What Determines AI Adoption?

Abstract

Over the recent years, firms have showed an increased demand for leveraging the performance potential of Artificial Intelligence (AI) technologies. However, the general applicability of AI to many settings raises important managerial questions about whether aspects such as potential for in-house versus external AI development, the presence of complementary technological resources (e.g. access to cloud computing), or even the specific use of AI (e.g. product/service development, operational management) would guide firm-level decisions underlying AI adoption. While recent research has provided some insight on worker-level decisions about using AI, knowledge about firm-level AI adoption, as well as heterogeneities in adoption, remains scant. To address this empirical gap, this preliminary paper provides one of the first comprehensive assessments of firm-level determinants of AI adoption while accounting for the heterogeneous sources, applications, and complementary technological resources of AI in organizations. Specifically, this paper reports the first results of a novel firm-level AI adoption survey deployed to a representative sample of South Korean firms from 2017 and 2018. Econometric assessments identified that firm size and use of existing intangible assets are important for AI adoption. However, while these characteristics are significant for AI adoption regardless of whether the technology was produced in-house or obtained from a vendor, analyses suggest that there may be heterogeneities on the determinants of AI adoption depending on the operational application of AI. Furthermore, AI adoption also ensues in tandem with the adoption of other digital technologies (e.g. big data, cloud computing, and the Internet of Things) and with contemporaneous firm reorganization.