This study examines the interaction effect between transformational leadership, transactional leadership, wireless IT competence, ambidexterity and supply chain management capability in increasing innovation capability. By using a knowledge-based view and dynamic capability theory basis, this research has provided an exploration of the supply chain of a merchant marine college and its impact on innovation capability using a quantitative method approach. The authors collected data from a cross-section of 673 questionnaires distributed to Managers in 3 managerial classifications from top, middle and bottom in the technical service unit of the merchant marine college under the Indonesian Ministry of Transportation. A total of 523 data were collected from questionnaires that could be continued for data analysis. The results of this study indicate that all the hypotheses put forward in this study are accepted, and the role of mediating variables in this research has succeeded in demonstrating their role in mediating each antecedent variable to increase innovation capability. The theoretical implication of this research is the growth of cloud or virtual supply chains facilitated by digital wireless communications, and internet technology is advancing logistics and supply chain innovations. Also, it can reinforce theory and dynamic capability.

This study examines the impact of relaxation of Foreign Direct Investment (FDI) access on the trans-regional investment behavior of Indonesian enterprises. Utilizing data from publicly listed companies between 2010 and 2022, this study investigates how easing FDI restrictions influences the geographic distribution of subsidiaries. The findings indicate that FDI access relaxation significantly promotes trans-regional investment, driven by reduced government subsidies and lower market intervention. These mechanisms enable enterprises to expand beyond local markets by decreasing their dependency on government support and lowering their transaction costs. Robustness tests, including first-order difference models and dynamic distribution tests, confirmed the reliability of the results. This study suggests that a further reduction in FDI restrictions can enhance domestic market integration, mitigate capital market segmentation, and promote inclusive economic growth. This study contributes to the literature by providing empirical evidence of the economic benefits of FDI access policies and offers new strategies for addressing market segmentation in Indonesia.

This research examined the connections of efficiency, regulatory and good corporate governance (GCG) variables on the achievement or performance of financial aspects and social reporting from Indonesia’s Shariah banks generally. Data and samples collected in this research include 34 Shariah banks, which are administered in the central bank, Bank of Indonesia for the year of 2009 until 2022. The financial achievement was determined with return on assets, and social reporting was determined with dummy 1 if the banks issue Islamic social reporting, and 0 otherwise. Regulation variables were measured with nonperforming financing (NPF), capital adequacy ratio (CAR), financial to deposit ratio (FDR), and net operating margin (NOM). Corporate governance variables were measured with firm age, board education, and board meeting. Efficiency variable was assessed with operating costs to revenue. The results show that regulation variables significantly impact financial achievement and social reporting, except NPF and CAR have nothing significant influence on Islamic social reporting. Efficiency variables significantly impact Islamic social reporting and financial achievement. Corporate governance variables significantly influence financial achievement and Islamic social reporting. Meanwhile firm age and board meetings have no remarkable influence on financial achievement.

Changes are occurring in customers demanding that companies produce environmentally friendly products. Apart from that, there is pressure from the government so that companies carrying out business activities can follow the rules and regulations set. The company has developed a system that uses technology to support production process activities properly and adequately. Blockchain technology makes green supply chain implementation faster and more flexible to continue improving supply chain performance. Data is collected on companies implementing blockchain technology and environmentally friendly programs. Data was collected at 512 manufacturing companies in Indonesia using Google Forms, which was distributed via email and social media. Data analysis used PLS to answer all research hypotheses. The research results showed that blockchain technology significantly influenced green supply chain management of 0.816, green practice of 0.370, supply chain flexibility of 0.115, and green supply chain performance of 0.150. Green supply chain management is a commitment for companies that have become top management commitments to adopt by reducing consumption of materials that impact the environment, which affects green supply practices of 0.473, supply chain flexibility of 0.244, and green supply chain performance of 0.247. Green supply practice, as a form of company best practice application, can influence supply chain flexibility by 0.428 and green supply chain performance by 0.214. Supply chain flexibility that has been running in manufacturing companies has had a significant impact on green supply chain performances of 0.331. This research provides a practical contribution to top management's commitment to running a green supply chain to increase company performance. Contribution for regulators and rule makers to continue to carry out continuous monitoring of business actors. Theoretical contributions to enrich theories about the green environment, green supply chain management, and blockchain technology.

Materialism has emerged as a growing global drift in the past decade. This study highlights the influence of materialism on consumers' attitudes and actions regarding the purchase of environmentally sustainable products (ESPs) in a country such as Pakistan, which environmental orientations are in the developing stage. Materialistic perspectives can hinder the formation of positive attitudes towards environmentally sustainable products, especially when consumers engage in product comparisons. To evaluate the effects of materialism and product comparisons on purchasing intentions and behaviors, a survey was conducted among university students. The results revealed that consumers tend to avoid purchasing experience goods primarily because these items require a higher degree of involvement and risk assessment due to their intangible and experiential nature. This avoidance is particularly pronounced when the subjective norms and attitudes toward these goods are influenced by materialistic values, which often prioritize tangible, ownership-oriented products.

