Understanding Chinese consumer engagement in social commerce: The roles of social support and swift guanxi

Jiabao Lin (College of Economics and Management, South China Agricultural University, Guangzhou, China)
Lei Li (College of Economics and Management, South China Agricultural University, Guangzhou, China)
Yanmei Yan (College of Economics and Management, South China Agricultural University, Guangzhou, China)
Ofir Turel (Mihaylo College of Business and Economics, California State University, Fullerton, Fullerton, California, USA)

Internet Research

ISSN: 1066-2243

Publication date: 6 February 2018

Abstract

Purpose

Building on the Chinese guanxi perspective, the purpose of this paper is to develop a theoretical model that explains the indirect effects of social support from friends on social commerce intentions, as mediated through the relational aspects that potential buyers develop with sellers.

Design/methodology/approach

Hypotheses are tested with partial least squares (PLS)-graph applied to data collected via a survey of social media users (n=511). SPSS and PLS-graph are the statistical analysis tools used in this study.

Findings

Relationship exists in social commerce interactions and its quality can be captured by swift guanxi and trust. These swift relationships matter as they drive users’ behavioral intentions on social commerce sites. The informational and social support people receive from friends helps in improving the relationship quality and can indirectly influence user behaviors on these sites.

Research limitations/implications

This study has relied on a convenient sampling and this may limit the generalizability of the findings. Future research should employ broader and more random sampling techniques to re-validate and extend the findings.

Originality/value

The interpersonal aspect of relationship quality has received little attention in the social commerce literature. This study develops a theoretical model that explains consumers’ intention in social commerce. The findings reveal the mechanisms through which different types of social support indirectly influence social commerce intentions. They provide a unique glimpse into consumer behavior in Chinese settings, in which the guanxi aspect of relationship quality highly matters.

Keywords

Citation

Lin, J., Li, L., Yan, Y. and Turel, O. (2018), "Understanding Chinese consumer engagement in social commerce", Internet Research, Vol. 28 No. 1, pp. 2-22. https://doi.org/10.1108/IntR-11-2016-0349

Download as .RIS

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


1. Introduction

The rising popularity of social media has led to the emergence of a new type of e-commerce, known as social commerce, which involves commercial activities performed via social media platforms such as Facebook, Twitter, and WeChat (Zhou et al., 2013; Chen et al., 2016). Social commerce adds support from a large number of online peers to the shopping process (Chen and Shen, 2015). Since social commerce is a combination of social media and e-commerce, it is different from traditional e-commerce (Huang and Benyoucef, 2013). Specifically, it adds social interactions to the traditionally existent commerce features (Zhang et al., 2014; Busalim and Hussin, 2016). This has changed the role of customers from recipients of information to content producers (Curty and Zhang, 2013), and allowed customers to gain insights and support via social interaction with others (Hajli and Sims, 2015). Hence, while purchase decisions may be considered in a similar fashion on e-commerce and social commerce platforms, the social interactions on social commerce sites add another important source of information for consumers, which in turn can help them making purchase decisions. One relatively unexplored aspect of these social interactions is the possible formation of informal relationships between buyers and other members of the social commerce sites and the effects of these relationships on the subsequent purchase decisions.

Relationship is known to have significant effects on human behavior (Morgan and Hunt, 1994; Jiang et al., 2016). It is particularly important in social commerce because it drives repeat purchases and interactions (Liang et al., 2011; Zhang et al., 2016; Wu et al., 2017). A recent survey has found that nearly 83 percent of respondents tend to share shopping information with their online friends, and almost 67 percent of the respondents would make their purchase decision based on the recommendations from online friends (Marsden, 2016). Because shopping information received from friends is viewed as more valuable and credible than company-produced information, it may influence online purchasing and play a key role in social commerce (Hu et al., 2016). In contrast, word of mouth in traditional e-commerce often depends on people one does not know and have no formal or informal relationship with. As such, the nature of relationship in social commerce can be an important contributor to purchase decisions. Nevertheless, little is known about the interpersonal aspect of relationship quality and this can too promote customer engagement in social commerce.

Given the presumed importance of relationship facets in social commerce, it is also interesting to consider what can predict them. One key benefit of social commerce is the social support it provides to its users (Mo and Coulson, 2008; Oh and Syn, 2015). This support can serve as a basis for forming relationship with sellers as it motivates trust, collaboration, and reciprocity (Liang et al., 2011; Chen and Shen, 2015). We hence also suggest that and examine how various types of social support (informational and emotional) drive the relationship quality facets that we emphasize in this study.

We ultimately aim to address three research questions:

RQ1.

Does social support from website friends exert influence on building relationships with sellers?

RQ2.

What is the effect of relationship quality on social commerce intentions?

RQ3.

Does relationship quality meditate the relationship between social support and social commerce intentions?

The findings are expected to be beneficial to sellers in their endeavors to effectively advance customer relationship and thus facilitate the success of social commerce.

2. Theoretical background

2.1 Trust transfer theory

Trust transfer refers to a mechanism capturing the way one’s trust in a known person/entity can be passed on to another relatively unknown person/entity through the association between them (Stewart, 2003, 2006). Trust transfer, theoretically rooted in social comparison theory, can be understood as a cognitive process that reduces the dissonance associated with holding divergent beliefs about other parties perceived to be associated (Festinger, 1954). Trust transfer involves three parties: the trustor who makes judgments on whether to trust others, the trustee whose trustworthiness is assessed by the trustor, and the third party who is the broker in the trust belief transfer process. The underlying logic is that if the trustor has a strong belief in the trustworthiness of the third party and there is a close relationship between the trustee and the third party, the trustor’s trust in the third party may be transferred to the trustee (Stewart, 2003, 2006). In this regard, the third party and the trustee are often called as the source and the target of trust transfer, respectively. Trust also can be transferred from different sources, such as individuals or a context, using communication or cognitive processes (Stewart, 2006). In this study, the trustor is a buyer, the trustee is a seller, and the third party is the members of the social commerce site. We draw upon the trust transfer idea to explain how the relationship between a seller and a buyer is formed in the context of social commerce.

2.2 Social support

Social support theory evaluates the influence of social network characteristics on an individual’s ability to cope with life events (Maier et al., 2015). Social support represents an individual’s perceived available social resources, such as information generated by both formal support groups and informal assisting relationships; it leads people to feel that they are being cared for, loved, and esteemed, and thus obligated to fulfill mutual obligations (Gottlieb and Bergen, 2010). In an online context, joining a community offers users a sense of belongingness, and through interactions with other members, group connections can be enhanced. Social commerce consists of three types of activities involving recommendations and referrals, ratings and reviews, and forums and communities (Hajli, 2012); these platforms empower consumers to interact with other consumers to get supportive information and immense themselves into an environment of assistance (Hajli, 2014). Therefore, opinions, recommendations, advice, and knowledge as well as emotional concerns about services or products that are generated by social commerce consumers are valuable for consumers’ decision-making process (Zhou et al., 2013). We thus consider these as social values which are beneficial to consumers’ informed decisions.

Social support is a multidimensional construct and whose components could differ from context to context (Cutrona and Russell, 1990). People need emotional and informational support (which are intangible) as well as tangible support when they are under health or work stress (Chiu et al., 2015). Since interactions on the internet are virtual in nature and often involve messages, online social support that may help social media users usually is intangible in nature, including informational support and emotional support (Coulson, 2005). Informational support refers to providing information and advice, which can help another person solve problems (Liang et al., 2011). Emotional support captures the provision of emotional concerns such as caring, sympathy, and understanding to another person (Taylor et al., 2004). These two types of messages are the major support mechanisms for social interactions in social commerce. Informational support could provide solutions, plans, or interpretation. Emotional support focuses on expressing one’s concerns and hence can help solving problems indirectly.

