Vol. 13 No. 4 (2025)
December 2025

Articles

Lecturers’ pathways to integrating artificial intelligence in business and economics curricula

Published 2025-12-28

  • Witold Nowiński
    • ,
  • Mohamed Yacine Haddoud
    • ,
  • Julien Issa

Witold Nowiński
WSB University in Poznan

Mohamed Yacine Haddoud
British University in Dubai, UAE and Liverpool John Moores University

Julien Issa
University of Amsterdam & Vrije Universiteit Amsterdam

Abstract

Objective: This article aims to identify the factors affecting business and economics lecturers’ inclusion of artificial intelligence (AI) in the curricula.

Research Design & Methods: We applied a quantitative approach to test a research model based on the theory of planned behaviour. We used partial least squares structural equation modelling to verify hypotheses using a sample of 133 university lecturers from business and economics.

Findings: The study reveals that key background factors, including prior AI education and prior AI use, indirectly contribute to the inclusion of AI in the curricula. AI education contributed by enhancing lecturers’ cognitive attitudes and self-efficacy and AI use only contributed through self-efficacy. Contrary to expectations, previous instances of AI integration in teaching have had an insignificant influence on the inclusion of AI into the curriculum.

Implications & Recommendations: The inclusion of AI in business and economics university teaching is a precondition for equipping graduates with skills expected in the job market. Based on the findings of this study, two paths seem to be particularly helpful in achieving this objective: improving lecturers’ attitudes via AI education and improving their self-efficacy through personal AI use.

Contribution & Value Added: The contribution of this study consists of identifying the factors that influence lecturers’ intentions to incorporate AI into their curricula. Shedding light on these determinants can guide higher education policies and support the development of strategies to promote the effective incorporation of AI into current teaching programmes.

Keywords

artificial intelligence, higher education, curriculum, theory of planned behaviour, business education

(PDF) Save

Author Biography

Witold Nowiński

Associate Professor at the WSB Merito University in Poznań, Poland. His research interests include entrepreneurship, strategic management, and management education, including new technology applications in business and education.

Mohamed Yacine Haddoud

Associate Professor at British University in Dubai, UAE, and Liverpool John Moores University, UK. His research interests include international entrepreneurship and management education.

Julien Issa

Lecturer at Oral Radiology & Digital Dentistry, ACTA, Amsterdam. His research interests involve AI applications in dentistry and education.

