An Overview of Literature on E-commerce Customer Loyalty

An Overview of Literature on E-commerce Customer Loyalty

Yun YueJianhua Xiao Shaoyu Luo 

School of Economics and Management, Wuyi University, Jiangmen, Guangdong, China

Corresponding Author Email: 
1146942842@qq.com
Page: 
1-6
|
DOI: 
http://dx.doi.org/10.18280/rces.020401
Received: 
|
Accepted: 
|
Published: 
30 December 2015
| Citation

OPEN ACCESS

Abstract: 

Customers’ loyalty is the key for electronic commercial enterprises to remain the advantage of competition. In this research, we retrieved 478 papers on e-commerce customer loyalty from CNKI, Springerlink and EBSCOHOST and Emerald databases. This review examines the cultivating, the organizing, the models, and the information systems of e-commerce customer loyalty. The general situation of international e-commerce customer loyalty research could be comprehended after a summary.

Keywords: 

E-commerce, Customer loyalty, Empirical research, Model, Information system

1. Introduction

20% of corporate customers create 80% profits. And the enterprises’ profits would increase 25%-85% if customers’ retention rate increased 5%. Customer loyalty plays an important role for the development of enterprises [1]. Customer loyalty in e-commerce is the extension of the traditional customer loyalty for Internet. In China, e-commerce develops rapidly. By the end of June in 2014, the total sales of e-commerce were 5.85 trillion, and the total sales of online retail market were 1.08 trillion, rising by 34.5% and 43.9% respectively than those in the same period of previous year [2]. However, low customer loyalty degree, loss of customers, and lots of complaints on Internet shopping hinder the development of electronic commerce. Customer loyalty in e-commerce is the key competition of enterprises. So, comprehension of customer loyalty in e-commerce both at home and abroad can guide sustainable development of our electronic commercial enterprises.

2. Literature Retrieval

Papers from CNKI and other databases were retrieved for the research development of e-commerce customer loyalty.

2.1 CNKI

In order to comprehend the Chinese scholars’ research development in e-commerce of customer loyalty, literature retrieval is made from CNKI. When searching with the key words “customer loyalty” and “e-commerce”, the result list has 34 papers. It is verified that in this retrieval mode we cannot retrieve proper research literature about customer loyalty in e-commerce effectively for some relevant literature papers missing. Various retrieval methods are necessary for enriching relevant research literature. The relevant results are showed in table 1. In the table, KY is keywords; TI is title; and AB is abstract.

Table 1. China National Knowledge database retrieval results

Retrieval methods

Literature constitution

China Academic Journal Network Publishing Database

23

52

136

875

1068

China Proceedings of conference Full-text Database

1

1

2

26

23

China Doctoral Dissertations Full-text Database

7

16

9

465

20

China Masters’ Theses Full-text Database

1

1

144

25

429

Special Journals

1

1

3

21

21

International Conference Papers Database

0

0

3

10

13

China Academic Journal Full-text Database Series

1

0

0

8

3

Total

34

71

297

1430

1577

 

NOTE: ①KY= ‘customer loyalty’*’e-commerce’; ②TI= ‘customer loyalty’*’e-commerce’; ③AB=‘customer loyalty’*’e-commerce’; ④KY=‘customer loyalty’; ⑤TI=‘customer loyalty’.

From Table 1, the number of the papers in the search list with key words of “customer loyalty + e-commerce” is totally 34; the number of the papers with title keywords of “customer loyalty + e-commerce” is totally 71, the number of the papers with abstract keywords of “customer loyalty + e-commerce” is totally 297. The search results by key words and titles are conservative. The results searched by abstract covers the full aspects, but some of the results don’t belong to the study of customer loyalty in e-commerce. Therefore three kinds of retrieval methods of ①, ② and ③ are represented as result sets U1, U2 and U3. The combination results are U1∩U3=25, U2∩U3=61, and U1∩(U2∪U3)=25. And there are total 316 papers retrieved through the above methods. And then these papers are screen further to obtain the papers that can best profile the current research of customer loyalty in electronic commerce.