2.3 Relationship quality

Relationship quality refers to the closeness or intensity of a given relationship (Thurau and Klee, 1997). It captures the strength of relationship between a buyer and a seller, and determines the probability of exchange between these parties in future encounters (Crosby et al., 1990). As such, it is a critical indicator of the health of the relationship between a buyer and a seller (Huang et al., 2014). Prior studies have shown that the relationship quality between consumers and companies can produce positive outcomes such as market performance, repurchase behavior, customer retention, and consumer loyalty (Palmatier et al., 2006; Zhang and Bloemer, 2008; Athanasopoulou, 2009). Information systems research has also shown that relationship quality is important in online shopping as it affects repurchase intentions (Zhang et al., 2011) and increases willingness to purchases or share shopping experiences (Hajli, 2015).

Relationship quality is usually conceptualized and operationalized as a multidimensional construct. It includes facets such as trust, commitment, and satisfaction (Athanasopoulou, 2009; Jiang et al., 2016). Different studies have also utilized a variety of construct combinations to indicate the relationship quality. For example, Lages et al. (2005) defined relationship quality as the amount of information sharing, communication quality, long-term orientation, and satisfaction with a relationship, whereas Su et al. (2016) conceptualized two distinct dimensions of relationship quality, including customer satisfaction and customer-company identification. Drawing on the research of Ou et al. (2014), we use swift guanxi and trust as two key elements of relationship quality in the context of Chinese social commerce. Trust is a buyer’s psychological expectation that a seller will not engage in opportunistic behavior based on a set of specific beliefs including the seller’s ability, benevolence, and integrity (Gefen et al., 2003; Turel and Gefen, 2013).

Trust is a vital component for building a successful relationship in online shopping environments (Turel et al., 2008; Hsu et al., 2014; Debei et al., 2015). Uncertainty in social commerce should be relatively high given the variety of user-generated information and the lack of face-to-face interactions (Hajli et al., 2017). Trust plays an important role in reducing risk and uncertainty toward sellers and increase tendencies to purchase on social commerce sites (Ha et al., 2016; Farivar et al., 2017). Trust develops over time after the first transaction and is regarded as the transactional basis of relationship development (Shaalan et al., 2013).

Swift guanxi is another facet of relationship quality. It refers to a buyer’s perception of a swiftly formed interpersonal relationship with a seller in the online marketplace (Ou et al., 2014). The main advantage of social commerce is that it has a relationship-oriented online community (Hajli, 2015). Through social media, individuals can form a strongly interpersonal relational network, which can be a basis of social commerce success (Liu et al., 2016). Considering that China has a relatively weak institutional and legal environment, swift guanxi, as an informal buyer-seller relationship characterized by mutual reciprocity, plays a vital role in predicting social commerce intentions (Ou et al., 2014). This has been demonstrated in various studies (Lisha et al., 2017). To further illustrate this idea, the descriptive study by China Internet Network Information Center showed that social media such as WeChat was be used to build swift guanxi (CNNIC, 2015). Therefore, in China’s social commerce context, swift guanxi is an important element of the relationship between a seller and a buyer, reflecting an interpersonal aspect of the relationship.

Guanxi generally refers to the relationships or social connections based on mutual interests and benefits (Lee et al., 2001). It is a significant cultural value dominating Chinese people’s behavior; it can be based on many factors including locality and dialect, fictive kinship, family, workplace, social clubs, and friendship (Wu and Chiu, 2016). Guanxi is often considered as a special type of relationship that bonds the exchange partners through reciprocal exchange of favors and mutual obligations (Luo, 1997). It is conducting key driver of business in China because having the proper guanxi can bring about many benefits (Fan, 2002). Guanxi has its own unique characteristics distinguishable from relational exchange in western societies (Shaalan et al., 2013). First, guanxi is more personal than impersonal in that it mainly works on the basis of friendship (Wu and Chiu, 2016). Guanxi is a personalized relationship based on the reciprocal exchange of personalized care and favors and its affective value is more important than its monetary value in social interactions. By contrast, relational exchange tends to have at least some economic and impersonal involvement, which leads to calculative commitment and expectation of mutuality in the relationship. Second, guanxi is focused more on a particularistic, rather than a universalistic relationship (Wang, 2007). Guanxi is highly network-specific and thus normally does not generalize to the members of outside social networks, while relational exchange has a universalistic nature in that the network is relatively open to any exchange partners as long as one plays by the rule of the game. Third, the guiding principles of relational behaviors in guanxi are morality and social norms. However, guiding principles of a relational exchange in the west are legality and rules (Lee et al., 2001).

Swift guanxi is an extension of the traditional guanxi. It captures the properties of consumers’ relationships with merchants in the online marketplace, in which relationships are not always lasting. Specifically, swift guanxi differs from traditional guanxi in four aspects including relationship duration, resources, status and the role of technology in communication (Chang et al., 2014; Ou et al., 2014). First, the swift nature of guanxi in online transactions is core dissimilarity. Traditional guanxi is built mostly for long-term cooperation, while online buyers are unlikely willing to spend a lot of time building guanxi with online sellers (Tim et al., 1999). If buyers cannot confirm the product function from one seller, they may just switch to another seller (Chou et al., 2016). Second, limited and controlled resources play an important role in building traditional guanxi (Arias, 1998), but increased availability of substitute products and services in online market lessens the level of dependence on any particular seller (Benito et al., 2015). These resources are less significant for swift guanxi, as it forms immediately and independent of resources. Third, the status of buyers and sellers in social commerce is relatively equal since many buyers and sellers interact together and do not differ in terms of resources in the online marketplace (Zhang et al., 2016), but status matters in traditional guanxi due to the fact that senior people often have more resources than junior ones (Xin and Pearce, 1996). Fourth, the face-to-face interaction is the primary mode used to build traditional guanxi, while communication technologies greatly matter in facilitating interaction to establish interpersonal relationships in the online marketplace (Liu et al., 2008; Ou et al., 2014). Therefore, swift guanxi is different from traditional guanxi as a result of the inherent nature of online transactions and the immediate relational needs of online users.

3. Research model and hypotheses

This study explores the drivers of social commerce intentions from the Chinese guanxi lens and based on the social-relational factors. Figure 1 shows the research model, which represents the effects of social support in enabling and enhancing relationship quality, and the impacts of relationship quality on social commerce intentions. In this section, we discuss the pivotal interrelationships between the variables.

3.1 Effects of social support

3.1.1 The role of informational support in relationship quality

Information support is the type of support that provides individuals with advice, guidance, or useful information to help them solve problems, generate new ideas, or make good decisions (Chen and Shen, 2015). The more frequently consumers solve problems or generate solutions with the assistance of information offered by online friends, the more positive valence toward these relationships and exchanges they develop (Liang et al., 2011). If people can consistently obtain instrumental assistance, such as valuable advice and immediate help from their online friends in a social networking website, they are more likely to feel connected to friends as well as develop stronger trust in them (Cropanzano and Mitchell, 2005; Akoorie et al., 2013) and via trust transfer in the website (Stewart, 2003, 2006). When consumers lack personal experience with a seller in social commerce, their reliable friends can convey relevant and positive information about the seller’s values, product quality, service quality, or even satisfaction of prior customers, by association (Jiang et al., 2008; Jin et al., 2009). In other words, with information support from friends, consumers are more likely to trust sellers on the same platform. Relaying on the trust transfer idea, we also contend that the buyers are also more likely to develop swift guanxi with the sellers, since these sellers are affiliated with a trustworthy website. Moreover, swift guanxi is easier to develop when support is provided because the risk of being wrong in these swift relationships can be mitigated by the informational support people receive. We hence hypothesize that:

H1a.

Informational support in a social networking website is positively related to swift guanxi with sellers in the website.

H1b.

Informational support in a social networking website is positively related to trust in sellers in the website.

