Factors affecting customer satisfaction with logistics service quality in Nam Tu Liem District

 Corresponding author: Nguyen Quang Huy Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Factors affecting customer satisfaction with logistics service quality in Nam Tu Liem District Nguyen Quang Huy * Hanoi University of Industry, Ha Noi, Viet Nam. World Journal of Advanced Research and Reviews, 2025, 26(03), 2176-2188 Publication history: Received on 05 May 2025; revised on 16 June 2025; accepted on 19 June 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.26.3.2322 Abstract In the face of intensifying competition from international logistics providers, customer satisfaction has emerged as a crucial determinant of success for domestic logistics firms in Vietnam, particularly in Nam Tu Liem District. This study investigates the key factors influencing customer satisfaction with logistics service quality in the district. Using a quantitative approach, data were collected from 137 logistics service users through structured questionnaires. The SERVQUAL model was adapted and validated using Cronbach's Alpha, Exploratory Factor Analysis (EFA), correlation analysis, and multiple linear regression. The findings reveal five significant factors—service capacity, price, information quality, trust, and tangibles—that positively affect customer satisfaction. These results offer a reliable measurement framework for logistics service evaluation and provide practical implications for enhancing customer satisfaction in Vietnam's growing logistics industry. Keywords: Logistics; Customer satisfaction; Service quality; Nam Tu Liem District 1. Introduction In the era of globalization and the rapid expansion of international trade, logistics services have become a critical driver of business success. In developing economies like Vietnam, logistics not only facilitates supply chain connectivity and integrated logistics management—which are essential in emerging markets (Coyle et al., 2016)—but also plays a pivotal role in promoting sustainable economic growth, as highlighted in global supply chain literature (Christopher, 2016). The remarkable expansion of the logistics industry, especially in urban areas, presents both opportunities and challenges for businesses aiming to enhance service quality and maintain customer satisfaction. Nam Tu Liem, a fast-developing district in Hanoi, exemplifies this transformation in emerging urban logistics. The district has experienced significant growth in logistics enterprises, accompanied by substantial infrastructure development and evolving customer expectations. Despite increasing attention to logistics research in major cities like Ho Chi Minh City, there remains a lack of focused studies on service quality and customer satisfaction in districts such as Nam Tu Liem. This research gap presents an opportunity to generate valuable insights tailored to emerging urban areas. The objective of this study is to identify the key factors affecting customer satisfaction with logistics service quality in Nam Tu Liem District. Grounded in the SERVQUAL framework, the study examines how dimensions such as service capacity, price, information quality, trust, and tangibles influence customer satisfaction. The findings aim to contribute to both theoretical understanding and practical strategies for improving logistics service quality and fostering customer loyalty. World Journal of Advanced Research and Reviews, 2025, 26(03), 2176-2188 2177 Given the increasingly competitive nature of the logistics sector, understanding the drivers of customer satisfaction is vital, as prior studies have shown that service quality directly influences satisfaction across various service environments (Ali et al., 2021). Insights from this study will enable logistics businesses to better align their service offerings with customer expectations and to implement more targeted and effective quality improvement strategies. The paper is structured as follows: Section 2 reviews the relevant literature and theoretical frameworks; Section 3 outlines the research methodology; Section 4 presents the empirical findings; and Section 5 discusses the implications and concludes the study. 2. Literature review and hypotheses 2.1. Logistics service quality and customer satisfaction Logistics service quality (LSQ) is a vital component in determining competitive advantage in the global market. It goes beyond transportation to include the provision of reliable, timely, and value-added services. Grönroos (1984) emphasized that service quality comprises multiple dimensions, including reliability, responsiveness, and tangibles, which all influence customer perceptions. The SERVQUAL model, proposed by Parasuraman et al. (1985), is widely applied to assess service quality across five dimensions: reliability, responsiveness, assurance, empathy, and tangibles. These dimensions provide a framework for understanding the gap between customer expectations and actual service experiences. Oliver (1997) noted that satisfaction arises when service performance meets or exceeds expectations, which supports the view that customer satisfaction is ultimately an emotional response to the service experience (Burger and Cann, 1995). In the logistics sector, on-time delivery, accurate tracking, and professional communication are central to customer satisfaction (Bowersox et al., 2002). 