发表机构
School of Management, Binghamton University, State University of New York（纽约州立大学宾汉姆顿学院管理学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个面向理论导向的智能体AI系统，通过三阶段流程（扩展搜索空间、重建知识状态、评估理论化需求）帮助研究人员从文献空白中识别理论机遇，并以人类监督智能体AI为例验证其有效性。
AI 中文摘要
生成式AI（GenAI）能够探索大量文献并生成看似合理的研究想法，但识别出缺失、研究不足、矛盾或潜在关联的内容本身并不能揭示理论应在何处推进。我们开发了一个面向理论导向的智能体AI系统，通过将既定的理论化方法融入文献探索与评估，帮助研究人员识别潜在的理论化机遇。该系统通过三个阶段运作。第一阶段扩展理论搜索空间并构建一个临时的候选知识图谱。第二阶段通过基于来源的证据提取和理论状态重建，独立地重建文献所支持的内容。第三阶段评估重建的知识状态，以确定未解决的配置是否值得进行理论发展或其他研究行动，并且在值得进行理论发展时，确定哪种理论化方法合适。我们通过对组织中智能体AI系统的人类监督进行端到端分析来展示该系统。分析表明，仅凭文献空白不足以识别理论机遇。例如，“透明度到信任”被路由到基于机制的理论化，因为这种关系在先前研究中被反复记录，而解释透明度如何塑造信任的生成机制仍未被充分说明。通过结合大规模文献处理、结构化知识表示和理论化引导的诊断，该系统作为一个面向理论的研究助手，支持研究人员识别后续研究中具有理论意义的方向。
英文摘要
Generative AI (GenAI) can explore large bodies of literature and generate plausible research ideas, but identifying what is missing, understudied, contradictory, or potentially connected does not by itself reveal where theory should advance. We develop a theory-oriented agentic AI system that helps researchers identify potential theorizing opportunities by incorporating established theorizing approaches into literature exploration and evaluation. The system operates through three stages. Stage 1 expands the theoretical search space and constructs a provisional Candidate Knowledge Graph. Stage 2 independently reconstructs what the literature supports through source grounded evidence extraction and theory-state reconstruction. Stage 3 evaluates the reconstructed knowledge state to determine whether an unresolved configuration warrants theory development or another research action and, when theory development is warranted, which theorizing approach is appropriate. We demonstrate the system through an end-to-end analysis of human oversight of agentic AI systems in organizations. The analysis shows that literature gaps alone are insufficient for identifying theoretical opportunities. For example, "transparency to trust" is routed to mechanism-based theorizing because the relationship is repeatedly documented in prior studies, while the generative mechanism explaining how transparency shapes trust remains insufficiently specified. By combining large-scale literature processing, structured knowledge representation, and theorizing-guided diagnosis, the system serves as a theory-oriented research assistant that supports researchers in identifying theoretically meaningful directions for subsequent research.