The published time of the papers is given in figure 1. With the rapid development of e-commerce, the customer loyalty research in e-commerce becomes hot spots. The study appeared in 2000. And after the year 2005, the number of relevant research papers rapidly increased. The numbers became considerable large between 2009 and 2014.

Figure 1. The numbers of research papers on customer loyalty in e-commerce

2.2 Other databases

There are 162 papers retrieved by searching databases of Springerlink, EBSCOHOST and Emerald with the advanced search option with the keywords of “e-commerce + electronic commerce” and “customer loyalty + e-loyalty” in them in pairs. Among the retrieved papers, 126 papers come from Springerlink; 19 papers come from EBSCOHOST and 17 papers are from Emerald.

3. Cultivation of E-Commerce Customer Loyalty

E-commerce customer loyalty is also called as e-loyalty, which is the powerful weapon and the critical element to the Internet retail e-commerce, it could help the Internet merchants gain the victory in the future. And even some researchers thought the e-loyalty as the life-saving straw from the heavens [3,4,5]. After realizing the importance of e-loyalty, the researchers thought more frequently about how to cultivate e-loyalty.

In quantitative researches, some scholars put forward the detailed measures to cultivate customer loyalty by analyzing the factors influencing customer loyalty. Cui proposed an e-loyalty model, according to the driving factors [6]. In the model, he put forward measures to enhance the customers’ satisfaction by optimizing the design of the website and setting up the website brand values. Liu analyzed driving factors, service quality, switching costs, brand images, site security, trust, and customer personal characteristics of customer loyalty. She proposed several ways including attracting the attentions of customers, caring for customers, paying attention to personalized consumption, enhancing service guarantees, making barriers to exit, and communicating with customers by combining with the Law of Reichheld for cultivating customer loyalty [7]. Du evaluated the influences on customer loyalty of solutions of providing quality products and services, increasing the interaction degree of the web sites, enhancing the reputation of the web sites, meeting the personalized needs, treating consumers’ complaints correctly, increasing customer switching costs, and organizing loyal operation team with high efficiency [8].

In qualitative researches, some scholars proposed the corresponding measures of cultivating customer loyalty by stating customer characteristics and meaning, the obstacles in cultivating customer loyalty. Summarizing the researches of Diao, Zhao, Kang and Xie, we think cultivating customer loyalty should start with good reputation and website image, personalized service, experience marketing network, network community, emotional experience, customer care communication, customer database management analysis and the improvement of platform values or transfer costs [9-12].

4. Organizing for Customer Loyalty

Organizing ways for customer loyalty involves B2C, C2C and O2O.

4.1 B2C customer loyalty

B2C is one of the main modes of electronic commerce retail market. The researches about B2C E-commerce customer loyalty are an important part. And the research content can be divided into three parts: (1) the influencing factors of customer loyalty in B2C; (2) relationship between factors and customer loyalty; (3) evaluation and improvement of customer loyalty. The literature about the evaluation of customer loyalty is less than other two parts.

There are many papers about the influencing factors of customer loyalty in B2C. The journal papers and degree thesis analyze the influencing factors of customer loyalty from theoretical and empirical points. A large number of literature on empirical research build models and propose assumptions basing on theoretical analysis of relevant influencing factors. The practical influencing factors are found through the analysis and structural equation confirmation of questionnaire data. Literature only from the perspective of theoretical analysis is less. There are two kinds of models as follows. Li regarded the customer loyalty as a whole and analyzed the connotation and driving factors [13]. He constructed a driving model of customer loyalty among customer perceived value, customer satisfaction, network customer trust, switching costs and customer network loyalty. The empirical research indicated the influences between each factor. Xiao separated customer loyalty in B2C into attitude loyalty and behavior loyalty and empirically analyzed the influences of customer trust, service quality, switching costs, customer dependent on attitude and behavior loyalty [14]. To sum up all the literature, some consider the influencing factors among customer trust, satisfaction, switching costs and customer loyalty. Some also consider the antecedents of customer trust, satisfaction and switching costs. The main influencing factors of customer loyalty contain customer trust, customer satisfaction, switching costs, perceived value, commitment and risk.

