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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
  • Published:
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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).

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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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