The links between e-logistics service quality, attitude and repurchase intention of Gen Z in e-commerce Open Access

Logistics has become a powerful differentiator for businesses by its contribution to achieving a cost or value advantage (Grant, 2012; Christopher, 2016). In e-commerce, logistics activities are performed to support the e-commerce business; within the context of e-commerce, logistics encompasses the strategic planning, implementation of essential logistics systems and processes, as well as the management activities and operational structure of a company (Büyüközkan et al., 2008). Logistics in e-commerce includes all activities that support the flow of goods from the point of supply to the point of consumption in electronic purchases and sale transactions, specifically as several basic activities: order receipt, inventory control, warehouse management, scheduling, organization of transportation and delivery (Joong-Kun Cho et al., 2008; Turban et al., 2018). With the rapid development of e-commerce over the past decade, logistics has emerged as a critical determinant of the success and sustainable development of e-commerce businesses (Gonzalez et al., 2023). Consequently, enhancing the quality and control of logistics services has become imperative for businesses as these activities significantly impact customer experience and their loyalty to e-commerce companies (Vu et al., 2025; Do et al., 2023; Vasić et al., 2021).

Gen Z, referring to individuals born between 1997 and 2012, grew up in an era of rapid technological advancements (Seemiller and Grace, 2016). Representing approximately one-third of the global population (Miller and Lu, 2018), Gen Z is poised to become a dominant force in the shopping landscape (Fromm, 2018). With their extensive technological proficiency, innovative mindset and high levels of educational attainment, Gen Z is projected to spearhead customer behavior in e-commerce in the coming years (Vieira et al., 2020). According to the General Statistics Office of Vietnam, Gen Z is expected to account for nearly one-third of the working-age population (PwC, 2021) and approximately 30% of the consumer market by the end of 2025 (Minh, 2022a, b). The prominent role of Gen Z customers in business activities has attracted significant scholarly attention. As of 2024, around 57 million Vietnamese customers shop online, with Gen Z comprising 43% of this group. Gen Z customers are particularly recognized for their rapid adaptability to emerging e-commerce services and platforms, such as livestreaming and mobile shopping (Vu, 2024). Hence, understanding the consumption behavior of this customer segment can help businesses devise long-term market development strategies.

Numerous studies have investigated the consumption behavior of this demographic across various markets (Lestari, 2019; Dabija and Lung, 2019), including Vietnam (Nguyen and Nguyen, 2020). However, research examining the impact of logistics service quality on customer behavior in e-commerce remains relatively sparse in the Vietnamese context. While several studies have proposed and validated evaluation scales for e-service quality and e-LSQ (Grant, 2004; Mentzer et al., 2001; Parasuraman et al., 2005), and others have explored the effects of logistics service quality on customer perception and behavior (Murfield et al., 2017; Jain et al., 2020), the findings have been inconsistent, specifically regarding the considerable differences in the effects of various dimensions of e-LSQ on customer behavior.

For instance, Xing et al. (2010) emphasized the role of condition in customer satisfaction, whereas Murfield et al. (2017) refuted this relationship. Additionally, the role of timeliness in influencing customer perceptions and behavior has been found to be inconsistent, heavily dependent on the characteristics and operational methods of the services provided (Murfield et al., 2017; Griffis et al., 2012). Furthermore, research on logistics service quality often focuses on port services and business-oriented approaches, with emerging studies predominantly investigating e-service quality rather than logistics service quality (Vu et al., 2020). Therefore, strengthening research on the role of e-LSQ carries significant implications both theoretically and practically.

This study was initiated to address specific research gaps in the relationship between e-LSQ and Gen Z customer behavior. Despite e-LSQ receiving significant academic attention regarding its definition, measurement and associated outcomes (Gil-Saura and Ruiz-Molina, 2011), previous studies have yielded inconsistent results in identifying the dimensions that represent e-LSQ (see Table 1). These inconsistencies have prompted scholars to call for further exploration of the topic (Wang et al., 2024; Sun and Karia, 2023). To the best of our knowledge, efforts to identify and establish a measurement scale for e-LSQ remain limited, particularly from the perspective of Gen Z customers.

