Service innovation in telecommunication: The path to customer loyalty through enhanced customer satisfaction

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This study focuses on the Nepalese telecom sector, where service innovation is crucial for increasing customer satisfaction and gaining loyalty. In the rapidly evolving technological landscape, telecom service providers must continuously improve their offerings and customer experiences to draw in new customers and maintain their competitiveness. Based on a sample of 245 telecom customers, this study investigates how innovative service offerings lead to higher satisfaction levels and, in turn, greater customer loyalty. This study uses structural equation modeling (SEM) and a quantitative research approach to examine the association between customer satisfaction, customer loyalty, and service innovation in Nepal’s telecom industry. The study confirmed that service innovation (β = 0.23, p ≤ 0.01) directly influences customer satisfaction and significantly (β = 0.32, p ≤ 0.01) enhances customer loyalty in the Nepalese telecom sector. However, the study finds a negligible indirect impact of service innovation (β = 0.12, UL = 0.292, LL = -0.003) on customer loyalty when customer satisfaction is considered a mediator, indicating that customer satisfaction does not fully influence this relationship in the Nepalese telecom sector, suggesting that other variables, such as price sensitivity, switching costs, and consistency in service quality may also affect the relationship between service innovation, customer satisfaction, and loyalty. The results of this study will help the Nepalese telecom sector reevaluate its service innovation attempts, guiding executives to update their programs by offering new, reliable, high-quality services at affordable prices while considering different aspects of service innovation for strategic planning to attract loyal customers.

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    • Figure 1. Result of structural model
    • Table 1. Scale reliability
    • Table 2. Demographic profile
    • Table 3. Factor loadings and descriptive statistics analysis
    • Table 4. Validity concern
    • Table 5. Model fit indicators
    • Table 6. Test of hypotheses
    • Table 7. Mediation analysis
    • Conceptualization
      Manoj Kumar Chaudhary
    • Investigation
      Manoj Kumar Chaudhary, Madan Dhungana, Rajesh Kumar Chaudhary
    • Supervision
      Manoj Kumar Chaudhary, Dinesh Mani Ghimire, Madan Dhungana
    • Visualization
      Manoj Kumar Chaudhary, Madan Dhungana, Madhav Adhikari, Shubhechchha Thapa
    • Writing – original draft
      Manoj Kumar Chaudhary, Madan Dhungana, Shubhechchha Thapa
    • Writing – review & editing
      Manoj Kumar Chaudhary, Dinesh Mani Ghimire, Madan Dhungana, Rajesh Kumar Chaudhary, Madhav Adhikari, Shubhechchha Thapa
    • Data curation
      Dinesh Mani Ghimire, Rajesh Kumar Chaudhary, Madhav Adhikari, Shubhechchha Thapa
    • Formal Analysis
      Dinesh Mani Ghimire, Rajesh Kumar Chaudhary, Madhav Adhikari, Shubhechchha Thapa
    • Methodology
      Dinesh Mani Ghimire, Madan Dhungana, Madhav Adhikari
    • Validation
      Dinesh Mani Ghimire, Madan Dhungana, Rajesh Kumar Chaudhary, Madhav Adhikari
    • Resources
      Madan Dhungana, Rajesh Kumar Chaudhary
    • Project administration
      Rajesh Kumar Chaudhary, Madhav Adhikari, Shubhechchha Thapa
    • Software
      Rajesh Kumar Chaudhary