166 HỘI THẢO KHOA HỌC QUỐC GIA VỀ LOGISTICS VẦ QUẢN LÝ CHUỖI CUNG ỨNG VIỆT NAM LẦN THỨ 4 (CLSCM-2024) IMPACT OF LAST MILE DELIVERY ON CUSTOMER EXPERIENCE IN FASHION INDUSTRY IN HANOI ẢNH HƯỞNG CỦA GIAO HÀNG CHẶNG CUỐI ĐẾN TRẢI NGHIỆM KHÁCH HÀNG TRONG NGÀNH THỜI TRANG TẠI HÀ NỘI NGUYEN MAI PHUONG1* , NGUYEN LE HANH LINH 2 , NGO CHAU ANH 3 1Athena Group Investment and Trading Joint Stock Company 2Mondelez Kinh Do Viet Nam Joint Stock Company 3Pearson College UWC *Email:
[email protected] Abstract As e-commerce continues to boom, the role of last mile delivery has become increasingly significant when considering customer experience. Dealing with last mile delivery presents both an opportunity and a challenge for retail companies to meet customer demands and provide the greatest experience. This article explores the impact of last mile delivery on customer experience for residents of Hanoi in the fashion industry. Five hypotheses were established to examine the relationships of delivery speed, delivery cost, delivery tracking, delivery options, return policy and customer experience. The results, after data analysis, revealed that return policy have the greatest impact and that Hanoi residents do not care about delivery cost. Based on the factors influencing customer experience, suggestions were made for domestic fashion firms targeting Hanoi customers. Keywords: Last mile delivery, customer experience, e-commerce, fashion companies. Tóm tắt Khi thương mại điện tử tiếp tục phát triển, vai trò của giao hàng chặng cuối trở nên ngày càng quan trọng khi xem xét trải nghiệm khách hàng. Giải quyết vấn đề giao hàng chặng cuối mang lại cả cơ hội và thách thức cho các công ty bán lẻ để đáp ứng nhu cầu của khách hàng và mang lại trải nghiệm tốt nhất. Bài viết này khám phá tác động của giao hàng chặng cuối đến trải nghiệm khách hàng đối với cư dân Hà Nội trong ngành công nghiệp thời trang. Năm giả thuyết đã được thiết lập để kiểm tra mối quan hệ giữa Tốc độ giao hàng, Chi phí giao hàng, Theo dõi giao hàng, Các tùy chọn giao hàng, Chính sách hoàn trả với Trải nghiệm khách hàng. Kết quả, sau khi phân tích dữ liệu, cho thấy chính sách hoàn trả có ảnh hưởng lớn nhất và người dân Hà Nội không quan tâm đến chi phí giao hàng. Dựa trên các yếu tố ảnh hưởng đến trải nghiệm khách hàng, tác giả đã đưa ra các gợi ý cho các công ty thời trang nội địa nhắm đến khách hàng Hà Nội. Từ khóa: Giao hàng chặng cuối, trải nghiệm khách hàng, thương mại điện tử, công ty thời trang. 1. Introduction According to Opengov Asia [27], Vietnam's e- commerce market has experienced steady expansion, with an average annual growth rate ranging from 16% to 30% over the last four years, making it the fastest- growing in the world. In this year, fashion items continue to lead in sales on e-commerce platforms. Specifically, in the first five months of 2024, the fashion category on e-commerce platforms reached VND 29 trillion in sales, a 67% increase compared to the same period in 2023 [26]. Being Vietnam's capital and a major economic hub, Hanoi is crucial to development of fashion industry. While last mile delivery service is acknowledged as a crucial element in e-fulfillment and a major catalyst for the expansion of the e-commerce sector [25], it also presents challenges for online merchants. The changes in consumer behavior and the rapid growth of e-commerce have caused disruptions in supply chains and heightened the demands on last mile delivery [23]. From the consumer's viewpoint, the experience with last mile delivery can be a decisive factor in whether they choose to return to the same retailer or brand [30]. Rapid advancements in e-commerce have led to a shortage of current research on last mile delivery and its impact on customer experience in Hanoi's fashion industry. Additionally, we are living and working in Hanoi, which gives us a deep understanding of the
