Today, the concept of “smart ports”, which are designed to enhance operational efficiency and competitiveness, has emerged as a key paradigm in the port and maritime industry. As an integral component of maritime transportation networks, container terminals in ports play a crucial role in facilitating international trade and stimulating economic growth. Accordingly, the Korean government has steadily pursued policies and investments to improve the technological capabilities of ports, focusing on container terminals. However, there is still a lack of comprehensive research that thoroughly analyzes the operational efficiency of container terminals in domestic ports undergoing smart port technology implementation. Therefore, this study analyzes the operational efficiency of 20 container terminals across five major domestic ports, which are forerunners in smart port transformation, as declared by the Korean government. This study employs a Principal Component Analysis (PCA)–Data Envelopment Analysis (DEA) approach to increase the effectiveness of the analysis and addresses efficiency in two aspects: static and dynamic. The findings indicate that container terminals under Ulsan Port and Busan Port (New) have the best operational efficiency. In particular, Ulsan Port demonstrates significant improvement in terms of efficiency changes. The conclusion emphasizes the pivotal role of technological progress in achieving higher efficiency among container terminals. In addition to providing policymakers and port managers with useful insights into smart port transformation, this study is expected to make theoretical contributions to expanding the perspectives of port efficiency research by simultaneously encompassing static and dynamic analyses.

The purpose is to present a knowledge management model for R&d organizations and discuss how to employ it for their innovation. This paper first derives generic elements for effective KM implementation through literature reviews. Then, considering the elements, this paper develops an integrative KM model for enabling R&D innovation. Next, this paper analyzes the case of Samsung Advanced Institute of Technology (SAIT), which has performed KM‐based innovation activities on the basis of the model presented in this paper. Finally, this paper discusses the lessons and implications from SAIT’s knowledge management initiatives. To achieve KM‐based innovation successfully in an R&D organization, its generic characteristics should be considered essentially in designing KM activities. From the case analysis of SAIT KM which employs this perspective, the following critical success factors are found: KM measurements metrics to stimulate a strong relationship between KM activities and the organization’s competitiveness, knowledge templates to enable the consistent management of core knowledge, various information group activities to generate meaningful new ideas, and IT systems and rules to satisfy researchers’ individual aspiration for self development. The research limitations/implications are that to generalize the usefulness of the proposed model, more cases need to be analyzed. The practical implications are that the proposed R&D KM model and analyses on the real‐life KM case of SAIT may be employed as significant benchmarking information in other R&D organizations. This paper presents an integrative KM model considering the generic features of R&D organizations.

Purpose: Social networking services (SNSs) have become deeply ingrained into our daily life. However, it has often been reported that users experience negative feelings regarding SNS usage. This phenomenon presents challenges for SNS providers in retaining or increasing their customer base. This study focuses on SNS fatigue, a negative psychological state that can lead to discontinuance intention among SNS users. This study proposes two distinctive SNS-specific contexts and investigates how they alleviate the negative effect of SNS fatigue on user behavior.Design/methodology/approach: Drawing upon the context-specific theorization perspective, a model involving moderation effects of the SNS-specific contexts on the relationship between user's SNS fatigue and discontinuance intention was proposed. The model was tested using survey data of active SNS users.Findings: The results indicate that SNS fatigue leads to user discontinuance intention. However, the SNS-specific contexts, such as social interaction context and platform service context, negatively moderate the relationship between SNS fatigue and user discontinuance intention.Originality/value: The findings of this study are expected to help SNS providers develop strategies to improve their services for effective user retention.

Creating knowledge within a team for developing new products and services is considered a primary means for improving organizational performance. Drawing upon the socio-technical perspective, we investigate the blended effects of social (learning culture, teamwork quality, and knowledge complexity) and technical (IT support) factors on team-level knowledge creation and team performance. We propose a model that features synergetic interactions between social and technical factors in this knowledge creation process. The model was tested by utilizing data from a field survey of industry managers. The results show significant interactions between social and technical factors, which influence team-level knowledge creation and, in turn, team performance. Our findings can be used to develop socio-technical initiatives to enhance the process of creating team-level knowledge within firms.

The availability of open, relevant, and up-to-date public data is becoming an increasingly important dimension of national competitiveness and sustainable development. It serves as a foundation for novel technologies, such as big data analytics, machine learning, and artificial intelligence, to take root and flourish, and it can help improve the quality and efficiency of government decision making and render governments more transparent and accessible to the public. Often referred as Open Government Data, or OGD, governments around the world have committed resources to constructing various OGD platforms. However, building a robust and effective OGD system has proved difficult, as the promise of OGD has not been realized fully around the world. At this important juncture, this study aims to explore the relationship between national technological and organizational capacities and environmental factor and the quality of OGD systems. In addition, national innovativeness and the degree of “globalization” in a country and their moderating effects between the predictors and OGD performance are examined. Our findings indicate strong positive effects of national technological capacity, government organization capacity, and globalization on OGD quality and a positive moderating effect of national innovativeness.

Recently, the live commerce market has experienced rapid growth, accompanied by increasingly intense competition. To improve business performance in this dynamic environment, it is essential to foster competent streamers and create effective commerce environments. Therefore, this study developed a research model based on the stimulus–organism–response (S-O-R) framework, focusing on streamer competencies and the commerce environment, to explore ways to effectively enhance live commerce business performance. Data for this study were collected through a questionnaire and analyzed using statistical techniques with 390 respondents. The results revealed that streamers’ competencies (expertise, demonstration skills, and interactive ability) significantly influence consumers’ internal states (perceived functional value of products and perceived trust in product recommendations), which in turn significantly influence purchase intentions. Moreover, the physical surroundings of the studio and the social surroundings, including peers’ perceptions of live commerce, were found to moderate the relationships between consumers’ internal states and their purchase intentions. This study holds academic significance in that it presents a model that effectively understands the mechanisms influencing viewers’ purchase decisions in live commerce contexts. The findings and practical implications discussed in this study are expected to provide valuable insights for developing strategies to enhance the performance of live commerce.