References

  1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-t
  2. Ajzen, I. (2002). Residual effects of past on later behavior: Habituation and reasoned action perspectives. Personality and Social Psychology Review, 6(2), 107-122. https://doi.org/10.1207/S15327957PSPR0602_02
  3. Ajzen, I. (2011). The theory of planned behaviour: Reactions and reflections. Psychology & Health, 26(9), 1113-1127. https://doi.org/10.1080/08870446.2011.613995
  4. Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314-324. https://doi.org/10.1002/hbe2.195
  5. Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. In D. Albarracín, B. Johnson, & M. Zanna (Eds.), The handbook of attitudes (pp. 173-222). Lawrence Erlbaum Associates.
  6. Aslan, A., & Zhu, C. (2016). Influencing factors and integration of ICT into teaching practices of pre-service and starting teachers. International Journal of Research in Education and Science, 2(2), 359-370.
  7. Bae, J., Won, D., Lee, C., & Pack, S.M. (2020). Adolescent participation in new sports: Extended theory of planned behavior. Journal of Physical Education and Sport, 20, 2246-2252. https://doi.org/10.7752/jpes.2020.s3301
  8. Bewersdorff, A., Zhai, X., Roberts, J., & Nerdel, C. (2023). Myths, mis-and preconceptions of artificial intelligence: A review of the literature. Computers and Education: Artificial Intelligence, 4, Article 100143. https://doi.org/10.1016/j.caeai.2023.100143
  9. Budhathoki, T., Zirar, A., Njoya E.T., & Timsina, A. (2024). ChatGPT adoption and anxiety: A cross-country analysis utilising the unified theory of acceptance and use of technology (UTAUT). Studies in Higher Education, 49(5), 831-846. https://doi.org/10.1080/03075079.2024.2333937
  10. Carr, J.C., & Sequeira, J.M. (2007). Prior family business exposure as intergenerational influence and entrepreneurial intent: A theory of planned behavior approach. Journal of Business Research, 60(10), 1090-1098. https://doi.org/10.1016/j.jbusres.2006.12.016
  11. Chatterjee, S., & Bhattacharjee, K.K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25, 3443-3463. https://doi.org/10.1007/s10639-020-10159-7
  12. Chai, C.S., Liang, S., & Wang, X. (2024). A survey study of Chinese teachers’ continuous intentions to teach artificial intelligence. Education and Information Technologies, 29(11), 14015-14034. https://doi.org/10.1007/s10639-023-12430-z
  13. Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159.
  14. Davis, F.D., Bagozzi, R.P., & Warshaw, P.R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.https://doi.org/10.1287/mnsc.35.8.982
  15. Dunn, R., Hattie, J., & Bowles, T. (2018). Using the theory of planned behavior to explore teachers’ intentions to engage in ongoing teacher professional learning. Studies in Educational Evaluation, 59, 288-294. https://doi.org/10.1016/j.stueduc.2018.10.001
  16. Fernández-Batanero, J.M., Montenegro-Rueda, M., Fernández-Cerero, J., & López Menéses, E. (2023). Adoption of the Internet of things in higher education: opportunities and challenges. Interactive Technology and Smart Education, 21(2), 292-307. https://doi.org/10.1108/ITSE-01-2023-0025
  17. Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312
  18. Foulger, T.S., Buss, R.R., & Su, M. (2021). The IT2 survey: contextual knowledge (XK) influences on teacher candidates’ intention to integrate technology. Educational Technology Research and Development, 69(5), 2729-2760. https://doi.org/10.1007/s11423-021-10033-4
  19. Gollwitzer, P.M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54(7), 493-503. https://doi.org/10.1037/0003-066X.54.7.493
  20. Habibi, A., Riady, Y., Samed Al-Adwan, A., & Awni Albelbisi, N. (2023). Beliefs and knowledge for pre-service teachers’ technology integration during teaching practice: An extended theory of planned behavior. Computers in the Schools, 40(2), 107-132. https://doi.org/10.1080/07380569.2022.2124752
  21. Hair Jr, J.F., Hult, G.T.M., Ringle C.M., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage.
  22. Hair Jr, J.F., Matthews, L.M., Matthews, R.L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107-123. https://doi.org/10.1504/IJMDA.2017.087624
  23. Harvard Humanitarian Initiative (n.d.) KoBoToolbox [Computer software]. Retrieved from https://www.kobotoolbox.org/ on May 15, 2023.
  24. Hassani, H., Silva, E.S., Unger, S., TajMazinani, M., & Mac Feely, S. (2020). Artificial intelligence (AI) or intelligence augmentation (IA). What is the future? AI, 1(2), 143-155. https://doi.org/10.3390/ai1020008
  25. Hmoud, H., Al-Adwan, A.S., Horani, O., Yaseen, H., & Al Zoubi, J.Z. (2023). Factors influencing business intelligence adoption by higher education institutions. Journal of Open Innovation: Technology, Market, and Complexity, 9(3), Article 100111. https://doi.org/10.1016/j.joitmc.2023.100111
  26. Hornberger, M., Bewersdorff, A., & Nerdel, C. (2023). What do university students know about artificial intelligence? Development and validation of an AI literacy test. Computers and Education: Artificial Intelligence, 5, Article 100165. https://doi.org/10.1016/j.caeai.2023.100165
