Symposium on Service Innovation in Big Data Area

eWOM in Mobile : a study about Chinese Wechat use

Mengmeng Song1, Lin Qiao2, Tao Hu3 Abstract Drawing upon Theory of Reasoned Action(TRA) and the perspective of the social influence theory(PSI), the paper proposes an integrated model to identify the impacts of tie strength and trust on eWOM and how eWOM constructs (opinion passing and opinion giving) affect purchase intentions via WeChat in . Structural equation model was employed to confirm the validity of the model. The results reveal the significant influence of tie strength on subjective norm among WeChat users, and equal importance of opinion passing and opinion giving should also be considered in eWOM management. Our findings offer insight to how boosted trust in WeChat encourages engagement in eWOM which ultimately enhances purchase intention. Implications and contributions are discussed. Keywords tie strength; subjective norm; purchase intention; eWOM

1 Introduction As the proliferation of mobile social media platform and the ever-growing trend of shopping via mobile devices, mobile social service has drastically changed peoples’ lives regarding work, shop, social interaction, etc (Nikou and Bouwman 2015). According to report4, up until the beginning of 2016, over half of online activities have been conducted via mobile phones in China. The number of social media users in China reached 659 million, exceeding the sum of that in the US and Europe, and 87.1% of the social media users in China interact via mobile phones. 1.1 eWOM in mobile social media Social Media is defined as “a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of User Generated Content (See-To and Ho 2014). Thus mobile social media can be interpreted as mobile applications that enable the communication among users. eWOM, developed from traditional WOM, are interpreted by (Hennig-Thurau, Gwinner, Walsh and Gremler 2004) as a subjective opinion a previous or potential consumer remark on certain product or a firm, spreading to numerous potential customers online. Previous research have studied eWOM from the perspective of brand companies (Chu, Sung 2015; Chang and Wu 2014),

Foundation: Research on the Analysis of the Reliability of Tourism Information Service in MSN and itsApplication, No.71361007; The research of mobile information services based on digital certificates and RDF,No.714263. 1 Mengmeng Song: lecturer, Hainan University, College of Tourism, Haikou, 570228, China 2 Lin Qiao: postgraduate, Hainan University, College of Tourism, Haikou, 570228, China 3 Tao Hu: professor, Hainan University, College of Tourism, Haikou, 570228, China

Symposium on Service Innovation in Big Data Area

Specifically, quantities of researches have been done on the effects of eWOM on information adoption and purchase intention in the and shopping (Erkan and Evans 2016; Filieri 2015; Park and Lee 2009; Wen-Chin et al. 2015; Yang et al. 2015; Yang et al. 2015). Further, the antecedents for eWOM in facilitate communication and spread of positive eWOM, thereby boosting confidence and willingness to purchase recommended product/service have been examined, including trust (Che and Cao 2014), tie strength (Rozzell, Piercy and Carr 2014), expertise (Lee and 2012), relationship quality (Purnasari and Yuliando 2015), subjective norm and trust (Che and Cao 2014) etc. Others, however, endeavored to investigate the effect of eWOM on information adoption from perspectives of review valence (Lee and Koo 2012; Qiu et al. 2012), or just simply study one single valence of eWOM (Chang and Wu 2014; Che and Cao 2014). The development of social media platform has accelerated the expansion of eWOM, as the mobile social media prevailed throughout the world, people can interact with each other with portable devices whenever and wherever they want (Xu, Kang, Song and Clarke 2015), making it easier to reach larger number of users with higher credibility, thus strengthening the effect of eWOM via mobile social media. However, little research has addressed the issue of the affecting mechanism of eWOM in the context of mobile social media, which is the currently the dominant tool for social interaction worldwide. Overall, eWOM is widely acknowledged as a crucial factor in influencing purchase intention and ultimately purchase behavior, and social media has been widely recognized as a facilitator in eWOM dissemination (Jeff et al. 2014). The study adopts qualitative and quantitative methods to analyze the effect of eWOM and the motivation behind eWOM via mobile social media platform. 1.2 WeChat WeChat (WeiXin in Chinese), as WhatsApp and in foreign countries, serves as an convenient text and voice communication service in China, and have been prevailing all over the country ever since its emergence. With full compatibility and accessibility of WeChat for free, WeChat users can interact with friends in various forms (one-to-many messaging, video chat, voice message, etc.) through diverse mobile operating systems such as iphone, android and windows. Boosted by the steady development of Internet information communication technology and the popularity of mobile smartphones, mobile social media usage has grown exponentially in China. For WeChat, MAU (monthly active users) reached 762 million at the end of the quarter in 2016 and covers more than 200 countries in over 20 languages (Tencent 2016). Thus, WeChat ranks among one of the most widely and frequently used mobile social media nationwide (Tencent 2015). The prevalence of WeChat has enabled users to interact with each other at ease, and has strengthened the social relationship between users, accelerating the spread of eWOM in (Gao and Zhang 2013). Quantities of theoretical research have been conducted on eWOM service in social networking sites (Ladhari and Michaud 2015; Lee et al. 2012), and findings suggest Symposium on Service Innovation in Big Data Area that Chinese users are inclined to consider the recommended products/services in social networking sites (HBR 2012), verifying the significance of eWOM in information adoption and purchase intention (East et al. 2007; Erkan and Evans 2016). As mobile social media penetrates into peoples’ daily lives, increasing ever faster than that of social networking services, whether the spreading mechanism of eWOM in web environment still works for that in mobile social media in China is worth to be examined. Specifically, many research have empirically studied eWOM under the context of RenRen, , Twitter and other social networking sites (SNS) (Shen et al. 2016; Balaji et al. 2016), few studies can be found empirically exploring the antecedents of eWOM and how eWOM affect purchase intention via mobile social media in China (Che and Cao 2014). Further, unlike eWOM in web social networking sites, where the content of eWOM might come from the unknown or even merchants, eWOM in WeChat is more trustworthy because of its credible source of eWOM (WeChat friends bonded with social intimacy). Henceforth, this study aims to investigate the subdivided dimensions of eWOM behavior, namely opinion giving and opinion passing, to investigate how social tie, subjective norm and trust affect dissemination of eWOM among WeChat users in China, which has scarcely been studied before. Additionally, a theoretical model was proposed to depict the influencing mechanism of eWOM in determine the purchase inclination in the context of WeChat.