Some scholars analyzed the detailed factors of customer loyalty in B2C, such as shopping experience, service quality, customer value, logistics service quality and so on. The study provided the detailed reference for B2C enterprise in depth improvement and practice. The literature about the evaluation of customer loyalty is less and the method has certain limitation. Hou built the evaluation system of customer loyalty for B2C enterprises [15]. He used hierarchical analysis and fuzzy comprehensive evaluation to research one website empirically. Then the relevant suggestions were put forward. Zhang evaluated customer loyalty in B2C from two aspects [16]. One is RFM method to evaluate customers’ behavior loyalty and the other is grey clustering method to evaluate attitude loyalty. She classified customer and formulated the cultivation strategies.

4.2 C2C customer loyalty

Though B2C developing rapidly in recent years, C2C has occupied a large proportion in E-commerce market. With the gradual maturity of customers’ psychology and behavior, they gradually shift to B2C market. The challenges of customer loyalty for C2C enterprises have been more sever. The relevant researches of customer loyalty in C2C mainly contain three parts: (1) relationship study between service quality and customer loyalty; (2) influence factors of customer loyalty; (3) evaluation and improvement of customer loyalty.

Compared with B2C market, abundant researches about service quality were done in C2C market. Zhou constructed a model involving electronic service quality, customer trust, customer satisfaction and customer loyalty and did a survey in students of Zhejiang University and users of Taobao community [17]. The results showed that electronic service quality had a direct influence on e-commerce customer loyalty. He also put forward some relevant suggestions to Taobao sellers. Chen studied the relationship between service quality and customer loyalty in C2C [18]. Tao did a research on the impact of logistics service quality to customer loyalty. At the same time, the mechanism affecting research between service quality and customer loyalty also involved [19].

Scholars studied the influence factors of customer loyalty in C2C from different angles. Some built the model of customer loyalty in network and then studied empirically. Some analyzed influence factors through theoretical analysis. Liu put driving factors like customer participation, perceived risk and quality of customer loyalty in the model [20]. She did empirical study and found decreasing customer perceived risk, increasing customer participation and establishing good relationship quality could be effective to maintain customer loyalty. Some literature directly studied effects of influence factors of customer loyalty or through satisfaction and trust to affect customer loyalty. Some others still considered from attitude and behavior loyalty. In addition, Cui creatively introduced the culture factors of customer loyalty into network and looked forward the prospect of the future [21].

The literature on evaluation and improvement of customer loyalty in C2C is less. Li constructed evaluation system of customer loyalty including behavior and emotional loyalty [22]. He used RFM and Likert Scale to measure and divide customers and some presented relevant points to improve customer loyalty. Zhi came up with evaluation system composed of behavior, attitude and emotional variables [23]. She used grey evaluation and grey clustering method to classify customer loyalty and put forward the corresponding cultivating strategies of customer loyalty.

4.3 O2O Customer Loyalty

According to the different transaction objects in E-commerce market, B2C, C2C are two different classifications and they have the essential distinctions compared with O2O. The essence of O2O objects is a kind of service mode which connects online and offline and has nothing to do with transaction object. O2O is becoming more and more popular and has been applied in many industries, such as catering, tourism, clothing, hotel and others. The literature mainly contains the improvement of customer loyalty and the empire research about customer loyalty in O2O.

Scholars put forward methods to improve customer loyalty through theory and practice analysis. Zhang analyzed advantages and disadvantages of customer loyalty management in O2O [24]. Thus he presented the related measures to improve customer of loyalty enterprises. Zhao proposed improvement strategies for group-buying websites according to three aspects of customer values: function value, procedure value and social value [25].

