Influence of Employees’ Intention to Adopt AI Applications and Big Data Analytical Capability on Operational Performance in the High-Tech Firms

Chi-hsiang Chen ()
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Chi-hsiang Chen: Tamkang University

Journal of the Knowledge Economy, 2024, vol. 15, issue 1, No 158, 3946-3974

Abstract: Abstract As the application of artificial intelligence (AI) becomes more prevalent, it has attracted the attention of high-tech firms, which adopt AI applications in response to emerging societal, technological, and environmental challenges. In the AI application processes, big data analytical capacity has become increasingly important. Although AI may potentially revolutionise the markets, industries, and general business activities, the question remains how high-tech firms can implement AI in their operations effectively and efficiently, so as to enhance their operational performances. This study aims to explore whether high-tech firm employees’ intention to adopt AI applications and the firms’ big data analytical capability would affect the operational performance. Besides utilising the unified theory of acceptance and use of technology as a framework, this study also adopted the structural equation modelling (SEM) and related statistical analyses (using SPSS and LISREL). The results show that employees’ intention to adopt AI applications is positively related to integration capability and team collaboration, and big data analytical capability is positively related to integration capability but not to team collaboration. Moreover, both integration capability and team collaboration are positively correlated with operational performance. Sobel t test was employed to test the mediating effect, and found that integration capability is a significant mediator in the influence of big data analytical capability on operational performance. Employees’ intention to adopt AI applications and big data analytical capability can effectively enhance the goals associated with achieving high operational performances.

Keywords: AI application; Intention; High-tech firm; Collaboration; Operational performance (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13132-023-01293-x

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