INTRODUCTION

There has been an increasing popularization of the Internet in China in recent times and as such, the number of rural Internet users in China keeps increasing. As of December 2017, the proportion of Internet users in China’s rural areas was 27%, with a size of 209 million, an increase of 7.93 million or 4% compared with the end of 2016 (CNNIC, 2018). The increasing popularity of e-commerce enables rural citizens to buy many different kinds of commodities.

A common expression, “the last kilometer” has become a major problem for express delivery to the countryside to overcome. This has been promoted by the lack of e-commerce talents in rural areas. Besides the lack of talent, rural e-commerce supervision and rural logistics system construction are not perfect (Ren, & He, 2017). The rural area is wide and sparsely populated with a scattered population. With transportation facilities a little out of date, all these challenges restrict the construction of rural e-commerce logistics and create a weak link in the development of rural e-commerce (Miao, 2017).

Since 2013, the context of the No. 1 Central Document has been focused on accelerating agricultural modernization (2013). In 2014, the government began to emphasize reforms and innovations in the sector. This gradually stepped up efforts towards driving a new development with new ideas in the sector. In 2015, The Ministry of Commerce built and improved the rural market system, with a focus on the development of rural electronics not only to expand rural consumption (Liu, 2017) but also to promote farmers' income, indirectly developing the rural economy. In 2018, the No. 1 Central Document proposed; vigorously build extensive infrastructure to promote the development of rural e-commerce, encourage and support various market entities to innovate and develop new agricultural industry models based on the Internet. It suggested the thorough implementation with a comprehensive demonstration of e-commerce in rural areas, while accelerating the modernization of rural circulation (2018).

In recent years, with the overall economic growth, the purchasing power of rural communities has been greatly improved, considering that the rural market is defined as a segment of the overall market of any economy. With the development of the Internet, the rural network market has become of great significance. The rural online shopping consumer groups are projected to grow day by day while the scale of the rural online shopping market will rise steadily (Mallika, 2014).

The terminal of rural logistics is an important factor affecting rural residents of the shopping network. The quality of rural network-shopping needs to be good at the end of the logistics distribution chain and this must be supported by the development of rural electricity. With this, rural residents are gradually putting a forward higher request to the quantity of service. At the end of the rural logistics services, the quality of services directly reflects on the rural residents’ satisfaction. The purpose of this study is to analyze the effect of terminal logistics of rural e-commerce logistics on rural residents’ online purchasing behavior and provide corresponding improvement and development countermeasures for the development of rural terminal logistics services to improve the quality of life of rural residents. The specific procedure is to study the effect of terminal logistic service quality on online shopping satisfaction, the effect of satisfaction on rural residents' online shopping behavior, and examine the mediating effects of satisfaction on terminal logistics service quality and online shopping behavior using the S-O-R model.

LITERATURE REVIEW

Logistics service quality

The development of logistics service quality has undergone different changes since the 1960s. The definition of logistics service quality research in the 1960s to 1980s, from the beginning of the definition of research, was represented by the 7R theory (Perreaul, & Russ, 1974). To LaLonde et al. (1976), it was representative of logistics service quality and defined as the marketing level. Mentzer et al. (1989), defined logistics as the marketing and distribution of two big means. Since the end of the 1980s, the most representative scales are SERVQUAL scale and LSQ scale, which are also the two most recognized scales in academia. They are a kind of evaluation method to study from the perspective of customers’ perception of service quality. LSQ is also used to conducts research through interviews and recordings. Until the 1990s, the focus of research on logistics service quality had shifted to the perspective of customer satisfaction. Through research, it has been found that different customers, different marketing modes, and different logistics service quality models vary. Based on the relationship that exists between the distribution service and personal products industry with the market share of the main manufacturers of personal products, Daugherty et al. (1998) gained information from the United States, the scholars put forward a series of assumptions. They introduced a concept model to show the relationship among logistics service customer satisfaction, loyalty, and the relationship between market shares. Their study found a positive relationship between logistics service performance and customer satisfaction and customer loyalty was positive to the relationship between them. Hartmann et al. (2001), found that the use of logistics packaging and the competitiveness of the logistics service industry has greatly increased in the past decade. Kilibarda et al. (2016) examined the importance of LSQ dimensions in freight forwarding companies which is based on the SERVQUAL model and surveys. They found that the assessment of the level of quality of service in various market segments is different. In the stimulus-organismresponse (S-O-R) framework, Yu et al. (2021) examined the effect of familiarity with the Huawei smartphone brand as a mediator between brand involvement and brand loy alty in China and explored behavioral and attitudinal loyalty as two major components of brand loyalty for rural consumers. To maintain an important role in this business environment, logistics service providers must find ways to improve their competitive advantages. At this point, the importance of flexibility for logistics providers is unknown. Therefore, based on the resource-based view, the conceptual model of logistics service providers and its influence on customer loyalty are studied.

