Determinants of customer satisfaction with parcel locker services in last-mile logistics

Lai, Po-Lin; Jang, Hyunmi; Fang, Mingjie; Peng, Ke Article Determinants of customer satisfaction with parcel locker services in last-mile logistics Asian Journal of Shipping and Logistics (AJSL) Provided in Cooperation with: Korean Association of Shipping and Logistics, Seoul Suggested Citation: Lai, Po-Lin; Jang, Hyunmi; Fang, Mingjie; Peng, Ke (2022) : Determinants of customer satisfaction with parcel locker services in last-mile logistics, Asian Journal of Shipping and Logistics (AJSL), ISSN 2352-4871, Elsevier, Amsterdam, Vol. 38, Iss. 1, pp. 25-30, https://doi.org/10.1016/j.ajsl.2021.11.002 This Version is available at: https://hdl.handle.net/10419/329686 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ Contents lists available at ScienceDirect The Asian Journal of Shipping and Logistics journal homepage: www.elsevier.com/locate/ajsl Determinants of customer satisfaction with parcel locker services in last-mile logistics Po-Lin Lai a , Hyunmi Jang b,⁎ , Mingjie Fang c , Ke Peng a a Department of International Logistics, Chung-Ang University, Seoul 06974, Republic of Korea b Graduate School of International Studies, Pusan National University, Busan, Republic of Korea c Department of Logistics, Service & Operations Management, Korea University Business School, Seoul, Republic of Korea article info Article history: Received 23 August 2021 Accepted 1 November 2021 Keywords: Parcel locker services Customer Satisfaction Last-mile logistics Service quality SERVQUAL abstract Based on the service quality (SERVQUAL) model and logistics service quality (LSQ) model, this study investigates the antecedents of customer satisfaction with parcel locker services in last-mile logistics. Data were collected from a survey of 321 consumers in China and analyzed using structural equation modeling. The results indicate that timeliness is the strongest predictor that positively impacts customer satisfaction with parcel locker services, while reliability and security are the predictive coefficients for the same, followed by responsiveness and tangibility, respectively. This study enriches the literature on SERVQUAL and LSQ by providing implications for logistics service providers. © 2021 The Authors. Production and hosting by Elsevier B.V. on behalf of The Korean Association of Shipping and Logistics, Inc. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). CC_BY_NC_ND_4.0 1. Introduction Numerous studies have revealed that the rapid development of e-commerce has considerably benefited the Chinese logistics industry (Janjevic & Winkenbach, 2020; Zeng, 2018). However, challenges that coexist with these opportunities have also attracted much attention. Specifically, because of the mutual interference of a series of distribution problems such as distribution interruption, distribution delay, low service quality, and imperfect distribution network, the traditional distribution model is gradually in a passive state during the last-mile distribution process (Boyer, Prud'homme, & Chung, 2009; Laseinde & Mpofu, 2017; Pronello, Camusso, & Valentina, 2017). The emergence of self-collection services is essential to alleviate the deficiencies of traditional last-mile logistics in many distribution problems, and in a true sense, to realize the transformation of the delivery service concept in methods (Xiong, Ji, Ding, & Zhang, 2020). In 2020, the self-collection market reached 35.4 billion RMB, a compound annual growth rate of 40%, and the research institute predicts that the proportion will be higher in the future, indicating that express containers will increase in popularity in the next few years. Parcel locker, also known as smart locker and automated parcel station (APS), is the service point involved in self-collection service (Wang, Yuen, Wong, & Teo, 2018; Yuen, Wang, Ng, & Wong, 2018). It helps last-mile service providers (LSPs) minimize failed deliveries, enabling them to deliver the parcels in less time-consuming trips. In addition, consumers can significantly reduce opportunity costs by choosing the time and place for pickup of the goods, namely any time of the day, per their convenience (Deutsch & Golany, 2018; Mostakim, Sarkar, & Hossain, 2019). The parcel locker has changed the conventional distribution and the roles of terminal delivery personnel and target consumers in the delivery process. In the traditional last-mile distribution, the process of consumer items was similar