管理工程学报2025，Vol.39Issue(3)：13-27,15.DOI:10.13587/j.cnki.jieem.2025.03.002
数据驱动企业-用户互动创新的情境价值研究
A study on the contextual value of data-driven enterprise-customer interaction innovation:The moderating role of product complexity and product competitive pressure
摘要
Abstract
In order to cope with the increasingly changeable,complex and ambiguous needs of customers,the innovation model of interaction between enterprises and customers has garnered significant attention from both academia and industry support,gradually evolving into an innovation model centered on acquiring and transforming diverse cognitive resources from customers.Currently,numerous enterprises have recognized the significance of interactive innovation between enterprises and customers.However,a persistent challenge in customer participation in innovation management persists:positive facilitation coexists with negative obstacle,which the obstacle is also difficult to detect and mitigate,particularly in digital era where expanding interactive innovation use cases are accompanied by heightened contextual sensitivity driven by data. Given the multifaceted nature of context,it is imperative for research on enterprise-customer interaction to have a specific focus.Consequently,the context that can accentuate diverse cognitive disparities is closely intertwined with the interactive innovation model,serving as the sole means to unveil practical issues pertaining to the dual-sided innovation value of this model.In this study,we selected product complexity as an indicator reflecting the extent of cognitive resources on product side,and product competitive pressure as an indicator reflecting the urgency for cognitive updates on that same front.By establishing a set of dual-dimensional contexts for comparison and reference,we meticulously delved into intricacies of the interactive innovation model in order to enhance product innovation,including investigating whether this model yields differential effects on both efficiency and effectiveness aspects. Based on an empirical study of 670 enterprises,our research reveals the following findings:1)The level of interaction between enterprises and customers positively influences the efficiency and effectiveness of product innovation.Overall,data analytics capability,which reflects the extent of data-driven approaches,has a positive regulatory effect on both efficiency and effectiveness.However,its impact is greater on efficiency than on effectiveness.This suggests that data analysis facilitates reducing preparation time for interactive innovation mode and lowering its usage cost.2)Interactive innovation mode exhibits significant variations in different"product complexity-product competitive pressure"contexts.In"high complexity-high pressure"contexts,the influence of interaction in promoting innovation efficiency and effectiveness,as well as the moderating effect of data analysis,diminishes.This indicates that when a product involves extensive knowledge and faces time constraints,even though interaction can bring about innovative potentialities,it is subject to higher risks of divergence in innovation outcomes.The"low complexity-high pressure"context is identified as most suitable for adopting a data-driven interactive innovation mode because there are minimal cognitive differences between parties involved,thus consensus formation becomes easier leading to feasible innovations. The research findings confirm and enhance the value of the interactive innovation model between enterprises and customers in promoting product innovation efficiency and effectiveness overall.Enterprises that adopt more interactive roles and complete interactive stages,combined with data analytics capability as a driving force,can improve the value of product innovation.However,upon closer examination,it is evident that the interactive innovation model is inherently more conducive to improving product innovation efficiency by reducing cycle time and cost required for market experiments and demand mining.The cultivation of data analytics capability further amplifies this advantage.Therefore,the primary benefit of the data-driven interactive innovation model between enterprises and customers is improved efficiency followed by enhanced effectiveness.关键词
企业与用户互动创新/数据分析能力/产品创新效率与效能/产品复杂性/产品竞争压力Key words
Enterprise and customer interaction innovation/Data analytics capability/Product innovation efficiency and effectiveness/Product complexity/Product competitive pressure分类
管理科学引用本文复制引用
夏正豪,肖静华..数据驱动企业-用户互动创新的情境价值研究[J].管理工程学报,2025,39(3):13-27,15.基金项目
国家自然科学基金重点项目(72032009、71832014) （72032009、71832014）
2024 年度国家博士后研究人员计划(GZC20242063) The Key Project of the National Natural Science Foundation of China(72032009,71832014) （GZC20242063）
2024 National Postdoctoral Researcher Funding Program(GZC20242063) （GZC20242063）
