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BIG data - BIG gains? Empirical evidence on the link between big data analytics and innovation

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  • Niebel, Thomas
  • Rasel, Fabienne
  • Viete, Steffen

Abstract

This paper analyzes the relationship between firms' use of big data analytics and their innovative performance in terms of product innovations. Since big data technologies provide new data information practices, they create novel decision-making possibilities, which are widely believed to support firms' innovation process. Applying German firm-level data within a knowledge production function framework we find suggestive evidence that big data analytics is a relevant determinant for the likelihood of a firm becoming a product innovator as well as for the market success of product innovations. These results hold for the manufacturing as well as for the service sector but are contingent on firms' investment in IT-specific skills. Subsequent analyses suggest that firms in the manufacturing and service sector rely on different data sources and data-related firm practices in order to reap the benefits of big data. Overall, the results support the view that big data analytics have the potential to enable innovation.

Suggested Citation

  • Niebel, Thomas & Rasel, Fabienne & Viete, Steffen, 2017. "BIG data - BIG gains? Empirical evidence on the link between big data analytics and innovation," ZEW Discussion Papers 17-053, ZEW - Leibniz Centre for European Economic Research.
  • Handle: RePEc:zbw:zewdip:17053
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    References listed on IDEAS

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    1. Stefanie Haller & Iulia Siedschlag, 2011. "Determinants of ICT adoption: evidence from firm-level data," Applied Economics, Taylor & Francis Journals, vol. 43(26), pages 3775-3788.
    2. Goodridge, PR & Haskel, J, 2015. "How does big data affect GDP? Theory and evidence for the UK," Working Papers 25156, Imperial College, London, Imperial College Business School.
    3. Petra Andries & Dirk Czarnitzki, 2014. "Small firm innovation performance and employee involvement," Small Business Economics, Springer, vol. 43(1), pages 21-38, June.
    4. Bertschek, Irene & Kesler, Reinhold, 2018. "Let the user speak: Is feedback on Facebook a source of firms' innovation?," ZEW Discussion Papers 17-015, ZEW - Leibniz Centre for European Economic Research.
    5. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, March.
    6. Bertschek Irene & Ohnemus Jörg & Viete Steffen, 2018. "The ZEW ICT Survey 2002 to 2015: Measuring the Digital Transformation in German Firms," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 238(1), pages 87-99, February.
    7. Zhong, Ray Y. & Huang, George Q. & Lan, Shulin & Dai, Q.Y. & Chen, Xu & Zhang, T., 2015. "A big data approach for logistics trajectory discovery from RFID-enabled production data," International Journal of Production Economics, Elsevier, vol. 165(C), pages 260-272.
    8. Jacques Mairesse & Pierre Mohnen, 2002. "Accounting for Innovation and Measuring Innovativeness: An Illustrative Framework and an Application," American Economic Review, American Economic Association, vol. 92(2), pages 226-230, May.
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    11. Anker Lund Vinding, 2006. "Absorptive capacity and innovative performance: A human capital approach," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 15(4-5), pages 507-517.
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    More about this item

    Keywords

    big data; data-driven decision-making; product innovation; firm-level data;

    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • L20 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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