The Intention to Adopt Artificial Intelligence for Software Testing by Organizations in Klang Valley to Achieve Sustainable Business Performance
DOI:
https://doi.org/10.24191/jikm.v15i2.8216Keywords:
Adoption Intention, Artificial Intelligence, Software Testing, Technology Organization Environment (TOE)Abstract
This study investigates factors influencing the intention to adopt Artificial Intelligence (AI) for software testing among organizations in Klang Valley, Malaysia. As software grows more complex, traditional testing struggles to keep up, increasing interest in AI. Despite its benefits like better defect detection and faster releases, adoption remains limited. Using the Technology, Organization, Environment (TOE) framework, this study examines the effects of technological, organizational, and environmental readiness. A survey of 219 software testers and engineers was analyzed using Structural Equation Modelling (SEM) via SmartPLS. Results show that organizational and environmental readiness strongly influence AI adoption, while technological readiness has a moderate effect. The findings provide useful insights for improving software testing and supporting sustainable business performance.
References
Harshad Vijay Pandhare. (2025). AI-driven software testing: Enhancing quality assurance through intelligent automation. Journal of Software Engineering Research, 15(2), 45-62.
Islam, M., Rahman, S., & Ahmed, K. (2023). Challenges in manual testing of complex software systems: A systematic review. International Journal of Software Testing, 12(4), 123-145.
Karhu, K., Gustafsson, R., & Lyytinen, K. (2025). Exploiting and defending in platform ecosystems. Strategic Management Journal, 46(1), 15-35.
Kusum, R., Sharma, P., & Verma, A. (2024). Software testing in the software development life cycle: A comprehensive review. Software Quality Journal, 32(3), 789-812.
Maroufkhani, P., Wagner, R., Wan Ismail, W. K., Baroto, M. B., & Nourani, M. (2020). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.
Masod, A., & Zakaria, N. H. (2024). Barriers to artificial intelligence adoption in software testing: An empirical study. Computers & Industrial Engineering, 178, 109134.
Nama, S. (2024). Artificial intelligence in software testing: Current trends and future directions. IEEE Software, 41(3), 78-85.
Ramdani, B., Chevers, D., & Williams, D. A. (2022). SMEs' adoption of enterprise applications: A technology-organisation-environment model. Journal of Small Business and Enterprise Development, 20(4), 735-753.
Tsang, E. W., Kwan, K. M., & Lightstone, K. (2017). Research methods in strategy and management. Emerald Publishing.
Vijayashree Shetty, K. (2020). The role of software testing in ensuring software quality and reliability. International Journal of Computer Applications, 175(8), 12-18.
Yogeshwar Kulkarni, M. (2024). Machine learning approaches in software testing: A systematic literature review. Empirical Software Engineering, 29(2), 45-78.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Irni Eliana Khairuddin, Noorizzati Apandi
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright of articles that appear in the journal belongs exclusively to Faculty of Information Science, Universiti Teknologi MARA (Publisher). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions or any other reproductions of similar nature.