to that in target handover, but in the parcel locker service, consumers not only take a single identity but also assume the role of distribution, which significantly reduces the pressure on the distribution terminal enterprises that they face due to lack of delivery staff (Chen, White, & Hsieh, 2020; Farooq, Fu, Hao, Jonathan, & Zhang, 2019). Although extant studies have argued that parcel lockers have great value in the self-collection service, scant evidence has been provided on the influencing factors of customer satisfaction regarding parcel lockers. Therefore, by combining the service quality (SERVQUAL) model and the logistics service quality (LSQ) model, this https://doi.org/10.1016/j.ajsl.2021.11.002 2092-5212/© 2021 The Authors. Production and hosting by Elsevier B.V. on behalf of The Korean Association of Shipping and Logistics, Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). CC_BY_NC_ND_4.0 ]]]] ]]]]]] ⁎ Correspondence to: 2, Busandaehak-ro, 63beon-gil, Geumjeong-gu, Busan 46241, Republic of Korea. E-mail addresses: [email protected] (L. Po-Lin), jangh0[email protected] (J. Hyunmi), [email protected] (F. Mingjie), [email protected] (P. Ke). The Asian Journal of Shipping and Logistics 38 (2021) 25–30 study aims to identify the determinants of service quality in parcel lockers and examine their impact on customer satisfaction. The rest of this paper is organized as follows. The next section reviews the relevant literature on parcel lockers, LSQ, and service quality and presents the hypotheses. Research methodology is presented in Section 3, followed by results and discussions. Finally, Section 5 concludes by summarizing the implications, limitations, and research agenda. 2. Literature review and hypotheses development 2.1. Parcel locker As discussed earlier, a parcel locker is a device that can provide consumers and LSPs with a 24/7 automatic and simplified parcel receiving and unloading process (Wang et al., 2018; Yuen et al., 2018). Online purchased parcels are placed in a nearby APS, a locker facility with a digitalized interface. Notification messages are then sent to consumers, informing them about the collection location and password needed to access the designated locker. The consumers can then plan accordingly to collect their parcels themselves (Wang et al., 2018). In the current research context, most self-collection facilities are located within walking distances from residential areas, which are highly accessible by non-motorized modes of transport. These facilities are mainly installed in subway entrances, supermarkets, campuses, and other convenient and crowded places (Lachapelle, Burke, Brotherton, & Leung, 2018). At this stage, the service provided by the parcel locker is primarily the collection and mailing of express parcels. When the LSPs use the parcel locker for delivery, the intelligent LSP cabinet system will first verify the LSPs’ identity. After appropriate verification, the LSPs can place the express in the parcel locker. The parcel locker will automatically dispatch the express identity and then send the generated pickup address and verification code to the consumer. After receiving the information, the consumer can arrive at the corresponding place conveniently and enter the verification code to collect the shipment. In addition, when consumers tend to use the parcel locker for a shipment, the parcel locker will first weigh the parcel and upload information of the consignment on the display screen. After that, the consumer can choose the shipping mode (low-slow/fast expensive). After paying the postage, the system will automatically open a cabinet. Consumers are only required to place the delivery in the cabinet. After that, the LSPs will come to pick up the package after verifying consumer identity at the APS. The system will open the cabinet where the LSPs need to be sent to facilitate the post. For customers, parcel locker is convenient and fast; it effectively protects customer privacy. Its 24-hour service can efficiently solve the pickup problem of office workers and students (Tsai & Tiwasing, 2021); for LSPs, the use of parcel locker can significantly improve delivery efficiency and reduce delivery waiting time and communication time (Pan, Zhang, Thompson, & Ghaderi, 2021). For example, with the use of the APS, LSPs can realize the centralized delivery of express for the property, the parcel locker receiving mode can save the management cost of the property and reduce the pressure of receiving the parcel. Moreover, the parcel locker is quiet and tidy, which is convenient for unified management. The property company can devote more human and material resources to provide a higher level of service to the owners (Chen et al., 2020; Vakulenko, Hellstrom, & Hjort, 2018). Overall, parcel locker delivery has played an essential role in enhancing the diversity of last-mile delivery, improving customer experience, reducing delivery costs, and building an efficient delivery system. Thus, it is of great significance in last-mile delivery (Chen et al., 2020; Deutsch & Golany, 2018; Liu et al., 2021; Rabe, Gonzalez-Feliu, Chicaiza-Vaca, & Tordecilla, 2021). 