  27. Horowitz, M.C., Kahn, L., Macdonald, J., & Schneider, J. (2023). Adopting AI: How familiarity breeds both trust and contempt. AI & Society, 39(4), 1721-1735. https://doi.org/10.1007/s00146-023-01666-5
  28. Jo, H. (2023). Decoding the ChatGPT mystery: A comprehensive exploration of factors driving AI language model adoption. Information Development, Advance online publication. https://doi.org/10.1177/02666669231202764
  29. Knabe, A.P. (2012). Applying Ajzen's theory of planned behavior to a study of online course adoption in public relations education. (Publication No. 186) [Doctoral dissertation, Marquette University]. E-Publications@Marquette, https://epublications.marquette.edu/dissertations_mu/186
  30. Knauder, H., & Koschmieder, C. (2019). Individualized student support in primary school teaching: A review of influencing factors using the theory of planned behavior (TPB). Teaching and Teacher Education, 77, 66-76. https://doi.org/10.1016/j.tate.2018.09.012
  31. Kohnke, L., Moorhouse B.L., & Zou, D. (2023). Exploring generative artificial intelligence preparedness among university language instructors: A Case Study. Computers and Education: Artificial Intelligence, 5, Article 100156. https://doi.org/10.1016/j.caeai.2023.100156
  32. Kong, S.C., Cheung, W.M.Y., & Zhang, G. (2023). Evaluating an artificial intelligence literacy programme for developing university students’ conceptual understanding, literacy, empowerment and ethical awareness. Educational Technology & Society, 26(1), 16-30. https://doi.org/10.30191/ETS.202301_26(1).0002
  33. Korseberg, L., & Elken, M. (2024). Waiting for the revolution: how higher education institutions initially responded to ChatGPT. Higher Education. https://doi.org/10.1007/s10734-024-01256-4
  34. La Barbera, F., & Ajzen, I. (2020). Control interactions in the theory of planned behavior: Rethinking the role of subjective norm. Europe's Journal of Psychology, 16(3), 401. https://doi.org/10.5964/ejop.v16i3.2056
  35. Lee, J., Cerreto, F.A., & Lee, J. (2010). Theory of planned behavior and teachers' decisions regarding use of educational technology. Educational Technology & Society, 13(1), 152-164. Retrieved from https://www.j-ets.net/collection/published-issues/13_1 on December 23, 2023.
  36. Lee, Y., & Lee, J. (2014). Enhancing pre-service teachers' self-efficacy beliefs for technology integration through lesson planning practice. Computers & Education, 73, 121-128. https://doi.org/10.1016/j.compedu.2014.01.001
  37. Lenart, R., Sypniewska, B.A., Chen, J., & Janowski, K. (2025). Antecedents of students’ behavioural intention to use generative artificial intelligence: Quantitative research. Entrepreneurial Business and Economics Review, 13(1), 7-21. https://doi.org/10.15678/EBER.2025.130101
  38. Lenski, A.E., Richter, D., & Lüdtke, O. (2019). Using the theory of planned behavior to predict teachers’ likelihood of taking a competency-based approach to instruction. European Journal of Psychology of Education, 34, 169-186. https://doi.org/10.1007/s10212-017-0356-7
  39. Luckin, R., Cukurova, M., Kent, C., & du Boulay, B. (2022). Empowering educators to be AI-ready. Computers and Education: Artificial Intelligence, 3, Article 100076. https://doi.org/10.1016/j.caeai.2022.100076
  40. Mohd Rahim, N.I., Iahad, N.A., Yusof, A.F., & Al-Sharafi, M.A. (2022). AI-based chatbots adoption model for higher-education institutions: A hybrid PLS-SEM-neural network modelling approach. Sustainability, 14(19), Article 12726. https://doi.org/10.3390/su141912726
  41. Newman, A., Obschonka, M., Schwarz, S., Cohen, M., & Nielsen, I. (2019). Entrepreneurial self-efficacy: A systematic review of the literature on its theoretical foundations, measurement, antecedents, and outcomes, and an agenda for future research. Journal of Vocational Behavior, 110, 403-419. https://doi.org/10.1016/j.jvb.2018.05.012
  42. Nowiński, W., Haddoud, M.Y., Lančarič, D., Egerová, D., & Czeglédi, C. (2019). The impact of entrepreneurship education, entrepreneurial self-efficacy and gender on entrepreneurial intentions of university students in the Visegrad countries. Studies in Higher Education, 44(2), 361-379. https://doi.org/10.1080/03075079.2017.1365359
  43. Nyale, D., Karume, S., & Kipkebut, A. (2024). A comprehensive analysis of the role of artificial intelligence in aligning tertiary institutions academic programs to the emerging digital enterprise. Education and Information Technologies, 1-20. https://doi.org/10.1007/s10639-024-12743-7
  44. O’Dea, X. (2024). Generative AI: Is it a paradigm shift for higher education?. Studies in Higher Education, 49(5), 811-816. https://doi.org/10.1080/03075079.2024.2332944
  45. Onjewu, A.K.E., Haddoud, M.Y., Tony-Okeke, U., Cao, D., & Nowiński, W. (2022). Dissecting the effect of family business exposure on entrepreneurial implementation intention. International Journal of Entrepreneurial Behavior & Research, 28(6), 1438-1462. https://doi.org/10.1108/IJEBR-05-2021-0350