2 Theoretical background 2.1 Source credibility theory Source credibility is the recipients’ trustworthiness toward the origin of information, having nothing to do with the information itself (Chaiken 1980). Source credibility in this study refers to the level that the reviewer is viewed as credible information provider and neutral opinion giver. The perceived risk of users to purchase online will be reduced if the source of information is credible. Prior studies have verified the crucial role source credibility plays in message credibility (Man et al. 2009; Wathen and Burkell 2002) and information usefulness (Peng et al. 2016), and many studies have been conducted on the antecedents of source credibility, including purchase experience (Kuan et al. 2012), trustworthiness (Cheung et al. 2008). 2.2 Theory of reasoned action (TRA) Ever since the proposal of TRA theory by Fishbein and Ajzen in 1977, the notion of behavior intention has been applied to various research in the individual level (Fishbein and Ajzen 1977). TRA assumes that attitudes and subjective norm will exert influence on behavioral intentions, and quantities of studies has adopted TRA to analyze the correlation between eWOM construct and intention to purchase (Reichelt 2013; Cheung and Thadani 2012). In this study, we select purchase intention and subjective norm as factors to further investigate the influencing power Symposium on Service Innovation in Big Data Area of subjective norm on eWOM behavior in the context of mobile social media. 2.3 Theory of planned behavior (TPB) Theory of planned behavior(TPB), extended from the Theory of Reasoned Action (TRA) (Fishbein and Ajzen 1977), incorporates the variable of behavior intention to describe the people’s willingness in certain behavior (Ajzen 1991).TPB model has been applied to investigate purchase intention in many studies (Paul et al. 2016).

