Ir al menú de navegación principal Ir al contenido principal Ir al pie de página del sitio

Vol. 25 Núm. 1 (2025)
Inquietud Empresarial 25(1) Enero-Junio

GESTIÓN, PRODUCCIÓN Y SERVICIOS

Trazando el terreno del marketing de influenciadores: Una revisión bibliométrica de Scopus y Wos

https://doi.org/10.19053/uptc.01211048.17388

Publicado 05-01-2025

  • Joshy John
    • ,
  • Nebu Cherian P
    • ,
  • Melby Joseph
    • ,
  • Shinta Sebastian
    • ,
  • Bose George

Joshy John
Marian College Kuttikkanam; Peermade, Kuttikkanam, Kerala 685531, India

Nebu Cherian P
Rajagiri College Of Social Sciences; Kalamassery, Kochi, Kerala 683104, India

Melby Joseph
Marian College Kuttikkanam; Peermade, Kuttikkanam, Kerala 685531, India

Shinta Sebastian
Marian College Kuttikkanam; Peermade, Kuttikkanam, Kerala 685531, India,Marian College Kuttikkanam; Peermade, Kuttikkanam, Kerala 685531, India

Bose George
Marian College Kuttikkanam; Peermade

Resumen

El marketing de influencers es una estrategia que utilizan las empresas para promocionar sus productos o servicios a través de asociaciones con personas o entidades populares, conocidas como influencers, en varias plataformas de redes sociales. Este estudio se embarca en un análisis bibliométrico del marketing de influencers, utilizando las bases de datos Scopus y Web of Science (WoS) para recopilar datos bibliográficos completos. Empleando la metodología del diagrama de flujo PRISMA, esta investigación identifica, selecciona e incluye meticulosamente artículos pertinentes en el análisis bibliométrico, abordando la creciente importancia del marketing de influencers en las estrategias digitales contemporáneas. La singularidad de este estudio radica en su método de combinar datos bibliográficos de Scopus y WoS, utilizando el software RStudio para fusionar y eliminar duplicados, asegurando un conjunto de datos sólido para el análisis. Nuestros hallazgos delinean un panorama de creciente producción científica anual dentro del dominio, destacando las fuentes más influyentes, los autores y la aplicación de la Ley de Lotka para evaluar la productividad de los autores. Un análisis más detallado a través de la espectroscopia del año de publicación de referencia, mapas temáticos y redes de coocurrencia revelan temas de tendencias en evolución y puntos focales temáticos dentro del campo. Los análisis factoriales e historiográficos, además de examinar las redes de colaboración de los países, proporcionan una comprensión más profunda del impacto global y la naturaleza interdisciplinaria de la investigación de marketing de influencers. Este estudio bibliométrico no solo traza la trayectoria académica y los contribuyentes clave de la literatura sobre marketing de influencers, sino que también identifica importantes lagunas de investigación e implicaciones prácticas, ofreciendo una valiosa hoja de ruta para futuras investigaciones y aplicaciones estratégicas en el panorama dinámico del marketing digital.

Códigos JEL: M310

Recibido: 22/03/2024. Aceptado: 10/07/2024. Publicado: 05/01/2025.

Palabras clave

Marketing de Influencia, Análisis Bibliométrico, Biblioshiny, RStudio

PDF (English)