3.1.2 The role of emotional support in relationship quality

Compared to informational support, emotional support often indirectly contributes to problem solving because it involves affective empathy, loving, and caring, and inspires encouragement (Madjar, 2008). It is complementary to informational support and provides a comprehensive understanding for social support. Receiving caring and warmth from friends in a social networking site can bring psychological benefits to consumers and satisfy their psychological needs (Hajli and Sims, 2015). Such benefits make consumers feel that the environment of social commerce is valuable and trustable (Shanmugam et al., 2016). Caring is a basis for trust building, as it improves the belief of benevolence (White, 2005). Therefore, when users receive strong emotional support from friends in a social networking website, they are more likely to trust their friends and keep intimate relationships with them (Aryee et al., 2002). Furthermore, on the basis of trust transfer mechanism, a consumer trust in a seller in a social networking website will be affected by information from their friends about the seller’s ability, benevolence, and integrity. In addition, emotional support can enable consumers to open up and look for help from others (Pfeil and Zaphiris, 2009). When other people care about a consumer, it signals to the consumer that the website, and its users by affiliation, has the consumer’s best interest in mind and that their intentions are altruistic. Thus sellers on a social networking website can cultivate the relationship with consumers by showing their efforts to foster members’ social relationships. Research has found such efforts as a signal that the sellers would not act opportunistically toward community members and have the ability to nurture a healthy, friendly, and trustworthy environment (Porter and Donthu, 2008). Ou et al. (2014) pointed out that interactivity enables consumers to build swift guanxi with sellers in the online marketplace; this interactivity can be reflected, in part, in the emotional support people receive. Hence, we propose:

H2a.

Emotional support in a social networking website is positively related to swift guanxi with sellers in the website.

H2b.

Emotional support in a social networking website is positively trust in sellers in the website.

3.2 Effects of relationship quality

3.2.1 The role of swift guanxi in social commerce intentions

Swift guanxi is a swiftly formed interpersonal relationship between buyers and sellers and is based on reciprocal favors from both the parties in the online marketplace (Ou et al., 2014). Its effects are rooted in social exchange theory (SET) which embraces the fundamental concepts of modern economics as a foundation for analyzing human behavior and relationships. The theory posits that when one party does something valuable for the other party, the receiving party tries to “reciprocate” with something valuable (Cropanzano and Mitchell, 2005). Guanxi comes into existence when one party does something valuable for the other, so that they have a reciprocal obligation to repay this debt (Akoorie et al., 2013). In this sense, guanxi is like a specific application in the Chinese context of the concept of “social exchange” of Blau (1964) which infers that care and affection from one party creates a moral obligation toward other party to reciprocate it with something valuable. Applying this notion at the social commerce context, when buyers have developed swift guanxi they will engage in positive social behaviors including reciprocation in the form of sharing information which they may believe that others value and transacting with others. Such transactions may even take place when a product is not perceived as ideal as the buyer has an obligation to reciprocate and buy from the seller. Hence, this paper proposes the following hypotheses:

H3a.

Swift guanxi is positively related to social shopping intention.

H3b.

Swift guanxi is positively related to social sharing intention.

3.2.2 The role of trust in social commerce intentions

Trust is a critical element in predicting consumers’ intentions to conduct business activities in an online marketplace (Shankar et al., 2002). Online trust generally results in positive behaviors such as buying, recommending, and developing social ties (Gefen and Straub, 2004), including in social media settings (Turel and Gefen, 2013). Consumers’ trust can also produce willingness to recommend products/services and merchants/brands to their online friends (Kim and Park, 2013). This happens because posting information increases one’s vulnerability to negative comments and clashes; trust makes people more comfortable with being vulnerable to others’ actions. The benevolence and integrity of sellers will smooth away consumers’ worries about opportunistic behaviors, such as deceptive advertising or inappropriate use of personal information. Indeed, previous studies have found that there is a significant relationship between trust and social commerce intention (Hajli, 2015). Based on the reciprocal nature of SET, if individuals have a strong perception of trust toward a social commerce site, they will be more inclined to seek product/service recommendations from the social commerce site and share their own consumption experience on the social commerce site. Hence, this paper proposes the following hypotheses:

H4a.

Trust is positively related to social shopping intention.

H4b.

Trust is positively related to social sharing intention.

4. Research methodology and data results

4.1 Scale development

A questionnaire survey was used to gather data for the examination of the conceptual model. All items were measured with a five-point Likert scale, ranging from “strongly disagree” (1) to “strongly agree” (5). The construct items were adopted from prior studies to ensure the content validity and were modified to fit the current research context. Social support consisted of informational support and emotional support facets, the items of which were adapted from the research of Liang et al. (2011). Swift guanxi was measured with items adapted from the research of Ou et al. (2014). Ou et al. (2014) conceptualized and operationalized swift guanxi as a formative construct. We therefore used the same formative conceptualization. The logic is that the dimensions do not have to co-vary (even though in our sample they may co-vary). Trust was measured with items adapted from the research of Kim and Park (2013). Social shopping and social sharing intentions were measured with items adapted from the research of Chen and Shen (2015). To achieve translation accuracy of the scales, several steps were taken. First, all of the items were translated into Chinese by one of the researchers. Second, another researcher re-translated them back to English independently. Third, we compared the two English versions and examined potential translation discrepancies; then we made subtle modifications to assure that the Chinese scales accurately conveyed the intended meanings of all the items. A pilot test involving 30 university students was conducted to collect feedback about the questionnaire and further modifications were made to refine some ambiguous expressions. Table AI shows the final items.

4.2 Data collection

The IT artifact on which this study focused on is WeChat because it is one of the most popular social commerce platforms in China, the number of its active users per month has reached 762 million (Tencent, 2016). It has functions like “Messages,” “Moments,” “Group Chat,” “Comments,” “Payment.” These functions enable its users to connect with each other, create unique profiles, share comments on products, and buy the products that sellers release over the platform. The questionnaire was created in a web page form and the data were collected through an online survey. One of the researchers in this study sent invitation messages to group chats and posted the questionnaire in “Moments” in WeChat (a function platform where WeChat users can post pictures, texts, and hyperlinks). Message recipients were asked to spread the survey to their friends and families. To encourage participation, a lottery draw for three RMD50 prizes, ten RMB20 prizes, and 20 RMB10 prizes was offered. As an initial screening method, the first question in the questionnaire asked whether the participants had social commerce experience on WeChat. To ensure that each respondent participated only once in the survey, each participant’s internet protocol address was tracked and examined. In total, we received 582 responses and found that the average completion time was about four minutes, which was close to the estimated completion time based on the pilot test. We deleted 52 responses that spent less than one minute on the survey. Another 19 responses were deleted due to the use of the same score for all items. Consequently, a total of 511 valid responses were obtained.

The demographics of the sample are shown in Table I. Among the respondents, 44.03 percent were male and 55.97 percent were female. The majority of the respondents was 20-34 years old (75.54 percent) and attained a bachelor’s degree or higher (86.9 percent). With respect to monthly income and occupation, 44.23 percent earned over RMB5,000 per month; 41.49 percent were company employees. As to experience with WeChat, almost all of the respondents had been using WeChat for more than one year (98.43 percent). To examine representativeness, we compared the respondent demographics with those provided in the report on WeChat (Tecent, 2015). The report showed that the majority (86.2 percent) of WeChat users were between 18 and 35 years old. The demographic information of the respondents in this research was reasonably consistent with the population of WeChat users in China, of which the majority of users were young people. Further, according to the report of social media user behavior in China published by China Internet Network Information Center (CNNIC, 2015), 55.2 percent of social media users were males and 44.8 percent were females, indicating men make up the majority of social media users, which had no difference with our sample. Therefore, the sample can be regarded as reasonably representatives of the WeChat user segment in China.

4.3 Data analysis

4.3.1 Measurement model testing

Partial least squares (PLS)-graph was used for testing the theoretical model because both reflective construct and formative construct were contained in our study. We tested the measurement model by looking into its reliability and validity (excluding swift guanxi because it is a formative construct). The Cronbach’s α and composite reliability (CR) scores of all factors exceeded 0.7, which indicated that the scales had reasonable reliability. Factor loadings were employed to evaluate the convergent validity. First, we conducted an exploratory factor analysis. In this process, the item ES1 and the item SO1 were dropped because their respective factor loading values were lower than 0.50. Then we conducted a confirmatory factor analysis to examine the factor loadings of the items and the average variance extracted (AVE) of each construct. The standard loadings ranged from 0.8299 to 0.9032 and were significant at the 0.001 level. The AVEs ranged from 0.713 to 0.803, demonstrating that the factor-related items exhibited relatively good convergent validity. The statistical test results are presented in Table II. Further evidence in support of discriminant validity was provided with the heterotrait-multitrait matrix (Henseler et al., 2015) values in the range of 0.53-0.73.

