Behavioral Sciences

Abusive supervision remains a pervasive and damaging phenomenon in organizations, prompting a critical need to understand preventive mechanisms. We adopt a leader-centric, actor-focused perspective to investigate how a positive organizational context can inhibit leaders’ destructive behaviors. Drawing on Affective Events Theory (AET), we propose that leaders’ awareness of their organization’s philanthropic activities serves as a positive, morally salient event that generates feelings of moral pride. This pride, in turn, is theorized to reduce the likelihood of abusive supervision. Furthermore, we posit that this process is contingent on leaders’ moral reputation maintenance concerns, such that the negative relationship between moral pride and abusive supervision is stronger for leaders who are highly concerned with being perceived as moral. We tested this model using a three-wave survey study involving 434 leaders. The results support our hypotheses, indicating that perceived philanthropy awareness is positively associated with moral pride, which, in turn, predicts lower abusive supervision. This indirect effect is significantly stronger for leaders with high moral reputation maintenance concerns. Our findings contribute to the literature by identifying a novel, positive, and self-regulatory pathway for preventing abusive supervision and showing that applying AET to understand how macro-level organizational good deeds can translate into improved micro-level leader conduct.

Grounded in regulatory focus theory, this study investigates the double-edged sword effect of generative AI adoption on sustainable entrepreneurial intentions and its underlying mechanisms. A questionnaire-based survey was conducted among 357 business students from public universities in China. The results reveal that generative AI adoption exerts a double-edged effect: it enhances sustainable entrepreneurial intentions by strengthening sustainable entrepreneurial self-efficacy through a promotion-focused pathway, while simultaneously undermining such intentions by heightening sustainable entrepreneurial fear of failure via a prevention-focused pathway. Moreover, artificial intelligence literacy moderates these relationships, amplifying the positive influence of generative AI adoption on entrepreneurial self-efficacy and attenuating its negative effect on fear of failure. This study enhances understanding of sustainable entrepreneurship amid the rise in generative AI, extends regulatory focus theory, and informs the development of AI-integrated sustainability education in academic institutions.

Co-design in healthcare settings requires teams to utilize each other’s knowledge effectively, but practical guidance and simple methods for observing collaboration are often lacking. We tested whether a lightweight AI assistant that guides the process—and automatically logs who speaks, when, and how work progresses—can make teamwork easier to manage and easier to track. Six four-person teams completed the same five-phase session. The assistant nudged timing, turn-taking, and artifact hand-offs; all interactions were recorded in a shared workspace. We assessed usability and acceptance, expert-rated product quality (technical performance), perceived team performance, and self-rated technical contribution, and we summarized basic log signals of participation and pacing (e.g., turn-taking balance, average turn duration). Analyses were descriptive. All teams finished the protocol with complete logs. Outcomes were favorable (expert ratings averaged 4.18/5; perceived performance 6.14/7; self-rated contribution 4.08/5). Teams with more balanced participation and clearer pacing tended to report better performance, whereas simply having more turns did not. A process-guiding AI assistant can quantify teamwork behaviors as markers of collective intelligence and support reflection in everyday clinical co-design; future work will examine the generalizability of these findings across different sites.