Developing enrollment intentions for work-based learning that uses artificial intelligence (AI): aspects for marketing interventions for higher education institutions

Purpose This study investigates the significant determinants that lead different work-field employees to develop intentions to enroll in educational programs that use artificial intelligence (AI). Venkatesh's (2000) technology acceptance model 3 (TAM3) is the foundation for the proposed research model in the current study, presenting a complete nomological network of technology adoption determinants (individual differences, system features, social influence and facilitating conditions). Design/methodology/approach Quantitative questionnaires were distributed January 2024 digitally, using Google Form, to employees enrolled in educational programs in higher education institutions for the purpose of work-based learning in Egypt. 405 questionnaires were analyzed, using statistical package for social science, version-26. Findings This study found that employees develop intentions for work-based learning with AI if the technology is useful and easy. Employees perceive AI as useful with subjective norms, job relevance, result demonstrability and quality. They perceive AI as easy based on their level of self-efficacy, computer anxiety and perception of control. Practical implications AI when used by higher education institutions can lead to admired learning among employees as it improves access to education opportunities, develops intelligent tutoring systems, personalizes education processes, etc. Thus, AI in work-based learning programs should be encouraged for fruitful education strategies and interventions. Originality/value This study contributes to the growing body of knowledge on AI in higher education, skills and work-based learning. A comprehensive investigation of the multifaceted drivers of AI adoption among employees seeking continuous learning can aid in the development of effective interventions to enhance technology adoption in education. The novelty lies in understanding how to endorse AI use in training courses for employees offered in higher education institutions.

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Publication Details

Journal
Higher Education Skills and Work-based Learning
Published
2025-06-30
DOI
https://doi.org/10.1108/heswbl-10-2024-0315
Citations
2
Primary Topic
Technology Adoption and User Behaviour
Type
article
Field-Weighted Citation Impact
8.34
Controls
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article

Developing enrollment intentions for work-based learning that uses artificial intelligence (AI): aspects for marketing interventions for higher education institutions

Eiman Negm
2 citations
Higher Education Skills and Work-based Learning
Technology Adoption and User Behaviour
8.34
article

Developing enrollment intentions for work-based learning that uses artificial intelligence (AI): aspects for marketing interventions for higher education institutions

Eiman Negm
article en
2 citations

Abstract

Purpose This study investigates the significant determinants that lead different work-field employees to develop intentions to enroll in educational programs that use artificial intelligence (AI). Venkatesh's (2000) technology acceptance model 3 (TAM3) is the foundation for the proposed research model in the current study, presenting a complete nomological network of technology adoption determinants (individual differences, system features, social influence and facilitating conditions). Design/methodology/approach Quantitative questionnaires were distributed January 2024 digitally, using Google Form, to employees enrolled in educational programs in higher education institutions for the purpose of work-based learning in Egypt. 405 questionnaires were analyzed, using statistical package for social science, version-26. Findings This study found that employees develop intentions for work-based learning with AI if the technology is useful and easy. Employees perceive AI as useful with subjective norms, job relevance, result demonstrability and quality. They perceive AI as easy based on their level of self-efficacy, computer anxiety and perception of control. Practical implications AI when used by higher education institutions can lead to admired learning among employees as it improves access to education opportunities, develops intelligent tutoring systems, personalizes education processes, etc. Thus, AI in work-based learning programs should be encouraged for fruitful education strategies and interventions. Originality/value This study contributes to the growing body of knowledge on AI in higher education, skills and work-based learning. A comprehensive investigation of the multifaceted drivers of AI adoption among employees seeking continuous learning can aid in the development of effective interventions to enhance technology adoption in education. The novelty lies in understanding how to endorse AI use in training courses for employees offered in higher education institutions.

Higher Education Skills and Work-based LearningVol. 15(5)
Arab Academy for Science, Technology, and Maritime Transport (EG)
Openalex Percentile: Top 3%
Technology Adoption and User Behaviour
8.34
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Developing enrollment intentions for work-based learning that uses artificial intelligence (AI): aspects for marketing interventions for higher education institutions — Eiman Negm · Higher Education Skills and Work-based Learning (2025) | TGRS Research Map | TGRS