Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 925 https://internationalpubls.com Analysis of Transport Department Services: Insights from Exploratory and Confirmatory Factor Analysis S.V.R. Murty1, V.V.S. Kesava Rao2 1Executive category Ph.D. Scholar in Department of Mechanical Engineering, College of Engineering(A), Andhra University, Visakhapatnam-3. Email: murtyamvi@gmail.com 2Professor, Department of Mechanical Engineering, College of Engineering(A), Andhra University, Visakhapatnam. Email: kesava9999@gmail.com Article History: Received: 12-11-2024 Revised: 17-12-2024 Accepted: 06-01-2025 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. Keywords:CFA, Transport management systems, Confirmatory factor analysis 1. Introduction Transport services play a pivotal role in facilitating mobility, ensuring compliance with regulatory frameworks, and promoting road safety. Effective management of transport-related activities, such as licensing, vehicle registration, permits, tax collection, penalty enforcement, and road safety measures, is essential for enhancing user satisfaction and operational efficiency. The increasing complexity of these services, driven by technological advancements and growing user expectations, necessitates a structured approach to evaluate and improve their performance. Evaluating the performance of road transport departments, which are responsible for enforcing transport-related regulations, is crucial for ensuring effective governance and public satisfaction. This literature review examines methodologies and frameworks pertinent to assessing such public sector transport enforcement agencies. The Department for Transport needs to establish comprehensive strategies for monitoring and evaluating transport services. Monitoring and Evaluation Strategy outlines a framework aimed at integrating high-quality evidence into departmental decision-making processes. This strategy mailto:murtyamvi@gmail.com mailto:kesava9999@gmail.com Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 926 https://internationalpubls.com emphasizes the importance of systematic learning from past and current activities to enhance future transport initiatives. The International Road Assessment Programme (iRAP) provides a framework for assessing road infrastructure safety, which can indirectly reflect the performance of transport departments responsible for road safety enforcement. iRAP focuses on identifying high-risk roads and recommending safety improvements, thereby contributing to the overall assessment of transport service quality. Monitoring and evaluation in the public sector, particularly within transport agencies, present several challenges. These include resource constraints, data collection difficulties, and the need for specialized expertise. Addressing these challenges is essential for developing effective monitoring and evaluation systems that can inform policy decisions and improve service delivery. Implementing robust monitoring and evaluation frameworks enables transport departments to identify areas requiring improvement, allocate resources efficiently, and enhance overall performance. By adopting best practices from established frameworks This chapter investigates the key dimensions of transport services through the application of both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). EFA is employed to uncover the latent structure of the data, grouping related items into cohesive factors that represent distinct dimensions of service efficiency and user experience. CFA is then used to validate these factors, ensuring their alignment with theoretical constructs and assessing the model's goodness-of-fit. The study focuses on six critical constructs of transport services: Licensing, Registration, Permits, Taxes, Penalties, and Road Safety. Data was collected on 25 measurement items, each designed to capture specific aspects of these constructs, such as clarity, accessibility, timeliness, compliance, and user satisfaction. The analysis revealed five key factors—Service Access, Process Support, Safety Operations, Ecoefficiency, and Penalty Awareness—that encapsulate the core dimensions of transport service delivery. Efficient transport service management is crucial for urban development, directly influencing user satisfaction, regulatory compliance, and safety outcomes. Research in this domain often emphasizes service quality dimensions such as accessibility, clarity, timeliness, and technological integration. Licensing and Registration Services Studies highlight the importance of clear procedures and user-friendly platforms in licensing and registration processes. Digital transformation in these areas significantly enhances user satisfaction and reduces delays. Permit Management The role of compliance with legal standards is vital in building trust among users. Providing timely and detailed permit information fosters a transparent and efficient service environment. Tax Efficiency and Incentives The impact of tax-related services on user satisfaction focuses on the ease of payment systems and the fairness of tax rates. Research also investigates the role of eco-friendly tax incentives in promoting sustainable practices, such as the adoption of electric vehicles. