Abstract

The Artificial intelligence (AI) healthcare market is expected to exceed $164 billion by 2029 (Fortune Business Insights, 2022). Indeed, AI healthcare implementation supports healthcare practitioners in preventing medication errors, diagnosing diseases, and tracking patients' health status (Liu et al., 2022). Previous studies have employed the Technological-Organizational-Environmental framework (TOE) to show that technological, environmental, and organizational factors positively affect AI adoption intentions by organizations (Polisetty et al., 2023). However, these intentions depend on further managerial evaluations. Implementation science has developed the Implementation Outcomes Framework (IOF) to capture such evaluations (Lewis et al., 2015). Given the above, this study integrates IOF with TOE to identify the determinants of AI adoption. Specifically, the authors investigate if several technological, organizational, and environmental factors, such as technology infrastructure, financial resources, and market pressure, foster the organizational evaluation of AI as acceptable, appropriate, and feasible. Indeed, although all the above factors could support AI adoption, organizations could not consider the implementation of AI as acceptable, appropriate, and feasible, and they could not be willing to adopt AI. This evaluation needs to be made in a case-to-case scenario because ethical AI considerations come into play. Indeed, a conflict between AI and ethics exists because the AI's capability to make autonomous decisions may not align with the traditional ethical principles of justice, beneficence, and non-maleficence (Mirbabaie et al., 2022). Therefore, it is essential to analyze if, although AI implementation is considered acceptable, appropriate, and feasible, organizations are not willing to adopt AI because they consider AI decisions to be in breach of the above principles. To sum up, this study aims to answer the following research questions: (i) to what extent does the interplay between technological, organizational, and environmental factors affect implementation outcomes? (ii) to what extent do ethical evaluations influence the relationship between implementation outcomes and AI adoption? Data are collected through a survey among healthcare organizations that are members of the Society for Information Management. This study will contribute to both information systems (IS) researchers and practitioners by identifying better possible determinants of AI organizational adoption in the healthcare sector.

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