Psychology of Leadership: Understanding AI Adoption, Self-Efficacy, Green Creativity, and Risk Perception among Oman’s Business Bosses

DOI: https://doi.org/10.15826/csp.2025.9.2.332

Abstract

Artificial intelligence (AI) is at the forefront of transformative changes in organizational innovation. This study examines the social psychology underpinning AI adoption among Oman’s top business leaders, including CEOs, founders, and senior executives, to explore how green creativity, positive mental well-being, and risk perception converge with self-efficacy as a critical moderating force. Significant positive correlations were identified between most variables in the data set of 214 prominent Omani leaders using structural equation modeling and SmartPLS 4 software. The findings illustrated how Oman’s business leaders harnessed AI to align technological capabilities with deeply ingrained cultural values and communal aspirations. By situating these insights within Oman’s strategic vision of economic diversification and sustainability, this study underscored AI’s potential to catalyze organizational performance and environmentally conscious innovation. In addition, the moderating role of self-efficacy highlighted the importance of leadership confidence in navigating the complexities of AI integration. These discoveries have important implications for scholars, policymakers, and industry practitioners in Oman and other collectivist and emerging markets. By combining technology and human psychology, this study accentuates the need for thoughtful integration of AI, ensuring that rapid digital transformations remain culturally resonant, ethically grounded, and person-centered in an era of continuous change.

Author Biographies

Fadi Abdelfattah, Modern College of Business and Science (MCBS), Muscat, Oman

Dr. Fadi Abdelfattah is an Associate Professor and the Head of the Business & Economics Department at the Modern College of Business and Science (MCBS) in Oman. His research spans consumer behavior, service quality, healthcare management, and knowledge-sharing practices. With significant experience in academic leadership and curriculum development, he plays a key role in driving institutional excellence and innovation in higher education.

Mohamed Salah, A’Sharqiyah University, Ibra, Oman; University of Karbala, Karbala, Iraq

Dr. Mohammed Salah is an Assistant Professor at A’Sharqiyah University in Oman. He specializes in the intersection of public administration, behavioral science, social psychology, and artificial intelligence. His research investigates how generative AI technologies reshape decision-making processes, user autonomy, and governance frameworks within public institutions. His work critically assesses AI-driven transformations under various psychological and structural impacts, focusing on sustainable and adaptive public service delivery.

 
Khalid Dahleez, A’Sharqiyah University, Ibra, Oman

Dr. Khalid Dahleez is an Associate Professor and the Dean of the College of Business Administration at A’Sharqiyah University in Oman. With over two decades of academic and managerial experience, he has supervised initiatives in academic planning, quality assurance, and community engagement. Actively involved in accreditation processes in Oman and Palestine, his teaching and research focuses on Leadership, Project Management, Strategic Management, Small Business, and Entrepreneurship. He has published over 50 peer-reviewed articles and has extensive experience supervising graduate students and managing donor-funded projects.

 
Hussam Al Halbusi, Lusail University, Doha, Qatar; Al-Bayan University, Baghdad, Iraq

Dr. Hussam Al Halbusi is an Assistant Professor at Lusail University in Qatar. His research interests include strategic management, innovation, leadership, and sustainability. He primarily focuses on improving performance across the public and private sectors through academic-practitioner collaboration and evidence-based strategy.

 

References


  • Abdelfattah, F., Dahleez, K., Al Halbusi, H., & Salah, M. (2025). Strategic green alliances: Integrating green dynamic capabilities, AI, and electronic entrepreneurial innovation for sustainability. Sustainable Futures, 9, Article 100433. https://doi.org/10.1016/j.sftr.2025.100433

  • Abdelfattah, F., Salah, M., Dahleez, K., Darwazeh, R., & Al Halbusi, H. (2025). Public policy and sustainability: How green core competence, government trust, and policy satisfaction influence green R&D investments in the private sector. Sustainable Futures, 9, Article 100461. https://doi.org/10.1016/j.sftr.2025.100461

  • Accenture. (2024). 360° value report 2024: Delivering value from every angle. https://www.accenture.com/content/dam/accenture/final/corporate/company-information/document/Accenture-360-Value-Report-2024.pdf

  • Ahmed, Z., Shew, A. M., Mondal, M. K., Yadav, S., Jagadish, S. K., Prasad, P. V., Buisson, M.-C., Das, M., & Bakuluzzaman, M. (2022). Climate risk perceptions and perceived yield loss increases agricultural technology adoption in the polder areas of Bangladesh. Journal of Rural Studies, 94, 274–286. https://doi.org/10.1016/j.jrurstud.2022.06.008

  • Al-Azri, K. M. (2012). Social and gender inequality in Oman: The power of religious and political tradition. Routledge. https://doi.org/10.4324/9780203115589