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 927 https://internationalpubls.com Penalty Management Digital integration in penalty management, such as online systems for penalty payments and disputes, improves user convenience. Public awareness campaigns are effective in promoting compliance and fostering positive perceptions of penalty enforcement. Road Safety Initiatives Data-driven approaches to accident analysis are essential for implementing integrated road safety measures. Collaborative efforts between transport departments and other agencies enhance the effectiveness of these initiatives. 1.1 Factor Analysis in Transport Research Factor analysis is widely applied in transport research to identify and validate key service dimensions. Exploratory Factor Analysis (EFA) uncovers latent constructs, while Confirmatory Factor Analysis (CFA) ensures the reliability and validity of measurement models. This chapter not only provides insights into the structural relationships among the various aspects of transport services but also highlights areas for improvement to enhance efficiency and user satisfaction. By integrating EFA and CFA methodologies, the study offers a robust framework for evaluating and optimizing transport service management systems. 2. Literature Review Efficient transport service management is crucial for urban development, directly influencing user satisfaction, regulatory compliance, and safety outcomes. Research in this domain often emphasizes service quality dimensions such as accessibility, clarity, timeliness, and technological integration. Parasuraman et al (1988) introduced SERVQUAL, a widely used model to measure service quality using factor analysis. The methodology can be adapted to transport services to evaluate efficiency and satisfaction. Pullen (1993) explored approaches to defining and assessing quality management processes in local public transportation. The research analyzed various evaluation methods, such as passenger waiting times, lost mileage, and an expanded range of performance measures and indices. Tyrinopoulos, Y., and Antoniou, C. (2008) examined public transport user satisfaction using factor analysis, identifying factors that affect transit service performance. Blanquart and Burmeister (2009) developed an alternative framework for evaluating freight transport performance, emphasizing diverse service configurations, each with unique performance indicators, rather than relying solely on traditional productivity measures Fabrigar and Wegener (2011) employed Exploratory and Confirmatory Factor Analyses on the Teacher’s Evaluation of Student’s Conduct Questionnaire (TESC). Chen, C., and Lai, W. T. (2011) used factor analysis and SEM to examine how different dimensions of service quality impact user satisfaction in public transport. Andrews, R., Boyne, G. A., and Walker, R. M. (2011) Used factor analysis to develop an empirical model of public service efficiency. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 928 https://internationalpubls.com Bachok, S., Osman, M. M., and Ponrahono, Z. (2014) used factor analysis to identify key determinants of public transport service quality, offering policy recommendations Yaya et al (2015) identified functional, convenience, and physical environment quality as key dimensions of public transport service quality, influenced by demographic factors like age, education, and license ownership. Younger commuters perceive lower service quality, while educated individuals are more responsive to quality-focused appeals. de Oña, J., & de Oña, R. (2015) reviewed different methodological approaches, including factor analysis, SEM, and CFA, used to assess public transport service quality. Ugo, F., & Usman, U. M. (2016) applied factor analysis to assess the efficiency of road transport services in Nigeria, highlighting factors influencing user satisfaction. The study made by Abenoza et al (2017) analysed nearly half a million records to identify key determinants of public transport satisfaction among five traveller segments in Sweden, emphasizing the importance of customer interface, operations, and network attributes, with tailored recommendations for each group Javid, M. A., & Sayed, T. (2018) Explored road safety performance evaluation through factor analysis. Kral et al (2018) identified key factors influencing public transport satisfaction and decision-making using statistical methods, offering insights for improving service management and customer retention. Yu, J., and Lee, H. (2019) examined transport policy effectiveness and public satisfaction using CFA and SEM techniques. Kilibarda et al (2020) made a systematic literature review of 98 papers across 56 journals identifies three primary research focuses in Logistics Service Quality (LSQ), analyzing commonly used dimensions and measurement approaches. Findings indicate a predominance of empirical studies reporting low LSQ levels, providing a foundation for future research and practical applications in transport and logistics. Public transport performance assessment methods include: (1) evaluating operational variables; (2) assessing user perceptions of service quality; and (3) integrating both approaches with outcome- oriented variables (Verma and Rastogi, 2022). A generalized methodology suitable for evaluating transit performance is also presented, with a focus on the approaches prevalent in India Sogbe et al (2024) made a systematic review of 104 papers published since 2000 identifies safety, security, comfort, reliability, and accessibility as key determinants influencing bus transport usage. The study highlights challenges in first mile and last-mile connectivity, particularly in developing countries, and suggests further research to address these issues. 