2.2. Service quality and customer satisfaction Scholars agree that service quality is directly or indirectly related to firm operations such as cost management, customer satisfaction, customer loyalty, customer retention, and profitability (Buyukozkan & Cifci, 2012; Gupta, 2018; Li & Shang, 2020). Service quality is generated by comparing consumers’ expectations of the service quality with their actual feelings after receiving the service. Service function quality and technical quality collectively constitute service quality. Among them, functional quality is a relatively subjective category, comprising personal judgment of consumers about the service personnel’s behavior, attitude, and many other factors. The technical quality of a service is similar to the traditional product quality; it exists objectively and can be objectively evaluated by customers (Gronroos, 1982). Services are different from production in that there is a process for their delivery. The customer perceived service quality, therefore, not only refers to the perceived results after service delivery but also reflects the process of service delivery and cannot be measured by the traditional theory of product quality service. Furthermore, service quality is considered the customer perception of the actual service with its before service than between the expectations of the results. At the same time, the service quality level is determined by the difference between the service level expected by customers before receiving the service and the service effect perceived by customers during the service process (Parasuraman et al., 1985). Although many scholars have defined the quality of service with their characteristics above, it is not difficult to conclude these viewpoints and identify the following features of quality of service: (1) Subjectivity: Service quality is not something objective, but the subjective feeling of consumers. (2) Differences: Consumers’ perception of service quality is affected by individual and environmental factors. Therefore, different customers have different perceptions of service quality in different environments. (3) Interaction: The communication between the service provider and the customer impacts the customer’s perception of service quality. 2.3. Conceptual framework and hypotheses development The theoretical foundation of this study is two-fold: SERVQUAL and LSQ. Following Parasuraman, Berry, and Zeithaml (2002), this study uses SERVQUAL as a part of our theoretical foundation. The SERVQUAL model has been widely adopted for measuring service quality in various fields. For instance, Li (2010) combined the characteristics of the exhibition, created the E-SERVQUAL model, and significantly added the professionalism dimension. Yu and Du (2013) modified the SERVQUAL model according to the current status and characteristics of the express service industry in the online shopping environment to increase the security dimension. Other studies, such as Zhang (2019), combined the characteristics of rural areas in Sichuan and divided the SERVQUAL scale into five dimensions of “service convenience, service responsiveness, service care, service tangibility, and service economy” to analyze the impact of the rural express terminals factor. In this study, the following four dimensions stemming from SERVQUAL are adopted in the model: Tangibles, which mainly refers to the site, environment, and all kinds of hardware facilities and equipment needed to provide service; Responsiveness refers to whether the LSPs can provide accurate, prompt service; Security refers to whether the service provided by LSPs can improve the customer’s trust and sense of security, which is mainly affected by the service attitude and serviceability of the service personnel; and Reliability explicitly measures whether the business can provide customers with error-free