Beyond ease and usefulness: a TAM perspective on marketing lecturers' adoption of generative AI

Khan, Imran and Godwin, Eun Sun and Refat, Nadia and Ahmed, Wasim and Akanbi, Lukman Adewale (2026) Beyond ease and usefulness: a TAM perspective on marketing lecturers' adoption of generative AI. The International Journal of Information and Learning Technology, 43 (3). pp. 223-240. ISSN 2056-4880

[thumbnail of Anonymons_manuscript.pdf]
Preview
Text
Anonymons_manuscript.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.

Download (398kB)

Abstract

Purpose

Over the years, marketing higher education has undergone a profound transformation due to advances in digital technology. Gen AI technology has progressively become more important both for teachers and students in marketing education. This study explores the use of Gen AI by marketing lecturers in higher education institutions (HEIs). We use the technology acceptance model (TAM) as a theoretical foundation. Using TAM, this study explores how marketing lecturers perceive Gen AI in improving teaching effectiveness and efficiency and how easy they find it in integrating it within their teaching practices.
Design/methodology/approach

The study employed online structured interviews to understand the adoption of Gen AI followed by a team-based thematic analysis method. A total of 22 structured interviews were conducted with marketing lecturers from different UK universities. Convenience and snowball sampling were used to shortlist the interviewees. Finally, team-based thematic analysis was performed.
Findings

The results show that HEIs lecturers see Gen AI for supporting teaching, developing marketing activities and structuring content. Secondly, marketing lecturers are using Gen AI to create assignment support video guidance and to provide generic feedback. Finally, marketing lecturers use Gen AI for administrative tasks.
Research limitations/implications

This study extends the literature on Gen AI and marketing higher education. This study also brings the social media data to understand the trends. Moreover, this study extends the TAM model. There are limitations, including specific geographical location, methodology and analysis techniques.
Originality/value

This study extends the literature on Gen AI and marketing higher education. This study also brings the social media data to understand the trends. Moreover, this study extends the TAM model. This study also guides the senior management in universities to invest more in the training of the marketing lecturers to effectively integrate Gen AI in education. Finally, this study explores the ethical use of Gen AI in teaching, assessments and feedback.

Item Type: Article
Identification Number: 10.1108/IJILT-12-2024-0291
Dates:
Date
Event
12 March 2026
Accepted
25 May 2026
Published Online
Uncontrolled Keywords: Generative AI, Higher education, Marketing lecturers, Technology acceptance model, United Kingdom
Subjects: CAH17 - business and management > CAH17-01 - business and management > CAH17-01-03 - marketing
Divisions: Business School > Management, Business and Marketing
Depositing User: Gemma Tonks
Date Deposited: 19 Aug 2026 09:46
Last Modified: 19 Aug 2026 09:46
URI: https://www.open-access.bcu.ac.uk/id/eprint/17173

Actions (login required)

View Item View Item

Research

In this section...