IDEAS home Printed from https://ideas.repec.org/a/aes/jetimm/v1y2018i1p156-165.html
   My bibliography  Save this article

Exploring Artificial Intelligence Techniques’ Applicability in Social Media Marketing

Author

Listed:
  • Adrian MICU

    (Dunarea de Jos University of Galati)

  • Alexandru CAPATINA

    (Dunarea de Jos University of Galati)

  • Angela-Eliza MICU

    (Ovidius University of Constanta)

Abstract

The increasing interest in Artificial Intelligence (AI)’s impact on Social Media Marketing (SMM) creates new opportunities to be captured by software developers. Marketers become aware of AI powerful tools role in leveraging competitive advantage in social media campaigns. This study aims to test correlations between the experience in the field of SMM and the level of knowledge regarding the applicability of Machine Learning (ML) in SMM and the frequency of using of ML algorithms in SMM campaigns and to identify the perceptions of the potential users of an Artificial Intelligence (AI)-based software, which will embed deep learning algorithms and convolutional neural networks to recognize logos of brands or companies involved in social media content, regarding its proposed capabilities. The AI Media software capabilities were embedded into three clusters (audience analysis, image analysis and sentiment analysis), being assessed through a 3 points scale, revealing necessary vs. expected functionalities in the eyes of digital agencies’ representatives of freelancers. The results outline a high interest and trust of potential users of AI Media software on its value proposition.

Suggested Citation

  • Adrian MICU & Alexandru CAPATINA & Angela-Eliza MICU, 2018. "Exploring Artificial Intelligence Techniques’ Applicability in Social Media Marketing," Journal of Emerging Trends in Marketing and Management, The Bucharest University of Economic Studies, vol. 1(1), pages 156-165, November.
  • Handle: RePEc:aes:jetimm:v:1:y:2018:i:1:p:156-165
    as

