Stefan Stepanovic
ABSTRACT
The digitalization in public administrations has seen, through the COVID-19 pandemic, the appearance of health surveillance technologies at the workplace. Wearable health devices, such as physiolytics, may then have an increasing role in the management of public agents. Still, little is known about the use of these systems in work settings, as research is mainly oriented towards ethical debates or legal considerations. Accordingly, we propose to consider a concrete case of implementation of physiolytics in a Swiss public administration. We particularly investigate employees’ use rates as well as the perceived opportunities and threats that are linked to physiolytics and health surveillance technologies. This is done through an action design research perspective, where we search to extract from the field guidelines and knowledge for practitioners. We especially highlight that physiolytics’ use steadily decline after the first weeks, due to the design of such devices, the fear of surveillance, and the impression of competition that these systems bring into the workplace. It is therefore vital for public managers to introduce interventions, such as regular feedback, gamification, or nudging to support the engagement of public agents and ensure the viability of such novel health initiatives.
- Guenduez, A. A., Mettler, T. and Schedler, K. Technological frames in public administration: What do public managers think of big data? Government Information Quarterly, 37, 1 (2020), 1-12.Google Scholar
Cross Ref
- Harteis, C. Machines, change and work: An educational view on the digitalization of work. In The Impact of Digitalization in the Workplace Springer, Cham, 2018, 1-10.Google ScholarCross Ref
- Wortmann, F. and Flüchter, K. Internet of things. Business & Information Systems Engineering, 57, 3 (2015), 221-224.Google ScholarCross Ref
- Calvo, R. A., Deterding, S. and Ryan, R. M. Health surveillance during covid-19 pandemic. British Journal of Medicine, 369 (2020), 1-2.Google Scholar
- Klievink, B., Romijn, B.-J., Cunningham, S. and de Bruijn, H. Big data in the public sector: Uncertainties and readiness. Information Systems Frontiers, 19, 2 (2017), 267-283.Google ScholarDigital Library
- Fernandez-Luque, L., Kushniruk, A. W., Georgiou, A., Basu, A., Petersen, C., Ronquillo, C., Paton, C., Nøhr, C., Kuziemsky, C. E. and Alhuwail, D. Evidence-based health informatics as the foundation for the COVID-19 response: a joint call for action. Methods of Information in Medicine, 59, 6 (2021), 183-192.Google Scholar
- Wilson, H. J. Wearables in the workplace. Harvard Business Review, 91, 11 (2013), 23-27.Google Scholar
- Dunleavy, P. Big data and policy learning. In Evidence-based policy making in the social sciences: methods that matter Policy Press, Bristol, 2016, 143-151.Google ScholarCross Ref
- Van Dijck, J. Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12, 2 (2014), 197-208.Google ScholarCross Ref
- Stepanovic, S., Mozgovoy, V. and Mettler, T. Designing visualizations for workplace stress management: Results of a pilot study at a Swiss municipality. In International Conference on Electronic Government Springer, Berlin, 2019, 94-104.Google ScholarDigital Library
- Mettler, T. and Wulf, J. Physiolytics at the workplace: Affordances and constraints of wearables use from an employee's perspective. Information Systems Journal, 29, 1 (2019), 1-29.Google ScholarCross Ref
- Kudyba, S. COVID-19 and the Acceleration of Digital Transformation and the Future of Work. Information Systems Management, 37, 4 (2020), 284-287.Google ScholarCross Ref
- Dinev, T., Xu, H., Smith, J. H. and Hart, P. Information privacy and correlates: an empirical attempt to bridge and distinguish privacy-related concepts. European Journal of Information Systems, 22, 3 (2013), 295-316.Google ScholarCross Ref
- Malhotra, N. K., Kim, S. S. and Agarwal, J. Internet users' information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research, 15, 4 (2004), 336-355.Google ScholarDigital Library
- Li, H., Wu, J., Gao, Y. and Shi, Y. Examining individuals’ adoption of healthcare wearable devices: An empirical study from privacy calculus perspective. International Journal of Medical Informatics, 88 (2016), 8-17.Google ScholarCross Ref
- Kari, T., Koivunen, S., Frank, L., Makkonen, M. and Moilanen, P. Perceived Well-being Effects During the Implementation of a Self-tracking Technology. In Proceedings of the 29th Bled eConference (Bled, Slovenia, 2016).Google Scholar
- Yassaee, M. and Mettler, T. Digital Occupational Health Systems: What Do Employees Think about it? Information Systems Frontiers (2017), 1-16.Google Scholar
