Research on Effect Reasoning on Enterprise Digital Transformation Performance with Big Data Analysis

Liangcan Liu; Jia Chen; Hang Ren; Tianhui Chen

doi:doi:10.11648/j.ijefm.20231101.15

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Research on Effect Reasoning on Enterprise Digital Transformation Performance with Big Data Analysis

Liangcan Liu, Jia Chen, Hang Ren, Tianhui Chen

Published in International Journal of Economics, Finance and Management Sciences (Volume 11, Issue 1)

Received: 12 January 2023 Accepted: 1 February 2023 Published: 6 February 2023

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Abstract

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.

Published in	International Journal of Economics, Finance and Management Sciences (Volume 11, Issue 1)
DOI	10.11648/j.ijefm.20231101.15
Page(s)	31-37
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Effect Reasoning, Failure to Learn, Digital Transformation

References

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APA Style

Liangcan Liu, Jia Chen, Hang Ren, Tianhui Chen. (2023). Research on Effect Reasoning on Enterprise Digital Transformation Performance with Big Data Analysis. International Journal of Economics, Finance and Management Sciences, 11(1), 31-37. https://doi.org/10.11648/j.ijefm.20231101.15

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ACS Style

Liangcan Liu; Jia Chen; Hang Ren; Tianhui Chen. Research on Effect Reasoning on Enterprise Digital Transformation Performance with Big Data Analysis. Int. J. Econ. Finance Manag. Sci. 2023, 11(1), 31-37. doi: 10.11648/j.ijefm.20231101.15

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AMA Style

Liangcan Liu, Jia Chen, Hang Ren, Tianhui Chen. Research on Effect Reasoning on Enterprise Digital Transformation Performance with Big Data Analysis. Int J Econ Finance Manag Sci. 2023;11(1):31-37. doi: 10.11648/j.ijefm.20231101.15

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@article{10.11648/j.ijefm.20231101.15,
  author = {Liangcan Liu and Jia Chen and Hang Ren and Tianhui Chen},
  title = {Research on Effect Reasoning on Enterprise Digital Transformation Performance with Big Data Analysis},
  journal = {International Journal of Economics, Finance and Management Sciences},
  volume = {11},
  number = {1},
  pages = {31-37},
  doi = {10.11648/j.ijefm.20231101.15},
  url = {https://doi.org/10.11648/j.ijefm.20231101.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20231101.15},
  abstract = {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.},
 year = {2023}
}

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TY  - JOUR
T1  - Research on Effect Reasoning on Enterprise Digital Transformation Performance with Big Data Analysis
AU  - Liangcan Liu
AU  - Jia Chen
AU  - Hang Ren
AU  - Tianhui Chen
Y1  - 2023/02/06
PY  - 2023
N1  - https://doi.org/10.11648/j.ijefm.20231101.15
DO  - 10.11648/j.ijefm.20231101.15
T2  - International Journal of Economics, Finance and Management Sciences
JF  - International Journal of Economics, Finance and Management Sciences
JO  - International Journal of Economics, Finance and Management Sciences
SP  - 31
EP  - 37
PB  - Science Publishing Group
SN  - 2326-9561
UR  - https://doi.org/10.11648/j.ijefm.20231101.15
AB  - 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.
VL  - 11
IS  - 1
ER  -

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Author Information

Liangcan Liu

School of Management, Guizhou University of Finance and Economics, Guiyang, China
Jia Chen

School of Management, Guizhou University of Finance and Economics, Guiyang, China
Hang Ren

School of Management, Guizhou University of Finance and Economics, Guiyang, China
Tianhui Chen

School of Management, Guizhou University of Finance and Economics, Guiyang, China

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Plain Text BibTeX RIS

APA Style

Liangcan Liu, Jia Chen, Hang Ren, Tianhui Chen. (2023). Research on Effect Reasoning on Enterprise Digital Transformation Performance with Big Data Analysis. International Journal of Economics, Finance and Management Sciences, 11(1), 31-37. https://doi.org/10.11648/j.ijefm.20231101.15

Copy | Download

ACS Style

Liangcan Liu; Jia Chen; Hang Ren; Tianhui Chen. Research on Effect Reasoning on Enterprise Digital Transformation Performance with Big Data Analysis. Int. J. Econ. Finance Manag. Sci. 2023, 11(1), 31-37. doi: 10.11648/j.ijefm.20231101.15

Copy | Download

AMA Style

Liangcan Liu, Jia Chen, Hang Ren, Tianhui Chen. Research on Effect Reasoning on Enterprise Digital Transformation Performance with Big Data Analysis. Int J Econ Finance Manag Sci. 2023;11(1):31-37. doi: 10.11648/j.ijefm.20231101.15

Copy | Download

@article{10.11648/j.ijefm.20231101.15,
  author = {Liangcan Liu and Jia Chen and Hang Ren and Tianhui Chen},
  title = {Research on Effect Reasoning on Enterprise Digital Transformation Performance with Big Data Analysis},
  journal = {International Journal of Economics, Finance and Management Sciences},
  volume = {11},
  number = {1},
  pages = {31-37},
  doi = {10.11648/j.ijefm.20231101.15},
  url = {https://doi.org/10.11648/j.ijefm.20231101.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20231101.15},
  abstract = {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.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Research on Effect Reasoning on Enterprise Digital Transformation Performance with Big Data Analysis
AU  - Liangcan Liu
AU  - Jia Chen
AU  - Hang Ren
AU  - Tianhui Chen
Y1  - 2023/02/06
PY  - 2023
N1  - https://doi.org/10.11648/j.ijefm.20231101.15
DO  - 10.11648/j.ijefm.20231101.15
T2  - International Journal of Economics, Finance and Management Sciences
JF  - International Journal of Economics, Finance and Management Sciences
JO  - International Journal of Economics, Finance and Management Sciences
SP  - 31
EP  - 37
PB  - Science Publishing Group
SN  - 2326-9561
UR  - https://doi.org/10.11648/j.ijefm.20231101.15
AB  - 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.
VL  - 11
IS  - 1
ER  -

Copy | Download