E-Commerce Engagement: A Prerequisite for Economic Sustainability—An Empirical Examination of Influencing Factors

Abstract

Economic sustainability for firms of all sizes and sectors is likely to depend on some type of online commercial activity. While technical barriers to e-commerce are not high, adaptability to new online markets is a critical part of sustainable economic growth for many firms. The Chinese e-commerce market has increased dramatically to become larger than that of the United States, Europe, and Japan combined. This study explores the underlying factors that influence Chinese online consumers’ acceptance and patronage of the online shopping platforms where those firms must operate. Firm competition in the e-commerce platform in China is highly competitive, making exploration of the factors that influence consumer purchase behaviour more valuable. After an extensive qualitative focus group study, a quantitative online survey of 691 savvy Chinese online shoppers was completed. When the data was subjected to structural equation modelling (SEM) for analysis, it was found that a model of three factor constructs explains whether an online shopping platform would be the preferred online shopping platform of choice. E-commerce platform preference (EPP) can predict purchase intention (PI) and site commitment (SC). The results explain why e-commerce platforms should address important EPP factors such as: order fulfilment and delivery process, company image enhancers, the variety of products offered, the design of the online shopping platform, trust of its recommendation system, and finally, awareness of the online shopping platform itself. These findings may be of interest to e-commerce practitioners as well as those whose research interests include e-commerce and consumer behaviour.

1. Introduction

1.1. Sustainability

Consideration of sustainability as a concept is not a recent phenomenon, over time evolving into a fairly well developed theoretical paradigm that is most frequently conceptualized by the use of the three pillars model [1], each with a separate assessment, Triple Bottom Line, or 3BT [2]. Separate discussions on ecological, social, and economic areas is a simplistic view [3] and presents a challenge for those seeking empirical analysis of the three pillars due to the inseparable and interdependent nature of factors that comprise them [4,5]. Further, there exists a lack of consensus around a precise definition of each of the pillars; indeed, some have proposed a 4th or even more additional pillars to encompass other areas of concern including cultural sustainability [6]. Consensus on how the model is segmented and described may not be necessary and there is, however, a consensus on the relevance of concern for sustainability in all three areas. Examples include ecological sustainability as manifested by the discussion of climate change [7], social sustainability viewed through the lenses of inequality of global wealth distribution and the problems that arise from increasing poverty [8,9,10], and finally, economic sustainability, which is a necessary precursor to addressing both social and ecological sustainability related concerns [11,12,13].

1.2. Economic Sustainability

Economic sustainability is complicated by the shifting backdrop of global economic trends caused in part by changing social factors [14], and ecological factors which in turn influence business decisions [15], demonstrating the interconnectedness of the three pillars. Another important factor in the study of economic sustainability is the rapidly changing technologies that both drive and are driven by commercial activity. Advances in technology as key drivers in both globalization and e-commerce have changed the commercial landscape dramatically within a relatively few years, creating opportunities for great successes as well as dismal failures of established firms [16,17]. Economic sustainability for existing firms has depended in many cases upon their ability to transition to online commerce [17,18]. Likewise, sustainable commercial growth for existing firms has often depended on firms ability to adapt to new, and often international, markets [16,19] and the challengers that come with the intercultural aspects of international commerce [20,21]. It is the nexus of these two trends that this study will examine, specifically e-commerce acceptance and global expansion by an established firm.

1.3. E-Commerce

As e-commerce has matured, the direct B2C via company website model has largely disappeared, replaced by the model where sellers and buyers connect via an online platform such as e-bay, Craig’s List, or Amazon.com in the US, and Taobao or JD in China [22]. The platform model provides opportunities for micro, small and medium sized firms to engage in commerce on a competitive basis, thus creating a viable avenue for social and economic sustainability [23].

It is within this dynamic and complex context that this study will explore consumer preferences and behaviours in use of e-commerce platforms and EPP.

China currently has the largest e-commerce markets in the world, mostly boosted by small and medium sized businesses (SMEs) [24,25]. In 2017, e-commerce market revenue in China was USD 558.9 billion, and it is projected to grow to USD 1262.5 billion by 2023, almost doubling in a six-year period [26]. E-commerce represents more than 35% of China’s retail sales, the highest proportion in the world [27].

Against this backdrop, Weise [28] reported that Amazon China had closed its Chinese e-commerce business. Amazon International originally entered the Chinese e-commerce market by purchasing local online book retailer, Joyo.com, in 2004 and later changing its name to Amazon China. As an online retailer, Amazon China was selling its own inventory and using its own logistics network, unlike its major competitor Taobao that is owned and operated by Alibaba. These online business models rely heavily on individual SMEs to sell a wide variety of products and use local delivery companies to lower prices and create delivery efficiency, which appeals to Chinese consumers’ loyalty. The Chinese market can be complicated and it is not an easy market to operate in, due to its uncertain legal and regulatory environment; however, Walmart China has been thriving and expanding its business into the online domain by partnering with the second most dominant online retailer in China, JD.com, and is now offering speedy home deliveries within hours [28].

1.4. Gap in the Literature

The objective of this paper is threefold: The first is to determine the factors that contributed to Amazon China’s withdrawal from the Chinese market from an online consumer’s perspective. Specifically, it is proposed that certain factors constitute and lead to e-commerce platform preference (EPP). Second, an analysis of the effect of EPP factors on consumers’ purchase intention (PI) and site commitment (SC) is undertaken, and third, whether there is a relationship between purchase intention and site commitment is explored.

This study provides an empirical rationalization which states that the Amazon China’s pull out and its failure to compete for online customers in China were related to fundamental business strategy failures in marketing, advertising, e-commerce basics, cultural adaptation, and supply chain logistics, rather than other macro-economic or administrative factors. To investigate why Chinese online consumers did not embrace the Amazon China platform—unlike its counterparts in the United States, Japan, or Europe—a survey of previous e-commerce and marketing literature was done, and we used existing scales to review these factors. New scales and constructs for a qualitative perspective employing a focus group were developed, and the factors affecting the development of online shoppers’ EPPs and the relationship of these preferences with online store purchase intentions, continued site commitment for repeat business, and customer loyalty was explored and discussed. Influencing factors include not offering a free and efficient delivery process for minimum purchase orders and failure to launch an Amazon mobile application (app) in its most basic design at an early stage to compete with local competitors like Taobao. The necessity of an app is based on Chinese consumers being very mobile-oriented, with 66% of their online purchases done on a mobile phone app [29].

