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Comparison and positioning of NGOs aimed at children from the perspective of social marketing on Twitter

Author

Listed:
  • Araceli Galiano-Coronil

    (University of Cadiz)

  • Marina Yong Alcedo-Velázquez

    (University of Cadiz)

  • Sofía Blanco-Moreno

    (University of Leon)

  • Luis Bayardo Tobar Pesántez

    (Politecnica Salesiana University of Ecuador)

Abstract

The role of Non-Governmental Organizations (NGOs) in disseminating and protecting children’s rights is fundamental by increasing society’s knowledge about the reality that children face, thus mobilizing citizen attention. In this paper, we present an original study on social media data, specifically Twitter, to analyze childhood NGOs, evaluating the success of their content (through the likes obtained by publications) from the perspective of social marketing and prospective theory. In addition, it examines the positioning of organizations concerning the types of messages identified. The methodological approach is based on data mining, content analysis, and simple correspondence analysis through which the typology of the messages and positioning map are determined. The results suggest that these organizations generate predictable communication by publishing on specific topics and only increasing the number of tweets in emergencies when they are requiring urgent help. Some tweets show an immediate risk to which children are exposed if they do not receive help, which aligns with one of the premises of the Prospect Theory. Furthermore, a more significant number of posts does not necessarily imply a greater number of likes. Three types of messages have been determined: informative tweets that point out risks (type 1), impartial dialogue tweets (type 2), and action tweets that highlight benefits (type 3), confirmed through the Kruskal-Wallis test to have a relationship with impact. The positioning map shows that type 3 messages, which World Vision Spain opts for, are the most popular, followed by type 1, which Educo leans towards. Finally, there are those of type 2, with which UNICEF Spain is associated. The main implication is that our analysis validates the use of social media such as Twitter to analyze NGOs and proposes these social media platforms to be an important tool in mobilizing the community. In addition, this study offers parameters when constructing the messages for use in social marketing campaigns according to decisions that involve risk or certainty.

Suggested Citation

  • Araceli Galiano-Coronil & Marina Yong Alcedo-Velázquez & Sofía Blanco-Moreno & Luis Bayardo Tobar Pesántez, 2024. "Comparison and positioning of NGOs aimed at children from the perspective of social marketing on Twitter," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-16, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-02611-7
    DOI: 10.1057/s41599-024-02611-7
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    References listed on IDEAS

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    1. Araceli Galiano-Coronil & Juan José MierTerán-Franco, 2019. "The Use of Social Digital Networks by NGDO from a Social Marketing Perspective," Social Sciences, MDPI, vol. 8(6), pages 1-23, June.
    2. Balaji, M.S. & Jiang, Yangyang & Jha, Subhash, 2021. "Nanoinfluencer marketing: How message features affect credibility and behavioral intentions," Journal of Business Research, Elsevier, vol. 136(C), pages 293-304.
    3. de Leeuw, Jan & Mair, Patrick, 2009. "Simple and Canonical Correspondence Analysis Using the R Package anacor," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i05).
    4. Gloria Jiménez-Marín & Rodrigo Elías Zambrano & Araceli Galiano-Coronil & Rafael Ravina-Ripoll, 2021. "Business and Energy Efficiency in the Age of Industry 4.0: The Hulten, Broweus and Van Dijk Sensory Marketing Model Applied to Spanish Textile Stores during the COVID-19 Crisis," Energies, MDPI, vol. 14(7), pages 1-10, April.
    5. Rodrigo Elías Zambrano & Gloria Jiménez-Marín & Araceli Galiano-Coronil & Rafael Ravina-Ripoll, 2021. "Children, Media and Food. A New Paradigm in Food Advertising, Social Marketing and Happiness Management," IJERPH, MDPI, vol. 18(7), pages 1-14, March.
    6. Moro, Sérgio & Rita, Paulo & Vala, Bernardo, 2016. "Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach," Journal of Business Research, Elsevier, vol. 69(9), pages 3341-3351.
    7. Lei Huang & Amelia Clarke & Natalie Heldsinger & Wen Tian, 2019. "The communication role of social media in social marketing: a study of the community sustainability knowledge dissemination on LinkedIn and Twitter," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(2), pages 64-75, June.
    8. Ester Guijarro & Cristina Santadreu-Mascarell & Beatriz Blasco-Gallego & Lourdes Canós-Darós & Eugenia Babiloni, 2021. "On the Identification of the Key Factors for a Successful Use of Twitter as a Medium from a Social Marketing Perspective," Sustainability, MDPI, vol. 13(12), pages 1-15, June.
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