Unlocking the potential of loyalty programs in reference to customer experience with digital wallets

  • Received January 6, 2023;
    Accepted March 15, 2023;
    Published March 30, 2023
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/im.19(1).2023.20
  • Article Info
    Volume 19 2023, Issue #1, pp. 233-243
  • TO CITE АНОТАЦІЯ
  • Cited by
    3 articles
  • 782 Views
  • 392 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

The emergence of digital technology has fundamentally transformed how businesses generate value for their customers. One of the critical components of this paradigm shift in digital transformation is improving customer experience, which benefits both consumers and organizations.
This study aims to evaluate customer experience and its influence on customer satisfaction and loyalty in the digital wallet domain. It also analyzes the moderating role of loyalty programs. This study was conducted in the Indian context since, alongside advancements in technology and a focus on digitalization, there has been a substantial increase in the acceptance of cashless payment options. The data from 349 respondents using the snowball sampling technique were collected through Google Forms, and SmartPLS 4.0 was used for analysis.
The results showed that loyalty and satisfaction are significantly influenced in the digital wallet domain if organizations work on factors affecting customer experience. The results also proved that loyalty programs moderate the relationship between customer experience and customer satisfaction.
This analysis successfully unlocked the potential of loyalty programs and established that loyalty programs do not moderate customer loyalty. However, organizations must note that poorly designed loyalty programs are just like any other sale promotion scheme, which adds up to the promotional expense without achieving the overall long-term objective of sustaining loyal consumers.

view full abstract hide full abstract
    • Figure 1. Conceptual model
    • Table 1. Demographic analysis
    • Table 2. Cronbach’s alpha, composite reliability, and average variance extracted
    • Table 3. Discriminant validity
    • Table 4. Hypotheses testing
    • Conceptualization
      Vidushi Vatsa, Bhawna Agarwal, Ruchika Gupta
    • Investigation
      Vidushi Vatsa, Ruchika Gupta
    • Methodology
      Vidushi Vatsa, Bhawna Agarwal
    • Software
      Vidushi Vatsa
    • Validation
      Vidushi Vatsa
    • Visualization
      Vidushi Vatsa, Ruchika Gupta
    • Writing – original draft
      Vidushi Vatsa
    • Writing – review & editing
      Vidushi Vatsa, Bhawna Agarwal, Ruchika Gupta
    • Data curation
      Bhawna Agarwal, Ruchika Gupta
    • Formal Analysis
      Bhawna Agarwal, Ruchika Gupta
    • Supervision
      Bhawna Agarwal, Ruchika Gupta
    • Resources
      Ruchika Gupta