IDEAS home Printed from https://ideas.repec.org/a/spr/joamsc/v49y2021i4d10.1007_s11747-020-00739-x.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11747-020-00739-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11747-020-00739-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Prasanna Tambe, 2014. "Big Data Investment, Skills, and Firm Value," Management Science, INFORMS, vol. 60(6), pages 1452-1469, June.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. James G. March, 1991. "Exploration and Exploitation in Organizational Learning," Organization Science, INFORMS, vol. 2(1), pages 71-87, February.
    14. 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.
    15. 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.
    16. 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.
    17. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    18. 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.
    19. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marina Proença & Tomas Sparano Martins, 2024. "The role of absorptive capacity in the use of digital marketing analytics for effective marketing decisions," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(3), pages 687-700, September.
    2. Li, Lixu & Ye, Fei & Zhan, Yuanzhu & Kumar, Ajay & Schiavone, Francesco & Li, Yina, 2022. "Unraveling the performance puzzle of digitalization: Evidence from manufacturing firms," Journal of Business Research, Elsevier, vol. 149(C), pages 54-64.
    3. Herhausen, Dennis & Bernritter, Stefan F. & Ngai, Eric W.T. & Kumar, Ajay & Delen, Dursun, 2024. "Machine learning in marketing: Recent progress and future research directions," Journal of Business Research, Elsevier, vol. 170(C).
    4. Li, Lixu & Zhu, Wenwen & Wei, Long & Yang, Shuili, 2022. "How can digital collaboration capability boost service innovation? Evidence from the information technology industry," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    5. Junlan Tan & Yasir Ahmed Solangi, 2024. "Assessing impact investing for green infrastructure development in low-carbon transition and sustainable development in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(10), pages 25257-25280, October.
    6. Jan-Benedict E. M. Steenkamp & Alberto Maydeu-Olivares, 2023. "Unrestricted factor analysis: A powerful alternative to confirmatory factor analysis," Journal of the Academy of Marketing Science, Springer, vol. 51(1), pages 86-113, January.
    7. Bag, Surajit & Rahman, Muhammad Sabbir & Srivastava, Gautam & Chan, Hau-Ling & Bryde, David J., 2022. "The role of big data and predictive analytics in developing a resilient supply chain network in the South African mining industry against extreme weather events," International Journal of Production Economics, Elsevier, vol. 251(C).
    8. G. Tomas M. Hult & Forrest V. Morgeson III & Udit Sharma & Claes Fornell, 2022. "Customer satisfaction and international business: A multidisciplinary review and avenues for research," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 53(8), pages 1695-1733, October.
    9. Marlini Moodley & Dipolelo Fungile & Farai Nyika & Winiswa Mavutha, 2023. "Understanding Marketing Communications Strategies During and Post Covid 19: A South African Perspective," International Review of Management and Marketing, Econjournals, vol. 13(2), pages 36-46, March.
    10. Sáez-Ortuño, Laura & Forgas-Coll, Santiago & Huertas-Garcia, Ruben & Sánchez-García, Javier, 2023. "What's on the horizon? A bibliometric analysis of personal data collection methods on social networks," Journal of Business Research, Elsevier, vol. 158(C).
    11. Philp, Matthew & Jacobson, Jenna & Pancer, Ethan, 2022. "Predicting social media engagement with computer vision: An examination of food marketing on Instagram," Journal of Business Research, Elsevier, vol. 149(C), pages 736-747.
    12. Alina Sorescu & Martin Schreier, 2021. "Innovation in the digital economy: a broader view of its scope, antecedents, and consequences," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 627-631, July.
    13. Dalenogare, Lucas Santos & Le Dain, Marie-Anne & Benitez, Guilherme B. & Ayala, Néstor F. & Frank, Alejandro G., 2022. "Multichannel digital service delivery and service ecosystems: The role of data integration within Smart Product-Service Systems," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    14. Sleep, Stefan & Gala, Prachi & Harrison, Dana E., 2023. "Removing silos to enable data-driven decisions: The importance of marketing and IT knowledge, cooperation, and information quality," Journal of Business Research, Elsevier, vol. 156(C).
    15. Horia Mihalcescu & Ana-Maria Dumitrache (Bajan) & Gheorghe Orzan, 2022. "Impact of Digitization and Big Data on Romanian Companies - a Qualitative Research," Journal of Emerging Trends in Marketing and Management, The Bucharest University of Economic Studies, vol. 1(1), pages 126-132, November.
    16. Aimé, Isabelle & Berger-Remy, Fabienne & Laporte, Marie-Eve, 2022. "The brand, the persona and the algorithm: How datafication is reconfiguring marketing work☆," Journal of Business Research, Elsevier, vol. 145(C), pages 814-827.
