Featuring scholarly and practitioner insights from HR and people leaders, industry experts, and researchers.

This research introduces a comprehensive framework for maintaining resilient leadership during periods of intense crisis and social fragmentation. It defines hope as a multidimensional, learnable skill involving cognitive planning, social trust, and purposeful action rather than a mere emotional state. The author highlights how information overload and eroding trust deplete a leader’s capacity to envision the future, which directly hurts organizational performance and employee wellbeing. To combat this, the source suggests evidence-based strategies such as transparent communication, distributed decision-making, and the celebration of small victories to restore collective confidence. Furthermore, it emphasizes the importance of material support and structured recovery to ensure that hope is built into the very infrastructure of the organization. Ultimately, the text argues that by practicing hope intentionally, leaders can foster adaptability and transcendent meaning even when conditions are most challenging.

Abstract: This article examines the role of management quality as institutional infrastructure in higher education, drawing on recent longitudinal evidence linking manager performance to employee salary progression, internal mobility, and retention. While colleges and universities invest heavily in student success initiatives and financial planning, people management is often treated as an assumed competency rather than a cultivated strategic capability. The evidence suggests this assumption carries significant costs. Over multiple years, employees reporting to high-performing managers experience measurably faster advancement and broader institutional mobility than peers led by weaker managers—differences that compound over time and directly affect institutional capacity to execute strategic priorities. This article synthesizes research from organizational behavior, human capital development, and higher education administration to propose evidence-based interventions institutions can implement to strengthen management quality, including structured development pathways, transparent performance ecosystems, and distributed leadership models that treat management capability as strategic infrastructure rather than administrative overhead.

How Great Leaders Build Hope in Chaos — a concise, research-backed guide inspired by Dr. Jonathan H. Westover’s synthesis of hope theory and organizational leadership. Learn evidence-based strategies across six domains—cognitive, affective, behavioral, social, spiritual/existential, and developmental—to cultivate and communicate hope during prolonged uncertainty. Packed with practical tactics: transparent communication, distributed sensemaking, designed small wins, recovery practices, material supports, and purpose reconnection. Ideal for leaders in healthcare, education, tech, and manufacturing seeking resilient, adaptive leadership tools. Like and share if this helped you build hope at work. #Leadership #Hope #Resilience #Sensemaking #AdaptiveLeadership #OrganizationalDevelopment #SnyderHopeTheory OUTLINE: 00:00:00 - The Echo Chamber of Uncertainty 00:00:46 - Charting a Course Through the Fog 00:01:41 - Naming the Fear, Finding the Footing 00:02:33 - Building Momentum with Small Wins 00:04:01 - Weaving Hope into a Collective Story

This conversation explores how artificial intelligence should be integrated into hiring processes, specifically when candidates are allowed to choose between human and AI interviewers. They introduce the concept of "choice-as-signal," noting that an applicant's preference for a specific screening method reveals private information about their underlying abilities. While providing this choice is often viewed as a way to protect worker autonomy, firms that strategically interpret these choices can improve match quality and organizational performance. The study finds that hybrid systems—which combine the specialized precision of AI in analytical tasks with human judgment in interpersonal areas—significantly outperform single-method approaches. Ultimately, the text argues that optimal AI adoption requires moving beyond simple automation toward a collaborative design that balances information revelation with applicant welfare. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This text highlights the critical role of management quality as an essential but often neglected foundation for success in higher education. Research indicates that effective supervisors significantly boost employee retention, salary growth, and internal career movement, yet many universities incorrectly assume leadership skills develop naturally from technical expertise. To address this, the article suggests that institutions should treat management as a strategic priority by implementing structured training, mentorship, and transparent feedback systems. By shifting away from "trial by fire" transitions and investing in leadership development, colleges can improve their execution of goals and organizational resilience. Ultimately, the source argues that prioritizing people management is a controllable lever that directly counters modern pressures like enrollment volatility and budget constraints. These findings emphasize that strong leadership at all levels is necessary to sustain a thriving academic workforce and fulfill long-term institutional missions. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Abstract: This article examines how firms should integrate artificial intelligence into labor-market screening when applicants can choose between human and AI interviewers. Drawing on a natural field experiment involving 70,000 job applicants and recent theoretical advances in mechanism design, we show that AI adoption is fundamentally a design problem rather than a simple substitution decision. When applicants select their preferred interviewer, this choice itself becomes an informative signal about underlying abilities—a phenomenon we term "choice-as-signal." The welfare implications depend critically on whether firms incorporate this signal into hiring decisions and whether applicants anticipate such use. Evidence suggests that hybrid screening systems combining human and AI evaluation outperform either technology alone, and that specialized assignment—matching each screener to the dimensions they assess most accurately—can improve match quality. These findings challenge conventional automation narratives and reveal novel trade-offs between worker autonomy and information revelation in AI-augmented hiring.

Abstract: Leaders across sectors increasingly report difficulty sustaining hope amid accelerating crises, information overload, and fractured social trust. This article synthesizes psychological research on hope theory with organizational scholarship on sensemaking and leadership to offer evidence-based strategies for cultivating and communicating hope during prolonged uncertainty. Drawing on Snyder's hope theory, recent multidimensional models of hope, and research on adaptive leadership, we examine why hope feels uniquely challenging in contemporary organizational contexts and outline six practical domains—cognitive, affective, behavioral, social, spiritual/existential, and developmental—through which leaders can strengthen their own hope and foster collective resilience. Case examples from healthcare, technology, education, and manufacturing illustrate how organizations sustain hope through transparent communication, distributed sensemaking, and deliberately designed moments of collective efficacy. The article concludes that hope is not merely an emotional state to be recovered but a dynamic, relational capacity that leaders can intentionally practice and amplify, even—and especially—when it feels most elusive.

This presentation explores the adoption paradox where high organizational investment in artificial intelligence often results in failure due to misaligned human trust. It distinguishes between cognitive trust, based on rational competence, and emotional trust, rooted in affective safety, identifying four distinct psychological configurations that dictate how employees interact with AI. When these trust dimensions are lacking, workers may manipulate or withdraw their digital data, creating a negative feedback loop that degrades the system’s accuracy and utility. To resolve this, the author advocates for holistic strategies that include transparent communication, ethical governance, and psychological support rather than relying on technical improvements alone. Ultimately, the source argues that successful implementation depends on managing the human-AI relationship through a culture of procedural justice and individual empowerment.