Online purchasing behavior

Li et al. (1999) proposed and tested a model of consumers’ online shopping behavior. In the model, it is assumed that consumers' online purchasing behavior is affected by population, channel knowledge, characteristics of perceived channels, and shopping orientation. Tuli et al. (2004), found that rural consumers had special habits and behaviors, as such marketers found it difficult to apply marketing skills in cities, unlike rural consumers. Lennon et al. (2010) used the innovation diffusion theory to study the process of rural residents on online clothes shopping. With the use of 2003 and 2000 research samples, their findings supported the innovation diffusion theory, before the practice and characteristics of decision-making units (education, income, innovation) to trust structure influence. The study also revealed that their influence before the purchasing behavior on the characteristics of decisionmaking units (education, income), indirectly affected online clothing shopping. Kundu (2013) studied the influencing factors of consumers’ purchasing decisions on FMCG and provided suggestions for advertisers on how to improve the awareness of farmers’ buyers. According to the results, rural consumers like electronic and print media advertising. Duhan et al. (2012) tracked the brand awareness of rural consumers and found that rural consumers face many problems when deciding on which brand to use, and the increase of brand awareness is influenced by information technology. Pooja Jain (2017) presented a conceptual model for measuring key dimensions of Internet acceptance in agriculture and studied its impact on rural market consumption. This is an empirical study that confirms the Internet's acceptance of changes in agriculture and consumption. Hu et al. (2020) found that customer service quality, payment security, and logistics quality significantly affected consumers’ willingness to buy fresh produce online.

HYPOTHESES AND METHODS

Research hypotheses

According to the S-O-R theory which represents Stimulus, Organism, and Response, consumers’ purchase intention or behavior represents a Response whose reaction mechanism is the result produced by the combined effect of stimulus and perception of consumer organism (Mehrabian, & Russell, 1974). In this study, the quality of terminal logistics service quality was represented by a stimulus factor (S), rural consumer satisfaction was denoted by the organism (O), and rural consumer purchasing behavior (purchase frequency, purchase amount, purchase quantity) was proved by the response (R). Among them, the design of rural terminal logistics service quality scale is based on the dimensions of SERVQUAL (model of service quality assessment) proposed by PZB and LSQ model proposed by Tennessee. The SERVQUAL model was adjusted to adapt to the objective of this study by rearranging some of the dimensions of the model. Some dimensions (which do not conform to the characteristics of rural terminal logistics service quality assessment) of the model were removed while some new dimensions (which reflects the characteristics of rural area terminal logistics services) were added to conform to the desired objective of this study. These above-mentioned modifications to the model in combination with the present situation of rural logistics service, forms the rural e-commerce terminal logistics service quality evaluation dimensions used in this study (Parasuraman et al., 1985, 2002).

The dimension of PZB's SERVQUAL model includes five aspects: Reliability, Responsiveness, Assurance, Empathy, and Tangibles. Combined with the LSQ model, 16 items in 5 dimensions of reliability, responsiveness, attitude, convenience, and economy were designed in this paper, and the scale used in studying them was as follows:

(1) Retained “Reliability”

Reliability is the ability to fulfill service commitments in case of errors during service. For rural e-commerce terminal logistics service, it is also applicable to ensure the intact, accurate, and other service processes of goods. Based on this, the reliability dimension will be retained.

