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Exploiting big data for customer and retailer benefits

Exploiting big data for customer and retailer benefits Purpose – Mobile checkout in the retail store has the promise to be a rich source of big data. It is also a means to increase the rate at which big data flows into an organization as well as the potential to integrate product recommendations and promotions in real time. However, despite efforts by retailers to implement this retail innovation, adoption by customers has been slow. The paper aims to discuss these issues. Design/methodology/approach – Based on interviews and focus groups with leading retailers, technology providers, and service providers, the authors identified several emerging in-store mobile scenarios; and based on customer focus groups, the authors identified potential drivers and inhibitors of use. Findings – A first departure from the traditional customer checkout process flow is that a mobile checkout involves two processes: scanning and payment, and that checkout scenarios with respect to each of these processes varied across two dimensions: first, location – whether they were fixed by location or mobile; and second, autonomy – whether they were assisted by store employees or unassisted. The authors found no evidence that individuals found mobile scanning to be either enjoyable or to have utilitarian benefit. The authors also did not find greater privacy concerns with mobile payments scenarios. The authors did, however, in the post hoc analysis find that mobile unassisted scanning was preferred to mobile assisted scanning. The authors also found that mobile unassisted scanning with fixed unassisted checkout was a preferred service mode, while there was evidence that mobile assisted scanning with mobile assisted payment was the least preferred checkout mode. Finally, the authors found that individual differences including computer self-efficacy, personal innovativeness, and technology anxiety were strong predictors of adoption of mobile scanning and payment scenarios. Originality/value – The work helps the authors understand the emerging mobile checkout scenarios in the retail environment and customer reactions to these scenarios. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Operations & Production Management Emerald Publishing

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0144-3577
DOI
10.1108/IJOPM-03-2015-0147
Publisher site
See Article on Publisher Site

Abstract

Purpose – Mobile checkout in the retail store has the promise to be a rich source of big data. It is also a means to increase the rate at which big data flows into an organization as well as the potential to integrate product recommendations and promotions in real time. However, despite efforts by retailers to implement this retail innovation, adoption by customers has been slow. The paper aims to discuss these issues. Design/methodology/approach – Based on interviews and focus groups with leading retailers, technology providers, and service providers, the authors identified several emerging in-store mobile scenarios; and based on customer focus groups, the authors identified potential drivers and inhibitors of use. Findings – A first departure from the traditional customer checkout process flow is that a mobile checkout involves two processes: scanning and payment, and that checkout scenarios with respect to each of these processes varied across two dimensions: first, location – whether they were fixed by location or mobile; and second, autonomy – whether they were assisted by store employees or unassisted. The authors found no evidence that individuals found mobile scanning to be either enjoyable or to have utilitarian benefit. The authors also did not find greater privacy concerns with mobile payments scenarios. The authors did, however, in the post hoc analysis find that mobile unassisted scanning was preferred to mobile assisted scanning. The authors also found that mobile unassisted scanning with fixed unassisted checkout was a preferred service mode, while there was evidence that mobile assisted scanning with mobile assisted payment was the least preferred checkout mode. Finally, the authors found that individual differences including computer self-efficacy, personal innovativeness, and technology anxiety were strong predictors of adoption of mobile scanning and payment scenarios. Originality/value – The work helps the authors understand the emerging mobile checkout scenarios in the retail environment and customer reactions to these scenarios.

Journal

International Journal of Operations & Production ManagementEmerald Publishing

Published: Apr 4, 2016

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