基于大数据分析的企业数字化转型绩效影响推理研究

Liangcan Liu, Jia Chen, Hang Ren, Tianhui Chen
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引用次数: 1

摘要

:由于现代企业与传统企业之间存在诸多差异,许多学者注意到传统成熟企业的研究成果并不能完全应用于现代数字化企业的相关研究。因此,近几十年来,数字化转型研究领域出现了许多新的理论。在多年的发展过程中,对其定义、概念维度和测量方法的研究越来越多。虽然已经有学者意识到效果推理会对企业数字化转型绩效产生影响,但关于效果推理对企业数字化转型绩效影响的实证研究相对较少。数字化转型是指企业通过数字化技术实现重大技术变革,其本质是一个不断探索的过程。本研究从过程视角出发,运用大数据分析揭示效果推理对企业数字化转型的影响。结果表明,效果推理和失败学习能够提高企业数字化转型绩效。实证学习理论被用于研究情境。有助于丰富和发展管理创新的理论研究,理解和指导中国企业通过大数据分析实现数字化转型
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Effect Reasoning on Enterprise Digital Transformation Performance with Big Data Analysis
: Due to the various differences between modern enterprises and traditional enterprises, many scholars have noticed that the research results of traditional mature enterprises can not be fully applied to the relevant research of modern digital enterprises. Therefore, many new theories have emerged in the field of digital transformation research in recent decades. In the course of many years of development, there have been more studies on its definition, conception dimension and measurement. Although some scholars have realized that effect reasoning will play a role in the performance of enterprise digital transformation, there are relatively few empirical studies on the impact of effect reasoning on the performance of enterprise digital transformation. Digital transformation refers to the realization of significant technological change by enterprises through digital technology, and its essence is a process of continuous exploration. This study is carried out to reveal the impact of the effect reasoning on enterprises’ digital transformation with big data analysis from a process perspective. The results show that the effect reasoning and failure learning improves enterprise digital transformation performance. Empirical learning theory is used for the research context. It is helpful to enrich and develop the theoretical research on management innovation, and understand and guide Chinese enterprises to realize digital transformation with big data analysis
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