    Download full text from publisher

    File URL: http://www.etimm.ase.ro/RePEc/aes/jetimm/2018/ETIMM_V01_2018_66.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. de Vries, Lisette & Gensler, Sonja & Leeflang, Peter S.H., 2012. "Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing," Journal of Interactive Marketing, Elsevier, vol. 26(2), pages 83-91.
    2. Singh, Sangeeta & Sonnenburg, Stephan, 2012. "Brand Performances in Social Media," Journal of Interactive Marketing, Elsevier, vol. 26(4), pages 189-197.
    3. Lee, In, 2018. "Social media analytics for enterprises: Typology, methods, and processes," Business Horizons, Elsevier, vol. 61(2), pages 199-210.
    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. Veronika Tarnovskaya, 2017. "Reinventing Personal Branding Building a Personal Brand through Content on YouTube," Journal of International Business Research and Marketing, Inovatus Services Ltd., vol. 3(1), pages 29-35, November.
    2. Muhammad Waqas & Zalfa Laili Binti Hamzah & Noor Akma Mohd Salleh, 2021. "Customer experience: a systematic literature review and consumer culture theory-based conceptualisation," Management Review Quarterly, Springer, vol. 71(1), pages 135-176, February.
    3. Gensler, Sonja & Völckner, Franziska & Liu-Thompkins, Yuping & Wiertz, Caroline, 2013. "Managing Brands in the Social Media Environment," Journal of Interactive Marketing, Elsevier, vol. 27(4), pages 242-256.
    4. Sara Amabile & Francesca Conte & Agostino Vollero & Alfonso Siano, 2022. "Measuring and evaluating CSR information and involvement strategies on corporate Facebook pages," Italian Journal of Marketing, Springer, vol. 2022(3), pages 341-369, September.
    5. Mithun S. Ullal & Iqbal Thonse Hawaldar & Rashmi Soni & Mohammed Nadeem, 2021. "The Role of Machine Learning in Digital Marketing," SAGE Open, , vol. 11(4), pages 21582440211, October.
    6. Capatina, Alexandru & Kachour, Maher & Lichy, Jessica & Micu, Adrian & Micu, Angela-Eliza & Codignola, Federica, 2020. "Matching the future capabilities of an artificial intelligence-based software for social media marketing with potential users’ expectations," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    7. Swani, Kunal & Milne, George R. & Miller, Elizabeth G., 2021. "Social media services branding: The use of corporate brand names," Journal of Business Research, Elsevier, vol. 125(C), pages 785-797.
    8. Liu, Xia & Shin, Hyunju & Burns, Alvin C., 2021. "Examining the impact of luxury brand's social media marketing on customer engagement​: Using big data analytics and natural language processing," Journal of Business Research, Elsevier, vol. 125(C), pages 815-826.
    9. Sang-Hyeak Yoon & Hee-Woong Kim, 2019. "What content and context factors lead to selection of a video clip? The heuristic route perspective," Electronic Commerce Research, Springer, vol. 19(3), pages 603-627, September.
    10. Schaefers, Tobias & Falk, Tomas & Kumar, Ashish & Schamari, Julia, 2021. "More of the same? Effects of volume and variety of social media brand engagement behavior," Journal of Business Research, Elsevier, vol. 135(C), pages 282-294.
    11. Suomi, Kati & Luonila, Mervi & Tähtinen, Jaana, 2020. "Ironic festival brand co-creation," Journal of Business Research, Elsevier, vol. 106(C), pages 211-220.
    12. SZIKSZAI-NÉMETH Ketrin, 2020. "Personal Branding In Team Sports Marketing," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 416-424, July.
    13. Yousef Ahmad El Dameh & Hamad AL Ghadeer, 2021. "The Impact of Traditional Direct Marketing on Creating Brand Awareness: Case Study on IKEA in Jordan," International Journal of Business and Management, Canadian Center of Science and Education, vol. 14(3), pages 130-130, July.
    14. Halloran, Timothy J. & Lutz, Richard J., 2021. "Let's Give Them Something to Talk About: Which Social Media Engagements Predict Purchase Frequency?," Journal of Interactive Marketing, Elsevier, vol. 56(C), pages 83-95.
    15. Spotts, Harlan E. & Weinberger, Marc G. & Assaf, A. George & Weinberger, Michelle F., 2022. "The role of paid media, earned media, and sales promotions in driving marcom sales performance in consumer services," Journal of Business Research, Elsevier, vol. 152(C), pages 387-397.
    16. Coelho, Pedro Simões & Rita, Paulo & Santos, Zélia Raposo, 2018. "On the relationship between consumer-brand identification, brand community, and brand loyalty," Journal of Retailing and Consumer Services, Elsevier, vol. 43(C), pages 101-110.
    17. Blanca I. Hernández-Ortega & Michael A. Stanko & Rishika Rishika & Francisco-Jose Molina-Castillo & José Franco, 2022. "Brand-generated social media content and its differential impact on loyalty program members," Journal of the Academy of Marketing Science, Springer, vol. 50(5), pages 1071-1090, September.
    18. Tony Cooper & Constantino Stavros & Angela R. Dobele, 2019. "The levers of engagement: an exploration of governance in an online brand community," Journal of Brand Management, Palgrave Macmillan, vol. 26(3), pages 240-254, May.
    19. Giannis Milolidakis & Demosthenes Akoumianakis & Chris Kimble, 2013. "Digital traces for business intelligence: A case study of mobile telecoms service brands in Greece," Post-Print halshs-00954440, HAL.
    20. Pera, Rebecca & Viglia, Giampaolo & Furlan, Roberto, 2016. "Who Am I? How Compelling Self-storytelling Builds Digital Personal Reputation," Journal of Interactive Marketing, Elsevier, vol. 35(C), pages 44-55.

    More about this item

    Keywords

    Social Media Marketing; machine learning; deep learning; image analysis; audience analysis; sentiment analysis.;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

    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:aes:jetimm:v:1:y:2018:i:1:p:156-165. 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: Lucian Onisor (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.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.