3. Transport Department The Transport Department operates under the provisions of Sections of the Motor Vehicle Act. It is primarily tasked with enforcing the Motor Vehicle Act, and the associated rules and regulations. Among the various modes of transport, road transport stands out as the most effective for relatively short distances, particularly for connecting rural areas with towns and cities, where other forms of transport are less feasible. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 929 https://internationalpubls.com In this context, the Transport Department collaborates with other organizations to enhance transport infrastructure and strives to ensure the provision of efficient, adequate, and economical road transport services for passengers and goods. In fulfilling its statutory responsibilities, the department has evolved into a significant revenue-generating entity for the government through the collection of motor vehicle taxes. Various functions of the department are presented below. Licensing: The Licensing function of the transport department focuses on ensuring clarity in licensing procedures by simplifying the process and making it easy to understand. It emphasizes timely issuance of licenses by processing applications within the promised timeframe. Transparency is a key aspect, as driving tests and evaluations are conducted fairly and objectively. Additionally, the department ensures that licensing services are accessible through multiple channels, including both online platforms and physical offices. Registration: The Registration function streamlines the vehicle registration process to make it accessible and convenient for users. Services are offered through online platforms and physical offices, with an emphasis on user-friendly systems. The department provides clear guidelines and documentation requirements to prevent confusion and ensures staff are supportive and responsive during the registration process. Permits: The Permits function ensures timely issuance of permits while adhering to legal and safety standards. The department is responsive to permit-related queries and provides complete and clear information regarding terms, validity, and conditions associated with permits. This transparency helps users navigate the permit process effectively. Taxes: The Taxes function prioritizes ease of payment by offering efficient systems for both online and in-person tax payments. It ensures that tax rates are fair and competitive compared to other states, fostering compliance. Services are also tailored to facilitate interstate vehicle registrations and tax payments, enhancing customer satisfaction. Furthermore, the department promotes eco-friendly practices by providing tax incentives for adopting electric and hybrid vehicles. Penalties: The Penalties function focuses on fairness and efficiency in managing penalties, ensuring users are satisfied with the procedures. Revenue from penalties is utilized effectively for public benefits, such as improving road safety or transport infrastructure. The department provides a digital platform for users to check, pay, or dispute penalties conveniently and organizes educational campaigns to raise awareness about legal compliance and the consequences of violations. Road Safety: The Road Safety function plays a crucial role in improving safety measures. The department collects and analyses road accident data to implement preventive strategies. It ensures vehicle fitness through regular inspections and enforces speed monitoring systems to reduce speeding. Collaboration with police, health, and other departments enhances the effectiveness of integrated road safety initiatives. Additionally, the department ensures the availability of emergency response services to provide quick assistance during road accidents. 4. Research Methodology The research methodology followed in this study consists of “identification of model variables with a comprehensive literature review, validation of literature review results with illustrative case study, Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 930 https://internationalpubls.com and questionnaire survey to construct model, analysis of data, and model creation with Factor Analysis and Validation through Confirmatory factor analysis. In this study, two research methods were used. Firstly, Factor Analysis used as a data reduction technique to simplify large datasets by identifying a smaller set of underlying dimensions (factors) that capture most of the variance in the observed variables. Then The factors identified through Factor Analysis (FA), which reveal the underlying structure of the data, are subsequently validated using Confirmatory Factor Analysis (CFA) to confirm the hypothesized relationships and ensure the robustness, reliability, and validity of the measurement model. The methodology is explained in the following steps. Step 1: Identification of model variables: The selection of variables should be grounded in existing literature, theories, or prior research to ensure a sound conceptual basis. Step 2: Design the Questionnaire survey: After identifying model variables, is designing a questionnaire survey to collect data. A well-designed questionnaire ensures that the observed variables accurately capture