  • Al Balushi, Y., Locke, S., & Boulanouar, Z. (2019). Determinants of the decision to adopt Islamic finance: Evidence from Oman. ISRA International Journal of Islamic Finance, 11(1), 6–26. https://doi.org/10.1108/IJIF-02-2018-0020

  • Al Harrasi, N., El Din, M. S., & Al Balushi, B. (2021). Towards an artificial intelligence (AI)-driven government in Sultanate of Oman: Transforming and augmenting leadership competencies. In T. Esakki (Ed.), Managerial issues in digital transformation of global modern corporations (pp. 244–256). IGI Global. https://doi.org/10.4018/978-1-7998-2402-2.ch016

  • Al Zadjali, H. (2020). Building the right AI governance model in Oman. In Y. Charalabidis, M. A. Cunha, & D. Sarantis (Eds.), ICEGOV’20: Proceedings of the 13th International Conference on Theory and Practice of Electronic Governance (pp. 116–119). ACM. https://dl.acm.org/doi/10.1145/3428502.3428516

  • Al Zuheimi, M. H. (2025). Development of new technologies and intellectual property regulations in the Sultanate of Oman. In N. Naim, A. AlDebasi, & D. Price (Eds.), Intellectual property and innovation. The political economy of the Middle East (pp. 79–107). Palgrave Macmillan. https://doi.org/10.1007/978-981-96-4020-1_4

  • Allioui, H., & Mourdi, Y. (2023). Unleashing the potential of AI: Investigating cuttingedge technologies that are transforming businesses. International Journal of Computer Engineering and Data Science, 3(2), 1–12. https://www.ijceds.com/ijceds/article/view/59

  • Alsheddi, A. (2020). The impact of socio-cultural and religious values on the adoption of technological innovation: A case study of Saudi Arabia [Doctoral dissertation, University of Canberra]. University of Canberra Research Portal. https://doi.org/10.26191/29mt-7v40

  • Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191

  • Bandura, A. (1986). Social foundations of thought and action: A Social Cognitive Theory. Prentice-Hall.

  • Bruch, H., & Vogel, B. (2011). Fully charged: How great leaders boost their organization’s energy and ignite high performance. Harvard Business Review Press.

  • Chen, Y.-S., & Chang, C.-H. (2013). The determinants of green product development performance: Green dynamic capabilities, green transformational leadership, and green creativity. Journal of Business Ethics, 116(1), 107–119. https://doi.org/10.1007/s10551-012-1452-x

  • Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2) 189–211. https://doi.org/10.2307/249688

  • Davenport, T. H., & Ronanki, R. (2018, January). Artificial intelligence for the real world. Harvard Business Review. https://hbr.org/2018/01/artificial-intelligence-for-the-real-world

  • Dawson, J. F. (2014). Moderation in management research: What, why, when, and how. Journal of Business and Psychology, 29(1), 1–19. https://doi.org/10.1007/s10869-013-9308-7

  • Delanoë, P., Tchuente, D., & Colin, G. (2023). Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions. Journal of Environmental Management, 331, Article 117261. https://doi.org/10.1016/j.jenvman.2023.117261

  • Di Vaio, A., Palladino, R., Hassan, R., & Escobar, O. (2020). Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research, 121, 283–314. https://doi.org/10.1016/j.jbusres.2020.08.019

  • Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, Article 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002

  • Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy [Editorial opinion paper]. International Journal of Information Management, 71, Article 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

  • Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3

  • Galluch, P. S. (2015). It’s all in your personality: Combatting technostress in the workplace. In SAIS 2015: Proceedings of the Southern Association for Information Systems Conference, Hilton Head Island, SC, USA March 20–21, 2015, Article 7. https://aisel.aisnet.org/sais2015/7/

  • Gharib, M. N., Jamil, S. A., Ahmad, M., & Ghouse, S. (2016). The impact of job stress on job performance: A case study on academic staff at Dhofar University. International Journal of Economic Research, 13(1), 21–33.

  • Ghobakhloo, M., Iranmanesh, M., Grybauskas, A., Vilkas, M., & Petraitė, M. (2021). Industry 4.0, innovation, and sustainable development: A systematic review and a roadmap to sustainable innovation. Business Strategy and the Environment, 30(8), 4237–4257. https://doi.org/10.1002/bse.2867

  • Ghouse, S. M., Barber, D., III, & Alipour, K. (2024). Shaping the future entrepreneurs: Influence of human capital and self-efficacy on entrepreneurial intentions of rural students. The International Journal of Management Education, 22(3), Article 101035. https://doi.org/10.1016/j.ijme.2024.101035

  • Ghouse, S. M., & Chaudhary, M. (2024). Artificial intelligence (AI) for tourism start-ups. In S. Manohar, A. Mittal, S. Raju, & A. Nair (Eds.), Innovative technologies for increasing service productivity (pp. 161–178). IGI Global. https://doi.org/10.4018/979-8-3693-2019-8.ch010