The possible underlying reasons for Amazon China pulling out from the Chinese market can be rationalized by EPP factors. These include the availability of a wide variety of products for online purchase, awareness campaigns to draw future customers and retain existing ones, an efficient order fulfilment and delivery process, the image of the company’s effect on consumer purchase, the design of the online shopping platform site, and trust in the online recommendations system of the shopping platform. Surprisingly, the price of the products sold on the shopping platform does not contribute to EPP behaviour development in online shoppers.

There is a gap in the literature where there is no existing EPP model that ties together all the different variables that could potentially influence consumers’ motivation to buy online from a specific e-commerce platform. In addition, there is little existing research that investigates the effect of the possible motivating variables on intention to purchase from an online platform and how these factors influence site commitment for repeat online customers specifically, within the context of China.

2. Theoretical Framework and Hypotheses

Many studies have asserted that online retail success may be dependent on factors such as providing excellent merchandising, retailing, ease of navigation, convenience of finding products online, ease of paying for those purchases, delivery efficiency, and enhanced security to protect online customers [30]. This review of current literature discusses these dimensions as well as the proposed relationships among them in relation to price, product variety, the online shop’s site design, company image, order fulfilment and delivery process, trust in product recommendation credibility, and promotional awareness campaigns aimed at attracting new online customers and retaining existing ones. How these factors relate to purchase intention (PI) from an online shopping platform and site commitment (SC) generated for future loyal customers to the online shopping platform is explored.

2.1. Price

Price is the amount of money given in exchange for tangible or intangible products; it also forms part of the marketing mix aimed at attracting more customers. Price influences how the product is perceived by intended consumers [31]. E-commerce growth is affected by several factors including the proliferation of technology, perceived security and value proposition [32]. However, assuming such factors are equal, and that the consumers are rational, a lower price indicates a higher quantity demand [33]. Customizing prices is becoming more popular and in the era of e-business, this strategy is ever more important, as it is both profitable and easy to implement [34]. Price has a direct effect on the benefits the consumer perceives when comparing the same product and its price among competitors [35]. According to Gefen and Devine [36], the price, and the perceived fairness of price [31] of products may affect consumer purchase behaviour. Online consumers are more influenced by the price of the products than offline consumers [37]. Moreover, due to the average living standard in China, most consumers are sensitive to the price of products [38]. For example, price consciousness is positively correlated with consumers’ trust for purchasing certain product types, such as electronic products [39]. Further, Zhao and Jin [40] found that some of the challenges Amazon China faced in the Chinese online market is the price competition from its direct competitors like Taobao and JD.com. The price war in the Chinese online marketplace placed enormous pressure on e-commerce companies’ profits [40]. Further, the authors state that Amazon lost its competitiveness in the Chinese online marketplace because of its excessive focus on the quality of its products, rather than the price attractiveness aimed at its major Chinese customer base, which led to a narrow customer segment, compared to its main competitor, Taobao [40]. Based on the above, we propose the following hypothesis:

H1: 

The price of products sold on an online shopping platform has a positive effect on e-commerce platform preference development.

2.2. Product Variety

Product variety in an online shopping context means that consumers can compare many choices from a wide range of products sold online, enabling them to make a better purchase decision [41]. Product variety has also been described as the availability of a wide range of commodities available for sale in a store or online that helps customers to view its diversity and select high-quality goods on an online shopping platform [42]. Moe [43] reported that, by its nature, online shopping gives consumers better decision-making opportunities, because of the availability of information on a wide variety of different products. Thus, the availability of a variety of products to choose from motivates customers to purchase from a specific online platform [44]. Consumers usually prefer to have more information to make a choice on various products available for purchase on an e-commerce site [45]. Moreover, the frequency of customers’ online shopping is positively correlated with the variety of products offered in the online shopping store [46]. Meanwhile, Pandian, et al. [47] found that—compared with shopping in physical retail stores—online shoppers have a better product evaluation capability due to the variety available to them online.

The positive influence of product variety on the consumer to improve product selection in an online shopping store can possibly contribute to explaining Amazon China’s withdrawal from the Chinese market. According to Liang et al. [48], product variety is very important to online shoppers and it is considered to be the foremost reason to shop online. Taobao, the Alibaba online shopping platform, has significant advantages in product variety categories offered for sale on its site, as it connects various SMEs to their end users [49]. It can therefore be said that if Amazon China’s product variety was of the same scope as that of their major competitor Taobao, it would have had a strong competitive advantage in the Chinese online shopping market.

H2: 

The product variety offering on an online shopping platform has a positive effect on e-commerce platform preference development.

2.3. Site Awareness

Site awareness is the extent to which a site is recognized by potential customers [50]. It also refers to consumers’ ability to remember a certain website when they need a specific product or service from that site [51]. Site awareness contributes greatly to the competitive advantage of an e-commerce platform within the marketplace and is a vital aspect of Chinese consumers’ selection process of an e-commerce platform when planning to purchase online [52]. Furthermore, Yoon [53] found that high consumer site awareness would influence online shoppers’ perceived trust and satisfaction with an e-commerce website. Additionally, site awareness is an essential purchase motive that a consumer associates with a specific online platform [54]. A company’s site awareness is among one of the most important factors that encourage consumers to select and evaluate the products or services an online shopping platform offers [55]. It is for this purpose that many online platforms provide additional external informational streams—rather than pure advertising—to attract additional customers and to retain current consumers [56].

The selection process of a suitable online seller from among many competing ones has become a time-consuming process for consumers; therefore, generating effective awareness campaigns is becoming increasingly important for customer acquisition and retention [57]. Online site awareness is the process of generating useful information online in the form of advertisement to attract new customers and retain current customers [56]. Competition among different online shopping platforms in China’s e-commerce sector is very intense and a few major e-commerce platforms—such as Taobao and JD.com—dominate the market; in comparison, Amazon China had the smallest scale of this vast Chinese e-commerce market [58]. The first big challenge for any businesses in the e-commerce domain is raising awareness to attract customers [59]. Amazon China failed in this aspect, as it had a low proportion of awareness and usage from Chinese online patrons [60].

H3: 

The site awareness of an online shopping platform has a positive effect on e-commerce platform preference development.

