Advertisement

Digital Occupational Health Systems: What Do Employees Think about it?

  • Maedeh Yassaee
  • Tobias Mettler
Article

Abstract

A high rate of work-related accidents or diseases around the world not only threatens the health and wellbeing of employees, but also causes a considerable annual economic burden for organizations. One promising use of information technology would therefore be the management and prevention of occupational accidents and employee absenteeism. Although some companies are starting to introduce digital occupational health initiatives, there is scarce evidence about the inhibiting factors which may discourage the wide adoption of such systems in the workforce. This paper presents qualitative and quantitative data of an exploratory study, which delves into the perceptions of employees towards the use of digital occupational health systems. Our results show that employees are usually aware of the enhanced possibilities for managing and improving their health and wellbeing through such corporate initiatives. However, privacy concerns and the additional mental pressure caused by such systems, significantly diminishes an employee’s willingness to adopt them.

Keywords

Digital occupational health Mixed methods Sensor-based systems Technology adoption 

References

  1. Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: a theoretical analysis and review of empirical research. Psychological Bulletin, 84(5), 888–918.CrossRefGoogle Scholar
  2. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs: Prentice-Hall.Google Scholar
  3. Al-Natour, S., & Benbasat, I. (2009). The adoption and use of IT artifacts: a new interaction-centric model for the study of user-artifact relationships. Journal of the Association for Information Systems, 10(9), 661–685.Google Scholar
  4. Atallah, L., Lo, B., Ali, R., King, R., & Yang, G.-Z. (2009). Real-time activity classification using ambient and wearable sensors. IEEE Transactions on Information Technology in Biomedicine, 13(6), 1031–1039.CrossRefGoogle Scholar
  5. Balci, B., Rosenkranz, C., & Schuhen, S. (2014). Identification of different affordances of information technology systems. In Proceedings of the European Conference on Information Systems, Tel Aviv, 9-11 June 2014, pp. 1-15.Google Scholar
  6. Bergkvist, L., & Rossiter, J. R. (2007). The predictive validity of multiple-item versus single-item measures of the same constructs. Journal of Marketing Research, 44(2), 175–184.CrossRefGoogle Scholar
  7. Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-fit measures in structural equation models. Sociological Methods & Research, 21(2), 205–229.CrossRefGoogle Scholar
  8. Bouchard, T. J. (1976). Field research methods: interviewing, questionnaires, participant observation, unobtrusive measures. In M. D. Dunnette (Ed.), Handbook of industrial and organizational psychology (pp. 363–414). Chicago: Rand-McNally College Publishing Company.Google Scholar
  9. Carpenter, D., McLeod, A., Hicks, C., & Maasberg, M. (2016). Privacy and biometrics: an empirical examination of employee concerns. Information Systems Frontiers, 1–20.  https://doi.org/10.1007/s10796-016-9667-5.
  10. Cheng, T., Migliaccio, G. C., Teizer, J., & Gatti, U. C. (2012). Data fusion of real-time location sensing and physiological status monitoring for ergonomics analysis of construction workers. Journal of Computing in Civil Engineering, 27(3), 320–335.CrossRefGoogle Scholar
  11. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Mahwah: Lawrence Erlbaum Associates.Google Scholar
  12. Corbellini, S., Ferraris, F., & Parvis, M. (2008). A system for monitoring workers’ safety in an unhealthy environment by means of wearable sensors. In Proceedings of the Instrumentation and Measurement Technology Conference, Victoria, 12-15 May 2008, pp. 951-955. Google Scholar
  13. Cortina, J. M. (1993). What is coefficient alpha? Examination of theory and applications. Journal of Applied Psychology, 78(1), 98–104.CrossRefGoogle Scholar
  14. Culnan, M. J., & Williams, C. C. (2009). How ethics can enhance organizational privacy: Lessons from the choicepoint and TJX data breaches. MIS Quarterly, 33(4), 673–687.Google Scholar
  15. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: theory and results. Dissertation, Massachusetts Institute of Technology Sloan School of Management, Cambridge.Google Scholar
  16. Elbanna, A., & Linderoth, H. C. (2015). The formation of technology mental models: The case of voluntary use of technology in organizational setting. Information Systems Frontiers, 17(1), 95–108.CrossRefGoogle Scholar
  17. Engels, J. A., van der Gulden, J. W., Senden, T. F., & van’t Hof, B. (1996). Work related risk factors for musculoskeletal complaints in the nursing profession: Results of a questionnaire survey. Occupational and Environmental Medicine, 53(9), 636–641.CrossRefGoogle Scholar