Citas

  1. Abbas, A. F., Jusoh, A., Masod, A., & Ali, J. (2021). A Bibliometric Analysis of Publications on Social Media Influencers Using Vosviewer. Journal of Theoretical and Applied Information Technology, 99(23), 5662–5676.
  2. Achuthan, K., Nair, V. K., Kowalski, R., Ramanathan, S., & Raman, R. (2023). Cyberbullying research — Alignment to sustainable development and impact of COVID-19: Bibliometrics and science mapping analysis. Computers in Human Behavior, 140, 107566. https://doi.org/10.1016/j.chb.2022.107566
  3. Agac, G., Sevim, F., Celik, O., Bostan, S., Erdem, R., & Yalcin, Y. I. (2023). Research hotspots, trends and opportunities on the metaverse in health education: A bibliometric analysis. Library Hi Tech. Advance online publication. https://doi.org/10.1108/LHT-04-2023-0168
  4. Ahuja, Y., & Loura, I. (2020). An investigative study of influencer marketing: Nuances, challenges and impact. Journal for Global Business Advancement. https://doi.org/10.1504/JGBA.2020.10035200
  5. Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377–386. https://doi.org/10.1162/qss_a_00019
  6. Birkle, C., Pendlebury, D. A., Schnell, J., & Adams, J. (2020). Web of Science as a data source for research on scientific and scholarly activity. Quantitative Science Studies, 1(1), 363–376. https://doi.org/10.1162/qss_a_00018
  7. Borgohain, D. J., Bhardwaj, R. K., & Verma, M. K. (2022). Mapping the literature on the application of artificial intelligence in libraries (AAIL): A scientometric analysis. Library Hi Tech. Advance online publication. https://doi.org/10.1108/LHT-07-2022-0331
  8. Brown, D., & Hayes, N. (2008). Influencer marketing. https://doi.org/10.4324/9780080557700
  9. Bu, Y., Parkinson, J., & Thaichon, P. (2022). Influencer marketing: Sponsorship disclosure and value co-creation behaviour. Marketing Intelligence and Planning, 40(7), 854–870. https://doi.org/10.1108/MIP-09-2021-0310
  10. Cartwright, S., Liu, H., & Davies, I. A. (2022). Influencer marketing within business-to-business organisations. Industrial Marketing Management, 106, 338–350. https://doi.org/10.1016/j.indmarman.2022.09.007
  11. Chen, C., Dubin, R., & Kim, M. C. (2014). Emerging trends and new developments in regenerative medicine: A scientometric update (2000 – 2014). Expert Opinion on Biological Therapy, 14(9), 1295–1317. https://doi.org/10.1517/14712598.2014.920813
  12. Farivar, S., Wang, F., & Yuan, Y. (2021). Opinion leadership vs. Para-social relationship: Key factors in influencer marketing. Journal of Retailing and Consumer Services, 59. https://doi.org/10.1016/j.jretconser.2020.102371
  13. Gavel, Y., & Iselid, L. (2008). Web of Science and Scopus: A journal title overlap study. Online Information Review, 32(1), 8–21.
  14. Godin, B. (2006). On the origins of bibliometrics. Scientometrics, 68(1), 109–133. https://doi.org/10.1007/s11192-006-0086-0
  15. Gonzales-Torres, X. B., Cruz-Cabrera, B. C., Méndez-Prada, M. C., & Acevedo-Martínez, J. A. (2023). Revisión bibliométrica de la literatura sobre economía social y solidaria en las áreas de economía, econometría, finanzas, negocios, gestión y contabilidad. Inquietud Empresarial, 23(1), e15523. https://doi.org/10.19053/01211048.15523
  16. Guleria, D., & Kaur, G. (2021). Bibliometric analysis of ecopreneurship using VOSviewer and RStudio Bibliometrix, 1989–2019. Library Hi Tech, 39(4), 1001–1024. https://doi.org/10.1108/LHT-09-2020-0218
  17. Hood, W. W., & Wilson, C. S. (2001). The Literature of Bibliometrics, Scientometrics, and Informetrics. Scientometrics, 52(2), 291–314.
