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Article

Empirical Research of Cold-Chain Logistics Service Quality in Fresh Product E-Commerce

by
Ling Wang
1,
Yuk-Ming Tang
2,3,*,
Ka-Yin Chau
2 and
Xiaoxuan Zheng
3
1
College of Marxism, Sichuan International Studies University, Chongqing 400031, China
2
School of Business, Shenzhen City Polytechnic, Shenzhen 518055, China
3
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
*
Author to whom correspondence should be addressed.
J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 2543-2556; https://doi.org/10.3390/jtaer19030122
Submission received: 28 June 2024 / Revised: 28 August 2024 / Accepted: 4 September 2024 / Published: 23 September 2024
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Abstract

:
Logistics service quality (LSQ) plays a vital role in providing excellent customer experience, particularly in e-commerce. Using mobile devices for food and fresh product orders is very common, but delivering these products is very challenging. In this study, we aim to evaluate the factors influencing logistics service quality (LSQ) in the context of fresh product e-commerce. The relevant literature was reviewed and a preliminary survey among field experts was conducted to establish the proposed LSQ scale. A qualitative study was carried out on fresh product e-commerce customers. A survey involving 222 participants was analyzed, and an LSQ evaluation scale was formed and evaluated scientifically and empirically. Research results showed that reliability, convenience, freshness, and personnel contact quality are the four key dimensions of the LSQ scale in the e-commerce platform for fresh and perishable items. The results of the study can help the managers of e-commerce companies to understand the LSQ criteria that determine customer satisfaction and consequently make the appropriate LSQ improvements.

