Normative Influence on the Adoption of Generative Artificial Intelligence in Higher Education

Abstract

Generative artificial intelligence (AI) has emerged as a transformative tool in various domains, including education, where it holds the potential to revolutionize learning experiences and outcomes. Despite its promising applications, the adoption and utilization of generative AI tools among students remain variable and complex. This article presents a mixed-methods approach to examine factors that influence student interest and use of generative AI in educational contexts. Using the theoretical foundations of the technology acceptance model (TAM), we explore the multifaceted determinants that shape students’ attitudes, perceptions, and behaviors toward utilizing generative AI technologies for learning purposes. Importantly, institutional influence has a positive influence on the intention to adopt ChatGPT as a learning tool. Insights into the challenges and opportunities associated with integrating generative AI into educational settings, as well as strategies for enhancing its acceptance and effectiveness among students are explored.

Keywords

artificial intelligence technology in classroom learning approaches and issues marketing education issues technology acceptance model survey methodology in-depth interviews partial least squares (PLS)

Get full access to this article

View all access options for this article.

References

Aghdaie

S. F. A.

Piraman

Fathi

(2011). An analysis of factors affecting the consumer’s attitude of trust and their impact on internet purchasing behavior. International Journal of Business and Social Science, 2(23), 147–158.

Ajzen

Fishbein

(1972). Attitudes and normative beliefs as factors influencing behavioral intentions. Journal of Personality and Social Psychology, 21(1), 1–9.

Al-Azawei

Alowayr

(2020). Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries. Technology in Society,62, 101325.

Anderson

L. W.

Krathwohl

D. R.

(Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of bloom’s taxonomy of educational objectives. Allyn & Bacon.

Autry

C. W.

Grawe

S. J.

Daugherty

P. J.

Richey

R. G.

(2010). The effects of technological turbulence and breadth on supply chain technology acceptance and adoption. Journal of Operations Management,28(6), 522–536.

Barrett

Pack

(2023). Not quite eye to AI: Student and teacher perspectives on the use of generative artificial intelligence in the writing process. International Journal of Educational Technology in Higher Education,20(1), 59.

Bashir

Madhavaiah

(2015). Consumer attitude and behavioral intention towards internet banking adoption in India. Journal of Indian Business Research,7(1), 67–102.

Bastani

Sungu

Kabakcı

Mariman

(2024, July 15). Generative AI can harm learning. The Wharton School Research Paper. https://ssrn.com/abstract=4895486

Bettman

J. R.

(1973). Perceived risk and its components: A model and empirical test. Journal of Marketing Research,10(2), 184–190.

10.

Bhattacherjee

Premkumar

(2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly,28, 229–254.

11.

Brand

Israeli

Ngwe

(2023). Using GPT for market research (Marketing Unit Working Paper no. 23-062). Harvard Business School.

12.

Bridges

(2021). The undergraduate marketing research course: Two decades of change. Journal of Marketing Education,43(3), 285–297.

13.

Chan

C. K. Y.

(2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education,20(1), 43.

14.

Clarke

T. B.

Murphy

Wetsch

L. R.

Boeck

(2018). Teaching search engine marketing through the Google Ad Grants program. Marketing Education Review,28(2), 136–147.

15.

Corbin

Strauss

(2014). Basics of qualitative research: Techniques and procedures for developing grounded theory. Sage.

16.

Davenport

Guha

Grewal

Bressgott

(2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science,48, 24–42.

17.

Davis

F. D.

(1989). Technology acceptance model: TAM. In Al-Suqri

M. N.

Al-Aufi

A. S.

(Eds.), Information seeking behavior and technology adoption (pp. 205–219).

18.

Dogra

Kaushal

(2021). An investigation of Indian generation Z adoption of the voice-based assistants (VBA). Journal of Promotion Management,27(5), 673–696.

19.

Eckhardt

Laumer

Weitzel

(2009). Who influences whom? Analyzing workplace referents’ social influence on IT adoption and non-adoption. Journal of Information Technology,24, 11–24.

20.

Essel

R. E.

(2022). Assessing the moderating role of trialability and perceived risk of E-Banking adoption in an emerging economy. Vision: The Journal of Business Perspective.

21.