  18. Hugh, D. C., Dolan, R., Harrigan, P., & Gray, H. (2022). Influencer marketing effectiveness: The mechanisms that matter. European Journal of Marketing, 56(12), 3485–3515. https://doi.org/10.1108/EJM-09-2020-0703
  19. Jarrar, Y., Awobamise, A. O., & Aderibigbe, A. A. (2020). Effectiveness of influencer marketing vs social media sponsored advertising. Utopia y Praxis Latinoamericana, 25(12), 40–54. https://doi.org/10.5281/zenodo.4280084
  20. John, S. P., & Supramaniam, S. (2023). Antecedents and Effects of Influencer Marketing Strategies: A Systematic Literature Review and Directions for Future Research. In Springer Proceedings in Business and Economics (pp. 113–119). https://doi.org/10.1007/978-3-031-31836-8_15
  21. Joseph, J., Jose, J., Jose, A. S., Ettaniyil, G. G., John, J., & D Nellanat, P. (2023). Unveiling the research impact: A visualization study of ChatGPT’s influence on the scientific landscape. Journal of Theoretical and Applied Information Technology, 101(22).
  22. Joshi, Y., Lim, W. M., Jagani, K., & Kumar, S. (2023). Social media influencer marketing: Foundations, trends, and ways forward. Electronic Commerce Research. https://doi.org/10.1007/s10660-023-09719-z
  23. Kay, S., Mulcahy, R., Sutherland, K., & Lawley, M. (2023). Disclosure, content cues, emotions and behavioural engagement in social media influencer marketing: An exploratory multi-stakeholder perspective. Journal of Marketing Management, 39(7–8), 550–584. https://doi.org/10.1080/0267257X.2022.2118815
  24. Komperda, R. (2017). Likert-type survey data analysis with R and RStudio. In ACS Symposium Series (Vol. 1260, pp. 91–116). https://doi.org/10.1021/bk-2017-1260.ch007
  25. Le, K., & Aydin, G. (2023). Impact of the pandemic on social media influencer marketing in fashion: A qualitative study. Qualitative Market Research, 26(4), 449–469. https://doi.org/10.1108/QMR-11-2021-0133
  26. Leung, F. F., Gu, F. F., Li, Y., Zhang, J. Z., & Palmatier, R. W. (2022). Influencer Marketing Effectiveness. Journal of Marketing, 86(6), 93–115. https://doi.org/10.1177/00222429221102889
  27. Lou, C., & Yuan, S. (2019). Influencer Marketing: How Message Value and Credibility Affect Consumer Trust of Branded Content on Social Media. Journal of Interactive Advertising, 19, 58–73. https://doi.org/10.1080/15252019.2018.1533501
  28. Luo, X., & Hussain, W. M. H. W. (2023). A Bibliometric Analysis and Visualization of Influencer Marketing. Journal of System and Management Sciences, 13(4), 59–73. https://doi.org/10.33168/JSMS.2023.0404
  29. Martínez-López, F. J., Anaya-Sánchez, R., Fernández Giordano, M., & Lopez-Lopez, D. (2020). Behind influencer marketing: Key marketing decisions and their effects on followers’ responses. Journal of Marketing Management, 36(7–8), 579–607. https://doi.org/10.1080/0267257X.2020.1738525
  30. Mero, J., Vanninen, H., & Keränen, J. (2023). B2B influencer marketing: Conceptualization and four managerial strategies. Industrial Marketing Management, 108, 79–93. https://doi.org/10.1016/j.indmarman.2022.10.017
  31. Mouritzen, S. L. T., Penttinen, V., & Pedersen, S. (2023). Virtual influencer marketing: The good, the bad and the unreal. European Journal of Marketing. Advance online publication. https://doi.org/10.1108/EJM-12-2022-0915
  32. Nadanyiova, M., Gajanova, L., Majerova, J., & Lizbetinova, L. (2020). Influencer marketing and its impact on consumer lifestyles. Forum Scientiae Oeconomia,, 8(2), 109–120. https://doi.org/10.23762/FSO_VOL8_NO2_7