3 Hypothesis development and research model 3.1 EWOM, trust and purchase intention Trust refers to reliance and positive belief on another person or entity (Kaushik et al. 2015), and is a determinant factor in ridding of uncertainty (Hajli 2015) and perceived risks (Mutz 2005) during transaction especially in the online context (Lien et al. 2015). Trust has also been defined as a “willingness to make oneself vulnerable to another in the presence of risk”. According to (Gremler et al. 2001), trust consists of three types: trust between people, trust between organization and trust between a person and organization. This study focalizes on trust between WeChat users and WeChat platform. Recently more studies have endeavored to analyze the subgroup of trust: ability, benevolence and integrity (Pavlou 2003; Xu et al. 2016). Purchase intention is depicted as the extent to which buyers are willing to purchase certain products. The more trustworthy the person feels towards the recommender, the less he/she feels about perceptions of risk, consequently the more likely the person is to purchase the object. Many scholars seek to find relationships between trust and purchase intention (Lien et al. 2015; See-To and Ho 2014; Mansour et al. 2014), and have proved that there is a significant positive effect of online trust on the intention to purchase online (Jarvenpaa et al. 2000). Trust towards the mobile social media empowers users to relieve their hesitation in conducting purchasing behavior (Hajli 2015). The positive impact of trust on consumers’ intention to purchase in e-commerce have already been validated (Lu et al. 2010; Gefen and Straub 2004), likewise, we assume that the relationship still holds water in the context of mobile social commerce since trust towards mobile social media reduce users’ perceived risk in purchasing intention. Therefore, the first hypothesis was proposed as follows: H1.Trust has a positive direct influence on purchase intention in WeChat. According to a recent survey conducted by William (2012), 4 out of 5 consumers expressed their skepticism about the product review in terms of the content and the motive behind the reviewer. Trust is an important factor affecting consumer behaviors (Giampietri 2017). For example, trust has been verified to have significant impact on eWOM construct in many studies (Neveen and Arik 2008). Based on empirical investigation of WeChat users in China, Che and Cao (2014) confirm that trust between WeChat users and WeChat positively affects WeChat users’ attitudes and ultimately exerts WOM behavior via WeChat. Additionally, trust also has Symposium on Service Innovation in Big Data Area significant influence on purchase intention both directly and indirectly (Gefen et al. 2003; Wen et al. 2012). Traditional WOM is an information source created by individuals and spread among consumers (Arndt 1967). Boosted by technology and social media, electronic WOM (eWOM), in which individuals spread opinions about products or services via virtual community, was developed (Noh et al. 2013).The positive relationship between eWOM and purchase intention has been well established by many scholars (Park and Kim 2008; Cheung and Thadani 2012). In this study, we investigate further the impacts of eWOM constructs on purchase intention respectively. According to Chu and Kim (2011), eWOM behavior can be reflected by three aspects: opinion seeking, opinion giving, and opinion passing. Like Facebook, twitter and other SNSs in foreign countries, WeChat enables users to look for suggestions and advice (opinion seeking), express prior or post-purchase opinions about products or services (opinion giving) and share friends’ opinions (opinion passing). The three dimensions constitute integral circulation of eWOM constructs. Positive eWOM acts as an effective and cost-effective method of promoting sales in products or service. Since the majority of eWOM are posted by unknown users, information credibility is doubted (Qiu et al. 2012). Comparatively, eWOM in WeChat, built on the reliable relationship among WeChat users, was generated and spread by users with spontaneity. Thus, it will positively enhance users’ trust towards WeChat, ultimately increasing willingness to purchase the product. Based on the above reasoning, we postulate the following hypothesis: H2. Trust has a positive influence on opinion passing in WeChat. H3. Trust has a positive influence on opinion giving in WeChat. H4. Opinion passing in WeChat affects intention to purchase. H5. Opinion giving in WeChat affects intention to purchase. 3.2 Purchase intention, trust and source credibility Source credibility refers to the level that the reviewers are viewed as trustworthy and credible for expressing product opinions. Many scholars have analyzed source credibility with integration of several theories. For instance, Ayeh (2015) combines the Technology Acceptance Model with the Source Credibility Theory to further analyze the mental factors behind the behavior of consumer-generated media (CGM), whereas studies the underlying impacts of source credibility on the acceptance level of information system. Recent studies have put more emphasis on the relationships between source credibility and other factors, such as attitudes, willingness to adopt information and intention to purchase (Shan 2016; Ayeh 2015; Filieri 2015). Thus we inference that if the source of information which is transferred via mobile social media is reliable, it is more likely for the user to adopt the information and ultimately purchase the recommended products or services. The hypotheses are summarized as follows: H6. Source credibility has positive a impact on purchase intention. Looking back upon previous studies, trust and expertise as widely acknowledged as two integral dimensions in source credibility (Fogg and Tseng 1999). Hence, the Symposium on Service Innovation in Big Data Area information will be perceived as accurate and reliable if the source is rich in expertise or trustworthy (Tormala and Petty 2004). According to O'Keefe (2002), trustworthiness is the extent to which the receivers feel confident in the motive behind the reviewer’s review. Trust factor in this study refers to the degree of reliability the receiver place upon mobile social media. The significant positive influence of expertise on attitude and behavioral intention has already been confirmed (Wang 2005), and according to trust transfer theory, the trustworthiness towards the reviewer can be conveyed to another unknown object, in which there exists specific relationship. Thus, drawing upon trust transfer theory, we postulates that in the context of mobile social media, the higher degree of reliability the receiver feels towards reviewer, the higher the perception of source credibility, which ultimately leads to higher level of trustworthiness to the mobile social media platform. H7. The more credible the source of product review, the higher level of trustworthiness the receivers feel towards mobile social media. 3.3 Subjective norm, eWOM and purchase intention Ajzen (1991) defined Subjective norm as a perceived social motivation to conduct a certain behavior. Similarly, subjective norm has been referred to as people’s expectations towards a specific action (Kim 2011; Alsajjan and Dennis 2010). Subjective norm reflects the degree to which a person can be influenced by others in terms of performance, a particular action and a common deed (Kaushik et al. 2015). According to the theory put forward by Venkatesh et al. (2003), subjective norm can exert a crucial influence on certain behavioral intention. Quantities of studies have been done to study the relation between subjective norm and intention of particular behavior, proving that subjective norm has positive influence on intention of a variety of conduct, namely mobile phone usage (Lopez-Nicolas et al. 2008), using blogs (Hsu et al. 2008), playing games on Internet (Hsu and Lu 2004), and purchasing online (Biswas and Roy 2015). According to theory of planned behavior (TPB), subjective norm are predictors of some mobile service behavior (Lu et al. 2008), and the majority of studies confirm an impact subjective norm displays on purchase intention (Zhao et al. 2014; Ágata et al. 2015; Zhou 2011; Leeraphong and Mardjo 2015; Kaushik et al. 2015). Nonetheless, others found insignificant relevance to purchase intention (Ajzen 1991) in which subjective norm are said to be related to users’ perceptions of opinions of other users. Further research is needed to verify the effect of subjective norm on purchase online in the context of mobile social media platform. Therefore, we raise the following hypothesis: H8. Subjective norm displays a positive effect on peoples’ intention of purchase a recommended product. According to Ajzen and Fishbein (1980), Ajzen (1991), subjective norms was driven by normative beliefs that impose psychological pressure on individuals to act in accordance with his/her friends and important others, which are believed to be friends in WeChat in our study. And eWOM conducts, including opinion passing via friends circle or group and opinion giving one to one or in friend circle in WeChat, Symposium on Service Innovation in Big Data Area are among those behaviors that people are exposed to every now and then when using . People are inclined to conform to behaviors of their friends in WeChat in the forms of clicking a “like” button, re-tweet a message and leave comment under the information, etc. based on the aforementioned theory and reasoning, we propose the following hypothesis: H9. Subjective norm exerts great impact on opinion passing in mobile social media. H10. Subjective norm exerts great impact on opinion giving in mobile social media. 3.4 The effects of tie strength on subjective norm, trust and purchase intention Tie strength, also called as social tie, relational closeness (Rozzell et al. 2014) and social distance, was defined as an integration of mutual intimacy and reciprocal relationship closeness (Mittal et al. 2008). Previous studies have proven the significant and positive impact of social tie on trust (Phua et al. 2017), engagement of eWOM (Brown and Reingen 1987). The level of tie strength can be categorized into different groups in the context of social networking sites. For example, most researchers have classified it into two groups, namely strong ties and weak ties (Rozzell, Piercy and Carr 2014), whereas Éva et al. (2013) proposed a more refined categories with the social tie “in-betweens” added. Some scholars endeavored to establish indicators for predicting tie strength, such as frequency of contact and recency of contact (Arnaboldi et al. 2013). In this study, the classification of tie strength are beyond consideration, since this study mainly focuses on the dissemination of eWOM in affecting purchase intention and how the antecedents of eWOM impose influence on intention to purchase. In the context of mobile social media, people are offered more convenient opportunity to interact with friends or families. Messages delivered by emotionally closer ties are more trustworthy with no relevance of the message content (Levin and Cross 2004). On the contrary, though, relationship in weak ties hinders the willingness to interact and communicate due to lack of trust (Levin and Cross 2004). According to trust transfer theory, trust can be transmitted among different entities, such as trust in suppliers’ salesperson to that in firms (Belanche et al. 2014; Kuan and Bock 2007). Thus we adopted trust transfer theory and hypothesize that in the context of mobile social media, intimacy and close relationship between two people will lead to higher level of trustworthiness in between, which will then be transferred to trust in the mobile social media platform itself. Consequently, we propose our hypothesis as follows: H11. Tie strength exerts a positive influence on trust towards mobile social media. On Facebook or twitter, users can receive information from other users, including the known and the unknown. Lin and Utz (2015) examines the emotional outcome that might be evoked while browsing Facebook, supposing that stronger feelings might be provoked if the information comes from a more intimate friends on Facebook. For example, the closer the relationship between two Facebook users, the happier the browser will be when looking through the post of the sender. Unlike Symposium on Service Innovation in Big Data Area

