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Big data initiatives in retail environments: Linking service process perceptions to shopping outcomes

  • Big Data Analytics in Operations & Supply Chain Management
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Abstract

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.

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Notes

  1. A pull-model supply chain is one where customers’ actual demands justify the entrance of products into the supply chain whereas in a push-model supply chain projected demands determine what enters the process (Houston Chronicle 2016).

References

  • 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.

  • Aloysius, J. A., Hoehle, H., & Venkatesh, V. (2016). Exploiting big data for customer and retailer benefits: A study of emerging mobile checkout scenarios. International Journal of Operations & Production Management, 36(4), 467–486.

    Article  Google Scholar 

  • Altintas, N., & Trick, M. (2014). A data mining approach to forecast behavior. Annals of Operations Research, 216(1), 3–22.

    Article  Google Scholar 

  • Alvesson, M., & Kärreman, D. (2007). Constructing mystery: Empirical matters in theory development. Academy of Management Review, 32(4), 1265–1281.

    Article  Google Scholar 

  • Angst, C. M., & Agarwal, R. (2009). Adoption of electronic health records in the presence of privacy concerns: The elaboration likelihood model and individual persuasion. MIS Quarterly, 33(2), 339–370.

    Article  Google Scholar 

  • 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 

  • Baker, J., Grewal, D., & Parasuraman, A. (1994). The influence of store environment on quality inferences and store image. Journal of the Academy of Marketing Science, 22(4), 328–339.

    Article  Google Scholar 

  • Bamberger, P. (2008). From the editors beyond contextualization: Using context theories to narrow the micro-macro gap in management research. Academy of Management Journal, 51(5), 839–846.

    Article  Google Scholar 

  • Blodgett, J. G., Hill, D. J., & Tax, S. S. (1997). The effects of distributive, procedural, and interactional justice on postcomplaint behavior. Journal of Retailing, 73(2), 185–210.

    Article  Google Scholar 

  • Brown, S. A. (2008). Household technology adoption, use, and impacts: Past, present, and future. Information Systems Frontiers, 10(4), 397–402.

    Article  Google Scholar 

  • Brown, S. A., Chervany, N. L., & Reinicke, B. (2007). What matters when introducing new information technology? Communications of the ACM, 50(9), 91–96.

    Article  Google Scholar 

  • Brown, S. A., Massey, A. P., & Ward, K. (2016). Handle mergers and acquisitions with care: The fragile nature of the user IT-service provider relationship. European Journal of Information Systems, 25(2), 170–186.

    Article  Google Scholar 

  • Chellappa, R. K., & Shivendu, S. (2010). Mechanism design for “free” but “no free disposal” services: The economics of personalization under privacy concerns. Management Science, 56(10), 1766–1780.

    Article  Google Scholar 

  • Chellappa, R. K., & Sin, R. G. (2005). Personalization versus privacy: An empirical examination of the online consumer’s dilemma. Information Technology and Management, 6(2–3), 181–202.

    Article  Google Scholar 

  • Chen, W., Song, J., Shi, L., Pi, L., & Sun, P. (2013). Data mining-based dispatching system for solving the local pickup and delivery problem. Annals of Operations Research, 203(1), 351–370.

    Article  Google Scholar 

  • Chongwatpol, J. (2015). Integration of RFID and business analytics for trade show exhibitors. European Journal of Operational Research, 244(2), 662–673.

    Article  Google Scholar 

  • Christopher, M., & Ryals, L. J. (2014). The supply chain becomes the demand chain. Journal of Business Logistics, 35(1), 29–35.

    Article  Google Scholar 

  • Chronopoulos, M., & Siddiqui, A. (2015). When is it better to wait for a new version? Optimal replacement of an emerging technology under uncertainty. Annals of Operations Research, 235(1), 177–201.

    Article  Google Scholar 

  • Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues for field settings. Boston, MA: Houghton Mifflin.

    Google Scholar 

  • Culnan, M. J., & Armstrong, P. K. (1999). Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation. Organization Science, 10(1), 104–115.

    Article  Google Scholar 

  • Culnan, M. J., & Bies, R. J. (2003). Consumer privacy: Balancing economic and justice considerations. Journal of Social Issues, 59(2), 323–342.