- Marcengo, A. and Rapp, A. Visualization of human behavior data: the quantified self. In Big Data: Concepts, Methodologies, Tools, and Applications, 2016, 1582-1612.Google ScholarCross Ref
- Lavallière, M., Burstein, A. A., Arezes, P. and Coughlin, J. F. Tackling the challenges of an aging workforce with the use of wearable technologies and the quantified-self. DYNA, 83, 197 (2016), 38-43.Google ScholarCross Ref
- Lupton, D. Self-tracking modes: Reflexive self-monitoring and data practices. In Proceedings of the 2015 Social Life of Big Data Symposium (Perth, Australia, 2014).Google Scholar
- Schall Jr, M. C., Sesek, R. F. and Cavuoto, L. A. Barriers to the adoption of wearable sensors in the workplace: A survey of occupational safety and health professionals. Human Factors, 60, 3 (2018), 351-362.Google ScholarCross Ref
- Yassaee, M., Mettler, T. and Winter, R. Principles for the design of digital occupational health systems. Information and Organization, 29, 2 (2019), 77-90.Google ScholarDigital Library
- Morozov, E. To save everything, click here: The folly of technological solutionism. Public Affairs, New York, 2013.Google Scholar
- Lupton, D. and Michael, M. ‘Depends on who's got the data’: Public understandings of personal digital dataveillance. Surveillance & Society, 15, 2 (2017), 254-268.Google ScholarCross Ref
- Mathur, A., Van den Broeck, M., Vanderhulst, G., Mashhadi, A. and Kawsar, F. 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 (Osaka, Japan, 2015).Google ScholarDigital Library
- Hamblen, M. Programs are used to weed out workers who raise premiums, one attorney says. Retrieved from https://www.computerworld.com/article/2937721/wearables/wearables-for-workplace-wellness-face-federal-scrutiny.html.Google Scholar
- Moore, P. and Piwek, L. Regulating wellbeing in the brave new quantified workplace. Employee Relations, 39, 3 (2017), 308-316.Google ScholarCross Ref
- Stepanovic, S., Mettler, T., Schmidt-Kraepelin, M., Thiebes, S. and Sunyaev, A. Wearable Health Devices in the Workplace: The Importance of Habits to Sustain the Use. In Proceedings of the 21st IEEE Conference on Business Informatics (Moscow, Russia, 2019).Google ScholarCross Ref
- Sein, M. K., Henfridsson, O., Purao, S., Rossi, M. and Lindgren, R. Action design research. MIS Quarterly, 35, 1 (2011), 37-56.Google ScholarCross Ref
- Mullarkey, M. T. and Hevner, A. R. An elaborated action design research process model. European Journal of Information Systems, 28, 1 (2019), 6-20.Google ScholarCross Ref
- Romme, A. G. L. and Meijer, A. Applying design science in public policy and administration research. Policy & Politics, 48, 1 (2020), 149-165.Google ScholarCross Ref
- Grossmeier, J. The art of health promotion: Linking research to practice. American Journal of Health Promotion, 31, 3 (2017), 251-261.Google ScholarCross Ref
- Kim, J. Y., Wineinger, N. E., Taitel, M., Radin, J. M., Akinbosoye, O., Jiang, J., Nikzad, N., Orr, G., Topol, E. and Steinhubl, S. Self-monitoring utilization patterns among individuals in an incentivized program for healthy behaviors. Journal of Medical Internet Research, 18, 11 (2016), 1-15.Google ScholarCross Ref
- Epstein, D. A., Ping, A., Fogarty, J. and Munson, S. A. A lived informatics model of personal informatics. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (New York, USA, 2015).Google ScholarDigital Library
- Limayem, M., Hirt, S. G. and Cheung, C. M. How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly (2007), 705-737.Google ScholarCross Ref
- Epel, E. S., Crosswell, A. D., Mayer, S. E., Prather, A. A., Slavich, G. M., Puterman, E. and Mendes, W. B. More than a feeling: A unified view of stress measurement for population science. Frontiers in Neuroendocrinology, 49 (2018), 146-169.Google ScholarCross Ref
- Spath, P. Introduction to healthcare quality management. Health Administration Press Chicago, IL, Chicago, USA, 2009.Google Scholar
- Ruckenstein, M. Visualized and interacted life: Personal analytics and engagements with data doubles. Societies, 4, 1 (2014), 68-84.Google ScholarCross Ref
- Pantzar, M. and Ruckenstein, M. The heart of everyday analytics: emotional, material and practical extensions in self-tracking market. Consumption Markets & Culture, 18, 1 (2015), 92-109.Google ScholarCross Ref
- Swan, M. Sensor mania! The internet of things, wearable computing, objective metrics, and the quantified self 2.0. Journal of Sensor and Actuator Networks, 1, 3 (2012), 217-253.Google ScholarCross Ref
- Fox, P. and Hendler, J. Changing the equation on scientific data visualization. Science, 331, 6018 (2011-02-11 00:00:00 2011), 705-708.Google ScholarCross Ref
