Mast, M., Gatica-Perez, D., Frauendorfer, D., Nguyen, L., & Choudhury, T. (2015). Social sensing for psychology: Automated interpersonal behavior assessment. Current Directions in Psychological Science, 24(2), 154–160.CrossRefGoogle Scholar
Schmidt, A., Beigl, M., & Gellersen, H.-W. (1999). There is more to context than location. Computers & Graphics, 23(6), 893–901.CrossRefGoogle Scholar
Scott, J. C., & Reynolds, D. H. (2010). Handbook of workplace assessment (Vol. 32). John Wiley & Sons.Google Scholar
Shami, N. S., Muller, M., Pal, A., Masli, M., & Geyer, W. (2015). Inferring employee engagement from social media. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 3999–4008).CrossRefGoogle Scholar
Shami, N. S., Nichols, J., & Chen, J. (2014). Social media participation and performance at work: A longitudinal study. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 115–118).CrossRefGoogle Scholar
Shami, N. S., Yang, J., Panc, L., Dugan, C., Ratchford, T., Rasmussen, J. C., … Ferrar, J. (2014). Understanding employee social media chatter with enterprise social pulse. In Proceedings of the 17th ACM conference on computer supported cooperative work & social computing.CrossRefGoogle Scholar
Shilton, K. (2009). Four billion little brothers? Privacy, mobile phones, and ubiquitous data collection. Communications of the ACM, 52(11).CrossRefGoogle Scholar
Silva, G. E., Goodwin, J. L., Sherrill, D. L., Arnold, J. L., Bootzin, R. R., Smith, T., … Quan, S. F. (2007). Relationship between reported and measured sleep times: The sleep heart health study (SHHS). Journal of Clinical Sleep Medicine, 3(6), 622–630.CrossRefGoogle ScholarPubMed
Smith, F. J. (2014). Organizational surveys: The diagnosis and betterment of organizations through their members. Psychology Press.CrossRefGoogle Scholar
Smith, M., Szongott, C., Henne, B., & Von Voigt, G. (2012). Big data privacy issues in public social media. In 2012 6th IEEE international conference on digital ecosystems and technologies (DEST) (pp. 1–6).CrossRefGoogle Scholar
Smyth, J. M., Sliwinski, M. J., Zawadzki, M. J., Scott, S. B., Conroy, D. E., Lanza, S. T., … Almeida, D. M. (2018). Everyday stress response targets in the science of behavior change. Behaviour Research and Therapy, 101, 20–29.CrossRefGoogle ScholarPubMed
Snyder, M. (1979). Self-monitoring processes. In Berkowitz, L. (Ed.), Advances in experimental social psychology (Vol. 12, pp. 85–128). Elsevier.Google Scholar
Soto, C. J., & John, O. P. (2017). The next big five inventory (bfi-2): Developing and assessing a hierarchical model with 15 facets to enhance bandwidth, fidelity, and predictive power. Journal of Personality and Social Psychology, 113(1), 117–143.CrossRefGoogle ScholarPubMed
Soto, M., Satterfield, C., Fritz, T., Murphy, G. C., Shepherd, D. C., & Kraft, N. (2021). Observing and predicting knowledge worker stress, focus and awakeness in the wild. International Journal of Human-Computer Studies, 146, 102560.CrossRefGoogle Scholar
Srivastava, S. B., & Goldberg, A. (2017). Language as a window into culture. California Management Review, 60(1).CrossRefGoogle Scholar
Steinberg, H., Sykes, E. A., Moss, T., Lowery, S., LeBoutillier, N., & Dewey, A. (1997). Exercise enhances creativity independently of mood. British Journal of Sports Medicine, 31(3), 240–245.CrossRefGoogle ScholarPubMed
Steindl, C., Jonas, E., Sittenthaler, S., Traut-Mattausch, E., & Greenberg, J. (2015). Understanding psychological reactance. Zeitschrift für Psychologie, 223(4), 205–214.CrossRefGoogle ScholarPubMed
Strack, R., Von Der Linden, C., Booker, M., & Strohmayr, A. (2014, October 6). Decoding global talent. BCG Perspectives.Google Scholar