167 HỘI THẢO KHOA HỌC QUỐC GIA VỀ LOGISTICS VẦ QUẢN LÝ CHUỖI CUNG ỨNG VIỆT NAM LẦN THỨ 4 (CLSCM-2024) city's market, customer psychology, and convenient access to data collection, making Hanoi an ideal location for conducting our research. This study broadens the understanding of the customer’s e-retail journey by examining the factors of last mile delivery on customer experience. The empirical findings offer domestic fashion firms in Hanoi new insights into customer experience, helping them refine the process and improve customer experience. 2. Theoretical background 2.1. Last mile delivery In the context of B2C, Gevaers [7] defines last mile delivery as the final segment in a B2C delivery service, where the consignment is delivered to the recipient’s home, a collection point, or other designated locations. The rising use of online platforms for transactions results in greater online consumption, which has unavoidably fueled the expansion and importance of delivery services [13]. For sellers, as skilled last mile delivery companies, delivery service providers provide full third-party benefits to a wide range of industrial and commercial sectors. For customers, last mile delivery is crucial because it is where they interact with and experience the brand. 2.2. Customer experience According to Shaw and Ivens [22] describe customer experience as the interaction between suppliers and consumers, emphasizing that it is the emotional response resulting from these interactions and continuously evaluated throughout the engagement period. In the early economic literature, such as Keynes [12], the significance of customer experience as a consumer motivator was recognized. Research by Capgemini Research Institute [24] revealed that 80% of consumers are willing to pay more for a better experience across various sectors, with approximately 9% willing to raise their expenditure by more than 50% if necessary. Providing outstanding customer experience is essential for businesses aiming to maintain their position and succeed in today's market. 2.3. Factors of last mile delivery affecting customer experience Before delivery is completed, the quality of the delivery service, including factors such as delivery costs and efficiency, has a direct impact on customers' evaluations of service quality, emphasizing the importance of addressing these aspects to improve customer experience [1]. Online shoppers are particularly sensitive to delivery speed, hence this element has a significant influence on consumer experience and intentions to buy again [4]. If companies have a shipment tracking system, it will reduce the number of delayed shipments while also providing great transparency to their consumers. In the post-delivery phase, study of Wahab and Khong [28] show that speed of response and returnability have an influence on the consumer experience while purchasing online. A robust return policy can help manage service failures, such as poor product quality or incorrect items, thereby enhancing customer experience and building loyalty [12]. In conclusion, the focus will be on analyzing the influence of factors Delivery speed, Delivery cost, Delivery tracking, Delivery options and Return policy. 3. Research methodology 3.1. Research hypothesis 3.1.1. Delivery speed Delivery speed is described as the time taken from receiving a customer order to its final delivery [17]. Yan and Wenxia [31] highlight that slow or inefficient delivery can cause customers to lose patience and enthusiasm, diminishing satisfaction and increasing the likelihood of delivery failures or returns. Xie [29] emphasizes it is critical to boost delivery speed and guarantee that customers receive their orders on time. Thus, it is hypothesized that: H1: There is a positive relationship between delivery speed and customer experience. 3.1.2. Delivery cost Delivery cost refers to the amount customers must pay for last mile delivery services. This cost significantly affects customer experience [29]. Lower delivery fees can enhance the customer experience, whereas higher delivery fees can negatively impact it [9]. Besides, unconditional free shipping and flat-rate shipping create different consumer preferences for online offers, influenced by the degree of consumer skepticism about shipping fees and the presence of external reference prices [21]. Thus, the following hypothesis is proposed: H2: There is a negative relationship between delivery cost and customer experience. 3.1.3. Delivery tracking Delivery tracking is an online service that provides information about the progress of the shipments. The presence of an order tracking and tracing system
