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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.

Mobile self-checkout refers to scanning products using a mobile device inside a brick-and-mortar store and completing the checkout process on mobile devices. Even though mobile self-checkout has been used in other industries for several years, it is a new application in the fashion industry and only limited numbers of retailers have implemented mobile self-checkout in their stores. The purpose of this study is to understand consumers' acceptance of mobile self-checkout in fashion retail stores by analyzing determinants of using a new system.

Part of the Unified Theory of Acceptance and Use of Technology (UTAUT) was used as a theoretical framework. Openness to experience, variety seeking and adventure shopping were added to the model. Empirical data (with 229 valid responses) were collected from the top 20 metropolitan areas in the US via Qualtrics Panel services. Exploratory factor analysis, confirmatory factor analysis, structural equation modeling and multi-group moderation were used to estimate construct validity and test the proposed hypotheses and theoretical framework.

The results indicated that consumers' intentions toward using mobile self-checkout in fashion retail stores were predicted by facilitating conditions, social influence and openness to experience. Moreover, consumers' previous experience of using mobile self-checkout in fashion retail stores moderated the path from facilitating conditions to behavioral intention and the path from social influence to behavioral intention. In addition, different genders and smartphone usage frequency did not vary significantly on the model paths.

The findings show how fashion retailers can understand consumers' preference and their willingness to use mobile self-checkout in fashion retail stores. Moreover, the authors addressed ways for fashion retailers to promote mobile self-checkout in the future.

As a new technology in the fashion industry, literature is deficient concerning consumers' intention to adopt mobile self-checkout. This research provided suggestions for fashion retailers about adopting and improving acceptance of mobile self-checkout. Results will lead to theoretical and managerial implications for future technology development.

Mobile technologies are increasingly used as a data source to enable big data analytics that enable inventory control and logistics planning for omnichannel businesses. The purpose of this paper is to focus on the use of mobile technologies to facilitate customers’ shopping in physical retail stores and associated implementation challenges.

First, the authors introduce three emerging mobile shopping checkout processes in the retail store. Second, the authors suggest that new validation procedures (i.e. exit inspections) necessary for implementation of mobile-technology-enabled checkout processes may disrupt traditional retail service processes. The authors propose a construct labeled “tolerance for validation” defined as customer reactions to checkout procedures. The authors define and discuss five dimensions – tolerance for: unfair process; changes in validation process; inconvenience; mistrust; and privacy intrusion. The authors develop a measurement scale for the proposed construct and conduct a study among 239 customers.

The results show that customers have higher tolerance for validation under scenarios in which mobile technologies are used in the checkout processes, as compared to the traditional self-service scenario in which no mobile technology is used. In particular, the customers do not show a clear preference for specific mobile shopping scenarios.

These findings contribute to our understanding of a challenge that omnichannel businesses may face as they leverage data from digital technologies to enhance collaborative planning, forecasting, and replenishment processes. The proposed construct and measurement scales can be used in future work on omnichannel retailing.

The present paper explores the concept of a cashier-free checkout system from customers' perspective. Thus, the study attempts to find out the overall sentiment of people towards cashier-free checkout retail stores.

The study culled 7,348 comments from 10 videos about the Amazon Go store available on YouTube. The paper extracted positive and negative sentiments as well as their affective scores from the comments using Python 3.10.

The results revealed that all key service attributes of Amazon Go stores have a positive impact on customers' overall in-store shopping experience except pay. Similarly, the viewpoints expressed about these stores positively influence their overall in-store shopping experiences except for job. The job has an adverse effect on the outcome variable compared to like and support.

This paper states how a cashier-free checkout system is a better and more innovative retailing solution than in-store cash counter practices. Additionally, the paper provides guidance on how to deal with textual data and quantitative ratings separately when establishing empirical relationships.

Retailers and suppliers are facing the challenge of reconfiguring systems to accommodate increasingly mobile customers expecting multichannel options supporting quick and secure digital payment. The purpose of this paper is to harness the learning from the implementation of self-checkout and combines it with available information relating to mobile scanning and mobile point-of-sale (MPOS).

