Big data initiatives in retail environments: Linking service process perceptions to shopping outcomes
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Given the enormous amount of data created through customers’ transactions in retail stores, it comes as no surprise that retailers are actively seeking initiatives to leverage big data and offer their customers superior services that provide mutual, previously unattainable benefits. Nonetheless, fulfilment of such a strategic aim requires customers to adopt and embrace emerging technology-driven services. Exploring customers’ perceptions of such big data initiatives in retail environments, we develop a model examining the effects of technology enablers and privacy concerns on critical shopping outcomes including repatronage intentions, store image, and intention to use medium in the context of recently identified service configurations. We conduct an exploratory study to understand customers’ reactions toward emerging shopping scenarios and to enhance our survey instrument and then conduct an online survey (n = 442) to test our model. We found that customers’ usefulness perceptions of emerging services positively affected their intentions to use medium, and that their privacy concerns about the amounts of personal information, being collected through emerging services, negatively affected their repatronage intentions and store image. We discuss the implications of our work for research and practice.
KeywordsBig data initiatives Retail stores Emerging service processes Technology enablers Privacy concerns Shopping outcomes
- Aloysius, J. A.,& Venkatesh, V. (2013). Mobile point-of-sale and loss prevention: An assessment of risk. Retail Industry Leaders Association. http://waltoncollege.uark.edu/lab/JAloysius/RILA%20Report/MobilePOSReport.pdf. Accessed 11 April 2016.
- Baesens, B., Bapna, R., Marsden, J. R., Vanthienen, J., & Zhao, J. L. (2014). Transformational issues of big data and analytics in networked business. MIS Quarterly, 38(2), 629–631.Google Scholar
- Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues for field settings. Boston, MA: Houghton Mifflin.Google Scholar
- Day, G. S., Schoemaker, P. J. H., & Gunther, R. E. (2004). Wharton on managing emerging technologies. New York: Wiley.Google Scholar
- Fosso Wamba, S., Edwards, A., & Sharma, R. (2012). Big data as a strategic enabler of superior emergency service management: Lessons from the New South Wales State Emergency Service. Society for Information Management and MIS Quarterly Executive Pre-ICIS 2012 SIM Academic Workshop, 1-3.Google Scholar
- Goller, B., & Hoffmann, S. (2013). Leveraging big data for precision in-store marketing: Turning real-time data into big-time insights. Retail Property Insights, 20(1), 30–36.Google Scholar
- Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
- Houston Chronicle (2016). http://smallbusiness.chron.com/push-vs-pull-supply-chain-strategy-77452.html. Accessed 11 April 2016.
- Laufer, R. S., Proshansky, H. M., & Wolfe, M. (1974). Some analytic dimensions of privacy. In R. Kuller (Ed.), Architectural psychology: Proceedings of the lund conference (pp. 353–372). Stroudsburg, PA: Dowden, Hutchinson, and Ross.Google Scholar
- Nunnally, J. C. (1978). Psychometric theory. New York, NY: McGraw-Hill.Google Scholar
- Ringle, C. M., Wende, S., & Becker, J. M. (2005). Smart PLS 2.0. http://www.smartpls.de. Accessed 13 Dec 2015.
- RIS Research. (2015). Store systems study: Making stores matter. http://risnews.edgl.com/retail-research/2015-RIS/IHL-Store-Systems-Study--Making-Stores-Matter97362. Accessed 11 April 2016.
- Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Belmont, CA: Wadsworth Cengage learning.Google Scholar
- Simmons, D. D. (1965). Invasion of privacy and judged benefit of personality-test inquiry. Journal of General Psychology, 79, 77–81.Google Scholar
- Straub, D., Boudreau, M. C., & Gefen, D. (2004). Validation guidelines for IS positivist research. The Communications of the Association for Information Systems, 13(1), 380–427.Google Scholar
- Venkatesh, V., Hoehle, H., Aloysius, J. A., & Burton, S. (2017). Leveraging customers’ mobile devices and auto-ID technologies in an in-store design and evaluation of auto-ID enabled shopping assistance artifacts: Two retail store laboratory experiments. MIS Quarterly.Google Scholar
- Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.Google Scholar
- Wirthman, L. (2013). Forbes. http://www.forbes.com/sites/emc/2013/12/16/what-your-cellphone-is-telling-retailers-about-you/#7196cb71df22. Accessed 11 April 2016.