Ferrell

O. C.

Ferrell

(2020). Technology challenges and opportunities facing marketing education. Marketing Education Review,30(1), 3–14.

22.

Fishbein

Ajzen

(1975). Belief attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.

23.

Grewal

Guha

Beccacece Satornino

Becker

(2025). The future of marketing and marketing education. Journal of Marketing Education,47(1), 61–77.

24.

Guha

Grewal

Atlas

(2024). Generative AI and marketing education: What the future holds. Journal of Marketing Education,46(1), 6–17.

25.

Gullifer

J. M.

Tyson

G. A.

(2014). Who has read the policy on plagiarism? Unpacking students’ understanding of plagiarism. Studies in Higher Education,39(7), 1202–1218.

26.

Habib

Vogel

Anli

Thorne

(2024). How does generative artificial intelligence impact student creativity? Journal of Creativity,34(1), 100072.

27.

Hair

Alamer

(2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics,1(3), 100027.

28.

Hair

J. F.

Anderson

R. E.

Tatham

R. L.

Black

W. C.

(1998). Multivariate data analysis with readings (5th ed.). Prentice-Hall.

29.

Hair

J. F.

Jr. Babin

B. J.

Krey

(2017). Covariance-based structural equation modeling in the Journal of Advertising: Review and recommendations. Journal of Advertising,46(1), 163–177.

30.

Hair

J. F.

Jr. Howard

M. C.

Nitzl

(2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research,109, 101–110.

31.

Hair

J. F.

Risher

J. J.

Sarstedt

Ringle

C. M.

(2019). When to use and how to report the results of PLS-SEM. European Business Review,31(1), 2–24.

32.

Hair

J. F.

Sarstedt

Ringle

C. M.

Mena

J. A.

(2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science,40, 414–433.

33.

Harrison

D. E.

Ajjan

(2019). Customer relationship management technology: Bridging the gap between marketing education and practice. Journal of Marketing Analytics,7(4), 205–219.

34.

Hatcher

(1994). A step-by-step approach to using SAS system for factor analysis and structural equation modeling. The SAS Institute.

35.

Hong

J.-C.

Lin

P.-H.

Hsieh

P.-C.

(2017). The effect of consumer innovativeness on perceived value and continuance intention to use smartwatch. Computers in Human Behavior,67, 264–272.

36.

Hooda

Gupta

Jeyaraj

Giannakis

Dwivedi

Y. K.

(2022). The effects of trust on behavioral intention and use behavior within e-government contexts. International Journal of Information Management,67, 102553.

37.

Jumani

Z. A.

Sukhabot

(2020). Identifying the important attitude of Islamic brands and its effect on buying behavioural intentions among Malaysian Muslims: A quantitative study using smart-PLS. Journal of Islamic Marketing,12(2), 408–426.

38.

Karahanna

Straub

D. W.

Chervany

N. L.

(1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly,23, 183–213.

39.

Kaur

Arora

(2023). Understanding customers’ usage behavior towards online banking services: An integrated risk–benefit framework. Journal of Financial Services Marketing,28(1), 74–98.

40.

Kwak

Seo

Y. H.

Ahn

J. W.

(2022). Nursing students’ intent to use AI-based healthcare technology: Path analysis using the unified theory of acceptance and use of technology. Nurse Education Today,119, 105541.

41.

Liang

Wang

Luo

Yan

Fan

(2023). The relationship between student interaction with generative artificial intelligence and learning achievement: Serial mediating roles of self-efficacy and cognitive engagement. Frontiers in Psychology,14, Article 1285392.

42.

Lin

C. H.

Shih

H. Y.

Sher

P. J.

(2007). Integrating technology readiness into technology acceptance: The TRAM model. Psychology & Marketing,24(7), 641–657.

43.

C. S.

Liu

Yao

J. E.

(2003). Technology acceptance model for wireless Internet. Internet Research,13(3), 206–222.

44.

Manrai

Yadav

P. D.

Goel

(2022). Factors affecting adoption of digital payments by urban women: Understanding the moderating role of perceived financial risk. Technology Analysis & Strategic Management,1–13.

45.

Mariani

M. M.

Perez-Vega

Wirtz

(2022). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing,39(4), 755–776.

46.