In review of the literature, the paper provides an overview of different modes of mobile payment systems, and a consideration of some of the benefits that they offer to retailers and their customers. The main focus, drawing upon telephone interviews with retail security professionals in Australia and New Zealand, is on anticipating and mitigating against the potential risks, vulnerabilities and impact on shrinkage.

With the market being flooded with software and products, retailers are exposed to a compelling case for mobile payment, but it was found that they are not as cognisant of the potential risks.

Further research is needed on the different permutations of mobile POS and how it impacts on the customer journey and rates of internal and external theft.

Suggestions for future empirical research on the risks and vulnerabilities that moving to mobile payment can usher in are provided.

The paper links research from diverse fields, in particular criminology, to elucidate the potential impact of mobile technologies on retail theft and internal technological and process issues, before offering possible solutions.

The purpose of this paper is to investigate the role of in-store mobile usage on purchase decision making in order to understand whether and to what extent the use of the device changes the shopper behavior in terms of planned and unplanned purchases even across different retail store formats.

Data were obtained using a structured questionnaire from 406 respondents interviewed in hypermarket and discount stores, after the checkout. Data were measured through t-tests and the analysis of variance.

The mobile intensifies a process of preparation making it popular and the same across the different store formats, confirming how the growing convergence, making store formats less distinctive in the eyes of the consumer, has somewhat flattened and standardized the pre-shopping out-of-store preparation.

The pervasiveness and the versatility of the mobile, and its ability to influence the decision-making processes, leads to important managerial questions and implications regarding the effectiveness of in-store marketing initiatives and the need to review the mix of out-of-store and in-store investments, with the knowledge that the consumer will continue to become even more prepared and well informed in the future.

Mobile devices could be used out-of-store, as a tool for shopping preparation, and in-store as a tool for self-regulation. Therefore, it becomes crucial to understand how the mobile influences the decision-making process as well as the buying behavior of shoppers.

This paper aims to investigate the effect of artificial intelligence (AI)-enabled checkouts on consumers’ purchase intent and evaluations of the retailing atmosphere. It also offers novel findings pertaining to the mediating role of arousal and moderation by innovativeness importance on consumers’ responses toward AI-enabled checkouts.

Three pilot studies, two field studies and one online experiment featuring 1,567 respondents were conducted by manipulating checkout methods.

AI-enabled checkouts lead to a higher level of arousal, which, in turn, yields more favorable store atmosphere evaluations and higher purchase intent. Furthermore, the positive effect of AI-enabled checkouts is moderated by consumers’ innovativeness importance.

This research contributes to the emerging body of AI research and demonstrates a novel perspective by illuminating that under certain circumstances, AI-enabled checkouts lead to more positive outcomes relating to store atmosphere evaluations and purchase intent, as well as the unintended effect of heightened arousal.

This study shows how marketers and practitioners can promote consumers’ evaluations and patronage likelihood effectively by harnessing AI-enabled checkouts for those who consider innovativeness as important.

This research documents the novel findings of how and when AI-enabled checkouts garner more favorable consumer responses.

Purpose: This paper presents a review of the current state and potential capabilities for application of robots, artificial intelligence and automated services (RAISA) in hotel companies.

Design/methodology/approach: A two-step approach was applied in this study. First, the authors make a theoretical overview of the robots, artificial intelligence and service automation (RAISA) in hotels. Second, the authors make a detailed overview of various case studies from global hotel practice.

Findings: The application of RAISA in hotel companies is examined in connection with the impact that technology has on guest experience during each of the five stages of the guest cycle: pre-arrival, arrival, stay, departure, assessment.

Research implications: Its implications can be searched with respect to future research. It deals with topics such as how different generations (guests and employees) perceive RAISA in the hotel industry and what is the attitude of guests in different categories of hotels (luxury and economy) towards the use of RAISA. It also shows what is the attitude of different types of tourists (holiday, business, health, cultural, etc.) and what kinds of robots (androids or machines) are more appropriate for different types of hotel operations.

Practical implications: The implications are related to the improvement of operations and operational management, marketing and sales, enhancement of customer experience and service innovation, training and management.

Originality/value: This book chapter complements and expands research on the role of RAISA in the hotel industry and makes some projections about the use of technologies in the future.