知识管理动态能力、AI驱动的知识共享、基于知识的组织支持与组织学习对工作绩效的探索：来自中国科技公司的证据
The Explore of Knowledge Management Dynamic Capabilities, AI-Driven Knowledge Sharing, Knowledge-Based Organizational Support, and Organizational Learning on Job Performance: Evidence from Chinese Technological Companies
AI总结：
本研究基于RBT和KBV，通过129份中国科技企业问卷，运用CFA和SEM实证发现KOS、AI驱动的知识共享、KMDC和组织学习均正向影响组织绩效，且教育水平、员工技能和技术创新显著调节上述关系。
摘要：
基于资源基础理论（RBT）和知识基础观（KBV），本研究考察了中国企业中基于知识的组织支持（KOS）、AI驱动的知识共享（KS）、组织学习（OL）和知识管理动态能力（KMDC）对组织绩效（OP）的影响。特别是，本研究探讨了这些因素之间的关系，以及教育水平、员工技能和技术创新等控制变量，以全面理解它们对绩效管理的影响。虽然近期关于组织绩效的研究主要集中在数字业务战略和高层决策上，但对数字成熟度、工作场所活动和沟通相关动态的作用关注有限。本研究通过整合有助于组织内总体工作绩效的关键因素来弥补这些空白。此外，为了实证检验所提出的假设，数据收集自中国各企业员工填写的129份有效问卷。研究采用验证性因子分析（CFA）来验证测量结构，并采用结构方程模型（SEM）来评估假设关系。研究结果揭示了几个重要见解：（1）KOS、AI驱动的KS、KMDC和OL各自对OP具有直接正向影响，强调了它们在提升组织成果中的关键作用。（2）控制变量，包括教育水平、员工技能和技术创新，显著调节KOS、AI驱动的KS、KMDC、OL与OP之间的关系，进一步放大了它们的影响。
英文摘要：
Drawing upon Resource-Based Theory (RBT) and the Knowledge-Based View (KBV), this study investigates the impact of Knowledge-Based Organizational Support (KOS), AI-Driven Knowledge Sharing (KS), Organizational Learning (OL), and Knowledge Management Dynamic Capabilities (KMDC) on Organizational Performance (OP) in Chinese firms. In particular, this research explores the relationships among these factors, alongside control variables such as education level, staff skills, and technological innovation, to provide a comprehensive understanding of their influence on performance management. While recent studies on organizational performance have predominantly concentrated on digital business strategies and high-level decision-making, limited attention has been given to the role of digital maturity, workplace activities, and communication-related dynamics. This study addresses these gaps by consolidating critical factors that contribute to overarching job performance within organizations. Moreover, to empirically test the proposed hypotheses, data were collected from 129 valid questionnaires completed by employees across various Chinese firms. The research employed confirmatory factor analysis (CFA) to validate the measurement constructs and structural equation modeling (SEM) to evaluate the hypothesized relationships. The findings reveal several significant insights: (1) KOS, KS with AI, KMDC, and OL each have a direct positive effect on OP, emphasizing their critical roles in enhancing organizational outcomes. (2) Control variables, including education level, staff skills, and technological innovation, significantly moderate the relationships between KOS, KS with AI, KMDC, OL, and OP, further amplifying their impact.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
