Browsing by Author "Alrawaf, Fahad Ibrahim Saad"

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SUCCESSION PLANNING IN THE PUBLIC SECTOR: A SYSTEMATIC REVIEW, MANAGERIAL IMPACT ANALYSIS, AND THE EMERGING ROLE OF ARTIFICIAL INTELLIGENCE IN HUMAN CAPITAL DEVELOPMENT
(Saudi Digital Library, 2026) Alrawaf, Fahad Ibrahim Saad; Tantardini, Michele
Succession planning is a critical strategic tool for public organizations to prepare and promote future leaders in response to leadership shortages. Despite its importance, research on its concepts, benefits, challenges, and influencing factors remains limited, as does understanding of its effectiveness and future potential, including the role of artificial intelligence (AI). This dissertation comprises three studies. The first study systematically reviews succession planning in public organizations, clarifying its core concepts, benefits, challenges, and success factors, while identifying gaps for future research. The second study quantitatively examines the impact of succession planning programs on work motivation, legacy motivation, job satisfaction, and turnover intentions, using Self-Determination Theory, Legacy Motivation Theory, and Social Exchange Theory. The analysis focuses on the National Program for Succession and Leadership Development, launched by the Ministry of Human Resources and Social Development in December 2024 and approved by the Saudi Council of Ministers in February 2025, which aligns with the Ambitious Nation pillar of Saudi Vision 2030. Results show positive effects on motivation and job satisfaction while reducing turnover intentions, introducing Legacy Motivation Theory as a new perspective. The third study employs a qualitative approach to explore HR professionals’ perceptions of AI in succession planning, guided by Human Capital Theory. Findings highlight AI’s potential to improve productivity, the need to develop digital competencies, and the importance of equity and transparency to avoid bias. Overall, the dissertation advances theoretical understanding and offers practical recommendations, emphasizing AI‑enabled succession planning, localized platforms to reduce algorithmic bias, ethical training, and strong governance frameworks to guide responsible AI adoption.
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