2.4. Site Commitment

Commitment is the intention to establish and preserve a relationship [61]. Consumer commitment refers to establishing a relationship with a brand and keeping this relationship stable with short-term sacrifices but long term advantages [62]. Site commitment refers to shoppers establishing and maintaining a constant valued relationship with online retailers to fulfil their purchase needs [63]. Site commitment plays an essential role in forming and maintaining a consumer’s ongoing loyalty and obtaining their repeat business, which further contributes to the site’s continuance in operations and future growth [64,65,66]. Site commitment can be influenced by many factors including post-purchase peer reviews [67,68]. Site commitment is therefore a necessary factor that guides customers to establishing and maintaining a relationship with an online store [69]. High site commitment means that an online shopper can be introduced to a platform’s new products or services with very minimal effort and with potential higher benefits [70]. For example, Park and Kim [30] showed that site commitment can affect purchase behaviour, as there is a positive relationship between site commitment and purchase behaviour.

H4: 

Site commitment to an online shopping platform has a positive effect on e-commerce platform preference development.

2.5. Intention to Purchase

Intention refers to an unsolidified act of will or a goal that a person is working toward to achieve [71] and purchase intention is the potential or likelihood of a user buying a product or service [72]. Measuring the real transactional behaviour of a buyer directly is difficult; hence, measuring purchase intention is a good substitute for real purchasing behaviour [73]. The theory of reasoned action argues that behavioural intentions are antecedents to specific behaviours of an individual [74]. Intention to purchase products or services can be influenced by related factors such peer influence via social media interactions [75], perceived brand value and community [76], word-of-mouth communication with others of shared ethnicity [21], as well as internalized personal behaviours [77], all of which combined can demonstrate consumers’ willingness and plans to buy a particular product or service [78]. Therefore, intention to purchase can directly forecast the behaviour of a future buyer [79]. Further, the intention to purchase can directly predict the actual purchase behaviours made by consumers [79].

H5: 

E-commerce platform preference for an online shopping platform has a positive effect on intention to purchase from that platform.

H6: 

Intention to purchase from an online shopping platform has a positive effect on site commitment (customer loyalty development).

2.6. Trust in Online Product Recommendations

Consumers’ intention to purchase products is based on the recommendations made by others in the same online shopping platforms of the intended product to purchase [80]. An online recommendation to increase sales volumes is regarded as word-of-mouth marketing [81]. However, Chinese online shoppers are sometimes overwhelmed with information and their purchase behaviour can easily be influenced by any of several sources [75] and, in particular, by an informative and fair recommendation system in the shopping platform’s system [82]. Trust in online recommendations influences positively site commitment if consumers have higher trust in the credibility of the online product recommendations [83].

H7: 

Trust in online recommendations credibility of an online shopping platform has a positive effect on e-commerce platform preference development.

2.7. Company Image

Consumers are affected by many factors during the shopping process, including store image, store reputation, store awareness, and product features. These factors not only affect consumers’ perception of the quality of goods or services, but also their evaluation of the goods or services [84]. Brand image as defined by [85] is “the set of beliefs, ideas, and impression that a person holds regarding an object” (p. 273). Consumers acquire a series of values related to both the product and company image; therefore, the company image is the “sum of value” of the enterprise [86]. In fact, many consider company image as a guarantee of quality assurance that minimizes underperformance issues in certain products [87].

Online shopping platforms’ image will affect consumers’ purchase intention to varying degrees. For instance, a study [88] on gender role in brand image and purchase intention found that brand image positively influenced purchase intention specially more on female e-shoppers as it lessens the risk perception. Brand image and its associated company image is often used by consumers as a standard to evaluate the quality of goods or services available on the platform. Thus, company image becomes a relevant basis of judgment for product or service selection and generates intention to purchase, eventually establishing site commitment [89]. Further, Treacy and Wiersema [90] confirm that positive experiences will eventually lead to a positive image.

E-commerce platform image can create loyalty in customers when they are exposed to a particular product or service they like and having an enhanced brand image could be a consequence of the product or service itself. The more positive the store image is, the easier it is to gain and attract more customers by affecting their purchase intention. The relationship between the retailer’s image and the consumer’s willingness to purchase from that retailer is greatly intertwined relationship. For instance, if the customer has a positive perception of the image of the online store, it will have a favourable effect on the consumer’s online purchase intention [91].

H8: 

The company image of an online shopping platform has a positive effect on e-commerce platform preference development.

2.8. Site Design

An online store’s success is very often determined by the quality of the website’s design [92]. The relationship between website design and e-commerce performance has been thoroughly investigated. Website design reflects users’ preferences for the website’s user interfaces, design, and navigation [93]. Moreover, creating useful data for online content requires a full understanding of several disciplines, such as customer habits, particularly those users who are lead users, those who may provide valuable input into the evolution of the customer interaction [94], as well as information and data warehouse [95]. For example, Cho and Park [96] found that e-commerce customer satisfaction was influenced by the website design. Additionally, how content is arranged on the website is highly indicative of how well-designed the website is [92]. When purchasing online, customers prefer to interact through a technology interface, rather than with employees. Thus, website design aspects are useful influencing factors of online retailers’ ability to guarantee customer quality, satisfaction, and loyalty in the long term [97].

Similarly, efficiency and ease of order fulfilment are also crucial parts of the website’s service quality; ease of use and a well-organized design of the different webpages contribute to meeting customers’ needs for quick service, providing an excellent interaction experience [98]. An e-commerce platform must also use seamless integrated web designs across multiple connection platforms—such as desktops, smartphones, and tablets—where elements such as colours, size of fonts, pictures, and other design elements should be similar across all media to create a reconciled style in e-commerce for branding competitiveness [99]. In the case of Amazon China, they used to focus more on primary functions such as displaying the products and its reviews with less colours, which looked simplistic [100]. Chinese consumers, however, want to know the current trends among their friends [75] and other influencers [75] so they can browse using the vogue button in Taobao. Amazon could have enriched its online platform similarly to make it more attractive for Chinese customers [101].

Compared with the Chinese mainstream online shopping platforms like Taobao and JD.com, Amazon China’s website design was not customized for the tastes of Chinese online shoppers. In fact, the Amazon China platform seemed very similar to the Amazon US platform, with no features catering to the local culture and customs that is necessary to attract Chinese online shoppers [100].

H9: 

The website design of an online shopping platform has a positive effect on e-commerce platform preference development.