1. Introduction

Due to the emergence of advanced technologies such as virtual reality, Artificial Intelligence (AI), and smart devices, global consumers now have a wide range of options when it comes to purchasing fresh food, such as using mobile phones, online e-commerce platforms, etc. [1]. These technological advancements allow customers to conveniently order products electronically and receive them promptly without being limited to a single mode of purchase. In Mainland China, the transaction volume of fresh food e-commerce is projected to experience an average annual growth rate of 35% in the foreseeable future. Moreover, a significant proportion of users, approximately 63.8%, have developed a habit of ordering fresh products online, with some making purchases more than once a week [2]. The global cold chain market is valued to reach over USD 300 billion by 2025. This indicates a significant number of companies are involved in cold-chain logistics, including those serving the fresh product sector [3]. The driving force behind this trend among these customers is primarily attributed to the widespread adoption of mobile devices and IoT technologies. Notably, as of June 2019, there were 847 million Chinese netizens classified as smart device users, with a significant concentration among the younger demographic. With nearly half of Chinese netizens below the age of 30, the fresh e-commerce markets in China are thriving. The trade volume in the fresh e-commerce market has reached approximately USD 20 billion, while the overall fresh market trade volume stands at around USD 250 billion in China [4]. The mobile IoT not only breaks down customers’ physical barriers but also provides retailers with information on the changing customer demand by examining customers’ mobile footprints [5]. With comprehensive data analytics of individuals, retailers can provide appropriate marketing campaigns for different customers in accordance with their demographic characteristics like gender, age, and location. The size of the e-commerce platform for fresh and perishable items is expanding, in which logistics service quality (LSQ) plays a significant part.
LSQ has focused significant attention on customer satisfaction, starting in the recent two decades. The implementation of cold-chain logistics service quality is crucial for ensuring the freshness, quality, and safety of fresh foods such as fruits, vegetables, meat, and dairy products that usually require proper temperature control, humidity management, and timely delivery. It also helps meet customer expectations, leading to higher customer satisfaction and loyalty. The logistics service capacity of a company is essential in establishing an intimate relationship between the company and its customers [6]. The association between customer satisfaction and improvement in LSQ was validated in various empirical studies. In general, LSQ includes two main key elements, namely, promoting consumer service and providing distribution service. In addition, LSQ is recognized as having subjective and objective perspectives. Specifically, the personal viewpoint emphasizes customer-centric logistics service, while the objective perspective focuses on physical distribution [7]. Logistics service quality (LSQ) factors, such as customer service and reliability, directly influence customer satisfaction and impact customer return motivation [2]. Repetitive purchase behavior and positive word-of-mouth are advantageous for a firm in a competitive environment. LSQ for the fresh product industry plays an important role because of the vulnerable and perishable nature of fresh products due to the relatively short product life cycle. The logistics of fresh produce is also challenging due to the critical requirement of prompt and effective quality control requirements [8]. Therefore, the traditional LSQ assessment could potentially be inapplicable to the e-commerce of fresh products. Since the perishability of fresh food requires faster delivery and tighter supply chain process control, a full and half cold chain for fresh products is necessary, requiring innovative and advanced technologies (e.g., decision support system, enterprise resource planning, robotics, augmented reality, virtual reality, Internet of Things, unmanned vehicles), professionals, and scientific management [9].
In the context of e-commerce, logistics mostly serve customers instead of the service provider since the delivery mode and time of delivery depend on the customer’s needs and favorites. The determination of LSQ concentrates on the full logistics distribution chain, although the “Last Mile” logistics for fresh product e-commerce typically takes place after consumer orders have been placed. To mitigate the risk of last-mile logistics operations and manage last-mile handovers, logistics firms may need to develop integrated omnichannel solutions and implement logistics strategies via the deployment of industrial 4.0 and the scaling up of unattended delivery [10,11]. Fewer earlier research studies were worried about the LSQ model concentrating on the adoption of e-commerce associated with fresh products since the typically adopted logistics quality assessment instruments are not appropriate for the fresh products online market. Therefore, the quality level of logistics service is a crucial element for consumers in choosing online fresh product retailers. The research recognizes that LSQ plays a vital role in providing excellent customer experience, especially in the e-commerce sector. With the increasing popularity of using mobile devices to order food and fresh products, the delivery of these products poses significant challenges. The primary objective of this research is to evaluate the factors influencing customer satisfaction with LSQ in the context of fresh product e-commerce, focusing specifically on fresh and perishable items. By identifying these factors, we aim to determine ways to enhance service quality levels on e-commerce platforms. Our approach involves conducting a comprehensive literature review and analyzing industry best practices to identify key questions for a questionnaire that measures LSQ dimensions relevant to fresh product e-commerce. Next, customer feedback is gathered through the questionnaire to quantify their perceptions of LSQ. This data is then analyzed using exploratory factor analysis to identify the most significant LSQ dimensions. Based on the analysis results, customer satisfaction towards LSQ in fresh product e-commerce can be explained based on reliability, convenience, freshness, and personnel contact quality. A model is developed based on these dimensions to explain customer satisfaction and identify areas for improvement in fresh product e-commerce logistics. The study’s findings offer valuable insights and benefits to various stakeholders, including e-commerce managers, fresh product providers, marketing and sales officers, logistics and operations representatives, customers, and delivery service providers. These insights enable them to comprehend the factors that influence customer satisfaction, enhance fresh product quality, formulate better marketing strategies, and plan for improved delivery services, as well as make different improvements to enhance LSQ. Moreover, the proposed model enhances understanding of the impact of each factor and establishes a solid theoretical foundation for future research and development in this area. This research contributes a customer-centric framework for evaluating LSQ in fresh product e-commerce, identifying key dimensions and providing actionable insights for businesses to enhance customer satisfaction and improve service quality.

2. Literature Review

2.1. Fresh Product and E-Commerce

Fresh products, which are perishable and not preserved by smoking, canning, or freezing, are essential items closely tied to our daily lives. They exhibit common characteristics such as seasonal demand, perishability, reliance on natural contexts of production, and sensitivity to temperature [12]. However, many developing countries face challenges in meeting the demand for fresh products due to limited resources for sustainable economic growth and poverty reduction [13]. Traditional perspectives on fresh product logistics do not apply here since the speed of deterioration is greatly influenced by environmental conditions and other factors. Perishable goods with shorter shelf life, such as leafy vegetables and poultry, experience a rapid decline in sensory characteristics and are highly vulnerable to unpredictable atmospheric conditions [14]. To address these challenges, logistics services need to be equipped with quick turnaround times, specific handling protocols for humidity, temperature, and cleanliness, as well as advanced cold chain technology like radio frequency identification, refrigeration technology, and wireless sensor networks for data collection [15,16]. Researchers have studied the optimization of logistics networks for fresh product logistics to ensure the hygienic, aesthetic, and nutritional features of these products during transportation [9]. Insufficient storage facilities and a poor transportation system pose obstacles in the fresh product supply chain, leading to increased customer complaints, food losses, and waste [17]. Innovative solutions like the Mainbox device, which offers efficient food storage with suitable construction and functioning systems, have been introduced [18]. Additionally, new ideas like lockers/boxes for storage and delivery have been proposed to minimize temperature loss and maintain freshness, although their bulky packaging necessitates door-to-door transportation and home delivery services by logistics companies [8]. As a result, there is a growing need to offer online fresh product purchases in e-commerce.
The emergence of globalization, changes in consumption patterns, the diversification of fresh food, the improvement in living standards, and advancements in e-commerce technology have created a pressing demand for a reliable fresh product supply chain [16]. The perishable food industry has experienced significant growth and prosperity worldwide, contributing a substantial percentage to the GDP of countries like China [19]. The integration of “offline + online + logistics” in the innovative Business to Consumer (B2C) retail model, supported by big data analytics, resource information sharing, advanced technology, and intelligent logistics, has fostered a consumer-centric retail experience [20]. However, the new retail environment has also brought forth challenges in the fresh product logistics network, including high transportation costs, degradation of fresh products, and weaknesses in the circulatory system of fresh food. In China, the deterioration rate of cold chain fresh products reached approximately 10 percent in 2018 [21]. Vertical B2C e-commerce platforms, operated by retailers, offer various categories of fresh products and have their own logistics capabilities for last-mile delivery. Therefore, apart from the condition of fresh products, logistics service plays a critical role in determining customer satisfaction. Researchers like Dani emphasize the importance of factors to consider when establishing perishable cold chains [22].

