Unlocking AI Potential: Effort Expectancy, Satisfaction, and Usage in Research
, Wendy Ven Ye Teh, Anita AdnanThis study investigates the key factors influencing the adoption and use of artificial intelligence (AI) applications among researchers, focusing on effort expectancy, satisfaction, perceived ease of use, and perceived usefulness, which shaped attitudes and drove AI adoption as a research assistant.
AI tools have rapidly become game-changers in academic research, transforming tasks such as literature retrieval, writing, editing, and data analysis. Despite their potential, barriers like high effort expectancy, inconsistent user satisfaction, and ethical concerns regarding over-reliance and plagiarism continue to hinder widespread adoption. A pressing gap exists in understanding how AI impacts the efficiency and integrity of academic research workflows.
A quantitative approach using structural equation modeling (SEM) was employed. Data was collected from 120 active researchers who use AI tools for academic tasks, including literature reviews, writing support, and data visualization.
This study contributes to the understanding of how key factors, such as effort expectancy and satisfaction, affect AI adoption in academic research. It emphasizes the importance of reducing cognitive load and improving user satisfaction to promote widespread AI adoption. It also underscores the importance of intuitive AI design and institutional support in shaping researchers’ engagement with AI tools, which could enhance productivity and research outcomes.
The findings reveal that effort expectancy, satisfaction, perceived ease of use, and perceived usefulness significantly influence attitude and actual use of AI tools, with attitude serving as a key mediator. The model demonstrated moderate to high explanatory power (R² = 0.409 to 0.459) and predictive relevance (Q² = 0.171 to 0.409), highlighting the substantial role of effort expectancy and satisfaction in shaping perceived ease of use and usefulness. These findings emphasize the importance of reducing cognitive load and improving user satisfaction to encourage the adoption of AI tools in research.
Institutions and AI developers should focus on reducing the learning curve of AI tools by enhancing their intuitiveness and providing targeted training and technical support. Ethical AI use should also be promoted to address concerns about over-reliance and plagiarism. Institutions should foster a culture that normalizes AI integration in research practices.
Researchers should be informed of the long-term effects of AI adoption on research quality and integrity and how institutional support can foster positive attitudes toward AI tools in academic research.
The broader adoption of AI tools in academic research could enhance productivity and efficiency, leading to more breakthroughs in various fields and benefiting society by accelerating research and innovation. Additionally, AI can democratize access to research resources, particularly for underfunded institutions and early-career researchers, by enabling broader participation in cutting-edge research and fostering equity and diversity in academic contributions.
Future studies should focus on the role of user experience in AI adoption, particularly how different user groups interact with AI tools. Longitudinal studies could provide insights into how attitudes toward AI change as users become more familiar with the tools.
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