IDEAS home Printed from https://ideas.repec.org/a/inm/orserv/v14y2022i2p77-89.html

How Artificial Intelligence Enhances Human Learning Abilities: Opportunities in the Fight Against COVID-19

Author

Listed:
  • Cristina Mele

    (Department of Economics, Management, Institutions, University of Naples Federico II, Naples 80126, Italy)

  • Marialuisa Marzullo

    (Department of Economics, Management, Institutions, University of Naples Federico II, Naples 80126, Italy)

  • Swapnil Morande

    (Department of Economics, Management, Institutions, University of Naples Federico II, Naples 80126, Italy)

  • Tiziana Russo Spena

    (Department of Economics, Management, Institutions, University of Naples Federico II, Naples 80126, Italy)

Abstract

This paper widens the focus on how artificial intelligence (AI) can foster the learning abilities of human actors, adopting a wider view with respect to a strict focus on tasks and activities. The interaction between AI and human learning has not been investigated in service research. Placing its theoretical roots in work by Huang and Rust [Huang MH, Rust RT (2021) Engaged to a robot? The role of AI in service. J. Service Res. 24(1):30–41.] in service research and on Bloom’s revised taxonomy in education studies [Anderson LW, Krathwohl DR, Airasian PW, Cruikshank KA, Mayer RE, Pintrich PR, Raths J, Wittrock MC (2001) A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives (Longman, London).], this study offers an integrative framework for the ways AI enhances human learning abilities. Some cases in the context of COVID-19 offer insightful illustrations of the framework.

Suggested Citation

  • Cristina Mele & Marialuisa Marzullo & Swapnil Morande & Tiziana Russo Spena, 2022. "How Artificial Intelligence Enhances Human Learning Abilities: Opportunities in the Fight Against COVID-19," Service Science, INFORMS, vol. 14(2), pages 77-89, June.
    • Handle: RePEc:inm:orserv:v:14:y:2022:i:2:p:77-89
    DOI: 10.1287/serv.2021.0289
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/serv.2021.0289
    Download Restriction: no
    File URL: https://libkey.io/10.1287/serv.2021.0289?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
    ---><---

    References listed on IDEAS

    as
    1. Stahl, B.C. & Andreou, A. & Brey, P. & Hatzakis, T. & Kirichenko, A. & Macnish, K. & Laulhé Shaelou, S. & Patel, A. & Ryan, M. & Wright, D., 2021. "Artificial intelligence for human flourishing – Beyond principles for machine learning," Journal of Business Research, Elsevier, vol. 124(C), pages 374-388.
    2. Jarrahi, Mohammad Hossein, 2018. "Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making," Business Horizons, Elsevier, vol. 61(4), pages 577-586.
    3. Paschen, Ulrich & Pitt, Christine & Kietzmann, Jan, 2020. "Artificial intelligence: Building blocks and an innovation typology," Business Horizons, Elsevier, vol. 63(2), pages 147-155.
    4. Meuter, Matthew L. & Ostrom, Amy L. & Bitner, Mary Jo & Roundtree, Robert, 2003. "The influence of technology anxiety on consumer use and experiences with self-service technologies," Journal of Business Research, Elsevier, vol. 56(11), pages 899-906, November.
    5. Tuck Siong Chung & Michel Wedel & Roland T. Rust, 2016. "Adaptive personalization using social networks," Journal of the Academy of Marketing Science, Springer, vol. 44(1), pages 66-87, January.
    6. Tuck Siong Chung & Roland T. Rust & Michel Wedel, 2009. "My Mobile Music: An Adaptive Personalization System for Digital Audio Players," Marketing Science, INFORMS, vol. 28(1), pages 52-68, 01-02.
    7. Rodgers, Waymond & Yeung, Fannie & Odindo, Christopher & Degbey, William Y., 2021. "Artificial intelligence-driven music biometrics influencing customers’ retail buying behavior," Journal of Business Research, Elsevier, vol. 126(C), pages 401-414.
    8. 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.
    9. Perez-Vega, Rodrigo & Kaartemo, Valtteri & Lages, Cristiana R. & Borghei Razavi, Niloofar & Männistö, Jaakko, 2021. "Reshaping the contexts of online customer engagement behavior via artificial intelligence: A conceptual framework," Journal of Business Research, Elsevier, vol. 129(C), pages 902-910.
    10. Albrecht, Tobias & Rausch, Theresa Maria & Derra, Nicholas Daniel, 2021. "Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals’ forecasting," Journal of Business Research, Elsevier, vol. 123(C), pages 267-278.
    11. Mele, Cristina & Russo Spena, Tiziana & Kaartemo, Valtteri & Marzullo, Maria Luisa, 2021. "Smart nudging: How cognitive technologies enable choice architectures for value co-creation," Journal of Business Research, Elsevier, vol. 129(C), pages 949-960.
    12. Zhu, John Jianjun & Chang, Yung-Chun & Ku, Chih-Hao & Li, Stella Yiyan & Chen, Chi-Jen, 2021. "Online critical review classification in response strategy and service provider rating: Algorithms from heuristic processing, sentiment analysis to deep learning," Journal of Business Research, Elsevier, vol. 129(C), pages 860-877.
    13. Elina Jaakkola, 2020. "Designing conceptual articles: four approaches," AMS Review, Springer;Academy of Marketing Science, vol. 10(1), pages 18-26, June.
    Full references (including those not matched with items on IDEAS)

