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

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  • Michael Song

    (School of Economics and Management, Xi’an Technological University, Xi’an 720021, China)

  • Haili Zhang

    (School of Economics and Management, Xi’an Technological University, Xi’an 720021, China)

  • Jinjin Heng

    (School of Economics and Management, Xi’an Technological University, Xi’an 720021, China)

Abstract

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.

Suggested Citation

  • Michael Song & Haili Zhang & Jinjin Heng, 2020. "Creating Sustainable Innovativeness through Big Data and Big Data Analytics Capability: From the Perspective of the Information Processing Theory," Sustainability, MDPI, vol. 12(5), pages 1-23, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1984-:d:328749
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    Cited by:

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    2. Huynh, Minh-Tay & Nippa, Michael & Aichner, Thomas, 2023. "Big data analytics capabilities: Patchwork or progress? A systematic review of the status quo and implications for future research," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    3. Zhengang Zhang & Yu Shang & Linyuan Cheng & Antao Hu, 2022. "Big Data Capability and Sustainable Competitive Advantage: The Mediating Role of Ambidextrous Innovation Strategy," Sustainability, MDPI, vol. 14(14), pages 1-17, July.
    4. Oesterreich, Thuy Duong & Anton, Eduard & Teuteberg, Frank & Dwivedi, Yogesh K, 2022. "The role of the social and technical factors in creating business value from big data analytics: A meta-analysis," Journal of Business Research, Elsevier, vol. 153(C), pages 128-149.
    5. Md Ahsan Uddin Murad & Dilek Cetindamar & Subrata Chakraborty, 2022. "Identifying the Key Big Data Analytics Capabilities in Bangladesh’s Healthcare Sector," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    6. Yonghong Ma & Xiaomeng Yang & Sen Qu & Lingkai Kong, 2022. "Research on the formation mechanism of big data technology cooperation networks: empirical evidence from China," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1273-1294, March.

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