the underlying constructs, facilitating reliable and valid results in subsequent analyses. Step 3: Data Collection: The questionnaire survey designed in the previous step is administered to gather responses from participants. This step is critical as the quality, accuracy, and representativeness of the collected data directly impact the validity and reliability of the subsequent analyses Step 4: Conduct the Factor analysis: At the end of this step, the factors are identified, labelled, and validated, providing a clear understanding of the latent constructs in the data. The identified factors can now be used in subsequent analyses, such as Confirmatory Factor Analysis (CFA), to test and validate the measurement model. Factor analysis is conducted using SPSS 20.0 Step 5: Analyse the results of FA: The results of the factor analysis are interpreted, validated, and aligned with the research objectives. The finalized factor structure provides a simplified, meaningful representation of the dataset, ready for further validation or integration into more advanced analyses Step 6: Conduct Confirmatory Factor analysis: After identifying and interpreting factors through Factor Analysis (FA), the next step is to perform Confirmatory Factor Analysis (CFA) to validate the factor structure. CFA is a hypothesis-driven technique used to confirm whether the observed variables reliably measure the latent constructs identified in FA. This step ensures the robustness, reliability, and validity of the measurement model, aligning it with theoretical expectations. Step 7: Analyse the results of CFA: The results of the CFA are thoroughly analysed, and the validated measurement model is finalized. The findings confirm that the constructs are reliable, valid, and appropriately measured by their observed variables. Step 8: Report Findings: Concise summary of the factor analysis (FA) and confirmatory factor analysis (CFA) findings is needed to highlight the factors retained, their reliability, validity, and the fit of the final model. Also, findings contribute to the existing body of knowledge? and how can the results be applied in real-world contexts (e.g., policymaking, organizational improvement)? Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 931 https://internationalpubls.com 5. Case Study This empirical case study evaluates the effectiveness of the Transport Department's services across licensing, registration, permits, taxes, penalties, and road safety based on user feedback. Questionnaire is distributed to 400 persons out of which 377 usable surveys are received. The survey is conducted in a major city in Andhra Pradesh. The questionnaire is presented in Appendix3.1. There are 25 statements under 6 functions of the department. To measure these functions, indicators were evaluated by respondents according to a 1 to 5-point Likert scale. Demographics are presented in table 1 Table 1: Demographics Category Sub-Category Number of Respondents Percentage (%) Age Groups (years) 18 to 30 150 39.8 31 to 50 131 34.7 Above 51 96 25.5 Gender Male 217 57.6 Female 150 39.8 Prefer not to say 10 2.7 Stakeholder Categories Individual Applicants 188 49.9 Transport Operators 77 20.4 Driving School Representatives 61 16.2 General Public 51 13.5 Mode of Interaction Online 226 60 In-person 113 30 Both 37 10 5.1 Data on the model variables: In the Study, items under 6 functions are considered and presented in table 2. Table 2: Measurement Items S. No. Measurement items ITEM 1 Clarity of Licensing Procedures ITEM 2 Timeliness of License Issuance ITEM 3 Transparency in Testing and Evaluation ITEM 4 Accessibility of Licensing Sen· ices ITEM 5 Ease of Access to Registration Services ITEM 6 User-Friendliness of Online Platforms ITEM 7 Support from Staff Dining Registration ITEM 8 Clarity of Registration Procedures ITEM 9 Timeliness of Permit Issuance ITEM 10 Compliance with Legal and Safety Standards ITEM 11 Support for Permit-Related Queries ITEM 12 Adequacy of Information Provided on Permits ITEM 13 Ease of Payment Process ITEM 14 Competitive Tax Rates ITEM 15 Customer Satisfaction Across Borders Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 932 https://internationalpubls.com S. No. Measurement items ITEM 16 Intact of Tax Incentives on Adoption of Green Initiatives ITEM 17 User Satisfaction with Penalty Procedures ITEM 18 Use of Penalty Revenue for Public Benefits ITEM 19 Digital Integration in Penalty Management ITEM 20 Educational Campaigns on Penalties ITEM 21 Road Accident Data Collection and Analysis ITEM 22 Inspection and Maintenance of Vehicles ITEM 23 Speed Monitoring and Control ITEM 24 Collaboration with Ollier Departments ITEM 25 Availability of Emergency Response Services Data on the measurement items collected from the survey are presented in appendix A.2. Basic statics of the items are presented in table 3.3 5.1.1 Explanatory Factor Analysis (EFA) Tabachnick and Fidell (2001) propose a general guideline that a minimum of 300 cases is required for factor analysis. Hair et al. (1995) recommend a sample size of at least 100. Comrey (1973) offers a more detailed classification, rating sample sizes as follows: 100 as poor, 200 as fair, 300 as good, 500 as very good, and 1000 or more as excellent. In this study 377 samples are considered. Basic statistics of the survey is presented in table 3. Table 3: Basic Statistics of the Survey Variable N Mean Std.dev Minimum Q1 Median Q3 Maximum Item 