International Journal of Information Management 40 (2018) 88–102

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International Journal of Information Management journal homepage: www.elsevier.com/locate/ijinfomgt

Who will attract you? Similarity effect among users on online purchase intention of movie tickets in the social shopping context

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Senhui Fua, Qing Yanb, , Guangchao Charles Fengc a

Sun Yat-sen Business School, Sun Yat-sen University, China School of Journalism and Communication, Jinan University, China c School of Communication, Shenzhen University, China b

A R T I C L E I N F O

A B S T R A C T

Keywords: Similarity effect Perceived usefulness Perceived enjoyment Trust transfer Purchase intention

With the popularity and growth of social networking sites, users often rely on the advice and recommendations from online friends when deciding to purchase movie tickets. The relationship between users' reviews and movie ticket purchase intention in the context of social media has been demonstrated in several studies, but few studies have explored users' perceptions of the similarity effect on online purchase intention or the psychological mechanisms of the similarity effect. From an interpersonal relationship perspective, we propose that similarity (including external and internal similarity) is an important cue for users who are deciding to purchase movie tickets online. Built on SOR (Stimuli-Organism-Response) Model and drawn upon trust transfer and information technology acceptance theories, we examined whether similarity could enhance users' online purchase intention of movie tickets. The results of a PLS analysis demonstrated that both external and internal similarity significantly affected users’ perceived usefulness, enjoyment and trust transfer, which in turn exerted profound impacts on users’ social shopping behaviors.

1. Introduction

distribution channel that allows customers to rapidly book movie tickets with convenience and substantial price savings. Therefore, it is ideal for consumers to purchase the tickets online. The purchasing rate was more than 35% in many European countries in 2011, and 34% of Internet users in the US have experienced purchasing movie tickets online (Fritz & Schwartzel, 2015). The Chinese movie market has grown at a faster rate. A total of 57.5 percent of movie tickets were sold online in 2015 (Papish, 2016), and the box office performance in China increased from 370 million to 6.78 billion, with a growth rate of 30% each year between 2006 and 2015, which contributes to the secondhighest-grossing movie ranking in the world (Feng, 2017). In contrast, social shopping provides a new platform for expressing moviegoers' opinions and intentions to purchase movie tickets. Movie reviews are vital for the movie industry (Harrison-Walker, 2001). In general, positive reviews are associated with higher movie sales (Rui, & Whinston, 2013; Robinson, Goh, & Zhang, 2012; West & Broniarczyk, 1998), which further influence users’ purchase intention (Robinson et al., 2012). As such, purchasing movie tickets online is a trend, and more people will use this approach to buy tickets on social commerce sites. Although online sales of movie tickets are increasing, some users hesitate to purchase tickets online. Users also have no specific loyalty to a given

The absence of human and social elements is one significant challenge that hinders the growth of e-commerce (Lu, Fan, & Zhou, 2016). The emergence of social shopping could help to ameliorate this situation. Social shopping is an emerging worldwide trend that has rapidly been developing in China. The beginning of social shopping was viewed as a subtype of e-Commerce that uses social media to support and enhance social interactions between customers (Marsden, 2010). After adding interpersonal interactions, social media gradually became the core platform for online shopping. Meanwhile, compared to the traditional “broadcast style” communication of e-commerce, social shopping depends on “penetration style” interpersonal communication for twoway communication and emphasizes users' contributions and usergenerated content. This e-commerce method combines social networking and shopping to satisfy the needs for obtaining information before shopping and sharing personal experiences online after use (Stampino, 2007). Therefore, there has been growing interest in both academia and industry in the study of the effects of social networking sites and their influences on consumer behavior, including online purchase intention (Chan, Lei, Leong, Ng, & Wong, 2016). For the movie industry, social shopping provides another

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Corresponding author at: School of Journalism and Communication, Jinan University, Guangzhou, IN 510632, China. E-mail addresses: [email protected], [email protected] (Q. Yan).

https://doi.org/10.1016/j.ijinfomgt.2018.01.013 Received 17 February 2017; Received in revised form 26 January 2018; Accepted 27 January 2018 0268-4012/ © 2018 Elsevier Ltd. All rights reserved.

