Suchergebnisse

According to the International Employee Assistance Professionals Association (2011), employee assistance programs (EAPs) are integrated programs in which various methods are used to provide organizations and their employees with different services. However, despite the increasing implementation of EAPs, quantitative meta‐analyses on EAPs have yet to be conducted. Thus, following Rosenthal's (1991) recommendations for meta‐analysis, we performed a meta‐analysis of 14 quantitative studies on EAPs in Taiwan. Results indicated that EAPs are negatively correlated with work stress and positively correlated with organizational commitment, job satisfaction, and social support. This study also offers prescriptions for the organizational conduct of EAPs with respect to job satisfaction, organizational commitment, social support, and work stress.

Artificial intelligence (AI) applications have been emerging in these past years and affecting multiple dimensions of the public sector. The government utilizes AI to transform policy implementation and service delivery, but AI can also threaten citizens' privacy and social equity due to its potential biases. These concerns increase citizens' perceived uncertainty concerning AI. In an uncertain environment, trust transfer serves as a way to improve citizens' trust in AI-enabled government systems. However, little research has explored trust transfer between the public sector and the system. This study examines whether a context-based trust transfer mechanism can explain the trust-building of the AI-enabled government system. The study conducted a survey and analyzed the collected data using factor-score-based regression analysis. The research results indicate that trust transfer occurs for the AI-enabled government system. Trust in an administrative process, local government, and political leaders can be transferred to trust in governmental AI systems. The findings can advance the theoretical development of trust transfer theory and be used to develop recommendations for the public sector.