Journal of Leadership Studies最新文献

In healthcare settings where structural change is slow and leadership is distributed across roles, the integration of artificial intelligence (AI) into leadership education introduces both promise and complexity. The current paper distinguishes between generative AI, systems that create new content and insights, and algorithmic AI, which automates predefined tasks within leadership training contexts. Yet, these tools risk embedding algorithmic bias and reinforcing Western-centric leadership ideals, raising ethical, cultural, and pedagogical concerns. The current paper examines the implications of AI-enabled leadership education through theoretical lenses, including complexity leadership, cultural dimensions theory, and relational pedagogy. It explores how AI systems reshape power within learning environments, shifting emphasis from relational mentorship to behavioral optimization. Drawing on Foucauldian concepts of discipline and visibility, the analysis shows how data-driven models may prioritize conformity over ethical discernment and reduce leadership to a technical artifact. To address these risks, the paper proposes a Relational-AI Pedagogy Model that positions AI as a supportive tool within a relational and culturally adaptive leadership education framework. This approach balances the efficiencies of AI with human judgment, mentorship, and cultural responsiveness. By integrating technological and relational strengths, the model offers a path for pharmaceutical organizations to develop KOLs who are not only scientifically credible but also ethically grounded and culturally responsive within diverse leadership contexts.

The rapid ascent of generative artificial intelligence (AI) presents higher education leaders with urgent challenges of pedagogy, ethics, and institutional adaptation. Yet many leadership responses have been top-down or vendor-driven, sidelining the faculty who are closest to teaching and learning. The current reflective case study examines The Generator, a faculty-led interdisciplinary AI lab at Babson College, as a model of distributed and relational leadership in the AI era. Drawing on theories of distributed leadership, relational leadership, and collective action, we explore how The Generator enacts a values-driven leadership practice through its decentralized lab structure, faculty-led programs, and signature “Family Conversations.” These practices foreground care, trust, and inclusion in decisions about AI adoption, which offers an alternative to purely efficiency-driven models of technological leadership. We argue that The Generator provides a transferable model for how faculty can lead institutional adaptation to AI in ways that are mission-aligned and pedagogically informed while emphasizing that each institution must adapt leadership practices to its own context, mission, and values. The case contributes to broader conversations about leadership and governance amid technological disruption, suggesting that distributed, relational leadership practices may be essential for guiding higher education through the uncertainties of the AI age.

Prior research has shown that laissez-faire leadership can have detrimental consequences on employees and organizations such as increased unethical behavior, workplace incivility, and employee burnout. However, little is known about the relationship between laissez-faire leadership and important leader outcomes. Based on social information processing theory and conservation of resources theory, laissez-faire leadership is likely positively related to follower counterproductive work behavior, which was predicted to indirectly relate to leader turnover intentions through leader emotional ill-being (i.e., negative affect, emotional exhaustion). Additionally, theory suggests that performance pressure would exacerbate the serial indirect relationship between laissez-faire leadership and leader turnover intentions. Two time-lagged studies of full-time working leaders (N = 533) across a variety of industries and cultures showed support for the hypothesized serial mediation in both Study 1 and Study 2, but Study 2 failed to provide support for the moderating hypothesis regarding performance pressure. The studies' results contribute to the literature by demonstrating how laissez-faire leadership can be related to significant negative consequences for both followers and the leaders themselves.

The dynamic nature of leadership education and the importance of bridging theory to practice call for research to understand how students perceive the relevance and effectiveness of incorporating real-world application such as current events into their learning experience. This study explores the perceptions of students regarding the use of current events articles from The New York Times as supplementary resources in learning leadership concepts and theories. A convergent parallel mixed methods design was employed across three different sections (online, hybrid & in person) during the fall 2023 semester at a large, land-grant university. A survey containing quantitative and qualitative items was sent to students in each class at the end of the semester for extra credit. Findings from the study show a positive perception among students regarding the usage of current events articles from The New York Times in supplementing their learning about leadership concepts and theories. The supplemental learning tool also gave students a better understanding of real-world issues and scenarios and enhanced their interest in learning more about leadership issues. The research team concludes that the integration of The New York Times current articles proved to be highly effective in supplementing traditional learning materials in the leadership course.

The current article examines the evolving relationship between leadership, artificial intelligence (AI), and language through the lens of structuration theory and critical discourse analysis's sensemaking theory. Through a content analysis of terminology governance practices across industries, we identify key leadership practices and opportunities to shape how organizations name, understand, and implement AI technologies. The study addresses the current state of “AI anomia”—the collective inability to name AI-related tools, including in ethical ways—and presents a practical framework for leaders to govern AI terminology while maintaining human agency in technological adoption. Findings suggest that effective AI terminology governance requires that leaders balance standardization with cultural integration, technical precision with public understanding, and innovation with ethical considerations.

In 2020, as communities navigated significant challenges, University of Wisconsin educators searched for ways to inspire hope in community members. This led to interviews across Wisconsin focused on the question, “How do leaders inspire hope and relational action in communities through story?” Interviews revealed that through storytelling, community leaders create shifts in mental models, leading to community innovation. Individuals who recognize an opportunity and communicate a vision and strategy create positive pivotal community change. Sharing these stories and lessons learned create hope and inspire a new group of people to act and innovate as leaders who shape community change.

The proliferation of artificial intelligence (AI) is transforming the nature of teamwork, leading to the emergence of human-agent teams across various domains. From cognitive assistants supporting astronauts to AI-driven automation in autonomous vehicles, AI is increasingly integrated as an active team member rather than a passive tool. The current paper explores the distinctions between human-human, agent-agent, and human-agent teams, providing insights into the roles AI can assume within collaborative environments. We examine established teamwork models, such as Shared Mental Model (SMM) and Co-Active Design, and present a human-AI team framework to understand how AI can effectively enhance joint activity, communication, and decision-making within teams. Additionally, the paper identifies current challenges with integrating AI into human-agent teams, including the lack of adaptive teamwork models and engineering limitations. By addressing these challenges and leveraging structured frameworks, organizations can optimize AI-human collaboration, fostering high-performance teams capable of excelling in complex, dynamic work environments.