Exploiting Big Data for Customer and Retailer Benefits: A Study of Emerging Mobile Checkout Scenarios
Abstract: 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. Based on interviews and focus groups with leading retailers, technology providers and service providers, we identified several emerging in-store mobile scenarios; and based on customer focus groups, we identified potential drivers and inhibitors of use. 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: (a) location—whether they were fixed by location or mobile and (b) autonomy—whether they were assisted by store employees or unassisted. We found no evidence that individuals found mobile scanning to be either enjoyable or to have utilitarian benefit. We also did not find greater privacy concerns with mobile payments scenarios. We did, however, in our post-hoc analysis find that mobile unassisted scanning was preferred to mobile assisted scanning. We 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, we found that individual differences including computer self-efficacy, personal innovativeness, and technology anxiety were strong predictors of adoption of mobile scanning and payment scenarios.