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The relationship between big data capability, supply chain collaborative innovation and operational performance in Chinese manufacturing sector
Journal of Industrial Engineering and Engineering Management ; 37(3):51-59, 2023.
Article in English | Scopus | ID: covidwho-20239659
ABSTRACT
Big data has now become a hot topic and focus of common concern in academia and practitioners. Data collection, storage, analysis, and processing require enterprises to improve their big data capability (BDC), which are increasingly valued by companies to create greater value. Supply chain collaborative innovation (SCCI) can enable companies to improve products and services. Many scholars have realized the positive effect of BDC on the improvement of enterprise performance, however, most of the extant literature focuses on direct effects of BDC on enterprise performance, and there is a lack of analysis of its impact transmission process. From the perspective of SCCI, and based on the theory of resource-based view (RBV) and theory of dual innovation (DI), this research analyzes the intermediary effect of SCCI as a complementary asset between BDC and enterprise operational performance (EOP), and reveals that BDC affect EOP by way of impact transmission mechanism. All the relationships between the three constructs (BDC, SCCI and EOP) are investigated by the empirical study. Subject to systematical review of existing literature from both China and overseas countries, and based on the theory of RBV, this paper categorizes BDC into three sub-capabilities big data resource integration (BDRI), big data in-depth analysis (BDIDA) and big data application (BDA);based on the theory of dual innovation, SCCI is divided into two variables breakthrough innovation (BI) and gradual innovation (GI). A theoretical framework is then developed and three research hypotheses are proposed accordingly H1. Big data capabilities have a positive effect on supply chain collaborative innovation;H2. Supply chain collaborative innovation has a positive effect on enterprise operational performance;H3. Supply chain collaborative innovation plays a mediating role in the relationship between big data capabilities and enterprise operational performance. Empirical research methods using a large-scale questionnaire collection and analysis of primary data are employed to test whether the research hypothesis are accepted or rejected, and conclusions are drawn subsequently. The research employs questionnaire survey to collect data from Chinese manufacturers in the automotive and electronics sectors as these two sectors are ideal to investigate the impact transmission mechanism between big data capability and enterprise operational performance, as they have relatively good understanding and high level of informatization with good BDC. Sample enterprises are located in both coastal provinces and inland areas. The questionnaire was designed subject to both intensive and extensive literature review. The data collection started in early May 2020 and ended in November 2020. Due to Covid-19, most of the questionnaires were collected by emails or Questionnaire Star. A total of 330 responses were received and 15 responses were discarded due to data missing or reliability judgement, leaving 315 valid responses for further data analysis. Exploratory factor analysis was performed employing SPSS 25. 0. The KMO and Bartlett sphere test results were good, then the principal component analysis was used to extract the factors whose characteristic roots were greater than 1. The results showed that all the items of BDC were aggregated into 3 factors, and the cumulative variance explanation rate reached 76. 548%;all the items in SCCI were aggregated into 2 factors, and the cumulative variance explanation rate reached 83. 757 %;all items of the EOP were aggregated into one factor, and the variance explanation rate was 77. 530%, which is consistent with the dimensions of the scale designed in this study, indicating that the quality of the questionnaire data was good. This was followed by reliability test and validity test and all the test results were satisfying. Structural equation modelling (SEM) was used for the data analysis employing Amos 24. The test results show χ2 / df =1. 823<3, RMSEA =0. 062<0. 08, CFI = 0. 953>0. 9, NFI = 0. 903>0. 9, TLI = 0. 947>0. 9. All ndicators are within the acceptable range, indicating that the model fits well with the sample data. The results of path analysis show that BDRI has a significant impact on both BI and GI;the BDIDA does not reach the significance level for the path of BI and GI;the BDA has a positive effect on BI and GI respectively;the BI and GI have a positive effect on EOP respectively. Following this, the mediating role of SCCI was tested by Bootstrap. The test results show that BI does not play a mediating role between BDC and EOP;however, GI does play a mediating role between BDC and EOP. The conclusions are drawn as follows by the empirical study. Firstly, BDC can positively promote SCCI;Secondly, SCCI can positively affect EOP;Thirdly, SCCI plays a mediating role between BDC and EOP. Theoretically, this research reveals that intermediary effect of SCCI as a complementary asset between BDC and EOP, which enriches literature by adding mechanism of other influencing factors on the path of BDC positively affecting EOP. Practically, this study has clarified a specific transmission path for BDC to improve EOP, i. e., enterprises should vigorously cultivate big data to improve their EOP, meanwhile focus more on the intermediary effect of SCCI © 2023, Journal of Industrial Engineering and Engineering Management.All Rights Reserved.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Observational study / Qualitative research / Reviews Language: English Journal: Journal of Industrial Engineering and Engineering Management Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Observational study / Qualitative research / Reviews Language: English Journal: Journal of Industrial Engineering and Engineering Management Year: 2023 Document Type: Article