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Essays on the Adoption and Diffusion of Big Data Analytics and Artificial Intelligence Technology

par Ameye, Nicolas
Président du jury Van Pottelsberghe, Bruno
Promoteur van Zeebroeck, Nicolas
Publication Non publié, 2023-05-25

Thèse de doctorat

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Essays on the Adoption and Diffusion of Big Data Analytics and Artificial Intelligence TechnologyThe motivation behind this thesis lies around developing the academic literature, on one hand, on the impact of a specific technology on an organization’s strategy, as well as, on the other hand, on the characteristics and components driving and inhibiting the adoption and diffusion of a specific technology inside an organization.By investigating the drivers and challenges of adopting and diffusing Artificial Intelligence (AI) in an organization, this research aims to answer to the following research question: “What are the main complements and antecedents to the adoption and diffusion of Big Data Analytics and Artificial Intelligence technology, at an organizational level?”.To answer that question, we must first understand how the established models of rank, order, stock and epidemic effects influence the adoption of AI technology. Different streams of works have highlighted four main groups of factors affecting the diffusion of new technologies within or across firms: rank, order, stock and epidemic effects. This thesis examines how these factors influence both the adoption and diffusion of Artificial Intelligence technology across and within firms.Second, this thesis extends the established models to incorporate the effects of uncertainty and competitive intensity on the adoption behaviors of AI technologies among firms. We investigate how uncertainty and competitive intensity affect the adoption behaviours of AI technology among firms.Third, this thesis investigates how technological and managerial complementarities influence the adoption and diffusion of AI technology. To this aim, we extend the established models to incorporate effects of technological and managerial complementarities in the adoption and diffusion of Artificial Intelligence technology among firms. We investigate how technological and managerial complementarities help in facilitating inter-firm diffusion, in driving intra-firm diffusion and in reducing the barriers to AI technology adoption.Fourth, this thesis investigates the discrepancies in adoption and use of AI technology between SMEs and large organizations. To this aim, we explore the determinants and patterns of inter- and intra-firm diffusion at both SMEs and large organization levels.A first finding of this thesis highlights the influence of industry-level adoption on a focal firm’s own adoption. This thesis points at the presence of herding behaviors by which firms tend to follow the crowd. As the share of adopters in the industry increases, the crowd gets bigger and provides a more compelling reason to adopt. However, as our results suggest, these herding behaviors are fragile, exacerbated by competitive forces, and counterbalanced by certain sources of uncertainty while strengthened by others. A second finding of this thesis highlights the importance of pre-existing digital capabilities in the adoption of AI technology. The adoption of AI requires a high degree of maturity and a significant stock of complementary digital technology. This is most likely due to the cumulative nature of AI technology that heavily relies on the information and process infrastructure of the firm. But this implies that leapfrogging on the technology is very unlikely with AI, encouraging firms to build the right foundations (in terms of infrastructure, systems, processes and skills) early on.