Martins

Oliveira

Popovič

(2014). Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management,34(1), 1–13.

47.

Mogavi

R. H.

Deng

Kim

J. J.

Zhou

Kwon

Y. D.

Metwally

A. H. S.

Tlili

Bassanelli

Bucchiarone

Gujar

Nacke

L. E.

Hui

(2024). ChatGPT in education: A blessing or a curse? A qualitative study exploring early adopters’ utilization and perceptions. Computers in Human Behavior: Artificial Humans,2(1), 100027.

48.

Mollick

E. R.

Mollick

(2022). New modes of learning enabled by AI chatbots: Three methods and assignments. SSRN.

49.

Mollick

E. R.

Mollick

(2023). Assigning AI: Seven approaches for students, with prompts. arXiv:2306.10052.

50.

Mollick

E. R.

Mollick

(2024). Instructors as innovators: A future-focused approach to new AI learning opportunities, with prompts. arXiv:2407.05181.

51.

Nunnally

J. C.

Bernstein

I. H.

(1994). Psychometric theory (3rd ed.). McGraw-Hill.

52.

Gonsalves

Quamina

L. T.

(2024). Generative AI in higher education assessments: Examining risk and tech-savviness on student’s adoption. Journal of Marketing Education,47, 138–155.

53.

Pavlou

P. A.

(2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce,7(3), 101–134.

54.

Peres

Schreier

Schweidel

Sorescu

(2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing,40(2), 269–275.

55.

Pierce

Jiang

(2021). Exploring cultural influences on mobile marketing acceptance. International Journal of Internet Marketing and Advertising,15(1), 1–28.

56.

Pitardi

Marriott

H. R.

(2021). Alexa, she’s not human but. . . Unveiling the drivers of consumers’ trust in voice-based artificial intelligence. Psychology & Marketing,38(4), 626–642.

57.

Reavey

Zahay

Rosenbloom

(2021). Updating the marketing research course to prepare the marketing generalist. Journal of Marketing Education,43(3), 333–353.

58.

Robson

Mills

A. J.

(2022). Teaching, fast and slow: Student perceptions of emergency remote education. Journal of Marketing Education,44(2), 203–216.

59.

Sams

(2018, April 26). McKinsey: AI will have the most impact in Marketing. https://www.marketingaiinstitute.com/blog/mckinsey-ai-will-have-the-most-impact-in-marketing

60.

Sarstedt

Ringle

C. M.

Hair

J. F.

(2021). Partial least squares structural equation modeling. In Handbook of market research (pp. 587–632). Springer International Publishing.

61.

Sevim

Yüncü

Hall

E. E.

(2017). Analysis of the extended technology acceptance model in online travel products. Journal of Internet Applications and Management,8(2), 45–61.

62.

Spiller

L. D.

Kim

D.-H.

Aitken

(2020). Sales education in the United States: Perspectives on curriculum and teaching practices. Journal of Marketing Education,42(3), 217–232.

63.

Strzelecki

(2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 1–14.

64.

Sullivan

Kelly

McLaughlan

(2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning & Teaching,6(1), 1–10.

65.

Sultana

(2020). Determining the factors that affect the uses of Mobile Cloud Learning (MCL) platform Blackboard-a modification of the UTAUT model. Education and Information Technologies, 25(1), 223–238.

66.

Thompson

R. L.

Higgins

C. A.

Howell

J. M.

(1994). Influence of experience on personal computer utilization: Testing a conceptual model. Journal of MIS, 11(1), 167–187.

67.

Triandis

H. C.

(1971). Attitude and attitude change. Wiley.

68.

Venkatesh

Bala

(2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.

69.

Venkatesh

Davis

F. D.

(2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.

70.

Venkatesh

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.

71.

Venkatesh

Thong

J. Y.

(2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376.

72.

Vlačić

Corbo

Costa

Silva

Dabić

(2021). The evolving role of artificial intelligence in marketing: A review and research agenda. Journal of Business Research, 128, 187–203.

73.

Warsono

Yuwono

Putranti

(2023). Analyzing technology acceptance model for collaborative governance in public administration: Empirical evidence of digital governance and perceived ease of use. International Journal of Data and Network Science,7(1), 41–48.