2.9. Order Fulfilment and Delivery

Online customers assign a lot of importance to how easily they can find products when shopping online. Features such as a live platform comprising short videos provide online shoppers with more accurate information about their purchases. Furthermore, providing video content to online shoppers enables e-commerce platforms to convert browsing customers into buying customers [102,103]. Similarly, ease of payment when making purchases contributes to convenience, which online shoppers often name as the reason they shop mostly online. After-sales service is another significant component that adds to whether the company’s order fulfilment is adequate and convenient [104]. Offering after-sales service to customers shows the company’s commitment to customers and represents an aspect of its quality offerings to customers [105]. However, many consumers think that after-sales service can hardly be ensured in the depersonalized world of e-commerce, which may affect whether they prefer to purchase online [106]. Order fulfilment considerations such as shipping costs need to be considered, as the shipping cost can increase the total cost of the final products, which, in turn, can affect customer site commitment [107,108]. Therefore, order fulfilment comprising how customers find, evaluate, and pay for the products and the ease of delivery and after-sales support may contribute to customers developing a preference for a certain e-commerce website. Fulfilment was also found to be one of the key ingredients in e-service quality adoption considerations in the e-tail industry [109].

H10: 

The order fulfilment and delivery of an online shopping platform has a positive effect on e-commerce platform preference development.

2.10. Summary of the Hypotheses

Based on our review of the literature above, we propose to test the following hypotheses in the research model below. We aim to determine if the seven factors of price, product variety, website design, company image, order fulfilment and delivery, trust in online recommendation, and website awareness lead to the development of EPP for an e-commerce website. We also propose that EPP could predict online purchase intention (PI) as well as site commitment (SC) to a certain online shopping platform. Moreover, we propose that online PI could predict the development of site commitment, which refers to a customer buying mostly from one online store or developing customer loyalty.

Table 1. Research Variables—Definitions.

Variable	Definition
Price	Price is the amount of money required for tangible or intangible transactions, and it is also part of the marketing mix to attract and gain more customers.
Variety	An improved ability to compare a mix of choices to a wide range of products, and eventually the possibility to make a better purchase decision to select a product for purchase.
Purchase intention	The likelihood of a user actually buying a product or a service.
Site awareness	Similar to brand awareness, site awareness means the extent to which potential customers recognize a website.
Site commitment	Creating a lasting desire in future and current online buyer to maintain a valuable relationship with an online seller or to prevent the tendency of that potential online buyer to change or move to another online retailer, thus disrupting long-term engagement of loyalty and repeat sales with customers for the online portal store.
Trust in site recommendation	The willingness of a consumer to trust the product recommendations of shoppers.
Company image	How consumers perceive company image is related to branding, public relationship work, journalism, staff, and consumers’ advocacy group. To establish a company image that meets public expectations is crucial for market competitiveness of enterprises through spending a large amount of money on advertising and marketing.
Site design	It is a process about platform development for generating a website through various tools and applications in order to achieve a satisfying look that focuses on figurative elements. Furthermore, the website designer has to consider more on their stakeholders, the target of the platform, and attractive appeal of the design.
Order fulfilment and delivery	How buyers process their online orders, viewing, selecting, comparing, and feeling through the different types of product through live demos, available information, then paying for it, and ultimately getting delivery of their purchases.

presents all nine variables in the research model.

Hypothesis summary:

H1: 

The price of products sold on an online shopping platform has a positive effect on e-commerce platform preference development.

H2: 

The product variety offering of an online shopping platform has a positive effect on e-commerce platform preference development.

H3: 

The site awareness of an online shopping platform has a positive effect on e-commerce platform preference development.

H4: 

Site commitment to an online shopping platform has a positive effect on e-commerce platform preference development.

H5: 

E-commerce platform preference for an online shopping platform has a positive effect on intention to purchase from that platform.

H6: 

Intention to purchase from an online shopping platform has a positive effect on site commitment or customer loyalty development.

H7: 

Trust in online recommendations of an online shopping platform has a positive effect on e-commerce platform preference development.

H8: 

The company image of an online shopping platform has a positive effect on e-commerce platform preference development.

H9: 

The website design of an online shopping platform has a positive effect on e-commerce platform preference development.

H10: 

The order fulfilment and delivery of an online shopping platform has a positive effect on e-commerce platform preference development.

3. Methodology

This study uses several scales that were designed, tested, and validated in previous studies to construct an online survey that explored online shoppers’ perceptions regarding EPP, as well as its relationship to developing intention to purchase from an e-commerce platform and to generating continued site commitment for repeat business and continued patronage. Three new constructs were designed to determine the outcome of focus group feedback on factors such as order fulfilment and order processing in an online shopping environment. The new constructs were tested to ascertain how these variables contribute to the development of online shoppers’ preference for buying from an online platform, a factor which we named e-commerce platform preference or EPP. Moreover, the relationship between EPP and PI as well as the relationship between PI and SC were tested using structural equation modelling.

3.1. Qualitative Analysis—Focus Group

A group of twenty Chinese students from an undergraduate American school of business in China were selected as a focus group to share their preferences and experiences when shopping online. Students were informed of the purpose of the focus group meeting and were encouraged to share their experiences and preferences (or lack thereof) when they shopped on the Amazon China website, compared with other Chinese online shopping platforms. Their feedback mainly indicated that even though they fancied shopping on Amazon China because of the implication that this company sells foreign and “authentic” brands, they felt that essential matters that promote site commitment and customer loyalty were not addressed on the platform, such as a wide array of products to choose from. They found the website design to not be customized according to Chinese design styles and colours, which affected transaction completion. Furthermore, there were more critical issues, such as free delivery—as provided by its competitors for most purchases—not being offered based on minimum purchases as Amazon China did.

Focus group participants also reported that Amazon China did not initially offer a mobile app to easily complete purchases or use debit and credit cards for transactions, whereas Taobao is a pioneer in the mobile payment app area and caused the increased dependence of Chinese society on mobile payment. Moreover, issues such as after-sales service or the resolution of disputes were not easily handled by Amazon China, unlike the time-based complaint resolution process on Taobao. Live product demo videos that could showpiece products’ key features and benefits to give the buyer the guarantee and accuracy of their purchases were not incorporated for many products. This is also in contrast with Taobao, who increasingly use short video product demonstrations that enhance customers’ exactitude of their purchases. Most participants in the focus group were also concerned about the image of the company “Amazon China” as a foreign entity that was competing with a valued local Chinese vanguard brand with excellent company image association. We incorporated the focus group’s findings into an online survey to obtain a complete picture of the Chinese online consumers’ expectations from online shopping platforms. The scale items in the fulfilment and delivery process construct, company image, and website design were mainly adopted from this focus group’s feedback.

