IDEAS home Printed from https://ideas.repec.org/p/ems/euriar/107428.html
   My bibliography  Save this paper

Learning with a purpose: the balancing acts of machine learning and individuals in the digital society

Author

Listed:
  • Liberali, G.

Abstract

No abstract is available for this item.

Suggested Citation

  • Liberali, G., 2018. "Learning with a purpose: the balancing acts of machine learning and individuals in the digital society," ERIM Inaugural Address Series Research in Management EIA-2018-074-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam..
    • Handle: RePEc:ems:euriar:107428
    as

    Download full text from publisher

    File URL: https://repub.eur.nl/pub/107428/EIA2018074MKT.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. John R. Hauser & Guilherme (Gui) Liberali & Glen L. Urban, 2014. "Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph," Management Science, INFORMS, vol. 60(6), pages 1594-1616, June.
    2. Glen L. Urban & Guilherme (Gui) Liberali & Erin MacDonald & Robert Bordley & John R. Hauser, 2014. "Morphing Banner Advertising," Marketing Science, INFORMS, vol. 33(1), pages 27-46, January.
    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. De Bruyn, Arnaud & Viswanathan, Vijay & Beh, Yean Shan & Brock, Jürgen Kai-Uwe & von Wangenheim, Florian, 2020. "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 91-105.
    2. 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.
    3. Babur De los Santos & Sergei Koulayev, 2017. "Optimizing Click-Through in Online Rankings with Endogenous Search Refinement," Marketing Science, INFORMS, vol. 36(4), pages 542-564, July.
    4. José Niño-Mora, 2020. "Fast Two-Stage Computation of an Index Policy for Multi-Armed Bandits with Setup Delays," Mathematics, MDPI, vol. 9(1), pages 1-36, December.
    5. Gui Liberali & Alina Ferecatu, 2022. "Morphing for Consumer Dynamics: Bandits Meet Hidden Markov Models," Marketing Science, INFORMS, vol. 41(4), pages 769-794, July.
    6. John R. Hauser & Guilherme (Gui) Liberali & Glen L. Urban, 2014. "Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph," Management Science, INFORMS, vol. 60(6), pages 1594-1616, June.
    7. Song Lin & Juanjuan Zhang & John R. Hauser, 2015. "Learning from Experience, Simply," Marketing Science, INFORMS, vol. 34(1), pages 1-19, January.
    8. Davide Proserpio & John R. Hauser & Xiao Liu & Tomomichi Amano & Alex Burnap & Tong Guo & Dokyun (DK) Lee & Randall Lewis & Kanishka Misra & Eric Schwarz & Artem Timoshenko & Lilei Xu & Hema Yoganaras, 2020. "Soul and machine (learning)," Marketing Letters, Springer, vol. 31(4), pages 393-404, December.
    9. Lambillotte, Laetitia & Magrofuoco, Nathan & Poncin, Ingrid & Vanderdonckt, Jean, 2022. "Enhancing playful customer experience with personalization," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    10. 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.
    11. Kristen Giombi & Catherine Viator & Juliana Hoover & Janice Tzeng & Helen W Sullivan & Amie C O’Donoghue & Brian G Southwell & Leila C Kahwati, 2022. "The impact of interactive advertising on consumer engagement, recall, and understanding: A scoping systematic review for informing regulatory science," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-18, February.
    12. Michael Trusov & Liye Ma & Zainab Jamal, 2016. "Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting," Marketing Science, INFORMS, vol. 35(3), pages 405-426, May.
    13. Jan Krämer & Daniel Schnurr & Michael Wohlfarth, 2019. "Winners, Losers, and Facebook: The Role of Social Logins in the Online Advertising Ecosystem," Management Science, INFORMS, vol. 65(4), pages 1678-1699, April.
    14. Kalaignanam, Kartik & Kushwaha, Tarun & Rajavi, Koushyar, 2018. "How Does Web Personalization Create Value for Online Retailers? Lower Cash Flow Volatility or Enhanced Cash Flows," Journal of Retailing, Elsevier, vol. 94(3), pages 265-279.
    15. S. Mills & S. Costa & C. R. Sunstein, 2023. "AI, Behavioural Science, and Consumer Welfare," Journal of Consumer Policy, Springer, vol. 46(3), pages 387-400, September.
    16. Pilli, Luis & Swait, Joffre & Mazzon, José Afonso, 2022. "Jeopardizing brand profitability by misattributing process heterogeneity to preference heterogeneity," Journal of choice modelling, Elsevier, vol. 43(C).
    17. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2017. "Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments," Marketing Science, INFORMS, vol. 36(4), pages 500-522, July.
    18. Kurt P. Munz & Minah H. Jung & Adam L. Alter, 2020. "Name Similarity Encourages Generosity: A Field Experiment in Email Personalization," Marketing Science, INFORMS, vol. 39(6), pages 1071-1091, November.
    19. Christian Hildebrand & Anouk Bergner, 2021. "Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 659-676, July.
    20. Alexander Bleier & Maik Eisenbeiss, 2015. "Personalized Online Advertising Effectiveness: The Interplay of What, When, and Where," Marketing Science, INFORMS, vol. 34(5), pages 669-688, September.

    More about this item

    Keywords

    e-commerce; informatiemaatschappij; adverteren; kunstmatige intelligentie; machine learning; multi-armed bandits; marketing science; online advertising; digital marketing; clinical trials;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ems:euriar:107428. 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: RePub (email available below). General contact details of provider: https://edirc.repec.org/data/erimanl.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.