    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. Huang, Ming-Hui & Dev, Chekitan S., 2020. "Growing the service brand," International Journal of Research in Marketing, Elsevier, vol. 37(2), pages 281-300.
    2. 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.
    3. 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.
    4. Chong Guan & Yu-Chen Hung & Wenting Liu, 2022. "Cultural differences in hospitality service evaluations: mining insights of user generated content," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1061-1081, September.
    5. 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.
    6. Ritu Agarwal & Michelle Dugas & Guodong (Gordon) Gao & P. K. Kannan, 2020. "Emerging technologies and analytics for a new era of value-centered marketing in healthcare," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 9-23, January.
    7. Sullivan, Yulia & Fosso Wamba, Samuel, 2024. "Artificial intelligence and adaptive response to market changes: A strategy to enhance firm performance and innovation," Journal of Business Research, Elsevier, vol. 174(C).
    8. Wedel, Michel & Bigné, Enrique & Zhang, Jie, 2020. "Virtual and augmented reality: Advancing research in consumer marketing," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 443-465.
    9. Volkmar, Gioia & Fischer, Peter M. & Reinecke, Sven, 2022. "Artificial Intelligence and Machine Learning: Exploring drivers, barriers, and future developments in marketing management," Journal of Business Research, Elsevier, vol. 149(C), pages 599-614.
    10. Roy, Sanjit K. & Tehrani, Ali N. & Pandit, Ameet & Apostolidis, Chrysostomos & Ray, Subhasis, 2025. "AI-capable relationship marketing: Shaping the future of customer relationships," Journal of Business Research, Elsevier, vol. 192(C).
    11. Paolo Franco, 2023. "Older consumers and technology: A critical systematic literature review," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 92-121, June.
    12. Ma, Liye & Sun, Baohong, 2020. "Machine learning and AI in marketing – Connecting computing power to human insights," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 481-504.
    13. Makarius, Erin E. & Mukherjee, Debmalya & Fox, Joseph D. & Fox, Alexa K., 2020. "Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization," Journal of Business Research, Elsevier, vol. 120(C), pages 262-273.
    14. 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.
    15. Benedict G. C. Dellaert, 2019. "The consumer production journey: marketing to consumers as co-producers in the sharing economy," Journal of the Academy of Marketing Science, Springer, vol. 47(2), pages 238-254, March.
    16. Rust, Roland T., 2020. "The future of marketing," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 15-26.
    17. 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.
    18. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    19. Black, J. Stewart & van Esch, Patrick, 2020. "AI-enabled recruiting: What is it and how should a manager use it?," Business Horizons, Elsevier, vol. 63(2), pages 215-226.
    20. Paschen, Jeannette & Wilson, Matthew & Ferreira, João J., 2020. "Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel," Business Horizons, Elsevier, vol. 63(3), pages 403-414.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:inm:orserv:v:14:y:2022:i:2:p:77-89. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.