Antecedents of big data analytics and artificial intelligence adoption on operational performance: the ChatGPT platform

Abstract

Purpose

This study aims to measure the integrated impact of big data analytics and artificial intelligence (BDA-AI) adoption by using the ChatGPT generative AI online platform as a BDA-AI tool on the operational and environmental performance.

Design/methodology/approach

This study considers Taiwanese professionals who engage with ChatGPT; the sample consists of 388 online users.

Findings

This study’s main finding is that the considered antecedents – including technological, organizational and environmental contexts, tangible resources and workforce skills – are significantly associated with BDA-AI adoption. Notably, BDA-AI adoption exhibits a significant relationship with operational performance, environmental performance and environmental process integration. Moreover, environmental process integration is significantly correlated with environmental performance. Lastly, operational performance is significantly correlated with environmental performance.

Originality/value

This study contributes to the heavily lacking but developing literature on the antecedents and consequences of BDA-AI adoption. Its theoretical foundation consists of the technological-organizational-environmental model, Roger’s diffusion of innovation theory and resource-based view theory.

Citation

Chen, C.-T., Chen, S.-C., Khan, A., Lim, M.K. and Tseng, M.-L. (2024), "Antecedents of big data analytics and artificial intelligence adoption on operational performance: the ChatGPT platform", Industrial Management & Data Systems, Vol. 124 No. 7, pp. 2388-2413. https://doi.org/10.1108/IMDS-10-2023-0778