- Dikhit, R. S. Internet of Things with Wearable Devices at Enterprise Radar. Journal of Mobile Computing & Application, 3, 1 (2016), 1-8.Google Scholar
- Khakurel, J., Melkas, H. and Porras, J. Tapping into the wearable device revolution in the work environment: a systematic review. Information Technology & People, 31, 3 (2018), 791-818.Google ScholarCross Ref
- AlMarshedi, A., Wanick, V., Wills, G. B. and Ranchhod, A. Gamification and behaviour. In Gamification Springer, Berlin, 2017, 19-29.Google ScholarCross Ref
- Deterding, S. Eudaimonic Design, or: Six Invitations to Rethink Gamification. In Rethinking Gamification Meson Press Lüneburg, 2014, 305-323.Google Scholar
- Hosseini, M., Shahri, A., Phalp, K. and Ali, R. Four reference models for transparency requirements in information systems. Requirements Engineering, 23, 2 (2018), 251-275.Google ScholarDigital Library
- Miele, F. and Tirabeni, L. Digital technologies and power dynamics in the organization: A conceptual review of remote working and wearable technologies at work. Sociology Compass, 14, 6 (2020), 1-13.Google ScholarCross Ref
- Weston, M. Wearable surveillance–a step too far? Strategic Human Ressources Review, 14, 6 (2015), 214-219.Google Scholar
- Türken, S., Nafstad, H. E., Blakar, R. M. and Roen, K. Making sense of neoliberal subjectivity: A discourse analysis of media language on self-development. Globalizations, 13, 1 (2016), 32-46.Google ScholarCross Ref
- Toner, J. Exploring the dark-side of fitness trackers: Normalization, objectification and the anaesthetisation of human experience. Performance Enhancement & Health, 6, 2 (2018), 75-81.Google ScholarCross Ref
- Mettler, T. and Wulf, J. Health promotion with physiolytics: What is driving people to subscribe in a data-driven health plan. PLOS One, 15, 4 (2020), 1-19.Google ScholarCross Ref
- Patel, M. S., Asch, D. A. and Volpp, K. G. Wearable devices as facilitators, not drivers, of health behavior change. Journal of the American Medical Association, 313, 5 (2015), 459-460.Google Scholar
- Bove, L. A. Increasing patient engagement through the use of wearable technology. The Journal for Nurse Practitioners, 15, 8 (2019), 535-539.Google ScholarCross Ref
- Jones, J., Gouge, C. and Crilley, M. Design principles for health wearables. Communication Design Quarterly Review, 5, 2 (2017), 40-50.Google ScholarDigital Library
- Lušic, M., Fischer, C., Bönig, J., Hornfeck, R. and Franke, J. Worker information systems: State of the art and guideline for selection under consideration of company specific boundary conditions. Procedia 41 (2016), 1113-1118.Google Scholar
- Attig, C. and Franke, T. Abandonment of personal quantification: A review and empirical study investigating reasons for wearable activity tracking attrition. Computers in Human Behavior, 102 (2020), 223-237.Google ScholarDigital Library
- Deterding, S., Dixon, D., Khaled, R. and Nacke, L. From game design elements to gamefulness: Defining "gamification". In Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, MindTrek 2011 (Tampere, Finland, 2011).Google ScholarDigital Library
- Sunstein, C. R. People prefer system 2 nudges (kind of). Duke LJ, 66 (2016), 121.Google Scholar
- Leonard, T. C. Richard H. Thaler, Cass R. Sunstein, Nudge: Improving decisions about health, wealth, and happiness. Constitutional Political Economy, 19 (2008), 356–360Google ScholarCross Ref
- Hausman, D. M. and Welch, B. Debate: To nudge or not to nudge. Journal of Political Philosophy, 18, 1 (2010), 123-136.Google ScholarCross Ref
- Sunstein, C. R. Nudging: a very short guide. Journal of Consumer Policy, 37, 4 (2014), 583-588.Google ScholarCross Ref
- Bucher, T., Collins, C., Rollo, M. E., McCaffrey, T. A., De Vlieger, N., Van der Bend, D., Truby, H. and Perez-Cueto, F. J. Nudging consumers towards healthier choices: a systematic review of positional influences on food choice. British Journal of Nutrition, 115, 12 (2016), 2252-2263.Google ScholarCross Ref
- Woodend, A., Schölmerich, V. and Denktas, S. “Nudges” to Prevent Behavioral Risk Factors Associated With Major Depressive Disorder. American Journal of Public Health, 105, 11 (2015), 2318-2321.Google Scholar
- Davison, R. M. and Martinsons, M. G. Context is king! Considering particularism in research design and reporting. Journal of Information Technology, 31, 3 (2016), 241-249.Google ScholarCross Ref
Index Terms
- Safe return to the workplace: Perceived opportunities and threats in the use of health surveillance technologies in public administrations
Index terms have been assigned to the content through auto-classification.
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