services during the whole journey. Additionally, several representative studies that echo these dimensions are summarized in Table 1. L. Po-Lin, J. Hyunmi, F. Mingjie et al. The Asian Journal of Shipping and Logistics 38 (2021) 25–30 26 Consensus has formed on the association between service quality and customer satisfaction (Cameran, Moizer, & Pettinicchio, 2010; Dam & Dam, 2021; Nguyen, Pham, Tran, & Pham, 2020; Rha, 2012). Specifically, there is increasing recognition that service quality can be a robust predictor of customer satisfaction (Putro & Rachmat, 2019; Santouridis & Trivellas, 2010). A firm’s competitive advantage is satisfying clients better than its rivals, surpassing clients’ needs and wants better than its competitors (Minta, 2018). Since customer satisfaction significantly impacts a company’s customer relationship maintenance and market share of its products/services, the academic and practical circles have been full of a strong interest in this issue. Through empirical research, some scholars have proved that customer satisfaction plays a vital role in a company’s marketing function. For example, it affects customer retention and loyalty, prompts customers to repeat purchases, and improves an enterprise’s market share and profit level, thus forming a competitive advantage. Therefore, more and more enterprises attach importance to customer satisfaction and consider it a significant performance index. Although many scholars have defined customer satisfaction, the definition accepted by most scholars was put forward by Oliver Richard (1997): Customer satisfaction is a kind of psychological state of customers after their needs are satisfied. It is a kind of value judgment of customers on the extent to which products or services can meet their own needs. Customer satisfaction also includes satisfaction, happiness, curiosity, surprise, and other forms of expression. In this definition, customer satisfaction, as a psychological response, is generated from comparing the customers’ actual perception of the service and their expectation of the service. Customers will buy a product or service because this kind of product or service can meet their needs, such as providing pleasure and pain relief. Sun, Du, and Li (2011) considered online shopping as the background. They used the SERVQUAL model to confirm an apparent relationship between service quality and customer satisfaction of express delivery companies and to show that tangibility, reliability, responsiveness, and other dimensions have different influences on customer satisfaction and customer loyalty, thus demonstrating the importance of service quality to enterprises. Along the same lines, Ma (2014) believed that customer satisfaction had become the pursuit of various enterprises nowadays; through the analysis of customer satisfaction, they can provide and improve the quality of service. By analyzing the service quality of China express, building an analysis model, administering a questionnaire for data collection, and by examining the data, Ma (2014) identified that five dimensions of service quality and customer satisfaction had a positive correlation. Thus, logistics enterprises can improve the five dimensions of service quality and improve customer satisfaction. Wang and Lu (2017) constructed evaluation indexes and analyzed them with analytic hierarchy process and fuzzy mathematics, providing a reference for other express delivery enterprises. Based on the above interpretation, the following hypotheses are proposed: H1. Tangibility has a positive impact on overall customer satisfaction. H2. Responsiveness has a positive impact on overall customer satisfaction. H3. Security has a positive impact on overall customer satisfaction. H4. Reliability has a positive impact on overall customer satisfaction. H5. Timeliness has a positive impact on overall customer satisfaction. 3. Methodology 3.1. Survey design and measurement items This study applies structural equation modeling (SEM) to test its research model and hypotheses. This method is considered appropriate because it involves analyzing the relationships between the latent constructs, which are multidimensional (Yuen et al., 2019). (Table 2). In this study, a quantitative survey technique was used for data collection. The questionnaire survey was mainly divided into three parts. The first part explains that the survey is intended for academic research and that the answers are anonymous. The second part aims to explore the demographic characteristics of the interviewees; information was collected on gender, age, education, occupation, monthly income, and region. The third part of the questionnaire contains some opinions and feelings of the respondents on the use of parcel lockers (Table 3); a five-point Likert scale from “Strongly disagree” (1) to “Strongly agree” (5) is adopted to measure these items. 