Facebook, WeChat, as a typical example of mobile social media in China, is a virtual community comprised of people that know each other. Thus the social tie between WeChat users are more closely interwined, intensifying the positive emotional feelings brought about by positive reviews from intimate friends. eWOM constructs consist of information sharing and reviews giving are prevalent among users in mobile social media, so based on what theorized as bellowed, the effect of eWOM are determined by social intimacy rather than the mere content of reviews of products or services, which ultimately exerts impact on intention to purchase the recommended products or services. Hence we put forward the following assumption: H12. Tie strength significantly affect purchase intention in mobile social media. Subjective norm explains the extent to which an individual are affected and pushed by invisible pressure by important others’ beliefs to accept an attitude or conduct certain behaviors. Dozens of researches have been done on the influence of subjective norm on innovation adoption and purchase intention online (Venkatesh et al. 2012; Jalilvand and Samiei 2012; Pavlou and Fygenson 2006), but little study has been conducted upon the antecedents of subjective norms. Based on the definition of subjective norm mentioned above, we assume that the degree of acting in conformity with others are in positive ratio to the intimacy between those two individuals, since the closer the relationship between the individuals, the more likely the receiver is affected both emotionally and in action by the attitude and comments of the reviewer in mobile social media. Hence we come to the hypothesis as followed: H13. Tie strength significantly and positively affect subjective norms in mobile social media.

Fig.1. Proposed eWOM model for WeChat

4 Research methodology 4.1 Source of measures for questionnaire survey method We used the questionnaire survey methodology to validate the research hypotheses. The questionnaires were comprised of three parts. The first part is the demographic questions including gender, age group, education background, identity, income range, frequency of online shopping via mobile devices, expenses on mobile shopping, Symposium on Service Innovation in Big Data Area types of items shopped online and so on. The second part of the questionnaire depicts basic condition of WeChat usage, such as most frequently used mobile social media, main function used, frequency of retweet and comment. The last part constitutes 24 items to measure the following construsts: Scales for tie strength stem from previous studies (Chu and Kim 2011; Levin and Cross 2004). Measures for trust were adapted from Yoon and Occe˜na (2015), Chu and Kim (2011), Chu and Choi (2011), Roy et al. (2001). The measurement of source credibility were based on the work of Yoon and Occe˜na (2015), Chu and Kim (2011) and Chu and Choi (2011); Roy et al. (2001). The subjective norm scales were extracted from Sussman and Siegal (2003), Berlo et al. (1969). Opinion passing originated from Ajzen (1991), Davis et al. (1989), Fishbein and Ajzen (1977), Kaushik et al. (2015). To measure opinion giving, scales were modified from Chu and Kim (2011), Sun et al. (2006). Items for purchase intention were altered from Liang et al. (2011), Coyle and Thorson (2001), Erkan and Evans (2016). All the items were measured with a five-point Likert scale ranging from strongly agree (1) to strongly disagree (5). Since the study was conducted in China and original items were in English, drawing from the experiment of Baozhou, we adopted the translations process as follows for a guarantee of precision and wording. In the beginning, we let a Chinese-speaking researcher translate the original questionnaire into Chinese; then, another researcher transfers it back to English by himself. Finally, two researchers modified the Chinese version after comparison and mutual discussion. In ensuring the readability of the questionnaire, the first draft was used for a pilot test (Winston et al. 2002) among 15 WeChat users to acquire feedback about their understanding of the questionnaire. The result showed that question design is logically consistent and well understood. After minor modification based on suggestions from the respondents, the final version of questionnaire was examined by the initial translators and is presented in Appendix. 4.2 Data collection and sample The research targeted at WeChat users aged between 20 to 40 in China. Like Facebook, WeChat is a mobile social medium which provides users with a convenient access to real-time video chats, text messaging, voice messaging and group chats. Moreover, it enables users to share, comment, retweet and forward photos and information concerning daily life, products and services (Xu et al. 2015). Data was collected randomly through both online and offline. Online surveys were distributed via Sojump, a professional questionnaire design and survey platform in China. Paper questionnaires were handed out to staff in the neighboring company and people who take training classes in Hainan University, age of whom ranged from 20 to 40. Online data collection went on for 20 days, and is told to be voluntary and anonymous. Questionnaires are not allowed to be finished by the same person/ IP address twice. After exclusion of incomplete and invalid papers, 8 pieces were retrieved. 80 complete questionnaires were delivered by paper and 66 were returned. Most studies targeted at students for related research since they are the mainstay Symposium on Service Innovation in Big Data Area as online consumers and mobile users in China (Walczuch and Lundgren 2004; Wang and Yu 2015; Jang et al. 2016), but in order to increase the validity and reliability of the data, we enlarge the sample towards people aged between 20 and 40 with experience of using WeChat. Thus, it would be more reasonable for this study. The descriptive information of the participants was summarized in Table 1. Among them 54% were female and most respondents are well educated (Over 91.8% reached a high education degree of bachelor.) The average age of the respondents was 26.5, in which the age group of 20-40 accounted for 94.8%, conforming to the prerequisite of having experience of using WeChat. 69.2% of respondents were active WeChat users with frequency of using WeChat several times a day to socialize (93.6%), work (35.5%) and read news (34.2%). All participants had an experience of purchasing online, 43.3% of the respondents’ average expenditure on purchasing via mobile devices every month were 101-500 yuan (=319.5 US$), while most of the respondents (43.9%) earn no more than 5000 yuan per month.