Additionally, previous studies have focused heavily on customer satisfaction, treating this variable as a mediator transitioning to repurchase intention (Akıl and Ungan, 2022; Uvet et al., 2024). As such, the direct effects of e-LSQ dimensions on repurchase intention have received insufficient attention. Specifically, we posit that in the context of Gen Z customers, who view e-commerce shopping as a habitual activity, the role of satisfaction may diminish significantly (Sharma and Dutta, 2025). These customers may continue to repurchase out of habit, even if their satisfaction with a product or service is not high. Therefore, clarifying the direct relationships between these dimensions and repurchase intention is even more meaningful.

Moreover, while attitude has been identified as a critical factor influencing young customers’ behavior (Plötz et al., 2023; Tiwari and Joshi, 2020; Lina et al., 2022), there is limited research connecting this variable to the relationship between e-LSQ and customer behavior. Identifying and integrating new factors to elucidate the mechanisms through which e-LSQ influences customer behavior holds significant promise for advancing e-LSQ research and offering practical insights for e-commerce businesses. Given the importance of understanding Gen Z customer behavior and the aforementioned gaps, this study aims to address the following research questions:

This study focuses on Vietnam, an emerging economy with one of the fastest growth rates in Southeast Asia. Notably, the e-commerce market in Vietnam has experienced rapid expansion, recording a market value of $5 billion in 2019 and it is projected to reach $33 billion by the end of 2025. This growth is primarily fueled by the increasing online purchase of products such as garments, electronics, home appliances and personal hygiene items (Van Nam et al., 2022). Furthermore, the logistics sector, which plays a critical role in supporting e-commerce, is expanding at an annual rate of 20–25%, with an estimated market value of $50–60 billion. This upward trajectory is anticipated to persist due to the high rate of internet penetration and the growing trend of online shopping (Hai and Quyet, 2023).

This paper is structured into five sections. The first section emphasizes the importance of the research and outlines the key research questions the study aims to address. The second section provides a comprehensive review of the relevant literature and develops the research hypotheses. The third section presents Study 1, which focuses on identifying appropriate dimensions of e-LSQ through qualitative research. The fourth section details the findings from Study 2, which involve in quantitative analyses to validate the measurement scales and test the proposed research hypotheses. Finally, the fifth section includes a thorough discussion of the findings, practical implications and the conclusion.

The Cognition-Affection-Conation (CAC) theory is a psychological framework that explains human behavior through three sequential stages: cognition (thinking), affection (feeling) and conation (acting). Specifically, cognition refers to the mental processes involved in acquiring knowledge and understanding through thought, experience and perception. Affection describes the emotional responses or feelings that arise from cognitive evaluations, while conation is often considered the behavioral intention or action resulting from cognitive and affective processes (Neyrinck et al., 2006).

In the context of e-commerce, this theory offers a robust perspective for understanding how customers transition from perceiving services on online platforms to making purchasing decisions (Ding and Lee, 2024). This study proposes a conceptual model based on the CAC theory, wherein: e-LSQ represents cognition, describing customers’ perceptions following their experiences; attitude serves as a variable representing their emotional responses to e-LSQ; and finally, repurchase intention is regarded as a behavioral outcome symbolizing conation.

Logistics activities, whether managed in-house or outsourced, aim to meet customer requirements by ensuring convenience in terms of location, timing and condition. This involves making sure that products are delivered to the right place, at the right time and in an undamaged state (Emerson and Grimm, 1996). The evolution of this concept to align with customer expectations signifies the outcome of the logistics process (Grant, 2012). Consequently, a number of studies in the field of logistics and supply chain management have adopted the approach of measuring service quality from the perspective of the service customers, as evidenced by the works of Emerson and Grimm (1996), Mentzer et al. (2001), Grant (2004), and Rafiq and Jaafar (2007).

To measure logistics service quality, some authors have focused on physical distribution (Emerson and Grimm, 1996). To evaluate the results of the distribution activities of enterprises to customers, Mentzer et al. (1989) introduced a model that was further developed by Bienstock et al. (1997), consisting of three factors: timeliness, availability and order condition. From the initial three factors, Mentzer et al. (2001) introduced a model with nine factors: information quality, ordering procedure, order release quantity, timeliness, accuracy, order quality/condition, order status, order mishandling and employee contact quality. However, the perspective of the logistics approach of the above studies is not consistent across studies. While Emerson and Grimm (1996) researched the quality of business logistics by surveying retail businesses as customers of a large tool manufacturer, Mentzer et al. (2001) surveyed customers of the Defense Logistics Agency, an enterprise providing inbound logistics services for their clients.

