The adoption of artificial intelligence innovation in Thai public hospital: Multiple case studies

Abstract

Adoptions of AI-based innovation in the healthcare sector have increasingly received attention from scholars in innovation studies. However, previous research has not adequately provided an account of the pathway leading to the adoption of AI technology in healthcare organizations, nor has there been a thorough understanding of how various factors may impact different stages of the innovation adoption process. This research, conducted using public hospitals in Thailand as case studies, aims to investigate the processes involved in adopting AI innovations in Thai public hospitals, as well as the factors influencing this adoption. The study uses Rogers’ diffusion of innovation (DOI) theory as a main theoretical background. The study also integrates the DOI theory with other frameworks, including the technology-organizationenvironment (TOE) framework, concepts of interorganizational relationships (IORs), and the technological innovation system (TIS) framework. Utilizing a qualitative-based, multiple case studies approach, the research collected data through in-depth interviews from February to July 2023, and follow-up interviews from September 2023 to February 2024, complemented by secondary data. In total, 52 key informants were interviewed. These included hospital administrators, healthcare practitioners, IT personnel, technology developers, and representatives from policy and promotion agencies, regulators, funding agencies, and research institutions. Thematic analysis was conducted on the transcribed materials in order to synthesize a storyline and to derive relevant insights. The study investigated seven cases of AIrelated innovation adoption in public hospitals in Thailand. These cases vary in terms of sources of innovations and hospital characteristics. The study identified characteristics specific to the innovation adoption process in each case. It also categorized the factors influencing adoption into five groups: organizational factors, ecosystem factors, interorganizational relationships factors, individual factors, and innovation factors. Furthermore, the study established connections between each step of the innovation adoption process and these adoption factors. This research contributes to a better understanding of the innovation adoption process, particularly concerning AI technology adoption, and it builds upon existing literature to enhance knowledge of Rogers’ DOI theory. The research also extends the application of the TOE framework by incorporating determinants related to interorganizational relationships and individual factors in analyzing the factors influencing innovation adoption.