2.2. Service Quality and LSQ

Service quality is a crucial factor in customer satisfaction, and numerous research studies have examined its dimensions and impact [23]. However, in the context of logistics, there is a specific focus on LSQ, which refers to the quality of services provided by logistics providers and plays a vital role in meeting customer expectations and ensuring customer satisfaction [4].
The measurement of LSQ requires a comprehensive understanding of the specific industry and context being studied, leading to the development of industry-specific LSQ frameworks [24]. In the context of e-commerce logistics for fresh products, LSQ dimensions need to be adjusted to address the unique challenges and requirements of this domain [25]. Researchers have identified various dimensions of LSQ in e-commerce logistics, including online ordering, network management, online order handling, freshness, punctuality, resilience, reliability, responsiveness, assurance, empathy, tangibility, order accuracy, delivery speed, product condition, communication, and customer support [26]. These dimensions capture the key aspects of logistics service that influence customer satisfaction in the fresh product e-commerce sector.
To measure LSQ in the context of fresh product e-commerce logistics, researchers have developed and validated scales specific to the domain. For example, the Fresh Food E-commerce Logistics Service Quality (FFELSQ) scale assesses LSQ in the fresh food e-commerce context, considering dimensions such as product freshness, delivery speed, packaging quality, order accuracy, and customer service [27]. Another study proposed a scale for measuring LSQ in online grocery shopping, including dimensions such as delivery reliability, product quality, customer service, and website quality. These scales provide researchers and practitioners with tools to measure and improve LSQ in the specific context of fresh product e-commerce logistics.

3. Theoretical Framework

3.1. Methodology Overview

The study begins with a comprehensive literature review to establish the proposed LSQ scale and research model, which are then evaluated by field experts. Subsequently, a questionnaire is designed to measure the various dimensions. Initially, secondary research is conducted to develop a systematic LSQ scale. Following this, primary research involves the creation and distribution of a closed-ended survey questionnaire.
To align with the research objectives and design, we adapt the traditional SERVQUAL model to develop a new LSQ scale specifically for fresh products. Previous research studies have often been unsuitable for fresh products, as LSQ is typically utilized in B2B settings rather than B2C contexts. The emergence of e-commerce highlights the importance of customer retention and satisfaction. However, many studies have focused predominantly on the cost and operational factors of LSQ, neglecting customer perspectives or specific elements within the LSQ dimensions [28]. Ding et al. [27] explored LSQ scales for assessing online self-service quality, emphasizing website design and relevant information systems like WEBQUAL and PIRQUAL and related systems like e-TailQ and E-S-Qual. Jain et al. [29] examined an eLSQ model and purchase intention for retailing. Nevertheless, these models primarily addressed general commodities such as books, CDs, and videos rather than fresh products. There is a notable deficiency in empirical research on LSQ in the context of fresh product e-commerce.
The improvement in living standards, lifestyle changes, the introduction of marketing strategies (e.g., incentive programs), and the COVID-19 pandemic have created unexpected opportunities for the fresh product e-commerce market. Despite this, fresh product e-commerce still faces challenges in competing with traditional methods due to logistics and food freshness preservation issues [4]. Building on previous research frameworks, we investigated dimensions such as reliability, freshness, convenience, and personnel contact quality, encompassing factors like timeliness, information quality, order issues, delivery, flexibility, and customer support. An improved conceptual research model is suggested, as exhibited in Figure 1.