Tams, S. (2008). Constructing self-efficacy at work: A person-centered perspective. Personnel Review, 37(2), 165–183.CrossRefGoogle Scholar
Tay, L., Woo, S. E., Hickman, L., & Saef, R. M. (2020). Psychometric and validity issues in machine learning approaches to personality assessment: A focus on social media text mining. European Journal of Personality, 34(5), 826–844.CrossRefGoogle Scholar
Tett, R. P., Jackson, D. N., & Rothstein, M. (1991). Personality measures as predictors of job performance: A meta-analytic review. Personnel Psychology, 44(4), 703–742.CrossRefGoogle Scholar
Thom, J., & Millen, D. R. (2012). Stuff IBMers say: Microblogs as an expression of organizational culture. In Sixth international AAAI conference on weblogs and social media.Google Scholar
Thomas, A. G., Dennis, A., Rawlings, N. B., Stagg, C. J., Matthews, L., Morris, M., … Johansen-Berg, H. (2016). Multi-modal characterization of rapid anterior hippocampal volume increase associated with aerobic exercise. Neuroimage, 131, 162–170.CrossRefGoogle ScholarPubMed
Thom-Santelli, J., Millen, D. R., & Gergle, D. (2011). Organizational acculturation and social networking. In Proceedings of the ACM 2011 conference on computer supported cooperative work (pp. 313–316).CrossRefGoogle Scholar
Torous, J., & Nebeker, C. (2017). Navigating ethics in the digital age: Introducing connected and open research ethics (core), a tool for researchers and institutional review boards. Journal of Medical Internet Research, 19(2), e6793.CrossRefGoogle Scholar
Tsui, A. S., Nifadkar, S. S., & Ou, A. Y. (2007). Cross-national, cross-cultural organizational behavior research: Advances, gaps, and recommendations. Journal of Management, 33(3), 426–478.CrossRefGoogle Scholar
Tufekci, Z. (2014). Big questions for social media big data: Representativeness, validity and other methodological pitfalls. In Eighth international AAAI conference on weblogs and social media.CrossRefGoogle Scholar
Umematsu, T., Sano, A., Taylor, S., & Picard, R. W. (2019). Improving students’ daily life stress forecasting using LSTM neural networks. In 2019 IEEE EMBS international conference on biomedical & health informatics (BHI) (pp. 1–4).CrossRefGoogle Scholar
Vaessen, J., & Raimondo, E. (2020). Conducting evaluations in times of covid-19 (coronavirus). https://ieg.worldbankgroup.org/blog/conducting-evaluations-times-covid-19-coronavirusGoogle Scholar
Vardi, Y. (2001). The effects of organizational and ethical climates on misconduct at work. Journal of Business Ethics, 29(4), 325–337.CrossRefGoogle Scholar
Viswesvaran, C., & Ones, D. S. (2000). Perspectives on models of job performance. International Journal of Selection and Assessment, 8(4), 216–226.CrossRefGoogle Scholar
Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., … Campbell, A. T. (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 (pp. 3–14).CrossRefGoogle Scholar
Warschauer, M., & Ames, M. (2010). Can one laptop per child save the world’s poor? Journal of International Affairs, 64(1), 33–51.Google Scholar
Watkins Allen, M., Coopman, S. J., Hart, J. L., & Walker, K. L. (2007). Workplace surveillance and managing privacy boundaries. Management Communication Quarterly, 21(2), 172–200.CrossRefGoogle Scholar
Wearne, T. A., Lucien, A., Trimmer, E. M., Logan, J. A., Rushby, J., Wilson, E., … McDonald, S. (2019). Anxiety sensitivity moderates the subjective experience but not the physiological response to psychosocial stress. International Journal of Psychophysiology, 141, 76–83.CrossRefGoogle Scholar
Weiss, H. M. (2002). Deconstructing job satisfaction: Separating evaluations, beliefs and affective experiences. Human Resource Management Review, 12(2), 173–194.CrossRefGoogle Scholar
