How to Combine Resources and Big Data Analysis Capabilities to Achieve Ambidextrous Innovation?A fsQCA Research based on Resource Orchestration Theory
With the wave of the digital age,Chinese manufacturing industry is rapidly developing while also facing mul-tiple external challenges.Manufacturing firms need to strengthen their ambidextrous innovation to address challenges,in order to establish sustainable competitive advantage and achieve high-quality development.Therefore,manufacturing firms should effectively allocate resources and big data analysis capabilities to explore customers'implicit needs and diverse needs,so as to create customer value and achieve high ambidextrous innovation is an important management issue to be explored.This paper aims to answer the following key question:Are resource and capability elements necessary conditions for triggering high ambidextrous innovation?What resources and big data capabilities can effectively explore customers'implicit needs and/or utilize diverse demands to form high ambidextrous innovation,and which resource configurations cannot form high ambidextrous innovation?Based on resource orchestration theory,this study sorts out the complex impact mechanism of resource reconstruction,big data analysis capabilities,and customer needs on ambidextrous innovation,and establishes a research framework from the configuration perspective.According to the development status of the manufacturing industry and the differences in re-gional development levels,this study selected five representative provinces(i.e.,Inner Mongolia,Shaanxi,Shandong,Jiang-su and Guangdong)for questionnaire survey,and finally obtained 364 manufacturing firm samples for analysis.The ques-tionnaire variables were measured using the Likert 7-point measurement method,and all the results showed well reliability and validity.Then,the percentile calibration,necessity condition analysis,configuration analysis and robustness test were conducted by using fuzzy set qualitative comparative analysis(fsQCA)method.The findings show that:(1)a single resource or capability element is not necessary for achieving high ambidextrous innovation,but technical skills,data-driven decision culture and customer need diversity do play a general role;(2)three paths produce high ambidextrous innovation,including resource reconfiguration supports the utilization diversity need of big data analysis capability;the exploration tacitness need and utilization diversity need of big data analysis capability;resource reconfiguration supports the exploration tacitness need and utilization diversity need of big data technology capability;(3)four paths generate non-high ambidextrous innovation,and the absent of customer need diversity is the key reason of non-high ambidextrous innovation.Based on the resource arrangement theory and configuration perspective,the research conclusions reveal the complex causal mechanism of multiple factors influencing ambidextrous innovation,and explores the synergistic effect of different re-sources and big data analysis capabilities on ambidextrous innovation in more granularity.This deepens the understanding of the multiple driving paths of ambidextrous innovation in Chinese manufacturing industry,and provides a new methodologi-cal perspective for analyzing the realization of ambidextrous innovation and firm development.Moreover,this study brings practical enlightenment for enterprises to enhance their competitive advantages and achieve high-quality development.On the one hand,in the face of the digital era,firms need to strengthen their technical skills related to big data analysis,and make scientific decisions based on data analysis results to meet diverse customer needs,so as to achieve ambidextrous inno-vation of exploring new products/services or optimizing existing products/services.On the other hand,firms should make full use of traditional resources and capabilities,while adopting big data analysis capabilities to reasonably arrange new and old resources and capabilities to meet customer needs,and thus achieve well ambidextrous innovation.
NETL
NSTL
万方数据
