Indoor positioning systems have attracted considerable attention from practitioners and firms seeking to optimize the consumer shopping experience with the goal of attaining increased revenue and profitability. Acknowledging the importance of indoor positioning systems in store layout optimization, we conducted a field experiment for 11 months in order to develop algorithms for connecting indoor positioning data with customer transaction data. Using fingerprinting as a primary data collection technique, we compared positioning and transaction data before and after critical store layout optimization decisions in order to identify which customer movement patterns generated the highest sales. In contrast to previous works on indoor positioning systems, which focused solely on developing algorithms or techniques to increase accuracy rates, our algorithms in principle integrate computing and marketing perspectives. Our findings can be applied to store layout optimization and personalized marketing.

Connected cars, which are vehicles connected to wireless networks through the convergence of automotive and information technologies, have become an important topic of academic and industrial research on automobiles. In this research, we conducted a field experiment to understand vehicle maintenance mechanisms of a connected car platform. Specifically, we investigated the feasibility of prognostics and health management under different driving circumstances, with varying vehicle models, vehicle conditions, drivers' propensity for speeding, and road conditions. We collected sensor data through a two-stage model of vehicle communication using an on-board diagnostics scanner and data transmission using wireless communication. We found that device defects can be predicted based on driving situations such as the driving mode, mechanical characteristics, and a driver's speeding propensity.

PurposeAlthough Leader-Member Exchange (LMX) has been studied within the domain of organizational psychology, it has not received major attention from information systems literature and researchers. Drawing on LMX theory and the theory of reasoned interaction, this study investigates the roles of LMX, individual member's attitude toward system adoption, supervisor influence and goal commitment on mandatory system implementation. Specifically, our model investigates the moderating role of LMX and how it can be interpreted for an individual member's system adoption in an organization.Design/methodology/approachPartial least square (PLS) technique with the field survey of 148 participants was used for this analysis.FindingsThe empirical test results in the field setting show that attitude and supervisor influence positively affect goal commitment in the system implementation. Furthermore, LMX is a significant moderator between attitude and goal commitment within organizations, but this moderating effect is not valid in the relationship between supervisor influence and goal commitment since it is a more complex process influenced by variables other than supervisor influence.Originality/valueThe results showed that the model in this research has high explanatory and predictive power and is valuable in offering insights and guidance for implementers initiating technology-related changes within organizations.

Within the technology acceptance literature, the issue of top management support and commitment has been studied extensively; however, the issue of leadership per se has not been addressed directly. A missing piece of the leadership puzzle as it relates to technology acceptance is an exploration of how top management support gets translated in the organizational hierarchy. This study introduces leader-member exchange (LMX) to better understand this missing piece. Specifically, this research explores the role direct supervisors play in the acceptance process by end users based on the moderated model of LMX and supervisor influence. The empirical test results in the field setting show that LMX is a significant moderator for most of the technology acceptance variables within organizations. The study explores the role of the quality of the relationship between supervisors and employees as end users. It also highlights the role of LMX and supervisor influence as a conduit for the acceptance process among end users in the organization.

Retailers need accurate movement pattern analysis of human-tracking data to maximize the space performance of their stores and to improve the sustainability of their business. However, researchers struggle to precisely measure customers’ movement patterns and their relationships with sales. In this research, we adopt indoor positioning technology, including wireless sensor devices and fingerprinting techniques, to track customers’ movement patterns in a fashion retail store over four months. Specifically, we conducted three field experiments in three different timeframes. In each experiment, we rearranged one element of the visual merchandising display (VMD) to track and compare customer movement patterns before and after the rearrangement. For the analysis, we connected customers’ discrete location data to identify meaningful patterns in customers’ movements. We also used customers’ location and time information to match identified movement pattern data with sales data. After classifying individuals’ movements by time and sequences, we found that stay time in a particular zone had a greater impact on sales than the total stay time in the store. These results challenge previous findings in the literature that suggest that the longer customers stayed in a store, the more they purchase. Further, the results confirmed that effective store rearrangement could change not only customer movement patterns but also overall sales of store zones. This research can be a foundation for various practical applications of tracking data technologies.