    Article  Google Scholar 

  • Davis, F. D., & Venkatesh, V. (2004). Toward preprototype user acceptance testing of new information systems: Implications for software project management. IEEE Transactions on Engineering Management, 51(1), 31–46.

    Article  Google Scholar 

  • Day, G. S., Schoemaker, P. J. H., & Gunther, R. E. (2004). Wharton on managing emerging technologies. New York: Wiley.

    Google Scholar 

  • Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61–80.

    Article  Google Scholar 

  • Dinev, T., McConnell, A. R., & Smith, H. J. (2015). Research commentary–Informing privacy research through information systems, psychology, and behavioral economics: Thinking outside the “APCO” box. Information Systems Research, 26(4), 639–655.

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  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.

  • Fosso Wamba, S., Akter, S., Coltman, T., & Ngai, E. W. T. (2015a). Guest editorial: Information technology-enabled supply chain management. Production Planning & Control, 26(12), 933–944.

    Article  Google Scholar 

  • Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015b). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246.

    Article  Google Scholar 

  • Froehle, C. M., & Roth, A. V. (2004). New measurement scales for evaluating perceptions of the technology-mediated customer service experience. Journal of Operations Management, 22(1), 1–21.

    Article  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 

  • Grace, D., & O’Cass, A. (2005). An examination of the antecedents of repatronage intentions across different retail store formats. Journal of Retailing and Consumer Services, 12(4), 227–243.

    Article  Google Scholar 

  • Guan, J. C., Yam, R. C., Mok, C. K., & Ma, N. (2006). A study of the relationship between competitiveness and technological innovation capability based on DEA models. European Journal of Operational Research, 170(3), 971–986.

    Article  Google Scholar 

  • Günther, O., & Spiekermann, S. (2005). RFID and the perception of control: The consumer’s view. Communications of the ACM, 48(9), 73–76.

    Article  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 

  • Hong, W., & Thong, J. Y. L. (2013). Internet privacy concerns: An integrated conceptualization and four empirical studies. MIS Quarterly, 37(1), 275–298.

    Article  Google Scholar 

  • Houston Chronicle (2016). http://smallbusiness.chron.com/push-vs-pull-supply-chain-strategy-77452.html. Accessed 11 April 2016.

  • Hui, K. L., Teo, H. H., & Lee, S. Y. T. (2007). The value of privacy assurance: An exploratory field experiment. MIS Quarterly, 33(1), 19–33.

    Article  Google Scholar 

  • Johns, G. (2006). The essential impact of context on organizational behavior. Academy of Management Review, 31(2), 386–408.

    Article  Google Scholar 

  • Kou, G., & Lou, C. (2012). Multiple factor hierarchical clustering algorithm for large scale web page and search engine clickstream data. Annals of Operations Research, 197(1), 123–134.

    Article  Google Scholar 

  • Laufer, R. S., & Wolfe, M. (1977). Privacy as a concept and a social issue: A multidimensional developmental theory. Journal of Social Issues, 33(3), 22–42.

    Article  Google Scholar 

  • 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.

  • Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114–121.

    Article  Google Scholar 

  • Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users’ information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research, 15(4), 336–355.

    Article  Google Scholar 

  • Maull, R., Geraldi, J., & Johnston, R. (2012). Service supply chains: A customer perspective. Journal of Supply Chain Management, 48(4), 72–86.

    Article  Google Scholar 

  • Nunnally, J. C. (1978). Psychometric theory. New York, NY: McGraw-Hill.

    Google Scholar 

  • Ohkubo, M., Suzuki, K., & Kinoshita, S. (2005). RFID privacy issues and technical challenges. Communication of the ACM, 48(9), 66–71.

    Article  Google Scholar 

  • Pavlou, P. A. (2011). State of the information privacy literature: Where are we now and where should we go. MIS Quarterly, 35(4), 977–988.

    Article  Google Scholar 

  • Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

    Article  Google Scholar 

  • Rai, A., & Bajwa, D. S. (1997). An empirical investigation into factors relating to the adoption of executive information systems: An analysis of EIS for collaboration and decision support. Decision Sciences, 28(4), 939–974.

    Article  Google Scholar 

  • Rai, A., & Patnayakuni, R. (1996). A structural equation model for CASE adoption behaviour. Journal of Management Information Systems, 13(2), 205–234.

    Article  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.