Given the use of self-reported cross-sectional data, we first used Harman’s single-factor test to examine potential common method bias (CMB) (Podsakoff et al., 2003). We ran an exploratory unrotated factor analysis on all first-order construct items. The first factor explained 38.6 percent of the variance, which does not account for the majority of covariance of the variables. Hence, this test suggested that CMB is not a serious problem in this study. Second, following the research of Liang et al. (2007), we included a common method factor in the PLS model. As shown in Table AII, the results demonstrated that the method factor loadings (R22) were insignificant and the indicators’ substantive variances (R12) were substantially greater than their method variances (R22). We therefore concluded that CMB is not a substantial problem in this study.

Table III gives the results of discriminant and convergent validity tests. Numbers on the diagonal were the square roots of AVEs of variables and the else numbers were the correlation coefficients. The square roots of AVEs of variables were larger than the respective correlation coefficients. This means that the scale has fairly good discriminant validity. Few correlation coefficients were high but within the acceptable range.

This study also examined multicollinearity in the formative construct of swift guanxi. Table IV presented the formative indicator weights and variance inflation factors (VIFs). Different weights of items toward their construct were obtained, indicating that these exert distinct effects. Of tremendous importance was the fact that all the VIFs were less than the threshold of 10 (Petter et al., 2007), suggesting that multicollinearity was not a problem in our study. Thus, we confirmed that swift guanxi can be conceptualized as a formative construct.

4.3.2 Structural model testing

PLS was used for generating parameter estimates and bootstrapping with 1,000 re-samples was done to derive t-statistics. The results are shown in Figure 2. All hypotheses were supported. According to the path coefficients, swift guanxi (β=0.184, p<0.01) and trust (β=0.467, p<0.001) had positive effects on social shopping intentions, and swift guanxi (β=0.315, p<0.001) and trust (β=0.328, p<0.001) also had positive effects on social sharing intentions. As to the determinants of relationship quality, informational support had positive effects on trust (β=0.273, p<0.001) and swift guanxi (β=0.356, p<0.001). Emotional support also had positive influences on trust (β=0.271, p=0.001) and swift guanxi (β=0.279, p<0.001). The explained variances of social shopping intention, social sharing intention, swift guanxi, and trust were 38.5, 36.7, 34.6, and 25.3 percent, respectively. As a secondary analysis, we also used consistent PLS bootstrapping (Dijkstra and Henseler, 2015), which is more conservative compared to traditional bootstrapping, to estimate path coefficient significance. The levels of significance remained the same. Overall, these values demonstrate that informational support and emotional support can sufficiently explain the formation of swift guanxi and trust; in turn, swift guanxi and trust substantially contribute to social shopping intention and social sharing intention. To explain potential influences of individual differences on social commerce intentions, we considered as gender, age, education, and length of time using WeChat as control variables in determining social commerce intentions. The results showed that the control factors had no significant effect on social commerce intentions, which is consistent with the research of Zhang et al. (2014).

4.3.3 Post hoc test

As per the proposed model, swift guanxi and trust are the mediators of the relationship between social support and social commerce intentions. We tested the mediated effects suggested by Baron and Kenny (1986). The results are reported in Table V. First, there were significant effects of informational support and emotional support (independent variable (IV)) on social shopping intention and social sharing intention (dependent variable (DV)) without involving swift guanxi and trust (mediating variable (MV)). Second, there were significant effects of informational support and emotional support (IV) on swift guanxi and trust (MV). Third, there were significant effects of swift guanxi and trust (MV) on social shopping intention and social sharing intention (DV). Finally, in the presence of swift guanxi and trust (MV), the effects of informational support and emotional support (IV) on social shopping intention and social sharing intention (DV) were reduced, though they were still significant. Taken together, the results suggest that swift guanxi and trust (MV) act as partial mediators. Next, we conducted the Sobel’s (1982) standard errors test to further ascertain the mediating relationship. This reveals that the internal mechanism of social support effects on social commerce intentions.

We used the path comparison method proposed by Li et al. (2013) to test H1a-H4b (Table VI). The result showed that informational support had a stronger impact on swift guanxi than emotional support did. No significant difference between the impacts of informational support and emotional support on trust was found. Trust had a significantly higher positive effect on social shopping intention than swift guanxi; however, they had no statistically significant difference in their effects on social sharing intention.

5. Discussion

This study describes an initial attempt to investigate the drivers of social commerce intentions from the Chinese guanxi perspective. A research model is developed and empirically examined with survey data and PLS analyses. The results provide adequate evidence to support the proposed hypotheses and the implied model. This study contributes to research in social commerce and relationship marketing by discussing important findings, related to the Chinese context, and to the key role guanxi plays in commerce in China.

First, both informational support and emotional support were found to be significant determinants of swift guanxi and trust. This demonstrates that social support from friends plays an important role in allowing and facilitating relationship building on social commerce sites. This happens in part via association, as per the trust transfer perspective, and is also supported via social support and trust-building theories. This view extends previous research that suggests that social support from friends positively affect the relationship with them (Liang et al., 2011; Chen and Shen, 2015). In social commerce contexts, the social platform provides support mainly in the form of information exchanges involving knowledge, advice, and opinions, or caring, understanding, and empathy. Based on friendship and connections among friends, consumers are likely to take social support into consideration, which helps to facilitate the relationship with sellers in social commerce. In addition, informational support has greater effects on swift guanxi than emotional support, indicating that consumers are more concerned with instrumental information in social commerce that is interest related, compared with emotional support. Instrumental or emotional information exchanges can generate relational benefits and will promote a positive relationship between sellers and buyers. They indirectly influence user behaviors on such sites, and can therefore be relevant intervention targets for website developers or sellers.

Second, swift guanxi, as one core dimension of relationship quality, exerts positive effects on social shopping intention and social sharing intention. This indicates that interpersonal relationships can ignite consumers’ desire to share useful information with others (or at least reduce the perceived risk associated with such actions) and buy recommended products or service, lubricating social commerce engagement. Furthermore, swift guanxi has a stronger effect on social sharing intention than on social shopping intention. This means that quickly formed interpersonal relationships facilitate friendly conditions and consumers’ sense of exchanging favors, which stimulates this form of information sharing in social commerce.

Third, trust, as another key dimension of relationship quality, is found to be an important determinant of social commerce intentions. Furthermore, trust has a stronger effect on social shopping intention than swift guanxi, while they have almost the same effects on social sharing intention. This is consistent with prior studies that emphasize the prominent role of trust in motivating consumers to purchase and share their opinions or transaction experiences (Pappas, 2016). Like traditional e-commerce, social commerce has risks, such as privacy exposure, false information, and business fraud. Trust reflects a high degree of consumers’ perceived safety in social commerce. If consumers trust the parties involved in social commerce, risk concerns about sharing experiences or information will no longer be a barrier that reduces consumers’ willingness to engage in social commerce. This result also supports the relationship between trust and word of mouth referrals described in the research of Kim and Park (2013), which suggested that consumers who have formed positive beliefs toward social commerce are likely to share helpful information with others through social media.

6. Contributions and implications

6.1 Theoretical contributions

The current study contributes to the existing research in at least three ways. First, relationship is particularly important in social commerce because relationship is an essential foundation on which social commerce is built (Liang et al., 2011). Previous studies on relationship in social commerce focus mainly on relationship quality among members or relationship quality between members and social media platforms (Chen and Shen, 2015; Hajli, 2015; Shanmugam et al., 2016), largely overlooking the relationship quality between buyers and sellers. Further, most studies focus on the stable, long-term relational exchange between parties, as manifested in trust, commitment, and satisfaction (Zhang et al., 2016; Hsu et al., 2017). However, the interpersonal and immediate aspect of relationship quality has received little attention. In China, guanxi, as a close and pervasive interpersonal relationship facet, is distinguishable from relational exchange in the west. Drawing on the research of Ou et al. (2014), we use swift guanxi and trust as two key elements of relationship quality in the context of Chinese social commerce. The focus on swift guanxi in this context is relatively innovative, yet important, given the Chinese context. The focus on trust is also important as it captures a key enabler of transacting online. Together, the trust-swift guanxi focus allows us to develop a well-rounded picture of key factors, relational and transactional, that can drive social commerce behaviors, especially in the Chinese context. Ultimately, our research provides empirical evidence to support and elucidate how an interpersonal relationship exists in social commerce, and further enriches the concept of swift guanxi and extends it from traditional e-commerce to the embryonic paradigm of social commerce.

