Strategic Prioritization of Service Delivery and User Satisfaction Determinants in Transport Department

Building upon the validated factors identified through Confirmatory Factor Analysis (CFA), this paper focuses on the strategic prioritization of service delivery and user satisfaction determinants within the Transport Department. Following CFA, Hierarchical Clustering was implemented to explore the structural relationships among the confirmed constructs– Licensing and User Accessibility, Process Support and Compliance Services, Safety and Emergency Management, Penalty Management, and Public Awareness. The insights gained from clustering were further integrated with the CRITIC method to enhance the objectivity of priority weighting. Employing Multi-Criteria Decision-Making (MCDM) techniques such as CRITIC, and Entropy, the study evaluates the relative importance of these determinants. Stakeholder judgments and quantitative data are synthesized to rank the constructs based on their impact on service quality and user satisfaction. The integration of hierarchical clustering with the CRITIC and Entropy methods provide a novel perspectives for grouping and weighting interrelated criteria, thereby strengthening the robustness of strategic insights. The findings highlight critical priority areas for policy intervention and resource allocation, offering a data-driven roadmap for improving public service delivery in the transport sector. This contributes to informed decision-making and strategic planning for sustainable, efficient, and user-cantered transport management.

Authors

Publication Details

Journal
Journal of Information Systems Engineering & Management
Published
2025-03-20
DOI
https://doi.org/10.52783/jisem.v10i3.6551
Citations
1
Primary Topic
Transportation Planning and Optimization
Type
article
Field-Weighted Citation Impact
2.18
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article

Strategic Prioritization of Service Delivery and User Satisfaction Determinants in Transport Department

Susan A. Murty
1 citations
Journal of Information Systems Engineering & Management
Transportation Planning and Optimization
2.18
article

Strategic Prioritization of Service Delivery and User Satisfaction Determinants in Transport Department

Susan A. Murty
article en
1 citations

Abstract

Building upon the validated factors identified through Confirmatory Factor Analysis (CFA), this paper focuses on the strategic prioritization of service delivery and user satisfaction determinants within the Transport Department. Following CFA, Hierarchical Clustering was implemented to explore the structural relationships among the confirmed constructs– Licensing and User Accessibility, Process Support and Compliance Services, Safety and Emergency Management, Penalty Management, and Public Awareness. The insights gained from clustering were further integrated with the CRITIC method to enhance the objectivity of priority weighting. Employing Multi-Criteria Decision-Making (MCDM) techniques such as CRITIC, and Entropy, the study evaluates the relative importance of these determinants. Stakeholder judgments and quantitative data are synthesized to rank the constructs based on their impact on service quality and user satisfaction. The integration of hierarchical clustering with the CRITIC and Entropy methods provide a novel perspectives for grouping and weighting interrelated criteria, thereby strengthening the robustness of strategic insights. The findings highlight critical priority areas for policy intervention and resource allocation, offering a data-driven roadmap for improving public service delivery in the transport sector. This contributes to informed decision-making and strategic planning for sustainable, efficient, and user-cantered transport management.

Journal of Information Systems Engineering & ManagementVol. 10(3)
Openalex Percentile: Top 14%
Transportation Planning and Optimization
2.18
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Strategic Prioritization of Service Delivery and User Satisfaction Determinants in Transport Department — Susan A. Murty · Journal of Information Systems Engineering & Management (2025) | TGRS Research Map | TGRS