3.2. Addressing Previous Research Shortcomings

To fulfill customers’ expectations, it is essential to assess the LSQ by analyzing the customers’ needs [30]. Jiang Tian [31] suggested that although the logistic information traceability of an e-commerce platform might not be directly related to the level of profits for suppliers, the relationship between reliability and LSQ of e-commerce should be further studied. While elements like timeliness of communication experience and response, information and complaints, and shipping costs are found to have a positive impact on the LSQ, there is still a lack of focus on the freshness and personnel contact quality in previous studies [32,33].
The proposed LSQ measure addresses several shortcomings of previous research. Firstly, we explicitly align our LSQ dimensions with SERVQUAL’s reliability dimension, ensuring consistency and comparability with established service quality measures. Secondly, unlike prior studies that emphasized cost and operational factors, our model prioritizes customer perspectives by incorporating dimensions including freshness and personnel contact quality. Finally, we adapt the traditional LSQ measures to the specific context of fresh product e-commerce, addressing unique challenges such as perishability and immediate delivery needs. This comprehensive approach provides a more nuanced and relevant understanding of LSQ in the dynamic world of online fresh food delivery.

3.3. LSQ Dimensions

The “Last Mile” is described as the transfer of products from local distribution centers to the front door of consumers. Customers do not perceive tangible products that are delivered to them before it comes to the order receipt stage. This means that “last mile delivery” is the pure logistics function that the consumer can directly perceive. This is the most important and ultimate phase where consumers receive products from e-retailers [34]. Therefore, the period when customers make the orders until they receive the products is a critical connection between logistics service providers and customers, directly influencing customer satisfaction. In this paper, the customer-oriented LSQ evaluation is based on the “last mile period”. In particular, four main aspects were illustrated in the current study as the key logistics disciplines of involvement in the B2C fresh product e-commerce customers: “Reliability, Freshness, Convenience, and Personnel contact quality” (Table 1).
Reliability: The ability to deliver a service that is guaranteed reliably and correctly [4,35], which is also one of the five dimensions of SERVQUAL. It is regarded as the reliability in timeliness, information quality, and order accuracy in this research. In the B2C fresh product e-commerce platform, information quality can be reflected in the descriptions of specific parcel delivery times and short back-order times to be posted instantly on the web page. Based on the presented information, customers are able to view the delivery time and check whether it matches the description, which is “on-time” delivery or timeliness performance. In addition, Parasuraman [38] also remarked on reliability as keeping the records accurate, which could be related to order accuracy. It means that logistics service providers should accurately keep records of customers’ fresh product orders without any erroneous entries.
Freshness: A crucial element for customers when they are selecting fruits and vegetables [26,36]. Several aspects exist when consumers perceive the freshness of food. So, Péneau [39] identified the importance of both non-sensory and sensory components for freshness via a questionnaire. On the one hand, non-sensory factors include time, management processes, and position. On the other hand, sensory factors contain outlook, state, taste, and texture.
Freshness in the LSQ scale means “whether the fresh products are in a good sensory condition and whether they are professionally handled during the last mile logistics process”. Customers are raising their expectations of healthy and fresh eating and a wide range of order fulfillment choices, so the original dimensions of the order condition and order quality should be primarily covered in the freshness dimension in response to so as reflect the e-grocery/e-commerce fresh product context.
Convenience: Fresh food generates a community-based business model. The model addresses the convenience of online ordering and aligns with the last-mile logistics service [4]. As such, customers are able to choose when to receive orders, including after-work delivery, weekend delivery, and desired-date delivery. This is especially important for fresh product logistics services because, unlike traditional parcels without the characteristics of perishability and vulnerability [15], fresh product parcels cannot be stored in delivery lockers until customers are available to accept the parcels. Digital transformation facilitates traditional retail outlets to move near the customer communities. In this case, the convenience of receiving fresh product parcels is a necessary factor for customer satisfaction.
Personnel contact quality: This item should be secured by the LSQ dimension. It mainly concerns how the logistics service provider (LSP) staff pays attention to the customer [26]. Responsiveness is the tendency to support customers by offering proactive service. Empathy is explained by the firm providing its customers with personalized care and consideration. Assurance is the skills and attitudes of employees and their potential to inspire confidence and trust. As such, it can be assumed that such measurements are associated with the quality of personnel communication. When purchasing fresh products online, couriers and online customer service staff are the primary contact points for customers. Thus, the personnel contact quality immediately connected to customer satisfaction is relevant to the service flow given by couriers and online customer service. Responsiveness indicates the time between customers making orders and receiving their order notifications. Further, online customer service staff and couriers are expected to respond quickly when customers contact them. Assurance and empathy require the online service staff and couriers to show professional knowledge regarding fresh products and cold chain logistics, as well as concern for customer requests.

