Orchestrating big data analytics capability for sustainability: A study of air pollution management in China

2019 ◽  
pp. 103231 ◽  
Author(s):  
Dan Zhang ◽  
Shan L. Pan ◽  
Jiaxin Yu ◽  
Wenyuan Liu
2020 ◽  
Vol 12 (5) ◽  
pp. 1984
Author(s):  
Michael Song ◽  
Haili Zhang ◽  
Jinjin Heng

Service innovativeness is a key sustainable competitive advantage that increases sustainability of enterprise development. Literature suggests that big data and big data analytics capability (BDAC) enhance sustainable performance. Yet, no studies have examined how big data and BDAC affect service innovativeness. To fill this research gap, based on the information processing theory (IPT), we examine how fits and misfits between big data and BDAC affect service innovativeness. To increase cross-national generalizability of the study results, we collected data from 1403 new service development (NSD) projects in the United States, China and Singapore. Dummy regression method was used to test the model. The results indicate that for all three countries, high big data and high BDAC has the greatest effect on sustainable innovativeness. In China, fits are always better than misfits for creating sustainable innovativeness. In the U.S., high big data is always better for increasing sustainable innovativeness than low big data is. In contrast, in Singapore, high BDAC is always better for enhancing sustainable innovativeness than low BDAC is. This study extends the IPT and enriches cross-national research of big data and BDAC. We conclude the article with suggestions of research limitations and future research directions.


2019 ◽  
Vol 11 (24) ◽  
pp. 7145 ◽  
Author(s):  
Shengbin Hao ◽  
Haili Zhang ◽  
Michael Song

Literature suggests that big data is a new competitive advantage and that it enhance organizational performance. Yet, previous empirical research has provided conflicting results. Building on the resource-based view and the organizational inertia theory, we develop a model to investigate how big data and big data analytics capability affect innovation success. We show that there is a trade-off between big data and big data analytics capability and that optimal balance of big data depends upon levels of big data analytics capability. We conduct a four-year empirical research project to secure empirical data on 1109 data-driven innovation projects from the United States and China. This research is the first time reporting the empirical results. The study findings reveal several surprising results that challenge traditional views of the importance of big data in innovation. For U.S. innovation projects, big data has an inverted U-shaped relationship with sales growth. Big data analytics capability exerts a positive moderating effect, that is, the stronger this capability is, the greater the impact of big data on sales growth and gross margin. For Chinese innovation projects, when big data resource is low, promoting big data analytics capability increases sales growth and gross margin up to a certain point; developing big data analytics capability beyond that point may actually inhibit innovation performance. Our findings provide guidance to firms on making strategic decisions regarding resource allocations for big data and big data analytics capability.


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