Grant (2004) and Rafiq and Jaafar (2007) modified the model of Mentzer et al. (2001) in their studies. Grant (2004) investigated logistics service quality in food processing in the UK with a model adapted the from Mentzer et al. (1989)’s, including core variables such as availability, time (order cycle time and delivery time) and customer service experience as outputs for a structured model based on actual service experience. Rafiq and Jaafar (2007) have inherited Mentzer et al. (2001) and replaced variables of information quality and ordering process with variables of communication, including timeliness, accuracy, completeness and reliability, and adding more variables to the ordering process, namely simplicity, flexibility, time and effort. Rafiq and Jaafar (2007) studied the quality of logistics services provided by 3PL service providers in the UK. The data used for analysis and evaluation is from a survey of corporate customers, including manufacturing companies, wholesalers, retailers and professional service enterprises providing output logistics services (outbound logistics) or both inbound and outbound logistics for your company. Among the studies on logistics service quality from customer experience, many focus on service quality performed by professional logistics service providers for corporate customers (Mentzer et al., 2001; Rafiq and Jaafar, 2007; Phan et al., 2021).

E-logistics service quality (e-LSQ) comprises various dimensions that collectively define the efficacy and customer satisfaction of logistics services within the e-commerce sector. These dimensions encompass operational factors such as reliability and timeliness, relational elements including empathy and professionalism, and the technological influences that enhance service delivery (Ta et al., 2025; Gil-Saura and Ruiz-Molina, 2011). A thorough understanding and continuous improvement of these dimensions is pivotal in enhancing customer satisfaction and fostering customer loyalty, positioning e-LSQ as a crucial focus area for e-commerce enterprises (Wang et al., 2024; Rao et al., 2011).

Similar to logistics service quality, e-LSQ has seen the continuous development of measurement scales over recent decades, primarily driven by the concurrent growth of e-commerce (Wang et al., 2024; Ta et al., 2025). The dimensions used to represent e-LSQ are varied, encompassing aspects such as operational (Wang et al., 2024), social (Sun and Karia, 2023) and technological factors (Junior et al., 2020). Many of these dimensions are inherited and evolved from theories of service quality and logistics service quality. A comprehensive summary of these dimensions and key studies is detailed in Table 1.

Attitude and repurchase intention are two pivotal concepts in customer behavior research, and fostering these factors is regarded as a key strategy for market development by businesses (Sahranavard et al., 2024). Attitude towards online shopping is defined as a customer’s emotional evaluation, either positive or negative, associated with the act of making purchases via the internet (Chiu et al., 2005). Several prominent theoretical models of behavior, including the Theory of Reasoned Action (TRA) (Fishbein and Ajzen, 1975), the Technology Acceptance Model (TAM) (Davis, 1989),and the Theory of Planned Behavior (TPB) (Ajzen, 1991), have identified attitude as a critical determinant of individual behavioral outcomes. Meanwhile, repurchase intention refers to a customer’s inclination or willingness to repurchase the same brand or product in the future (Xu et al., 2015). This factor is often conceptualized as an outcome of positive product or service experiences, particularly when customers feel satisfied, perceive high value or hold favorable attitudes toward the product or service (Sharma et al., 2024).

In this study, the identification of dimensions representing e-LSQ was conducted following the procedure outlined by Nguyen et al. (2024). This process involved in-depth interviews with three experts, including two PhD holders specializing in marketing and an experienced logistics professional from e-commerce enterprises. Additionally, focus group discussions were conducted, involving 15 Gen Z customers who actively use e-commerce platforms. The findings from this phase facilitated the selection of dimensions and refinement of measurement scales prior to the quantitative analysis in Study 2. The procedure was as follows:

• Step 1: The authors proposed constructs and items for the research model based on a comprehensive literature review.

• Step 2: Three in-depth interviews with experts and a focus group discussion with 15 Gen Z customers were conducted to refine the measurement scales and the research model.

• Step 3: The reliability of the proposed scales was evaluated using exploratory factor analysis (EFA) and Cronbach’s alpha analysis, incorporating feedback from the interviews and involving 75 respondents.

