Service Recovery, Justice and Customer Satisfaction: Evidence from Internet Service Users in Nepal
DOI:
https://doi.org/10.3126/ija.v4i1.92375Keywords:
Customer Satisfaction, Employee Behavior, Fairness in Pricing, Follow-up, Internet Service Providers, TimelinessAbstract
Background: In Nepal’s highly competitive internet service market, service failures are inevitable. Effective service recovery has become a critical determinant of customer satisfaction and loyalty, yet empirical evidence on this dynamic within the country’s Internet Service Provider (ISP) sector remains limited.
Objectives: This study aims to evaluate the influence of key service recovery dimensions—timeliness, employee behavior, fairness in pricing, and follow-up—on customer satisfaction among WorldLink internet users in the Kathmandu Valley.
Methods: A quantitative, cross-sectional research design was employed. Data were collected from 442 WorldLink users in Kathmandu Valley using a structured online questionnaire. The proposed model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM), grounded in justice theory.
Findings: The results demonstrate that all four service recovery dimensions have a significant positive influence on customer satisfaction. Timeliness (β = 0.329) and fairness in pricing (β = 0.299) emerged as the strongest predictors, followed by employee behavior (β = 0.245) and follow-up (β = 0.118). The model explained 75.4% of the variance in customer satisfaction, underscoring its strong explanatory power.
Conclusion: The study concludes that a holistic service recovery strategy, integrating procedural, interactional, and distributive justice, is essential for enhancing customer satisfaction in Nepal’s ISP sector. While WorldLink excels in timeliness and employee behavior, strengthening follow-up mechanisms is a key area for improvement.
Novelty: This research is among the first to empirically validate the applicability of justice theory to service recovery in Nepal’s ISP sector, offering a comprehensive analysis of multiple recovery dimensions using robust PLS-SEM techniques within a unique emerging market context.
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