168 HỘI THẢO KHOA HỌC QUỐC GIA VỀ LOGISTICS VẦ QUẢN LÝ CHUỖI CUNG ỨNG VIỆT NAM LẦN THỨ 4 (CLSCM-2024) enhances the quality of physical distribution services in online retail [30]. Additionally, Gevaers et al. [8] find that tracking availability is the third most important feature, following delivery cost and delivery speed. A well-implemented tracking system significantly enhances the customer experience when purchasing products [15]. Thus, it is hypothesized that: H3: There is a positive relationship between delivery tracking and customer experience. 3.1.4. Delivery options Delivery options provide clients with lists of alternatives for delivery methods and locations, which can affect their purchase and repurchase decisions. Different delivery types can enhance customer experience by optimizing service quality and influencing purchase decisions, particularly during the last mile delivery stage [14]. Delivery options are a crucial part of order fulfillment that helps build online trust, significantly impacting consumer purchase intentions [2]. Similarly, Rao et al. [20] find that delivery options significantly influence the perceived quality of delivery services, which in turn affects consumer experience and repurchase intentions. Thus, it is hypothesized that: H4: There is a positive relationship between delivery options and customer experience. 3.1.5. Return policy Return of order is a phrase used to describe post- sales activities that entail processing consumer returns owing to varied criteria [18]. The management of consumer returns is critical to improving customer service [16]. Returning the items is evaluated based on characteristics such as the ease of collection, the availability of a well-defined return policy, and the inclusion of return costs [3]. Thus, it is hypothesized that: H5: There is a positive relationship between return policy and customer experience. 3.2. Research method Desk research methodology: Desk research was conducted by analyzing relevant documents, selecting, and citing previous reports and studies to build a theoretical framework, design a questionnaire, and systematize the scientific basis for the topic. Quantitative research method: Step 1: Descriptive statistics will be utilized to detail the characteristics of the survey's sample groupings. Step 2: Cronbach’s Alpha is employed to assess the reliability of the variables measuring each factor. Step 3: Exploratory Factor Analysis is used to reveal the underlying structure of a large set of variables, while Confirmatory Factor Analysis tests specific theoretical relationships to ensure the data fits the proposed model adequately. Step 4: Pearson correlation analysis is carried out to examine the linear relationship between the dependent and independent variables. Step 5: Linear regression analysis is conducted to assess the impact of independent variables on the dependent variable and the extent of their influence. Step 6: Independent Sample T-Test is used to assess mean differences when qualitative variables include only two values. The One-way ANOVA test is used to compare mean values for variables with two or more options, addressing the limitations of the T- Test. 3.4. Data collection The minimum sample size for EFA and CFA analysis is calculated using the formula n=5*m or n=10*m, where n represents the minimum sample size and m denotes the number of variables observed [9]. With 22 observed variables, the minimum sample size required for CFA and EFA analysis is 220 samples. The study targeted individuals residing in Hanoi who are above 15 years old and have used last mile delivery when buying fashion items. The sample was acquired through convenience sampling, a non- random method where data was gathered from volunteers who agreed to participate by completing the online questionnaire. Participants responded to the questions using a 5-point Likert scale, ranging from ‘strongly disagree’ to ‘strongly agree’. 4. Data analysis and findings 4.1. Descriptive data analysis An online survey was disseminated via Google Forms to respondents, with the survey period spanning from May 8th, 2024 to May 15th, 2024. The survey targeted 281 participants in Hanoi who had previously purchased fashion products online. After excluding unsuitable responses, the final sample size was 257. The demographic breakdown of the respondents revealed that 62.4% were female, 34.9% were male, and 2.7% preferred not to disclose their gender. Shopee emerged as the most popular e-commerce
