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The determinants that contribute to reducing stock price crash risk have garnered attention from scholars and practitioners. However, our understanding of the relationship between board diversity and stock crash risk, as well as the contextual factors that influence this relationship, remains limited. To address this gap, this study aims to investigate how different attributes of board diversity affect stock price crash risk, particularly under conditions of higher performance hazard and ownership concentration.

Using a two-stage least squares fixed-effects estimator, the authors analyze a panel data set of 1,792 firm-year observations across 282 firms listed on the KOSPI200 from 2010 to 2019.

Relation-oriented diversity reduces future stock price crash risk, particularly when firms experience performance shortfalls and have concentrated ownership structures, but task-oriented diversity has no significant effects. The results imply that only relation-oriented diversity strengthens governance mechanisms by curtailing managerial bad news withholding behaviors, and the role of relation-oriented diversity in reducing stock crash risk becomes more crucial when firms have higher performance hazard and concentrated ownership.

This study makes crucial contributions as follows: the authors contribute to the stock crash risk literature by shifting the focus from how to when board diversity matters in assessing stock crash risk; the authors extend the board diversity research and enhance scholarly understanding of the effects of board diversity on corporate governance by highlighting that not all aspects of board diversity improve firm governance mechanisms; and the authors widen the lens from a single attribute to multiple attributes of diversity to reveal the effects of diversity on boards in assessing future crash risk.

The purpose of this study is to provide the benchmarking guideline to improve managing maintenance man-hour in producing sorties of fighter jet aircraft in the Korean Air Force by measuring relative efficiency of aircraft in a fleet.

Using actual operational data from 2014 to 2018, this paper measures the relative efficiencies of aircraft by data envelopment analysis (DEA) model. To analyze the efficiencies across different operational units and among aircraft with different attributes, non-parametric statistical techniques and tobit regression are employed.

The overall management of maintenance man-hour turns out to be 63.2% efficient compared to the best practice. Operational unit possessing aircraft with different configurations shows the poorest performance implying efficiency depends on the complexity of job processes. Identification of aircraft on the best-practice frontier indicates the efficiency is closely related with the reliability of components and the age of aircraft.

Performance of aircraft maintenance has a tremendous impact on military readiness, utilization of work force, and the quality and safety of flight operations. This study confirmed there exists plenty of room for performance enhancement and presents a reference to find a balance in the performances of maintenance practices across different installations as well as among different types of aircraft.

Application of DEA to engineering systems has been limited in literature, especially in military operations. Thus, this study presents meaningful guidelines to which fleet manager can refer to when establishing strategic maintenance plans to balance the utilization of manpower.

This study investigates the key factors that influence customer satisfaction when interacting with augmented reality shopping assistance applications (ARSAPs). ARSAPs grant consumers the capability to experience products in a virtually simulated user environment before product acquisition. With the development of mobile e-commerce due to breakthroughs in smartphone and augmented reality (AR) technologies, there is an increasing potential for these emergent AR mobile services, yet there is a need for further improvement.

This study initially explored the key satisfaction factors for ARSAPs by utilizing topic modeling of a collection of actual user reviews. These factors are subsequently revisited and complemented by existing literature, and finally verified through logistic regression analysis supported by sentiment analysis.

This study identified the key factors that influence customer satisfaction with ARSAPs, including visuality, sense of reality, credibility, format, completeness, understandability, relevance, flexibility, response time, reliability, availability, ease of use and privacy. In particular, two additional factors (i.e. visuality and sense of reality) were newly identified as important in the context of AR, despite their previous omissions in existing literature.

This study is the first to investigate the key factors that influence customer satisfaction with ARSAPs from users' perspectives, utilizing topic modeling of a large amount of real-world data on actual user feedback. By identifying new factors (i.e. visuality and sense of reality) that were not identified in previous literature, this study provides important academic implications for a broader understanding of AR and related technologies that are essential elements of the metaverse. This study also provides valuable insights for developers and companies in the e-commerce industry on how to optimize AR applications and develop more targeted and effective marketing strategies in this field.

Previous studies have empirically examined the positive outcomes organizations can achieve by engaging in environmentally responsible actions, but the underlying mechanism of the reasons why publics engage in supportive behaviors for those organizations has not been examined in light of theoretical foundations. To fill this gap, this paper builds on a foundation of norm activation theory to explore the effect of publics' awareness of environmental consequences on perceived environmental responsibility of organizations and organizational norm, which can have an impact on publics' supportive behavior toward environmentally responsible organizations. The potential relationships between social media use for environmental information and other variables are also explored.

Drawing on norm activation theory, an online survey was conducted among 288 adults in the United States. Participants were recruited from Amazon Mechanical Turk.

Findings indicate the impact of individuals' awareness of environmental consequences on their ascription of responsibility to organizations, which influences organizational norm and supportive behavior intention toward organizations.

This study is one of the first attempts to explore the underlying mechanism of publics' support for environmentally responsible organizations.

Upon the realization of the need for guideline in cross-organizational data integration, in an exploratory manner, this study developed a public data governance framework, specifically, the governance for integrated public data (GIPD) framework and identified the influential factors of its successful implementation. This framework was then subjected to an analysis of a real data integration case in the South Korean public sector to test its efficacy.

To develop the GIPD framework, the authors conducted an extensive meta study, focus group interviews and the analytic hierarchy process involving field experts. Further, the authors performed topic modeling on documents from Korean research and development data integration projects, and compared the extracted factors to those of the GIPD to illustrate the latter's usefulness in a real case.

Legislation, policy goals and strategies, operation organization, decision-making council, financial support size and objective, system development and operation, data integration, data generation, system/data standardization and master data management were derived as the 10 important factors in implementing the GIPD framework. The illustrative case of Korea revealed that decision-making council, financial support size and objective, legislation, data generation and data integration were insufficient.

Although this study reveals important findings, it has a few limitations. First, the potential factors for data governance might vary depending on the attribute of the “interviewee” (such as their career or experience period) and the goal and area of GIPD framework building. Second, the inherent limitation of topic modeling in determining topics from groups of extracted keywords means that topics may be interpreted in various ways, depending on the perspective of the expert.

This study is highly significant in that it provides a starting point for discussions on the issue of data integration among public institutions. Therefore, although this study examined public data governance based on R&D data, it will contribute to providing a sufficient guideline for any type of inter-institutional data governance framework, what to discuss and how to discuss between institutions.

The findings are expected to provide a roadmap to formulate practical guidelines on inter-institutional data cooperation and a diagnostic matrix to improve the existing data governance system, especially in the public sector, from the existing practice of empirical analysis using a mixed methodology approach.