The research scales were developed with reference to several studies on e-LSQ and customer behavior in e-commerce. Specifically, the items representing e-LSQ were derived from the works of Murfield et al. (2017), Griffis et al. (2012), Xing and Grant (2006), Rabinovich and Bailey (2004) and Rao et al. (2011). The attitude scale was adapted from Vu et al. (2023), incorporating items such as “Online shopping in E-commerce is a good idea,” “Online shopping in E-commerce is a smart solution” and “I enjoy shopping in E-commerce.” Similarly, the repurchase intention scale was informed by Sullivan and Kim (2018), consisting of three items: “If I were to buy the product again, I would likely buy it from the same E-commerce site,” “If I could, I would like to reuse this e-commerce site for my next purchase” and “I would like to revisit this E-commerce site to purchase products in the near future.”

Before conducting interviews, the items were translated from English to Vietnamese and cross-verified by two linguistic experts. The interviews facilitated the elimination of items that were unsuitable for the research context. Moreover, based on recommendations from experts and customers, several additional items were integrated into the scale, including “Customers can choose/suggest the time of day for delivery,” “Deliveries are completed on time,” “When out of stock, items are quickly replenished,” “E-commerce site offers various delivery options” and “E-commerce site supports item exchanges after delivery.” The additional items were selected through a rigorous process. First, they had to be proposed and endorsed by experts then thoroughly discussed during focus group interviews. To pass this stage, the items needed to be validated by customers — the respondents of the focus group interviews — ensuring their alignment with the customers’ experiences of e-logistics services. Finally, the items were subjected to pre-testing to ensure their reliability before being officially included in the research measurement scale.

The selected items derived from the qualitative research phase were validated through EFA and Cronbach’s alpha reliability analysis, with a sample size of 75 respondents. The results indicated that the items converged into four variable groups representing the four components of e-LSQ (see Table 2), with Cronbach’s alpha values exceeding 0.7 for all components. These four concepts – timeliness, availability, condition and return – are components consistently highlighted in the literature on e-LSQ (see Table 3).

Thus, Study 1 identified four dimensions representing e-LSQ along with the corresponding items of the measurement scale. Based on these findings, Study 2 will proceed to formulate research hypotheses and validate the measurement scales as well as the proposed hypotheses.

When an order is placed, customers start forming an expectation towards when they will receive the ordered products for consumption or wait for the order to be delivered. Therefore, in this respect, customers can observe timeliness based on the speed at which orders are delivered and stability. In addition, in e-commerce, with the support of technology, orders are often delivered to personal addresses (e.g. private homes) where customers sometimes are not available to receive shipments. Therefore, logistics services are also expected to be flexible, allowing customers to choose the date and time to receive orders and the ability to deliver at the time chosen by the customer (Murfield et al., 2017). Many studies found the positive impact of timeliness on satisfaction and loyalty of customer purchasing in e-commerce (Akıl and Ungan, 2022; Murfield et al., 2017). Asghar and Mahmud (2020) indicated specifically that timeliness could improve online purchase intention. Furthermore, delivery speed is recognized as an essential aspect customers consider when selecting a retailer (Riley and Klein, 2021). However, there has not been much investigation into the role of timeliness on attitude and repurchase intention of the Gen Z customer. Based on these arguments, hypothesis H1a is proposed:

While shopping within the brick-and-mortar retailing sector, customers can observe availability bywhether an item is on the shelf or not; however, in e-commerce, thanks to the information integration function among the stakeholders and the view of e-commerce platforms, information will be displayed during the transaction whether the item has enough stock to purchase. Customer experience will be worse than their expectations when accessing an e-commerce site when the item they want to buy is unavailable, as well as alternative products and services. In addition, due to the distance between the place and time of purchase and receipt, customers expect logistics services to allow them to control the delivery of orders. Sousa and Voss (2012) stated that convenience, information availability and accessibility are advantages of online channels in the retail sector. Several studies, including one by Heim and Sinha (2001), demonstrated that product availability improves significantly customer loyalty. Particularly, Hausman and Siekpe (2009) and Kumar and Kashyap (2018) posited that product availability critically impacts purchase intention, thus promoting sales. Therefore, this study formulates the following hypothesis:

The successful delivery of an order also reflects the efficient operation of the supporting logistics system. Shopping on e-commerce platforms often does not allow customers to inspect products directly and products go through a shipping process to reach customers (Taher, 2021) and, as a result, customers bear risks when goods are damaged during transportation, when product quality is not as expected or when products are not delivered sufficiently (Xing et al., 2010). Murfield et al. (2017) reported that condition is an important component of e-LSQ, promoting customer satisfaction and loyalty. Therefore, if the condition aspect is guaranteed, it can potentially improve customer attitudes and lead them to continue shopping on e-commerce platforms. As such, hypothesis H1c is proposed:

Convenient and easy return methods are essential in helping customers use online shopping. Because shopping on e-commerce platforms is limited due to customer inability to check items directly when purchasing, the choice depends heavily on the product information provided on the interface (Taher, 2021). As a result, customers may perceive risks when purchasing goods if the product cannot meet their expectations. Pei et al. (2014) emphasize that retailer return policies can vigorously promote customer trust and purchase intention in e-commerce. Similarly, Yu and Kim (2019) stated that online retailers’ return policy is an effective method to ensure benefits for customers and can promote customers’ purchase intentions. Therefore, hypothesis H1d is proposed:

Several emprical studies have demonstrated that attitude significantly improves customer repurchase intention in various contexts. For example, Vu et al. (2023) found a positive influence of attitude on continuance intention of Gen Z customers using food delivery applications. Wang et al. (2013) emphasized attitude as a key determinant for improving repurchase intention in remanufactured products consumption. Riley and Klein (2021) illustrated promoting attitude on online purchasing intention of young customers. However, several studies explored the gap between attitude and behavioral intention (Vu et al., 2024) and Nguyen et al. (2019) explained the differences between these factors due to financial barriers. Vermeir and Verbeke (2006) reported that the specific gap between attitude and behavioral intention of young customers is dependent on perceived effectiveness. Based on inconsistent results, this study proposes hypothesis H2 to investigate the impact of attitude on repurchase intention of Gen Z customer in the e-commerce context.

Despite the significant interest of scholars in the relationship between service quality and purchasing intention, and the diverse array of studies evaluating the impact of service quality on purchasing intention across various contexts (Monoarfa et al., 2024; Huang et al., 2011), gaps in knowledge remain. These include result inconsistencies across different contexts and the subsequent need to explore underlying mechanisms to gain a deeper understanding of service quality’s role of in influencing purchasing intention (Kuo et al., 2012; Choudhury, 2013). Consequently, integrating intermediary factors into this relationship is a strategy widely adopted by researchers to provide more comprehensive insights.

Huang et al. (2011) identified the mediating role of satisfaction in the relationship between service quality and purchasing intention within the context of hospital studies. Similarly, in a Taiwanese study on mobile phone consumption Liao et al. (2022) indicated the existence of an indirect effect of perceived service quality on purchasing intention through two mediators: brand image and customer satisfaction. These studies suggest a transformative process whereby high-quality service perceptions enhance customers’ positive attitudes and evaluations of products and services, which, in turn, facilitate outcomes such as repurchase and loyalty (Nasir et al., 2024; Zarei et al., 2019).

However, to the best of our knowledge, no studies have evaluated the mediating role of attitude in the relationships between the dimensions of e-LSQ and the repurchase intention of Gen Z customers in the e-commerce context. This represents a significant research gap, as attitude is often considered a crucial factor in predicting the customer behavior of young customers (Plötz et al., 2023; Tiwari and Joshi, 2020; Lina et al., 2022). According to scholars, service quality can substantially influence customer experience, leading to changes in perception and evaluation of products/services (Nasir et al., 2024; Zarei et al., 2019), thereby resulting in positive behavioral outcomes, potentially including repurchase intention.

This study proposes to evaluate the mediating role of attitude in the relationships among the four dimensions of e-LSQ - timeliness, availability, condition and return–and the repurchase intention of Gen Z customers. The proposed hypotheses are as follows:

Based on hypotheses development section, the proposed research model is depicted as Figure 1.

Data from the official survey were collected, and the study employed Structural Equation Modeling (SEM) techniques to assess both measurement and structural models. SEM methodologies are typically categorized into two principal approaches: Covariance-Based SEM (CB-SEM) and Partial Least Squares SEM (PLS-SEM). The PLS-SEM approach is particularly advantageous in predictive research, as it prioritizes maximizing the explained variance of dependent variables. This characteristic renders it especially suitable for studies aimed at identifying key drivers or forecasting outcomes. Furthermore, PLS-SEM is highly appropriate for exploratory research contexts, where uncovering and testing new relationships between latent variables is a critical objective. This method enables researchers to iteratively refine models, ensuring their robustness. A notable strength of PLS-SEM lies in its reduced sensitivity to non-normal data distributions, making it a preferred analytical tool in cases where the dataset does not meet the normality assumptions required by CB-SEM (Usakli and Rasoolimanesh, 2023). In this study, there is a focus on certain paths between the dimensions of e-LSQ and repurchase intention that have remained unexplored in prior research. Additionally, examining the mediating role of attitude represents a novel approach aimed at providing a more comprehensive explanation for repurchase intention. To perform the quantitative analyses, the study utilized SmartPLS software version 4.0.9.2, with evaluation criteria aligned to the thresholds recommended by Hair et al. (2017).