1 377 2.8355 0.6838 1 3 3 3 5 Item 2 377 2.8806 0.6988 1 3 3 3 5 Item 3 377 2.878 0.7264 1 3 3 3 5 Item 4 377 2.8568 0.7076 1 3 3 3 5 Item 5 377 2.8462 0.6901 1 3 3 3 5 Item 6 377 2.8408 0.6733 1 3 3 3 4 Item 7 377 2.8223 0.6863 1 3 3 3 4 Item 8 377 2.8408 0.6733 1 3 3 3 4 Item 9 377 2.8833 0.7595 1 3 3 3 5 Item 10 377 2.8435 0.7325 1 3 3 3 5 Item 11 377 2.8408 0.7004 1 3 3 3 4 Item 12 377 2.8515 0.7816 1 3 3 3 5 Item 13 377 2.878 0.6926 1 3 3 3 4 Item 14 377 2.8541 0.6704 1 3 3 3 5 Item 15 377 2.8462 0.6978 1 3 3 3 5 Item 16 377 2.8462 0.6666 1 3 3 3 5 Item 17 377 3.2812 0.6688 1 3 3 4 5 Item 18 377 3.305 0.6398 2 3 3 4 5 Item 19 377 3.3103 0.616 2 3 3 4 4 Item 20 377 3.3236 0.5937 2 3 3 4 4 Item 21 377 2.8223 0.7054 1 3 3 3 4 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 933 https://internationalpubls.com Item 22 377 2.8859 0.6844 1 3 3 3 5 Item 23 377 2.8621 0.7049 1 3 3 3 5 Item 24 377 2.8196 0.6798 1 3 3 3 4 Item 25 377 2.809 0.7188 1 3 3 3 5 Varimax rotation which was developed by (Thompson 2004) is the most common form of rotational methods for exploratory factor analysis and will often provide a simple structure that produces a more interpretable and simplified solution 6. Results and Discussion EFA was performed using SPSS (IBM Corp, 2020) statistical software packages. To evaluate the appropriateness of the data for factor analysis, the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett’s Test of Sphericity were applied. Initially, Exploratory Factor Analysis (EFA) was conducted without rotation, utilizing maximum likelihood extraction and retaining factors with eigenvalues greater than 1. Furthermore, EFA was also performed using varimax rotation. The results so obtained are discussed below. Table 4: KMO and Bartlett’s test Results KMO measure 0.867 Bartlett's Test of Sphericity Approx. Chi-Square 5052.67 Degrees of freedom 300 Significance 0 The KMO measure serves as an index that evaluates how well each variable in a dataset is predicted by the other variables without error. A value closer to 1 signifies a greater proportion of variance that may be attributed to common variance, indicating suitability for factor analysis. A KMO value of 0.867 is regarded as excellent, suggesting that a significant portion of the variance in the variables can be accounted for by underlying factors, making factor analysis an appropriate method for the data. Bartlett's Test of Sphericity assesses whether the correlation matrix is an identity matrix, indicating unrelated variables unsuitable for factor analysis. A significant p-value (< 0.05) rejects this null hypothesis. In this case, the p-value is 0, confirming significant relationships among variables and supporting the suitability of factor analysis. Table 5: Communalities Factor Extraction F1 0.658 F2 0.673 F3 0.680 F4 0.674 F5 0.683 F6 0.731 F7 0.715 F8 0.712 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 934 https://internationalpubls.com Factor Extraction F9 0.754 F10 0.745 F11 0.648 F12 0.716 F13 0.708 F14 0.718 F15 0.739 F16 0.718 F17 0.516 F18 0.592 F19 0.582 F20 0.498 F21 0.712 F22 0.717 F23 0.727 F24 0.661 F25 0.746 Above table shows the communalities from an exploratory factor analysis (EFA) it is suggested that these variables correlate well with the factors extracted and are satisfactorily represented within the factor structure. Table 6: Eigen Values and Loadings Component Initial Eigenvalues Loadings Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % 1 5.545 22.180 22.180 5.545 22.180 22.180 4.086 16.343 16.343 2 3.596 14.384 36.564 3.596 14.384 36.564 3.607 14.427 30.771 3 3.173 12.691 49.255 3.173 12.691 49.255 3.591 14.365 45.135 4 2.860 11.438 60.694 2.860 11.438 60.694 3.554 14.215 59.350 5 1.849 7.396 68.090 1.849 7.396 68.090 2.185 8.740 68.090 Above table shows the components having eigen values >1.0. The results support retaining five factors, as each contributes significantly to explaining the variance in the dataset, with a cumulative total of over 68% variance explained. Each factor likely represents a distinct underlying construct. The factor loadings for each variable on these factors to interpret what each factor represents need to be examined. Table 7: Rotated Component Matrix Component 1 2 3 4 5 Item l 0.801 Item 2 0.814 Item 3 0.819 Item 4 0.804 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 935 https://internationalpubls.com Component 1 2 3 4 5 Item 5 0.810 Item 6 0.850 Item 7 0.839 Item 8 0.825 Item 9 0.858 item 10 0.859 Item 11 0.789 Item 12 0.843 Item 13 0.833 Item 14 0.821 Item 15 0.837 Item 16 0.841 Item 17 0.687 Item 18 0.744 Item 19 0.760 Item 20 0.683 Item 21 0.829 Item 22 0.835 Item 23 0.846 Item 24 0.793 Item 25 0.858 The Rotated Component Matrix indicates how each question (item 1 through item 25) loads on five distinct components, following a factor rotation. This matrix is essential in identifying which questions share common underlying factors and how they group together. Factor 1: Licensing and User Accessibility (Items 1 to 6) This factor focuses on ensuring efficient and user-friendly licensing and registration processes. It includes the clarity of licensing procedures, timeliness of license issuance, transparency in testing and evaluation, accessibility of licensing services, ease of access to registration services, and user- friendliness of online platforms. The emphasis is on providing accessible and efficient systems for users to navigate