Integrating the TOE framework and DOI theory to dissect and understand the key elements of AI adoption in sociotechnical systems

Authors

DOI:

https://doi.org/10.18489/sacj.v36i2/17679

Keywords:

Adoption, Organisation, Sociotechnical, Design Science Research, Artificial Intelligence, Generative Artificial Intelligence, ChatGPT

Abstract

This study is interested in increasing the likelihood of achieving the organisational adoption of artificial intelligence that has a positive  outcome. We argue that the technological-organisational-environmental framework provides a sound theoretical lens for analysing  how an organisation’s context influences the adoption and integration of artificial intelligence solutions. Furthermore, the diffusion of  innovation theory is proposed to identify enablers for transforming organisations. Together with the combination of technological-organisational-environmental and diffusion of innovation, the stages of diffusion are proposed as an evaluation paradigm to evaluate the effectiveness of the enabling factors. Furthermore, the elements and objectives of artificial intelligence adoption in the context of  data-driven organisations are included. From this, the research develops a comprehensive framework for studying the technical and  social AI adoption elements in an organisational environment where complex symbiotic relationships prevail. This study uses  generative artificial intelligence as a novel approach to exploring the framework’s usability. The evidence from our research indicates  that the developed framework can advance our comprehension of what drives the success or failure of artificial intelligence adoption  in organisations. Theoretically, it provides a tool for dissecting and understanding the key elements influencing this process.

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Published

2024-12-11

Issue

Section

Special Issue

How to Cite

[1]
Smit, D. et al. 2024. Integrating the TOE framework and DOI theory to dissect and understand the key elements of AI adoption in sociotechnical systems. South African Computer Journal. 36, 2 (Dec. 2024). DOI:https://doi.org/10.18489/sacj.v36i2/17679.

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