2.2. International research on service quality and customer satisfaction Globally, numerous studies have examined the relationship between service quality and customer satisfaction in logistics. Tjendana and Pranitasari (2024) found that physical factors—such as facility appearance and operational efficiency—significantly influence customer satisfaction in maritime transportation and logistics. Similarly, Abbas (2023) highlighted that service reliability and the quality of customer interaction are crucial in shaping satisfaction within public transport logistics. Lai et al. (2022) investigated customer satisfaction in last-mile logistics, focusing on parcel locker systems. Their research revealed that responsiveness and the quality of customer interaction were key contributors to satisfaction. These findings underscore the importance of dimensions such as responsiveness, reliability, and physical evidence in enhancing service quality. Despite the breadth of international studies, much of the literature focuses on specific sectors—such as maritime shipping or last-mile delivery—rather than offering a holistic view applicable to emerging logistics markets like Vietnam. 2.3. Domestic research on logistics service quality and customer satisfaction In Vietnam, LSQ has gained growing research attention. Nhung et al. (2022) examined customer satisfaction in sea freight among SMEs in Ho Chi Minh City, identifying reliability, empathy, and price as significant factors. Hanh and Bich (2022) reported that customers in Northern Vietnam placed strong emphasis on service capacity, responsiveness, and tangible service quality. Hiep et al. (2024), in their study of logistics firms in Binh Duong province, emphasized the impact of operational quality, staff professionalism, and service innovation. Meanwhile, Nguyen Thanh Nam and Le Thu Hang (2021) applied the SERVQUAL framework to evaluate satisfaction with express delivery services in Hanoi, finding all five SERVQUAL dimensions to be significant predictors of satisfaction. These studies confirm that the SERVQUAL model is applicable in Vietnam, while also revealing the importance of regional characteristics in shaping customer expectations and perceptions. World Journal of Advanced Research and Reviews, 2025, 26(03), 2176-2188 2178 2.4. Research gap While existing literature offers valuable insights into LSQ and customer satisfaction, specific studies focused on Nam Tu Liem District are lacking. As a growing logistics hub with diverse businesses and developing infrastructure, Nam Tu Liem presents unique challenges and expectations not fully addressed in broader national studies. Moreover, although the SERVQUAL model is well-established, its application in the context of local infrastructure and regional service characteristics remains underexplored—despite evidence that infrastructure and technology play essential roles in shaping logistics service quality (Arabelen and Kaya, 2021). This study addresses this gap by examining how these dimensions influence satisfaction among logistics users in Nam Tu Liem, thereby offering practical recommendations for service improvement. 3. Data and Research methods 3.1. Research model This study employs the SERVQUAL model proposed by Parasuraman et al. (1985), which is widely recognized for assessing service quality across various service sectors. The model encompasses five core dimensions: reliability, responsiveness, assurance, empathy, and tangibles. For this study, the model was adapted to reflect five hypothesized factors affecting customer satisfaction in the logistics context: service capacity, price, information quality, trust, and tangibles. The proposed research model is illustrated in Figure 1. Figure 1 Proposed research model 3.2. Hypotheses Based on the research model, the following hypotheses are proposed: • H1: Service capacity has a positive impact on customer satisfaction with logistics service quality. • H2: Price has a positive impact on customer satisfaction with logistics service quality. • H3: Information quality has a positive impact on customer satisfaction with logistics service quality. • H4: Trust has a positive impact on customer satisfaction with logistics service quality. • H5: Tangibles have a positive impact on customer satisfaction with logistics service quality. 