Citas bibligráficas
Martinez, C., Vidurrizaga, N. (2024). El conocimiento del cliente, intimidad con el cliente y compromiso en relación con la lealtad del cliente de billeteras electrónicas [Trabajo de investigación, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/673506
Martinez, C., Vidurrizaga, N. El conocimiento del cliente, intimidad con el cliente y compromiso en relación con la lealtad del cliente de billeteras electrónicas [Trabajo de investigación]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2024. http://hdl.handle.net/10757/673506
@misc{renati/416917,
title = "El conocimiento del cliente, intimidad con el cliente y compromiso en relación con la lealtad del cliente de billeteras electrónicas",
author = "Vidurrizaga Placencia, Nicolle Samantha",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2024"
}
In the context of a competitive market, customer knowledge has been identified as a valuable and scarce asset that enables organizations to adapt to market changes. Furthermore, the influence of customer knowledge on customer intimacy and loyalty has been explored. Customer intimacy has been highlighted as a key driver in building customer loyalty in a highly competitive market, where customers value personalized experiences and individualized attention. Additionally, the effect of commitment on customer loyalty has been investigated in various sectors. In the banking context, gaps in research have been identified regarding the impact of customer knowledge on customer intimacy, commitment, and loyalty. Therefore, it is suggested to delve into these relationships and conduct analyses in emerging markets and other online sectors to better understand customers' relationship with e-wallets. Data will be obtained from a sample of 400 participants, selected through non-probabilistic convenience sampling. These participants will receive structured questionnaires divided into three sections: filter questions, demographic questions, and questions related to the variables under study. Additionally, the results will be analyzed using a structural equation modeling approach, applying the Partial Least Squares (PLS) technique and the Smart-PLS software.
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