Second, from the trust transfer perspective, we examine potential transfers from social support from friends to relationships with (trust in) sellers on social commerce sites; these transfers can not only build trust but also promote swift guanxi as they can serve as a basis for relationship expectations and reciprocity. In essence, we posit that trust transfer logic can apply not only for building trust in an unknown party but also to develop further aspects of the relationship with this party. Although previous studies have discussed the effects of social support from friends on the relationship with them (Liang et al., 2011; Chen and Shen, 2015), this association in the context of Chinese social commerce is unclear. We enrich the existing research by identifying two key types of social support from friends, namely informational support and emotional support, and revealing their important yet different impacts on swift guanxi and trust between buyers and sellers. Informational support has a stronger effect on swift guanxi than emotional support, while they have no difference in their effects on trust. These findings provide a theoretical account explaining how sellers can improve the interpersonal relationship with buyers. We also provide evidence that informational support and emotional support pose not only indirect effects on social commerce intentions mediating by swift guanxi and trust, but also have direct effects. This insight extends the applicability of trust transfer and social support theories as means to build relationships on social commerce sites.

Third, we show that the logic of trust transfer theory can be used not only for explaining trust building, but also for building other aspects of relationship, including swift guanxi. Applied in our context, we show that social support from peers on the site can serve as a buffer to protect people from relationship risks the site introduces, especially regarding unknown sellers. By providing this layer of assurance and through mental affiliation, users develop trust in sellers and also allow themselves to develop swift guanxi with these sellers. Both trust and swift guanxi require one to be vulnerable to others. Hence, inflammation that can help alleviating these risks can be used to promote both facets of relationships. Future research can further examine if the logic of trust transfer can be applied to understanding the development of other relationship quality dimensions.

6.2 Practical implications

Several practical implications are noteworthy. First, a supportive environment facilitates swift guanxi establishment and trust cultivation; therefore, we call for efforts to be made to create such an environment in social commerce platforms. Both informational support and emotional support should be encouraged in social commerce, for example by providing comments such as “are you sure that is what you want to say?” to make people stop and think before they provide negative or toxic comments or engage in questionable practices. To provide informational support, when consumers are in search of help for certain products or service, experts in the company should be notified, based on a repository of consumer actions. To enhance emotional support, practitioners should be concerned with the emotional dynamics of consumers. Specifically, they should encourage social commerce users to share personalized and interest-based stories with other users and help people with personal experiences. They can do so by using “gamification” principles and provide users with dashboard, rating systems, and bonus points. In this regard, social commerce users are able to have emotional exchanges with others, which facilitates relationship enhancement with merchants by creating the kind of warm environment that people desire.

Second, relational elements play an important role in motivating consumers’ intentions to participate in social commerce shopping and sharing. Managers should consider how to maintain close, reciprocal, and trustworthy relationships with consumers. In the social commerce context, sellers should make full use of social media; through interactions with consumers, they can express their knowledge about products/service and their caring for consumers. Again, “gamification” principles and features can be used to enhance this facet. Additionally, consumers should also be allowed to ask sellers for more detailed information about purchases to make sure that they can reach agreement on the transaction with merchants; thus, swift guanxi can act as lubrication for consumers’ social commerce engagement.

Third, as social shopping and sharing are two components of social commerce, practitioners should pay particular attention to both of them. To intrigue potential social shopping, practitioners could push hot topics and products experts to the possible consumers based on their previous browsing and search history. To activate consumers’ sharing actions, privacy security should be assured to reduce consumers’ perceived risk in sharing their experiences. Also, “one click to share” over different platforms and incentives in monetary or other forms for sharing could become fertile ground for cultivating frequent sharing behaviors.

6.3 Limitations and future research

Several limitations provide attractive opportunities for future research. First, this study has relied on a convenient sampling and this may limit the generalizability and representativeness of the findings. Future research should employ broader and more random sampling techniques to re-validate and extend our findings. Second, the respondents are WeChat users and the results may not be directly applicable to other social commerce platforms, such as Facebook or SinaWeibo. Future research should widen the sample sources. Third, traditional PLS may lead to inconsistency problems. Future studies can use consistent PLS instead of traditional PLS to improve statistical validity. Fourth, our research model is based on the Chinese context, where guanxi is a long-standing and popular cultural orientation. Additional studies should be conducted to explore the applicability of our research model in other cultural contexts. Fifth, this study employs swift guanxi and investigates it from the consumers’ perspective. As swift guanxi is a dyadic concept, future studies should examine it from a dyadic perspective. Moreover, although the overall model explains 38.5 percent of the variance in social shopping intention and 36.7 percent of the variance in social sharing intention, predictors beyond the one’s we considered can extend these values and should be incorporated in future research. Sixth, our study focused on two facets of relationship quality, trust and swift guanxi, as these are two key manifestations of the interpersonal and transactional sides of relationships. Nevertheless, there can be many other facets of relationship quality. We hence call for future research to expand our treatment or relationship quality and perhaps include more nuance facets of this concept. Lastly, our study focuses on social commerce as a distinct type of service, separate from traditional e-commerce, and illuminates differences between these two types of services. Nevertheless, there seems to be some convergence in this domain where e-commerce sites implement social commerce features and vice versa. We hence call for future research to monitor this trend and consider in the future amalgamated e-commerce and social commerce sites.

Figures

Research model

Figure 1

Research model

Structural model testing results

Figure 2

Structural model testing results

Demographic statistics

Attributes Options Frequency Percentage
Gender Male 225 44.03
Female 286 55.97
Age <20 18 3.52
20~29 305 59.69
30~34 81 15.85
35~39 61 11.94
40~44 19 3.72
45~49 10 1.96
50~54 14 2.74
55~59 1 0.20
>60 2 0.38
Education Below college 23 4.50
Junior college 44 8.61
Bachelor’s degree 350 68.50
Master’s degree or higher 94 18.39
Monthly income <RMB3,000 202 39.53
RMB3,000-4,999 83 16.24
RMB5,000-5,999 85 16.63
RMB6,000-7,999 63 12.33
>RMB8,000 78 15.27
Occupation Student 199 38.94
Company employee 212 41.49
Government personnel 15 2.94
Non-profit organization personnel 66 12.91
Others 19 3.72
Length of time using WeChat (years) <1 8 1.57
2-3 288 56.36
4-5 165 32.29
>5 50 9.78

Notes: n=511

Reliability and validity

Variables Items Factor loadings t-value AVE CR Cronbach’s α
Informational support IS1 0.8490 39.756 0.748 0.899 0.830
IS2 0.8756 59.980
IS3 0.8698 59.044
Emotional support ES2 0.8615 56.175 0.738 0.894 0.835
ES3 0.8383 54.296
ES4 0.8763 58.767
Trust TRU1 0.8760 72.813 0.713 0.926 0.898
TRU2 0.7953 43.405
TRU3 0.8299 61.712
TRU4 0.8661 63.618
TRU5 0.8531 56.467
Social shopping intention SO2 0.8870 77.139 0.803 0.891 0.846
SO3 0.9032 101.178
Social sharing intention SA1 0.8823 56.608 0.796 0.921 0.872
SA2 0.8990 104.766
SA3 0.8957 70.283

Discriminant validity

Factor IS ES TRU SO SA SG
IS 0.865
ES 0.710 0.859
TRU 0.465 0.463 0.844
SO 0.483 0.460 0.608 0.896
SA 0.520 0.552 0.571 0.710 0.892
SG 0.553 0.532 0.779 0.546 0.568

Notes: IS, informational support; ES, emotional support; TRU, trust; SO, social shopping intention; SA, social sharing intention; SG, swift guanxi