(2) Retained “Responsiveness”

Responsiveness means helping customers and responding quickly to their needs for service. Studies have found that in the process of service, service efficiency alone cannot bring about a full reaction, enterprise service quality and efficient service will equally play a positive role on consumer perception, not only to increase customer satisfaction but at the same time to also increase customer loyalty. All of these put together can improve the efficiency of service, hence greatly improving the quality of service to consumers

(3) Changed “Assurance” and “Empathy” into “Attitude”

Assurance refers to the ability to communicate with customers politely and respectfully as well as pay attention to their needs and seeking to meet their needs. Empathy refers to the ability to pay enough attention to customers. These two dimensions belong to the attitude of logistics staff towards consumers, so combine them into the attitude dimension. In the process of online shopping, consumers are often unable to truly feel the quality of the goods before they buy them. As a result of information asymmetry, consumers are not satisfied with the goods they receive. Therefore, the attitude of logistics service is not only related to the delivery process but also the return service caused by goods error is an important index to be considered.

(4) Replaced “Tangibles” with “Convenience”

“Tangible” refers to a tangible environment, such as equipment, personnel, materials, and other external visible and personally experienced environments. This is an essential dimension for the evaluation of offline services. However, in the context of e-commerce, computers, mobile phones, and other electronic devices are the media for the completion of the whole transaction process. Online consumers pay more attention to the convenience of home delivery, logistics information query, and other services, especially because of the rural geographical location and the particularity of the population's living condition. Service “convenience” is one of the immediate needs for current rural consumers. Therefore, “Tangibles” was replaced with “Convenience”.

(5) Add “Economy”

For online shopping consumers, the high quality and low price of products is an important reason for consumers to choose online channels. Moreover, compared with urban consumers, rural consumers are more sensitive to price, therefore the logistics price should be considered in the logistics service quality evaluation system. In addition, ecommerce shopping also has the situation of return and exchange of goods, and the extra logistics cost is also a concern for consumers, so the dimension of "economy" was added. Due to the particularity of rural environment and geographical location, the phenomenon of additional transportation costs is common, so for the agent to buy and sell, the unreasonable added value of logistics is also an index that needs to be investigated.

As shown in Figure 1, based on the above assumptions, a theoretical model of e-commerce terminal logistics service quality on rural residents’ purchasing behavior was proposed.

According to the reconstructed scale of terminal logistics service quality and the objectives of the research, the following assumptions were made:

(1) Relationship between terminal logistics service quality and rural consumer satisfaction

There is a lot of literature on the relationship between logistics service quality and satisfaction. With the rapid development of E-commerce in China, consumers have higher and higher requirements on the quality of logistics services and find it more and more difficult to be satisfied. The same situation also happens in rural areas. With the improvement of rural economic level and the construction of public facilities, only online shopping can no longer meet the needs of rural consumers who have the habit of online consumption. As such, their expectations on the quality of terminal logistics service are getting higher and higher. First, this paper assumes that the five dimensions of rural e-commerce terminal logistics service quality have a significant positive correlation with satisfaction, so the fol- lowing hypothesis was proposed (Politis et al., 2014;Fomell et al., 1996;Silva et al., 2014;Athanassopoulos et al., 2001):

(2) The relationship between satisfaction and rural consumers’ purchasing behavior

Satisfaction reflects perception, and satisfaction assessment is measured by the gap between what the customer expects and what the customer actually feels. Customer satisfaction has long been regarded by many scholars as one of the main factors affecting purchasing behavior. Customer satisfaction is closely related to purchasing behavior and has a strong functional relationship. It can be considered that customer satisfaction has a positive impact on rural consumers’ purchasing behavior (Sun, & Liu, 2014). Therefore, the following hypothesis was proposed:

(3) The mediating effect of satisfaction

From the perspective of psychology, the positive perception of consumers will produce a tendency through the influence of stimulus factors on behaviors, while the negative perception will produce avoidance behaviors through the influence of stimulus factors on behaviors. Satisfaction refers to the degree of consumer perception. Therefore, it is assumed that the effect of satisfaction mediator on the service quality of rural terminal logistics on the online purchasing behavior, and hypothesis were proposed (Gil- Saura et al., 2011):

