When AI heals the healers: AI adoption and explainability drive service innovation and reduce emotional exhaustion

PURPOSE: With the growing integration of AI in healthcare, its capabilities, such as automation, predictive analytics, enhanced diagnostics, and increased efficiency, hold strong potential to reduce emotional exhaustion among healthcare workers. Drawing on the Conservation of Resources (COR) Theory, this study proposes that AI adoption in healthcare can alleviate healthcare workers' emotional exhaustion by enabling service innovation. DESIGN/METHODOLOGY/APPROACH: To support our proposition, three-wave self-reported data were collected over six months from doctors working in technologically advanced hospitals across four provinces of Pakistan. FINDINGS: The structural equation modeling (SEM) results revealed that AI adoption was negatively related to emotional exhaustion via service innovation. Findings also showed that this indirect relationship was stronger when the level of explainable AI was high than when it was low. ORIGINALITY/VALUE: The study significantly contributes to the literature by revealing that AI adoption can substantially alleviate emotional exhaustion, contrasting with previous research, which predominantly suggests that technology adoption heightens emotional exhaustion among healthcare workers.