  • Ghouse, S. M., Shekhar, R., Ali Sulaiman, M. A. B., & Azam, A. (2024). Green purchase behaviour of Arab millennials towards eco-friendly products: The moderating role of eco-labelling. The Bottom Line. Advanced online publication. https://doi.org/10.1108/BL-08-2023-0246

  • Gupta, S., Ghardallou, W., Pandey, D. K., & Sahu, G. P. (2022). Artificial intelligence adoption in the insurance industry: Evidence using the technology–organization–environment framework. Research in International Business and Finance, 63, Article 101757. https://doi.org/10.1016/j.ribaf.2022.101757

  • Hair, J. F., Harrison, D. E., & Ajjan, H. (2022). Essentials of marketing analytics. McGraw Hill.

  • Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling. SAGE.

  • Hassan, M. S., Al Halbusi, H., Razali, A., Ariffin, R. N. R., & Williams, K. A. (2023). The Swedish gamble: Trust in the government and self-efficacy in the battle to combat COVID-19. Current Psychology, 42(21), 17935–17950. https://doi.org/10.1007/s12144-022-02947-w

  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

  • Kaplan, R. S., & Mikes, A. (2012). Managing risks: A new framework. Harvard Business Review. https://hbr.org/2012/06/managing-risks-a-new-framework

  • Kock, N., & Lynn, G. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546–580. https://doi.org/10.17705/1jais.00302

  • Kulkarni, S., Valeri, M., & William, P. (Eds.). (2025). Driving business success through eco-friendly strategies. IGI Global. https://doi.org/10.4018/979-8-3693-9750-3

  • Kumar, V., Pallathadka, H., Sharma, S. K., Thakar, C. M., Singh, M., & Pallathadka, L. K. (2022). Role of machine learning in green supply chain management and operations management. Materials Today: Proceedings, 51(Pt. 8), 2485–2489. https://doi.org/10.1016/j.matpr.2021.11.625

  • Lukat, J., Margraf, J., Lutz, R., van der Veld, W. M., & Becker, E. S. (2016). Psychometric properties of the positive mental health scale (PMH-scale). BMC Psychology, 4, Article 8. https://doi.org/10.1186/s40359-016-0111-x

  • Marr, B. (2019). Artificial intelligence in practice: How 50 successful companies used AI and machine learning to solve problems. Wiley.

  • Mohammed, H. S., Ariffin, R. N. R., Mansor, N., & Al Halbusi, H. (2021a). Rebelliousness and street-level bureaucrats’ discretion: Evidence from Malaysia. Journal of Administrative Science, 18(1), 173–198.

  • Mohammed, H. S., Ariffin, R. N. R., Mansor, N., & Al Halbusi, H. (2021b). The moderating role of willingness to implement policy on street-level bureaucrats’ multidimensional enforcement style and discretion. International Journal of Public Administration, 46(6), 430–444. https://doi.org/10.1080/01900692.2021.2001008

  • Oman Vision 2040 Implementation Follow-up Unit. (n.d.). https://www.oman2040.om/?lang=en

  • Ordoñez de Pablos, P. (2023). Digital innovation, competitiveness and governments: Insights from Oman and other countries in the digital era [Editorial]. Journal of Science and Technology Policy Management, 14(5), 801–806. https://doi.org/10.1108/JSTPM-09-2023-218

  • Podsakoff, N., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879

  • Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569. https://doi.org/10.1146/annurev-psych-120710-100452

  • Puthukulam, G., Ravikumar, A., Sharma, R. V. K., & Meesaala, K. M. (2021). Auditors’ perception on the impact of artificial intelligence on professional skepticism and judgment in Oman. Universal Journal of Accounting and Finance, 9(5), 1184–1190. https://doi.org/10.13189/ujaf.2021.090527

  • Ransbotham, S., Khodabandeh, S., Fehling, R., LaFountain, B., & Kiron, D. (2019, October 15). Winning with AI: Pioneers combine strategy, organizational behavior, and technology. MIT Sloan Management Review; Boston Consulting Group. https://sloanreview.mit.edu/projects/winning-with-ai

  • Ringle, C., da Silva, D., & Bido, D. de S. (2014). Structural equation modeling with the SmartPLS. Brazilian Journal of Marketing, 13(2), 56–73. https://doi.org/10.5585/remark.v13i2.2717

  • Rodway, P., & Schepman, A. (2023). The impact of adopting AI educational technologies on projected course satisfaction in university students. Computers and Education: Artificial Intelligence, 5, Article 100150. https://doi.org/10.1016/j.caeai.2023.100150

