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A Combination of Self-Reported Data and Social-Related Neural Measures Forecasts Viral Marketing Success on Social Media

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  • Motoki, Kosuke
  • Suzuki, Shinsuke
  • Kawashima, Ryuta
  • Sugiura, Motoaki

Abstract

Consumers often share product-related content (e.g., advertising), and highly shared advertising has a huge impact on consumer behavior. Despite its apparent effectiveness, prediction of whether such advertising will be highly shared is a poorly understood area of marketing. Advances in brain imaging techniques may allow researchers to forecast aggregate consumer behavior beyond subjective reports. Using neuroimaging techniques, previous research has established models showing that expectations of self-related outcomes (potential for self-enhancement) and the social impact of sharing (potential for social approval) contribute to the likelihood of users sharing non-commercial static content (i.e., text-based health news). However, whether this finding can be applied to forecasting the virality of dynamic commercial stimuli, which is more relevant to interactive marketing (i.e., video ads), remains unknown. Combining brain imaging techniques, cross-validation methods, and real-world data regarding sharing on social media, the present study investigated whether brain data can be used to forecast the viral marketing success of video ads. We used neuroimaging (functional magnetic resonance imaging: fMRI) to measure neural activity during three sets of theory-driven neural measures implicated in value, self, and social (mentalizing) processes while 40 participants viewed video ads that brands had posted on Facebook. Contrary to previous findings regarding value-related virality in non-commercial static content, our results indicate that social-related neural activity contributes significantly to forecasting the virality of dynamic marketing-related content. The model that included both social-related neural measures and subjective intentions to share forecasted viral marketing success better than the model that included only social-related neural measures. The model that included only subjective intention to share did not forecast viral marketing success. Overall, these findings provide a novel connection between neurophysiological measures and real-world dynamic commercial content. Contrary to previous neuroforecasting findings, social-related but not value-related neural measures can significantly improve our ability to predict market-level sharing of video ads.

Suggested Citation

  • Motoki, Kosuke & Suzuki, Shinsuke & Kawashima, Ryuta & Sugiura, Motoaki, 2020. "A Combination of Self-Reported Data and Social-Related Neural Measures Forecasts Viral Marketing Success on Social Media," Journal of Interactive Marketing, Elsevier, vol. 52(C), pages 99-117.
    • Handle: RePEc:eee:joinma:v:52:y:2020:i:c:p:99-117
    DOI: 10.1016/j.intmar.2020.06.003
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    1. Sarah G. Moore & Brent McFerran, 2017. "She Said, She Said: Differential Interpersonal Similarities Predict Unique Linguistic Mimicry in Online Word of Mouth," Journal of the Association for Consumer Research, University of Chicago Press, vol. 2(2), pages 229-245.
    2. Friestad, Marian & Wright, Peter, 1994. "The Persuasion Knowledge Model: How People Cope with Persuasion Attempts," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(1), pages 1-31, June.
    3. Jonah Berger & Raghuram Iyengar, 2013. "Communication Channels and Word of Mouth: How the Medium Shapes the Message," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 40(3), pages 567-579.
    4. Casado-Aranda, Luis-Alberto & Liébana-Cabanillas, Francisco & Sánchez-Fernández, Juan, 2018. "A Neuropsychological Study on How Consumers Process Risky and Secure E-payments," Journal of Interactive Marketing, Elsevier, vol. 43(C), pages 151-164.
    5. Huang, Jinsong & Su, Song & Zhou, Liuning & Liu, Xi, 2013. "Attitude Toward the Viral Ad: Expanding Traditional Advertising Models to Interactive Advertising," Journal of Interactive Marketing, Elsevier, vol. 27(1), pages 36-46.
    6. Kristine de Valck & Roberts V. Kozinets & Andrea C. Wojnicki & Sarah J.S. Wilner, 2010. "Networked Narratives: Understanding Word-of-Mouth Marketing in Online Communities," Post-Print hal-00458424, HAL.
    7. Ho, Jason Y.C. & Dempsey, Melanie, 2010. "Viral marketing: Motivations to forward online content," Journal of Business Research, Elsevier, vol. 63(9-10), pages 1000-1006, September.
    8. Casado-Aranda, Luis-Alberto & Dimoka, Angelika & Sánchez-Fernández, Juan, 2019. "Consumer Processing of Online Trust Signals: A Neuroimaging Study," Journal of Interactive Marketing, Elsevier, vol. 47(C), pages 159-180.
    9. Alec Smith & B. Douglas Bernheim & Colin F. Camerer & Antonio Rangel, 2014. "Neural Activity Reveals Preferences without Choices," American Economic Journal: Microeconomics, American Economic Association, vol. 6(2), pages 1-36, May.
    10. Phelps, Joseph E. & Lewis, Regina & Mobilio, Lynne & Perry, David & Raman, Niranjan, 2004. "Viral Marketing or Electronic Word-of-Mouth Advertising: Examining Consumer Responses and Motivations to Pass Along Email," Journal of Advertising Research, Cambridge University Press, vol. 44(4), pages 333-348, December.
    11. Shehu, Edlira & Bijmolt, Tammo H.A. & Clement, Michel, 2016. "Effects of Likeability Dynamics on Consumers' Intention to Share Online Video Advertisements," Journal of Interactive Marketing, Elsevier, vol. 35(C), pages 27-43.
    12. Nelson-Field, Karen & Riebe, Erica & Newstead, Kellie, 2013. "The emotions that drive viral video," Australasian marketing journal, Elsevier, vol. 21(4), pages 205-211.
    13. Quesenberry, Keith A. & Coolsen, Michael K., 2019. "Drama Goes Viral: Effects of Story Development on Shares and Views of Online Advertising Videos," Journal of Interactive Marketing, Elsevier, vol. 48(C), pages 1-16.
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