Table 1. Demographic profile of questionnaires participants {N=224} Profile category Frequency Percentage Age Less than 20 years 6 2.60% 21-25 years 156 66.88% 26-40 years 65 27.92% Over 40years 6 2.60% Gender Male 107 46% Female 126 54% Education High school/secondary school 7 3.0% Associate degree(2 years) 12 5.2% Bachelor degree 134 57.6% postgraduate degree or above 80 34.2% Occupation Student 100 43.0% Administrative unit 12 5.2% Public service sector 31 13.5% Enterprise 67 28.7% Freelance 9 3.9% others 13 5.7% Monthly household income 0-1000 65 27.8% 1001-5000 102 43.9% 5001-10000 43 18.3% >10000 23 10.0% WeChat use frequency Several timeson each day 161 69.2% About once a day 65 27.8% Weekly 4 1.7% Scarely 3 1.3% Monthly expenditures via mobile devices <100 82 35.1% 101-500 101 43.3% Symposium on Service Innovation in Big Data Area

501-1000 34 14.7% >1000 16 6.9% Usage of WeChat(multiple choice) Social interaction 218 93.6% Work 83 35.5% Shopping 23 9.8% Merchandise 8 3.4% News 80 34.2% Financial transaction 26 11.1%

5 Data analysis and results As suggested by Anderson and Gerbing (1988), we adopt a two-step approach to examine the validity of the measurement models and test the relations among potent variables in structural models. SEM was used to perform the analysis procedure due to its edge over other statistical analysis methods (Gefen et al. 2011). The analyzing tools chosen are Amos 17. For the sake of accurate evaluation and sufficient statistical persuasion, we rechecked our sample size following the rule of Chin (1998). In order to ensure the accuracy of the results of data analysis, we conducted exploratory factor analysis (EFA) using Principal Axis extraction and Direct Oblimin rotation. We went through theKaiser–Meyer–Olkin (KMO) test and the Bartlett sphericity test to ensure properness of the data. The results, as depicted in Table, demonstrating good results for the following factor analysis. Seven factors were extracted via principal component analysis, each of which showing an eigenvalue value exceeding one. Moreover, factor loadings of less than 0.5 were suppressed and those with cross-loading phenomenon were abandoned. Factor loadings catering to the baseline requirements of 0.6 (Hair et al. 1992), indicating that all factors are accepted in the model. The final remaining factors account for of the total unique variance. 5.1 Measurement model 5.1.1 Reliability Reliability is employed to check to stability of the measures adopted in the model (Sapsford, 2006). Construct reliability was evaluated via the index of composite reliability (CR) (Wasko and Faraj 2005) and Cronbach's alpha. A satisfactory reliability was suggested by CR values over 0.7. As shown in Table 2, all CR values are well over the 0.7 benchmark. If the results are all over 0.5, then the measures are accepted with internal consistency. As presented in Table2, all the Cronbach’s alphas are within realm of acceptance, indicating a satisfactory level of internal consistency (0.932). Based on the above analysis, we are confirmed about the reliability and validity of the model. 5.1.2 Validity The convergent validity of constructs were calculated by factor loadings and average variance extracted (AVE) (Fornell and Larcker 1981). As shown in Table 3, Symposium on Service Innovation in Big Data Area the standardized factor loading of the constructs on its affliated variables outnumbers the 0.7. Besides, all the AVE value exceed criterion of 0.5 (Fornell and Larcker 1981; Hair et al. 2010), manifesting good convergent validity and inner uniformity of the measures (Fornell and Larcker 1981). In discriminant validity, as suggested by (Chin 1998; Wasko and Faraj 2005), loadings for every item of the potent variables should exceed the squared reciprocity between any randomized two variables. Evidently, as depicted in Table 2 , the overall outcome indicates a good condition of discriminant validity in the model. Confirmatory factor analysis (CFA) was applied to examine the model fit, reliability and validity in the measurement model. The measurement model is made up of 7 latent variables. Furthermore, to evaluate the level of multi-collinearity, we adopt variance inflation factors (VIF) put forward by Kleinbaum and Kupper (1988). A regression analysis was carried out to examine the relationship between purchase intention and other 6 variables. The VIF values are all within the standard of 3.3, given by (Diamantopoulos and Siguaw 2006; Diamantopoulos 2011). Thus multi-collinearity was beyond worry in this study (Pedhazur 1982). In a nutshell, the good-fitting measurement model is verified with ample reliability and validity prior to the following section of structure model analysis (Kenny 2014).

