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ISSN : 1225-8504(Print)
ISSN : 2287-8165(Online)
Journal of the Korean Society of International Agricultue Vol.32 No.4 pp.309-314
DOI : https://doi.org/10.12719/KSIA.2020.32.4.309

Research on Factors Influencing Chinese Consumers' Intention to Buy Agricultural Fresh Products Online - Evidence from Tangshan City

Shuang-yu Hu, Shi-yong Piao, Zhi-run Li, Yu-cong Sun, Xuan-you Jin, Jong-In Lee
Department of Agricultural and Resource Economics, Kangwon National University, Republic of Korea
Corresponding author (Phone) +82-33-250-8668 (E-mail) leejongin@kangwon.ac.kr
June 26, 2020 November 12, 2020 November 27, 2020

Abstract


This paper is based on field survey data and applies it to the logistic model to analyze consumers' online shopping intention to buy fresh produce. According to the total effect of online shopping intention, it was revealed that consumers' age, educational level, and monthly household income significantly influence willingness to buy fresh produce online. The product quality, price and brand name were the main factors affecting consumers' willingness to buy fresh produce online. The customer service quality, payment security, and logistics quality significantly also affected consumers' willingness to buy fresh produce online. In conclusion, it is important for vitalizing online sales of fresh produce to ensure the quality, use well-known brand name, and improve logistics service and e-commerce platforms.



중국 하북성 탕산시 소비자의 온라인 농산물 구매에 영향을 미치는 요인에 관한 연구

호쌍우, 박세영, 이지윤, 손우총, 김현우, 이종인
강원대학교 농업자원경제학과

초록


    INTRODUCTION

    With the development of e-commerce for agricultural products and the increasing standards of living, online shopping of fresh produce have been gradually accepted by consumers as a new shopping model. Consumers complete transactions through a fictional online environment, which is very different from the traditional method of purchasing fresh produce. In the meantime, people are putting forward higher requirements for the quality and convenience of purchasing fresh produce. According to the data released by China Internet Network Information Center (CNNIC), as of June 2019, the number of Internet users in China have reached 854 million, with an Internet penetration rate of 61.2%. The development of the Internet has brought huge development opportunities for the development of fresh e-commerce. In the context of the rapid development of the Internet, the e-commerce platforms conduct fresh produce transactions through the Internet, reducing intermediate channels and saving costs, enabling consumers to quickly buy fresher and lower-priced products and shorten the distance between merchants, producers, and consumers. However, the development of online shopping of fresh produce in China is relatively slow. The growth of consumers' demand for fresh produce is accompanied by many challenges at the same time. The market audience for fresh e-commerce is still not high at the moment, and most consumers prefer traditional sales models such as farmers' markets and supermarkets. When consumers buy fresh produce online, they have a higher expectation on the quality, freshness, timeliness of logistic services, and other aspects of fresh produce. Online shopping is more complicated than offline transactions. Getting clarity on consumers' psychology, exploring the key factors that affect consumers' willingness to buy fresh produce online and improving the efficiency of online sales is therefore an unavoidable theoretical and practical subject. Within the above mentioned contexts, this study conducted a survey on consumers through the administration of questionnaires. The parameters surveyed had as aims, to understand the current situation of consumers' perception or intentions on online shopping for fresh produce, and to investigate the main factors affecting online shopping for fresh produce and from the findings put forward suggestions to e-commerce platforms and retail sellers of fresh produce.

    THEORETICAL BASIS

    The research on the factors influencing consumers' willingness to buy agricultural products online has attracted the academic community's attention. The analysis of factors affecting consumers' willingness to buy fresh produce online mainly includes the following two dimensions.

    • (1) The website service quality. Ahn (2004) regards website quality as an external variable and believes the website quality consists of website information quality, delivery quality, service quality, and system quality. Yang Chaojun (2014) studied e-commerce and realised that the online shopping environment and the assistance from customer service staff could significantly affect consumers' purchasing intentions. Sangyong Kim and Youngjun Lim (2001) believe that the stability of the Internet, the page's design, and the level of detail of product information would affect consumers' online shopping of fresh produce. In order to understand how website quality affects perceived quality and purchase intention under the condition of information asymmetry, Yang Xiaopeng (2015) constructed a theoretical model for the influence of various dimensions of website quality on perceived quality and purchase intention under the condition of information asymmetry. His findings revealved that the effect of website quality, perceived quality, and perceived quality all had a significant positive impact on purchase intention.

    • (2) The fresh produce attribute. Hughes (2008) studied consumers' attention to attributes when buying fruits and vegetables and proved that quality and safety of products were the key factors that determine whether consumers should buy or not. Li Aihong (2011) researched on the influencing factors for consumers' online shopping of fruits and concluded that the most important factors are preservation measures, express delivery efficiency, brand types, and price. Cui Ming (2012) believes that freshness, product quality, reputation, product packaging, and other factors will affect consumers' purchase intention. He Dehua et al., (2014) found that the higher consumers' expectation for product safety and quality, the lower their intention to purchase fresh produce through online shopping. And by taking pork as the research object, Zhang Yayan (2014) studied the influencing factors of purchase intention for safe agri-food.

