Publications

Teams that combine human intelligence with artificial intelligence (AI) have become indispensable for solving complex tasks in various decision-making contexts in modern organizations. However, the factors that contribute to AI convergence, where human team members align their decisions with those of their AI counterparts, still remain unclear. This study integrates signaling theory with self-determination theory to investigate how specific signals—such as signal fit, optional AI advice, and signal set congruence—affect employees' AI convergence in human–AI teams. Based on four experimental studies conducted in facial recognition and hiring contexts with approximately 1100 participants, the findings highlight the significant positive impact of congruent signals from both human and AI team members on AI convergence. Moreover, providing an option for employees to solicit AI advice also enhances AI convergence; when AI signals are chosen by employees rather than forced upon them, participants are more likely to accept AI advice. This research advances knowledge on human–AI teaming by (1) expanding signaling theory into the human–AI team context; (2) developing a deeper understanding of AI convergence and its drivers in human–AI teams; (3) providing actionable insights for designing teams and tasks to optimize decision-making in high-stakes, uncertain environments; and (4) introducing facial recognition as an innovative context for human–AI teaming.

Diverse and inclusive teams are not merely a moral imperative but also a catalyst for scientific excellence in robotics. Drawing from literature, a comprehensive citation analysis, and expert interviews, we derive seven main benefits of diversity and inclusion and propose a leadership guide for roboticists to reap these benefits.

Workers are increasingly embracing Artificial Intelligence (AI) to optimise various aspects of their operations in the workplace. While AI offers new opportunities, it also presents unintended challenges that they must carefully navigate. This paper aims to develop a deeper understanding of workers’ experiences with interactions with automated agents (AA) in the workplace and provide actionable recommendations for organisational leaders to achieve positive outcomes. We propose and test a simulation model that quantifies and predicts workers’ experiences with AA, shedding light on the interplay of diverse variables, such as workload, effort and trust. Our findings suggest that lower-efficiency AA might outperform higher-efficiency ones due to the constraining influence of trust on adoption rates. Additionally, we find that lower initial trust in AA could lead to increased usage in certain scenarios and that stronger emotional and social responses to the use of AA may foster greater trust but result in decreased AA utilisation. This interdisciplinary research blends a systems dynamics approach with management theories and psychological concepts, aiming to bridge existing gaps and foster the sustainable and effective implementation of AA in the workplace. Ultimately, our research endeavour contributes to advancing the field of human-AI interaction in the workplace.

Social Virtual Reality (VR) platforms have surged in popularity in recent years, including among people with disabilities (PWD). Previous research has documented accessibility challenges, harassment, and negative experiences for PWD using disability signifiers in VR, primarily focusing on those with visible disabilities who encounter negative experiences. Yet, little is known about the experiences of people with invisible disabilities in social VR environments, and whether positive experiences are also common. To address these gaps, we designed inclusive avatars (avatars with disability signifiers) and investigated the lived experiences of 26 individuals with both visible and invisible disabilities immersing themselves in social interactions in VRChat for a week. We utilized a mixed methods experience sampling design and multilevel regression to explore the relationships between social interactions of PWD in VR and various psychological outcomes. Our results indicate that PWD, both visible and invisible, experienced positive and negative social interactions in VR. These interactions, in turn, significantly influenced users’ overall experience with inclusive avatars, affecting aspects such as emotional responses, engagement levels, satisfaction with the avatar’s design, and perceptions of inclusion in VR. Qualitative interviews of 18 participants allowed for a more nuanced exploration of the experiences of PWD by giving voice to users who are rarely studied in depth. Findings provided unique insights into both the positive and negative experiences of PWD, as well as identified key design factors influencing user experience in social VR.

The gig economy has expanded beyond platform-based work and is also transforming standard organizations that are accustomed to stable employment arrangements and long-term-oriented HRM practices. The shift towards gig workers and blended teams disrupts standard HR practices due to the short-term, transactional nature of gig work. This research investigates the implications of gig work on HRM practices in standard organizations. Specifically, we 1) examine the trends and perspectives of HR professionals on the use of gig work in standard organizations, 2) investigate whether HR professionals apply standard HRM practices for gig workers, and 3) conduct a longitudinal analysis of HRM perspectives applicable to gig workers before and post-COVID-19 pandemic. To achieve these research objectives, we employ natural language processing techniques to analyze more than 500 YouTube videos of HR professionals offering their opinions about gig work. The findings suggest that despite the widely conceived notion that gig workers are ‘self-managed’, various HRM practices are utilized in the context of gig work.