Artificial Intelligence Facets and System-Level Properties as Drivers of Use: A Critical Review, Research Framework and Agenda
本文综述52篇文献，识别AI的拟人化、自主性等特征及可解释性、可靠性等系统属性，构建框架解释这些因素如何影响AI使用，并提出未来研究方向。
Artificial intelligence (AI) introduces a new paradigm that challenges established information systems (IS) frameworks, prompting a reassessment of IS use in AI-driven systems. To address this, we conducted a three-iteration multi-approach review of 52 articles centered on AI facets and system-level properties as drivers of AI use. Our review identifies and classifies AI’s facets (anthropomorphism, autonomy, inscrutability, and learning) and system-level properties (explainability, transparency, and reliability). We propose working definitions to resolve conceptual issues. Building on these findings, we inductively develop a framework that positions AI use as a process in which AI facets disrupt or enable user interactions, while system-level properties mediate these effects, individually or in combination. Together, these elements shape established and emerging forms of AI use and outcomes. Leveraging our framework and iterations insights, we critically assess extant understanding and propose a research agenda with actionable paths to support future IS research addressing AI use complexities.