3.3. Variables and measurement scales The measurement scales in this study were adapted from previous validated research, primarily based on the SERVQUAL model by Parasuraman et al. (1985, 1988). Observed variables for service capacity, price, information quality, trust, tangibles, and customer satisfaction were selected from relevant studies to ensure content validity and contextual relevance. All items were measured using a five-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). World Journal of Advanced Research and Reviews, 2025, 26(03), 2176-2188 2179 Table 1 Measurement Items for constructs and sources Variable Sign Observation variable Quote Customer satisfaction CS1 Are you satisfied with the quality of the company's logistics services? Lassar et al. (2000) CS2 I will continue to use the company's logistics services. CS3 I would be willing to recommend the company's services to others. Service capacity SC1 Staffs have solid professional knowledge, full service consulting, suitable for customer characteristics. Zeithaml, V.A., Parasuraman, A., Bery, L.L., and Malhotra,A. (2000) SC2 Staff have professional and polite working attitude. Collier, J.E., andStevens,C.K.(2002) SC3 Staffs handle problems quickly, accurately and promptly. Zeithaml, V.A., Parasuraman, A., Bery, L.L., and Malhotra,A. (2000) SC4 Staff always provides necessary service information and update goods status for customers. Verhoef, P.C., Lemon, K.N., Yeniyurt, S., and Parasuraman, A. (2009) Price P1 The price of the service the company offers is consistent with the service the company provides Zeithaml, V.A., Parasuraman, A., Bery, L.L., and Malhotra,A. (2000) P2 The company's service prices are competitive with those of its competitors in the market. P3 The company has a discount policy for customers. P4 The service prices provided by the company are stable and have little fluctuation. Information quality IQ1 The company uses information technology applications and customer data exchange platforms when performing services. Jian and Zhenpeng (2008), Thai (2008), Thai (2013) IQ2 Businesses introduce and apply technological innovations in customer service IQ3 Customers can always find information about the services that businesses provide Trust TR1 The company performs services as committed to customers quickly. Zeithaml, V.A., Parasuraman, A., Bery, L.L., and Malhotra,A. (2000) TR2 The company does the service right the first time. Parasuraman, A., Zeithaml, V.A. and Bery, L.L. (1998); Collier, J.E., andStevens,R.(2002) TR3 The company provides services on time as committed. Zeithaml, V.A., Parasuraman, A., Bery, L.L., and Malhotra,A. (2000) TR4 The company performs the service accurately, without any errors. Collier, J.E., andStevens,R.(2002) TR5 The company always cares about supporting and solving customers' difficulties. Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1988) Tangible TM1 Modern company facilities and equipment. Zeithaml, V.A., Parasuraman, A., Bery, L.L., and Malhotra,A. (2000) TM2 Staff dressed politely and professionally TM3 The company's arrangement of goods is convenient during transportation and delivery. Bazargan,M.(2000) TM4 The company's transaction office is conveniently located. Babin,B,J., and Griffin,M.C.(1998) World Journal of Advanced Research and Reviews, 2025, 26(03), 2176-2188 2180 TM5 Photo brochures that are relevant to the business's services look great. Parasuraman and et. al., (1988) Source: Author's summary 3.4. Research Method This study applies a quantitative research approach to examine the factors influencing customer satisfaction with logistics service quality in Nam Tu Liem District. Data were collected through a structured questionnaire designed based on the SERVQUAL framework and adapted from previous validated studies. The questionnaire included closed-ended questions measured on a five-point Likert scale (1 = strongly disagree, 5 = strongly agree), covering constructs such as service capacity, price, information quality, trust, tangibles, and customer satisfaction. A non-probability convenience sampling method was employed, targeting individuals who had prior experience using logistics services in the study area. A total of 137 valid responses were obtained over a two-week data collection period through both online and faceto-face channels. To assess the reliability and validity of the measurement scales, a series of statistical analyses were conducted. Internal consistency reliability was evaluated using Cronbach’s Alpha, with coefficients above 0.6 deemed acceptable (Nunnally and Bernstein, 1994). Exploratory Factor Analysis (EFA) was then performed to identify the latent structure of the constructs, with the suitability of the data verified using the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity. Pearson correlation analysis was used to explore the linear relationships between variables. Finally, multiple linear regression using the stepwise method was applied to test the proposed hypotheses and determine the significance and explanatory power of each independent variable. Multicollinearity was checked through the Variance Inflation Factor (VIF), with all values remaining below the threshold of 10, indicating no serious multicollinearity issues. This methodological approach ensures the robustness of the findings and supports the validity of the conclusions drawn regarding customer satisfaction in the logistics service context. 