Formative indicator weights and VIFs

Construct Item VIF Weighta
Swift guanxi (SG) SG1 3.25 0.418
SG2 2.63 0.349
SG3 2.89 0.386

Note: aAll weights are significant at p<0.001

Results of testing for mediating effects

IV→MV IV+MV→DV Sobel test
IV MV DV IV→DV IV SE IV MV SE Mediating effects (t)
IS SG SO 0.563*** 0.561*** 0.037 0.305*** 0.460*** 0.049 7.98
IS SG SA 0.575*** 0.561*** 0.037 0.329*** 0.437*** 0.045 8.18
IS TRU SO 0.563*** 0.456*** 0.039 0.303*** 0.571*** 0.045 8.60
IS TRU SA 0.575*** 0.456*** 0.039 0.364*** 0.462*** 0.043 7.91
ES SG SO 0.567*** 0.567*** 0.040 0.295*** 0.480*** 0.049 8.06
ES SG SA 0.641*** 0.567*** 0.040 0.406*** 0.415*** 0.044 7.85
ES TRU SO 0.567*** 0.476*** 0.041 0.288*** 0.586*** 0.045 8.67
ES TRU SA 0.641*** 0.476*** 0.041 0.431*** 0.442*** 0.042 7.80

Notes: IV, independent variable; MV, mediator; DV, dependent variable. ***p<0.001

Results of path comparison tests

Path coefficient or comparison t-statistic Conclusion
H1a βIS→SG=0.356 6.303*** Supported
H2a βES→SG=0.279 4.860*** Supported
βIS→SG (0.356)>βES→SG (0.279) 3.341*** Difference detected
H1b βIS→TRU=0.273 3.974*** Supported
H2b ΒES→TRU=0.271 4.127*** Supported
βIS→TRU (0.273)>βES→TRU (0.271) 1.051 (ns) No difference detected
H3a βSG→SO=0.184 2.718** Supported
H4a βTRU→SO=0.476 7.692*** Supported
βTRU→SO (0.476)>βSG→SO (0.184) 3.596*** Difference detected
H3b βSG→SA=0.315 5.170*** Supported
H4b βTRU→SA=0.328 5.427*** Supported
βTRU→SA (0.328)>βSG→SA (0.315) 1.126 (ns) No difference detected

Notes: IS, informational support; ES, emotional support; TRU, trust; SG, swift guanxi; SO, social shopping intention; SA, social sharing intention. One-tailed tests were performed as the directional differences were hypothesized. **p<0.01; ***p<0.001

Questionnaire items

Variables Items Contents of items
Informational support IS1 WeChat friends offer suggestions when I am in need of assistance
IS2 When I am faced with problems, WeChat friends give me information to help me address my problems
IS3 When I encounter difficulties, WeChat friends help me find out why and give me advice
Emotional support ES1 When I encounter difficulties, WeChat friends stand with me on the same side
ES2 When I encounter difficulties, WeChat friends give me comfort and encouragement
ES3 When I encounter difficulties, WeChat friends listen to me when I talk about my private feelings
ES4 When I encounter difficulties, WeChat friends express their concerns for me
Swift guanxi SG1 Sellers on WeChat and I can understand each other
SG2 Sellers on WeChat and I treat each other as we treat our friends
SG3 Sellers on WeChat and I have harmonious relationships
Trust TRU1 Sellers on WeChat are trustworthy
TRU2 Sellers on WeChat take my best interests into consideration
TRU3 Sellers on WeChat realize his/her promises
TRU4 I believe in the information provided by sellers on WeChat
TRU5 Sellers on WeChat leave people with an impression that they keep their promises
Social shopping intention SO1 I think about the purchase experiences shared by others on WeChat “Moments” when I want to shop
SO2 Before I shop, I ask members of WeChat “Moments” to give me suggestions
SO3 I am willing to purchase products recommended by members of WeChat “Moments”
Social sharing intention SA1 When members of WeChat “Moments” want to get advice on purchasing a certain product, I am willing to provide my related experiences and suggestions
SA2 I would like to share my purchase experiences with members of WeChat “Moments”
SA3 I would like to recommend products that are worthy of buying to members of WeChat “Moments”

Common method bias analysis

Construct Indicator Substantive factor loadings (R1) R12 Method factor loading (R2) R22
Informational support IS1 0.893*** 0.733 −0.050 0.001
IS2 0.826*** 0.759 0.060 0.001
IS3 0.877*** 0.753 −0.010 0.000
Emotional support ES2 0.886*** 0.755 −0.023 0.000
ES3 0.780*** 0.678 0.058 0.002
ES4 0.909*** 0.781 −0.034 0.001
Trust TRU1 0.937*** 0.771 −0.069 0.001
TRU2 0.930*** 0.641 −0.150 0.006
TRU3 0.724*** 0.682 0.119 0.004
TRU4 0.819*** 0.747 0.053 0.001
TRU5 0.820*** 0.726 0.038 0.001
Swift guanxi SG1 0.868*** 0.763 0.006 0.000
SG2 0.900*** 0.772 −0.025 0.001
SG3 0.827*** 0.716 0.022 0.000
Social shopping intention SO2 0.897*** 0.800 −0.003 0.000
SO3 0.895*** 0.806 0.003 0.000
Social sharing intention SA1 0.875*** 0.782 0.011 0.000
SA2 0.895*** 0.806 0.003 0.000
SA3 0.907*** 0.801 −0.014 0.000

Note: ***p<0.001

Appendix

Table AI

Table AII

References

Akoorie, M., Ahmed, I., Ismail, W.K.W., Amin, S.M. and Nawaz, M.M. (2013), “A social exchange perspective of the individual guanxi network: evidence from Malaysian-Chinese employees”, Chinese Management Studies, Vol. 7 No. 1, pp. 127-140.

Arias, J.T.G. (1998), “A relationship marketing approach to guanxi”, European Journal of Marketing, Vol. 32 Nos 1/2, pp. 145-156.

Aryee, S., Budhwar, P.S. and Chen, Z.X. (2002), “Trust as a mediator of the relationship between organizational justice and work outcomes: test of a social exchange model”, Journal of Organizational Behavior, Vol. 23 No. 3, pp. 267-285.

Athanasopoulou, P. (2009), “Relationship quality: a critical literature review and research agenda”, European Journal of Marketing, Vol. 43 Nos 5/6, pp. 583-610.

Baron, R.M. and Kenny, D.A. (1986), “The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations”, Journal of Personality and Social Psychology, Vol. 51 No. 6, pp. 1173-1182.

Benito, O.G., Partal, M.M. and Martin, S.S. (2015), “Brands as substitutes for the need for touch in online shopping”, Journal of Retailing and Consumer Services, Vol. 27, pp. 121-125.

Blau, P.M. (1964), Exchange and Power in Social Life, Wiley, New York, NY.

Busalim, A.H. and Hussin, A.R.C. (2016), “Understanding social commerce: a systematic literature review and directions for further research”, International Journal of Information Management, Vol. 36 No. 6, pp. 1075-1088.

Chang, A., Guo, C., Zolin, R. and Yang, X. (2014), “Guanxi as a complex adaptive system: definition, description and underlying principals”, Journal of Asia Business Studies, Vol. 8 No. 2, pp. 81-103.

Chen, J. and Shen, X.L. (2015), “Consumers’ decisions in social commerce context: an empirical investigation”, Decision Support Systems, Vol. 79, pp. 55-64.

Chen, X., Pan, Y. and Guo, B. (2016), “The influence of personality traits and social networks on the self-disclosure behavior of social network site users”, Internet Research, Vol. 26 No. 3, pp. 566-586.

Chiu, C.M., Huang, H.Y., Cheng, H.L. and Sun, P.C. (2015), “Understanding online community citizenship behaviors through social support and social identity”, International Journal of Information Management, Vol. 35 No. 4, pp. 504-519.

Chou, S.Y., Shen, G.C., Chiu, H.C. and Chou, Y.T. (2016), “Multichannel service providers’ strategy: understanding customers’ switching and free-riding behavior”, Journal of Business Research, Vol. 69 No. 6, pp. 2226-2232.