Some of the focus group statements described their opinion on factors they considered to be important when shopping online during a lengthy class engagement. The following factors were shown to have influenced their online purchase behaviour and customer loyalty: “I buy products online because I am loyal to the company’s culture and orientation” and “I feel proud if it is a Chinese company”. Another statement was “I buy products from Taobao because the company Alibaba is engaged in a lot of social responsibility and environmental programs that help Chinese society”. Another participant stated that “good customer service and a good company reputation are important factors that influence my online purchase decision making process”. One student clarified this statement, further adding: “JD.com is a famous Chinese brand but had a bad reputation due to its CEO [allegedly] misbehaving towards a female, which led me not to buy from the JD platform”. It can therefore be said that it is not an issue of pure nationalism, but a perceived overall company image or corporate social responsibility. For example, as a company image related response, they chose a certain online shopping platform “because it resonated with them due to its high social responsibility” and because of “good value and service as well as a good company reputation”.

Considering order processing and delivery, participants stated that “comparing the website designs of Amazon China and of Taobao, the latter seems more appealing to me and the orientation of the website is more logical”. These statements were also made: “It is easier to complete the transaction”, “I couldn’t understand the logic of Amazon China website, as I was used to Taobao”, “payments were not mobile app payments”, and “Amazon China used debit and credit cards, not very efficient, as we no longer carry cards”. These comments indicate that the design, logical flow of the website, and ease of paying for purchases are important measurements when shopping online. In terms of customer service, these statements were provided: “It is very difficult to get the company to resolve issues after the sale on Amazon China” and “returning products was not seamless and shipping back was also cumbersome”. Regarding product evaluations, the following statement was provided: “I could not use the customized feature of live product demos on Amazon China, so it’s difficult to evaluate products for quick purchase”. The focus group described payment choices, after-sales services, and product demonstrations as important components of the online shopping experience that influence consumers’ choices and online purchase behaviour.

3.2. Survey Questionnaire Design

To collect research data for this study, a survey was designed using constructs from previous research as well as the focus group findings. We designed nine latent variables which are measured by 31 observed variables. For instance, price is measured by three questions: “Buying products on Amazon China may be more expensive than on another online platform”. All of the variables considered were measured on a 6-point Likert scale (1 = strongly disagree, to 6 = strongly agree). The survey was then distributed to students from an American university in China as well as their friends and family members via WeChat, a popular social media app in China, to reach more diverse demographics.

All of the following scales are detailed in Appendix A. Price measure scale items were adopted from the work of [36]; product variety scale items were adopted from Clemes et al. [110]; site awareness and site commitment items were both adopted from Park and Kim Park and Kim [30] study; intention to purchase items were adapted from Pavlou and Gefen [111]; and trust in product recommendation scale items were adopted from the scale of [80], which was originally adapted from Gefen and Devine [36]. The company image scale (four observed variables), order process and delivery scale (four observed variables) and website design scale (three observed variables) items were all derived from the focus group. For instance, “I chose Amazon China because it has high company social responsibility” is one of the questions of company image scale.

3.3. Response Rate and Sample Size Determination

Using an a priori sample size determination with an online calculator [112,113,114], it was determined that the minimum sample size needed to detect effect was 226. The minimum sample size for model structure was 144, after calculating the anticipated effect size at 0.3, desired statistical power level at 0.8, a probability level of 0.01, and factoring in nine latent variables and 31 observed variables. The collected sample size of 691 was justified for the structural equation modelling analysis in this study. After eight weeks of email follow up with the target participants, 691 final usable samples were collected with the desired demographic distribution from almost every province in China with a response rate of 76.8%.

3.4. Data Collection Process

Before the survey was distributed, it was emailed to 50 participants as a pre-test survey. The initial data collection goal was to find participants from different regions that represent Chinese consumers’ behaviours when purchasing from an online platform. The questionnaire was distributed to students on a university campus via WeChat. In addition, to create snowball sampling for survey participation, we also asked them to send the survey link to their friends and family members to further ensure good representation. After eight weeks, 691 usable responses were received to the questionnaires from different age groups, education, income, gender, and different provinces in China.

3.5. Demographic Data

The essential demographic survey information is presented in

Table 2. Socio-demographic breakdown of online shoppers in this study.

	Frequencies	Valid %
Gender
Male	285	41
Female	406	59
Age
18–24	391	57
25–31	116	17
32–38	70	10
39–55	112	16
Above 55	2	0
Average monthly salary
¥0–¥2999	292	42
¥3000–¥4499	102	15
¥4500–¥5999	97	14
¥6000–¥7999	75	11
¥8000–¥9999	42	6
¥10,000–¥14,999	46	7
¥15,000–¥19,999	12	2
More than ¥20,000	25	4
Education level
None	7	1
Middle school	13	2
High school	68	10
Technical school	11	2
College	118	17
Bachelor’s degree	416	60
Master’s degree	46	7
PhD degree	12	2
Online Shopping Frequency
More than one time a day	46	7
Daily	25	4
2–3 times a week	220	32
Weekly	187	27
Monthly	157	23
Rarely	56	8
Geographic residence
Metropolis	183	26
Large city	280	41
City	193	28
Town	21	3
Rural	14	2

. The questionnaire instrument collected data from individual respondents with high online shopping experience. Of the participants, 41% were male and 59% female, and almost 95% of the respondents were from urban areas. Of these, 26% of participants were from a metropolis, 41% were from large cities, 28% were from small cities, and 5% were from a small towns and rural areas. We can therefore confidently claim that the survey is geographically representative of the Chinese population. Further, the participants of the three age groups 25–31, 32–38, and 39–55 were evenly distributed with 10–17% in each age group and together representing 43% of the total study population. The millennial generation (age group 18–24) comprised the remaining 57% of the population. Therefore, the age ranges between 18 and 55 comprise more than 99% of the survey responses and covers the majority of Chinese internet users.

Additionally, for more than half of the respondents, their monthly income is less than CNY 3000 which is consistent with the fact that almost all participants are students. Furthermore, the proportion of respondents gradually decreased to 7% as the monthly income increase to CNY 15,000, and still, 6% of participants had a monthly income higher than CNY 15,000. Approximately 92% of the participants shop online at least once a month, 27% shop online once a week, and nearly 7% shop more than once a day. This data indicates that our participants have enough knowledge of shopping online.