licensing and registration processes. Factor 2: Support and Compliance Services (Items 7 to 11) This factor emphasizes the quality of support services and adherence to legal and safety standards. It includes support from staff during registration, clarity of registration procedures, timeliness of permit issuance, compliance with legal and safety standards, and support for permit-related queries. The focus is on ensuring user satisfaction while maintaining compliance and addressing user concerns effectively. Factor 3: Information and Financial Systems (Items 12 to 16) This factor deals with the adequacy and efficiency of information and financial systems. It includes the adequacy of information provided on permits, ease of payment processes, competitive tax rates, Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 936 https://internationalpubls.com customer satisfaction across borders, and the impact of tax incentives on green initiatives. The focus is on ensuring reliable information systems and financial processes to support customer satisfaction and encourage sustainable practices. Factor 4: Penalty Management and Public Awareness (Items 17 to 20) This factor focuses on effective penalty administration and increasing public awareness. It includes user satisfaction with penalty procedures, the use of penalty revenue for public benefits, digital integration in penalty management, and educational campaigns on penalties. The aim is to ensure fair and transparent penalty management while promoting public understanding and acceptance of the system. Factor 5: Safety and Emergency Management (Items 21 to 25) This factor highlights the importance of safety measures and emergency response systems. It includes road accident data collection and analysis, inspection and maintenance of vehicles, speed monitoring and control, collaboration with other departments, and availability of emergency response services. The aim is to enhance safety standards and preparedness for emergencies through systematic management. 6.1. Confirmatory Factor Analysis (CFA) Integrating EFA with CFA is beneficial as EFA provides data-driven insights into the underlying structure of the variables, while CFA validates this structure within a hypothesis-driven framework. In this study, CFA is employed to confirm and validate the factor structure identified through EFA. Specifically, CFA tests whether the data aligns with a hypothesized factor model derived from theoretical foundations or prior analyses, such as those from EFA. To conduct CFA, the following hypotheses are developed. Licensing and User Accessibility H1: Items 1-6 are expected to load on the latent factor "Licensing and user accessibility." Support and Compliance Services H2: Items 7-11 are expected to load on the latent factor will load significantly on a latent factor representing "Support and Compliance Services." Information and Financial Systems H3: Items 12-16 are expected to load on the latent factor will load significantly on a latent factor representing "Information and Financial Systems". Penalty Management and Public Awareness H4: Items 17-20 are expected to load on the latent factor will load significantly on a latent factor representing "Penalty management and Public Awareness". Safety and Emergency Management H5: Items 20-25 are expected to load on the latent factor will load significantly on a latent factor representing "Safety and Emergency management". In this chapter, Confirmatory Factor Analysis is employed to assess the critical factors for evaluating performance Transportation department. This approach aims to provide valuable insights for these better functioning of the department. The proposed methodology is outlined in the following steps. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 937 https://internationalpubls.com Selection of Variables. The variables identified based on the EFA are utilized for CFA. LISREL 8.8 software is used to conduct confirmatory factor analysis. The results obtained are discussed below. Table 8: Measurements Items and Result of Reliability Test S.No. Latent Construct Cronbach's Alpha Measurement Items Cronbach's Alpha 1 Licensing and User Accessibility 0.9046 Clarity of Licensing Procedures 0.8902 Timeliness of License Issuance 0.8888 Transparency· in Testing and Evaluation 0.8880 Accessibility· of Licensing Services 0.8888 Ease of Access to Registration Services 0.8872 User-Friendliness of Online Platforms 0.8830 2 Support and Compliance Services 0.7031 Support from Staff During Registration 0.6859 Clarity of Registration Procedures 0.6847 Timeliness of Permit Issuance 0.6382 Compliance with Legal and Safety Standards 0.6279 Support for Permit-Related Queries 0.6286 3 Safety and Emergency Management 0.7663 Adequacy of Information Provided on Permits 0.8757 Ease of Payment Process 0.6818 Competitive Tax Rates 0.6751 Customer Satisfaction Across Borders 0.6549 Impact of Tax Incentives on Adoption of Green Initiatives 0.6804 4 Information and Financial Systems 0.7805 User Satisfaction with Penalty Procedures 0.6570 Obtain Suitable Factors from EFA Selecting the Factors Use the data on the factors collected in CFA Specify the Measurement Model and implement through LISREL Analyse the model constructs and their items for validation Obtain the Fitness metrics of the Model and specify the final measurement model. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 938 https://internationalpubls.com S.No. Latent Construct Cronbach's Alpha Measurement Items Cronbach's Alpha Use of Penalty Revenue for Public Benefits 0.6240 Digital Integration in Penalty Management 0.7918 Educational Campaigns on Penalties 0.7979 5 Penalty· Management and Public Awareness 0.8980 Road Accident Data Collection and Analysis 0.8763 Inspection and Maintenance of Vehicles 0.8756 Speed Monitoring and Control 0.8731 Collaboration with Oilier Departments 0.8824 Availability of Emergency Response Services 0.8707 High Cronbach's Alpha values indicate that the scale is reliable and consistently measures whatever construct it is intended to. Composite reliability, average variance extracted of the latent constructs and measurement items are presented in table 9. Table 9: Composite Reliability and Average Variance Extracted Transport Dept. Function Measurement Item Loadings Error ΑΧΈ CR Licensing and User Accessibility Clarity of Licensing Procedure s 0.77 0.41 0.6146 0.9053 Timeliness of License Issuance 0.77 0.4 Transparency in Testing and Evaluation 0.78 0.39 Accessibility of Licensing Services 0.77 0.4 Ease of Access to Registration Services 0.79 0.38 User-Friendliness of Online Platforms 0.82 0.33 Support and Compliance Services Support from Staff During Registration 0.79 0.37 0.6405 0.8989 Clarity of Registration Procedure s 0.8 0.36 Timeliness of Permit Issuance 0.84 0.29 Compliance with Legal and Safety Standards 0.82 0.32 Support for Permit-Related Queries 0.74 0.45 Information and Financial Systems Adequacy of Information Provided on Permits 0.79 0.37 0.6376 0.8979 Ease of Payment Process 0.79 0.37 Competitive Tax Rates 0.8 0.36 Customer Satisfaction Across Borders 0.81 0.35 Impact of Tax Incentives on Adoption of Green Initiatives 0.8 0.36 Penalty Management and Public Awareness User Satisfaction with Penalty Procedures 0.62 0.62 0.4734 0.7821 Use of Penalty Revenue for Public Benefits 0.66 0.37 Digital Integration in Penalty Management 0.6 0.36 Educational Campaigns on Penalties 0.59 0.35 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 939 https://internationalpubls.com Transport Dept. Function Measurement Item Loadings Error ΑΧΈ CR Safety and Emergency Management Road Accident Data Collection and Analysis 0.8 0.37 0.6364 0.8974 Inspection and Maintenance of Vehicles 0.8 0.36 Speed Monitoring and Control 0.81 0.35 Collaboration with Other Departments 0.76 0.42 Availability of Emergency Response Services 0.82 0.32 Item loadings on their respective constructs range from 0.59 to 0.82, indicating a moderate relationship with their latent variables. This confirms their suitability as indicators of the intended constructs. The AVE assesses the variance a construct captures from its items relative to measurement error. A value above 0.5 is preferred, indicating the construct explains over half of the item variance. For the latent constructs, AVE exceeds 0.6, except for penalty management and public awareness. CR evaluates the reliability of a latent construct, offering a more precise measure than Cronbach’s alpha by considering factor loadings. A CR above 0.7 is acceptable, above 0.8 is good, and above 0.9 is excellent. In this study, CR values exceed 0.78, indicating a well-specified model. The strong loadings and generally high AVE and CR values suggest that your model is well-specified and the constructs are measured reliably. These results can be confidently used for further analysis, such as examining the relationships between these constructs and other variables or outcomes. Model Evaluation Criteria The model fitting process involves determining the goodness-of fit between the hypothesized model and the sample data. Given below is a description of the goodness-of–fit indicators used to evaluate model fitness in (CFA) Table 10: Fit Indices Model fit indices Values Chi-square or degree of freedom (df) (388.71/265) =1.467 Goodness of fit index (GFI) 0.9236 Adjusted goodness of fit index (AGFI) 0.9063 Normed fit index (NFI) 0.9508 Comparative fit index (CFI) 0.9823 Incremental fit index (IFI) 0.9823 Relative fit index (RFI) 0.9443 Root mean square error of approximation (RMSEA) 0.035 Standardized RMR 0.019 The model fitting process assesses the goodness-of-fit between the hypothesized model and the sample data. Below is a summary of the fit indicators used to evaluate model fitness in CFA. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 940 https://internationalpubls.com Chi Square Goodness of Fit (χ2/d.f) The Chi-square goodness-of-fit metric evaluates the alignment between theoretical specifications and empirical data in CFA. A higher Chi-square value indicates a poorer fit. In this study, χ²/d.f = 1.467, indicating a good fit. Goodness-of-fit Index The Chi-square test assesses model fit in CFA, with higher values indicating poorer fit. In this study, χ²/d.f = 1.467, confirming a good fit. Adjusted goodness-of-fit index The AGFI adjusts the GFI for degrees of freedom. In this study, AGFI = 0.9063, indicating a good fit. Normed Fit Index (NFI) It ranges between zero to one. A Normed fit index of one indicates perfect fit. In this study, NFI = 0.9508 indicates good fit. Incremental fit index (IFI) It ranges between zero to one. An incremental index of one indicates perfect fit. In this study, IFI = 0.9823 indicates good fit. Relative Fit Index (RFI) RFI coefficient values range from zero to one with values close to one indicating superior fit. In this