169 HỘI THẢO KHOA HỌC QUỐC GIA VỀ LOGISTICS VẦ QUẢN LÝ CHUỖI CUNG ỨNG VIỆT NAM LẦN THỨ 4 (CLSCM-2024) platform among Hanoi people (61.6%), followed by TikTok Shop and Lazada. For last mile delivery services, Shopee Xpress provided the best experience for 44.2% of respondents, followed by Giao Hàng Nhanh (22.9%) and Giao Hàng Tiết Kiệm (15.1%). 4.2. Reliability testing It is evident that all six variables meet the reliability requirements, with Cronbach’s Alpha coefficients exceeding the acceptable threshold of 0.6, and even >0.7. Besides, all observed variables have a corrected item- total correlation score >0.3, meeting the required level. Consequently, all observed variables are accepted and will be subjected to further analysis. 4.3. Exploratory Factor Analysis (EFA) Based on KMO coefficient, the factor analysis is completely suitable for the data set. In the Bartlett test, sig. (<0.001) <0.05 proves that there is a correlation in the factors between observed variables. Besides, with the smallest coefficient reaching 1.073>1, it means that all 5 research components are not removed from the model. In addition, the total variance extracted reaches 69.79% (>50%) indicating that only 30.21% of the included observed variables are lost. It is concluded that the model is suitable. Similar to EFA analysis for the independent variable, the results for the dependent variable summarized in the Table 3 above shows that factor analysis is appropriate. 4.4. Confirmatory Factor Analysis Based on Table 4, all indices to assess the model fit satisfy the required threshold and the overall model is suitable. Besides, Unstandardized and Standardized Regression Weights show that all observed variables in this model explain well for their corresponding parent factor with no estimate values <0.5. According to the results shown in the Appendix, CR meets the standard, being greater than 0.7. AVEs of all latent variables are greater than or equal to 0.5; therefore, the convergent validity of the scale is ensured. MSV is less then AVE and Square Root of AVE is larger than all the inter-construct correlation, which means that the proposed scale ensures the discriminant validity as all the conditions are met. 4.5. Pearson correlation analysis From the result in the Appendix, it can be seen that the value Sig. of the variables DS, DC, DT, DO, RP when considered in correlation with CE are all less than 0.05, showing that those 5 independent variables are all correlated with the dependent variable CE. In addition, it is alleged that if sig. value <0.05 and Pearson correlation value >0.4, attention should be paid to the possibility of multicollinearity [5]. Thus, it is suspected that multicollinearity may exist between DT & DS, DT & DC, DT & RP, DO & RP since the Pearson correlation values of these pairs are higher than 0.4. However, this is just a question. Therefore, to determine whether multicollinearity literally occurs between independent variables, the VIF value will be used in linear regression analysis in the next part. Table 1. Reliability Test Result Variables N Cronbach's Alpha DS 3 0.777 DC 3 0.725 DT 3 0.788 DO 4 0.868 RP 4 0.824 CE 5 0.899 Source: Results from SPSS 29.0 software Table 2. Test for independent variables KMO Measure 0.852 Bartlett’s Test Approx. Chi- square 1929.620 df 136 Sig. <0.001 Lowest Eigenvalue Coefficient 1.073 Total Variance Extracted 69.79% Source: Results from SPSS 29.0 software Table 3. Test for dependent variables KMO Measure 0.842 Bartlett’s Test Approx. Chi- square 801.557 df 10 Sig. <0.001 Lowest Eigenvalue Coefficient 3.589 Total Variance Extracted 71.778% Source: Results from SPSS 29.0 software Table 4. Model fit summary and threshold comparison Index χ2/df GFI CFI RMSEA PCLOSE Threshold ≤ 3 ≥ 0.8 ≥ 0.8 ≤ 0.06 ≥ 0.05 Result 1.898 0.891 0.940 0.059 0.052 Source: Self-synthesized by me
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