大数据分析能力与制造企业服务创新绩效:一个链式中介模型  

Big Data Analytics Capability and Service Innovation Performance of Manufacturing Enterprises:A Chain Mediating Model

作　　者：刘念 简兆权[2] 王鹏程 Liu Nian;Jian Zhaoquan;Wang Pengcheng(School of Management,Wuhan Polytechnic University,Wuhan 430048,China;School of Business Administration,South China University of Technology,Guangzhou 510640,China)

机构地区：[1]武汉轻工大学管理学院,湖北武汉430048 [2]华南理工大学工商管理学院,广东广州510640

出　　处：《科技管理研究》2021年第24期125-135,共11页Science and Technology Management Research

基　　金：国家自然科学基金面上项目“外部组织整合、知识获取对新服务开发绩效的影响:关系质量和吸收能力的调节作用”(71672061);广东省自然科学基金面上项目“外部知识搜寻、知识流动对服务创新的影响:大数据分析能力的调节效果”(2019A1515011526);中央高校基本科研业务费专项资金项目“基于平台与价值共创的制造业服务化研究”(C2180120)。

摘　　要：围绕动态能力理论,挖掘制造企业服务创新绩效提升的前因变量,探究大数据分析能力对制造企业服务创新绩效的影响及其作用机制,并利用285家制造企业数据检验研究假设。基于层级回归分析和Bootstrap方法的实证结果表明,大数据分析能力不仅正向影响制造企业服务创新绩效,也正向影响资源拼凑与组织敏捷性;资源拼凑与组织敏捷性均有助于制造企业服务创新绩效的提升;资源拼凑与组织敏捷性在大数据分析能力和制造企业服务创新绩效的关系中起部分中介作用,且为链式中介。研究结论的实践启示为:制造企业需重视对大数据分析的投资,通过提升大数据分析能力实现高资源拼凑、高组织敏捷性和服务创新高绩效;同时需重视采取资源拼凑战略行为,通过有意识地将现有资源进行组拼以率先实现对新服务需求的满足、创造出优于竞争对手的服务创新成果。Based on dynamic capability theory, this paper excavates the antecedent of improving service innovation performance of manufacturing enterprises,the effect of big data analytics capability on service innovation performance of manufacturing enterprises and its role mechanism. The paper conducts survey of 285 manufacturing enterprises’ data to test the research hypotheses. Using hierarchical regression analysis and Bootstrap method, the results support the positive relationship between big data analytics capability and manufacturing enterprises’ service innovation performance,between big data analytics capability and resource bricolage as well as organizational agility, which both contribute to the improvement of service innovation performance of manufacturing enterprises resource bricolage and organizational agility play the partial mediating role in the relationship between big data analytics capability and service innovation performance of manufacturing enterprises, and it is a chain intermediary. The practical enlightenment of the research conclusion is that manufacturing enterprises need to value the investment in big data analysis, achieve high resource pooling, high organizational agility and new service performance through improving the ability of big data analysis;at the same time, enterprises should pay attention to the adoption of resource pooling strategic behavior,in order to take the lead in meeting new service needs and create service innovation results superior to competitors through consciously grouping existing.

关 键 词：大数据分析能力 制造企业服务创新绩效 资源拼凑 组织敏捷性 

分 类 号：F270.7[经济管理—企业管理] F224[经济管理—国民经济]