Table 2. Factor loadings for individual items Construct/item Loading(>0.7) Cronbach’s alpha Tie strength(TS) 0.774 TS1 0.821 TS2 0.819 Trust(T) 0.780 Trust1 0.500 Trust2 0.768 Trust3 0.826 Source Credibility(SC) 0.881 SC1 0.823 SC2 0.845 SC3 0.841 Subjective norm(SN) 0.593 SN1 0.481 SN2 0.620 SN3 0.541 Opinion Passing 0.749 OP1 0.701 OP2 0.772 OP3 0.549 Opinion Giving(OG) 0.837 OG1 0.836 OG2 0.870 OG3 0.819 Purchase Intention(PI) 0.800 PI1 0.659 PI2 0.772 PI3 0.701 Note: CR, construct reliability; SMC, * p < 0.001. TS=Tie strength; SN=Subjective norm; SC=Source credibility; OP=Opinion passing; OG=Opinion giving; PI=Purchase intention;

Symposium on Service Innovation in Big Data Area

Table 3. Descriptive data, correlations and the square root of AVE Constructs TS SN Trust SC OP OG PI TS 1 SN 0.488 1 Trust 0.313 0.493 1 SC 0.243 0.446 0.682 1 OP 0.160 0.352 0.401 0.480 1 OG 0.061 0.235 0.379 0.425 0.619 1 PI 0.240 0.345 0.330 0.403 0.565 0.473 1 Mean 2.558 2.658 2.936 3.140 3.057 3.571 2.80 Standard deviation 0.893 0.714 0.812 0.849 0.890 0.955 0.891 Note: The bolded diagonal elements are the square root of average variance extracted (AVE) for each variable. 5.2 Structural model After confirming the validity of the measurement model, the structural-path analysis was conducted among exogenous and endogenous variables. of the structure model was shown in Figure 2 as below, presenting a good model fit compared with the recommended value respectively (x2/df=1.876 ， GFI=0.993 ， NFI=0.996 ， CFI=0.990，RMSEA=0.086), a comparative fit index (CFI) value of 0.926 (N0.9), normed fit index (NFI) value of 0.917, and root mean square error of approximation (RMSEA) value of 0.067 (N0.05).). In spite of different thresholds of RMSEA given by scholars, ranging from 0.01 to 0.1 (Maccallum et al. 1996; Hu and Bentler 1998; Browne and Cudeck 1993), we could take it as one of the indicators of fitting goodness.

Fig. 2. Path analysis result Note: * significant at the .05 level, ** significant at the .01 level, *** significant at the .001 level. Table 4. Results of hypothesis testing Hypothesis Path Path coefficient C.R Supported? H1 Trust→ Purchase Intention 0.619* -2.059 Y H2 Trust→ Opinion Passing 0.561*** 5.005 Y H3 Trust→ Opinion Giving 0.565*** 5.418 Y H4 Opinion Passing→ Purchase Intention 0.504*** 4.136 Y H5 Opinion Giving→ Purchase Intention 0.283** 2.724 Y H6 Source Credibility→ Purchase Intention 0.557* 2.243 Y H7 Source Credibility→ Trust 0.866*** 6.669 Y H8 Subjective Norm→ Opinion Passing 0.155* 1.789 Y H9 Subjective Norm→ Opinion Giving -0.004 -0.053 N H10 Tie Strength→ Trust 0.141* 2.255 Y H11 Tie Strength→ Purchase Intention 0.271** 2.818 Y H12 Tie Strength→ Subjective Norm 0.731*** 4.897 Y Note: *p < 0.05, **p < 0.01, ***p < 0.001.C.R e Critical Ratio. Symposium on Service Innovation in Big Data Area

5.3 Hypothesis testing The majority of path coefficients present p value below 0.05, suggesting that presumed relationships were statistically significant under the level, supporting the assumed hypotheses. The impacts of Trust, Opinion Passing and Opinion Giving on intention to purchase(b= 0.619, t= -2.059, p > 0.01), (b=0.561, t= 4.136, p < 0.001), (b= 0.565, t= 2.724, p < 0.01)on were found significant, showing that opinion passing and opinion giving are both key indicators of purchase intention. Trust exerts positive impact on the corresponding eWOM dimensions, including opinion passing and opinion giving, corroborating hypotheses from H4 to H5 (b = 0.504, t = 5.005, p < 0.001) and (b = 0.283, t = 5.418, p < 0.01). Conformed with prior research, the effect of source credibility on purchase intention was marked, thus verifying H6 (b = 0.557, t = 1.789, p >0.01). The results of H7 validation favors the previous findings (Erkan and Evans 2016), indicating the crucial role that source credibility plays in trust towards WeChat platform (b = 0.866, t =6.669, p < 0.001). Further, the findings reveals that subjective norm significantly influences opinion passing, thus supporting H8 (b = 0.155, t = 1.789, p>0.01), whereas the relationship between subjective norm and opinion giving is insignificant, henceforth, H9 is not empirically supported (b = -0.004, t = -0.053, p>0.1). Additionally, the hypothesis 10 &11 also demonstrate that social closeness proves to exert impact on trust and ultimately on purchase intention (b = 2.255, t = 2.607, p > 0.01), (b = 2.818, t = 2.607, p<0.01), which mirrors prior findings (Rozzell et al. 2014). Finally, a potent relationship was validated between tie strength and subjective norm, thereby backing H12 (b = 0.731, t = 4.897, p < 0.001). Results of path coefficients and hypotheses testing are illustrated in Figure 3 and Table 5.