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2.1. The stimuli-organism-response model in social shopping

website, as information redundancy and price competition encourage them to continually change portals. Business practitioners face the challenge of determining how to personalize a user's purchasing path and the specific types of social strategies that are the most effective for facilitating users’ engagement in social shopping, such as purchasing movie tickets online. Given the low cost, it is useful for practitioners to know the primary factors that affect the use of social shopping websites for purchasing movie tickets. Interpersonal relationships exist in the context of social shopping, and interpersonal interactional factors have received much attention (Hsiao, Lin, Wang, Lu, & Yu, 2010). For interpersonal interaction, similarity is an important cue upon which people build personal relationships, which has been highlighted in recent literature (Brack & Benkenstein, 2012; Brendl, Chattopadhyay, Pelham, & Carvallo, 2005; Burger, Messian, Patel, del Prado, & Anderson, 2004; Guéguen, Martin, & Meineri, 2011; Jiang, Hoegg, Dahl, & Chattopadhyay, 2010; Kachersky, Sen, Kim, & Carnevale, 2014; Martin, Jacob, & Gueguen, 2013) that has found that similarity (demographic, incidental or overall similarity) has a positive effect. Similarity refers to, “the degree to which people who interact are similar in beliefs, education, social status, and the like” (Rogers & Bhowmik, 1970). In sociology, there is a similarity effect when people prefer those who are similar to their own characteristics. Little research has examined the types of social shopping websites that drive users to purchase movie tickets online or the psychological mechanisms that are used to complete transactions on these sites. Deka (2017) indicated that it is important to understand factors that influence online purchasing behavior. Drawing from the literature above, this study infers that investigating the impacts of interpersonal interaction factors (e.g., similarities between users in the social shopping context) on users’ social purchase intention should be a promising area for research. However, there has been little effort to examine factors that contribute to similarities between users on social shopping websites. To address this gap in research, the present study aims to explore the similarity factor in the social shopping context. Then, we use concepts from psychology to propose a new method of categorizing similarity and introduce a new theoretical explanation for how similarity affects users’ decision-making processes. The results from our study demonstrate that there is a positive effect of similarity (external and internal similarity) on users' online purchase intention for movie tickets and that internal similarity has a larger effect than external similarity. We also incorporate “perceived enjoyment”, “perceived usefulness” and “trust transfer," from the fields of information technology and organizational behavior, in our research to employ an interdisciplinary perspective for examining users' purchase intention for movie tickets online. The rest of the paper is organized as follows. The next section reviews the theoretical background for this study. The subsequent sections develop a research model that is based on the above-described theories and present the research hypotheses. The research methodology and data analysis results are presented in the following sections. In the last section, we discuss the findings and their theoretical and practical implications, as well as limitations and future directions for research.

2.1.1. Stimuli Previous research on social shopping has indicated that the experience of consumers in the context of social media differs from offline shopping, as consumers have social interactions with others (Hajli, 2014) and all users eventually interact with other users online (Bagozzi & Dholakia, 2006; Kozinets, 2010). Once a user interacts with others online, it is likely that they will become a recurring member of the community, and as time passes, they are more likely to become a source of information and social interaction (Kozinets, 2010). Thus, similarity between members in the social community is an important stimulus that influences users' intentions and behaviors on a social shopping website. 2.1.2. Organism Most human behaviors are driven by mental states, which are often affected by how we relate to a specific stimulus. This type of organism includes cognitive and affective reactions. Previous environmental psychology research defined cognitive reactions as ‘the mental process occurring in individuals’ minds when they interact with the stimulus’(Eroglu, Machleit, & Davis, 2001), and affective reactions are related to individuals’ emotional responses when they are stimulated by the environment (Sun & Zhang, 2006). In this context, we introduce perceived usefulness (PU) and perceived enjoyment (PE), which are constructs from the TAM Model, to further explain users’ cognitive and affective reactions when interacting with similar members on a social shopping website. In addition, trust and trust transfer are a type of reaction that includes both cognitive and affective factors. 2.1.3. Response Consistent with the S-O-R model, responses represent the final outcomes and decisions of users based on cognitive and affective reactions and include approach or avoidance behaviors (Sherman, Mathur, & Smith, 1997). In the context of social shopping, the response has two important aspects, namely, social shopping intentions and social sharing intentions (Chen & Shen, 2015). Because this study focuses on consumers' online purchases of movie tickets, we only discuss social shopping intentions. Thus, this paper explores the effects of similarity on social shopping from the perspectives of perception and trust transfer. 2.2. The similarity effect: external and internal similarity In sociology, we describe the similarity effect as the way in which people strongly prefer those who have similar characteristics to their own. The concept of similarity has been widely studied in the areas of psychology and marketing. Based on the hypothesis that similarity leads to attraction (Byrne, 1971), many scholars have examined similarities for different personal attributes, including demographic information (Hitsch, Hortaçsu, & Arielyet, 2010), trusting in those who have the same fingerprint (Burger et al., 2004), wearing the same clothing (Buckley & Roach, 1981), and preferring those who have similar facial features (Bailenson, Pontikakis, Mauss, Gross, Jabon, & Hutcherson, 2008). In the context of brand names, consumers prefer brands that have their initials, e.g., Tonya prefers the brand “Twix” (Brendl et al., 2005). In addition, people also prefer those who have similar attitudes (Fisher, 1974; Simons, Berkowitz, & Moyer, 1970) or interests (Martin et al., 2013). In addition, Guéguen et al. (2011) demonstrated that similarity promoted people’s implicit behaviors. Research on interpersonal attraction in social psychology has shown that people like others who share similarities with themselves because human beings are social animals. When people interact with similar others, they will experience smoother communication and better understanding, and they will be able to predict the reactions of the other person. In addition, similarity can stimulate a feeling of connection to