  33. Nguyen, H. D., Nguyen, K. V., Hoang, S. N., & Huynh, T. (2020). Design a Management System for the Influencer Marketing Campaign on Social Network. In Lecture Notes in Computer Science (Vol. 12575, pp. 139–151).Springer. https://doi.org/10.1007/978-3-030-66046-8_12
  34. Palermo García, B., & Salazar-Velázquez, R. (2023). Transporte en la logística verde: Análisis bibliométrico. Inquietud Empresarial, 23(2), e15998. https://doi.org/10.19053/01211048.15998
  35. Racine, J. S. (2012). RStudio: A platform-independent IDE for R and Sweave. Journal of Statistical Software, 42(1), 1–28. JSTOR.
  36. Ribeiro, M. I., Fernandes, A. J., & Fernandes, A. P. (2020). Influencer marketing: A bibliometric analysis of scientific production from the scopus database; [Influencer marketing: Uma análise bibliométrica da produção científica da base de dados scopus]. RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao, (E34), 77–90.
  37. Souza de Cursi, E. (2023). Some Tips to Use R and RStudio. In E. Souza de Cursi (Ed.), Uncertainty Quantification using R (pp. 1–108). Springer International Publishing. https://doi.org/10.1007/978-3-031-17785-9_1
  38. Syed, T. A., Mehmood, F., & Qaiser, T. (2023). Brand–SMI collaboration in influencer marketing campaigns: A transaction cost economics perspective. Technological Forecasting and Social Change, 192, 122580. https://doi.org/10.1016/j.techfore.2023.122580
  39. Tanwar, A. S., Chaudhry, H., & Srivastava, M. K. (2022). Trends in Influencer Marketing: A Review and Bibliometric Analysis. In Journal of Interactive Advertising, 22(1), 1–27. https://doi.org/10.1080/15252019.2021.2007822
  40. Thangavel, P., & Chandra, B. (2023). Two Decades of M-Commerce Consumer Research: A Bibliometric Analysis Using R Biblioshiny. Sustainability, 15(15), 11835. https://doi.org/10.3390/su151511835
  41. Valadez-Solana, B. D., Cruz-Cabrera, B. C., Huesca-Gastélum, M. I., & Castillo-Leal, M. (2023). Emprendimiento social e innovación social: Un análisis bibliométrico. Inquietud Empresarial, 23(2), e15874. https://doi.org/10.19053/01211048.15874
  42. Waghmare, P. (2021). Bibliometric Analysis of Global Research Trends on E-Waste Management from Scopus Database seen through Biblioshiny. Library Philosophy and Practice, 2021, 1–16.
  43. Wang, N. (2023). Influencer Marketing Strategies in Foreign Marketplaces. In Springer Proceedings in Business and Economics (pp. 86–90). Springer. https://doi.org/10.1007/978-3-031-31836-8_11
  44. Wulandari, T. A., & Assidiq, M. I. (2023). Influencer marketing strategy of a start-up company as the provider of fast-moving consumer goods (FMCG) products in Indonesia. Journal of Eastern European and Central Asian Research, 10(4), 569–579. https://doi.org/10.15549/jeecar.v10i4.1365
  45. Yamashita, R. (2016). What is influencer marketing? Screen Printing, 107(4), 24–25.
  46. Yu, Y., Li, Y., Zhang, Z., Gu, Z., Zhong, H., Zha, Q., Yang, L., Zhu, C., & Chen, E. (2020). A bibliometric analysis using VOSviewer of publications on COVID-19. Annals of Translational Medicine, 8(13), 816. https://doi.org/10.21037/atm-20-4235
  47. Zhou, S., Blazquez, M., McCormick, H., & Barnes, L. (2021). How social media influencers’ narrative strategies benefit cultivating influencer marketing: Tackling issues of cultural barriers, commercialised content, and sponsorship disclosure. Journal of Business Research, 134, 122–142. https://doi.org/10.1016/j.jbusres.2021.05.011

Descargas

Los datos de descargas todavía no están disponibles.

Artículos similares

1 2 3 4 5 6 > >> 

También puede {advancedSearchLink} para este artículo.