4. Results

To test the direct and indirect relationships between the constructs, this study employed structural equation modelling [90,115]. This analysis method enabled the study of the direct relationship among the study’s constructs (latent variables) while simultaneously measuring the effects of the observed variables on those latent constructs. AMOS v24 software was used to allow multiple estimation methods and selected a maximum likelihood as the choice of estimation method for this study. Additionally, we verified the issue of multicollinearity, when one or more of the independent variables are highly intercorrelated [116]. Thus, this would affect the estimates of the regression coefficients to yield statistically significant results [117]. More importantly, multicollinearity leads to distortion in the predictive ability of the independent variables on the dependent variables [118]. In this study, the Tolerance and Variance Inflation Factor (VIF) were assessed to determine the existence of significant multicollinearity issues in the independent variables. According to [117] multicollinearity would be a concern if the VIF value is higher than 5 and tolerance value is <0.20; notably though, other studies also cite a VIF value of 10 and above.

Table 3. Multicollinearity test.

Independent Variables	Tolerance	VIF
Product Price	0.93	1.07
Product Variety	0.42	2.37
Site Design	0.25	4.04
Company Image	0.20	5.00
Order Fulfilment & Delivery	0.23	5.00
Site Recommendation Trust	0.30	3.39
Site Awareness	0.38	2.62

VIF: Variance Inflation Factor.

indicates that multicollinearity for the most variables is within the acceptable limits of VIF values <5 and tolerance values exceeded 0.20, as suggested by [116], even though two variables approach close to the limit. Thus, significant multicollinearity issues are not found in the present study data. Furthermore, we examined the possibility of a Common Method Bias (CMB) in the research variables. To test that we followed the Harman single-factor test in factor analysis using SPSS v25 with nine factors were extracted using the Principal Axis Factoring, and the first factor Total Variance Explained showed 50.8% of variance, which is slightly more than the 50% marker. Hence, taking into consideration the somewhat presence of a common method variance, we acknowledge that this is one of the limitations of our study.

4.1. Confirmatory Factor Analysis

A nine-factor measurement model with reflective scales was used to estimate the model with a confirmatory factor analysis (CFA) to determine if the observed variables and their latent factor structures fit the hypothesized model. In particular, this study employed CFA for the next step analysis of using causal models in SEM [106].

Table 4. First Order Model Fit Indices for Confirmatory Factor Analysis.

Model	χ2	df	χ2/df	CFI	TLI	RMSEA	SRMR
First order	1516.078	364	4.165	0.940	0.929	0.068	0.040

, the CFA first order model fit indices showed the following results in: χ² = 1516.078, DF = 364, p = 0.001 and the Chi-squared goodness of fit is significant, which indicates poor fit to the data; however, due to the large sample size (n = 691) it is not expected to fit. Table 4 shows the other fit indices with an acceptable or excellent level of fit: CFI = 0.940, TLI = 0.929, SRMR = 0.040, RMSEA = 0.068 [119]. We evaluated two CFA models. The first-order CFA consisted of nine latent factors that showed excellent convergent and discriminant validity measures.

Table 5. Factor Loadings and Reliability Assessment.

Constructs/Indicators	Factor Loadings	CR	AVE	CA	Mean	SD
Product Price	-	0.900	0.749	0.833	3.429	[0.94]
Price1	0.865
Price2	0.855
Price3	0.877
Product variety	-	0.878	0.706	0.787	3.178	[0.87]
Variety1	0.852
Variety2	0.848
Variety3	0.822
Site Design	-	0.940	0.887	0.871	3.624	[1.02]
Design1	0.942
Design2	0.942
Design3	0.941
Company image	-	0.925	0.754	0.888	3.807	[1.13]
Image1	0.910
Image2	0.908
Image3	0.840
Image4	0.812
Order Fulfilment	-	0.942	0.765	0.922	3.843	[1.10]
Process2	0.905
Process1	0.895
Process4	0.875
Process3	0.875
Process5	0.820
Site Recommendation Trust	-	0.962	0.894	0.941	3.837	[1.09]
Trust1	0.947
Trust2	0.947
Trust3	0.942
Intention to purchase	-	0.957	0.881	0.933	3.816	[1.07]
Intention2	0.945
Intention3	0.936
Intention1	0.934
Site commitment	-	0.936	0.785	0.906	4.005	[1.2]
Commitment2	0.904
Commitment3	0.898
Commitment4	0.885
Commitment1	0.856
Site awareness	-	0.903	0.756	0.834	3.651	[1.10]
Awareness2	0.895
Awareness3	0.979
Awareness1	0.836

Note: AVE: Average variance extracted; CR: Composite Reliability; CA: Cronbach’s Alpha.

lists the variables that are correlated under each latent factor and explain only their unique latent variable and discriminant validity, which measures if the variables correlate with other factors other than their latent variable. The average variance extracted is a measure of convergent validity for all nine constructs, ranging between 0.706 and 0.894. These values exceed 0.5 and are indicative of the high reliability of convergent validity among the latent factors. Further, composite or construct reliability [120] measures the internal consistency within a scale items ranged between 0.878 and 0.962, which exceeds 0.7 and is indicative of good inter-item reliability for the model scales [117,121]. The second-order CFA model consisted of three first order and seven first order representative of EPP indicators. Similarly, the second-order fit indices in

Table 6. Second Order Model Fit Indices for Confirmatory Factor Analysis.

Model	χ2	df	χ2/df	CFI	TLI	RMSEA	SRMR
Second order	1936.105	390	4.964	0.920	0.911	0.076	0.046

show acceptable or excellent levels of fit: CFI = 0.920, TLI = 0.911, SRMR = 0.046, RMSEA = 0.076 [120]. Figure 1 presents the second-order factor inter-correlations for the ten factors, with statistical significance set at p < 0.001.

The EPP (E-commerce Platform Preference) is based on a second-order factor confirmatory factor analysis (CFA) where the EPP is an endogenous variable and not directly observed but emergent as a latent variable and inferred from the measured first-order variables. The EPP second-order factor CFA was used based on theoretical model assumptions. We found that the second-order factor models allowed the advantage of a more parsimonious model that higher-order factors underlie the data. Moreover, given the researchers’ investigation of various factors found in prior literature (product price, variety of products, site design, company image, order fulfilment, site trust, and site awareness) as the underlying dimensions that may influence E-commerce Engagement, there exists the need for empirical testing for a second-order factor which is both theoretically and statistically acceptable and [116,122]. The researchers theorized that, individually first-order measured indicators alone cannot explain the phenomena under investigation in the study to explain the influencing factors of e-commerce engagement. Therefore, the need for the inclusion of a second-order factor which can be used to test the assumption that the correlations among the set of the first-order factors is accounted for one or more higher order factors to further adequately explain a priori theoretical model [123].