Ethiopia's bottled water industry faces mounting pressure to balance economic growth with environmental responsibility. This study investigates the effect of green supply chain management (GSCM) on Ethiopian bottled water companies' economic performance, with environmental performance as a potential mediator. We employ structural equation modelling (SEM) on survey data from managers of 99 bottled water firms in Ethiopia. The findings revealed that while some GSCM practices indirectly enhance the bottom line through improved environmental impacts, others, like investment recovery initiatives, directly enhance economic performance. Notably, the study demonstrates that GSCM fosters an environmentally sustainable future for Ethiopia's bottled water industry, where environmental responsibility ultimately leads to long-term economic performance. This research offers valuable insights for policymakers and stakeholders seeking to promote balanced environmental and economic growth within the Ethiopian bottled water industry, moving beyond mere “greenwashing” towards genuine sustainability.

Climate change creates considerable issues for watershed management, especially in areas prone to erosion and sediment production. The purpose of this study was to examine and map the sediment yield response to future climatic scenarios in the Songwe Watershed. The Soil and Water Assessment Tool (SWAT), which is integrated with Regional Climate Models (RCM) under Representative Concentration Pathways (RCPs) 8.5, was used to evaluate the possible consequences on sediment transport dynamics within the watershed. The simulated results from the four Regional Climate Models (CCLM4, HIRAM5, RACMO22T, and RCA4 RCMs) showed that sediment yields increased for future estimates from 2011 to 2100 under RCP 8.5, owing mostly to increased rainfall and altered hydrological cycles. The results reveal that the average annual sediment yield could increase by 30-50% under RCP 8.5. scenario. Sediment yield mapping highlights crucial hotspots, notably in steep terrain and places with minimal vegetation cover, that are extremely susceptible to erosion, providing useful insights for focused intervention measures. The study emphasized the need for adaptive watershed management methods to counteract the negative effects of climate change on soil erosion and sediment crusade.

The advent of Industry 4.0 has revolutionized manufacturing, integrating advanced technologies to enhance efficiency and sustainability. However, the transition to sustainable green manufacturing presents numerous challenges. This paper analyzes these challenges using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). By incorporating expert opinions and fuzzy logic, various obstacles are evaluated and prioritized in the implementation of green manufacturing practices in the context of Industry 4.0. The analysis reveals that market uncertainty in the economic landscape ranks as the top challenge, followed by high costs of implementation, maintenance, security, and integration. Uncertain benefits and trade-offs are also found as significant barriers. Key factors include the need for substantial investments, cybersecurity concerns, integration difficulties, and the complexities of predicting returns on investment. From the study, it is also evident that the impact of Industry 4.0 on supply chains and emissions from Electronics manufacturing is also a critical issue. The study provides actionable insights and strategic recommendations for policymakers and industry leaders to facilitate the adoption of sustainable green manufacturing practices in the era of Industry 4.0.

In the era of Industry 4.0, advanced manufacturing systems are increasingly integrating cyber and physical components, making them susceptible to sophisticated cyber-attacks. Addressing these vulnerabilities is crucial for maintaining the integrity and efficiency of manufacturing processes. This study introduces a comprehensive game-theoretic model to tackle cybersecurity challenges in such systems. The interaction between cyber attackers and defenders is modeled as a strategic game, incorporating a cost function that includes production losses, recovery from attacks, and maintaining of defense strategies. Both deterministic and probabilistic approaches are employed: linear programming identifies optimal strategies, achieving Nash equilibrium under ideal conditions, while the Quantal Response Equilibrium (QRE) method captures player behavior under uncertainty. The optimization problem is solved using the CPLEX library in Python, ensuring robust and efficient computation of optimal mixed strategies. The methodology is demonstrated through a numerical example, highlighting the identification of potential vulnerabilities and optimal defense strategies. The analysis reveals that the defender's learning curve is longer and more complex than the attacker's, emphasizing the necessity for advanced and adaptive defense strategies. This comprehensive approach not only predicts attacker behavior but also suggests effective defense mechanisms tailored to specific threats. The findings underscore the importance of strategic decision-making in enhancing the cybersecurity resilience of cyber-physical manufacturing systems, offering valuable insights for mitigating cybersecurity risks effectively. The most significant result indicates the critical need for timely and adaptive defense mechanisms to counter sophisticated cyber threats, ensuring the sustained operation and security of modern manufacturing environments.

Supply chain disruptions pose significant challenges to global economic stability, necessitating advanced predictive tools for effective risk management. As Machine Learning (ML) offers promising solutions for enhancing resiliency, this study investigates its applications in supply chain management. Utilizing a systematic literature review, we examined recent research to identify effective ML models and techniques, focusing on both supervised and unsupervised learning. Our analysis covered various industries to understand the adaptability and effectiveness of these models in mitigating supply chain risks. The results highlight the growing implementation of ML in anticipating disruptions, with supervised learning demonstrating superior predictive precision under specific conditions. At the same time, unsupervised approaches offer valuable insights in data-scarce scenarios. Context-specific data surfaced as crucial in model accuracy, underscoring the need for tailored approaches. This study concludes that integrating ML with current supply chain systems can significantly enhance operational resilience, advocating for continued exploration of novel data sources and interdisciplinary collaborative efforts.