درخت حوزه‌های تخصصی

Objective : This study examines the impacts of Extended Producer Responsibility (EPR) on product life extension and market dynamics. It investigates the relationship between the physical and emotional durability of products in the apparel supply chain, highlighting the importance of the secondary market in prolonging product life and value. By understanding the connections among producers, consumers, and the secondhand market, the study aims to develop a practical approach that helps planners derive optimal synergistic solutions, considering the needs of consumers, ecological considerations, and the economic factors within the apparel market. Methods : The interactions among consumers, producers, and the government are demonstrated through agent-based simulations across five scenarios. The market includes various groups of consumers to represent its heterogeneous nature. The simulation, conducted in NetLogo, models the behavior of both producers and buyers in the context of government intervention. Results : Simulation results indicate that EPR policies, combined with government interventions such as incentives, penalties, and regulations, can effectively promote the management of products at the end of their life cycle. In scenarios that utilized incentives, producers were more likely to buy and sell secondhand products, reducing the production of new items and increasing recycling rates. These results underscore the critical importance of collaboration among the government, the public, and producers to implement EPR policies successfully. Conclusion : Effective implementation of EPR policies requires government market intervention calibration. When the government applies a reward-and-penalty system to enforce EPR, product waste is reduced, and producer profitability surpasses other scenarios.

The advancement of artificial intelligence (AI) and its growing societal importance are reshaping decision-making processes and policy analysis roles. This study examines the readiness of manufacturing companies in Malaysia to embrace AI technology, considering its potential to enhance decision-making, productivity, quality control, job automation, and data analysis. Focusing on the Technology, Organization, and Environment (T-O-E) readiness framework, the research investigates the relationship between these dimensions and AI adoption readiness among manufacturing companies in Shah Alam, Selangor, Malaysia. AI adoption readiness serves as the dependent variable, while technological, organizational, and environmental readiness dimensions act as independent variables. The study applies the T-O-E framework to AI readiness and proposes a framework for assessing AI readiness at the manufacturing level. It identifies factors influencing readiness within the technological, organizational, and environmental dimensions, including relative advantage, compatibility, resources, competitive pressure, top management support, and government regulations. Through rigorous analysis, patterns, trends, and correlations are revealed, highlighting a significant link between the T-O-E readiness dimensions and AI adoption readiness. Notably, organizational readiness emerges as a key driver of AI adoption in Malaysian manufacturing companies. The results of this investigation have broad implications, offering suggestions to improve organizational preparedness and unlock AI’s potential benefits for businesses in the industrial sector. Additionally, the research lays the groundwork for further studies on AI readiness across various industries and international contexts. As AI becomes increasingly integrated into manufacturing processes, adaptive businesses gain competitive advantages on a global scale. These advantages include increased productivity, informed decision-making, streamlined quality control, improved customer satisfaction, and potential contributions to economic growth. The study concludes by recommending strategies to reinforce organizational readiness and emphasizes the need for future research to deepen understanding of AI adoption readiness in the manufacturing industry. The integration of AI technology offers benefits such as enhanced productivity, decision-making, quality control, and customer satisfaction, granting businesses a competitive edge in the digital landscape and increasing stakeholder interest.