IDEAS home Printed from https://ideas.repec.org/a/spr/joamsc/v50y2022i6d10.1007_s11747-022-00906-2.html
   My bibliography  Save this article

The future of digital technologies in marketing: A conceptual framework and an overview

Author

Listed:
  • Kirk Plangger

    (King’s College London)

  • Dhruv Grewal

    (Babson College
    University of Bath)

  • Ko Ruyter

    (King’s College London)

  • Catherine Tucker

    (MIT Sloan School of Management, Massachusetts Institute of Technology)

Abstract

Digital technologies are key to achieving competitive advantage across marketing and retailing contexts. At the same time, marketing managers are confronted with a variety of challenges surrounding the strategic use of these technologies and the need to re-think their digital strategies. Importantly, managers need to develop a deeper understanding of consumer attitudes towards and engagement with (or lack thereof) digital technologies. Marketing strategists can benefit from academic marketing’s thought leadership to learn how they can transform these challenges into strategic opportunities for competitive advantage. This can lead to technological innovations that create customer, firm, and societal value. Some of these benefits are described in the articles that make up this special issue. We propose a framework that focuses on the role of strategic resources used by managers to develop and deliver on the promise of digital technologies. Then, we report insights from 16 boardroom interviews with senior marketing managers resulting in three broad themes: decentralized marketing, metamodern customer experiences, and marketing mechanization. We close with a research agenda to motivate additional thought-leading research in this fast-growing area.

