研究非技术用户对智能分析的信任如何影响决策质量。
Beyond Adoption Intention How Trust in Augmented Analytics Relates to Perceived Decision Quality Among Non-Technical BI Users
- 用认知外包理论分析信任与决策质量的关系。
- 250名越南从业者数据表明信任正向提升决策质量。
- 适合关注人机协作与决策效率的研究者和管理者。
智能分析已改变商业智能（BI）系统支持决策的方式，使非技术经理从手动分析转向依赖自动化洞察。当前的BI研究常忽视AI驱动分析的认知机制及其对决策质量的直接影响。本研究基于认知外包理论，探讨非技术型BI用户对智能分析的信任与其感知决策质量之间的关系。数据来自2025年1月至3月间越南不同组织角色的250名商务专业人士，采用偏最小二乘结构方程模型（PLS-SEM）进行分析。结果表明，智能分析功能与感知易用性、有用性及对BI系统的信任正相关。信任与有用性共同影响BI采纳意愿和感知决策质量。值得注意的是，在所研究的非专业用户样本中，信任与感知决策质量呈正相关。通过将智能分析视为认知外包，本研究拓展了BI采纳研究的范畴，纳入了感知决策成果，深化了对组织中人机交互的理解。
原文摘要 · Abstract (English)
Augmented analytics has transformed how Business Intelligence (BI) systems support decision-making, shifting non-technical managers from manual analysis toward dependence on automated insights. Current BI research often overlooks the cognitive mechanisms and the direct impact of AI-enabled analytics on decision quality. This study employs the theory of cognitive delegation to investigate the association between trust in augmented analytics and perceived decision quality among non-technical BI users. Data were collected from 250 business professionals across various organizational roles in Vietnam between January and March 2025 and analyzed using partial least squares structural equation modeling (PLS-SEM). Findings indicate that augmented analytics capabilities are positively associated with perceived ease of use, usefulness, and trust in BI systems. Trust and usefulness are jointly associated with BI adoption intention and perceived decision quality. Notably, trust is positively related to perceived decision quality, as observed within the studied sample of non-specialist users. By framing augmented analytics as cognitive delegation, this study expands BI adoption research to include perceived decision outcomes and contributes to the understanding of human-AI interaction in organizations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。