While AI has the potential to enhance productivity in organizations, some still hesitate to adopt it and are cautious about incorporating it into their daily operations. This reluctance is especially pronounced in sectors such as healthcare, where decision-making carries significant consequences. As part of this project, we interviewed OPO Executive Directors and COOs to understand better the process of integrating a new technology into their operations. The expanded technology, organization, and environment (TOE) framework outlines the primary factors that influence the organizational adoption of new information technologies; we used this framework to structure our interview process.
The Technology-Organization-Environment (TOE) framework is a theoretical model used to explain how organizations adopt and implement new technologies by examining three key dimensions: technology, organization, and environment. The technology context considers the characteristics of the technology, such as its relative advantage, complexity, and compatibility with existing systems. The organization context focuses on internal factors like the size of the organization, top management support, available resources, and an innovative culture. The environment context examines external influences, including market trends, regulatory requirements, competitive pressure, and the availability of external support infrastructure. Most of the time, technology adoption follows a bottom-up approach, where employees or departments experiment with and advocate for new tools or systems before gaining formal approval from leadership. After the technology has proven its validity, companies will share the information with each other, and slowly, more and more companies will adopt the technology until universal adoption. This is a typical innovation curve starting with early adopters. Together, these dimensions provide a comprehensive view of the factors driving technology adoption, making the TOE framework a widely applicable tool across various industries and innovations.
Two contrasting approaches to the adoption process for AI adoption in transplant emerged. One perspective advocates for top-down, national-level coordination of AI system implementation to ensure consistency across the country. However, a bottom-up approach, aligned with the innovation adoption curve and existing diffusion of innovation theories, also surfaced. This analysis indicated that a bottom-up strategy can result in uneven adoption, leading to equity concerns. Variations among OPOs—such as organization size, available resources, and AI literacy—are likely contributing factors to this uneven adoption and implementation. This has implications for how regulators think about encouraging technological innovation.