Determinants of customers’ perception towards digital payment system

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Type of the article: Research Article

Abstract
In today’s competitive age, marketers have realized the need to identify the factors influencing customers’ perceptions of digital payments. This study helps in developing sustainable marketing strategies for the firm. The study’s major goal was to examine the factors influencing customers’ perception towards digital payment in Nepal. Causal comparative research design has been used. This study has used the primary data, collected through the structured questionnaire based on the 6-point Likert scale. Purposive sampling was employed, and 394 sample size was taken because at least 384 responses are representative sample for the unknown population. The study population was consumers who use digital payment systems in Nepal. The survey was conducted from June to October 2024 in Nepal. The questionnaire was distributed to the digital payment system users because they might have adequate knowledge regarding the digital payment systems. The path analysis examined the impact of ease of use, self-efficacy, perceived trust, perceived usefulness, and privacy and security on customers’ perception towards digital payment using the structural equation modeling. The result showed that ease of use (β = .189, p < 0.05), self-efficacy (β = .595, p < 0.05), and perceived usefulness (β = .168, p < 0.05) significantly impact customer perception towards digital payment. However, it was also discovered that perceived trust (β = .084, p > 0.05) and privacy and security (β = .044, p > 0.05) do not considerably influence customers’ perception towards digital payment in the Nepalese market. It is concluded that ease of use, self-efficacy, and perceived usefulness play an essential role in the perception development of customers towards digital payment system. Still, consumers have no trust and do not believe in the security towards digital payment. The study’s findings might be helpful for business people and future researchers to understand new market conditions and growth.

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    • Figure 1. Variables
    • Figure 2. Structural model
    • Table 1. Questionnaire structure
    • Table 2. Profile of the respondents
    • Table 3. Internal consistency and common method bias
    • Table 4. Internal validity insights
    • Table 5. Estimated parameters of the regression routes
    • Conceptualization
      Gyan Mani Adhikari, Bharat Rai, Ganesh Bhattarai
    • Formal Analysis
      Gyan Mani Adhikari, Bharat Rai, Ganesh Bhattarai
    • Investigation
      Gyan Mani Adhikari, Bharat Rai
    • Methodology
      Gyan Mani Adhikari, Bharat Rai, Ganesh Bhattarai
    • Project administration
      Gyan Mani Adhikari, Ganesh Bhattarai
    • Validation
      Gyan Mani Adhikari, Bharat Rai, Ganesh Bhattarai
    • Visualization
      Gyan Mani Adhikari
    • Writing – original draft
      Gyan Mani Adhikari, Bharat Rai
    • Data curation
      Bharat Rai, Ganesh Bhattarai
    • Supervision
      Bharat Rai, Ganesh Bhattarai
    • Writing – review & editing
      Bharat Rai, Ganesh Bhattarai