  18. Faraj, S., and Azad, B. (2012). The materiality of technology: An affordance perspective. In: Leonardi, P. M., Nardi, B. A., Kallinikos, J. (Eds.) Materiality and organizing: Social interaction in a technological world. Oxford University Press, Oxford. pp. 237–258.Google Scholar
  19. Fayarda, A.-L., & Weeks, J. (2014). Affordances for practice. Information and Organization, 24(4), 236–249.CrossRefGoogle Scholar
  20. Fingas, R. (2015). IBM adopts Apple watch for internal fitness initiative & Watson-linked health app. http://appleinsider.com/articles/15/10/27/ibm-adopts-apple-watch-for-internal-fitness-initiative-watson-linked-health-app2016. Accessed 7 Sept 2016.
  21. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.CrossRefGoogle Scholar
  22. Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using PLS-graph - tutorial and annotated example. Communications of the Association for Information Systems, 16(5), 91–109.Google Scholar
  23. Gibson, J. J. (Ed.). (1979). The theory of affordances, the ecological approach to visual perception. Boston: Houghton Mifflin Harcourt.Google Scholar
  24. Greenhalgh, T., Robert, G., MacFarlane, F., Bate, P., & Kyriakidou, O. (2004). Diffusion of innovations in service organizations: Systematic review and recommendations. The Milbank Quarterly, 82(4), 581–629.CrossRefGoogle Scholar
  25. Grgecic, D., Holten, R., & Rosenkranz, C. (2015). The impact of functional affordances and symbolic expressions on the formation of beliefs. Journal of the Association for Information Systems, 16(7), 580–607.Google Scholar
  26. Hu, H., Xu, J., and Lee, D. L. (2010). PAM: An efficient and privacy-aware monitoring framework for continuously moving objects, IEEE Transactions on Knowledge and Data Engineering, 22(3), 404–419.Google Scholar
  27. Hutchby, I. (2001). Technologies, texts and affordances. Sociology, 35(2), 441–456.CrossRefGoogle Scholar
  28. Lee, S.-H., Nohb, S.-E., & Kim, H.-W. (2013). A mixed methods approach to electronic word-of-mouth in the open-market context. International Journal of Information Management, 33(4), 687–696.CrossRefGoogle Scholar
  29. Lekaa, S., Jaina, A., Zwetslootb, G., & Coxa, T. (2010). Policy-level interventions and work-related psychosocial risk management in the european union. Work & Stress: An International Journal of Work, Health & Organisations, 24(3), 298–307.CrossRefGoogle Scholar
  30. Leonardi, P. M. (2011). When flexible routines meet flexible technologies: affordance, constraint, and the imbrication of human and material agencies. MIS Quarterly, 35(1), 147–168.Google Scholar
  31. Leonardi, P. M. (2013). When does technology use enable network change in organizations? A comparative study of feature use and shared affordances. MIS Quarterly, 37(3), 749–775.Google Scholar
  32. Leonardi, P. M., & Barley, S. R. (2010). What’s under construction here? Social action, materiality, and power in constructivist studies of technology and organizing. The Academy of Management Annals, 4(1), 1–51.CrossRefGoogle Scholar
  33. Maier, J. R. A., Fadel, G.M., & Battisto, D. G. (2009). An affordance-based approach to architectural theory, design, and practice. Design Studies, 30(4), 393–414.Google Scholar
  34. Majchrzak, A., & Markus, M. L. (2012). Technology affordances and constraints in management information systems (MIS). In E. Kessler (Ed.), Encyclopedia of management theory. Thousand Oaks: Sage Publications.Google Scholar
  35. Markus, M. L., & Silver, M. S. (2008). A foundation for the study of IT effects: a new look at Desanctis and Poole’s concepts of structural features and spirit. Journal of the Association for Information Systems, 9(10), 609–632.Google Scholar
  36. McFarlane, D. C., and Latorella, K. A. (2002). The scope and importance of human interruption in humancomputer interaction design. Human-Computer Interaction, 17(1), 1–61.Google Scholar
  37. Moore, G., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.CrossRefGoogle Scholar
  38. Nieuwenhuijsen, K., Bruinvels, D., & Frings-Dresen, M. (2010). Psychosocial work environment and stress-related disorders, a systematic review. Occupational Medicine, 60(4), 277–286.CrossRefGoogle Scholar
  39. Norman, D. A. (1988). The psychology of everyday things. New York: Basic Books.Google Scholar
  40. Norman, D. A. (1999). Affordance, conventions, and design. ACM Interactions, 6(3), 38–43.CrossRefGoogle Scholar
  41. Olson, P. (2014). Wearable tech is plugging into health insurance. http://www.forbes.com/sites/parmyolson/2014/06/19/wearable-tech-health-insurance/2016. Accessed 7 Sept 2016.
