数据驱动的产品研发转型：组织惯例适应性变革视角的案例研究

现有研究多聚焦于大数据对产品研发流程与绩效的影响,对基于经验的产品研发转变为数据驱动的产品研发的转型路径、实现机制与转型范式缺乏理论探讨。本文基于组织惯例适应性变革的整合视角,通过对韩都衣舍的纵向案例研究,提出从基于经验的产品研发转变为数据驱动的产品研发的两阶段转型模型,剖析了数据驱动的产品研发转型的组织惯例适应性变革机制,提出了数据驱动的产品研发转型范式理论模型,对"数据驱动"的内涵进行了创新性阐述。本文从转型路径、实现机制与转型范式3个方面构建了数据驱动的产品研发转型理论,研究结论对数字经济时代数据驱动产品创新的企业实践提供了政策启示。

Data-Driven R&D Transformation of New Product:A Case Study from the Perspective of Adaptive Change in Organizational Practices

In the digital era, many companies start to use big data for new product development(NPD), and try to build up the data-driven dynamic capability. However, the process of realizing data-driven NPD is very difficult for most of the pre-digital companies. For example, if the transformation process is too radical, both the management teams and employee will feel hard to accept data-driven practice; and if the transformation process is too incremental, the new practice may be out of value. As a result, how companies transform from experience-based NPD to data-driven NPD,is not only the key area of digital innovation, but also the hot spot of organization theory in digital economy.From the academic perspective, data-driven NPD transformation is the process of big data analytic enabled NPD routine change, which is also the research focus for a long time. However, past research mostly focuses on the impacts of big data on the NPD process and performance, and there is few theoretical discussion on the transformation paths,realization mechanism and transformation paradigm of data-driven NPD.Based on above, this paper conducts a case study of Han Du, a Chinese famous Internet brand, from the integrated perspective of organizational routine and adaptive change. In order to discuss the theory of data-driven NPD transformation,this paper try to answer the following three questions: firstly, how companies can transform from experience-based NPD to data-driven NPD? Secondly, during the data-driven NPD transformation process, how companies adaptively change different routines? Thirdly, what is the main characteristic of data-driven NPD transformation paradigm? The research remain different degree of theoretical cap for these three questions.This study uses exploratory case study method, the reasons are as followed: firstly, this paper focuses on the datadriven NPD transformation process, which is suitable for the case study method illustrating the question of"How".Secondly, there are few company which can successfully transform from experience-based NPD to data-driven NPD, which is a new phenomenon and new question that can be studied by case study. Last but not the least, since the organizational routine change and adaptive change are both situational construct, case study can be used to discuss this kind of research question in order to explore new ideas and conclusion. And this paper collects data by semi-structure interview, anthropological ethnography, enterprise files and also the second-handed data.Based on the findings, this paper put forward the two stage transformation model from experience-based NPD to data-driven NPD.Meanwhile, this study proposes that the data-driven NPD transformation realization mechanism is the organizational routine adaptive change mechanism, and comes up with the theoretical transformation paradigm model of data-driven NPD,firstly illustrating the meaning of data-driven.This study theoretically contributes to the data-driven NPD theory by coming up with the transformation path, realization mechanism and transformation paradigm, which is a theory contribution to the big data research and NPD research.The practical suggestion also includes the following three points: firstly, the company can use organizational routine adaptive change mechanism to cope with the conflict resulting from the data-driven NPD transformation process. Secondly,when the data-driven routine has been introduced to the company, mangers can improve the routine by big data assets development, big data analytics enhancing and big data conscious training. Thirdly, both the business function team and data science function team should cooperate and work together for the same goals. These three suggestion provide new insights for the enterprise practice for data-driven product innovation in the digital economy era.