Soyean Kim’s research while affiliated with Kyung Hee University and other places

This research seeks to determine whether IT leaders are willing to leverage Artificial Intelligence (AI) recommendations to augment their decision-making when considering which IT projects to select based on the projects’ contribution towards organizational goals and objectives and the IT leaders’ level of knowledge and trust in AI. The research design utilized a quantitative survey of 113 IT leaders to understand their willingness to accept AI recommendations that augment their own decision-making while considering organizational criteria for selecting the best IT projects to invest in. AI recommendations were measured based on factors contained within a conceptual AI-driven online recommendation system, while decision-making was measured based on factors such as uncertainty, consequences of decisions, information & goals, motivation, and self-regulation that were designed and validated to examine certain aspects that influence decision-making. IT project contributions were measured based on relevance, risk, reasonableness, basis research return, and business return associated with the project. The research results showed the relationship between IT Project Contributions and IT leader decision was significant in that the leader's perception of IT project contributions towards organizational goals and objectives has a positive influence on the IT project selection decision. Unfortunately, the remaining hypothesized relationships showed no significant outcomes. The research results did not show that IT Leader Decisions may be influenced by AI recommendations, nor the greater knowledge of AI an IT Leader has, the stronger the relationship will be in support of or against AI recommendations, nor that the greater trust in AI an IT Leader has, the stronger the relationship will be in support of or against AI recommendations. This research will provide important insights to understand IT project selection and human-AI collaboration by IT leaders in organizations.

Disclosure of intimate personal stories is an essential part of many marketplace conversations. In four experiments, this research investigates whether a social media influencer’s sharing of sensitive self-disclosure (i.e., personal information involving risk and vulnerability) can either enhance or diminish their persuasive impact. In Study 1, we find that the impact of sensitive self-disclosure on persuasion is contingent on the expected types of influencer-audience relationships (communal vs. exchange). An influencer’s sensitive self-disclosure enhances persuasion when the expected influencer-audience relationship is communal, characterized by emotional connection and mutual care. In exchange relationships, where the audience expects a more information-oriented approach, sensitive self-disclosure diminishes the influencer’s persuasive impact. Study 2 demonstrates that these effects are mediated by trust in communal relationships and by perceived violations of relationship norms in exchange relationships. Studies 3 and 4 identify the boundary conditions for these effects. Study 3 shows that extremely sensitive self-disclosure, which triggers a norm violation, reduces persuasion in a communal relationship. Study 4 reveals that solicited sensitive self-disclosure, which triggers conflicting norms, increases persuasion in an exchange relationship. This research widens the scope of persuasion literature by incorporating sensitive self-disclosure as a novel framework for understanding online influencer-audience persuasion.

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.

Purpose Although 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/approach Partial least square (PLS) technique with the field survey of 148 participants was used for this analysis. Findings The 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/value The 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.

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.

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.

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.

This research examines the relationship between expectations and concerns in the general public's intention to accept the Korea-China FTA. It empirically analyzes whether the image of Korea-China bilateral relations and the perception of Chinese products influence individuals' intentions to accept the ETA with a mediating effect (Study I). The results indicate that the negative impact of FTA concerns on its acceptance appears to be smaller than the positive impact of FTA expectations. We also conduct an opinion mining analysis to investigate individuals' feelings, opinions, and sentiments on the Korea-China ETA and test the hypothesized relationships (Study 2). Further, this research analyzes public opinions on the Korea-China FTA in three sensitive product categories-cars, agricultural and marine products, and electronics (Study 3). The number of individuals who support the Korea-China ETA is significantly greater than that of individuals who oppose it, regardless of their perception of Chinese products. Finally, this research offers theoretical and practical policy implications.

We examine how and why bloggers and other influencers might share secrets online. Based on an exploratory survey of bloggers and the literature on secret sharing, we examine the use of secret sharing as a strategy for audience building. Our results suggest that online influencers disclose intimate personal information, and that, for a subset of these people, this means sharing information they considered secret. In contrast to secret sharing in other domains, secret sharing by influencers does not appear to be particularly emotionally taxing or therapeutic. Rather, online influencers share secrets for strategic reasons in order to build relationships with their audience and their brand following. As such secrets are usually about the self and for the audience’s direct benefit. Future research should look at the consequences of sharing secrets and when this strategy is likely to be effective, as well as how public personas such as bloggers might redefine what they consider secret over time.