Abandono en servicios - Una revisión bibliométrica
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Publicado
11-05-2022
Hugo Ribeiro
Belém Barbosa
António C. Moreira
Ricardo Rodrigues
https://orcid.org/0000-0002-0410-6430
Belém Barbosa
António C. Moreira
Ricardo Rodrigues
Resumen
El objetivo de este artículo es identificar las investigaciones más impactantes sobre la pérdida de clientes y trazar la estructura conceptual e intelectual de su campo de estudio. Los datos han sido recogidos de la base de datos WoS, que comprenden 338 artículos publicados entre 1995 y 2020. Varias técnicas bibliométricas fueron aplicadas, incluyendo el análisis de co-palabras, cocitaciones, acoplamiento bibliográfico y redes de coautoría. Para realizar los análisis se utilizaron el software R y el Bibliometrix/Biblioshiny. Los resultados identifican los autores, artículos y revistas más influyentes y activos sobre el tema. Más específicamente, a través de las cocitaciones y el acoplamiento bibliográfico, fue posible mapear los artículos más antiguos (análisis retrospectivo) y la investigación más actual (análisis prospectivo). El análisis retrospectivo, basado en las cocitaciones, reveló que los fundamentos de este campo de investigación son constructos como la calidad del servicio, la satisfacción, la lealtad y el cambio de comportamientos. El análisis prospectivo, realizado a través del acoplamiento bibliográfico, reveló que la investigación actual está inmersa en el análisis predictivo, los conglomerados, la minería de datos y los algoritmos. Los resultados proporcionan una sólida orientación para seguir investigando en este campo.
Cómo citar
Ribeiro, H., Barbosa, B., C. Moreira, . A., & Rodrigues, R. (2022). Abandono en servicios - Una revisión bibliométrica. Cuadernos De Gestión, 22(2), 97–121. https://doi.org/10.5295/cdg.211509hr
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Keywords
Churn de Clientes, Análisis Bibliométrico, Análisis de Cocitación, Acoplamiento Bibliográfico, Mapeo de la Ciencia, Biblioshiny
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Buckinx, W., & Van den Poel, D. (2005). Customer base analysis: Partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. European Journal of Operational Research, 164(1), 252-268. https://doi.org/10.1016/j.ejor.2003.12.010
Burez, J., & Van den Poel, D. (2007). CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Systems with Applications, 32(2), 277-288. https://doi.org/10.1016/j.eswa.2005.11.037
Burez, J., & Van den Poel, D. (2009). Handling class imbalance in customer churn prediction. Expert Systems with Applications, 36(3), 4626-4636. https://doi.org/10.1016/j.eswa.2008.05.027
Burnham, T. A., Frels, J. K., & Mahajan, V. (2003). Consumer switching costs: A typology, antecedents, and consequences. Journal of the Academy of Marketing Science, 31(2), 109-126. https://doi.org/10.1177/0092070302250897
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Chen, P. Y., & Hitt, L. M. (2002). Measuring switching costs and the determinants of customer retention in Internet-enabled busi-nesses: A study of the Online brokerage industry. Information Systems Research, 13(3), 255-274. https://doi.org/10.1287/isre.13.3.255.78
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Coussement, K. (2014). Improving customer retention management through cost-sensitive learning. European Journal of Marketing, 48(3-4), 477-495. https://doi.org/10.1108/ejm-03-2012-0180
Coussement, K., & Van den Poel, D. (2009). Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers. Expert Systems with Applications, 36(3), 6127-6134. https://doi.org/10.1016/j.eswa.2008.07.021
De Caigny, A., Coussement, K., & De Bock, K. W. (2018). A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. European Journal of Operational Research, 269(2), 760-772. https://doi.org/10.1016/j.ejor.2018.02.009
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