    17. Sadek, Biland, 2024. "Components and Strategic Routes of Corporate Transformations," MPRA Paper 120332, University Library of Munich, Germany, revised 26 Feb 2024.
    18. Sultana, Saida & Akter, Shahriar & Kyriazis, Elias, 2022. "How data-driven innovation capability is shaping the future of market agility and competitive performance?," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    19. Cozzio, Claudia & Viglia, Giampaolo & Lemarie, Linda & Cerutti, Stefania, 2023. "Toward an integration of blockchain technology in the food supply chain," Journal of Business Research, Elsevier, vol. 162(C).
    20. Mariani, Marcello M. & Machado, Isa & Nambisan, Satish, 2023. "Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda," Journal of Business Research, Elsevier, vol. 155(PB).
    21. Abhishek Borah & Francesca Bonetti & Angelito Calma & José Martí-Parreño, 2023. "The Journal of the Academy of Marketing Science at 50: A historical analysis," Journal of the Academy of Marketing Science, Springer, vol. 51(1), pages 222-243, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dawn Iacobucci & Maria Petrescu & Anjala Krishen & Michael Bendixen, 2019. "The state of marketing analytics in research and practice," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(3), pages 152-181, September.
    2. Surajit Bag & Shivam Gupta & Ajay Kumar & Uthayasankar Sivarajah, 2021. "An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance," Post-Print hal-03188195, HAL.
    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. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    5. Hossain, Md Afnan & Akter, Shahriar & Yanamandram, Venkata, 2021. "Why doesn't our value creation payoff: Unpacking customer analytics-driven value creation capability to sustain competitive advantage," Journal of Business Research, Elsevier, vol. 131(C), pages 287-296.
    6. Caesarius, Leon Michael & Hohenthal, Jukka, 2018. "Searching for big data," Scandinavian Journal of Management, Elsevier, vol. 34(2), pages 129-140.
    7. Lin, Hsing-Er & Hsu, I-Chieh & Hsu, Audrey Wenhsin & Chung, Hsi-Mei, 2020. "Creating competitive advantages: Interactions between ambidextrous diversification strategy and contextual factors from a dynamic capability perspective," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    8. Miikka Blomster & Timo Koivumäki, 2022. "Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing," Information Systems and e-Business Management, Springer, vol. 20(1), pages 123-169, March.
    9. Elina H. Hwang & Param Vir Singh & Linda Argote, 2015. "Knowledge Sharing in Online Communities: Learning to Cross Geographic and Hierarchical Boundaries," Organization Science, INFORMS, vol. 26(6), pages 1593-1611, December.
    10. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    11. Mariani, Marcello M. & Borghi, Matteo & Laker, Benjamin, 2023. "Do submission devices influence online review ratings differently across different types of platforms? A big data analysis," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    12. Elisabetta Raguseo & Claudio Vitari, 2017. "Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects," Grenoble Ecole de Management (Post-Print) halshs-01923259, HAL.
    13. Youngtak M. Kim & John R. Busenbark & Seung-Hwan Jeong & Son K. Lam, 2022. "The performance impact of marketing dualities: a response surface approach to resolving empirical challenges," Journal of the Academy of Marketing Science, Springer, vol. 50(5), pages 915-940, September.
    14. Kim, Jaemin & Dibrell, Clay & Kraft, Ellen & Marshall, David, 2021. "Data analytics and performance: The moderating role of intuition-based HR management in major league baseball," Journal of Business Research, Elsevier, vol. 122(C), pages 204-216.
    15. Conboy, Kieran & Mikalef, Patrick & Dennehy, Denis & Krogstie, John, 2020. "Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda," European Journal of Operational Research, Elsevier, vol. 281(3), pages 656-672.
    16. Feng, Cong & Fay, Scott, 2022. "An empirical investigation of forward-looking retailer performance using parking lot traffic data derived from satellite imagery," Journal of Retailing, Elsevier, vol. 98(4), pages 633-646.
    17. 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.
    18. Hossain, Md Afnan & Akter, Shahriar & Yanamandram, Venkata, 2020. "Revisiting customer analytics capability for data-driven retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
    19. Candice WALLS & Brian BARNARD, 2020. "Success Factors of Big Data to Achieve Organisational Performance: Theoretical Perspectives," Expert Journal of Business and Management, Sprint Investify, vol. 8(1), pages 1-16.
    20. Elisabetta Raguseo & Claudio Vitari, 2017. "Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects," Post-Print halshs-01923259, HAL.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joamsc:v:49:y:2021:i:4:d:10.1007_s11747-020-00739-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.