Analysis of Transport Department Services: Insights from Exploratory and Confirmatory Factor Analysis

This work presents a comprehensive analysis of transport services using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) to identify and validate key dimensions influencing service efficiency and user satisfaction. Data was collected on 25 measurement items across six constructs, including Licensing, Registration, Permits, Taxes, Penalties, and Road Safety. EFA was employed to explore the underlying structure of the data and to group related items under cohesive factors. The results identified five distinct factors, namely Service Access, Process Support, Safety Operations, Eco Efficiency, and Penalty Awareness. CFA was then utilized to validate the factor structure and assess the model's fit to the observed data. The findings highlight critical areas for improvement in transport services, such as streamlining processes, enhancing accessibility, ensuring compliance, and promoting road safety initiatives. This study offers actionable insights for policymakers and stakeholders to optimize service delivery and enhance user satisfaction in transport management systems.

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Publication Details

Journal
Communications on Applied Nonlinear Analysis
Published
2025-02-26
DOI
https://doi.org/10.52783/cana.v32.4040
Primary Topic
Technology and Data Analysis
Type
article
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article

Analysis of Transport Department Services: Insights from Exploratory and Confirmatory Factor Analysis

V.V.S. Kesava Rao S.V.R. Murty
Communications on Applied Nonlinear Analysis
Technology and Data Analysis
article

Analysis of Transport Department Services: Insights from Exploratory and Confirmatory Factor Analysis

V.V.S. Kesava Rao S.V.R. Murty
article en

Abstract

This work presents a comprehensive analysis of transport services using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) to identify and validate key dimensions influencing service efficiency and user satisfaction. Data was collected on 25 measurement items across six constructs, including Licensing, Registration, Permits, Taxes, Penalties, and Road Safety. EFA was employed to explore the underlying structure of the data and to group related items under cohesive factors. The results identified five distinct factors, namely Service Access, Process Support, Safety Operations, Eco Efficiency, and Penalty Awareness. CFA was then utilized to validate the factor structure and assess the model's fit to the observed data. The findings highlight critical areas for improvement in transport services, such as streamlining processes, enhancing accessibility, ensuring compliance, and promoting road safety initiatives. This study offers actionable insights for policymakers and stakeholders to optimize service delivery and enhance user satisfaction in transport management systems.

Communications on Applied Nonlinear AnalysisVol. 32(9s)
Openalex Percentile: Top 92%
Technology and Data Analysis
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