    Online shopping is born with the rapid development of the Internet technology. Some scholars have found the consumers' individual characteristics as an important factor that affects consumers' choice for online shopping. In fact, consumers' purchasing behavior is a complex process and the factors that affect consumers' purchasing decisions mainly come from two aspects: firstly from the consumers themselves such as consumers' personal characteristics and psychological factors. Secondly from the consumers' external environment, such as social culture, social class, related groups, and family background. Kim et al., (2010) found that there were significant differences between online shoppers and ordinary consumers in age, level of education, and income. Wang Kexi (2017) uses a Logistic model to analyze that consumers' purchase intentions are affected by factors such as personal attributes, agricultural product quality, and e-commerce platforms.

    According to the above analysis, the influencing factors of consumers' online shopping of fresh produce are taken as independent variables and is shown in Table 1. The influencing factors of online shopping of fresh produce are integrated into the fresh produce attribute, consumer individual characteristic and the website quality. Based on the above three dimensions and considering the index of influencing factors, Logistic regression was used to analyze the data to provide suggestions for fresh e-commerce platforms.

    DATA AND MODEL

    The data used in this study was gotten from field surveys of Tangshan City from May to August 2018. The field survey was conducted by 15 students majoring in economics at the Tangshan Normal University. The data was collected through personal interviews and questionnaires. The research made out 180 questionnaires in Tangshan, among which 164 were valid questionnaires, accounting to about 91% of the total number of questionnaires sends out. The specific survey situation is shown in Table 2.

    According to the statistics of the collected valid questionnaires as shown in Table 2, among the surveyed samples, 67 were males, accounting for 40.9% of the surveyed population, and 97 were females, accounting for 59.1% of the surveyed population. In terms of profession, there were 101 clerks, accounting for 61.6% of the surveyed population, 3 civil servants, accounting for 1.8% of the surveyed population, and 19 workers, accounting for 11.7% of the surveyed population, there were equally 14 individual operators accounting for 8.5% of the surveyed population, 27 people in other professions, accounting for 16.4%. In terms of household monthly income, 50 people had a monthly income of less than 3500 yuan, accounting for 30.5%, and 55 people had a monthly income of 3500 to 6000 yuan, accounting for 33.5%, and 36 people had a monthly income of 6000 to 10000 yuan, accounting for 22%, 23 people had a monthly income above 10000 yuan, accounting for 14%. In summary, we can see that the sample distribution is relatively balanced, which is suitable for further research.

    By the use of Logistic regression, this study examined consumers who purchase fresh produce online. Logistic regression is a non-linear statistical analysis method. According to both domestic and overseas research results, this model is often used to analyze factors influencing consumers' behaviors. The results obtained from the analysis are expressed in the form of the probability of events. In this study, “You are willing to buy fresh produce online” is taken as the dependent variable “Y”, when “Y=1”, it means that consumers are willing to buy fresh produce online, when “Y=0”, it means that consumers are unwilling to buy fresh produce online, to analyze the influence factors of consumers' online shopping of fresh produce. Logistic regression fundamental formula is as follows:

    p i = Z ( α + β i ) = 1 1 + e ( α + β i ) ( i = 1 , 2 , 3 , , i )
    (1)

    ln ( p 1 p ) = β 0 + β 1 X 1 + β 2 X 2 + β 3 X 3 + β i X i ( i = 1 , 2 , 3 , i )
    (2)

    In (2), (when “Y=1”) indicates that consumers are willing to buy fresh produce online, (when “Y=0”) indicates the probability that consumers are not willing to buy. β0 is a constant term. Xi is independent variables, which represent each influence factor of consumers' online shopping of fresh produce, βi are the influence degree of the independent variables on the dependent variable Y.

    According to the theoretical basis proposed in the previous section, the domestic and overseas research status is shown on Table 1 where the empirical model of consumers' willingness to buy fresh produce is established.

    EMPIRICAL ANALYSIS AND RESULTS

    The reliability and factor analysis results of this study are shown on Table 4. Devellis (1991) proposed that when the reliability of the scale is above 0.8, the scale is very reliable, and when the reliability is above 0.6, it indicates that the reliability of the questionnaire can be passed. And It can be seen from the results that the Kaiser-Meyer-Olkin value is 0.83, which is suitable for factor analysis.

    This study used SPSS 22.0 to conduct Logistic regression on the sample data obtained from the field survey. The following conclusions are drawn from the model estimation results on Table 5 and Table 6.

    It can be seen from Table 5, that the Chi-square of the model is 268.35, and the P-value is 0.000, indicating that the model is significant. The results of Cox&Snell R2 and Nagelkerke R2 are 0.67 and 0.90, indicating that the model has a higher degree of Goodness of fit and the independent variable explains the dependent variable to a higher degree.