Data collection

The formal questionnaire consisted of an introduction and a text. The introduction is designed in the questionnaire to make the research object to be filled in the questionnaire more assured. Starting with simple words, the main purpose of the questionnaire alongside the research objective was presented to guarantee privacy, which ensures an understanding of the research objective and purpose as the actual state of the problem is expressed. All of these ensures the right information or responses are filled in the questionnaire. The questionnaire mainly included four parts: (1) Consumers scale of terminal logistics service quality of five dimensions and satisfaction, namely terminal logistics service quality at the end of the five dimensions - Reliability, Responsiveness, Attitude, Convenience, Economy. Likert scale was adopted, in which 1-5 respectively represented five meanings of “strongly disagree”, “strongly disagree”, “generally agree”, “strongly agree”, and “strongly agree”. (2) Consumers' online shopping behaviors, namely online experience years, purchase frequency, purchase amount, purchase quantity, online shopping stores, and purchase types were equally exploited. (3) Statistical variables of demographic and Socioeconomic characteristics, including gender, age, Marital status, education, occupation, and family annual income per capita of the respondents were requested. (4) Open-end questions, mainly of suggestions on service quality of terminal logistics processes. Parameters of service quality, satisfaction, and purchase behavior of terminal logistics were displayed in Table 1.

The objects of this investigation are the consumers who live in the rural areas of Jilin Province, having online purchasing behaviors. Rural residents above 18 years of age with online purchasing behaviors were selected for the survey. Questionnaires were distributed and data was collected by using the Questionnaire Star platform through the Internet in January 2018. SEM is a large-sample size analysis technique. Generally, the model requires that the ratio between observed variable and sample ranges from 1:10-1:15, and the sample size should be between 200 and 400 (Hair et al., 1988). This survey was conducted utilizing field research and an online questionnaire. In the end, 400 questionnaires were issued, and the recovery rate of 378 valid questionnaires was 100% and the effective rate was 94.50%, which meets the requirements of the desired analysis sample.

Analysis methods

Amos 24.0 software was used to analyze the data collected at two stages using methods of structural equation modeling and the mediator effect.

In the first stage, Structural Equation Modeling (SEM) was used to analyze the relationship between terminal logistics service quality and rural consumer satisfaction, and the relationship between satisfaction and rural consumers’ purchasing behavior as shown in the theoretical model. CE, S. put forward the concept of the latent variable, and Wright, S. put forward the method of path analysis for calculating the correlation between two variables (CE, 1904;Wright, 1934). Until the 1970s, Joreskog, K. G. proposed a structural equation model by integrating latent variables with path analysis using a mathematical matrix (Jöreskog, 1973). SEM is a method used to build estimation and test a causal model, its essence is that of a kind of generalized general linear model. Its greatest advantage is that it verifies the relationship between multiple independent variables and multiple dependent variables (Ulman, 2002).

In the second stage, testing the mediator effect was used to test whether satisfaction had any mediator effect between terminal logistics service quality and online purchasing behavior. The mediator effect refers to the influence on the relationship between variables (X and Y), which is not a direct causal chain but which is generated through the indirect influence of one or more variables (M). In this case, M is called the mediating variable, while the indirect influence of X on Y through M is called the mediator effect. The mediation relationship can be expressed as follows by the regression equation.

(3)

Where,

Traditionally, there are three methods to test the mediating effect, which are casual steps and products of coefficients and differences in coefficients (MacKinnon et al., 2002). There is a new procedure for testing the mediator effect which was proposed by Wen, Z. et al. as shown in Figure 2 (Wen et al., 2004). This program only uses the casual steps and Sobel test, as well as controls the error rate of the first type and the second type within a small probability. However, it can test partial mediator effect and full mediator effect, which is worthy of recommendation.

ESTIMATION AND RESULTS

Descriptive statistical analysis

Table 2 presents the descriptive statistics of the 378 samples. The description is done in three parts: the demographic characteristics of respondents, socioeconomic characteristics of the respondents, and the purchasing behavior of rural residents.

The demographic characteristics of respondents indicated that of the 378 respondents, 60.85% of them were women while 39.15% were male. This indicates that most of the respondents who shopped online were from the surveyed areas were women. For the sampled age distribution, it was observed that 34.13% of those surveyed were within the age range from 36-44 years as the most populated age class. The age range from 26-34 years had 25.13% of samples and was the second most distributed age class. The age range from 18-24 years had 20.37%, the 45-49 years had 12.7%. Meanwhile, these last categories had the least from the surveyed population above 50 years of age, 7.67%. A proportion of 71.69% of those samples were married while 26.19% of them were still single and those who had divorced or lost their wife or husband were only 2.12%.