  46. Ratten, V. (2024). Artificial intelligence: Building a research agenda. Entrepreneurial Business and Economics Review, 12(1), 7-16. https://doi.org/10.15678/EBER.2024.120101
  47. Rejali, S., Aghabayk, K., Esmaeli, S., & Shiwakoti, N. (2023). Comparison of technology acceptance model, theory of planned behavior, and unified theory of acceptance and use of technology to assess a priori acceptance of fully automated vehicles. Transportation Research Part A: Policy and Practice, 168, Article 103565. https://doi.org/10.1016/j.tra.2022.103565
  48. Ringle, C.M., Wende, S., & Becker, J.-M. (2022). SmartPLS 4 [Computer software]. SmartPLS. Retrieved from https://www.smartpls.com on May 3, 2023.
  49. Roy, R., Babakerkhell, M.D., Mukherjee, S., Pal, D., & Funilkul, S. (2022). Evaluating the intention for the adoption of artificial intelligence-based robots in the university to educate the students. IEEE Access, 10, 125666-125678. https://doi.org/10.1109/ACCESS.2022.3225555
  50. Sadaf, A., & Gezer, T. (2020). Exploring factors that influence teachers’ intentions to integrate digital literacy using the decomposed theory of planned behavior. Journal of Digital Learning in Teacher Education, 36(2), 124-145. https://doi.org/10.1080/21532974.2020.1719244
  51. Sanusi, I.T., Ayanwale, M.A., & Tolorunleke, A.E. (2024). Investigating pre-service teachers’ artificial intelligence perception from the perspective of planned behavior theory. Computers and Education: Artificial Intelligence, 6, Article 100202. https://doi.org/10.1016/j.caeai.2024.100202
  52. Sarker, I.H. (2022). AI-Based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3(2), Article 158. https://doi.org/10.1007/s42979-022-01043-x
  53. Schaeffer, P.R., Guerrero, M., & Fischer, B.B. (2021). Mutualism in ecosystems of innovation and entrepreneurship: A bidirectional perspective on universities’ linkages. Journal of Business Research, 134, 184-197. https://doi.org/10.1016/j.jbusres.2021.05.039
  54. Shiau, W.L., & Chau, P.Y. (2016). Understanding behavioral intention to use a cloud computing classroom: A multiple model comparison approach. Information & Management, 53(3), 355-365. https://doi.org/10.1016/j.im.2015.10.004
  55. Southworth, J., Migliaccio, K., Glover, J., Golver, J. Reed, D., McCarty, C., … & Thomas, A. (2023). Developing a model for AI across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4, Article 100127. https://doi.org/10.1016/j.caeai.2023.100127
  56. Strzelecki, A. (2024). Students’ acceptance of ChatGPT in higher education: An extended unified theory of acceptance and use of technology. Innovative Higher Education, 49(2), 223-245. https://doi.org/10.1007/s10755-023-09686-1
  57. Teo, T., Zhou, M., & Noyes, J. (2016). Teachers and technology: Development of an extended theory of planned behavior. Educational Technology Research and Development, 64, 1033-1052. https://doi.org/10.1007/s11423-016-9446-5
  58. Tondeur, J., Aesaert, K., Prestridge, S., & Consuegra, E. (2018). A multilevel analysis of what matters in the training of pre-service teacher's ICT competencies. Computers & Education, 122, 32-42. https://doi.org/10.1016/j.compedu.2018.03.002
  59. Tsao, J., Kochhar-Lindgren, G., & Lam, A.M.H. (2024). Institutionalising a transdisciplinary curriculum: assemblages, territories, and refrains. Higher Education. https://doi.org/10.1007/s10734-024-01250-w
  60. Tschannen-Moran, M., & Hoy, A.W. (2007). The differential antecedents of self-efficacy beliefs of novice and experienced teachers. Teaching and Teacher Education 23(6), 944-956. https://doi.org/10.1016/j.tate.2006.05.003
  61. Usabiaga, C., Núñez, F., Arendt, L., Gałecka-Burdziak, E., & Pater, R. (2022). Skill requirements and labour polarisation: An association analysis based on Polish online job offers. Economic Modelling, 115, Article 105963. https://doi.org/10.1016/j.econmod.2022.105963
  62. Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F.D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478. https://doi.org/10.2307/30036540
  63. Walter, Y. (2024). Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21(1), 15. https://doi.org/10.1186/s41239-024-00448-3
  64. Wang, Y., Liu, C., & Tu, Y.F. (2021). Factors affecting the adoption of AI-based applications in higher education. Educational Technology & Society, 24(3), 116-129. https://www.jstor.org/stable/27032860
  65. Watson, J.H., & Rockinson-Szapkiw, A. (2021). Predicting preservice teachers' intention to use technology-enabled learning. Computers & Education, 168, Article 104207. https://doi.org/10.1016/j.compedu.2021.104207
  66. Zhang, D., Peng, G., Yao, Y., & Browning, T.R. (2023). Is a college education still enough? The IT-labor relationship with education level, task routineness, and artificial intelligence. Information Systems Research, 35(3), 992-1010. https://doi.org/10.1287/isre.2021.0391
  67. Zhao, Y., Wang, N., Li, Y., Zhou, R., & Li, S. (2020). Do cultural differences affect users’ e-learning adoption?. British Journal of Educational Technology, 52(1), 20-41. https://doi.org/10.1111/bjet.13002

Downloads

Download data is not yet available.