4. Result 4.1. Descriptive statistics for the study sample Table 2 provides an overview of the respondents’ logistics service usage status and service duration. Out of 145 distributed questionnaires, 143 were deemed valid after screening for completeness and consistency. Among these, six respondents (4.2%) reported never having used logistics services in Nam Tu Liem District and were thus excluded from the analysis, yielding a final analytical sample of 137 respondents. Within this valid sample, a large majority (83.9%) indicated that they were currently using logistics services, while 16.1% had used such services in the past. This distribution highlights the relevance of the sample in capturing active user perspectives, which is critical for evaluating customer satisfaction with logistics service quality. Regarding service duration, the findings reveal that a significant portion of respondents (60.3%) had been using logistics services for over two years. This was followed by users with experience ranging from one to two years (24.7%), six months to one year (10.7%), and less than six months (4.3%). This distribution indicates a high level of service familiarity among respondents, thereby enhancing the reliability of their evaluations. These characteristics confirm that the sample comprises experienced logistics service users, ensuring that subsequent analyses are grounded in informed customer insights. Table 2 Summary of Logistics Service Usage among Respondents Category Subcategory Frequency Percentage (%) Service Usage Status (n=143) Previously Used 22 15.4 Currently Using 115 80.4 Never Used 6 4.2 Total 143 100 World Journal of Advanced Research and Reviews, 2025, 26(03), 2176-2188 2181 Category Subcategory Frequency Percentage (%) Valid Sample (n=137) Previously Used 22 16.1 Currently Using 115 83.9 Total 137 100 Duration of Use (n=137) Less than 6 months 6 4.3 6 months to 1 year 15 10.7 1 year to 2 years 34 24.7 More than 2 years 83 60.3 Total 137 100 Source: Author's compilation from calculation results 4.2. Reliability testing of the measurement scales To assess the internal consistency of the measurement scales and ensure the reliability of each construct, Cronbach’s Alpha coefficient was employed. This method evaluates the extent to which items within the same scale are correlated, thereby indicating the reliability of the construct. Following the standard threshold proposed by Nunnally and Bernstein (1994), a Cronbach’s Alpha value of 0.60 or higher is considered acceptable, and items with corrected item-total correlations below 0.30 are subject to removal. The results of the reliability analysis for each construct are summarized in Table 3. All six constructs—Service Capacity, Price, Information Quality, Trust, Tangibles, and Customer Satisfaction—achieved Cronbach’s Alpha values ranging from 0.799 to 0.952, exceeding the recommended threshold. Moreover, all items exhibited corrected item-total correlation values above 0.30, indicating strong internal consistency. Table 3 Results of reliability testing using cronbach’s alpha Observation variable Scale mean if variable excluded Scale variance if variable is excluded Total variable correlation Cronbach’s alpha if variables are excluded Service capacity(SC): Alpha = 0.799 SC1 11.94 0.732 0.723 0.692 SC2 11.99 0.882 0.542 0.782 SC3 11.98 0.875 0.527 0.788 SC4 12.03 0.705 0.670 0.721 Price (P): Alpha = 0.863 P1 10.65 1.818 0.777 0.799 P2 10.67 1.913 0.689 0.835 P3 10.63 1.941 0.673 0.841 P4 10.64 1.864 0.708 0.827 Information quality (IQ): Alpha = 0.847 IQ1 7.26 0.924 0.680 0.818 IQ2 7.24 0.875 0.711 0.790 IQ3 7.17 0.876 0.753 0.748 Trust (TR): Alpha = 0.884 TR1 13.96 3.727 0.746 0.853 TR2 14.04 3.653 0.738 0.855 World Journal of Advanced Research and Reviews, 2025, 26(03), 2176-2188 2182 Observation variable Scale mean if variable excluded Scale variance if variable is excluded Total variable correlation Cronbach’s alpha if variables are excluded TR3 14.06 3.850 0.678 0.869 TR4 14.00 3.868 0.711 0.861 TR5 14.01 3.794 0.728 0.857 Tangible means (TM): Alpha = 0.848 TM1 13.77 3.019 0.620 0.826 TM2 13.71 2.811 0.674 0.812 TM3 13.77 2.974 0.650 0.819 TM4 13.73 2.743 0.650 0.820 TM5 13.82 2.856 0.695 0.806 Satisfaction (CS): Alpha = 0.952 CS1 5.88 2.854 0.920 0.912 CS2 5.82 2.822 0.912 0.918 CS3 5.82 2.925 0.862 0.956 Source: Author's compilation from calculation results The results confirm that all constructs meet the criteria for internal consistency reliability. Therefore, all items are retained for subsequent factor analysis. 