From the surveyed result of socioeconomic characteristics of consumers, it was observed that 28.57% of rural residents had junior middle school or lower educational certificates, 21.43% had higher school education, 22.49% had a college education and 27.51% had bachelor’s degree or higher education. This characteristic is important as literate consumers had the ability to do shopping online. The occupation of respondents was as follows, 37.04% of samples were working as enterprise officials, 14.29% of them were working in individual operations. Within the annual per capita household income level ranges surveyed, a good number of respondents fell within the ￥0-￥9,999 range amounting to 36.24% of the population and 26.72% fell within the ￥10,000-￥29,999 range level.

This survey of online shopping consumer purchasing behavior revealed that the largest group of monthly purchase frequency was 2-3 times a month, accounting for 33.33%, 4-5 times once a month respondented 23.02%, and those who purchased 0-1 time once a month were 17.99%. For purchase amount on yearly bases, the number of respondents fell within ‘below ‘500’ and amounted to 0.00% of the sample population. 46.82% fell within the ￥ 500 - ￥999 range level, 28.30% fell within the ￥1000 - ￥1499 range level. The quantity on monthly ranged from 1 or below had 19.84% of the sampled population; quantity ranged from 4-5 had 22.22%. It is observed that 43.12% of those surveyed were within the purchased quantity on monthly range from 2-3 as the most populated age class. The least purchasing quantity categories of 6-7 and 8 or above were 6.08% and 8.73% respectively.

Reliability and validity

Confirmatory factor analysis is a part of SEM analysis. Thomopson, B proposed that SEM researchers should analyze the measurement model before implementing the analysis structure model because the measurement model can correctly reflect the aspects or factors of the study (Thompson, 2004). In this study, CFA was carried out for all the dimensions, and the 7 dimensions of the model were reliability, responsiveness, attitude, convenience, economy, satisfaction, and consumption behavior. The load on all dimensions were greater than 0.7 and significant, values of composite reliability were at the range of 0.8~0.9, values of the average of variance extracted fell into the range of 0.625~0.804, the values of a square of the multivariate correlation coefficient were between 0.551 and 0.891 (Table 3), which were conformed to the standard of Hair et al. (2006) and Fornell et al. (1981): (1) Values of factor load on all dimensions were greater than 0.5; (2) Values of composite reliability were greater than 0.6; (3) Values of the average of variance extracted were greater than 0.5; (4) The values of a square of the multivariate correlation coefficient were greater than 0.5. Therefore, all 7 dimensions have convergent validity.

Discriminant validity reflects whether the correlation between measure terms of different factors is as small as possible, and its evaluation criterion is that the square root of AVE value of each variable could be greater than the correlation coefficient between the variable and other variables. Table 4 displayed the results of the discriminant validity test. Although the AVE root value of some factors is not greater than the maximum correlation coefficient between the factor and other factors, the difference is very small, which can be considered to have good discriminant validity.

After the pretest model of reliability and validity, we moved on, to comprehend the structural model fitness and good model fitness which is a necessary condition for SEM analysis (Byrne, 2010). The structural equation modeling (SEM) analysis usually takes the χ2 to verify model fitness; however, the sample influences χ2, as the literature indicated (Bentler et al., 1980;Marsh et al., 1985;Marsh et al., 1988). Therefore, except for considering sample size, when taking the χ2 and the degree of freedom to measure model fitness, the standard should not be over 3 (Bagozzi et al., 1988; Chin et al., 1955). In addition, referring to opinions of Schreiber (2008), McDonald et al. (2002), Boomsma (2002), Jackson et al. (2009), Hoyle (1995), Schreiber et al. (2006), several indicators were selected for the overall model's fitness evaluation including Root Mean Square Error of Approximation (RMSEA), Goodness-of-Fit Index (GFI), Adjusted Goodness-of-Fit Index (AGFI), Normed Fit Index (NFI), Comparative Fit Index (GFI). The results (Table 5) revealed that χ2 /df =2.19, RMSEA = 0.056, GFI = 0.908, AGFI = 0.88, NNFI = 0.941, CFI = 0.967. Except that the AGFI was lower than the recommended 0.9, the others were all above, but it still meets the Baumgartner et al. (1996) and Doll et al. (1994) recommended level of above 0.8.