Empirical researches about customer loyalty in O2O involving group-buying, traveling and clothing industry. Most literature is degree thesis and the literature is few. Lei set up a model consist of customer satisfaction, brand trust and customer loyalty [26]. Basing on the questionnaire of customers in group-buying, he made structural equation confirmation and found brand trust is an intermediate variable for the formation of customer loyalty from customer satisfaction. Group-buying website should improve customer satisfaction and increase the marketing possession to establish brand trust effectively. In the end, achieving the transformation of customer satisfaction into customer loyalty will come true. Liu set up the relationship model consist of four aspects as service quality, customer satisfaction, trust and customer loyalty [27]. She found 7 aspects of service quality have influence on customers’ attitude and behavior loyalty through empirically studying of customers in group-buying site. The detailed suggestions and measures for group-buying operators were proposed. Yang established the influence factor model for tourist website according to customer value and transaction cost theory [28]. She did a questionnaire on Mango net to get the corresponding conclusions. Zhang built a model consist of service quality, perceived value and customer loyalty for travel agency [29]. He empirically researched the influence of service quality on customer loyalty from group-buying website and travel agency. Then he proposed the related management countermeasures and suggestions according to the conclusion. Liu combined the satisfaction of apparel industry from online and offline and studied the influence on customer loyalty [30].

5. Model Construction of Customer Loyalty

Since 2000 Frederick F., Reichheld, Phil Schefter published the “e-commerce loyal” in the Review of Harvard Business, customer loyalty has been more important than ever [31]. Scholars built rich loyalty models for customer loyalty to do empirical research in electronic commerce market.

There are many kinds of models as the stage model, the combination of online and offline model and the specific factors model and combined model. Liao C, etc. studied the impact of ordering and fulfillment stages on the willingness of customers to continuously use B2C website [32]. Chen C, Cheng C constructed an online and offline combined model for a bookstore basing on the information system success model (IS success model) and related cognitive behavioral model of emotional conative (tri-component behavioral model) [33]. They got corresponding conclusions after validation of the collected data. Zhuo J, Wei J, etc. found service quality have an impact on customer loyalty and satisfaction [34]. They analyzed the determinants of the quality of service in Chinese express industry. Castañeda J studied the relationship nature and intensity between customer loyalty and customer satisfaction in online market [35]. Scholars also analyzed the specific influence factors of satisfaction, trust and others that affected customer loyalty. The specific factors in models were constructed to make guidance for achieving customer loyalty in e-commerce. The combined models built loyalty model basing on the combination of relationship, value, transfer costs and FLOW theory and etc. There are “trust, satisfaction, loyal” model; “quality, trust, satisfaction, loyalty” model; “quality, satisfaction, loyalty” model; “quality, trust, loyalty” model; “value, satisfaction, loyalty” model; “FLOW, loyalty” model and so on.

The models involve a variety of factors that affect customer loyalty. Most of them are satisfaction, trust, quality, transfer cost, value and so on. Many scholars also studied other factors. Chou S, Hsu C analyzed the influence of shopping habits on online repurchase intention of customers [36]. San martín S, Jiménez N studied the purchase habits of Spanish online customer and considered the impact of gender on customer satisfaction and trust [37]. Chen J, Yen D, etc. studied the influence of different cultures on the customer loyalty of the e-commerce sites [38]. Román S studied the regulatory role to the customer satisfaction and loyalty by the type of product, the customer point of view and the age [39]. Other scholars also studied the influence of education, customer personal characteristics and collective characteristics on satisfaction, trust, loyalty or buy intentions.

6. Information Systems for Customer Loyalty

With the rapid development of Internet and technology, the information systems to cultivate and enhance customer loyalty have many kinds. The mainly of them are E-CRM, Recommendation and personalization system and Network community.

6.1 E-CRM

Customer relationship management has already made applications in the traditional market. It also has a positive role in the e-commerce market. Bahn D, Fischer P. Clicks and Mortar studied the supporting role of e-commerce customer relationship management system [40]. Gurău C, Ranchhod A, etc. combined customer lifetime value with customer loyalty and built a strategic that regard customer as center [41]. The strategic set forth requirements to the CRM system of network business. Hadaya P, Cassivi L studied the development and innovation of e-commerce and the regulation of E-crm [42]. Romano NJ, Fjermestad J analyzed the five research areas which were technology, knowledge management, business models, marketing and customer of customer relationship management in e-commerce [43].