4.3. Results of exploratory factor analysis (EFA) Exploratory Factor Analysis (EFA) was conducted to assess the construct validity of the independent variables and to identify the underlying factor structure. Prior to performing EFA, the suitability of the data was evaluated using the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity. The KMO value was 0.776—above the minimum threshold of 0.5—indicating sampling adequacy. Furthermore, Bartlett’s Test yielded a Chi-square value of 1302.700 with a significance level of p < 0.001, confirming that the correlation matrix was factorable (Table 4). Principal Component Analysis with Varimax rotation (Table 5) was applied to extract factors with eigenvalues greater than 1. The results revealed five distinct factors from the 21 observed variables, which collectively explained 68.624% of the total variance—well above the recommended 50% threshold. Table 4 KMO test of independent variables Kaiser – Meyer – Olkin Mesure of Sampling Adequacy 0.776 Bartlett's Test of Sphericity Appro Chi – Square 1302.700 DF 21.0 Sig 0.000 Source: Author's compilation from calculation results The rotated factor matrix indicated that each group of observed variables loaded clearly onto its corresponding latent construct, with no significant cross-loadings. This confirms that the five extracted factors align with the theoretical structure proposed in the research model. After testing KMO with 21 observed variables, the results showed that the KMO coefficient = 0.776 was between 0.5 and 1, meeting the conditions. At the same time, the Bartlett's test results showed that the statistical significance level with the Sig. = 0.000 system was less than 5%, proving that factor analysis was suitable for the data and the variables were related to each other. Based on the eigenvalue criterion = 2.126 greater than 1, a total of 5 factors were extracted, summarizing the information of 21 observed variables in the most effective way. The total variance of these 5 extracted World Journal of Advanced Research and Reviews, 2025, 26(03), 2176-2188 2183 factors was 68.624%, surpassing the 50% threshold, showing that they explained 68.624% of the data variation of the observed variables participating in EFA. Table 5 Rotation matrix of independent variables Observation variable Factor group 1 2 3 4 5 TR1 0.850 TR2 0.845 TR5 0.832 TR4 0.811 TR3 0.781 TM5 0.825 TM2 0.788 TM3 0.786 TM4 0.782 TM1 0.755 P1 0.881 P4 0.832 P3 0.818 P2 0.816 SC1 0.866 SC4 0.842 SC2 0.732 SC3 0.701 IQ3 0.888 IQ2 0.869 IQ1 0.848 Source: Author's compilation from calculation results 4.4. EFA analysis with dependent variable To assess the construct validity of the Customer Satisfaction scale, exploratory factor analysis was conducted on the three observed variables (CS1, CS2, and CS3). The Kaiser-Meyer-Olkin (KMO) measure was 0.758, and Bartlett’s Test of Sphericity was significant (Sig. = 0.000), indicating that the data were appropriate for factor extraction. The analysis identified a single factor with an eigenvalue of 2.736, which explained 91.190% of the total variance—well above the standard 50% threshold. This result confirms the unidimensionality of the construct. Additionally, all three items exhibited very high factor loadings (CS1 = 0.965, CS2 = 0.962, CS3 = 0.937), demonstrating strong internal consistency and convergent validity. These findings validate the use of the Customer Satisfaction scale in subsequent correlation and regression analyses. A summary of the factor analysis results is presented in Table 6. World Journal of Advanced Research and Reviews, 2025, 26(03), 2176-2188 2184 Table 6 Factor Analysis Summary for Customer Satisfaction Indicator Factor Loading CS1 0.965 CS2 0.962 CS3 0.937 KMO 0.758 Bartlett’s Test (Sig.) 0 Eigenvalue 2.736 Total Variance Explained (%) 91.19% Source: Author's compilation from calculation results 4.5. Correlation analysis results Pearson correlation analysis was conducted to examine the linear relationships between the five independent variables—Service Capacity, Price, Information Quality, Trust, and Tangibles—and the dependent variable, Customer Satisfaction. As shown in Table 7, all independent variables are positively and significantly correlated with Customer Satisfaction at the 0.01 level (two-tailed). Among these, Service Capacity exhibited the strongest correlation with Customer Satisfaction (r = 0.394), followed by Trust (r = 0.377), Tangibles (r = 0.370), Price (r = 0.301), and Information Quality (r = 0.200). According to Cohen’s (1988) guidelines, these coefficients indicate weak to moderate positive relationships. These results suggest that improvements in any of the five service dimensions are associated with increased customer satisfaction. The positive and statistically significant correlations also justify the inclusion of all five independent variables in the subsequent multiple regression analysis. Table 7 Correlation analysis results Corelations CS SC P IQ TR TM CS Pearson Corelation 1 0.394** 0.276** 0.200** 0.327** 0.302** Sig. (2-tailed) 0.000 0.001 0.019 0.000 0.000 SC Pearson Corelation 0.394** 1 -0.073 -0.058 -0.097 -0.007 Sig. (2-tailed) 0.000 0.399 0.499 0.259 0.932 P Pearson Corelation 0.276** -0.073 1 0.149 0.041 0.114 Sig. (2-tailed) 0.001 0.399 0.083 0.633 0.185 IQ Pearson Corelation 0.200** -0.058 0.149 1 -0.045 0.022 Sig. (2-tailed) 0.019 0.499 0.083 0.602 0.799 TR Pearson Corelation 0.327** -0.097 0.041 -0.045 1 0.078 Sig. (2-tailed) 0.000 0.259 0.633 0.602 0.364 TM Pearson Corelation 0.302** -0.007 0.114 0.022 0.078 1 Sig. (2-tailed) 0.000 0.932 0.185 0.799 0.364 Source: Author's compilation from calculation results