    It can be seen from Table 6, that from the consumers' characteristics variable, Consumers' gender and profession have no significant impact on willingness to buy fresh produce online. Consumers' age, educational level, and monthly household income influences the willingness to buy fresh produce online. Among them, in terms of age, younger consumers tend to buy more fresh produce online compared with the reference group. In terms of educational level and monthly income, the higher income and highly educated groups show a stronger willingness to buy fresh produce online. From the fresh produce attributes, the product's price, quality, and brand are the main factors affecting consumers' willingness to buy fresh produce online. The variable of product price is statistically significant at the significance level of 5%, the average change of the odd ratio of the product price to consumers' willingness to buy fresh produce online is between 9.04 and 16.15 times, indicating that compared with the reference category, the product price can significantly affect consumers' demand. Product quality factor passes the significance test. This indicates that product quality impacts consumers' willingness to buy. However, compared with the reference category, consumers who choose the moderately important category think that product quality factor has little effect on their online shopping intention. For every change in product quality factor by 1 unit, the odd ratio of online shopping will increase by 8.9 times compared with the reference category. The product's brand factor passed the significance test, indicating that the brand has a significant impact on consumers' online shopping intentions. In the website service quality, customer service quality variable is statistically significant at the significance level of 5%. The average change of the odd ratio of customer service quality to consumers' willingness to buy fresh produce online is between 3.30 and 5.75 times. The factor of logistics quality and the security of payment passed the significance test. While ensuring the safety of consumer assets, convenient and efficient logistics services can ensure the quality of fresh produce, and on the other hand, reduce the time cost for consumers.

    CONCLUSIONS

    Based on the data of 164 valid questionnaires selected from Tangshan city, this study analysed the factors influencing consumers' willingness to buy fresh produce online and explored the degree of influence of age, education, income, and other factors on the willingness to buy fresh produce online. (1) In terms of consumers' individual character, age, educational level, and income, are factors seen to have a significant impact on consumers' willingness to buy fresh produce online. Gender, profession, and whether they have experience in online shopping or not, were observed to have no obvious influence on consumers' online shopping intentions. The higher the educational level of consumers, the stronger their willingness to buy online. Compared to younger people, the elderly are less likely to do online shopping. At the same time, consumers with higher household incomes are more likely to buy online. (2) In terms of fresh produce attributes, price is always an important reference for consumers. And The better the quality and safety of the delivered fresh produce, the stronger consumers' intention is to buy online. The more famous the brand of fresh produce, the more consumers tend to shop online. (3) In terms of website service quality, the communication process between customer service and consumers can enhance consumers' willingness to buy online if there is an active recommended behavior. The better an ‘after-sales’ service, the stronger the intention of consumers to buy online. The safer and more convenient online payment is, the more consumers' purchasing intention can be promoted.

    Therefore, this study put forward the following suggestions through empirical analysis results. (1) E-commerce platforms guarantee the production of quality. The quality of the product itself is the foundation of the survival of the e-commerce platforms and the guide of consumer choice. However, in the competition of online sales, in order to attract more consumers, e-commerce platforms inevitably make lower prices and more service commitments than those in the same industry. As such, some e-commerce platforms may reduce the quality of their products in order to make up for the losses. Therefore, to effectively improve the quality of the fresh produce, e-commerce platforms will inevitably touch the processes of commodity procurement, storage, transportation, etc. In the supply chain, storing fresh produce according to their own characteristics is primordial to ensuring their quality. To ensure the quality of the product, logistics companies need to optimize the entire logistics system and update logistics refrigeration equipment and improve the product's service quality to reduce consumers' perceived risks. (2) E-commerce platforms need to improve on website service quality. The product sales website is a platform for consumers and businesses to communicate. Therefore website interface needs to be simple and clear, and the product information rich and detailed. Ensuring these alongside timely customer service response are important ways to improve website service quality. When designing the website interface, ecommerce platforms should fully consider the acceptability of all ages, genders, cultural levels to enrich and highlight the information in the most concise form. In communicating with consumers, customer service should provide appropriate suggestions for references. This could be a great way to increase consumers' willingness to buy fresh produce online.

    적 요

    본 연구에서는 농산물에 대한 온라인 쇼핑을 이용한 소비자 를 대상으로 설문조사를 실시하고 그 결과를 로지스틱 회귀분 석하여 농산물 온라인 쇼핑 구매에 영향을 미치는 요인을 분 석하였음.

    • 1. 소비자의 연령, 교육수준, 소득 등 요인이 농산물의 온라 인 쇼핑 구매에 영향을 미치는 것으로 나타났음.

    • 2. 농산물 속성에 의해 가격이 낮으며, 고품질, 유명 브랜드 일수록 온라인 쇼핑 구매에 긍정적인 영향을 미치는 것으로 나타났음.

    • 3. 온라인 서비스 부문에서는 신속한 지불, 고품질 서비스 등 요인이 온라인 쇼핑 구매에 긍정적인 영향을 미침.

    • 4. 결론적으로 농산물 품질, 고품질 온라인 서비스 등을 개 선하여야 온라인 쇼핑 구매가 활성화 될 수 있음.

    Figure

    Table

    The influencing factor variables of online shopping intention

    Summary statistics on characteristics of survey participants

    Variable and definition

    The results of reliability analysis and factor analysis

    Model summary

    The regression results

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