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Social Media Engagement Through Video Advertising: Informativeness and Self Brand Connection as Predictors

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Advances in Digital Science (ICADS 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1352))

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Abstract

The evolution of social media has been great and people have been looking for more information on these networks. Globally, there are more than 4 billion active users on social networks who brands seek to attract their products and services. In this context, our investigation found factors that influence social media engagement: informativeness, self-brand connection and advertising stimulation.

Through the literature review, a conceptual model was proposed that was later tested using PLS-SEM. Data were collected from 237 consumers and it was revealed that engagement in social media is explained by the variables identified by our model. Important contributions to the theory and management of the brand will be found in this investigation.

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Acknowledgment

This work is funded by National Funds through the FCT - Foundation for Science and Technology, I.P., within the scope of the project Refª UIDB/05507/2020. Furthermore, we would like to thank the Centre for Studies in Education and Innovation (CI&DEI) and the Polytechnic of Viseu for their support.

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Authors and Affiliations

  1. Centre for Studies in Education and Innovation (CI&DEI), Instituto Politécnico de Viseu, Viseu, Portugal

    Sónia Ferreira

  2. Instituto Politécnico de Viseu, Viseu, Portugal

    Sara Santos

  3. ESTGOH - Instituto Politécnico de Coimbra, Coimbra, Portugal

    Pedro Espírito Santo

Authors
  1. Sónia Ferreira
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  2. Sara Santos
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  3. Pedro Espírito Santo
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Corresponding author

Correspondence to Sónia Ferreira .

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Editors and Affiliations

  1. Institute of Certified Specialists (ICS), Perm, Russia

    Tatiana Antipova

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Ferreira, S., Santos, S., Santo, P.E. (2021). Social Media Engagement Through Video Advertising: Informativeness and Self Brand Connection as Predictors. In: Antipova, T. (eds) Advances in Digital Science. ICADS 2021. Advances in Intelligent Systems and Computing, vol 1352. Springer, Cham. https://doi.org/10.1007/978-3-030-71782-7_23

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