数字经济时代,数据已成为驱动企业技术创新和可持续发展的关键生产要素。企业如何有效利用数据要素以提升创新能力,从而增强竞争优势,是学术界与业界关注的重要议题。基于资源基础观与创新管理理论,系统分析其对企业利用式创新与探索式创新的影响机制,采用2008—2023年中国A股上市企业面板数据,构建双重固定效应模型进行实证检验。研究发现,数据要素应用对企业双元创新具有显著正向影响,且对利用式创新的影响更为突出。机制分析表明,数据要素应用通过激活内部冗余资源促进利用式创新,通过增强外部研发合作推动探索式创新。此外,大数据综合试验区设立与东部地区经济优势进一步强化数据要素应用对双元创新的促进作用。研究深化了资源基础理论在数据要素背景下的解释力,为政府完善数据要素市场制度和企业提升数据利用能力提供实践启示。

In the era of the digital economy, data has evolved from an auxiliary information resource into a core production factor driving technological innovation and sustainable development. Understanding how data factor empowers firms to achieve balanced exploitative and explorative innovation,thus enhancing their ambidextrous innovation capability,has become an essential question in both academic research and policy practice. Existing research primarily examines corporate innovation through the lenses of digital economy, digital transformation, and digital technology application, spanning macro, industrial, and digital capability levels. Recent studies have begun to explore the nexus between data factor and corporate innovation, highlighting their promotional effects, potential risks, and relevant theoretical models. However, significant deficiencies persist regarding mechanisms, contextual factors, and theoretical integration. Specifically, research on data factor and corporate ambidextrous innovation exhibits three major gaps: first, a lack of systematic characterization based on micro-level firm data; second, insufficient elaboration on how data factor embeds within the innovation process and exert differential impacts; and third, inadequate empirical testing that incorporates external environmental dynamics and firm heterogeneity, thereby limiting the generalizability of conclusions and practical impl ications.Thus, this study aims to systematically explore the mechanisms and boundary conditions through which the application of data factor influences firms’ innovation performance in both exploitative and explorative dimensions. Grounded in the resource-based view and innovation management theory, this paper constructs a dual-path framework of “stock resource activation” and “incremental resource integration”. It argues that data factor application can stimulate exploitative innovation by identifying and activating slack internal resources, while promoting explorative innovation by strengthening external R＆D collaboration networks. To empirically test these mechanisms, a two-way fixed effects model is established using panel data from 3 076 Chinese A-share listed firms between 2008 and 2023, yielding 29 900 firm-year observations. The level of data factor application is measured through text mining of corporate annual reports across four dimensions: data stock, data development capability, data-driven business applications, and data monetization. Exploitative and explorative innovation are distinguished by patent classification continuity, with slack reso urces and R＆D collaboration as mediating variables.Empirical results show that the application of data factor significantly promotes both exploitative and explorative innovation, with a stronger effect on exploitative innovation, suggesting that data utilization primarily enhances incremental rather than radical innovation. Mechanism analysis confirms two distinct transmission paths: data factor application reduces slack resources by improving resource recognition, allocation, and utilization efficiency, thereby enhancing exploitative innovation; simultaneously, it facilitates explorative innovation by optimizing partner identification, collaboration modes, and knowledge integration efficiency, underscoring the role of data in enabling heterogeneous resource synergy. Heterogeneity analysis shows that these effects are more pronounced under favorable institutional and regional conditions. Specifically, the establishment of national big data pilot zones and the economic advantages of eastern China significantly strengthen the positive relationship between data factor application and dual inno vation outcomes.This study makes three primary contributions. First, it advances micro-level research on data factor by integrating them into the analysis of firm-level innovation behavior, extending the theoretical frontier of data economy studies. Second, it deepens the understanding of how data-driven processes reshape firms’ resource allocation mechanisms, providing empirical evidence that data applications can transform slack resources into innovation assets and foster cross-organizational knowledge recombination. Third, it identifies the heterogeneous effects of institutional environments and regional development levels, offering new insights into how data infrastructure and policy design can enhance firms’ inno vation capability.From a managerial perspective, the findings suggest that firms should strengthen their data management and analytics capabilities to embed data-driven decision-making throughout innovation activities. For exploitative innovation, data can be used to optimize internal processes and accelerate incremental improvement; for explorative innovation, big data and collaborative platforms can help identify emerging opportunities and reduce innovation uncertainty. Policymakers are advised to accelerate data market reforms, promote cross-regional data circulation, and build supportive institutional frameworks,especially in underdeveloped areas,to ensure equitable access to data-driven innovation opportunities.