CNNIC (2015), “Social media user behavior in China”, available at: www.cnnic.net.cn/ (accessed May 10, 2017).

Coulson, N.S. (2005), “Receiving social support online: an analysis of a computer-mediated support group for individuals living with irritable bowel syndrome”, CyberPsychology & Behavior, Vol. 8 No. 6, pp. 580-584.

Cropanzano, R. and Mitchell, M.S. (2005), “Social exchange theory: an interdisciplinary review”, Journal of Management, Vol. 31 No. 6, pp. 874-900.

Crosby, L.A., Evans, K.R. and Cowles, D. (1990), “Relationship quality in services selling: an interpersonal influence perspective”, Journal of Marketing, Vol. 54 No. 3, pp. 68-81.

Curty, R.G. and Zhang, P. (2013), “Website features that gave rise to social commerce: a historical analysis”, Electronic Commerce Research and Applications, Vol. 12 No. 4, pp. 260-279.

Cutrona, C.E. and Russell, D.W. (1990), Social Support: An Interactional View, John Wiley & Sons, Oxford.

Debei, M.M., Akroush, M.N. and Ashouri, M.I. (2015), “Consumer attitudes towards online shopping: the effects of trust, perceived benefits, and perceived web quality”, Internet Research, Vol. 25 No. 5, pp. 707-733.

Dijkstra, T.K. and Henseler, J. (2015), “Consistent and asymptotically normal PLS estimators for linear structural equations”, Computational Statistics & Data Analysis, Vol. 81, pp. 10-23.

Fan, Y. (2002), “Guanxi’s consequences: personal gains at social cost”, Journal of Business Ethics, Vol. 38 No. 4, pp. 371-380.

Farivar, S., Turel, O. and Yuan, Y. (2017), “A trust-risk perspective on social commerce use: an examination of the biasing role of habit”, Internet Research, Vol. 27 No. 3, pp. 586-607.

Festinger, L. (1954), “A theory of social comparison processes”, Human Relations, Vol. 7 No. 2, pp. 117-140.

Gefen, D. and Straub, D.W. (2004), “Consumer trust in B2C e-Commerce and the importance of social presence: experiments in e-Products and e-Services”, Omega, Vol. 32 No. 6, pp. 407-424.

Gefen, D., Karahanna, E. and Straub, D.W. (2003), “Trust and TAM in online shopping: an integrated model”, MIS Quarterly, Vol. 27 No. 1, pp. 51-90.

Gottlieb, B.H. and Bergen, A.E. (2010), “Social support concepts and measures”, Journal of Psychosomatic Research, Vol. 69 No. 5, pp. 511-520.

Ha, H.Y., John, J., John, J.D. and Chung, Y.K. (2016), “Temporal effects of information from social networks on online behavior: the role of cognitive and affective trust”, Internet Research, Vol. 26 No. 1, pp. 213-235.

Hajli, M. (2012), “Social commerce adoption model”, Proceedings of the UK Academy of Information Systems Conference, Oxford, pp. 1-26.

Hajli, M.N. (2014), “A study of the impact of social media on consumers”, International Journal of Market Research, Vol. 56 No. 3, pp. 388-404.

Hajli, N. (2015), “Social commerce constructs and consumer’s intention to buy”, International Journal of Information Management, Vol. 35 No. 2, pp. 183-191.

Hajli, N. and Sims, J. (2015), “Social commerce: the transfer of power from sellers to buyers”, Technological Forecasting and Social Change, Vol. 94, pp. 350-358.

Hajli, N., Sims, J., Zadeh, A.H. and Richard, M.-O. (2017), “A social commerce investigation of the role of trust in a social networking site on purchase intentions”, Journal of Business Research, Vol. 71, pp. 133-141.

Henseler, J., Ringle, C.M. and Sarstedt, M. (2015), “A new criterion for assessing discriminant validity in variance-based structural equation modeling”, Journal of the Academy of Marketing Science, Vol. 43 No. 1, pp. 115-135.

Hsu, C.L., Chen, M.C., Kikuchi, K. and Machida, I. (2017), “Elucidating the determinants of purchase intention toward social shopping sites: a comparative study of Taiwan and Japan”, Telematics and Informatics, Vol. 34 No. 4, pp. 326-338.

Hsu, M.H., Chuang, L.W. and Hsu, C.S. (2014), “Understanding online shopping intention: the roles of four types of trust and their antecedents”, Internet Research, Vol. 24 No. 3, pp. 332-352.

Hu, X., Huang, Q., Zhong, X., Davison, R.M. and Zhao, D. (2016), “The influence of peer characteristics and technical features of a social shopping website on a consumer’s purchase intention”, International Journal of Information Management, Vol. 36 No. 6, pp. 1218-1230.

Huang, Q., Davison, R.M. and Liu, H. (2014), “An exploratory study of buyers’ participation intentions in reputation systems: the relationship quality perspective”, Information & Management, Vol. 51 No. 8, pp. 952-963.

Huang, Z. and Benyoucef, M. (2013), “From e-commerce to social commerce: a close look at design features”, Electronic Commerce Research & Applications, Vol. 12 No. 4, pp. 246-259.

Jiang, P., Jones, D.B. and Javie, S. (2008), “How third-party certification programs relate to consumer trust in online transactions: an exploratory study”, Psychology and Marketing, Vol. 25 No. 9, pp. 839-858.

Jiang, Z., Shiu, E., Henneberg, S. and Naude, P. (2016), “Relationship quality in business to business relationships – reviewing the current literatures and proposing a new measurement model”, Psychology & Marketing, Vol. 33 No. 4, pp. 297-313.

Jin, C., Cheng, Z. and Yunjie, X. (2009), “The role of mutual trust in building members’ loyalty to a C2C platform provider”, International Journal of Electronic Commerce, Vol. 14 No. 1, pp. 147-171.

Kim, S. and Park, H. (2013), “Effects of various characteristics of social commerce (s-commerce) on consumers’ trust and trust performance”, International Journal of Information Management, Vol. 33 No. 2, pp. 318-332.

Lages, C., Lages, C.R. and Lages, L.F. (2005), “The RELQUAL scale: a measure of relationship quality in export market ventures”, Journal of Business Research, Vol. 58 No. 8, pp. 1040-1048.

Lee, D.J., Pae, J.H. and Wong, Y.H. (2001), “A model of close business relationships in China (guanxi)”, European Journal of Marketing, Vol. 35 Nos 1/2, pp. 51-69.

Li, X., Hsieh, J.J.P.A. and Rai, A. (2013), “Motivational differences across post-acceptance information system usage behaviors: an investigation in the business intelligence systems context”, Information Systems Research, Vol. 24 No. 3, pp. 659-682.

Liang, H., Saraf, N., Hu, Q. and Xue, Y. (2007), “Assimilation of enterprise systems: the effect of institutional pressures and the mediating role of top management”, MIS Quarterly, Vol. 31 No. 1, pp. 59-87.

Liang, T.P., Ho, Y.T., Li, Y.W. and Turban, E. (2011), “What drives social commerce: the role of social support and relationship quality”, International Journal of Electronic Commerce, Vol. 16 No. 2, pp. 69-90.

Lisha, C., Goh, C.F., Yifan, S. and Rasli, A. (2017), “Integrating guanxi into technology acceptance: an empirical investigation of WeChat”, Telematics and Informatics, Vol. 34 No. 7, pp. 1125-1142.

Liu, H., Chu, H., Huang, Q. and Chen, X. (2016), “Enhancing the flow experience of consumers in China through interpersonal interaction in social commerce”, Computers in Human Behavior, Vol. 58, pp. 306-314.

Liu, Y., Li, Y., Tao, L. and Wang, Y. (2008), “Relationship stability, trust and relational risk in marketing channels: evidence from China”, Industrial Marketing Management, Vol. 37 No. 4, pp. 432-446.

Luo, Y. (1997), “Guanxi and performance of foreign-invested enterprises in China: an empirical inquiry”, Management International Review, Vol. 37 No. 1, pp. 51-70.

