Search results

This research investigates the impact of blockchain technology utilization (BTU) on corporate social responsibility (CSR) through the complementarity role of supply chain transparency (SCT) capabilities while also considering supplier risks.

This research tested hypotheses using covariance-based structural equation modeling using survey data from 197 managers who held decision-making responsibilities in supply chain operations.

The findings suggest that BTU improves CSR via increasing SCT. Supplier risk also plays a role, where engagement with risky suppliers magnifies the positive impact of BTU on SCT, mitigating risks and improving CSR goals. SCT was founded as a full mediator between BTU and CSR, emphasizing its significant role in promoting CSR practices which are becoming more crucial in the global supply chain.

Data were collected via the Prolific data collection platform. An avenue for future investigation could involve replicating or extending this research with participants from additional sources.

This research underscores the significance of employing transparent processes by embracing blockchain technology for better CSR practices. SCT promotes responsible corporate practices for customers’ growing concern for sustainability. This will help businesses to get a competitive advantage by ensuring customers’ concerns for CSR.

Besides the well-known feature of BTU for secure transactions, the findings of this study underscore the value of adopting blockchain technology for better CSR through SCT by being one of the few empirical studies to focus on the utilization of blockchain technology for sustainability practices.

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.

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.