4.2. Structural Model

The structural equation model was tested using AMOS 24 with latent factors. We used SEM to analyse both the structural model between the latent variables and the measurement relationships between the latent variables and their observable indicators. We used CFA to evaluate the validity of the manifest indicators associated with the underlying latent factors. Next, we used a multivariate analysis of the structural relationships among the latent variables to draw conclusion [124]. Table 4 shows the model fit used to assess the structural model to the absolute, comparative, and parsimonious fit indices to test if the research data fits the proposed model. To test the absolute fit model, the X² test for the model is not significant (28) 66.44, p < 0.001, even though CHI2/DF 2.37 is less than five. The comparative fit model GFI of 0.984 is greater than the cut-off of 0.90, AGFI 0.969, CFI 0.981, compared to the cut-off of 0.95, SRMR 0.028, and RMSEA 0.041. This shows that the overall model fits the results of the research data.

Table 7. Hypothesis Test Results.

			β	t-Values	p
EPP		PI	0.890	25.696	***
EPP		SC	0.849	14.175	***
EPP		Awareness	0.821	28.247	***
EPP		Trust	0.863	28.247	***
EPP		Fulfilment	0.937	32.477	***
EPP		Image	0.910	30.876	***
EPP		Design	0.867	28.469	***
EPP		Variety	0.744	22.677	***
PI		SC	0.099	1.8970	0.058
Intention		Intention1	0.905	38.466	***
Intention		Intention2	0.916	38.466	***
Intention		Intention3	0.901	36.960	***
Commitment		Commitment4	0.822	28.130	***
Commitment		Commitment3	0.870	28.131	***
Commitment		Commitment2	0.885	28.868	***
Commitment		Commitment1	0.794	24.464	***

Note: *** p < 0.001. β = Standardized path coefficients, t-values = critical ratios; EPP = E-commerce Platform Preference, PI = Purchase Intention, SC = Site Commitment.

shows the resulting values of the hypothesis testing. This is also indicated in a figure form in Appendix B.

Table 8. Hypothesis relationship with the structural model fit results.

Hypothesis	Support
H1. The price of products sold on AC is positively associated with e-commerce platform preference of AC	Not Supported
H2. The variety of products sold on AC is positively associated with e-commerce platform preference of AC	Supported
H3. The design of the AC website is positively associated with e-commerce platform preference of AC	Supported
H4. The image of AC is positively associated with e-commerce platform preference of AC	Supported
H5. The ease of the fulfilment process on AC is positively associated with e-commerce platform preference of AC	Supported
H6. The trust of AC is positively associated with e-commerce platform preference of AC	Supported
H7. The awareness of AC is positively associated with e-commerce platform preference of AC	Supported
H8. E-commerce preference is positively associated with AC site commitment	Supported
H9. E-commerce preference is positively associated with AC intention to purchase	Supported
H10. Intention to purchase on AC is positively associated with AC site commitment	Not Supported

shows the relationships among the study constructs and variables. The results of the structural equation model analysis show that eight of the ten hypotheses are significantly supported by the final model. E-commerce preference of a platform is influenced by the variety of the products sold on that platform; thus, H2 is supported (β = 0.744, t-value = 22.677, p < 0.001). The online shopping website’s aesthetic and adaptive cultural design was also validated, supporting H3 (β = 0.867, t-value = 28.469, p < 0.001). Trust of the recommendation system of online e-commerce platform was also validated, supporting H6 (β = 0.863, t-value = 28.247, p < 0.001), as was awareness of the online shopping platform, and hence, H7 is accepted (β = 0.821, t-value = 28.247, p < 0.001), while order fulfilment and delivery processes and company image significantly affect more than four factors of developing EPP (β = 0.937, t-value = 32.477, p < 0.001 and β = 0.910, t-value = 30.876, p < 0.001) respectively, thus supporting both H5 and H4. However, the price construct was not statistically significant to contribute to developing EPP (β = 0.099, t-value = 1.897, p = 0.058) because the t-value falls below z < 1.96; thus, H1—stating that price contributes to EPP, especially when it comes to the Chinese online shoppers’ preference or lack thereof—is not accepted. Table 8 summarizes the study hypotheses and its outcomes, and Figure 2 shows the SEM, the path coefficients, and the associated p-values.

5. Discussion

This study makes both theoretical and practical contributions to the topic of the factors that most impact online commerce platform preference sustainability from the customer acceptance perspective.

The purpose of this study was to discover through empirical study the factors that affect consumers’ online shopping platform preferences. This is investigated within the context of the maturity and vastness of the Chinese online marketplace. This study contributes to the literature in how the consumers’ online shopping platform preferences develop, in addition to understanding whether these EPP (E-commerce Platform Preference) attitudes led to purchase intentions on an e-commerce platform, and if their EPP had the strong influence necessary to achieve site commitment for continued customer loyalty necessary for sustained economic activity, which is imperative for an e-commerce site’s future business operations and existence, and more importantly, to the sustainable commercial activity of the countless small and medium sized firms that use the platform to connect with consumers. In contrast, previous studies in this field [125] studied the internal organizational incentives such as e-commerce adoption influenced by top management support, HR IT competence, and financial resources available that create e-business value that leads to e-commerce adoption. Another study, Wang and Ahmed [126], concluded from a macro view that e-commerce adoption is incentivized more from an external pressure, perceived benefits, as well as organizational readiness by itself. Moreover, Thaw, et al. [127] studied the factors that influence e-commerce adoption from the perspective of trust in perceived privacy, security, and the trustworthiness of the vendors. Further, Abubakar, Ilkan and Sahin [88] study looked into the effects that e-referral, eWOM (word of mouth), and gender have on brand image and its related positive impact on purchase intentions. While there is considerable research on e-commerce adoption, to the best of the authors’ knowledge, no research has conceptualized and studied e-commerce platform preference motivations in association with purchase intention on the site and finally site commitment for continuous and sustainable patronage. This study adds more to our understanding on how e-commerce platform preference attitudes develop and its close positive associations with purchase intention and site commitment in one single parsimonious model. The findings in this study contribute to the understanding of what factors and characteristics impact consumer acceptance of the e-commerce experience determined by the platform in a sustainable site commitment.