4. Methodology

4.1. Questionnaire Design

Before formal investigation, a preliminary survey was carried out to streamline items associated with each LSQ dimension and to identify the content validity of the items. The questionnaire mainly investigates how four key variables (i.e., freshness, reliability, staff contact quality, and convenience) in the proposed model influence the logistics service quality of fresh product e-commerce. In the questionnaire, we developed 23 items assessed by a 7-point Likert scale that varied from “1” (low expectation) to “7” (high expectation).
To validate the content and ensure the accuracy of the survey instrument, as well as minimize ambiguous wordings and double-barrel items, a face validity approach was adopted [40]. Five experts, industrial practitioners, or logistics associates who have rich experience in using the B2C fresh product vertical platform were invited to review the questions. The experts have been working logistics industry for over 10 years in managerial positions. A face-to-face meeting was held to explain each item of the measured dimensions. The experts were requested to specify for every item the degree to which they are concerned when doing fresh product online shopping, covering from “does not explain at all” (1) to “completely explains” (3). The experts who chose “does not explain at all” were expected to give reasons. They were also required to give suggestions for all the descriptions of items, making them more suitable for the fresh product e-commerce market. The item could be retained if at least three experts identify that the item’s content could contribute to the considered dimension. A total of 25 items were deleted from the preliminary survey, forming the 23 items in the formal investigation (see Table 2).

4.2. Data Collection and Analysis

This study determines the feedback of the participants who have experience using the B2C e-commerce platform for purchasing fresh products and their logistics services. The research study employed a series of quantitative research methods about descriptive statistics, reliability tests, validity analyses, and factor analysis. In total, 322 questionnaires were distributed through the social media channel using mixed sampling methods, namely snowball and convenience sampling. The QR code/link of the questionnaire was distributed through our social networks using snowball sampling in the first stage, focusing on the younger age group of the consumers due to their higher majority of participation in e-commerce [41,42]. Afterward, it was further circulated by individuals in this network to more target survey respondents.
The data screening process is conducted to make sure that the data are prepared to be adopted and cleaned prior to carrying out further statistical analysis. Missing data, irrelevant samples, outliers, unreliable answers, and duration spent below 100 s were considered as reasons to delete samples [43]. Also, those without experience in using e-commerce to buy fresh products and their delivery service were classified as invalid. Finally, 222 valid questionnaires were received, showing a valid feedback rate of 69%. Descriptive statistics mainly show survey respondent characteristics like gender, age group, and educational background.

5. Results

In this study, 222 valid questionnaires were received, and the sample characteristics of the received survey are illustrated in Table 3.

5.1. AVE, CR, Reliability, and Validity Analyses

Cronbach’s alpha provides an acceptable reliability examination according to relatively minimal assumptions [44]. As such, our study used Cronbach’s alpha to conduct the reliability assessment. In accordance with the results presented in Table 4, all the dimensions of Cronbach’s alpha were over 0.6. It indicates that the reliability of the four dimensions considered is acceptable for a preliminary study [45]. Prior to the factor analysis, the data is assessed using the Kaiser–Meyer–Olkin (KMO) measure to determine the sampling adequacy. The KMO indicates the proportion of variance in the variables that might be caused by underlying factors. The KMO value of 0.959, which is greater than 0.50, and the significance level of Bartlett is 0.000, which is smaller than 0.05, indicates that it is acceptable for the data collection [46]. Further, the two adopted statistical processes show that the data collection has extremely high sampling accuracy and appropriateness for the factor analysis. We also evaluated the AVE and CR of the items, in which AVE is 0.5 and CR is 0.78.

