AI-driven electronic customer relationship management and brand advocacy: The mediating role of consumption values in Vietnam’s digital banking sector

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Type of the article: Research Article

Abstract
The rapid adoption of artificial intelligence in digital banking is transforming how financial institutions manage customer relationships and encourage customer advocacy. However, empirical evidence explaining how artificial intelligence-driven electronic customer relationship management influences brand advocacy through different consumption values remains limited. This study aims to assess the effect of AI-driven electronic customer relationship management on brand advocacy and to examine the mediating roles of consumption values in Vietnam’s digital banking context. A quantitative cross-sectional survey was conducted with 468 users of digital banking services who had interacted with artificial intelligence-enabled customer service functions, and the data were analyzed using partial least squares structural equation modelling. The results show that AI-driven electronic customer relationship management has a positive and statistically significant direct effect on brand advocacy (β = 0.194, p < 0.001). Functional value (β = 0.092, p = 0.030), monetary value (β = 0.347, p < 0.001), epistemic value (β = 0.197, p < 0.001), and social value (β = 0.104, p = 0.010) also positively influence brand advocacy, whereas emotional value does not have a significant effect (β = 0.023, p = 0.703). Monetary value emerges as the strongest predictor of brand advocacy, and the model explains 63.4% of the variance in this construct. The findings indicate that artificial intelligence-enabled relationship management systems strengthen brand advocacy primarily when they deliver tangible economic, informational, and functional benefits to digital banking customers.

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. Structural model results
    • Table 1. Research sample structure (n = 468)
    • Table 2. Measurement model assessment
    • Table 3. Discriminant validity assessment using the HTMT criterion
    • Table 4. Hypotheses testing
    • Table A1. Measurement items
    • Conceptualization
      Nguyen Ha Thach, Pham Thi Kim Thanh, Pham Ngoc Kim Khanh, Nguyen Thu Hien
    • Data curation
      Nguyen Ha Thach, Pham Ngoc Kim Khanh, Nguyen Thu Hien, Phan Thi Huyen, Pham Thi Ngoc Dung
    • Funding acquisition
      Nguyen Ha Thach, Pham Thi Kim Thanh, Pham Ngoc Kim Khanh, Nguyen Thu Hien, Phan Thi Huyen, Pham Thi Ngoc Dung
    • Investigation
      Nguyen Ha Thach, Pham Thi Kim Thanh, Nguyen Thu Hien, Phan Thi Huyen, Pham Thi Ngoc Dung
    • Methodology
      Nguyen Ha Thach, Pham Thi Kim Thanh, Nguyen Thu Hien, Phan Thi Huyen, Pham Thi Ngoc Dung
    • Project administration
      Nguyen Ha Thach, Pham Thi Kim Thanh
    • Resources
      Nguyen Ha Thach, Pham Thi Kim Thanh, Pham Ngoc Kim Khanh
    • Software
      Nguyen Ha Thach, Phan Thi Huyen, Pham Thi Ngoc Dung
    • Supervision
      Nguyen Ha Thach
    • Validation
      Nguyen Ha Thach, Pham Thi Kim Thanh, Pham Ngoc Kim Khanh, Nguyen Thu Hien, Phan Thi Huyen, Pham Thi Ngoc Dung
    • Visualization
      Nguyen Ha Thach
    • Writing – original draft
      Nguyen Ha Thach, Pham Thi Kim Thanh, Pham Ngoc Kim Khanh, Nguyen Thu Hien, Phan Thi Huyen, Pham Thi Ngoc Dung
    • Writing – review & editing
      Nguyen Ha Thach
    • Formal Analysis
      Pham Thi Kim Thanh, Pham Ngoc Kim Khanh, Nguyen Thu Hien, Phan Thi Huyen, Pham Thi Ngoc Dung