科研管理 ›› 2025, Vol. 46 ›› Issue (1): 1-11.DOI: 10.19571/j.cnki.1000-2995.2025.01.001

• 论文 •    下一篇

组态视角下数据要素利用水平的影响因素研究

曹萍,陈由彬   

  1. 上海应用技术大学经济与管理学院,上海200235
  • 收稿日期:2024-04-19 修回日期:2024-11-22 出版日期:2025-01-20 发布日期:2025-01-09
  • 通讯作者: 曹萍
  • 基金资助:

Research on the influencing factors of data element utilization levels from the perspective of configuration

Cao Ping, Chen Youbin   

  1. School of Economics and Management, Shanghai Institute of Technology, Shanghai 200235, China
  • Received:2024-04-19 Revised:2024-11-22 Online:2025-01-20 Published:2025-01-09
  • Contact: Cao Ping

摘要:     数据要素已成为促进数字经济发展的重要的生产要素,研究数据要素利用水平对于促进数字经济的发展具有重要意义。本文基于技术、组织、环境(TOE)理论框架,结合模糊集定性比较分析方法(fsQCA)与人工神经网络(ANN)方法,并引入可解释机器学习模型SHAP,对数据要素利用水平的影响因素进行研究。基于我国30个省(自治区、直辖市)的数据,实证分析数据要素利用水平的影响因素。研究发现,技术、组织和环境因素三者之间协同联动形成了3种能够产生高数据要素利用水平的组态路径:创新-组织-开放综合型、技术-组织-环境综合型、基建-机构-环境综合型。其中科技创新水平与数据资源开放程度对于高数据要素利用水平影响最为显著,并且如果单一的追求数据资源的开放度,反而会对数据要素的利用产生负面影响。研究结论在理论层面明晰了数据要素利用水平的影响因素及组态路径,丰富了数据要素的相关研究,提出的混合fsQCA-ANN方法为解决类似的复杂组态问题研究提供了新的思路。在实践层面为我国优化数据要素利用政策、提升数据要素利用水平提供理论依据和管理启示。

关键词: 数据要素, 数字经济, 机器学习, 人工神经网络, fsQCA, SHAP

Abstract:     The data element has become an important production factor in promoting the development of the digital economy, and studying the utilization level of data elements is of great significance for advancing the growth of the digital economy. This paper, based on the Technology-Organization-Environment (TOE) theoretical framework, adopting the fuzzy-set qualitative comparative analysis method (fsQCA) and artificial neural network (ANN) approaches, introduced the interpretable machine learning model SHAP to investigate the influencing factors of data element utilization levels. Using the data from 30 provinces (autonomous regions and municipalities) in China, this study empirically analyzed the factors influencing data element utilization levels. The research found that the synergy among technological, organizational and environmental factors forms three configurational paths capable of achieving high data element utilization levels: innovation-organization-openness composite, technology-organization-environment composite, and infrastructure-institution-environment composite. Among these, technological innovation levels and data resource openness have the most significant impact on achieving high data element utilization levels, while a singular focus on data resource openness may have a negative effect on the utilization of data elements. The research conclusions have clarified the influencing factors and configurational paths of data element utilization levels at the theoretical level, enriched the relevant studies on data elements, and proposed a hybrid fsQCAANN method, thus offering a new perspective for addressing similar complex configurational problems. At the practical level, the findings will provide some theoretical support and managerial insights for optimizing data element utilization policies and enhancing data element utilization levels in China.

Key words: data element, digital economy, machine learning, artificial neural network, fsQCA, SHAP