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Retailers are implementing technology-enabled mobile checkout processes in their stores to improve service quality, decrease labor costs and gain operational efficiency. These new checkout processes have increased customer convenience primarily by providing them autonomy in sales transactions in that store employee interventions play a reduced role. However, this autonomy has the unintended consequence of altering the checks and balances inherent in a traditional employee-assisted checkout process. Retailers, already grappling with shoplifting, with an estimated annual cost of billions of dollars, fear that the problem may be exacerbated by mobile checkout and concomitant customer autonomy. The purpose of this paper is to understand the effect of mobile checkout processes in retail stores on cybercrime in the form of shoplifting enabled by a technology transformed the retail environment.

The authors conducted an online survey of a US sample recruited from a crowdsourced platform. The authors test a research model that aims to understand the factors that influence the intention to shoplift in three different mobile checkout settings − namely, smartphone checkout settings, store-provided mobile device checkout settings, and employee-assisted mobile checkout settings − and compare it with a traditional fixed location checkout setting.

The authors found that, in a smartphone checkout setting, intention to shoplift was driven by experiential beliefs and peer influence, and experiential beliefs and peer influence had a stronger effect for prospective shoplifters when compared to experienced shoplifters; in a store-provided mobile devices checkout setting, experiential beliefs had a negative effect on shoplifters’ intention to shoplift and the effect was weaker for prospective shoplifters when compared to experienced shoplifters. The results also indicated that in an employee-assisted mobile checkout setting, intention to shoplift was driven by experiential beliefs and peer influence, and experiential beliefs had a stronger effect for prospective shoplifters when compared to experienced shoplifters.

This study is the among the first, if not first, to examine shoplifters’ intention to shoplift in mobile checkout settings. We provide insights into how those who may not have considered shoplifting in less favorable criminogenic settings may change their behavior due to the autonomy provided by mobile checkout settings and also provide an understanding of the shoplifting intention for both prospective and experienced shoplifters in different mobile checkout settings.

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.

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.

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.

The work helps the authors understand the emerging mobile checkout scenarios in the retail environment and customer reactions to these scenarios.

The purpose of this paper is to identify variables important to acceptance and use of advanced technologies by apparel retail employees and to recommend management strategies for effective technology integration in retail stores.

Current or past retail employees (N=71) were introduced to and given time to use three technologies – a 3D body scanner, product configurator, and social networking – in a laboratory setting using a within subjects design. A questionnaire measured participants’ perceptions of each technology in terms of usefulness, enjoyment, ease of use, task importance, technology self-efficacy – overall participant confidence in using new technology – and usage intent.

Results showed that employee's perceived usefulness of technology was a mediating influence on usage intent for all three technology types. Enjoyment also emerged as a significant mediator for 3D body scanning technology usage intent. Employees’ high self-efficacy scores corresponded to higher ratings for usefulness of each technology, suggesting that more technologically confident employees would be more likely to use the three types of technology presented in this study as part of their work.

The controlled environment of the laboratory setting may limit the generalizability of results to actual retail store settings. Future evaluations of technology usage in actual retail store environment involving both employees and customers are recommended.

This research explores the use of advanced in-store technologies from the perspective of apparel retail employees, applying the Technology Acceptance Model. It provides insight as to why employees accept and use innovative technologies that are relevant to their jobs and increase and enhance the points of contact between employees and customers. This knowledge of employee technology usage in the store environment could be used to improve job performance and job satisfaction – issues that often confront apparel retailers.