study, RFI = 0.9443 indicates good fit. Comparative Fit Index (CFI) CFI ranges from 0 to 1, with higher values indicating better fit. A CFI above 0.90 suggests a well- fitting model. In this study, CFI = 0.9823, confirming a good fit. Root Mean Square Error of Approximation (RMSEA) RMSEA measures model fit for the population, not just the sample. Lower values indicate better fit, with <0.08 considered good. In this study, RMSEA = 0.035, confirming a good fit. Standardized RMR (SRMR) SRMR is the square root of the mean standardized residuals, with lower values indicating better fit. An SRMR < 0.05 is recommended. In this study, SRMR = 0.0019, confirming a good fit. Overall Measurement Model Fitness The chi-square statistic appears satisfactory; however, the χ²/df ratio provides a more reliable model fit assessment. A lower χ²/df value generally indicates a better fit. If most fit indices meet acceptable thresholds, the model can be considered a good fit. The CFA path diagram is presented below. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 941 https://internationalpubls.com 6.2. Discussion Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) serve distinct yet complementary roles in validating the structure of latent constructs within a dataset. In the case of the analysis of transport department services, EFA was used to uncover the underlying factor structure from empirical data, while CFA was employed to confirm and validate the identified factors. The integration of EFA and CFA ensures that the factors derived from the data are both statistically sound and theoretically meaningful. This discussion elaborates on the alignment of EFA and CFA results, the methodological consistency, and the implications of the findings. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 942 https://internationalpubls.com Alignment between EFA and CFA Results 1. Factor Structure Retention: o EFA identified five factors—Licensing and User Accessibility, Support and Compliance Services, Information and Financial Systems, Penalty Management and Public Awareness, and Safety and Emergency Management—which encapsulated key dimensions of transport services. o CFA validated the structure by confirming that these five factors fit the measurement model well, with high factor loadings and acceptable goodness-of-fit indices. 2. Reliability and Validity: o Internal Consistency: High Cronbach’s alpha and Composite Reliability (CR) values (>0.78) indicated strong reliability across the identified factors. o Convergent Validity: The Average Variance Extracted (AVE) values were above 0.6 for most constructs, except for the "Penalty Management and Public Awareness" factor, suggesting a strong relationship between items and their respective latent constructs. o Discriminant Validity: The correlation among constructs was lower than their AVE values, ensuring that each factor was measuring a distinct concept. 3. Goodness-of-Fit Measures in CFA: o The CFA results demonstrated excellent model fit with key fit indices: ▪ χ²/df = 1.467 (acceptable if < 3) ▪ GFI = 0.9236 (good fit, > 0.9) ▪ AGFI = 0.9063 (acceptable, > 0.9) ▪ CFI = 0.9823 (excellent fit, > 0.95) ▪ RMSEA = 0.035 (ideal fit, < 0.08) ▪ SRMR = 0.019 (ideal fit, < 0.05) These indices confirm that the five-factor structure identified in EFA was a good representation of the latent constructs. Interpretation of Key Findings: Conceptual Coherence o The five-factor structure reflects well-defined aspects of transport services, such as efficiency, accessibility, compliance, and public awareness. o Factors such as Penalty Management and Public Awareness and Safety and Emergency Management highlight the department’s role in enforcement and public welfare. Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 943 https://internationalpubls.com Strengths of EFA-CFA Integration o EFA helped in exploratory hypothesis generation, while CFA validated and confirmed those hypotheses, ensuring that the model was not only statistically significant but also conceptually meaningful. o The moderate to high factor loadings in CFA reinforced the robustness of the identified factors. Areas for Improvement o The AVE for Penalty Management and Public Awareness was slightly lower than the ideal threshold, suggesting that some items in this factor may need refinement or rewording for better construct validity. o Future research may include higher-order CFA models to assess interactions between factors and their influence on transport service efficiency. 7. Concluding Remarks The results of EFA and CFA in the transportation department study strongly correlate, demonstrating a well-structured and validated model for analysing transport services. The integration of both techniques ensures methodological rigor, helping policymakers and researchers draw meaningful conclusions about service effectiveness and user satisfaction. While the overall fit of the model is strong, minor refinements in measurement items could further improve construct validity. This study provides a robust framework for future assessments of transport service performance. The findings emphasize the need for improved user accessibility, transparent licensing and registration procedures, and efficient penalty management systems. By validating the factor structure, policymakers can rely on a well-tested model to assess and improve transport services. 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