基于大数据的学习组织能力的概念化和规模开发
Nesrin Alkan1, Deniz Ersan Yilmaz1, Bilal Baris Alkan2
1Faculty of Economics and Administrative Sciences, Akdeniz University, Antalya, Türkiye.
Frontiers in big data
|July 4, 2025
概括
本研究介绍了基于大数据的学习组织能力 (BD-LOC) 规模,这是衡量组织如何从大数据中学习的新工具. 经过验证的规模提供了一种可靠的方式来评估和提高学习能力,以获得战略优势.
科学领域:
- 组织学习 组织学习
- 大数据分析大数据分析
- 管理科学 管理科学
背景情况:
- 组织需要加强学习和适应能力,以获得竞争优势.
- 缺乏验证的工具来衡量大数据驱动的学习能力.
- 大数据显著影响组织的学习过程.
研究的目的:
- 开发和验证可靠的尺度来评估基于大数据的学习组织能力.
- 为大数据驱动的学习提供量化衡量.
- 在大数据的背景下解决有关学习组织评估的文献上的差距.
主要方法:
- 采用了两阶段的研究设计.
- 在232名经理的探索性因素分析 (EFA) 中,在三个因素中确定了22个项目.
- 对128名经理进行的确认因素分析 (CFA) 验证了尺度的结构和心理特征.
主要成果:
- EFA揭示了该规模的明确的三因素结构.
- CFA证实该模型与数据相匹配,并显示出良好的心理测量特性.
- 最终的基于大数据的学习组织能力 (BD-LOC) 尺度表现出高的内部一致性和构造有效性.
结论:
- 在大数据时代,BD-LOC尺度是评估组织学习能力的有效和可靠工具.
- 该工具帮助组织制定战略决策,创新和提高运营效率.
- 该研究有助于有效实施数字化转型战略.
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