3.2. Data collection An online pilot survey was conducted to test the feasibility of the questionnaire from April 1 to April 7, 2021, and distributed directly to 25 respondents via email, communication apps (wechat, line, WhatsApp etc.), and face to face interview. The results showed that all the respondents understood all the questions. The main requirement for the interviewees needs to have experience for using parcel locker. The research questionnaire was collected between April 9 and April 21, 2021. In total 351 questionnaires were collected, of which 321 were valid, and response rate was 91.45%. The questionnaire and links were posted on questionnaire platform, sent out by email, and communication apps to collect data. Because space does not limit the online transmission, it can increase the diversity of the sample and reduce the measurement result caused by the homogeneity of the sample. 4. Results and discussions 4.1. Demographic profile As seen in Table 3, there are more men than women, but the overall numbers are similar and evenly distributed, with the ratio of 50.2:49.8. In the age structure, the proportion aged 20–30 years is the largest, with a total of 147 persons, accounting for 45.8% of the total. Next, 40.8% are aged 31–40 years; users of parcel lockers are mainly in the 20–40 years age group, which is also in line with the distribution of express counter users. In terms of occupation, the Table 1 Representative studies on SERVQUAL. The author Influencing factors Parasuraman, Zeithaml, and Berry (1988) Tangibility, Responsiveness, Reliability, Assurance, Empathy Vaughan and Shiu (2001) Authorization of the organization, Humanization, Service personnel competence Brady and Cronin (2001) Interaction quality, Physical environment quality, Result quality Stank, Goldsby, Vickery, and Savitskie (2003) Timeliness, Reliability, Accuracy Parasuraman (2005) Efficiency, Fulfillment, System availability, Privacy Freeman and Rolland (2010) Labeled ease of use, Information content, Fulfillment reliability, Security, Privacy, After-sales service Rahim, Voon, and Mahdi (2016) Product development, Tangibility, Reliability, Responsiveness, Assurance, Empathy L. Po-Lin, J. Hyunmi, F. Mingjie et al. The Asian Journal of Shipping and Logistics 38 (2021) 25–30 27 respondents are mainly students and enterprise employees, 34.0% and 35.2%, respectively. In addition, there is a corresponding distribution among occupations such as public institutions and private owners. In terms of monthly income, the number of persons earning between 6001 and 9000 RMB amounts to 151, or 25.9%, respectively; the next are those earning 3001–6000 RMB or 22.4%. To sum up, the survey sample gender distribution is even; the age is concentrated in 20–40 years range, in line with the distribution of express counter users; the monthly income is mainly distributed in the 3000–9000 RMB range; the occupation distribution is wide; the sample range is wide, and has certain representativeness and persuasiveness. 4.2. Measurement model assessment To evaluate the reliability and validity of the measurement model fit, a confirmatory factor analysis is performed; the results are presented in Tables 4 and 5. The results in Table 4 show the following: (1) the ratio of the Chi-square value to the degree of freedom (χ2/df) = 1.516.(p < 0.05); (2) Tucker-Lewis index (TLI) = 0.961; (3) comparative fit index (CFI) = 0.966, (4) root mean square error of approximation (RMSEA) = 0.040, and (5) standardized root mean square residual (SRMR) = 0.038. It is found that the measurement model is a good fit for the sample as a result of the model fit indices being within the cut-off range recommended by Hu and Bentler (1999). In addition to the model fit, the measurement model is also evaluated for reliability, convergent validity, and discriminant validity. As regards reliability, Hair et al. (2010) proposed that the composite reliability (CR) of each construct should exceed 0.70, and standardized factor loadings (λ) should be higher than 0.50. As the results indicate, the CRs of the constructs are all above 0.80, which indicating the constructs of the