scholarly journals Big Data, Big Data Analytics Capability, and Sustainable Innovation Performance

2019 ◽  
Vol 11 (24) ◽  
pp. 7145 ◽  
Author(s):  
Shengbin Hao ◽  
Haili Zhang ◽  
Michael Song
Keyword(s):  
Big Data ◽  
Empirical Research ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The United States ◽  
Innovation Performance ◽  
Sales Growth ◽  
Gross Margin ◽  
Innovation Projects ◽  
Analytics Capability

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.

scholarly journals Creating Sustainable Innovativeness through Big Data and Big Data Analytics Capability: From the Perspective of the Information Processing Theory

2020 ◽  
Vol 12 (5) ◽  
pp. 1984
Author(s):  
Michael Song ◽  
Haili Zhang ◽  
Jinjin Heng
Keyword(s):  
Big Data ◽  
Information Processing ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The United States ◽  
Information Processing Theory ◽  
Processing Theory ◽  
Study Results ◽  
Analytics Capability ◽  
Cross National

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.

scholarly journals The Influence of Big Data Analytics on E-Commerce: Case Study of the U.S. and China

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Weiqing Zhuang
Keyword(s):  
Big Data ◽  
Global Economy ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The United States ◽  
Mediating Effect ◽  
Theoretical Research ◽  
Business Strategies ◽  
Future Research ◽  
The U.S

Big data analytics (BDA) is a wide and deep application in e-commerce, which impacts positively on the global economy, especially the U.S. and China who have done well. This paper seeks to examine the relative influence of theoretical research and practical activities of BDA in e-commerce to explain the differences between the U.S. and China according to the two main literature databases, Web of Science and CNKI, respectively, and by employing other samples that present retail e-commerce sales and the number of some data companies founded in the U.S. and China each year. We further determine the reasons leading to the difference between the U.S. and China in BDA in e-commerce, which can help managers devise appropriate business strategies in e-commerce for each of them, and provide a proof of the significant relationship of theoretical research and practical activities in BDA in e-commerce. In addition, the variables related to big data companies show a moderation effect rather than mediating effect relative to the practice of theoretical research in e-commerce in the United States, but they show a moderate effect and mediating effects in China. The results of this study help clarify doubts regarding the development of China’s e-commerce. Moreover, three orientations in e-commerce using BDA and the use of quantum computing in e-commerce to solve existing e-commerce problems are explored to provide better evidence for decision-making that could be valuable in future research.

scholarly journals Strategies to Implement Big Data Analytics in Telecommunication Organizations

2021 ◽  
Vol 10 (04) ◽  
pp. 064-076
Author(s):  
Dr. Delton Aneato ◽  
Dr. Cesar Castellanos
Keyword(s):  
Big Data ◽  
Competitive Advantage ◽  
Organizational Performance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The United States ◽  
Communication Strategy ◽  
Analysis Process ◽  
Successful Communication ◽  
Multiple Case

Information technology (IT) leaders who do not invest in big data projects may struggle to gain a competitive advantage and business insights to improve performance. Grounded in Kotter’s change and Six Sigma models, the purpose of this qualitative multiple case study was to explore strategies IT leaders use to implement big data analytics successfully. The participants comprised 4 IT leaders from 2 telecommunication organizations in the United States of America, who expertly used big data analytics strategies to promote and maximize competitive advantage. Data were collected from semistructured interviews, company documents, and project-related documents. The collected information was examined by utilizing a thematic analysis approach. Four themes emerged from the data analysis process communication, training, employee involvement in decisions, and teamwork strategy. A key recommendation from these findings is for IT leaders to use successful communication strategies to convey the vision and objectives to all organizational levels. The successful communication-strategy can help evaluate business trends, forecasts, improve overall organizational performance and competitive advantage. The implications for positive social change include the potential for job creation, thus catalysing economic growth within communities.

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