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What “V” of the big data support firms’ radical and incremental innovation?

Author

Listed:
  • Ferrigno, Giulio
  • Barabuffi, Saverio
  • Marcazzan, Enrico
  • Piccaluga, Andrea

Abstract

Despite the considerable attention from both academics and practitioners to the effects of big data on firms’ innovation performance, a noticeable research gap remains in understanding how big data influences different types of innovation—namely, radical and incremental innovation. Many studies recognize that big data can be a valuable source of innovation, as it enables firms to gather and incorporate insights from customers, partners, suppliers, and other stakeholders. However, prior research has rarely investigated this relationship through a granular lens, failing to distinguish the specific effects of big data on radical and incremental innovation.

Suggested Citation

  • Ferrigno, Giulio & Barabuffi, Saverio & Marcazzan, Enrico & Piccaluga, Andrea, 2025. "What “V” of the big data support firms’ radical and incremental innovation?," Technovation, Elsevier, vol. 146(C).
    • Handle: RePEc:eee:techno:v:146:y:2025:i:c:s0166497225001270
    DOI: 10.1016/j.technovation.2025.103295
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    References listed on IDEAS

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    1. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    2. James G. March, 1991. "Exploration and Exploitation in Organizational Learning," Organization Science, INFORMS, vol. 2(1), pages 71-87, February.
    3. Patrick Mikalef & Ilias O. Pappas & John Krogstie & Michail Giannakos, 2018. "Big data analytics capabilities: a systematic literature review and research agenda," Information Systems and e-Business Management, Springer, vol. 16(3), pages 547-578, August.
    4. Rakshit, Sandip & Mondal, Sandeep & Islam, Nazrul & Jasimuddin, Sajjad & Zhang, Zuopeng, 2021. "Social media and the new product development during COVID-19: An integrated model for SMEs," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    5. Rosita Capurro & Raffaele Fiorentino & Stefano Garzella & Alessandro Giudici, 2021. "Big data analytics in innovation processes: which forms of dynamic capabilities should be developed and how to embrace digitization?," European Journal of Innovation Management, Emerald Group Publishing Limited, vol. 25(6), pages 273-294, July.
    6. Ghasemaghaei, Maryam, 2021. "Understanding the impact of big data on firm performance: The necessity of conceptually differentiating among big data characteristics," International Journal of Information Management, Elsevier, vol. 57(C).
    7. Arianna Martinelli & Andrea Mina & Massimo Moggi, 2021. "The enabling technologies of industry 4.0: examining the seeds of the fourth industrial revolution [Mapping innovation dynamics in the Internet of Things domain: evidence from patent analysis]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 30(1), pages 161-188.
    8. Fosso Wamba, Samuel & Bawack, Ransome Epie & Guthrie, Cameron & Queiroz, Maciel M. & Carillo, Kevin Daniel André, 2021. "Are we preparing for a good AI society? A bibliometric review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    9. Erevelles, Sunil & Fukawa, Nobuyuki & Swayne, Linda, 2016. "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, Elsevier, vol. 69(2), pages 897-904.
    10. Xueli Huang & Geoffrey N. Soutar & Alan Brown, 2002. "New Product Development Processes in Small and Medium‐Sized Enterprises: Some Australian Evidence," Journal of Small Business Management, Taylor & Francis Journals, vol. 40(1), pages 27-42, January.
    11. Zhang, Haisu & Wu, Fang & Cui, Anna Shaojie, 2015. "Balancing market exploration and market exploitation in product innovation: A contingency perspective," International Journal of Research in Marketing, Elsevier, vol. 32(3), pages 297-308.
    12. Mariani, Marcello M. & Machado, Isa & Magrelli, Vittoria & Dwivedi, Yogesh K., 2023. "Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions," Technovation, Elsevier, vol. 122(C).
    13. Prasanna Tambe, 2014. "Big Data Investment, Skills, and Firm Value," Management Science, INFORMS, vol. 60(6), pages 1452-1469, June.
    14. Ully Y. Nafizah & Stephen Roper & Kevin Mole, 2024. "Estimating the innovation benefits of first-mover and second-mover strategies when micro-businesses adopt artificial intelligence and machine learning," Small Business Economics, Springer, vol. 62(1), pages 411-434, January.
    15. Cooper, Robert G., 1990. "Stage-gate systems: A new tool for managing new products," Business Horizons, Elsevier, vol. 33(3), pages 44-54.
    16. Radicic, Dragana & Petković, Saša, 2023. "Impact of digitalization on technological innovations in small and medium-sized enterprises (SMEs)," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    17. Constantine Andriopoulos & Marianne W. Lewis, 2009. "Exploitation-Exploration Tensions and Organizational Ambidexterity: Managing Paradoxes of Innovation," Organization Science, INFORMS, vol. 20(4), pages 696-717, August.
    18. Sivarajah, Uthayasankar & Kumar, Sachin & Kumar, Vinod & Chatterjee, Sheshadri & Li, Jing, 2024. "A study on big data analytics and innovation: From technological and business cycle perspectives," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    19. Ghasemaghaei, Maryam & Calic, Goran, 2020. "Assessing the impact of big data on firm innovation performance: Big data is not always better data," Journal of Business Research, Elsevier, vol. 108(C), pages 147-162.
    20. Ghasemaghaei, Maryam & Calic, Goran, 2019. "Does big data enhance firm innovation competency? The mediating role of data-driven insights," Journal of Business Research, Elsevier, vol. 104(C), pages 69-84.
    21. Chang, Victor, 2021. "An ethical framework for big data and smart cities," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    22. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    23. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    24. repec:ucp:bkecon:9780226316529 is not listed on IDEAS
    25. Ritala, Paavo & Keränen, Joona & Fishburn, Jessica & Ruokonen, Mika, 2024. "Selling and monetizing data in B2B markets: Four data-driven value propositions," Technovation, Elsevier, vol. 130(C).
    26. Ernesto Cassetta & Umberto Monarca & Ivano Dileo & Claudio Di Berardino & Marco Pini, 2020. "The relationship between digital technologies and internationalisation. Evidence from Italian SMEs," Industry and Innovation, Taylor & Francis Journals, vol. 27(4), pages 311-339, April.
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