measurement model are considered reliable. For the convergent validity, scholars have suggested that the constructs’ average variance extracted (AVE) should exceed 0.50 (Lai, Table 2 Measurement scale. Construct Measurement Items Source Tangibles (TAN) A1. Your satisfaction with the appearance design of the parcel locker (whether it is beautiful and fashionable). Parasuraman et al. (1988) A2. Your satisfaction with the grid size of the parcel locker (whether it can meet daily needs). A3. Your satisfaction with the number of slots in the parcel locker (whether it can meet your daily needs). Duc, Hong, and Phuc (2020) A4. Your satisfaction with the modernization of parcel locker facilities (such as face recognition, QR code pickup). A5. Your satisfaction with the upgrading speed of parcel locker facilities. Responsiveness (RES) B1. Do you think the parcel locker can accurately notify you of the pickup time? Parasuraman et al. (1988) B2. Do you think the parcel locker has a large number of outlets and a wide coverage? B3. Your satisfaction with parcel locker management staff regarding the processing speed of lost or damaged express parcels. Duc et al. (2020) B4. Your satisfaction with the speed of troubleshooting for parcel locker managers. Security (SEC) C1. I think parcel locker can accurately deliver the goods to your hands. Yu and Du (2013) C2. I think parcel locker pickup can guarantee the quality of the goods. C3. I think the parcel locker will not disclose or misappropriate information about the sender, recipient, and goods. C4. I believe that when paying fees to parcel locker companies, the payment environment or method is safe. Reliability (REL) D1. I think the functions and services promised by the parcel locker to customers can be completed in time. Parasuraman et al. (1988) D2. When I encounter problems during use, service providers of parcel lockers can provide solutions. D3. Do you think the parcel locker company is reliable and trustworthy? Duc et al. (2020) D4. Do you think parcel locker can provide delivery within the promised time and other functional services? D5. Do you think the networking function of the parcel locker can correctly record the express waybill in detail? Timeliness (TIM) E1. Do you think parcel lockers can provide intelligent services 24 h a day? Mentzer, Flint, and Hult (2001) E2. Your satisfaction with the notification speed of parcel lockers (whether the notification is timely or not). E3. When you use the parcel locker to send the courier, the package is picked up in time. E4. When you use the parcel locker to send the express, the parcel locker can update the logistics information in time. Overall Service Satisfaction (SAT) F1. Do you think using a parcel locker is the right choice? Duc et al. (2020) F2. Do you think the service provided by the parcel locker is in line with your expectations? F3. How satisfied are you with the overall service of the parcel locker? Table 3 Profile of survey respondents. Characteristics Observations Frequency (n = 321) Percentage (%) Gender Male 161 50.2 Female 160 49.8 Age < 20 years 20 6.2 20–30 years 147 45.8 31–40 years 131 40.8 > 40 years 23 7.2 Employment Student 109 34.0 Employee 113 35.2 Civil servants 40 12.5 Self-owned business 27 8.4 Other 32 10.0 Education Junior high school and below 48 15.0 High school 47 14.6 Undergraduate 188 58.6 Masters 36 11.2 Ph.D. and above 2 0.6 Monthly income (RMB) < 3000 70 21.8 3001–6000 72 22.4 6001–9000 83 25.9 9001–12000 68 21.2 > 12000 28 8.7 L. Po-Lin, J. Hyunmi, F. Mingjie et al. The Asian Journal of Shipping and Logistics 38 (2021) 25–30 28 Su, Tai, & Yang, 2020). Therefore, the convergent validity of the model is confirmed as AVEs in Table 4 are all above the threshold value. With regard to discriminant validity, the inter-correlations values between constructs should be less than their square root of AVE (Chang, Lu, & Lai, 2021). As shown in Table 5, all the constructs have fulfilled the above conditions, suggesting confirmation of discriminant validity. 