5.2. Factor Analysis

Factor analysis is the cornerstone of scale development that is conducted as an analytical test for data analysis. In this study, maximum likelihood (ML) and varimax rotation are adopted as the ML estimation is considered to be a better choice for analysis [41]. ML estimation allows for the computation of various goodness-of-fit indexes for the model, facilitates statistical significance testing of factor loadings and correlations among factors, and enables the calculation of confidence intervals.
Table 5 shows the KMO and Bartlett’s Test that examine the strength of the partial correlation. KMO values closer to 1.0 are considered ideal, while values less than 0.5 are unacceptable. From our result, a KMO value of 0.912 with a significant value of 0.00 < 0.05 is obtained, which indicates the presence of a strong partial correlation.
Table 6 shows the ML result in which the components with eigenvalues under 1.0 were removed. Factor 1 refers to staff contact quality, factor 2 represents freshness, factor 3 represents convenience, and factor 4 represents reliability. Four factors confirmed the rationale in the screen plot (Figure 2) following the ML analysis for 19 items, which revealed the selected eigenvalues from large (8.239) to small (1.076). The total cumulative variance elaborated climbed up considerably from 43.363% to 62.720%. From the ML, we propose the original 23 components can be reduced to 4 underlying factors in order to illustrate the proposed statistical models. Table 7 shows the rotated component matrix. The pattern of rotated factors indicates that the four segregated factors highlight that 19 items were maintained since most of them were above 0.5, and symbolized the right factor apart from L10 (procedures of requisitioning logistics information are easy to use), L14 (door-to-door service is available), L16 (the package of fresh products is convenient to use), and L18 (staff respond to customer requests promptly even if they are busy). Considering the sample size needed for significance, the cut-off loading value of 0.45 is used to retain the items [42].

6. Discussions

According to the previous literature review, much of the LSQ research was conducted under the business-to-business (B2B) context, which measures the producer’s internal performance of the distribution function, whereas the e-commerce market operates within the business-to-customer (B2C) context. The existing theories of the traditional LSQ evaluation scale may not be suitable for the e-commerce market for fresh and perishable items, so the relevance of LSQ should be developed based on the viewpoint of the customer. The newly established LSQ scale fills the past research gaps regarding LSQ and fresh product e-commerce. Moreover, ready-to-cook food and fresh products, which are perishable and vulnerable during the storage process, require the logistics service to have particular handling processes, state-of-the-art cold chain technology, and a short lead time [15].

6.1. Research Model and Results

Based on the analysis results, we found that staff contact quality, reliability, convenience, and freshness are the prime four aspects of logistics in fresh products. Firstly, the application of temperature monitoring and the stacking problem both prove the transportation process of fresh food has high requirements for the timeliness of fresh food. The outcomes of the fresh product e-commerce LSQ also showed that customers put the most expectation on “convenience” compared with “reliability”, “freshness”, and “staff contact quality”. Indeed, a crucial element of the logistics service is convenience, as we need to make sure door-to-door packages arrive in advantageous packs. Considering a flexible ordering volume, forecasting the data-intensive inventory model is important for e-commerce [47]. Consumers place their orders via online platforms at their homes or offices, expecting quicker delivery than purchasing from conventional physical stores and punctual delivery at any time [48]. Fresh product e-commerce customers choose to buy fresh products online mainly due to the consumers suppose that buying in physical stores is much more inconvenient than buying online. To align with the characteristics of fresh product e-commerce, the LSQ scale suggested herein can be adequately applied and theoretically established in the Mainland China markets. Fresh food e-commerce is a new field that has developed rapidly in recent years, and various enterprises have stepped into it. The most crucial element of fresh food e-commerce (“Fresh”) has also become a significant problem that all e-commerce companies must solve. For this reason, different companies choose different logistics and distributions, which have their characteristics and have their advantages, and disadvantages. In the research, basic requirements and measurement scales of logistics under various circumstances are defined and may be used to combine the actual situation of the practitioners and thus achieve the final objective of fresh food logistics.

6.2. Novelty and Implications

This study proposes a model that integrates the LSQ model and SERVQUAL to develop a specific LSQ model for fresh product e-commerce. By considering the similarities and differences between the requirements of fresh product e-commerce logistics service and traditional logistics service, it was determined that a generic LSQ model is insufficient for evaluating LSQ in the e-commerce of fresh products [49]. Fresh product e-commerce places particular emphasis on factors such as cold chain technology and lead time [50], which are not as significant in assessing LSQ in other markets. Our research is novel in its customer-centric framework, which prioritizes customer perspectives by incorporating unique dimensions of fresh products, including freshness and personnel contact quality [51]. This comprehensive approach provides a more nuanced and relevant understanding of LSQ in the dynamic world of online fresh food delivery.
This research offers both practical and theoretical implications for the field. For the practical implications, the study provides a specific LSQ model tailored for this market, highlighting the importance of factors like freshness, cold chain technology, and lead time. This model allows managers to analyze their LSQ systems, identify areas for improvement, and gain a competitive edge. It also offers actionable insights to stakeholders that identify key dimensions of LSQ to enhance customer satisfaction. It also provides strategies for logistics operators to enhance their supply chain capabilities, including investing in cold chain infrastructure and utilizing big data analytics. Theoretically, the study demonstrates the need for a context-specific LSQ framework for fresh product e-commerce, highlighting the limitations of generic LSQ models. This contributes to a deeper understanding of LSQ in this specialized market and establishes a solid foundation for future research in this area.