  42. Oreg, S. (2003). Resistance to change: Developing an individual differences measure. Journal of Applied Psychology, 88(4), 680–693.CrossRefGoogle Scholar
  43. Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012). A review of wearable sensors and systems with application in rehabilitation. Journal of Neuroengineering and Rehabilitation, 9(1), 21.Google Scholar
  44. Patel, M. S., Asch, D. A., & Volpp, K. G. (2015). Wearable devices as facilitators, not drivers, of health behavior change. Journal of the American Medical Association, 313(5), 459–460.CrossRefGoogle Scholar
  45. Piccoli, G., & Pigni, F. (2013). Harvesting external data: the potential of digital data streams. MIS Quarterly Executive, 12(1), 53–64.Google Scholar
  46. Pozzi, G., Pigni, F., & Vitari, C. (2014) Affordance theory in the IS discipline: A review and synthesis of the literature. In Proceedings of the 20th Americas Conference on Information Systems, Savannah, 7-9 August 2014, pp. 1-12.Google Scholar
  47. Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). A critical look at the use of PLS-SEM in MIS quarterly. MIS Quarterly, 36(1), iii–xiv.Google Scholar
  48. Schwaibold, M., Gmelin, M., von Wagner, G., Schochlin, J., & Bolz, A. (2002). Key factors for personal health monitoring and diagnosis devices. In Proceedings of the 2nd Conference on Mobile Computing in Medicine, Heidelberg, 11 April 2002, pp. 143-150.Google Scholar
  49. Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information privacy: Measuring individuals’ concerns about organizational practices. MIS Quarterly, 20(2), 167–196.CrossRefGoogle Scholar
  50. Strauss, A. L., & Corbin, J. (1998). Basics of qualitative research: techniques and procedures for developing grounded theory (2ed.). Newbury Park: Sage.Google Scholar
  51. Sun, J., & Qu, Z. (2015). Understanding health information technology adoption: A synthesis of literature from an activity perspective. Information Systems Frontiers, 17(5), 1177–1190.CrossRefGoogle Scholar
  52. Swan, M. (2012). Health 2050: the realization of personalized medicine through crowdsourcing, the quantified self, and the participatory biocitizen. Journal of Personalized Medicine, 2(3), 93–118.CrossRefGoogle Scholar
  53. Thompson, G. (1966). The evaluation of public opinion. In B. Berelson & M. Janowitz (Eds.), Reader in public opinion and communication (pp. 7–12). New York: Free Press.Google Scholar
  54. van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), pp. 695-704.Google Scholar
  55. van der Veer, G. C., & van Welie, M. (2003). Dutch-designing for users and tasks from concepts to handles. In D. Diaper & A. S. Neville (Eds.), The handbook of task analysis for human-computer interaction (pp. 155–173). New Jersey: Lawrence Erlbaum Associates.Google Scholar
  56. Vitari, C., & Pigni, F. (2014). DDGS affordances for value creation. In L. Caporarello, B. Di Martino, & M. Martinez (Eds.), Smart organizations and smart artifacts (pp. 9–16). New York: Springer.Google Scholar
  57. Vyas, D., Fitz-Walter, Z., Mealy, E., Soro, A., Zhang, J., & Brereton, M. (2015). Exploring physical activities in an employer-sponsored health program. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, Seoul, 18–23 April 2015, pp. 1421–1426.Google Scholar
  58. Wang, N., Carte, T., & Schwarzkopf, A. (2015). How should technology affordances be measured? An initial comparison of two approaches. In Proceedings of the 21st Americas Conference on Information Systems, Fajardo, Puerto Rico, 13–15 August 2015, pp. 1–14.Google Scholar
  59. Wilson, J. R., & Sharples, S. (2015). Evaluation of human work, (4ed.). London: CRC Press.Google Scholar
  60. World Health Organization (1995). Global strategy on occupational health for all: the way to health at work, recommendation of the second meeting of the who collaborating centres in occupational health. http://www.who.int/occupational_health/globstrategy/en/. Accessed 7 Sept 2016.
  61. Wu, P. F. (2012). A mixed methods approach to technology acceptance research. Journal of the Association for Information Systems, 13(3), 172–187.Google Scholar
  62. Wu, D. J., Ding, M., & Hitt, L. M. (2013). IT implementation contract design: Analytical and experimental investigation of IT value, learning, and contract structure. Information Systems Research, 24(3), 787–801.CrossRefGoogle Scholar
  63. Zijlstra, F. R., Roe, R. A., Leonora, A. B., and Krediet, I. (1999). Temporal factors in mental work: Effects of interrupted activities. Journal of Occupational and Organizational Psychology, 72(2), 163–185.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Institute of Information ManagementUniversity of St. GallenSt. GallenSwitzerland
  2. 2.Swiss Graduate School of Public AdministrationUniversity of LausanneChavannes-près-RenensSwitzerland

Personalised recommendations