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Explainable artificial intelligence is often presented as a means of making intelligent systems more understandable, trustworthy, and usable, yet explanation availability does not reliably produce adoption, appropriate reliance, accountability, or improved decisions. This realist review examines why explainable AI is adopted, ignored, rejected, discontinued, overused, underused, or misused across organizational, professional, technical, and decision settings. It applies context–mechanism–outcome reasoning to interpret how explanation design interacts with workflow integration, user and domain expertise, decision risk, organizational incentives, governance arrangements, and human-oversight conditions. The synthesis focuses on mechanisms including perceived transparency, cognitive accessibility, mental-model formation, perceived usefulness, trust calibration, accountability confidence, contestability awareness, algorithm aversion, algorithm appreciation, automation bias, and explanation overload. These mechanisms may support selective reliance and meaningful challenge, but they may also generate false reassurance, superficial compliance, defensive rejection, or uncritical dependence. The review distinguishes adoption from acceptance, trust from trustworthiness, reliance from appropriate reliance, and explanation usefulness from decision quality. It identifies plausible configurations in which professional expertise, uncertainty communication, traceability, feedback, and contestability support careful use, while poor workflow fit, limited accessibility, weak governance, and automation pressure suppress scrutiny or encourage misuse. The main contribution is a bounded realist synthesis and design agenda for evaluating explainable AI through context-sensitive human responses and decision outcomes rather than explanation availability alone. The configurations are explanatory propositions requiring human-centered, organizational, and decision-context validation; they are not universal causal laws, implementation protocols, or evidence of deployment readiness.