Madjar, N. (2008), “Emotional and informational support from different sources and employee creativity”, Journal of Occupational and Organizational Psychology, Vol. 81 No. 1, pp. 83-100.

Maier, C., Laumer, S., Eckhardt, A. and Weitzel, T. (2015), “Giving too much social support: social overload on social networking sites”, European Journal of Information Systems, Vol. 24 No. 5, pp. 447-464.

Marsden, P. (2016), “Top social commerce survey findings”, available at: http://socialcommercetoday.com/top-social-commerce-survey-findings-ripple6 (accessed October 1, 2016).

Mo, P.K.H. and Coulson, N.S. (2008), “Exploring the communication of social support within virtual communities: a content analysis of messages posted to an online HIV/AIDS support group”, Cyberpsychology & Behavior, Vol. 11 No. 3, pp. 371-374.

Morgan, R.M. and Hunt, S.D. (1994), “The commitment-trust theory of relationship marketing”, Journal of Marketing, Vol. 58 No. 3, pp. 20-38.

Oh, S. and Syn, S.Y. (2015), “Motivations for sharing information and social support in social media: a comparative analysis of Facebook, Twitter, Delicious, YouTube, and Flickr”, Journal of the Association for Information Science and Technology, Vol. 66 No. 10, pp. 2045-2060.

Ou, C.X., Pavlou, P.A. and Davison, R. (2014), “Swift guanxi in online marketplaces: the role of computer-mediated communication technologies”, MIS Quarterly, Vol. 38 No. 1, pp. 209-230.

Palmatier, R.W., Dant, R.P., Grewal, D. and Evans, K.R. (2006), “Factors influencing the effectiveness of relationship marketing: a meta-analysis”, Journal of Marketing, Vol. 70 No. 4, pp. 136-153.

Pappas, N. (2016), “Marketing strategies, perceived risks, and consumer trust in online buying behaviour”, Journal of Retailing and Consumer Services, Vol. 29, pp. 92-103.

Petter, S., Straub, D. and Rai, A. (2007), “Specifying formative constructs in information systems research”, MIS Quarterly, Vol. 31 No. 4, pp. 623-656.

Pfeil, U. and Zaphiris, P. (2009), “Investigating social network patterns within an empathic online community for older people”, Computers in Human Behavior, Vol. 25 No. 5, pp. 1139-1155.

Podsakoff, P.M., MacKenzie, S.B., Jeong Yeon, L. and Podsakoff, N.P. (2003), “Common method biases in behavioral research: a critical review of the literature and recommended remedies”, Journal of Applied Psychology, Vol. 88 No. 5, pp. 879-903.

Porter, C.E. and Donthu, N. (2008), “Cultivating trust and harvesting value in virtual communities”, Management Science, Vol. 54 No. 1, pp. 113-128.

Shaalan, A.S., Reast, J., Johnson, D. and Tourky, M.E. (2013), “East meets west: toward a theoretical model linking guanxi and relationship marketing”, Journal of Business Research, Vol. 66 No. 12, pp. 2515-2521.

Shankar, V., Urban, G.L. and Sultan, F. (2002), “Online trust: a stakeholder perspective, concepts, implications, and future directions”, The Journal of Strategic Information Systems, Vol. 11 Nos 3/4, pp. 325-344.

Shanmugam, M., Sun, S., Amidi, A., Khani, F. and Khani, F. (2016), “The applications of social commerce constructs”, International Journal of Information Management, Vol. 36 No. 3, pp. 425-432.

Sobel, M.E. (1982), “Asymptotic confidence intervals for indirect effects in structural equation models”, Sociological Methodology, Vol. 13, pp. 290-312.

Stewart, K. (2006), “How hypertext links influence consumer perceptions to build and degrade trust online”, Journal of Management Information Systems, Vol. 23 No. 1, pp. 183-210.

Stewart, K.J. (2003), “Trust transfer on the world wide web”, Organization Science, Vol. 14 No. 1, pp. 5-17.

Su, L., Swanson, S.R. and Chen, X. (2016), “The effects of perceived service quality on repurchase intentions and subjective well-being of Chinese tourists: the mediating role of relationship quality”, Tourism Management, Vol. 52, pp. 82-95.

Taylor, S.E., Sherman, D.K., Kim, H.S., Jarcho, J., Takagi, K. and Dunagan, M.S. (2004), “Culture and social support: who seeks it and why?”, Journal of Personality and Social Psychology, Vol. 87 No. 3, pp. 354-362.

Tecent (2015), “Report on WeChat”, available at: http://tech.qq.com/a/20150127/018482.htm#p=1 (accessed May 10, 2017).

Tencent (2016), “The second-quarter earnings of Tencent”, available at: www.tencent.com/zh-cn/content/at/2016/attachments/20160817.pdf (accessed August 18, 2016).

Thurau, T.H. and Klee, A. (1997), “The impact of customer satisfaction and relationship quality on customer retention: a critical reassessment and model development”, Psychology & Marketing, Vol. 14 No. 8, pp. 737-764.

Tim, A., Chris, S. and Wang, X. (1999), “The effect of channel relationships and guanxi on the performance of inter-province export ventures in the People’s Republic of China”, International Journal of Research in Marketing, Vol. 16 No. 1, pp. 75-87.

Turel, O. and Gefen, D. (2013), “The dual role of trust in system use”, Journal of Computer Information Systems, Vol. 54 No. 1, pp. 2-10.

Turel, O., Yuan, Y. and Connelly, C.E. (2008), “In justice we trust: predicting user acceptance of e-customer services”, Journal of Management Information Systems, Vol. 24 No. 4, pp. 123-151.

Wang, C.L. (2007), “Guanxi vs relationship marketing: exploring underlying differences”, Industrial Marketing Management, Vol. 36 No. 1, pp. 81-86.

White, T.B. (2005), “Consumer trust and advice acceptance: the moderating roles of benevolence, expertise, and negative emotions”, Journal of Consumer Psychology, Vol. 15 No. 2, pp. 141-148.

Wu, S.H., Huang, S.C.T., Tsai, C.Y.D. and Lin, P.Y. (2017), “Customer citizenship behavior on social networking sites: the role of relationship quality, identification, and service attributes”, Internet Research, Vol. 27 No. 2, pp. 428-448.

Wu, W.K. and Chiu, S.W. (2016), “The impact of guanxi positioning on the quality of manufacturer-retailer channel relationships: evidence from Taiwanese SMEs”, Journal of Business Research, Vol. 69 No. 9, pp. 3398-3405.

Xin, K.K. and Pearce, J.L. (1996), “Guanxi: connections as substitutes for formal institutional support”, Academy of Management Journal, Vol. 39 No. 6, pp. 1641-1658.

Zhang, H., Lu, Y., Gupta, S. and Zhao, L. (2014), “What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences”, Information & Management, Vol. 51 No. 8, pp. 1017-1030.

Zhang, J. and Bloemer, J.M.M. (2008), “The impact of value congruence on consumer-service brand relationships”, Journal of Service Research, Vol. 11 No. 2, pp. 161-178.

Zhang, K.Z.K., Benyoucef, M. and Zhao, S.J. (2016), “Building brand loyalty in social commerce: the case of brand microblogs”, Electronic Commerce Research and Applications, Vol. 15, pp. 14-25.

Zhang, Y., Fang, Y., Wei, K.K., Ramsey, E., McCole, P. and Chen, H. (2011), “Repurchase intention in B2C e-commerce – a relationship quality perspective”, Information & Management, Vol. 48 No. 6, pp. 192-200.

Zhou, L., Zhang, P. and Zimmermann, H.D. (2013), “Social commerce research: an integrated view”, Electronic Commerce Research and Applications, Vol. 12 No. 2, pp. 61-68.

Supplementary materials

INTR_28_1.pdf (9.9 MB)

Acknowledgements

This work was supported by the grants from the National Science Foundation of China (71501078, 71332001, 71633002, 71333004), a grant from the Excellent Young Teacher Foundation in Guangdong Province (YQ2015031), and a grant from the Philosophical and Social Science Foundation of Guangdong Province (GD14CGL10).

Corresponding author

Jiabao Lin can be contacted at: linjb@scau.edu.cn