Following the development of statements to capture observed variables across four dimensions – timeliness, availability, condition and return – as suggested by expert feedback, the authors designed a questionnaire to evaluate customer perceptions of each statement using a seven-point Likert scale. Due to the unavailability of precise information regarding the required sample size for this study, a convenience sampling method was applied. The questionnaires were distributed to various online shopping communities on social media platforms and included a comprehensive introduction to the research topic, ethical assurances stating that the collected data would be used solely for research purposes and clear explanations of the relevant terms before presenting the assessment content. To minimize issues related to self-selection bias, the survey links were continuously reposted on social media platforms to attract a wider range of participants. The data collection process adhered strictly to the principles outlined in the Declaration of Helsinki (General Assembly of the World Medical Association, 2014), ensuring participants were fully informed that their data would be used exclusively for research purposes. In addition, participants were required to confirm their adequate knowledge of the topic and affirm their voluntary participation. Furthermore, to ensure respondents had sufficient recent online shopping experience, the study targeted participants who had completed e-commerce transactions within the past month and who had a clear understanding of e-LSQ during the shopping process. The data collection phase took place from June 2024 to the end of September 2024. After excluding invalid or incomplete responses, the authors retained 290 valid responses from Gen Z customers. A summary of the demographic details of these respondents is presented in Table 4.

To prevent the appearance of common bias method issue, this study followed the recommendation of Podsakoff et al. (2003) during the data collection process. In addition, Harman’s single test was performed to evaluate the likelihood of the common bias method before submitting the data to the tests. Harman’s single factor test result indicated that the single factor supported less than 50% (32.30%) of the total variance. Notably, the full collinearity assessment indicates that all variance inflation factor (VIF) coefficients for the variables were below 3.3. Hence, the common bias method did not appear in this study (Malhotra et al., 2006; Kock, 2015).

All Cronbach’s alpha (α) values for the variables exceed 0.7, ranging from 0.704 to 0.879, and the composite reliability (CR) values are also above 0.7, ranging from 0.834 to 0.917 (Table 5). Each items outer loadings are greater than 0.7, ranging from 0.755 to 0.886. Therefore, the measurement’s reliability is confirmed (Hair et al., 2017). The average variance extracted (AVE) values exceed 0.5, with a range from 0.609 to 0.734, ensuring that the convergent validity test results meet the thresholds recommended by Hair et al. (2017).

The analysis of the heterotrait-monotrait (HTMT) ratio revealed that all correlation values were below 0.85. Therefore, in line with the criteria established by Henseler et al. (2015), the thresholds for discriminant validity were satisfied (Table 6).

The results of the proposed model testing with 5,000 bootstrap samples are presented in Table 7 and indicate that all four dimensions of e-LSQ have significantly positive impacts on repurchase intention. Timeliness has a specific positive impact on repurchase intention at β = 0.299, t-value = 7.409 and p < 0.001. Similarly, the availability has coefficient at β = 0.252, t-value = 6.240, p < 0.001; the condition has the impact at β = 0.247, t-value = 4.400 and p < 0.001; and the return has β = 0.314, t-value = 6.763 and p < 0.001. Thus, hypotheses 1a, 1b, 1c and 1d are supported. H2 tested the positive effect of attitude on the repurchase intention of Gen Z customers. The result confirmed the positive influence of attitude on repurchase intention at β = 0.201, t-value = 5.383 and p < 0.001. Thus, H2 is supported.

