Integrating social customer relationship management into customer lifetime value: Empirical evidence from Vietnamese banking

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Type of the article: Research Article

Abstract
In the context of rapid digital transformation in the banking sector of developing countries such as Vietnam, maintaining long-term customer value has become a critical challenge. Traditional Customer Lifetime Value (CLV) models, which mainly rely on transactional data, are often insufficient in capturing customer behavior in dynamic digital environments. This study aims to evaluate the integration of Social Customer Relationship Management (SCRM) into CLV models through the lens of the Technology-Organization-Environment (TOE) framework. Specifically, it analyzes how technological, organizational, and environmental contexts influence the implementation of SCRM, and how SCRM, in turn, affects three key components of CLV: customer acquisition, retention, and expansion. Data were collected from a survey of 425 banking professionals in Vietnam in October 2024 and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that SCRM has a positive and statistically significant impact on all three CLV components, with the strongest effect on customer retention (β = 0.325, p < 0.001). The technological (β = 0.181) and organizational (β = 0.198) contexts significantly influence the implementation of SCRM, while the environmental context does not show a meaningful impact. The study provides empirical evidence on the mediating role of SCRM and offers practical recommendations for banks to prioritize internal factors when developing strategies to enhance long-term customer value. As the empirical investigation was limited to the Vietnamese banking sector, the findings should be considered context-specific. To establish broader applicability, future studies should replicate this model in different national or industry contexts.

Acknowledgment
This research is partly funded by Industrial University of Ho Chi Minh City and University of Finance – Marketing.

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    • Figure 1. Proposed research model
    • Figure 2. PLS-SEM results
    • Table 1. Research sample structure (n = 425)
    • Table 2. Outer loadings, Cronbach’s Alpha, CR, AVE and VIF
    • Table 3. Discriminant validity assessment using the Fornell-Larcker criterion
    • Table 4. Hypothesis testing
    • Table A1. Survey measurements
    • Conceptualization
      Nguyen Ha Thach, Pham Thi Kim Thanh, Nguyen Thi Thanh Hien, Nguyen Thu Hien
    • Data curation
      Nguyen Ha Thach, Pham Thi Kim Thanh, Nguyen Thi Thanh Hien, Nguyen Thu Hien
    • Formal Analysis
      Nguyen Ha Thach, Pham Thi Kim Thanh, Nguyen Thi Thanh Hien, Nguyen Thu Hien
    • Investigation
      Nguyen Ha Thach, Pham Thi Kim Thanh, Nguyen Thi Thanh Hien, Nguyen Thu Hien
    • Methodology
      Nguyen Ha Thach, Pham Thi Kim Thanh, Nguyen Thi Thanh Hien, Nguyen Thu Hien
    • Project administration
      Nguyen Ha Thach, Pham Thi Kim Thanh, Nguyen Thi Thanh Hien, Nguyen Thu Hien
    • Resources
      Nguyen Ha Thach, Pham Thi Kim Thanh, Nguyen Thi Thanh Hien, Nguyen Thu Hien
    • Software
      Nguyen Ha Thach
    • Supervision
      Nguyen Ha Thach
    • Validation
      Nguyen Ha Thach, Pham Thi Kim Thanh, Nguyen Thi Thanh Hien, Nguyen Thu Hien
    • Visualization
      Nguyen Ha Thach
    • Writing – original draft
      Nguyen Ha Thach, Pham Thi Kim Thanh, Nguyen Thi Thanh Hien, Nguyen Thu Hien
    • Writing – review & editing
      Nguyen Ha Thach