On the Ethical Implications of Big Data in Service Systems
Big data analytics is a fast evolving phenomenon, and understanding its impact on service systems is a key research priority for service science. However, there is very little knowledge related to the potential ethical implications associated with the use of big data analytics in service today. This chapter therefore aims to identify some ethical implications that can arise in data-driven service systems. It is relevant to those who use big data to generate new value propositions, and for those who cocreate value in this context. The chapter also aims to inform managerial and policy guidelines for implementing, and ethically benefiting from, big data analytics in service.
KeywordsAlgorithmic decision making Big data Ethics Society
- Asadi-Someh, I., Breidbach, C.F., Davern, M.J., Shanks, G. (2016). “Ethical Implications of Big Data Analytics,” Proceedings of the 24th European Conference on Information Systems, Paper 24.Google Scholar
- Beverungen, D., Breidbach, C. F., Poeppelbuss, J., Tuunainen, V. K. (2016). “CFP ISJ: Smart Service Systems: An Interdisciplinary Perspective,” Information Systems Journal.Google Scholar
- Breidbach, C. F., and Maglio, P.P. (2015). “A Service Science Perspective on the Role of ICT in Service Innovation,” Proceedings of the 23rd European Conference on Information Systems (ECIS), Paper 33.Google Scholar
- Brust, L., Breidbach, C. F., Antons, D. and T. O. Salge (2017). “Service-Dominant Logic and Information Systems Research: A Review and Analysis Using Topic-Modeling”, Proceedings of the 38th International Conference on Information Systems (ICIS). Available online: http://aisel.aisnet.org/icis2017/ServiceScience/Presentations/7/ [accessed, January 28th 2018].
- Economist (2010), “Data, Data Everywhere”. Available online at http://www.economist.com/node/15557443 [Accessed April 25th 2013].
- Maglio, P. P., Breidbach, C. F. (2014) “Service Science: Toward Systematic Service System Innovation”. Bridging Data and Decisions, INFORMS Tutorials Series. Ed. by A. Newman, J. Leung, and J. C. Smith. Catonsville, MD, 161–170.Google Scholar
- Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A. H. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity, McKinsey Global Institute, San Francisco, CA.Google Scholar
- Martin, K. E. (2015). “Ethical Issues in the Big Data Industry,” MIS Quarterly Executive (14)2, 67–85.Google Scholar
- Mayer-Schönberger, V., Cukier, K. (2013). Big Data: A Revolution that will Transform how we Live, Work, and Think. Houghton Mifflin Harcourt.Google Scholar
- McAfee, A., Brynjolfsson, E., (2012), “Big Data: The Management Revolution”, Harvard Business Review, October, 61–68.Google Scholar
- Newell, S., Marabelli, M. (2015). “Strategic Opportunities (and Challenges) of Algorithmic Decision-making: A Call for Action on the Long-term Societal Effects of ‘Datification,’” The Journal of Strategic Information Systems, 1–12.Google Scholar
- Schroeck, M., R. Shockley, J. Smart, D. Romero-Morales and P. Tufano (2012) Analytics: The Real-World Use of Big Data, IBM Institute for Business Value, Somers, NY, USA.Google Scholar
- Srinivasan, A., Breidbach, C. F., Kolb, D. G. (2015). “Service Science,” Wiley Encyclopedia of Management, (7), 1–3.Google Scholar