Citas bibligráficas
Macedo, L., Moya, R. (2024). La influencia del odio a la marca y el perdón del cliente en el boca a boca negativo y la intención de recompra en los operadores de redes móviles [Trabajo de investigación, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/683969
Macedo, L., Moya, R. La influencia del odio a la marca y el perdón del cliente en el boca a boca negativo y la intención de recompra en los operadores de redes móviles [Trabajo de investigación]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2024. http://hdl.handle.net/10757/683969
@misc{renati/1060454,
title = "La influencia del odio a la marca y el perdón del cliente en el boca a boca negativo y la intención de recompra en los operadores de redes móviles",
author = "Moya Taipe, Rosely",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2024"
}
This study addresses the importance of consumer impact following a negative experience in mobile network operator companies and its relationship with brand hate, customer forgiveness, negative word of mouth, and repurchase intention. It emphasizes that service failures lead to negative experiences, fostering the development of intense brand hate among dissatisfied consumers. This hate manifests through negative word of mouth, encompassing unfavorable comments and critiques shared in both physical and digital environments. The concept of customer forgiveness emerges as a crucial element in mitigating brand hate, reducing the inclination toward negative word of mouth. This internal act of forgoing revenge contributes to the restoration of the relationship with the brand and, consequently, is positively linked to repurchase intention. The research is justified by the scarcity of studies on brand hate, especially in the context of mobile network operators. Additionally, the need to distinguish between product and service in investigating this phenomenon is underscored. A research model is proposed, to be evaluated through a quantitative and cross-sectional design, employing online surveys and the SEM model to analyze the proposed hypotheses.
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