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How and when do big data investments pay off? The role of marketing affordances and service innovation

Author

Listed:
  • Luigi M. De Luca

    (Cardiff University Business School)

  • Dennis Herhausen

    (KEDGE Business School)

  • Gabriele Troilo

    (Bocconi University and SDA Bocconi)

  • Andrea Rossi

    (Cardiff University Business School
    Centrica Plc, 4, Callaghan Square)

Abstract

Big data technologies and analytics enable new digital services and are often associated with superior performance. However, firms investing in big data often fail to attain those advantages. To answer the questions of how and when big data pay off, marketing scholars need new theoretical approaches and empirical tools that account for the digitized world. Building on affordance theory, the authors develop a novel, conceptually rigorous, and practice-oriented framework of the impact of big data investments on service innovation and performance. Affordances represent action possibilities, namely what individuals or organizations with certain goals and capabilities can do with a technology. The authors conceptualize and operationalize three important big data marketing affordances: customer behavior pattern spotting, real-time market responsiveness, and data-driven market ambidexterity. The empirical analysis establishes construct validity and offers a preliminary nomological test of direct, indirect, and conditional effects of big data marketing affordances on perceived big data performance.

Suggested Citation

  • Luigi M. De Luca & Dennis Herhausen & Gabriele Troilo & Andrea Rossi, 2021. "How and when do big data investments pay off? The role of marketing affordances and service innovation," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 790-810, July.
    • Handle: RePEc:spr:joamsc:v:49:y:2021:i:4:d:10.1007_s11747-020-00739-x
    DOI: 10.1007/s11747-020-00739-x
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    as
    1. James G. March, 1991. "Exploration and Exploitation in Organizational Learning," Organization Science, INFORMS, vol. 2(1), pages 71-87, February.
    2. Germann, Frank & Lilien, Gary L. & Fiedler, Lars & Kraus, Matthias, 2014. "Do Retailers Benefit from Deploying Customer Analytics?," Journal of Retailing, Elsevier, vol. 90(4), pages 587-593.
    3. Glenn B. Voss & Zannie Giraud Voss, 2013. "Strategic Ambidexterity in Small and Medium-Sized Enterprises: Implementing Exploration and Exploitation in Product and Market Domains," Organization Science, INFORMS, vol. 24(5), pages 1459-1477, October.
    4. Oliver J. Rutz & George F. Watson, 2019. "Endogeneity and marketing strategy research: an overview," Journal of the Academy of Marketing Science, Springer, vol. 47(3), pages 479-498, May.
    5. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    6. Pim Den Hertog, 2000. "Knowledge-Intensive Business Services As Co-Producers Of Innovation," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 4(04), pages 491-528.
    7. Ming-Hui Huang & Roland T. Rust, 2017. "Technology-driven service strategy," Journal of the Academy of Marketing Science, Springer, vol. 45(6), pages 906-924, November.
    8. John Hulland & Hans Baumgartner & Keith Marion Smith, 2018. "Marketing survey research best practices: evidence and recommendations from a review of JAMS articles," Journal of the Academy of Marketing Science, Springer, vol. 46(1), pages 92-108, January.
    9. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    10. 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.
    11. Prasanna Tambe, 2014. "Big Data Investment, Skills, and Firm Value," Management Science, INFORMS, vol. 60(6), pages 1452-1469, June.
    12. Roland T. Rust & Ming-Hui Huang, 2014. "The Service Revolution and the Transformation of Marketing Science," Marketing Science, INFORMS, vol. 33(2), pages 206-221, March.
    13. Kahn, Kenneth B. & Mentzer, John T., 1998. "Marketing's Integration with Other Departments," Journal of Business Research, Elsevier, vol. 42(1), pages 53-62, May.
    14. Constantiou, Ioanna D & Kallinikos, Jannis, 2015. "New games, new rules: big data and the changing context of strategy," LSE Research Online Documents on Economics 63017, London School of Economics and Political Science, LSE Library.
    15. Stefan Sleep & John Hulland & Richard A. Gooner, 2019. "THE DATA HIERARCHY: factors influencing the adoption and implementation of data-driven decision making," AMS Review, Springer;Academy of Marketing Science, vol. 9(3), pages 230-248, December.
    16. Raymond F. Zammuto & Terri L. Griffith & Ann Majchrzak & Deborah J. Dougherty & Samer Faraj, 2007. "Information Technology and the Changing Fabric of Organization," Organization Science, INFORMS, vol. 18(5), pages 749-762, October.
    17. Peter Ebbes & Michel Wedel & Ulf Böckenholt & Ton Steerneman, 2005. "Solving and Testing for Regressor-Error (in)Dependence When no Instrumental Variables are Available: With New Evidence for the Effect of Education on Income," Quantitative Marketing and Economics (QME), Springer, vol. 3(4), pages 365-392, December.
    18. Wamba, Samuel Fosso & Gunasekaran, Angappa & Akter, Shahriar & Ren, Steven Ji-fan & Dubey, Rameshwar & Childe, Stephen J., 2017. "Big data analytics and firm performance: Effects of dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 356-365.
    19. Christina Kuehnl & Danijel Jozic & Christian Homburg, 2019. "Effective customer journey design: consumers’ conception, measurement, and consequences," Journal of the Academy of Marketing Science, Springer, vol. 47(3), pages 551-568, May.
    Full references (including those not matched with items on IDEAS)

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