  • Salah, M., Abdelfattah, F., & Al Halbusi, H. (2024). The good, the bad, and the GPT: Reviewing the impact of generative artificial intelligence on psychology. Current Opinion in Psychology, 59, Article 101872. https://doi.org/10.1016/j.copsyc.2024.101872

  • Salah, M., Al Halbusi, H., & Abdelfattah, F. (2023). May the force of text data analysis be with you: Unleashing the power of generative AI for social psychology research. Computers in Human Behavior: Artificial Humans, 1(2), Article 100006. https://doi.org/10.1016/j.chbah.2023.100006

  • Salah, M., Al Halbusi, H., Ismail, M. M., & Abdelfattah, F. (2024). Chatting with ChatGPT: Decoding the mind of chatbot users and unveiling the intricate connections between user perception, trust and stereotype perception on self-esteem and psychological well-being. Current Psychology, 43(9), 7843–7858 https://doi.org/10.1007/s12144-023-04989-0

  • Saxena, S., & Al-Tamimi, T. A. S. M. (2018). Visioning “smart city” across the Gulf Cooperation Council (GCC) countries. Foresight, 20(3), 237–251. https://doi.org/10.1108/FS-11-2017-0068

  • Shipman, K., Burrell, D. N., & Huff, A. (2023). An organizational analysis of how managers must understand the mental health impact of teleworking during COVID-19 on employees. International Journal of Organizational Analysis, 31(4), 1081–1104. https://doi.org/10.1108/IJOA-03-2021-2685

  • Simons, T. L., & Peterson, R. S. (2000). Task conflict and relationship conflict in top management teams: The pivotal role of intragroup trust. Journal of Applied Psychology, 85(1), 102–111. https://doi.org/10.1037/0021-9010.85.1.102

  • Stajkovic, A. D., & Luthans, F. (1998). Self-efficacy and work-related performance: A meta-analysis. Psychological Bulletin, 124(2), 240–261. https://doi.org/10.1037/0033-2909.124.2.240

  • Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910

  • Tawfik, O. I., Durrah, O., Hussainey, K., & Elmaasrawy, H. E. (2023). Factors influencing the implementation of cloud accounting: Evidence from small and medium enterprises in Oman. Journal of Science and Technology Policy Management, 14(5), 859–884. https://doi.org/10.1108/JSTPM-08-2021-0114

  • Upadhyay, N., Upadhyay, S., Abed, S. S., & Dwivedi, Y. K. (2022). Consumer adoption of mobile payment services during COVID-19: Extending meta-UTAUT with perceived severity and self-efficacy. International Journal of Bank Marketing, 40(5), 960–991. https://doi.org/10.1108/IJBM-06-2021-0262

  • Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451–481. https://doi.org/10.1111/j.1540-5915.1996.tb00860.x

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

  • Wijayati, D. T., Rahman, Z., Fahrullah, A., Rahman, M. F. W., Arifah, I. D. C., & Kautsar, A. (2022). A study of artificial intelligence on employee performance and work engagement: The moderating role of change leadership. International Journal of Manpower, 43(2), 486–512. https://doi.org/10.1108/IJM-07-2021-0423

  • Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443–488. https://doi.org/10.1108/JEIM-09-2014-0088

  • Yap, S.-F., Xu, Y., & Tan, L. (2021). Coping with crisis: The paradox of technology and consumer vulnerability. International Journal of Consumer Studies, 45(6), 1239–1257. https://doi.org/10.1111/ijcs.12724

  • Yigitcanlar, T., Corchado, J. M., Mehmood, R., Li, R. Y. M., Mossberger, K., & Desouza, K. (2021). Responsible urban innovation with local government artificial intelligence (AI): A conceptual framework and research agenda. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), Article 71. https://doi.org/10.3390/joitmc7010071

  • Zahoor, N., Donbesuur, F., Christofi, M., & Miri, D. (2022). Technological innovation and employee psychological well-being: The moderating role of employee learning orientation and perceived organizational support. Technological Forecasting and Social Change, 179, Article 121610. https://doi.org/10.1016/j.techfore.2022.121610

  • Zaidan, E., Al-Saidi, M., & Hammad, S. H. (2019). Sustainable development in the Arab world–Is the Gulf Cooperation Council (GCC) region fit for the challenge? Development in Practice, 29(5), 670–681. https://doi.org/10.1080/09614524.2019.1628922

Published
2025-07-12
How to Cite
Abdelfattah, F., Salah, M., Dahleez, K., & Al Halbusi, H. (2025). Psychology of Leadership: Understanding AI Adoption, Self-Efficacy, Green Creativity, and Risk Perception among Oman’s Business Bosses. Changing Societies & Personalities, 9(2), 353-380. doi:10.15826/csp.2025.9.2.332
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Vol. 9, No. 2 (2025)
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Articles