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This study investigates the drivers and mitigators of workplace bullying in surgical environments in Australia and Aotearoa New Zealand, utilizing social identity theory (SIT) and contemporary models of psychological safety. It introduces the concept of personality traits as a bridging factor between drivers and mitigators.

Qualitative interviews were conducted with 31 surgeons. Thematic analysis, supported by NVivo, was used to map findings to the SIT framework and incorporate psychological safety and personality theory.

Key drivers of workplace bullying include entrenched hierarchical power structures, gender dynamics and early socialization in competitive environments. The “bad apple” phenomenon, where personality traits such as narcissism and low emotional intelligence exacerbate bullying, contrasts with individuals demonstrating empathy and interpersonal awareness, who mitigate such behaviors. Effective leadership, generational shifts and team stability through procedural inclusion are key mitigators that promote psychological safety and collaboration.

Sample diversity, self-reporting bias and temporal context may impact the generalizability of findings across broader health and surgical settings in contemporary practice.

Addressing entrenched power imbalances and fostering inclusive leadership and practices are critical to mitigating bullying. Promoting psychological safety and leveraging generational change can reshape surgical culture, leading to healthier teams and improved patient outcomes.

This study bridges SIT with theories of personality and psychological safety, offering new perspectives on how individual traits and systemic dynamics shape bullying behaviors and culture in surgical teams, highlighting the need for targeted interventions.

Applying practices from online role-playing games with multiple players, we employed Reacting to the Past (RTTP) pedagogy to engage students at several universities in a multi-week experience. As a pedagogical method, RTTP is ripe for potential use across an array of leadership education initiatives, including interinstitutional collaborations.

In this essay, we describe this student learning and engagement experience as well as address innovations and potential applications in similar leadership education contexts, preliminary student and facilitator feedback and lessons learned.

As part of The Justice Challenge’s eight-week Colloquium, students developed an understanding of food justice, systems thinking and transdisciplinary collaboration, as well as skills in perspective-taking, persuasion and teamwork. Through the RTTP gameplay, students engaged firsthand with multiple perspectives and goals, differing ways of interpreting information and situations, diverse factions and political pressures.

We believe our use of RTTP in The Justice Challenge is novel for two reasons. First, although RTTP fits remarkably well with the basic philosophy of honors education, it is less explored as a pedagogical approach in leadership education contexts. Second, our use of RTTP in an online, multi-institutional program highlights it as a useful pedagogical tool to foster leadership learning that can work regardless of group size or modality.

Past research has emphasised the potential for conversational artificial intelligence (AI) to disrupt services. Conversely, the literature recognises customer expectations as fundamental to service quality and customer satisfaction. However, the understanding of users’ expectations for conversational AI services is currently limited. Building upon previous research that has underscored the importance of users’ expertise, this study aims to provide valuable insights into the expectations of users with varying levels of expertise.

Forty-five semi-structured interviews were conducted, on three populations: experts, quasi-experts and non-experts from various countries including Japan, France and the USA. This includes 10 experts and 11 quasi-experts, as in professionals in conversational AI and related domains. And 25 non-experts, as in individuals without professional or advanced academic training in AI.

Findings suggest that users’ expectations depend on their expertise, how much they value human contact and why they are using these services. For instance, the higher the expertise the less anthropomorphism was stated to matter compared to technical characteristics, which could be due to a disenchantment effect. Other results include expectations shared by all users such as a need for more ethics including public interest.

The study provides insights into a key yet relatively unexplored area: it defines three major expectations categories (anthropomorphic, technical and ethical) and the associated expectations of each user groups based on expertise. To the best of the authors’ knowledge, it also highlights expectations never detected before as such in the literature such as explainability.