Vol. 7, Issue 1, Part C (2025)

This review paper highlights the evolution of data analysis techniques in service quality research, drawing on service quality studies from the SCOPUS database (2013-2022). Building on Khan & Adil (2013) review paper on data analysis technique, this paper extends the discourse by summarizing technique advancements and emerging trends. The analysis reveals a pronounced shift from traditional methods (e.g., descriptive statistics, regression analysis, t-tests) to advanced multivariate approaches, particularly structural equation modelling. Post-2019 trends highlight a shift toward PLS-SEM for testing model and also for testing complex mediation and moderation effects. Factor analysis (EFA and CFA) remains pivotal for validating service quality constructs. Importance-performance analysis has gained prominence as a valuable tool for evaluating key factors in service quality research. The emergence of data mining reflects a growing trend of interdisciplinary innovation. Despite the dominance of SEM-based techniques, there is persistent reliance on regression and ANOVA. By summarising decade-long trends, this study offers a roadmap for researchers to navigate methodological choices, emphasizing context-specific adaptability. This review advocates for emerging computational tools (softwares) in service quality research while retaining core psychometric principles. Future research should prioritize methodological transparency, reproducibility, and the integration of mixed-methods designs to bridge theoretical and practical gaps.