This paper proposes an incentive model to involve communities and industries in effectively managing coastal waste in Makassar, Indonesia. The model seeks to incentivize stakeholders to invest in waste management solutions and enable public stakeholders to monitor and evaluate the progress of waste management activities. The model actively encourages participation from all stakeholders and builds upon existing efforts to promote environmental accountability. The proposed model includes several key components. It focused on public and private partnerships that should be fostered to coordinate stakeholder approaches and provide capital investment. It also focused on a financial reward scheme that should be adopted to incentivize businesses and individuals that invest in waste management initiatives. Performance bonus awards and tax incentives are proposed as possible incentive schemes. Lastly, a regulatory framework should be developed to ensure environmental standards are met and regulated. The framework should include regular reporting and auditing requirements and the implementation of penalties for those who fail to comply. The proposed incentive model seeks to engage stakeholders in effectively managing coastal waste in Makassar, Indonesia, through public and private incentive schemes.

Leadership behavior is a critical component of effective management, significantly influencing organizational success. While extensive research has examined key success factors in road management, the specific role of leadership behaviors in road usage charging (RUC) management remains underexplored. This study addresses this gap by identifying and analyzing leadership behavior dimensions and their impact on management performance within the RUC context. Using a mixed-methods approach, focus group discussions with industry practitioners were conducted to define eight leadership behavior dimensions: Central-Level Leadership Guidance (LE1), Local-Level Leadership Guidance (LE2), Central-Level Leadership Commitment (LE3), Local-Level Leadership Commitment (LE4), Subordinate Understanding from Central-Level Leadership (LE5), Subordinate Understanding from Local-Level Leadership (LE6), Work Motivation (LE7), and Understanding Rights and Obligations (LE8). These dimensions were further validated through a quantitative survey distributed to 138 professionals involved in RUC management in Vietnam, with the data analyzed using structural equation modeling (SEM) and partial least squares (PLS) estimation. The findings revealed that LE3 (Central-Level Leadership Commitment) had the strongest direct impact on management performance (MP) and mediated the relationships between other leadership dimensions and management outcomes. This study contributes to the theoretical understanding of leadership in RUC management by highlighting the centrality of leadership commitment and offering practical insights for improving leadership practices to enhance organizational performance in infrastructure management.

Relational database models offer a pathway for the storage, standardization, and analysis of factors influencing national sports development. While existing research delves into the factors linked with sporting success, there remains an unexplored avenue for the design of databases that seamlessly integrate quantitative analyses of these factors. This study aims to design a relational database to store and analyse quantitative sport development data by employing information technology tools. The database design was carried out in three phases: (i) exploratory study for context analysis, identification, and delimitation of the data scope; (ii) data extraction from primary sources and cataloguing; (iii) database design to allow an integrated analysis of different dimensions and production of quantitative indicators. An entity-relationship diagram and an entity-relationship model were built to organize and store information relating to sports, organizations, people, investments, venues, facilities, materials, events, and sports results, enabling the sharing of data across tables and avoiding redundancies. This strategy demonstrated potential for future knowledge advancement by including the establishment of perpetual data updates through coding and web scraping. This, in turn, empowers the continuous evaluation and vigilance of organizational performance metrics and sports development policies, aligning seamlessly with the journal’s focus on cutting-edge methodologies in the realm of digital technology.

Since the proposal of the low-carbon economy plan, all countries have deeply realized that the economic model of high energy and high emission poses a threat to human life. Therefore, in order to enable the economy to have a longer-term development and comply with international low-carbon policies, enterprises need to speed up the transformation from a high-carbon to a low-carbon economy. Unfortunately, due to the massive volume of data, developing a low-carbon economic enterprise management model might be challenging, and there is no way to get more precise forecast data. This study tackles the challenge of developing a low-carbon enterprise management mode based on the grey digital paradigm, with the aim of finding solutions to these issues. This paper adopts the method of grey digital model, analyzes the strategy of the enterprise to build the model, and makes a comparative experiment on the accuracy and performance of the model in this paper. The results show that the values of MAPE, MSE and MAE of the model in this paper are the lowest. And the r^2 of the model in this paper is also the highest. The MAPE value of the model in this paper is 0.275, the MSE is 0.001, and the MAE is 0.003. These three indicators are much lower than other models, indicating that the model has high prediction accuracy. r² is 0.9997, which is much higher than other models, indicating that the performance of this model is superior. With the support of this model, the efficiency of building an enterprise model has been effectively improved. As a result, developing an enterprise management model for the low-carbon economy based on the gray numerical model can offer businesses new perspectives into how to quicken the shift to the low-carbon economy.

The fifth-generation technology standard (5G) is the cellular technology standard of this decade and its adoption leaves room for research and disclosure of new insights. 5G demands specific skillsets for the workforce to cope with its unprecedented use cases. The rapid progress of technology in various industries necessitates a constant effort from workers to acquire the latest skills demanded by the tech sector. The successful implementation of 5G hinges on the presence of competent individuals who can propel its progress. Most of the existing works related to 5G explore this technology from a multitude of applied and industrial viewpoints, but very few of them take a rigorous look at the 5G competencies associated with talent development. A competency model will help shape the required educational and training activities for preparing the 5G workforce, thereby improving workforce planning and performance in industrial settings. This study has opted to utilize the Fuzzy Delphi Method (FDM) to investigate and evaluate the perspectives of a group of experts, with the aim of proposing a 5G competency model. Based on the findings of this study, a model consisting of 46 elements under three categories is presented for utilization by any contingent of 5G. This competency model identifies, assesses, and introduces the necessary competencies, knowledge, and attributes for effective performance in a 5G-related job role in an industrial environment, guiding hiring, training, and development. Companies and academic institutions may utilize the suggested competency model in the real world to create job descriptions for 5G positions and to develop curriculum based on competencies. Such a model can be extended beyond the scope of 5G and lay the foundation of future wireless cellular network competency models, such as 6G competency models, by being refined and revised.

Research that discusses the impact of implementing Green Human Resource Management and environmentally friendly behavior, especially in sustainable tourism, is limited. It becomes crucial to understand how implementing good green human resource management practices in tourism sector organizations. To achieve the objectives of this research, a qualitative approach was used where the data and information collected were obtained through direct observation and interviews with tourism informants. The findings show the importance of environmentally friendly behavior as the implementation of green human resource management is able to improve tourism management. The uniqueness of this research is developing a model of human resource readiness in implementing environmentally friendly behavior towards sustainable tourism. This resource readiness will be reflected in the GHRM model in supporting sustainable tourism. The results of this research offer a model of sustainable Green Tourism which includes antecedents, implementation and results achieved. These antecedents come from internal and external (environmental ethics and management commitment) managers which will result in good GHRM implementation. This model will be the basis for implementing sustainable tourism in human resource management practices based on literature reviews and also tourism management practices.