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Using Artificial Intelligence on Social Media’s User Generated Content for Disruptive Marketing Strategies in eCommerce Abstract. This study examines how user-generated content on social media can act as a marketing research instrument in identifying consumer behaviour. The analysis was performed on a sample of 900 Instagram images containing the hashtag #thegoodlife and labeled the digital photos using a machine learning algorithm. The results highlight the visual representation of the clusters resulted using the K-mean method and practical managerial implications

Author

Listed:
  • Marius GERU

    (Dunarea de Jos University of Galati, Romania)

  • Angela Eliza MICU

    (Ovidius University of Constanta, Romania)

  • Alexandru CAPATINA

    (Dunarea de Jos University of Galati, Romania)

  • Adrian MICU

    (Dunarea de Jos University of Galati, Romania)

Abstract

No abstract is available for this item.

Suggested Citation

  • Marius GERU & Angela Eliza MICU & Alexandru CAPATINA & Adrian MICU, 2018. "Using Artificial Intelligence on Social Media’s User Generated Content for Disruptive Marketing Strategies in eCommerce Abstract. This study examines how user-generated content on social media can act," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 3, pages 5-11.
    • Handle: RePEc:ddj:fseeai:y:2018:i:3:p:5-11
    DOI: https://doi.org/10.26397/eai1584040911
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    References listed on IDEAS

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    1. Gheorghe ORZAN & Otilia-Elena PLATON & Cristian Dragos STEFĂNESCU & Mihai ORZAN, 2016. "Conceptual Model Regarding The Influence Of Social Media Marketing Communication On Brand Trust, Brand Affect And Brand Loyalty," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(1), pages 141-156.
    2. Hollebeek, Linda D. & Glynn, Mark S. & Brodie, Roderick J., 2014. "Consumer Brand Engagement in Social Media: Conceptualization, Scale Development and Validation," Journal of Interactive Marketing, Elsevier, vol. 28(2), pages 149-165.
    3. Chae, Bongsug (Kevin), 2015. "Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research," International Journal of Production Economics, Elsevier, vol. 165(C), pages 247-259.
    4. Delen, Dursun & Zolbanin, Hamed M., 2018. "The analytics paradigm in business research," Journal of Business Research, Elsevier, vol. 90(C), pages 186-195.
    5. Kaplan, Andreas M. & Haenlein, Michael, 2010. "Users of the world, unite! The challenges and opportunities of Social Media," Business Horizons, Elsevier, vol. 53(1), pages 59-68, January.
    6. Mollen, Anne & Wilson, Hugh, 2010. "Engagement, telepresence and interactivity in online consumer experience: Reconciling scholastic and managerial perspectives," Journal of Business Research, Elsevier, vol. 63(9-10), pages 919-925, September.
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    Cited by:

    1. Josef Schwaiger & Timo Hammerl & Johannsen Florian & Susanne Leist, 2021. "UR: SMART–A tool for analyzing social media content," Information Systems and e-Business Management, Springer, vol. 19(4), pages 1275-1320, December.

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