数字经济背景下，培育数据要素市场并引导企业积极参与数据要素市场化流通势在必行。基于势差理论和推拉理论，构建企业数据要素供给动力因素分析框架，采用必要条件分析（NCA）和模糊集定性比较分析（fsQCA）方法对311个数据要素型企业高管样本进行组态效应分析。结果显示，内外部动力因素共同驱动企业数据要素供给，凸显数据势能和数据势差等动力因素的重要性；企业数据要素供给意愿是多条件协同作用的结果，单个条件变量不能成为高供给意愿产生的必要条件；高供给意愿产生有5条组态路径，可归纳为政府支持—市场需求驱动型、内部—外部动力协同驱动型和政府支持—数据势差驱动型3种动力模式。结论可拓展势差理论、推拉理论在企业数据要素供给研究中的应用，为提升数据市场供方主体积极性提供启示。

In the digital economy, it is crucial to cultivate a data element market and encourage enterprise participation in the market-oriented circulation of data elements. However, existing theories have not developed an effective analytical framework for understanding the dynamic factors of enterprise data element supply. Due to the unique characteristics of data elements compared to other production elements, current research primarily consists of theoretical studies and lacks empirical analysis, which hampers the ability to effectively explain the complex synergistic mechanisms of dynamic factors. Therefore, how to realize the effective incentivization of data release and participation in market-oriented circulation becomes a critical issue.
To address this important question, this study follows the theories of potential difference and push-pull and aims to construct an analytical framework for the dynamic factors of enterprise data element supply from the perspectives of internal and external forces. Specifically, internal forces include data potential and economic benefits, while external forces encompass government support, data differentials, demand differentials, capability constraints, and risk constraints. Examining these forces is crucial for understanding the motivations and barriers that influence enterprise participation in data element supply activities. The study employs the NCA and fsQCA methods to analyze the configuration effects among a sample of 311 senior executives from data-driven enterprises. These methods enable a nuanced examination of the synergistic effects of various dynamic factors and provide a comprehensive understanding of the conditions that generate a high willingness to supply data elements.
The study yields several significant conclusions. Firstly, the key internal (data potential and economic benefits) and external (government support, data differentials, market demand, capacity constraints, and risk constraints, etc.) dynamic factors affecting the supply of data elements to enterprises are identified and the importance of dynamic factors such as data potential and data differentials is emphasized. Secondly, the formation of willingness to supply data factors is the result of multi-condition synergistic effects, without the presence of necessary conditions for the generation of high willingness. Thirdly, the study identifies five pathways that lead to high willingness to supply, and these five pathways are able to reflect the fact that different enterprises, in the light of their own actual situation, can achieve a high level of willingness to supply through a combination of internal and external dynamic factors. Fourthly, the study summarizes three dynamic models: "government support-market demand driven", "internal-external driving force synergistic driven" and "government support-data potential driven". A cross-sectional comparison of the three dynamic models shows that stronger government support and market demand play a universal role in high willingness to supply. Thus, to promote the generation of high willingness among enterprises, the two-wheel drive combining government support and market demand becomes the key. The government should actively explore the implementation of relevant incentives and support policies according to their characteristics, support the main body of enterprises’ supplying data elements, and stimulate the entry of more market demand for data elements to create a stable policy environment and a good market order. Meanwhile, when selecting the appropriate dynamic model, enterprises need to consider the context and specific circumstances, tailoring their approach to participate in data element supply more effectively.
This paper contributes significantly in three aspects. Firstly, this paper enriches the relevant research from a configurational perspective. Taking into account the characteristics of data elements that distinguish them from other production elements, a systematic analytical framework is proposed, which remedies the inadequacy of previous results that have mostly studied single-dimensional conditions of incentives or disincentives. Secondly,it extends the research scope of potential difference theory and push-pull theory and extends their application to data elements supply research, which is a useful supplement to the existing research based on potential difference theory and push-pull theory. Finally, the current research on data element supply is still at the stage of theoretical exploration, with a predominance of qualitative analysis in the literature and relatively few empirical studies. This paper pioneers the integration of NCA and fsQCA methods into the research on data element supply, enriching the methodological approaches in this field.