6.3. Limitations and Future Works

This article has several limitations that can be considered as future research directions. Firstly, the existing survey data were mainly gathered from consumers in the urban areas in the south of China. The expectation of fresh product e-commerce LSQ would be distinct among different geographical areas, notably between urban and rural areas. A larger sample size from various geographical areas, including urban and rural areas, can be collected to analyze the geographical differences in the future. Secondly, this study focuses on collecting feedback from young consumers aged 18 to 30 (81.17%) using snowball sampling. Further studies could consider surveying diverse population groups and between different nations to make comparisons in order to generalize our research study between different regions. Lastly, the study is mainly focused on the discipline of business management to address cold chain LSQ. In the future, we may highlight investigating the operations management innovation like the delivery issue through improvement in logistics and last-mile delivery. The integration of emerging concepts of operation management like capacity management, facility planning, and demand management into the last-mile delivery service.

7. Conclusions

This research aimed to investigate the impact of service quality on customer satisfaction within the realm of fresh product e-commerce. To achieve this objective, a comprehensive Localized Service Quality (LSQ) scale was meticulously developed through a rigorous and systematic process. Initially, a preliminary scale was constructed based on an extensive review of the existing literature, which included four primary dimensions and 48 items. The preliminary scale underwent refinement through valuable expert feedback, leading to a more targeted version comprising four dimensions and 23 items. To ensure practical relevance and accuracy, an online survey was administered to experienced online shoppers of fresh products. This engagement not only further refined the scale but also provided insights into customer expectations. As a result of this iterative process, the final LSQ scale emerged, consisting of four dimensions and 19 items. This refined scale serves as a robust tool for measuring and understanding service quality in the dynamic and competitive landscape of fresh product e-commerce.
Importantly, this research offers valuable insights for stakeholders, identifying key dimensions of LSQ that are essential for enhancing customer satisfaction. By understanding these dimensions, stakeholders can strategically focus their efforts to improve service delivery and foster customer loyalty. Furthermore, the findings provide actionable strategies for logistics operators to enhance their supply chain capabilities. Recommendations include investing in cold chain infrastructure to ensure the freshness and quality of products and utilizing big data analytics to optimize inventory management and demand forecasting. By implementing these strategies, logistics operators can not only improve service quality but also create a more resilient and responsive supply chain, ultimately leading to increased customer satisfaction in the fresh product e-commerce sector. Future research could consider surveying a variety of population groups and exploring differences among nations to enable comparisons and improve the generalizability of our findings across different regions. This approach would offer a more thorough understanding of service quality dynamics in fresh product e-commerce on a global scale.

Author Contributions

Conceptualization, X.Z.; methodology, X.Z. and Y.-M.T.; formal analysis, X.Z.; data curation, X.Z.; writing—original draft preparation, X.Z, Y.-M.T. and K.-Y.C.; writing—review and editing, Y.-M.T. and L.W.; supervision, Y.-M.T., L.W. and K.-Y.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data are available on request due to restrictions.

Conflicts of Interest

The authors declare no conflicts of interest.

