Determinants of Artificial Intelligence Adoption in Software Development: A Systematic Literature Review Using the TOE Framework

N.S.M. Isuru Sampath Senanayake
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N.S.M. Isuru Sampath Senanayake: Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka.

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Abstract: Aims: Artificial intelligence (AI) is reshaping software development through automation, predictive analytics, and enhanced decision-making across the development lifecycle. However, AI adoption in software development industry remains ad-hoc and unstructured, limiting its potential to enhance productivity. This study aims to systematically identify and synthesize the antecedents influencing AI adoption in software development using the Technology Organization Environment (TOE) framework. Methodology: A systematic literature review (SLR) was conducted following the PRISMA protocol. Data were gathered from 32 peer-reviewed journal articles published between 2015 and 2025 and systematically analyzed using the Technology–Organization–Environment (TOE) framework to extract and categorize AI adoption antecedents. Results: The analysis identified 92 distinct antecedents, comprising 31 technological, 34 organizational, and 27 environmental factors. Key technological determinants included relative advantage, compatibility, and security. Organizational factors such as top management support, organizational readiness, and human capital emerged as critical enablers. Environmental drivers, including competitive pressure, regulatory support, and ecosystem readiness, were less frequently examined. Contradictory findings were observed, particularly where security and compatibility functioned as both enablers and barriers depending on context. Conclusion: The review consolidates technological, organizational, and environmental antecedents influencing AI adoption in software development using the Technology Organization Environment framework. By synthesizing fragmented evidence across prior studies, the findings clarify key drivers, barriers, and contextual inconsistencies affecting structured AI adoption for productivity enhancement.

Date: 2026-02-07
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Published in Journal of Global Economics, Management and Business Research, 2026, 18 (1), pp.287-298

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05499845

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