IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Determinants of Public Sector Managers' Intentions to Adopt AI in the Workplace

Determinants of Public Sector Managers' Intentions to Adopt AI in the Workplace
View Sample PDF
Author(s): Khalid Majrashi (Digital Transformation and Information Center, Institute of Public Administration, Riyadh, Saudi Arabia)
Copyright: 2024
Volume: 11
Issue: 1
Pages: 26
Source title: International Journal of Public Administration in the Digital Age (IJPADA)
Editor(s)-in-Chief: Manuel Pedro Rodríguez Bolívar (Universidad de Granada, Spain)
DOI: 10.4018/IJPADA.342849

Purchase

View Determinants of Public Sector Managers' Intentions to Adopt AI in the Workplace on the publisher's website for pricing and purchasing information.

Abstract

This study investigated the determinants of public sector managers' intentions to adopt artificial intelligence (AI) systems within their organizations. An extended technology acceptance model (TAM) was developed, incorporating additional constructs including fairness, humanity, reliability, safety, transparency, accountability, privacy, security, trust, social norms, tolerance, impact, and isomorphic pressure. A survey was conducted among 330 public sector managers, and the data were analyzed using linear regression tests to evaluate the model. The results showed significant positive influences of both perceived usefulness and perceived impact on managers' attitudes and behavioral intentions toward AI adoption. Isomorphic pressure was also a significant determinant of managers' behavioral intentions toward adopting AI systems. Our findings also indicated that perceptions related to AI ethical principles, such as transparency, privacy, and security, influenced managers' trust in AI systems.

Related Content

Sohawm Sengupta, Anant Ayyagari, Rithika Archinapalli, Ming Zhang, Lesley Clack. © 2024. 12 pages.
Khalid Majrashi. © 2024. 26 pages.
Hanjin Mao, Meril Antony, Yujin J. Jung. © 2024. 34 pages.
Niaz Ahmad. © 2023. 17 pages.
Hsin-Ching Wu, Aroon P. Manoharan. © 2023. 22 pages.
Ali Emrouznejad, Vishal Panchmatia, Roya Gholami, Carolee Rigsbee, Hasan B. Kartal. © 2023. 22 pages.
Mohammad Nur Ullah, Sadia Anjum Hossain, Raiyana Tazin, Bikram Biswas. © 2023. 20 pages.
Body Bottom