To test mediation effect attitude in the relationship between four dimensions of e-LSQ and repurchase intention, we tested the indirect effects. The results showed that attitude mediates three out of the four relationships connecting the dimensions–namely, availability, condition and return — to repurchase intention. In particular, the indirect effects of availability (β = 0.028, t = 2.307, p < 0.05), condition (β = 0.037, t = 2.301, p < 0.05) and return (β = 0.035, t = 2.209, p < 0.05) on repurchase intention through attitude were all statistically significant. In addition, the direct effects between availability, condition and return and repurchase intention with the presence of attitude were still significant. Therefore, attitude partially mediates the relationship between availability, condition, return and repurchase intention. Thus, H3b, H3c and H3d are supported. In contrast to H3a, the indirect effect of timeliness and repurchase intention through attitude was not significant (β = 0.009, t = 0.660, p = 0.510). Thus, H3a is not supported.

Additionally, based on the R-square value, The SEM model accounts for 64.4% of the variance in repurchase intention and 16 per cebt of the variance in attitude. The effect size coefficients (f²) range from 0.082 to 0.203, corresponding to small and medium levels, respectively.

This study makes several theoretical contributions. Firstly, building upon prior research conducted in markets such as India, the UK and the US (Jain et al., 2020; Xing and Grant, 2006; Murfield et al., 2017; Rao et al., 2011), this study has developed a measurement scale by incorporating relevant items and validating them in Vietnamese context. Consequently, it provides a highly reliable measurement scale to evaluate e-LSQ from the perspective of Gen Z customers in the Vietnamese market–a rapidly growing economy with substantial potential for e-commerce expansion. These findings contribute significantly to the existing body of research on e-LSQ, enhancing both the understanding and methodologies for assessing service quality.

Furthermore, this study is a pioneering effort in applying the CAC theory by considering e-LSQ as the cognition factor to explain attitude and repurchase intention. The findings reinforce the relevance of CAC theory in predicting customer behavior (Neyrinck et al., 2006; Ding and Lee, 2024). Additionally, the identification of a new role for e-LSQ within the CAC framework opens up further avenues for exploring diverse approaches to evaluating e-LSQ and its impact on customer behavior. With these contributions, the study establishes a solid foundation for future research on related topics, supporting the refinement of measurement scales and the development of research models.

Secondly, this study not only developed a measurement scale reflecting e-LSQ but also assessed the impact of four dimensions – timeliness, availability, condition and return – on the repurchase intention of Gen Z customers. These dimensions collectively explain 64.4% of the variance in repurchase intention, a notably high level compared to similar studies. Specifically, timeliness was identified as a key determinant affecting repurchase intention and this finding aligns with the perspectives of Akıl and Ungan (2022), Murfield et al. (2017) and Asghar and Mahmud (2020) regarding the significance of this factor. However, in this study, timeliness emerged as the most influential factor, highlighting that young customers place considerable importance on the delivery of goods being on time as well as their ability to customize delivery times.

This study also highlights the significant role of return in predicting repurchase intentions. These findings align with the observations of Pei et al. (2014) and Yu and Kim (2019) regarding the importance of return policies in encouraging customers’ purchasing intentions. Gen Z customers, who are typically tech-savvy and have high expectations for their shopping experiences, place considerable importance on return policies (Serravalle et al., 2022). Therefore, return is seen as a critical dimension that enhances the repurchase intention of Gen Z customers.

Furthermore, this study indicates that availability positively impacts the repurchase intention of Gen Z customers. These results support the view that availability can boost sales and attract customers to choose products on e-commerce platforms (Hausman and Siekpe, 2009; Kumar and Kashyap, 2018). In addition, the study demonstrates the roles of availability and condition in influencing the repurchase intention of Gen Z customers, reinforcing the perspectives of several previous studies (Xing et al., 2010; Murfield et al., 2017; Hausman and Siekpe, 2009; Kumar and Kashyap, 2018). The diversity of products and the ability to ensure the quality of goods during transportation are essential to customers’ purchasing intentions in e-commerce. Notably, the gap between attitude and intention does not appear in this study, as attitude has been shown to significantly predict repurchase intention.

Notably, this study makes significant theoretical contributions by uncovering the mediating role of attitude in the relationship between the dimensions of e-LSQ and repurchase intention. To the best of our knowledge, this is the first attempt to demonstrate the mediating role of attitude in these relationships. The findings regarding the indirect effects of condition, availability and return through attitude enrich the understanding of the mechanisms through which e-LSQ influences the repurchase intention of Gen Z customers. Thus, attitude not only has a direct impact but also enhances the influence of e-LSQ on repurchase intention, improving the explanatory power of the research model’s dependent variable.

