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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.

In the aftermath of data breaches, many firms offer compensation to affected customers to recover from damaged customer sentiments. To understand the effectiveness of such compensation offerings, Goode et al. (2017) examined the effects of compensation offered by Sony following the PlayStation Network breach in 2011. Although Goode et al. (2017) present key insights on data breach compensation, it is unclear whether their findings generalize beyond the context of subscription-based gaming platforms whose customers are young and experience substantial switching costs. To address this issue, we conducted a methodological replication in a retail context with low switching costs.

In our replication, we examine the effects of compensation offered by Home Depot in the aftermath of its data breach in 2014. Home Depot is the largest home improvement retailer in the US and presents a substantially different context. Data were collected from 901 participants using surveys.

Our results were consistent with the original study. We found that in retail breaches, effective compensation needs to meet customers' expectations because overcompensation or undercompensation leads to negative outcomes, such as decreased repurchase intention.

Our study provides insights into the effectiveness of compensation in the retail context and confirms the findings of Goode et al. (2017).

Big Data Analytics provides a multitude of opportunities for organizations to improve service operations, but it also increases the threat of external parties gaining unauthorized access to sensitive customer data. With data breaches now a common occurrence, it is becoming increasingly plain that while modern organizations need to put into place measures to try to prevent breaches, they must also put into place processes to deal with a breach once it occurs. Prior research on information technology security and services failures suggests that customer compensation can potentially restore customer sentiment after such data breaches. The paper aims to discuss these issues.

In this study, the authors draw on the literature on personality traits and social influence to better understand the antecedents of perceived compensation and the effectiveness of compensation strategies. The authors studied the propositions using data collected in the context of Target’s large-scale data breach that occurred in December 2013 and affected the personal data of more than 70 million customers. In total, the authors collected data from 212 breached customers.

The results show that customers’ personality traits and their social environment significantly influences their perceptions of compensation. The authors also found that perceived compensation positively influences service recovery and customer experience.

The results add to the emerging literature on Big Data Analytics and will help organizations to more effectively manage compensation strategies in large-scale data breaches.

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.

Many manufacturers are exploring adopting smart technologies in their operations, also referred to as the shift towards “Industry 4.0”. Employees' contribution to high-tech initiatives is key to successful Industry 4.0 technology adoption, but few studies have examined the determinants of employee acceptance. This study, therefore, aims to explore how managers affect employees' acceptance of Industry 4.0 technology, and, in turn, Industry 4.0 technology adoption.

Rooted in the unified theory of acceptance and use of technology model and social exchange theory, this inductive research follows an in-depth comparative case study approach. The two studied Dutch manufacturing firms engaged in the adoption of Industry 4.0 technologies in their primary processes, including cyber-physical systems and augmented reality. A mix of qualitative methods was used, consisting of field visits and 14 semi-structured interviews with managers and frontline employees engaged in Industry 4.0 technology adoption.

The cross-case comparison introduces the manager's need to adopt a transformational leadership style for employees to accept Industry 4.0 technology adoption as an organisational-level factor that extends existing Industry 4.0 technology user acceptance theorising. Secondly, manager's and employee's recognition and serving of their own and others' emotions through emotional intelligence are proposed as an additional individual-level factor impacting employees' acceptance and use of Industry 4.0 technologies.

Synthesising these insights with those from the domain of Organisational Behaviour, propositions were derived from theorising the social aspects of effective Industry 4.0 technology adoption.