Unlocking innovation potential: the impact of artificial intelligence transformation on enterprise innovation capacity

Abstract

Purpose

Building upon the resource-based view (RBV) and related research, this paper empirically examines the impact and specific mechanisms of artificial intelligence transformation on corporate innovation capabilities. It provides micro-level evidence of AI’s influence on innovation behavior.

Design/methodology/approach

Drawing upon data from Chinese listed companies spanning the period from 2011 to 2022, this study employs a dual fixed-effects model and a mediation effects model to empirically analyze the influence of enterprise AI transformation on its innovation capability as well as the specific mechanisms involved.

Findings

The research reveals that AI transformation significantly enhances the innovation capability of enterprises. Heterogeneity analysis indicates that AI transformation exerts a stronger promoting effect on the innovation capability of non-technology firms, large enterprises and those within the manufacturing sector. Mechanism analysis further reveals that AI transformation enhances innovation capability by boosting enterprise profits, reducing costs and reinforcing internal control mechanisms. Further examination demonstrates that AI transformation elevates the quality, efficiency and eco-friendliness of enterprise innovation.

Originality/value

Firstly, this study employs text analysis methods from machine learning to construct artificial intelligence indicators at the firm level, providing stronger evidence of AI’s impact on corporate innovation capabilities. Secondly, it extends corporate innovation behavior to include innovation quality, efficiency and green innovation practices, offering a more comprehensive validation of AI’s role in fostering corporate innovation.

Citation

Jiang, L., Xuan, Y. and Zhang, K. (2024), "Unlocking innovation potential: the impact of artificial intelligence transformation on enterprise innovation capacity", European Journal of Innovation Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/EJIM-07-2024-0809