6.2 Recommendation and personalization system

Recommended system can indirectly affect satisfaction and customer loyalty by reducing search costs and improving customers’ utility. Personalization system not only can increase customer switching costs but also can obtain valuable information of customers. The researches include two aspects which are content and technology. Hinz O, Eckert J studied the impact of search and recommendation system for retailers and found that the technology was the competitive advantage [44]. Chellappa R, Sin R developed a simple model for empirical studies and found that the use of the personalized service are related to customers’ trust of the supplier [45]. They also proposed the recommendation to develop personalized service. Gao M, Liu K, etc. made an overview of the personalization system and described the advanced methods and techniques nowadays [46]. Repschlaeger J, Erek K, etc. used cloud computing to do an empirical research on customer preferences of startup enterprises [47]. Holland S, Kießling W utilized the preference of customers to do data mining and found the results can be applied to personalized product recommendations, personal customer service and one to one market [48].

6.3 Network community

Online communities, social networks and online reviews provide convenient platforms to exchange information for customers. This interaction also has influence on e-commerce customer loyalty. Hu N, Liu L, Zhang J regarded online reviews as reputation and analyzed the characteristics and prescription impact of online reviews on product sales [49]. Pai F, Yeh T built a relational model containing information-sharing, interaction, using attitudes and intentions [50]. They studied the intention differences between different groups of user and find features of information sharing and perception of entertainment can enhance the using intention of customers. The study of Song J, Kim Y revealed how social factors influence the using intention of services in virtual community, allowing managers and researchers to learn more about the using behavior of customers [51]. Albert L, Aggarwal N, etc. found the publishing frequency and perceived value of customers in hotel’s online community had impact on purchase intention [52]. Kim S, Yang K, etc. built a model to study the 292 members of the virtual community and found success factors [53]. The research results can give guidance to the virtual community in marketing practitioners.

7. Conclusion

E-commerce customer loyalty develops from the theory of traditional customer loyalty. Traditional customer loyalty comes from abroad. With the years’ research contributions of scholars, a more complete theoretical system were built including the definition of customer loyalty, customer loyalty classifications, drivers of customer loyalty, customer loyalty models, evaluation of customer loyalty and customer loyalty cultivating and improving.

Theoretical studies of E-commerce customer loyalty haven’t form a system, such as the definition of E-commerce customer loyalty. There are several types of views, but the explicitly accepted view hasn’t been determined. Domestic and abroad scholars committed to empirical researches of e-commerce customer loyalty models and influencing factors. Building various models, scholars considered customer loyalty as a static factor mostly and researched the relationship between it and other influencing factors. In reality life, customer loyalty change with the time and it is dynamic. The life cycle and value of customer are changing all the time. Considering life cycle and the value of the customer, relevant research to bind customer loyalty and value and lifecycle to achieve the best interests is lack. The difficulty is how to make the results more accurate and closer to the reality in measuring customer loyalty in e-commerce. Empirical researches concerning influencing factors of C2C, B2C, O2O are rich, but influencing factors are complex and diverse and the results will vary for samples from different regions. Domestic comparative literature about different areas and cultures with aboard is lack, such as the cultural differences around countries result in the different empirical results in customer loyalty. Many types of literature are on improving and cultivating of customer loyalty in e-commerce. E-CRM, network community, recommendation and personalized system are to nurture and enhance customer loyalty. Technology advancing, the big data, cloud computing, data mining, and knowledge discovery develop to be mature gradually. Applying technology to cultivate and improve customer loyalty in e-commerce companies and achieving customer loyalty better need to be researched deeply in the future, such as how to make better use of those technology to achieve customer classification and how to utilize the technology to maintain the friendly relationship between enterprises and customers.

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