research

Predictors of Health Care Practitioners’ Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology

Artificial intelligence–enabled clinical decision support systems (AI-CDSSs) offer potential for improving health care outcomes, but their adoption among health care practitioners remains limited. The meta-analysis identified predictors influencing health care practitioners’ intention to use AI-CDSSs based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Additional predictors were examined based on existing empirical evidence.

Financial stress and quit intention: the mediating role of entrepreneurs’ affective commitment

One primary reason entrepreneurs abandon their goals is due to financial difficulties. In one experimental and two field studies, we found a positive relationship between financial stress and quit intention, mediated by affective commitment to their entrepreneurial endeavors. The findings are in line with the challenge–hindrance stressor (CHS) framework and self-determination theory (SDT).

Challenge and threat appraisal of entrepreneurial errors: a latent profile analysis and examination of coping responses

According to transactional stress theory (TST), entrepreneurs’ coping strategies depend on viewing errors as challenges or threats. This study uses latent profile analysis to explore distinct profiles of challenge and threat appraisals among entrepreneurs. The findings reveal five appraisal profiles that highlight differences in error damage control and rumination, suggesting improvements for TST and error management interventions.

Advancing Mental Health Care with AI-Enabled Precision Psychiatry Tools: A Patent Review

We wrote a review on AI-enabled precision psychiatry patents published between 2015 and mid-October 2022. Multiple analytic approaches, such as graphic network analysis and topic modeling, are used to analyze the scope, content, and trends of the retained patents. The tools described aim to provide diagnosis, prediction of treatment responses, and prognosis of mental disorder symptoms. Additionally, about one-third of the tools suggest treatment options related to selection, adjustment, and management. The complexity of technology combinations has increased over the years. This review highlights the potential of AI-enabled precision psychiatry tools for adoption in practice.

Thriving at work: An investigation of the independent and joint effects of vitality and learning on employee health

Thriving at work has been defined as employees’ joint sense of vitality and learning. Based on the socially embedded model of thriving at work, we examine several competing operationalizations of thriving at work. We hypothesize effects of (a) composite thriving, (b) separate vitality and learning scores, and (c) the interaction between vitality and learning, and we explore effects of (d) the congruence between vitality and learning on self-rated physical and mental health.

Students’ Career exploration: a meta-analysis

On the basis of Lent and Brown’s (2013) model of career self-management (CSM), the meta-analysis examined the antecedents and outcomes of career exploration among college students (K = 109, N = 34,969 students). The findings highlight several implications for the further development of the CSM model, future research on students’ career exploration, and career development practice.

Thriving at work: a meta-analysis

Thriving at work refers to a positive psychological state characterized by a joint sense of vitality and learning. On the basis of Spreitzer and colleagues’ model, we present a comprehensive meta-analysis of antecedents and outcomes of thriving at work.