人工智能资源对双元创新绩效的影响机制 ——基于数字业务强度与组织学习的中介效应

Mechanisms of Artificial Intelligence Resources on Ambidextrous Innovation Performance： Based on mediating effects of digital business intensity and organizational learning

摘要:

人工智能目前已经成为组织的技术重点,是大数据环境下组织需重视并科学运用的资源之一。然而关于组织如何构建及配置人工智能资源的研究有限。与以往大多数文献从能力视角分析人工智能不同,本文从资源视角进行划分,首次将人工智能分为资产性资源以及社会性资源。其次，探索了两类人工智能资源对双元创新绩效的差异性作用。本文选择2011-2022年中国A股上市公司样本进行理论分析和实证检验,研究结果表明:(1)资产性、社会性人工智能资源均会促进企业双元创新绩效提升,且对探索式创新绩效的提升更加明显。(2)资产性、社会性人工资源对于双元创新绩效的提升路径中,数字业务强度和组织学习均起到了中介作用。在资产性资源提升双元创新绩效的路径中,组织学习的中介作用更为突出。(3)经济政策不确定性增强了人工智能资源对探索式创新绩效的提升效果。上述理论为管理者提供了人工智能资源分配的不同见解,推动了组织在人工智能领域的创新应用,并确保组织在竞争激烈的市场环境中保持领先地位。

Abstract:

Artificial Intelligence (AI) has now become a technological priority for organizations, and is one of the resources that organizations need to pay attention to and use scientifically in the Big Data environment. However, there is limited research on how organizations build and configure AI resources. Unlike most previous literature that analyzes AI from a capability perspective, this paper divides it from a resource perspective, and for the first time divides AI into asset-based resources as well as social resources. Second, it explores the differential effects of the two types of AI resources on ambidextrous innovation performance. This paper selects a sample of Chinese A-share listed companies from 2011 to 2022 for theoretical analysis and empirical testing, and the results show that (1) Both asset-based and social AI resources promote firms' ambidextrous innovation performance, and the enhancement of exploratory innovation performance is more obvious. (2) Both digital business intensity and organizational learning play a mediating role in the enhancement path of asset-based and social AI resources for ambidextrous innovation performance. The mediating role of organizational learning is more prominent in the path of asset-based resources for enhancing ambidextrous innovation performance. (3) Economic policy uncertainty enhances the enhancement effect of AI resources on exploratory innovation performance. The above theories provide managers with different insights into the allocation of AI resources, which drive organizations to make innovative applications in the field of AI and ensure that they stay ahead of the curve in a competitive market environment.