Wiley, J. (2010). Strategic employee surveys: Evidence-based guidelines for driving organizational success. John Wiley & Sons.Google Scholar
Woo, S. E., Chae, M., Jebb, A. T., & Kim, Y. (2016). A closer look at the personality-turnover relationship: Criterion expansion, dark traits, and time. Journal of Management, 42(2), 357–385.CrossRefGoogle Scholar
Woo, S. E., Jebb, A. T., Tay, L., & Parrigon, S. (2018). Putting the “person” in the center: Review and synthesis of person-centered approaches and methods in organizational science. Organizational Research Methods, 21(4), 814–845.CrossRefGoogle Scholar
Woo, S. E., Tay, L., Jebb, A. T., Ford, M. T., & Kern, M. L. (2020). Big data for enhancing measurement quality. In Woo, S. E., Tay, L., & Proctor, R. W. (Eds.), Big data in psychological research (pp. 59–85). American Psychological Association.CrossRefGoogle Scholar
Wright, A. G., & Woods, W. C. (2020). Personalized models of psychopathology. Annual Review of Clinical Psychology, 16, 49–74.CrossRefGoogle ScholarPubMed
Wright, T. A., & Cropanzano, R. (2000). Psychological well-being and job satisfaction as predictors of job performance. Journal of Occupational Health Psychology, 5(1), 84–94.CrossRefGoogle ScholarPubMed
Wu, F., Wu, T., & Yuce, M. R. (2019). Design and implementation of a wearable sensor network system for IoT-connected safety and health applications. In 2019 IEEE 5th world forum on internet of things (Wf-IoT) (pp. 87–90).CrossRefGoogle Scholar
Xu, X., Chikersal, P., Doryab, A., Villalba, D. K., Dutcher, J. M., Tumminia, M. J., … Mankoff, J. (2019). Leveraging routine behavior and contextually-filtered features for depression detection among college students. PACM IMWUT.CrossRefGoogle Scholar
Yilmaz, C., & Ergun, E. (2008). Organizational culture and firm effectiveness: An examination of relative effects of culture traits and the balanced culture hypothesis in an emerging economy. Journal of World Business, 43(3), 290–306.CrossRefGoogle Scholar
Yuan, N. J., Zhang, F., Lian, D., Zheng, K., Yu, S., & Xie, X. (2013). We know how you live: Exploring the spectrum of urban lifestyles. In Proceedings of the first ACM conference on online social networks (pp. 3–14).CrossRefGoogle Scholar
Zakaria, C., Balan, R., & Lee, Y. (2019). Stressmon: Scalable detection of perceived stress and depression using passive sensing of changes in work routines and group interactions. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1–29.CrossRefGoogle Scholar
Zenonos, A., Khan, A., Kalogridis, G., Vatsikas, S., Lewis, T., & Sooriyabandara, M. (2016). Healthyoffice: Mood recognition at work using smartphones and wearable sensors. In 2016 IEEE international conference on pervasive computing and communication workshops (percom workshops) (pp. 1–6).CrossRefGoogle Scholar
Zhang, H., De Choudhury, M., & Grudin, J. (2014). Creepy but inevitable?: The evolution of social networking. In Proceedings of the 17th ACM conference on computer supported cooperative work & social computing.CrossRefGoogle Scholar
Zhang, X., Pina, L. R., & Fogarty, J. (2016). Examining unlock journaling with diaries and reminders for in situ self-report in health and wellness. In Proceedings of the 2016 CHI conference on human factors in computing systems (pp. 5658–5664).CrossRefGoogle Scholar
Zhao, D., & Rosson, M. B. (2009). How and why people twitter: The role that micro-blogging plays in informal communication at work. In Proceedings of the ACM 2009 international conference on supporting group work (pp. 243–252).CrossRefGoogle Scholar
Zimmerman, R. D., Swider, B. W., Woo, S. E., & Allen, D. G. (2016). Who withdraws? Psychological individual differences and employee withdrawal behaviors. Journal of Applied Psychology, 101(4), 498–519.CrossRefGoogle ScholarPubMed