6 Discussions and implications The study aims at analyzing the mechanism behind eWOM spreading via mobile social media, developing a theoretical model to depict how WeChat as a mobile social media platform affect WeChat users’ purchase intention in China. WeChat has become predominant mobile social media platform in China. Furthermore, the study examines how control variables affect the purchase intention in the mobile social media context. 6.1 Key findings The study contributes to the few literature on the effects of trust, tie strength, source credibility and subjective norm on eWOM effectiveness in influencing purchase intention via WeChat. Unlike previous research, in which respondents were confined to university students (Hasbullah et al. 2016). This study extends the scope, targeting at those who have experience of using WeChat, making the research more accurate and rational. As shown in Table 3, of all 12 hypotheses, 11 were supported in the model. Theoretical and practical implications can be drawn from data analysis. Most important of all, drawing upon source credibility theory, tie strength was Symposium on Service Innovation in Big Data Area found to significantly and positively affect subjective norm with a direct effect of 0.76. This finding provides a supplement to previous studies regarding subjective norm, in which the impact of subjective norm on intention toward adoption and purchase has already been confirmed (Zhao et al. 2014). The critical role that tie strength plays in affecting subjective norm are consistent with social common sense, since relational closeness will directly determine the significance that the reviewer means towards the receiver, thus leading to psychological, attitudinal and behavioral uniformity between the reviewer and the receiver. With the popularity of WeChat among Chinese users, people can chat and interact with friends at ease, strengthening the social intimacy between users in WeChat. The strengthened social ties between users can intensify the motivation and willingness to comply with the perceived beliefs and attitudes of the reviewer, thus facilitating the dissemination of eWOM in the context of WeChat. Therefore, the significant, direct and positive impact of social closeness on subjective norm reflects that WeChat should constantly put forward new methods to motivate social interaction among WeChat users, intensifying the social intimacy between users. Also, the study expands the existing research to the context of mobile social media in China, revealing that trust has a significant and positive impact (0.661) on purchase intention, complying with existing studies concerning social media and B2B commerce (Wang et al. 2013; Chu and Kim 2011). Further, by shedding light on the relationship between trust and components of eWOM, the study found that trust displays a significant and direct influence on eWOM (opinion passing (0.615) and opinion giving (0.634) respectively, indicating that trust exerts equal importance on opinion passing and opinion giving in the environment of WeChat. The study shed light on the constructs of eWOM in mobile social media, contributing to eWOM literature, thus disclosing the fact that equivalent emphasis should be placed upon opinion passing and opinion giving in maintaining eWOM among WeChat. The higher the level of trust user feels towards WeChat platforms, the more likely he/she is to actively participate in eWOM behaviors, such as expressing reviews and sharing friends’ comments in WeChat circles or among chat groups. Besides, opinion giving and opinion passing also act as influential factors affecting purchase intention. In particular, opinion passing (0.53) is slightly more influential compared with opinion giving (0.31) in terms of purchase intention. This implies that compared with opinion giving, opinion passing serves as a more critical factor influencing intention to purchase the recommended product or service. Meanwhile, manufacturers should attach great importance to incentives of both eWOM conducts in WeChat, thus promoting the popularity and reputation of the products and boosting sales. Besides, we find the unrecognized relationship between subjective norm and opinion passing, extending theory of planned behavior. We looked into the influence that subjective norm imposes on opinion giving and opinion passing respectively. Prior studies neglected the normative influence on eWOM. Specifically, we make an attempt to consider further the relation between subjective norm and eWOM constructs. We found subjective norm positively affects opinion passing in WeChat (0.197), whereas its impact on opinion giving was insignificant. This might be Symposium on Service Innovation in Big Data Area attributed to the fact that opinion passing refers to the recognized reviews and information from others that you share in friend circle in WeChat, therefore more susceptible to perceived expectation of compliance with reviewers, whereas opinion giving is self-generated contents with spontaneity, less likely to be affected by subjective norm. Overall, the results demonstrates that users who are more susceptible to subjective norm will have more chance to pass opinion of the reviewers in friend circle, while the correlation between subjective norm and opinion giving is insignificant. As expected, tie strength exerts significant and positive influence on trust towards WeChat platform (0.158) and purchase intention (0.284), compatible with existing literature. However, the impact of tie strength on trust is slightly weaker than that on purchase intention, due to the fact that trust towards WeChat platform, transmitted from trust between users in the light of trust transfer theory, displays relatively weaker effect compared with direct impact of tie strength on purchase intention. Chinese WeChat users are reported to purchase a large variety of product online, which provides great room for sales promotion via mobile social media. The exponential growth of WeChat usage in China can be accounted for eagerness to interact with their friends Chang and Zhu (2011), leading to the explosively viral spread of eWOM both extensively and intensively. And WeChat developers themselves have cleverly come up with stimulus to further motivate social interaction with their friends through WeChat, such as the annually activity of “Chinese new year red envelope” , which began in January, 2014. And the overall number of red envelope delivered and received soared to 32.1 billion, with 516 million WeChat users involved. Other novel incentives includes a random envelope delivered to the friend in WeChat enables you an access to view the photo he/she posts on the friend circle. Regarding source credibility, the results presents great impact source credibility has on trust towards WeChat platform (0.886). It not only reconfirms the conclusion discussed in preceding research (Erkan and Evans 2016), but also adds to the conclusion a case from China. The more reliable the source of information is in WeChat, the more trustworthy the receiver feels towards WeChat platform. 6.2 Implications 6.2.1 Implications for research The theoretical implications for this study are threefold. First, this study supplements to current theory by identifying the relationship between tie strength and subjective norm. Deriving from rational behavioral theory, subjective norm is regarded as crucial factor affecting “attitude towards use”, “intention towards adoption” and “perceived usefulness” from psychological aspect (Kim et al. 2009; Casalò et al. 2010; Kaushik et al. 2015). Quantities of research has been done to confirm the positive influence of subjective norm on attitude and intention to adopt, however, few tried to investigate antecedents of subjective norm from social interaction perspectives. Hence, this study bridge the theoretical gap by exploring impacts that social factor exert on subjective norm in mobile social media context. Symposium on Service Innovation in Big Data Area