Hypothesis testing

After measuring the path relationship between observed variables and latent variables of the model with Amos 24.0, this study proposed six hypotheses tests as follows. Table 6 and Figure 3 show the details of completely standardized estimates and empirical results. According to the results, Reliable, Convenience, and Economy had a positive impact on satisfaction respectively. Therefore H1a, H1d, H1e, were supported. Finally, satisfaction had a positive impact on Consumption behavior and H2 was supported.

Mediating effects

It could be found from the results of formula (1) that only c2 and c5 were significant, while c1, c3, and c4 were not, indicating that Reliable, Attitude, and Convenience are not significantly correlated with Consumption Behavior, so the mediation effect analysis was stopped. From the results of Formula (2) and Formula (3), it could be found that a5, b5, and c5 were all significant, indicating that satisfaction had a mediator effect between Economy and consumption behavior. And a2 and b2 were not significant, so the Sobel test was needed to determine whether there was a mediating effect between Responsiveness and consumption behavior. Sobel was calculated using Calculation for the Sobel Test in the web, the result of the test statistic, St. Error and p-value of Sobel test were 4.313, 0.043, 0.000 (<0.05) separately, which indicated that there is a significant mediating effect between Economy and Consumption Behavior (Calculation for the Sobel Test) (Table 7).

In conclusion, the results of the hypothesis testing are as follows (Table 8): Distribution reliability, Service convenience had positive effects on satisfaction, Economy had a negative effect on satisfaction, and Satisfaction has a positive effect on online purchasing behavior. Satisfaction has a mediator effect between responsiveness and purchase behavior, it also had a mediating effect between Economy and purchase behavior.

CONCLUSIONS

The effect of five dimensions of rural e-commerce terminal logistics service quality on customers’ satisfaction were analyzed respectively. From the data analysis results, it was found that: Distribution reliability, service convenience all had positive effects on satisfaction, while Economy had a negative effect on satisfaction. Nevertheless, service responsiveness and attitude did not affect satisfaction. There is a gap between rural consumers' demand for online shopping logistics service quality because of immature rural online shopping development. For most rural residents, attitude and speed of the logistics personnel are not very important, while reliability, convenience and economic cost are currently valued. The satisfaction of rural online shopping consumers had a significant positive impact on purchasing behavior, indicating that the higher the satisfaction of customers’ logistics behavior in online shopping, the more likely they were to have positive purchasing behaviors such as purchase frequency, purchase amount and purchase quantity. Satisfaction has a mediator effect between responsiveness and purchase behavior, economy, and purchase behavior separately.

With the development of the economy and the improvement of online shopping platform technology and services, online shopping has become an important channel for consumers to buy goods. For online shopping consumers in rural areas, online shopping is a way to choose high-quality and high-efficiency goods because it is not limited by time, space and other factors, and the cost of online goods are relatively low compared with offline shopping, hence there is no price discrimination. The development of the Internet has narrowed the distance between the consumption habits of rural residents and urban residents, while the development of rural online shopping is based on the construction and improvement of the logistics system, the convenience of logistics distribution require not only the shortest time to complete the distribution, but also the highest efficiency to complete the distribution. Therefore, unblocking the "last kilometer" of rural logistics and solving the convenience and service attitude of rural terminal logistics are the necessary conditions for the development of rural e-commerce.

적 요

농촌 종점 물류는 농촌 주민들의 온라인 쇼핑에 영향을 주 는 중요한 요인임. 농촌 인터넷 쇼핑의 서비스 품질은 양호한 종점 물류 배송과 지원이 필요함. 농촌 이커머스의 발전에 의 하여 농촌 주민들이 서비스의 품질에 대한 기대치도 높아지고 있음. 본 연구에서는 이커머스 만족도를 통한 농촌 종점 물류 서비스 품 질이 농촌 주민의 온라인 구매행위에 어떠한 영향 미치는지 검토하고, 농촌 종점 물류 서비스의 발전을 위한 개 선 및 발전전략을 제시하였음. 본 연구에서는 구조방정식모델 (SEM)과 온충린의 매개효과 테스트를 적용하여 분석하였음.