Suggested Citation

  • Kirk Plangger & Dhruv Grewal & Ko Ruyter & Catherine Tucker, 2022. "The future of digital technologies in marketing: A conceptual framework and an overview," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1125-1134, November.
    • Handle: RePEc:spr:joamsc:v:50:y:2022:i:6:d:10.1007_s11747-022-00906-2
    DOI: 10.1007/s11747-022-00906-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11747-022-00906-2
    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/s11747-022-00906-2?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. Noble, Stephanie M. & Mende, Martin & Grewal, Dhruv & Parasuraman, A., 2022. "The Fifth Industrial Revolution: How Harmonious Human–Machine Collaboration is Triggering a Retail and Service [R]evolution," Journal of Retailing, Elsevier, vol. 98(2), pages 199-208.
    2. Manjunath Padigar & Ljubomir Pupovac & Ashish Sinha & Rajendra Srivastava, 2022. "The effect of marketing department power on investor responses to announcements of AI-embedded new product innovations," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1277-1298, November.
    3. Grewal, Dhruv & Roggeveen, Anne L. & Nordfält, Jens, 2017. "The Future of Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 1-6.
    4. Anja Lambrecht & Catherine Tucker, 2019. "Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads," Management Science, INFORMS, vol. 65(7), pages 2966-2981, July.
    5. Tim Hilken & Debbie I. Keeling & Ko Ruyter & Dominik Mahr & Mathew Chylinski, 2020. "Seeing eye to eye: social augmented reality and shared decision making in the marketplace," Journal of the Academy of Marketing Science, Springer, vol. 48(2), pages 143-164, March.
    6. Yuping Liu-Thompkins & Shintaro Okazaki & Hairong Li, 2022. "Artificial empathy in marketing interactions: Bridging the human-AI gap in affective and social customer experience," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1198-1218, November.
    7. Grewal, Dhruv & Gauri, Dinesh K. & Roggeveen, Anne L. & Sethuraman, Raj, 2021. "Strategizing Retailing in the New Technology Era," Journal of Retailing, Elsevier, vol. 97(1), pages 6-12.
    8. Felipe Thomaz & Carolina Salge & Elena Karahanna & John Hulland, 2020. "Learning from the Dark Web: leveraging conversational agents in the era of hyper-privacy to enhance marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 43-63, January.
    9. Bleier, Alexander & Goldfarb, Avi & Tucker, Catherine, 2020. "Consumer privacy and the future of data-based innovation and marketing," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 466-480.
    10. Venkatesh Shankar & Sohil Parsana, 2022. "An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1324-1350, November.
    11. Montecchi, Matteo & Plangger, Kirk & West, Douglas C., 2021. "Supply chain transparency: A bibliometric review and research agenda," International Journal of Production Economics, Elsevier, vol. 238(C).
    12. Sara Quach & Park Thaichon & Kelly D. Martin & Scott Weaven & Robert W. Palmatier, 2022. "Digital technologies: tensions in privacy and data," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1299-1323, November.
    13. Ertugrul Uysal & Sascha Alavi & Valéry Bezençon, 2022. "Trojan horse or useful helper? A relationship perspective on artificial intelligence assistants with humanlike features," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1153-1175, November.
    14. Okazaki, Shintaro & Eisend, Martin & Plangger, Kirk & de Ruyter, Ko & Grewal, Dhruv, 2020. "Understanding the Strategic Consequences of Customer Privacy Concerns: A Meta-Analytic Review," Journal of Retailing, Elsevier, vol. 96(4), pages 458-473.
    15. David A. Schweidel & Yakov Bart & J. Jeffrey Inman & Andrew T. Stephen & Barak Libai & Michelle Andrews & Ana Babić Rosario & Inyoung Chae & Zoey Chen & Daniella Kupor & Chiara Longoni & Felipe Thomaz, 2022. "How consumer digital signals are reshaping the customer journey," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1257-1276, November.
    16. Dan King & Sumitra Auschaitrakul & Chia-Wei Joy Lin, 2022. "Search modality effects: merely changing product search modality alters purchase intentions," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1236-1256, November.
    17. Navid Bahmani & Amit Bhatnagar & Dinesh Gauri, 2022. "Hey, Alexa! What attributes of Skills affect firm value?," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1219-1235, November.
    18. Dhruv Grewal & John Hulland & Praveen K. Kopalle & Elena Karahanna, 2020. "The future of technology and marketing: a multidisciplinary perspective," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 1-8, January.
    19. Arnold, Stephen J & Fischer, Eileen, 1994. "Hermeneutics and Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(1), pages 55-70, June.
    20. Thomas Davenport & Abhijit Guha & Dhruv Grewal & Timna Bressgott, 2020. "How artificial intelligence will change the future of marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 24-42, January.
    21. Sagarika Mishra & Michael T. Ewing & Holly B. Cooper, 2022. "Artificial intelligence focus and firm performance," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1176-1197, November.
    22. Lidan Xu & Ravi Mehta, 2022. "Technology devalues luxury? Exploring consumer responses to AI-designed luxury products," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1135-1152, November.
    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. Wolfgang Breuer & Jannis Bischof & Christian Hofmann & Jochen Hundsdoerfer & Hans-Ulrich Küpper & Marko Sarstedt & Philipp Schreck & Tim Weitzel & Peter Witt, 2023. "Recent developments in Business Economics," Journal of Business Economics, Springer, vol. 93(6), pages 989-1013, August.
    2. Morgan M. Bryant & Jen Riley & Tiffanie Turner-Henderson & Dexter Purnell, 2024. "Ready, set, go! Deploying the social listening stoplight activity to teach marketing analytics using qualitative techniques," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 169-181, June.
    3. Kamaal Allil, 2024. "Integrating AI-driven marketing analytics techniques into the classroom: pedagogical strategies for enhancing student engagement and future business success," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 142-168, June.
    4. Pappas, Alec & Fumagalli, Elena & Rouziou, Maria & Bolander, Willy, 2023. "More than Machines: The Role of the Future Retail Salesperson in Enhancing the Customer Experience," Journal of Retailing, Elsevier, vol. 99(4), pages 518-531.

