Abstract
Artificial Intelligence (AI) has emerged as a distinct form of ICT, revolutionizing manufacturing to the fourth industrial revolution. However, inherently complex and time-constrained operations restrict conservative industries from embracing AI transformation, leading to technological innovation. This study attempts to pave the way for AI transformation (leading to technological innovation) in conservative industries by developing and testing a value-based theoretical AI adoption framework. The proposed framework incorporates functional and conditional values as predictors to assess the industrial AI’s fitness to the conservative industry need. Service reliability is taken as a moderator to assess AI acceptance’s intention impact on its consistent use in routine operations in conservative industries. The model was tested in the construction and oil gas industries. A total number of 480 samples were collected from Pakistan. The results have indicated functional value as a significant predictor of the way forward with AI transformation in conservative industries. The other process variables like price value and performance expectancy have shown what drives AI acceptance intention in a conservative industry. The results also found service reliability as a necessity for the sustained use of AI in conservative industries. The findings provide useful insights for industrial AI companies on how such conservative industries envisage AI as a technological innovation and a potential solution to their problem. The framework shall also help conservative industries in evaluating potential AI proposals.
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