Multinational corporations(MNCs)play a pivotal role in driving sustainable development by effectively implementing Environmental,Social,and Governance(ESG)strategies.This study adopts the lens of Social Cognitive Theo...Multinational corporations(MNCs)play a pivotal role in driving sustainable development by effectively implementing Environmental,Social,and Governance(ESG)strategies.This study adopts the lens of Social Cognitive Theory to analyze how MNCs operating in China can optimize human capital to enhance ESG outcomes.By exploring the interplay between individual cognition,organizational culture,and incentive mechanisms,the research establishes a human capital-driven framework for ESG implementation.Key findings emphasize the importance of cultivating ESG awareness among employees,fostering an ESG-centric organizational culture,and designing robust incentive structures to align individual behaviors with corporate sustainability goals.This comprehensive approach offers practical insights for MNCs striving to balance profitability with social responsibility and environmental stewardship in a rapidly evolving global landscape.展开更多
Objectives:Economic growth and rapid development of health technology in China have created opportunities to strengthen health technology assessment (HTA) capacity.Over the time,HTA institutions have been established ...Objectives:Economic growth and rapid development of health technology in China have created opportunities to strengthen health technology assessment (HTA) capacity.Over the time,HTA institutions have been established to conduct HTA related work.This study reviewed the current status of HTA in China and analysed the challenges of HTA development in the context of health reform under'new normal'economy.Methods:Literature review and webpage searches were used to document the development of HTA in China.An institutional survey has also been conducted to collect information on the HTA research institutions in China.Results:The number of articles and research projects on HTA were rising and are continuing to rise.HTA development has made substantial progress in China in terms of growing number of research institutions and qualified HTA workforce.However,HTA has notable weaknesses such as low capacity for conducting HTA research,limited experience in HTA researchers,and lack of knowledge translation.Conclusion:Currently,the translation of HTA findings to policy-making is limited and the integration of HTA in the policy-making processes is still in its infancy.The HTA development in China has had opportunities due to demands of health care,health insurance,and health technology as a result of health reform.Capacity building and institutionalization of HTA are urgently needed for further development of HTA in China.展开更多
Territory risk analysis has played an important role in the decision-making of auto insurance rate regulation.Due to the optimality of insurance loss data groupings,clustering methods become the natural choice for suc...Territory risk analysis has played an important role in the decision-making of auto insurance rate regulation.Due to the optimality of insurance loss data groupings,clustering methods become the natural choice for such territory risk classification.In this work,spatially constrained clustering is first applied to insurance loss data to form rating territories.The generalized linear model(GLM)and generalized linear mixed model(GLMM)are then proposed to derive the risk relativities of obtained clusters.Each basic rating unit within the same cluster,namely Forward Sortation Area(FSA),takes the same risk relativity value as its cluster.The obtained risk relativities from GLM or GLMM are used to calculate the performance metrics,including RMSE,MAD,and Gini coefficients.The spatially constrained clustering and the risk relativity estimate help obtain a set of territory risk benchmarks used in rate filings to guide the rate regulation process.展开更多
Accurate prediction of fatal events in car accidents has significant health management implications.This research article explores the application of imbalanced data handling techniques in machine learning to enhance ...Accurate prediction of fatal events in car accidents has significant health management implications.This research article explores the application of imbalanced data handling techniques in machine learning to enhance prediction performance.By implementing these techniques on car accident data,health organizations can identify and forecast a fatal event,enabling more efficient and effective allocation of limited health resources.Concurrently,enhancing the performance of machine learning models through imbalanced data handling techniques can impact health management decisions.Our findings highlight the significance of imbalanced data handling techniques in predicting fatality in car accidents,ultimately contributing to improved road safety and better management of health resources.Moreover,the effective use of imbalanced data demonstrates a substantial improvement in the specificity of the prediction.Addressing the impact of machine learning techniques on imbalanced car accident data can significantly improve overall health outcomes.展开更多
文摘Multinational corporations(MNCs)play a pivotal role in driving sustainable development by effectively implementing Environmental,Social,and Governance(ESG)strategies.This study adopts the lens of Social Cognitive Theory to analyze how MNCs operating in China can optimize human capital to enhance ESG outcomes.By exploring the interplay between individual cognition,organizational culture,and incentive mechanisms,the research establishes a human capital-driven framework for ESG implementation.Key findings emphasize the importance of cultivating ESG awareness among employees,fostering an ESG-centric organizational culture,and designing robust incentive structures to align individual behaviors with corporate sustainability goals.This comprehensive approach offers practical insights for MNCs striving to balance profitability with social responsibility and environmental stewardship in a rapidly evolving global landscape.
文摘Objectives:Economic growth and rapid development of health technology in China have created opportunities to strengthen health technology assessment (HTA) capacity.Over the time,HTA institutions have been established to conduct HTA related work.This study reviewed the current status of HTA in China and analysed the challenges of HTA development in the context of health reform under'new normal'economy.Methods:Literature review and webpage searches were used to document the development of HTA in China.An institutional survey has also been conducted to collect information on the HTA research institutions in China.Results:The number of articles and research projects on HTA were rising and are continuing to rise.HTA development has made substantial progress in China in terms of growing number of research institutions and qualified HTA workforce.However,HTA has notable weaknesses such as low capacity for conducting HTA research,limited experience in HTA researchers,and lack of knowledge translation.Conclusion:Currently,the translation of HTA findings to policy-making is limited and the integration of HTA in the policy-making processes is still in its infancy.The HTA development in China has had opportunities due to demands of health care,health insurance,and health technology as a result of health reform.Capacity building and institutionalization of HTA are urgently needed for further development of HTA in China.
文摘Territory risk analysis has played an important role in the decision-making of auto insurance rate regulation.Due to the optimality of insurance loss data groupings,clustering methods become the natural choice for such territory risk classification.In this work,spatially constrained clustering is first applied to insurance loss data to form rating territories.The generalized linear model(GLM)and generalized linear mixed model(GLMM)are then proposed to derive the risk relativities of obtained clusters.Each basic rating unit within the same cluster,namely Forward Sortation Area(FSA),takes the same risk relativity value as its cluster.The obtained risk relativities from GLM or GLMM are used to calculate the performance metrics,including RMSE,MAD,and Gini coefficients.The spatially constrained clustering and the risk relativity estimate help obtain a set of territory risk benchmarks used in rate filings to guide the rate regulation process.
文摘Accurate prediction of fatal events in car accidents has significant health management implications.This research article explores the application of imbalanced data handling techniques in machine learning to enhance prediction performance.By implementing these techniques on car accident data,health organizations can identify and forecast a fatal event,enabling more efficient and effective allocation of limited health resources.Concurrently,enhancing the performance of machine learning models through imbalanced data handling techniques can impact health management decisions.Our findings highlight the significance of imbalanced data handling techniques in predicting fatality in car accidents,ultimately contributing to improved road safety and better management of health resources.Moreover,the effective use of imbalanced data demonstrates a substantial improvement in the specificity of the prediction.Addressing the impact of machine learning techniques on imbalanced car accident data can significantly improve overall health outcomes.