    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. Erik Hermann, 2022. "Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective," Journal of Business Ethics, Springer, vol. 179(1), pages 43-61, August.
    2. Poushneh, Atieh & Vasquez-Parraga, Arturo & Gearhart, Richard S., 2024. "The effect of empathetic response and consumers’ narcissism in voice-based artificial intelligence," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    3. David A. Schweidel & Yakov Bart & J. Jeffrey Inman & Andrew T. Stephen & Barak Libai & Michelle Andrews & Ana Babić Rosario & Inyoung Chae & Zoey Chen & Daniella Kupor & Chiara Longoni & Felipe Thomaz, 2022. "How consumer digital signals are reshaping the customer journey," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1257-1276, November.
    4. Jordan W. Moffett & Judith Anne Garretson Folse & Robert W. Palmatier, 2021. "A theory of multiformat communication: mechanisms, dynamics, and strategies," Journal of the Academy of Marketing Science, Springer, vol. 49(3), pages 441-461, May.
    5. Leah Warfield Smith & Randall Lee Rose & Alex R. Zablah & Heath McCullough & Mohammad “Mike” Saljoughian, 2023. "Examining post-purchase consumer responses to product automation," Journal of the Academy of Marketing Science, Springer, vol. 51(3), pages 530-550, May.
    6. Cambra-Fierro, Jesús & Polo-Redondo, Yolanda & Trifu, Andreea, 2021. "Short-term and long-term effects of touchpoints on customer perceptions," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    7. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    8. Akter, Shahriar & Hossain, Md Afnan & Sajib, Shahriar & Sultana, Saida & Rahman, Mahfuzur & Vrontis, Demetris & McCarthy, Grace, 2023. "A framework for AI-powered service innovation capability: Review and agenda for future research," Technovation, Elsevier, vol. 125(C).
    9. Dhruv Grewal & John Hulland & Praveen K. Kopalle & Elena Karahanna, 2020. "The future of technology and marketing: a multidisciplinary perspective," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 1-8, January.
    10. Ertugrul Uysal & Sascha Alavi & Valéry Bezençon, 2022. "Trojan horse or useful helper? A relationship perspective on artificial intelligence assistants with humanlike features," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1153-1175, November.
    11. Roggeveen, Anne L. & Grewal, Dhruv & Karsberg, John & Noble, Stephanie M. & Nordfält, Jens & Patrick, Vanessa M. & Schweiger, Elisa & Soysal, Gonca & Dillard, Annemarie & Cooper, Nora & Olson, Richard, 2021. "Forging meaningful consumer-brand relationships through creative merchandise offerings and innovative merchandising strategies," Journal of Retailing, Elsevier, vol. 97(1), pages 81-98.
    12. Guha, Abhijit & Grewal, Dhruv & Kopalle, Praveen K. & Haenlein, Michael & Schneider, Matthew J. & Jung, Hyunseok & Moustafa, Rida & Hegde, Dinesh R. & Hawkins, Gary, 2021. "How artificial intelligence will affect the future of retailing," Journal of Retailing, Elsevier, vol. 97(1), pages 28-41.
    13. Alabed, Amani & Javornik, Ana & Gregory-Smith, Diana, 2022. "AI anthropomorphism and its effect on users' self-congruence and self–AI integration: A theoretical framework and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    14. 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).
    15. Sara Quach & Park Thaichon & Kelly D. Martin & Scott Weaven & Robert W. Palmatier, 2022. "Digital technologies: tensions in privacy and data," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1299-1323, November.
    16. Stephanie M. Noble & Martin Mende, 2023. "The future of artificial intelligence and robotics in the retail and service sector: Sketching the field of consumer-robot-experiences," Journal of the Academy of Marketing Science, Springer, vol. 51(4), pages 747-756, July.
    17. Souka, Mohamed & Bilstein, Nicola & Decker, Reinhold, 2024. "Give me your data and I’ll dress you: A two-sided messaging approach to address privacy concerns surrounding in-store technologies," Journal of Business Research, Elsevier, vol. 172(C).
    18. Huang, Ming-Hui & Rust, Roland T., 2022. "A Framework for Collaborative Artificial Intelligence in Marketing," Journal of Retailing, Elsevier, vol. 98(2), pages 209-223.
    19. Abhijit Guha & Dhruv Grewal, 2022. "How robots will affect the future of retailing," AMS Review, Springer;Academy of Marketing Science, vol. 12(3), pages 245-252, December.
    20. Vieira, Valter Afonso & Rafael, Diego Nogueira & Agnihotri, Raj, 2022. "Augmented reality generalizations: A meta-analytical review on consumer-related outcomes and the mediating role of hedonic and utilitarian values," Journal of Business Research, Elsevier, vol. 151(C), pages 170-184.

    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:50:y:2022:i:6:d:10.1007_s11747-022-00906-2. 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.