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Jtaer 19 00122 g001
Figure 1. Proposed research model for LSQ of fresh product e-commerce.
Figure 1. Proposed research model for LSQ of fresh product e-commerce.
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Figure 2. The screen plot of the ML results. The red line represents the eigenvalue is equal to 1.
Figure 2. The screen plot of the ML results. The red line represents the eigenvalue is equal to 1.
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Table 1. List and source of LSQ dimensions for fresh product e-commerce.
Table 1. List and source of LSQ dimensions for fresh product e-commerce.
DimensionOriginal Dimensions
Reliability [4,35]Timeliness
Information quality
Order accuracy
Freshness [26,36]Order condition
Order quality
Convenience [4,37]Order procedures
Volume flexibility
Door-to-door
Convenient packs
Personnel Contact Quality [26,36]Responsiveness
Assurance
Empathy
Table 2. The items of LSQ for fresh product e-commerce after validation.
Table 2. The items of LSQ for fresh product e-commerce after validation.
DimensionItem No.Item Content
Reliability L1The time between placing a requisition and receiving the delivery is short.
L2Deliveries arrive on the date promised.
L3The logistics information and documentation provided by firms are accurate, adequate, and credible.
L4The firm is able to trace the delivery condition.
L5Deliveries rarely contain the wrong items and incorrect quantities.
Freshness L6Items received from the fresh product company are of good quality.
L7Items received from couriers are undamaged.
L8Safety and security in delivery (intact and without loss).
L9Products ordered from the fresh product company meet the expected requirements.
Convenience L10Procedures for requisitioning logistics information are easy to use.
L11Customers are able to adjust the order volume after placing orders.
L12Firms are able to adjust operations to meet urgent orders.
L13Delivery meets high or low volume requirements.
L14Door-to-door service is available.
L15Desirable date and time delivery are available.
L16The package of fresh products is convenient to use.
Personnel Contact Quality L17Staff give quick and prompt responses to customer’s needs and requirements.
L18Staff respond to customer requests promptly even if they are busy.
L19Staff knowledge and experience meet customer needs and requirements.
L20Couriers have a neat image and wear the company’s uniform.
L21Customers are able to feel safe in their transactions with the staff.
L22Staff receive adequate support from the respective firms to do their jobs well.
L23Staff have a good attitude and remain polite to customers.
Table 3. Sample characteristics.
Table 3. Sample characteristics.
Descriptive IndexFrequencyPercentage (%)
Gender
Male7232.82%
Female15067.18%
Age group
18–2918181.17%
30–493817.04%
Above 5031.79%
Education background
Secondary school and below		209.12%
Undergraduate degree14966.87%
Postgraduate degree and above5324.01%
Table 4. Reliability test for reliability, freshness, convenience, and staff contact quality.
Table 4. Reliability test for reliability, freshness, convenience, and staff contact quality.
DimensionNo. of Items Cronbach’s Alpha
Reliability5 0.83
Freshness4 0.84
Convenience7 0.86
Staff contact quality7 0.73
Average variance extracted (AVE)0.50
Composite reliability (CR)0.78
Table 5. KMO and Bartlett’s Test.
Table 5. KMO and Bartlett’s Test.
Kaiser–Meyer–Olkin Measure of Sampling Adequacy0.912
Bartlett’s Test of Sphericity Approx. Chi-Square2095.504
df171
Sig.0.000
Table 6. ML results.
Table 6. ML results.
Factor1234
Eigenvalues 8.2391.4981.1041.076
Cumulative Variance (%) 43.36351.24657.05662.720
Table 7. Rotated component matrix.
Table 7. Rotated component matrix.
1234
L1 0.635
L2 0.582
L3 0.734
L4 0.518
L5 0.476
L6 0.671
L7 0.591
L8 0.670
L9 0.681
L11 0.582
L12 0.581
L13 0.774
L15 0.452
L17 0.517
L19 0.657
L20 0.524
L21 0.720
L22 0.520
L23 0.627
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Wang, L.; Tang, Y.-M.; Chau, K.-Y.; Zheng, X. Empirical Research of Cold-Chain Logistics Service Quality in Fresh Product E-Commerce. J. Theor. Appl. Electron. Commer. Res. 2024, 19, 2543-2556. https://doi.org/10.3390/jtaer19030122

AMA Style

Wang L, Tang Y-M, Chau K-Y, Zheng X. Empirical Research of Cold-Chain Logistics Service Quality in Fresh Product E-Commerce. Journal of Theoretical and Applied Electronic Commerce Research. 2024; 19(3):2543-2556. https://doi.org/10.3390/jtaer19030122

Chicago/Turabian Style

Wang, Ling, Yuk-Ming Tang, Ka-Yin Chau, and Xiaoxuan Zheng. 2024. "Empirical Research of Cold-Chain Logistics Service Quality in Fresh Product E-Commerce" Journal of Theoretical and Applied Electronic Commerce Research 19, no. 3: 2543-2556. https://doi.org/10.3390/jtaer19030122

APA Style

Wang, L., Tang, Y. -M., Chau, K. -Y., & Zheng, X. (2024). Empirical Research of Cold-Chain Logistics Service Quality in Fresh Product E-Commerce. Journal of Theoretical and Applied Electronic Commerce Research, 19(3), 2543-2556. https://doi.org/10.3390/jtaer19030122

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