  • Sampson, S. E., & Spring, M. (2012). Customer roles in service supply chains and opportunities for innovation. Journal of Supply Chain Management, 48(4), 30–50.

    Article  Google Scholar 

  • Schoenherr, T., & Speier-Pero, C. (2015). Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36(1), 120–132.

    Article  Google Scholar 

  • 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 

  • Singh, J. (1988). Consumer complaint intentions and behavior: Definitional and taxonomical issues. The Journal of Marketing, 52(1), 93–107.

    Article  Google Scholar 

  • Smith, H. J., Dinev, T., & Xu, H. (2011). Information privacy research: An interdisciplinary review. MIS Quarterly, 35(4), 989–1016.

    Article  Google Scholar 

  • Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information privacy: Measuring individuals’ concerns about organizational practices. MIS Quarterly, 20(2), 167–196.

    Article  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 

  • Thong, J. Y. L., Hong, W., & Tam, K. Y. (2002). Understanding user acceptance of digital libraries: What are the roles of interface characteristics, organizational context, and individual differences? International Journal of Human–Computer Studies, 57(3), 215–242.

    Article  Google Scholar 

  • Thong, J. Y. L., Hong, S. J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human–Computer Studies, 64(9), 799–810.

    Article  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.

  • Venkatesh, V., & Agarwal, R. (2006). Turning visitors into customers: A usability-centric perspective on purchase behavior in electronic channels. Management Science, 52(3), 367–382.

    Article  Google Scholar 

  • Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.

    Article  Google Scholar 

  • Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: The competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Quarterly, 32(3), 483–502.

    Article  Google Scholar 

  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.

    Article  Google Scholar 

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

    Article  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 

  • Wang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98–110.

    Article  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.

  • Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85–102.

    Article  Google Scholar 

  • Zhong, R. Y., Huang, G. Q., Lan, S., Dai, Q. Y., Chen, X., & Zhang, T. (2015). A big data approach for logistics trajectory discovery from RFID-enabled production data. International Journal of Production Economics, 165, 260–272.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Supply Chain Management Department, University of Arkansas, Fayetteville, AR, USA

    John A. Aloysius

  2. Information Systems Department, University of Arkansas, Fayetteville, AR, USA

    Hartmut Hoehle, Soheil Goodarzi & Viswanath Venkatesh

Authors
  1. John A. Aloysius
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  2. Hartmut Hoehle
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  3. Soheil Goodarzi
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  4. Viswanath Venkatesh
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Corresponding author

Correspondence to Viswanath Venkatesh.

Appendices

Appendix 1: Demographics of scenario survey respondents

Demographic

Category

Percentage

Gender

Men

42.3

Women

57.7

Age groups

Under 20

5.0

20–29

63.1

30–39

17.9

40–49

8.1

50–59

4.8

60 or older

1.1

Annual income (in USD)

Under 10,000

12.9

10,000–19,000

9.3

20,000–29,000

12.9

30,000–39,000

9.5

40,000–49,000

10.6

50,000–74,000

19.7

75,000–99,000

14.0

100,000–150,000

7.7

Over 150,000

3.4

Job

Banking and finance

29.7

Education

1.6

Engineering

7.7

Government and military

3.6

ICT

34.6

Insurance and real estate

13.3

Marketing and advertising

1.4

Retail and wholesale

2.9

Student

1.8

Other

3.4

  1. n = 442

Appendix 2: Example of an emerging shopping scenario

Thank you for agreeing to participate in our Mobile Shopping study. This is what mobile shopping means. Imagine that on your visit to the store you select all the items you would like to purchase. You take your shopping cart to an employee who scans all items you put into your shopping cart. The picture below illustrates the mobile scanning process.

figure a

Once you have completed shopping, you take your shopping cart to the checkout area. The checkout area is equipped with mobile payment terminals that can access the information stored on the employee’s mobile scanning device. To check out, you swipe your mobile phone over the terminal and authorize the payment on your mobile phone. The picture below illustrates the mobile payment process.

figure b

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Aloysius, J.A., Hoehle, H., Goodarzi, S. et al. Big data initiatives in retail environments: Linking service process perceptions to shopping outcomes. Ann Oper Res 270, 25–51 (2018). https://doi.org/10.1007/s10479-016-2276-3

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  • DOI: https://doi.org/10.1007/s10479-016-2276-3

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