全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Perspectives and Experiences of Education Stakeholders: A Quantitative Study on the Adoption of Artificial Intelligence in Executive Training Using Structural Equation Modeling

DOI: 10.4236/iim.2024.162007, PP. 104-120

Keywords: Artificial Intelligence, Technology Acceptance, Intention to Use, UTAUT Model, Personal Innovativeness of Young Executive Trainees

Full-Text   Cite this paper   Add to My Lib

Abstract:

The recent increase in the use of artificial intelligence has led to fundamental changes in the development of training and teaching methods for executive education. However, the success of artificial intelligence in regional centers for teaching and training professions will depend on the acceptance of this technology by young executive trainees. This article discusses the potential benefits of adopting AI in executive training institutions in Morocco, specifically focusing on CRMEF Casablanca Settat. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), this study proposes a model to identify the factors influencing the acceptance of artificial intelligence in regional centers for teaching professions and training in Morocco. To achieve this, a structural equation modeling approach was used to quantitatively describe the impact of each factor on AI adoption, utilizing data collected from 173 young executive trainees. The results indicate that perceived ease of use, perceived usefulness, trainer influence, and personal innovativeness influence the intention to use artificial intelligence. Our research provides managers of CRMEFs with a set of practical recommendations to enhance the implementation conditions of an artificial intelligence system. It aims to understand which factors should be considered in designing an artificial intelligence system within regional centers for teaching professions and training (CRMEFs).

References

[1]  Andrea, K., Holz, E.M., Sellers, E.W. and Vaughan, T.M. (2015) Toward Independent Home Use of Brain Computer Interfaces: A Decision Algorithm for Selection of Potential End-Users. Archives of Physical Medicine and Rehabilitation, 96, 527-532.
https://doi.org/10.1016/j.apmr.2014.03.036
[2]  Ahmad, T. (2019) Scenario Based Approach to Re-Imagining Future of Higher Education Which Prepares Students for the Future of Work. Higher Education, Skills and Work-Based Learning, 10, 217-238.
https://doi.org/10.1108/HESWBL-12-2018-0136
[3]  Han, S.N. (2003) Individual Adoption of IS in Organisations. A Literature Review of Technology Acceptance Model.
[4]  Ajzen, I. and Fieshbein, M. (1975) Belief, Attitude, Intention and Behavior. Addison Wesley, Boston.
[5]  Ajzen, I. (1991) The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.
https://doi.org/10.1016/0749-5978(91)90020-T
[6]  Bandura, A. and Wood, R. (1989) Social Cognitive Theory of Organizational Management. The Academy of Management Review, 14, 361-384.
https://doi.org/10.5465/amr.1989.4279067
[7]  Triandis, H.C. (1980) Values, Attitudes, and Interpersonal Behavior. Nebraska Symposium on Motivation, 27, 195-259.
[8]  Davis, F.D., Bagozzi, R. and Warshaw, P.R. (1989) User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35, 982-1003.
https://doi.org/10.1287/mnsc.35.8.982
[9]  Rogers, E.M. (1995) Diffusion of Innovations. 4th Edition, The Free Press, Los Angeles.
[10]  Birch, A. (2009) Preservice Teachers’ Acceptance of Information and Communication Technology Integration in the Classroom. Thèse, University of Victoria, Victoria.
[11]  Agarwal, R. and Karahanna, E. (1998) On the Multi-Dimensional Nature of Compatibility Beliefs in Technology Acceptance.
http://discnt.cba.uh.edu/chin/digit98/first.pdf
[12]  Churchill, G.A. (1979) A Paradigm for Developing Better Measures of Marketing Constructs. Journal of Marketing Research, 16, 64-73.
https://doi.org/10.1177/002224377901600110
[13]  Cronbach, L.J. (1951) Coefficient Alpha and the Internal Structure of Tests. Psychometrica, 16, 297-334.
https://doi.org/10.1007/BF02310555
[14]  Gerbing, D.W. and Anderson, J.C. (1988) An Updated Paradigm for Scale Development Incorporating Unidimensionality and Its Assessment. Journal of Marketing Research, 25, 186-192.
https://doi.org/10.1177/002224378802500207
[15]  Usunier, J.C., Easterby-Smith, M. and Thorpe, R. (2000) Introduction à la recherché en gestion, Economica, 2nd Edition. 271 p.
[16]  Perrien, J., Cheron, E.-J. and Zins, M. (1983) Recherche en marketing: Méthodes et decisions. Gaetan Morin, Québec, 173.
[17]  Malhotra, N., Decaudin, J.M. and Bouguerra, A. (2004) études Marketing Avec SPSS. Pearson Education, Paris.
[18]  Evrard, Y., Pras, B. and Roux, E. (2003) Market. Etudes et recherches en Marketing. 3ème édition, Dunod, Paris.
[19]  Pichon, P.-E. (2006) Confiance et consommation alimentaire: De l’importance de la confiance dans les émetteurs des réducteurs de risque. 5ème Congrès International des Tendances du Marketing, Venise, January 2006, 10-15.
[20]  Cao, W., Fang, Z., Hou, G., Han, M., Xu, X., Dong, J. and Zheng, J. (2020) The Psychological Impact of the COVID-19 Epidemic on College Students in China. Psychiatry Research, 287, Article ID: 112934.
https://doi.org/10.1016/j.psychres.2020.112934
[21]  Feng, X., Zhang, W. and Chen, L. (2011) Distance Education in Rural China Achieves Inter-School Collaboration and Increased Access to Education. Distances et Savoirs, 9, 53-67.
https://doi.org/10.3166/ds.9.53-67
[22]  Chiu, C.-M. and Wang, E.T.G. (2008) Understanding Web-Based Learning Continuance Intention: The Role of Subjective Task Value. Information & Management, 45, 194-201.
https://doi.org/10.1016/j.im.2008.02.003
[23]  Chong, A.Y.L. (2013) Predicting m-Commerce Adoption Determinants: A Neural Network Approach. Expert Systems with Applications, 40, 523-530.
https://doi.org/10.1016/j.eswa.2012.07.068

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133