Innovative Adoption of AI-empowered Electronic Records and Information Management System in the Energy Sector

Artificial intelligence (AI) is considered a source of innovative technological transformation that has increasingly reshaped how firms govern, manage, and retrieve knowledge. However, its adoption in highly regulated sectors like energy remains underexplored in theory. This study examines the innovative adoption of AI-empowered electronic records and information management systems (ERIMS) in the energy sector of the United Arab Emirates (UAE), conceptualizing AI-empowered ERIMS as a form of knowledge management innovation entrenched within the energy sector. The study develops and examines an innovation readiness and adoption behavior model that draws on the technology readiness and acceptance model (TRAM) and the technology acceptance model (TAM). All the study variables were assessed using cognitive appraisal theory (CAT), which shapes employees’ responses to AI as an organizational innovation. The study employed a quantitative research design, with survey data analyzed using partial least squares structural equation modeling (PLS-SEM). The findings revealed that of the 26 relationships explored, only 7 proposed hypotheses were supported. Specifically, individuals evaluate the variables based on their ability to manage AI adoption in ERIMS and on their perceived benefits. The study adds to the theoretical literature and contributes to innovation studies by extending TAM and TRAM theories into the innovation adoption field in high-reliability settings, which, in turn, adds a knowledge management innovation layer to ERIMS governance and generates implications for innovation policy and organizational change strategies in energy-sector institutions undertaking digital transformation.