4.3. Structural model assessment A SEM analysis was conducted to test the hypotheses, and the results are shown in Tables 5 and 6. The same goodness-of-fit criteria (i.e., 1 < χ2/df < 3, CFI > 0.90, TLI > 0.90, SRMR < 0.08, and RMSEA < 0.06) were adopted to determine the fit of the proposed model. The presented results in Table 5 suggest a good model fit. The results indicate that Tangibility has a significant positive impact on overall service satisfaction (β = 0.154, p < 0.05); thus H1 was accepted; Responsiveness has a significant positive impact on overall service satisfaction (β = 0.165, p < 0.05), supporting H2; Security has a significant positive impact on overall service satisfaction (β = 0.203, p < 0.05), so H3 is established; Reliability has a significant positive impact on overall service satisfaction (β = 0.219, p < 0.05), so H4 was accepted; Timeliness has a significant positive effect on overall service satisfaction (β = 0.221, p < 0.05), generating support for H5. (Table 7). 5. Conclusion This study investigated the determinants of customer satisfaction with parcel locker service. Five dimensions—tangibility, responsiveness, security, reliability, and timeliness—were found to positively affect customers’ satisfaction. Among them, the path coefficient of timeliness was the strongest predictor, higher than the others. This factor plays the most crucial role in customer satisfaction of parcel locker services. People who use the parcel locker service attach great importance to timeliness. This result agrees with the findings of previous studies (Ferreira, Nunes, & Marques, 2020; Reilly, McKelvey-Walsh, Freundlich, & Brenner, 2011; Tuncer, Unusan, & Cobanoglu, 2021). Therefore, managers should endeavor to provide customers prompt and flexible services. For example, the operating system of the courier should be constantly optimized to avoid system crashes, and LSPs should be available to help customers when they have problems. In addition, our results also show that reliability and security are the second critical significant factors in explaining customer satisfaction. Our results support prior studies (Boronico, 1998; Grassi & Patella, 2006; Ihtiyar & Ahmad, 2015; McGranaghan, 2007), suggesting LSPs should pay attention to providing customers reliable and safe service. Lastly, the third strongest predictor for customer satisfaction is responsiveness, followed by tangibility. Thus, we suggest the LSPs make sure that equipment for parcel locker services should be visually appealing and neat to achieve better customer satisfaction. There are a few limitations and deficiencies in this study, such as the geographic restriction. As the investigation was conducted in China, the results may not be suitable to be applied directly to other cultural or geographical contexts. Therefore, we encourage future studies to examine the model in other contexts. Another drawback of this study may be the theoretical foundation. Our study only studied the customer satisfaction of parcel locker services from the lens of SERVQUAL and LSQ. Thus, future studies can apply other models such as the technology acceptance model or attitude theory to investigate customer satisfaction of parcel locker services. Table 4 Confirmatory factor analysis results. Construct Item λ AVE CR Tangibility (TAN) A1 0.762 0.544 0.857 A2 0.744 A3 0.737 A4 0.704 A5 0.741 Responsiveness (RES) B1 0.779 0.582 0.848 B2 0.731 B3 0.762 B4 0.779 Security (SEC) C1 0.778 0.581 0.847 C2 0.795 C3 0.697 C4 0.775 Reliability (REL) D1 0.786 0.573 0.870 D2 0.787 D3 0.737 D4 0.707 D5 0.765 Timeliness (TIM) E1 0.715 0.570 0.841 E2 0.773 E3 0.784 E4 0.747 Satisfaction (SAT) F1 0.789 0.650 0.847 F2 0.863 F3 0.763 Note: Model fit indices: χ 2 /df = 1.516, CFI = 0.966; TLI = 0.961; RMSEA = 0.040; SRMR= 0.038. Table 5 Construct correlation and square root of AVE. TAN RES SEC REL TIM SAT TAN 0.738 a RES 0.391 b 0.763 SEC 0.592 0.395 0.762 REL 0.430 0.581 0.539 0.757 TIM 0.272 0.494 0.392 0.692 0.755 SAT 0.493 0.542 0.564 0.644 0.575 0.806 a Square roots of AVE values are along the main diagonal; b Correlations of constructs are below the main diagonal. Table 6 Measurement model fit indices. CMIN df CMIN/DF NFI IFI TLI CFI GFI RMSEA 394.112 260 1.516 0.908 0.967 0.961 0.966 0.913 0.040 Table 7 Results of hypotheses testing. Hypotheses Path Estimate S.E. CR P-value Supported? H1 SAT < — TAN 0.154 0.055 2.269 0.023 Yes H2 SAT < — RES 0.165 0.062 2.461 0.014 Yes H3 SAT < — SEC 0.203 0.067 2.760 0.006 Yes H4 SAT < — REL 0.219 0.080 2.411 0.016 Yes H5 SAT < — TIM 0.221 0.071 2.790 0.005 Yes L. Po-Lin, J. Hyunmi, F. Mingjie et al. The Asian Journal of Shipping and Logistics 38 (2021) 25–30 29 Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Boronico, J. S. (1998). An investigation into the costs and benefits of reliability of service. Omega-International Journal of Management Science, 26(1), 99–114. Boyer, K. K., Prud'homme, A. M., & Chung, W. (2009). 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