Results ascertain that social intimacy serves as an extremely vital role in subjective norm, which deserve future research in relevant field combining theory of planned behavior (TPB). Second, rooted in literature related to eWOM, the study have significant contributions in providing deeper understanding in how trust towards mobile social media affect eWOM in subdivided aspects. Prior studies have mentioned that eWOM constructs comprised of two variables, namely opinion giving and opinion passing (Chu and Kim 2011), Tien Wang and Ralph (2015) overlooking the relationship among trust, subjective norm and eWOM as a multi-dimensional construct. The proposed model offers two edges. First, the segmentation of the construct uncovers which dimensions of eWOM are more susceptible to its antecedents such as trust and subjective norm, and which aspects of eWOM exerts greater influence on intention to purchase in the context of WeChat. Second, the theorization illustrates elaborately the influencing mechanism of trust on eWOM, ultimately purchase intention in details. As a whole, the study highlights the equal importance of opinion giving and opinion passing in facilitating eWOM and promoting sales. What also deserves to be mentioned is that the study empirically examines eWOM in a less-studied research area, China, targeting at a well-received mobile social media (WeChat). Previous studies, in the vast majority, focused on online networking sites or B2B sites such as Facebook, RenRen (from China)in western cultures ignoring the rapid popularity of mobile social media. In order to enrich the theoretical content in the context of mobile social media, we empirically conducted the research with case of WeChat in China. Future studies are suggested to make comparisons between eWOM models from different cultures. 6.2.2 Implications for practice Our research also offers managerial implications as follows. First, although the mediating effect that trust plays between tie strength and purchase intention has been confirmed by prior studies, rarely does scholars examine the effect on the context of mobile social media in China. Thus we provide sufficient evidence that social closeness between users plays an important role in determining users’ trust towards mobile social platform and subjective norm. This illustrates that WeChat practitioners should continually come up with novel stimulations to facilitate interactions among WeChat users, strengthening social intimacy and finally reinforcing effect of subjective norm and trust towards the platform itself. Second, the findings show that trust towards WeChat platform will equally lead to intention to purchase and engagement in eWOM, namely opinion giving and opinion passing. As expected, two dimensions in eWOM can significantly provoke intention to purchase the recommended product/service, specifically, opinion passing exerts greater influence in purchase intention. Therefore, mobile social network developer in China should establish multiple measures to enhance users’ trust towards the platform, thus driving users to actively participate in eWOM activities with spontaneity. Further, enterprise practitioners are advised to figure out enjoyable activities to arouse users’ willingness to express their opinions and feelings of the products and share the comments posted by their friends. For example, minor Symposium on Service Innovation in Big Data Area economical incentives such as coupons and gifts can be incorporated to encourage review posting in mobile social media by product manufacturers. The rapid spread of eWOM among trustworthy friend circle is a powerful ensurance of establishment of product reputation and image, ultimately leading to purchase intention and even actual purchase behavior.

7 Limitations and directions for future research Several limitations should be acknowledged beforehand in this study. First, this study explores impacts of tie strength, subjective norm, source credibility, trust in affecting eWOM conducts and purchase intention in a particular context, that is, among WeChat users in China. Future research is advised to generalize conclusion in wider areas, enriching data by incorporating cases of WeChat usage in other countries as well. Second, despite the fact that the survey was mainly conducted online, acquiring data from all over the nation, but the majority of respondents are in Haikou, a relatively under-developed province in China. Hence, in order to make the result more persuasive, it is suggested that future studies extending to other provinces and even make comparisons among different regions if possible. Third, instead of focusing merely on convenient sample of university student, this study extends the scope of the participant to whoever owns experience of using WeChat. It would be meaningful to compare intention to purchase the recommended product/services by friends in WeChat among different age groups, gender, educational background, economical status, etc. For example, age factor was confirmed to be a significant moderator moderating the effect of trust in C2C commerce (Yoon and Occe˜na 2015). Finally, considering its absolute focus on social and psychological factors affecting dissemination of eWOM and intention to purchase, several critical factors are beyond consideration in enhancing purchase intention , such as privacy concerns, perceived risks of commerce online (Featherman and Hajli 2015; Hajli and Lin 2014) and self-disclosure (Green et al. 2016). Therefore, the current research of purchase intention in the context of WeChat can be enriched if extra theories are applied to include variables such as privacy concerns and self-disclosure in the model.

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Appendix

Constructs and items Tie strength(TS) (Chu and Kim 2011; Levin and Cross 2004) TS1 Overall, I feel very important about the contacts on my “friends” list on social networks TS2 I communicate very frequently with the contacts on my “friends” list on my social network Trust(T) (Yoon and Occe˜na 2015; Chu and Kim 2011; Chu and Choi 2011; Roy et al. 2001) T1 A person from my online community (i.e., groups with whom I interact online) recommending a seller/buyer in C2C e-commerce reduces my risk in the transaction T2 I think that the information offered by this system is sincere and honest. T3 I feel confident about having discussions with the contacts on my “friends” list on social networks Source Credibility(SC) (Sussman and Siegal 2003; Berlo et al. 1969) Symposium on Service Innovation in Big Data Area

SC1 I think the reviewer is trustworthy SC2 I think the reviewer is an expert in this field SC3 I think the reviewer is highly rated by other WeChat users. Subjective norm(SC) (Ajzen 1991; Davis et al. 1989; Fishbein and Ajzen 1977; Kaushik et al. 2015) SN1 People who influence my behavior think that I should use the system. SN2 People who are important to me think that I should use the system. SN3 Using a system enhances my stature within my surroundings. Opinion Passing(OP) (Chu and Kim 2011; Sun et al. 2006) OP1 When I receive product related information or opinion from a friend, I will pass it along to my other contacts on social networks OP2 I tend to pass along my contacts’ positive reviews of products to other contacts on social networks OP3 I tend to pass along my contacts’ negative reviews of products to other contacts on social networks Opinion giving(OG) (Chu and Kim 2011; Sun et al. 2006) OG1 I often persuade my contacts on social networks to buy products that I like OG2 My contacts on social networks pick their products based on what I have told them OG3 On social networks, I often influence my contacts’ opinions about products Purchase Intention(PI) (Liang et al. 2012; Coyle and Thorson 2001; Erkan and Evans 2016) PI1 I will purchase the recommended products the next time I need the related products. PI2 If a friend called me to get my advice about which snacks to buy, I would advise them to buy the recommended products. PI3 I am willing to buy the products recommended by my friends on RenRen