However, contrary to expectations, timeliness does not exhibit a mediating effect through attitude. This outcome can be partly attributed to the fact that many Gen Z customers are still students (ages 15–23) who often have ample time since they are not yet working, reducing time-related pressures and awareness, leading to differences in evaluation and behavior. Additionally, in the Vietnamese market, where customers can receive goods multiple times and on behalf of others, the role of timeliness in repurchase intention through attitude remains unclear. As operational issues related to timeliness are not yet fully addressed, considering timeliness as a criterion for evaluating logistics services in e-commerce may not be widely recognized, resulting in attitude not serving as a mediator between timeliness and repurchase intention.

Finally, this study indicated that attitude positively influenced repurchase intention and its findings strengthen the opinions of Chiu et al. (2005), and Riley and Klein (2021) in that a good attitude leads Gen Z customers to shop on e-commerce platforms. Therefore, businesses trading on e-commerce platforms can focus on several communication strategies to raise customer awareness of the advantages of shopping via these methods. In addition, businesses need to improve essential aspects such as the quality of products, perceived usefulness, ease of use and customer service.

In addition to its theoretical contributions, this study offers practical implications for businesses aiming to enhance their e-commerce operations based on e-LSQ. Identifying the four crucial dimensions that constitute e-LSQ allows businesses to strategically and efficiently allocate resources to improve these specific aspects. This study suggests solutions for managing delivery teams, optimizing order distribution systems and meeting strict time requirements to enhance timeliness. Moreover, providing options for customizing delivery locations and times should be considered to increase customer convenience.

Businesses must also focus on forecasting market demand to develop effective storage solutions and ensure product availability for customers. Since transactions occur online, enterprises need to improve the quality of information provided, ensuring it is accurate, timely and detailed to support customers effectively. In addition, given that young customers often have limited financial budgets, guaranteeing the condition of goods is crucial for customer retention. This study provides empirical evidence that Gen Z customers prioritize the condition of products when deciding whether to continue using logistics and e-commerce services. Therefore, inadequate quantity or quality of delivered goods can significantly impact the perceived financial benefits, making it essential for businesses to have commitment policies to fulfill orders accurately and adequately. It is also vital that emphasis is put on product packaging practices to prevent damage during delivery.

Furthermore, this study highlights the importance of the return dimension and illustrates that product return policies should focus on enhancing customer benefits. The current e-commerce business environment in Vietnam faces ethical challenges where products may not match their advertisements. Therefore, return and exchange activities are crucial for creating a transparent business environment and protecting customer interests.

The discovery of the mediating mechanism of attitude suggests that improving customers’ attitudes involves helping them recognize the usefulness and advantages of e-commerce shopping, particularly regarding aspects based on e-LSQ such as convenience, time savings, diverse choices and return policies. These solutions will make the enhancement of e-LSQ more meaningful and effective.

E-commerce has grown rapidly over the past 2 decades, significantly altering customer habits and increasing market competition for customer goods. This study utilizes various research methods to identify and evaluate the impact of several e-LSQ dimensions on the repurchase intentions of Gen Z customers in Vietnam. The findings diversify academic resources and offer new insights into the role of e-LSQ in customer behavior. Additionally, the results reveal novel findings, such as the mediating role of attitude, suggesting that future studies should continue to explore and incorporate new factors to better explain the relationship between e-LSQ and customer behavior in the e-commerce context. Moreover, the study proposes practical implications for businesses to enhance the repurchase intentions of Gen Z customers based on e-LSQ, aiming for sustainable development.

Although this study has addressed the proposed research questions, there remain certain limitations that future studies should aim to overcome for a more comprehensive understanding. Due to the limited scope of sample collection, which was restricted to Vietnam, and the use of convenience sampling methods, this study is unable to compare findings across different national markets. This may limit the generalizability of the research. Moreover, the relatively modest sample size compared to the overall population raises concerns about representativeness. In particular, demographic and personal characteristics were not included in the analysis, suggesting that future research could focus on these variables to provide additional valuable insights for businesses. Additionally, the measurement scales developed in this study are based solely on customer perceptions and other stakeholders, such as suppliers and e-commerce business partners, have not been considered, indicating that future studies should adopt a multi-stakeholder approach to develop various measurement scales tailored to different groups. Lastly, the remarkable technological advancements in recent years underscore the need for future studies to continue exploring and updating new dimensions that represent e-LSQ, as well as their impact on customer behavior.