AI adoption by lecturers in the business study program

Abstract

The development of artificial intelligence (AI) has created new opportunities in the world of higher education, especially in supporting a more effective and efficient learning process. Nonetheless, the adoption of AI by lecturers, especially in business majors, still faces various challenges. Some lecturers doubt the advantages of AI, face difficulties in its use, feel that AI does not match the characteristics of the courses being taught, or are burdened by the cost and limitations of institutional support. Therefore, this study aims to analyze the internal and external factors that influence lecturers' intention to adopt AI in learning, focusing on the utilization of AI platforms such as OpenAI ChatGPT, Google Gemini, and Microsoft Copilot.

This research combines three main theoretical frameworks, namely the Diffusion of Innovation (DoI), Technology-Organization-Environment (TOE) Framework, and Task-Technology Fit (TTF). This study uses a quantitative approach with a survey method. The data was collected through a Google Form questionnaire that was distributed to business lecturers at various universities in Indonesia. The number of respondents analyzed was 417 people. Data analysis was carried out using the Partial Least Squares - Structural Equation Modeling (PLS-SEM) method through SmartPLS software version 4.

The results show that factors in the TOE framework, such as technological context, organization, and environment, have a significant effect on the perception of task-technology fit. Furthermore, TTF has a positive effect on AI adoption intentions. Additionally, the variables in DoI theory —namely, relative advantage and compatibility —have a positive effect on adoption, while complexity has a negative effect. The findings also show that gender moderates the relationship between complexity and adoption intention, as well as between compatibility and adoption intention. This means that the influence of the perception of the complexity and suitability of technology on AI adoption differs between male and female lecturers