Practical Contribution

These findings contribute to close a gap in the existing literature by establishing that online shopping site (platform) preference is strongly and positively determined by six of the seven essential characteristics investigated: (a) the variety of diverse items on sale, in line with previous research [41,42]; (b) the online shopping portal’s site design in terms of layout, colour, font, and design, similar to previous findings [30,92,93]; (c) developing trust in the online recommendation system of the e-commerce portal similarly in line with previous studies [80,81,82]; (d) ease of order fulfilment and low-cost delivery processes in agreement with previous studies [107,108]; (e) the company’s image or brand image in terms of its reputation within society [89,90,91]; and finally (f) high awareness of the online shopping platform through the media, word of mouth, or being generally well known by most people [51,52,56,58]. These findings are also based upon statements made during the focus group investigation: “…I didn’t even know there was an Amazon in China…” or “we have Taobao for all our online needs, why venture into another company?”

Specifically, this study found that EPP emergent factor also has a significant impact on consumers’ intention to purchase from an online shopping platform, in this case Amazon China, as well as site commitment for repeat purchases and customer loyalty to the same online shopping website. Meanwhile, site commitment and online customer loyalty are not affected by the intention to purchase alone. Customers may have the intention to purchase from an online store, but that intention alone does not create a loyal customer. These results show that the competitiveness and development of customer retention of an e-commerce platform from the online shoppers’ perspective depends on those six essential factors that generate EPP attitude (variety of products, website design, company image, order fulfilment and delivery process, trust in the recommendation system, and raising awareness of the e-commerce platform). Interestingly, the price factor in our original theoretical model was found to have no such significant influence on EPP development. This finding was surprising and contradictory to what Zhao and Jin [40] reported, that Amazon China’s main challenges were related to the untenable price competition with other online platforms such as Taobao and JD.com. In fact, it was expected that, as Amazon China was mostly offering more foreign expensive products than the local e-commerce sites, that the price element would be a strong determinant factor for EPP. According to Kim, et al. [35], the price of products could have a direct influence as a means of comparison among different competitors; however, in this case, price was not a major contributor in developing EPP behaviour that leads to PI (purchase intention) or SC (site commitment).

The effects of different variables on e-commerce preference, including product variety, site design, company image, order fulfilment and delivery process, trust in the recommendation system, and the awareness of the e-commerce platform were investigated and found to significantly impact EPP behaviour development. Among those factors, order fulfilment and delivery process (β = 0.937, t = 32.477, p < 0.001) was the most influential in EPP behaviour development, compared with the other factors [38]. Company image was the second most influential factor (β = 0.910, t = 30.876, p < 0.001). The other four variables were also shown to have a great influence on EPP, with β-values ranging between 0.7 to 0.9, significant t-values between 22 to 33, and p-values under 0.001. Remarkably, price was not found to be an influential factor for Chinese consumers’ preference in terms of their interaction with the Amazon China online platform (β = 0.099, t-value = 1.897, p-value = 0.058). Prior research indicates that Chinese consumers’ price sensitiveness might be due to the average living standards in the country [38]. These contradictory findings could be explained by changes in China since the previous research was completed, specifically that China lately witnessed increasing living standards and income levels.

From the examination of the relationship between EPP and intention to purchase products from an online shopping platform, it was found that intention to purchase is strongly affected by EPP (β = 0.890, t = 25.696, p < 0.001). Further, it was found that site commitment is also strongly influenced by EPP (β = 0.849, t = 14.175, p < 0.001). This shows that EPP can predict both the intention to purchase products from an online shopping platform and site commitment for sustained customer loyalty.

In terms of the relationship between intention to purchase products and site commitment, it was found that site commitment is not influenced by the behaviour of developing intention to purchase products from an online shopping platform (β = 0.099, t = 1.897, p = 0.058). This result is quite interesting, as it can be said that if one has a PI to an online shopping website, one could also develop the habit to repeatedly buy from the site to become committed as a repeat customer; however, that is not necessarily the case, as the Chinese e-commerce market is very competitive and diverse with many e-commerce sites in existence. Thus, the case of Amazon China abandoning this market indicates that the various reasons that affected the Chinese consumers’ lack of purchase intention and site commitment to its online platform can be explained by a failure to address related important factors, such as order fulfilment and delivery process, company image, variety of products offered, site design of the online shopping platform, trust of its recommendation system, and finally, a lack of awareness of the shopping platform itself. All of these factors are highly related to developing strong EPP behaviour and later strong purchase intentions and sustained site commitment in the form of customer loyalty.

6. Conclusions

This paper has attempted to make an advance in the understanding of the concept of how an e-commerce platform can be sustainable—specifically in the Chinese e-commerce market context. The emerging theme from this study is that the e-commerce platform company’s focus should be on improving their EPP (E-commerce Platform Preference) factors such as order fulfilment and delivery process, creating a more favourable company image, offering a large variety of products, tailoring the website design to local cultural tastes and habits, building a trustworthy online recommendation system, and increasing awareness of the e-commerce website. Furthermore, this study highlights two important insights. First, developing PI (Purchase Intention) behaviour alone does not lead to online shopping site commitment or customer loyalty and repeat purchasing. Second, online site commitment is generated by EPP factors; among these factors, order fulfilment and delivery process and the company image of the online shopping platform contribute the most, indicating that the prioritization of these six factors should be a starting point when practitioners design online shopping platforms.

This study is not without limitations. Although the data used in this study are based on the consumers’ perspective, it would be more informative if combined with direct company information, even though we acknowledge that publicly available data on Amazon China—such as its annual report (2018), which only reports a consolidated financial statement for the whole company—are limited. Additionally, the Chinese government regulates and protects its burgeoning local e-commerce market heavily through licensing requirements and other measures that may significantly limit foreign investments; thus, the extent of government intervention was not investigated in this study. Another limitation in the study is the presence of Common Method Bias (CMB) of 53% in the survey data, slightly above the oft cited threshold of 50% using Harman’s Single-Factor Test. Future research should determine how government interferences that protect the local Chinese e-commerce platforms may tip the scales against foreign ventures. Another important angle to study especially in the Chinese e-commerce market is the effect of company image (corporate image) on EPP factors and its eventual site commitment aspect.