Service quality dimensions as drivers of customer satisfaction in the telecommunications market: Evidence from an emerging economy

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Type of the article: Research Article

Abstract
Despite recent technological improvements in telecommunications services in emerging economies, many users still feel that the service does not meet their expectations. The main objective of this study is to analyze how the five dimensions of service quality influence customer satisfaction among users of a large telecommunications company operating in Lima, Peru. The research employed a quantitative explanatory design and surveyed 247 active customers with ongoing service contracts, using an instrument based on SERVPERF, which demonstrated strong validity and reliability indicators (KMO = 0.833; Cronbach’s α = 0.971). Data were analyzed using SPSS, applying an ordinal logistic regression model to evaluate both the general hypothesis and the individual effects of each dimension. The results indicate that all dimensions contribute significantly to explaining customer satisfaction, with reliability and responsiveness standing out as the most influential factors. The model demonstrated a good fit, explaining 57% of the variance in customer satisfaction (Nagelkerke’s R2 = 0.569). These findings suggest that improving the different dimensions of service quality is crucial to increasing satisfaction levels, strengthening user trust, and maintaining competitiveness. From a marketing perspective, this study contributes to the service marketing literature by explaining how perceived service quality dimensions shape customer satisfaction in the telecommunications market of an emerging economy, offering empirical insights that support customer experience management and value creation strategies.

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    • Table 1. Kaiser-Meyer-Olkin coefficient (KMO) and Bartlett’s test of sphericity
    • Table 2. Frequency distribution of perceived service quality and its dimensions
    • Table 3. Frequency distribution of satisfaction and its dimensions
    • Table 4. Hypothesis testing – lkelihood-ratio test and pseudo R-squared
    • Table 5. Specific hypotheses test – pseudo R-squared
    • Table A1. Questionnaire items used in the study sample
    • Conceptualization
      Arthur Giuseppe Serrato-Cherres, Celeste Lucero Barzola-Castro
    • Formal Analysis
      Arthur Giuseppe Serrato-Cherres, Franklin Cordova-Buiza
    • Project administration
      Arthur Giuseppe Serrato-Cherres
    • Visualization
      Arthur Giuseppe Serrato-Cherres
    • Writing – original draft
      Arthur Giuseppe Serrato-Cherres, Celeste Lucero Barzola-Castro
    • Methodology
      Franklin Cordova-Buiza
    • Supervision
      Franklin Cordova-Buiza
    • Validation
      Franklin Cordova-Buiza, Celeste Lucero Barzola-Castro
    • Writing – review & editing
      Franklin Cordova-Buiza, Jan Molina-Guillen
    • Funding acquisition
      Celeste Lucero Barzola-Castro
    • Investigation
      Celeste Lucero Barzola-Castro
    • Data curation
      Jan Molina-Guillen
    • Resources
      Jan Molina-Guillen
    • Software
      Jan Molina-Guillen