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Artificial Intelligence In Public Administration: How The Emotional Intelligence Of Civil Servants Influences Its Effective Implementation

Author

Listed:
  • Maria VESOLOVSKA

    (Department of Management of Organizations, Lviv Polytechnic National University, 5 Metropolyta Andreia St., Building 4, Room 407, Lviv, 79013, Ukraine)

  • Veronika KARKOVSKA

    (Department of Administrative and Financial Management, Lviv Polytechnic National University; 12 Stepana Bandery str., Lviv; 79013, Ukraine)

  • Oleh DUMA

    (Department of Administrative and Financial Management, Lviv Polytechnic National University)

Abstract

This study explores the influence of public servants’ emotional intelligence (EI) on the effectiveness of artificial intelligence (AI) implementation in public administration. As governments increasingly adopt AI to streamline administrative processes, the human factor—particularly emotional and cognitive adaptability—emerges as a critical determinant of success. A nationwide survey of 150 Ukrainian civil servants was conducted, employing the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) to assess EI levels. Additional instruments measured digital competencies, attitudes toward AI, perceived risks, and training needs. A regression model was developed to quantify the impact of emotional and professional variables on AI implementation outcomes. The findings reveal that high EI, when combined with strong digital skills, significantly enhances adaptability, fosters trust in AI systems, and improves the overall effectiveness of digital transformation. In contrast, technical proficiency alone does not guarantee successful implementation if accompanied by emotional rigidity or resistance to change. Key barriers identified include unequal digital literacy, skepticism toward automated decision-making, and insufficient managerial support. The study’s novelty lies in formalizing the EI–AI relationship through a predictive model that integrates emotional, cognitive, and behavioral dimensions. It also introduces a composite index of AI implementation effectiveness, grounded in both subjective perceptions and observable behaviors. Practical recommendations emphasize the need for integrated training programs that simultaneously develop emotional and technical competencies. These findings underscore the importance of a human-centered approach to digital governance, where emotional intelligence is not a peripheral trait but a strategic asset in managing technological change and enhancing public sector resilience.

Suggested Citation

  • Maria VESOLOVSKA & Veronika KARKOVSKA & Oleh DUMA, 2025. "Artificial Intelligence In Public Administration: How The Emotional Intelligence Of Civil Servants Influences Its Effective Implementation," REVISTA ADMINISTRATIE SI MANAGEMENT PUBLIC, Faculty of Administration and Public Management, Academy of Economic Studies, Bucharest, Romania, vol. 2025(45), pages 27-45.
    • Handle: RePEc:rom:rampas:v:2025:y:2025:i:45:p:27-45
    DOI: https://doi.org/10.24818/amp/2025.45-02
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    References listed on IDEAS

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    1. Bernd W. Wirtz & Jan C. Weyerer & Carolin Geyer, 2019. "Artificial Intelligence and the Public Sector—Applications and Challenges," International Journal of Public Administration, Taylor & Francis Journals, vol. 42(7), pages 596-615, May.
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    Keywords

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    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs
    • J82 - Labor and Demographic Economics - - Labor Standards - - - Labor Force Composition
    • M12 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Personnel Management; Executives; Executive Compensation
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration

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