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To be different, or to be the same? Corporate innovation under the impact of the Big Data Pilot Zone
数字技术所带来的革命性冲击使得企业现有的许多技术和技能过时。面临这种外部技术环境变革,企业会通过强化革新来谋求未来的繁荣,还是会依靠模仿行为以应对当前的不确定性?这成为了一个有待探索的问题。在世界各国谋求数字化变革的新时代,本文以中国建立大数据综合试验区为准自然实验,实证研究发现:大数据变革冲击下,企业的创新决策呈现出求同与存异并举的特征,企业的探索式创新水平得到提升,企业创新的同群效应得到增强。特别地,求同与存异并举的现象还受到补偿协奏逻辑的支配,即对于探索式创新程度较高、需要合法性补偿的企业,其技术创新的同群模仿现象更为显著。本文拓展和深化了关于企业战略平衡行为的文献,相关结论能够为实践中企业创新战略的制定提供一定参考。
Abstract
In the digital age, Chinese firms are undergoing a big data revolution, that is, a profound shift in their business operations, innovation, and development models, owing to the rapid advancements and widespread application of big data technologies. It entails how firms tackle the challenges and leverage the opportunities presented by digital technologies. Moreover, it involves utilizing big data to streamline business processes, enhance products and services, and ultimately bolster competitiveness, enabling them to adapt to the dynamic market demands. The emerging technology paradigm represented by big data systematically impacts the existing technological landscape and capabilities of firms. On the one hand, this revolution offers traditional firms a chance to foster unique value through digital technologies, empowering them to develop innovative products or services and establish a sustainable competitive edge. On the other hand, the dynamic nature of technological innovation in the digital age may introduce legitimacy challenges for firms, hindering the translation of their innovative activities into tangible economic outcomes. In light of these shifts in the external technological landscape, it remains to be explored whether firms would chart a path to future success by amplifying their innovation efforts or if they would choose to respond to the prevailing uncertainties through imitation. To delve into the strategic positioning decisions of firms amidst the transformative influence of big data, this article draws upon the theory of optimal distinctiveness and its concept of strategic orchestration. According to the theory of optimal distinctiveness, firms grapple with a complex interplay of multidimensional factors, enabling them to strike a balance between 'to be different' and 'to be the same' across various strategic dimensions. Building upon this premise, the paper endeavors to explore how corporate innovation activities, affected by the paradigm shift of big data, can effectively navigate the inherent tension between 'to be different' and 'to be the same'. The study seeks to validate the proposition that firms undergoing the impact of the big data revolution would prioritize differentiation in the direction and content of innovation, thereby pursuing exploratory and disruptive innovations, while simultaneously fostering similarities in the volume of innovation inputs. In August 2015, the State Council of the People's Republic of China issued the Outline of Operations to Stimulate the Development of Big Data, inaugurating a pilot program in selected regions of China to establish a number of Big Data Pilot Zones. This initiative provided a valuable research opportunity to examine how firms respond to an exogenous technological paradigm shift embodied by big data. Leveraging the quasi-natural experiment of Big Data Pilot Zones, this paper reveals several key findings. First, under the impact of the big data revolution, corporate innovation decisions show the characteristics of seeking conformity while reserving differentiation, manifested as the simultaneous increasing of the exploratory innovation investment of firms and the peer effects of innovation levels. This conclusion is further corroborated through a series of rigorous tests, including propensity score matching (PSM), parallel trend analysis, placebo testing, and alternative variable measurements. Second, the mechanism tests demonstrate that the impact of the big data revolution on firms' innovation decisions follows a compensatory orchestration logic. That means, the tendency for peer imitation of technological innovations is particularly pronounced among firms that allocate greater investments towards exploratory innovations and face challenges in legitimacy. This study's findings yield valuable managerial insights for firms grappling with the repercussions of digital technologies. Employing the concept of strategic orchestration, firms can consider the approach that combines both radical and conservative tactics in response to the influence of digital technologies. Firstly, firms can embrace digital innovation by implementing transformative measures in digitalization. This may involve adopting cutting-edge digital technologies, harnessing data analytics, and integrating artificial intelligence to bolster productivity, enhance customer experiences, and foster the creation of novel business models. Simultaneously, companies must uphold a certain degree of conservatism to ensure adherence to pertinent laws and regulations, as well as effective risk management throughout the digital transformation journey. This may encompass reinforcing data privacy protection, fortifying information security measures, and ensuring the sustainability and compliance of the digital transformation process. By striking a balance between proactive and cautious strategies, firms can effectively navigate the impacts of digital technologies, thereby sustaining their competitive edge and upholding their legitimacy.
大数据变革提升企业探索式创新水平
以大数据综合试验区建设为准自然实验,利用双重差分法发现,试验区建设对企业探索式创新(ExploInv)的回归系数为0.205,在1%水平上显著为正,表明大数据变革冲击显著提升了企业的探索式创新水平,即企业在创新方向上更倾向于'存异'。
大数据变革增强企业创新同群效应
大数据试验区建设与同群企业创新投入交互项(Policy×AveInv)对企业创新投入的回归系数为0.008,在5%水平上显著为正,说明大数据变革冲击显著增强了企业创新投入的行业同群效应,即企业在创新投入规模上更倾向于'求同'。
高探索式创新企业同群效应更显著
分组检验发现,高探索式创新组的交互项系数为0.009并通过10%显著性检验,而低探索式创新组系数为0.002且不显著,验证了补偿协奏逻辑:探索式创新程度高、需要合法性补偿的企业,其创新同群模仿现象更为显著。
核心解释变量
大数据试验区建设(Policy);同群企业创新投入(AveInv)
被解释变量
探索式创新(ExploInv);创新投入(Inv)
样本与数据
2011-2020年中国A股上市公司19480个公司-年度观测值,剔除金融行业、ST或退市及数据严重缺失的公司样本。公司财务数据来自CSMAR数据库,企业专利数据来自CnOpenData数据库。
识别方法 / 模型设定
以中国建立大数据综合试验区为准自然实验,采用双重差分法(DID)识别大数据变革冲击对企业创新决策的因果效应。
内生性及稳健性检验
更换变量测量方式(如以研发支出/总资产衡量创新投入、以IPC专利分类号衡量探索式创新)、更换标准误计算方式、倾向得分匹配(PSM)、安慰剂检验及平行趋势检验。