Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial Intelligence Facets and System-Level Properties as Drivers of Use: A Critical Review, Research Framework and Agenda

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月24日

查看全文
  • Marion Korosec-Serfaty,
  • Bogdan Negoita,
  • Sylvain Sénécal,
  • Pierre-Majorique Léger

摘要

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.