2. Theoretical background The following theories are integrated to develop the research model. The Stimuli-Organism-Response Model (SOR) provides a framework that describes similarity as a stimulus, while perceived usefulness, perceived enjoyment and trust are different types of organism, and the online purchasing intention for movie tickets is a type of response. The theories of trust transfer and information technology acceptance contribute to the rationale for choosing trust, perceived usefulness and perceived enjoyment as our mediating variables in the research model. 89

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Fig. 1. Research Model.

2.4. Trust transfer

others, which is consistent with the principle of attraction in social media. According to social response theory, which was proposed by Reeves and Nass (1996), people will apply the social norms in reality to computers that have human features. Therefore, online similarity can also stimulate connections to other members in the community and can lead to an increased willingness to interact. This type of similarity can be based on different factors, such as genetic, social, cultural, physical and psychological aspects. Theoretically, both external environmental factors and internal biological factors influence growth and development in humans. Similarities between humans can also be divided into external and internal similarities. Based on the above literature, external similarity can be obtained without deep interactions, e.g., name, age, gender, birthday, place of residence, while internal similarity can only be obtained through deep interactions, e.g., values, interests, attitudes or opinions and preferences. We use these two types of similarity as the two constructs in this study.

Trust transfer has received growing attention in the literature. Doney, Cannon, and Mullen (1998) proposed that trust transfer occurs when one party (a trustor) ascribes trustworthiness to an unfamiliar partner based on that partner’s association with a trusted third party. Recent studies demonstrated that trust transfer can occur both online and offline. For example, mutual trust among community members can be transferred to trust for the virtual community (Chen, Zhang, & Xu, 2009). To better understand the interplay between different levels of trust in the social community, we draw from this theory to examine how member–member trust can be transferred to community-member trust. Trust plays an important role in the exchange between consumers and service providers because there are many risks in the virtual world, and online consumers and service providers must determine their own interests based on trust. However, it is not easy for consumers to directly trust service providers. In contrast, consumers are more willing to trust other consumers rather than service providers. Therefore, trust toward members in the same social community is more valuable for marketers and service providers.

2.3. The TAM model The Technology acceptance model (TAM) is one of the most commonly employed theories for examining technology acceptance (Davis, 1989). There are two perceptions that affect people’s behavior intentions: perceived ease of use (PEOU) and perceived usefulness (PU). Perceived usefulness is “the degree to which a person believes that using a particular system would enhance his or her job performance,” while perceived ease of use is “the degree to which a person believes that using a particular system would be free of effort” (Davis, 1993). Based on several limitations, scholars have extended TAM by adding variables in different contexts and settings (Nguyen, 2015). Davis, Bagozzi, & Warsaw (1992) added perceived enjoyment (PE) as an antecedent for the user’s acceptance of information technology and found that perceived pleasure had a significant effect on the adoption intentions for a word processing program. Perceived enjoyment is the degree to which using technology is enjoyable (Davis et al., 1992). It refers to the hedonic attractiveness, aesthetic beauty, perceived pleasure, playfulness or fun that is derived from using a system or interface. In the context of social shopping websites that are supported by Web 2.0 social media technologies, users can easily interact with other users, that is, most users are already familiar with online shopping technologies, and the effect of PEOU decreases as users become familiar with a technology (Gefen, 2003). In addition, people are more likely to find similar others. Over time, they may perceive their information as useful and feel close to and enjoy other members in the same community and may be more willing to purchase products that are recommended by others. Thus, we do not examine the effect of PEOU but explore the effects of PU and PE.

3. Research model and hypotheses This study aims to examine the effects of similarity on consumers’ social shopping behaviors from the perspective of users’ perceptions and trust. Fig. 1 depicts the research framework, which reflects the influence of similarity (i.e., both external and internal similarity) on social shopping intentions, as well as the role of perceived usefulness, perceived enjoyment, and trust toward members and the community platform. In this section, we explain the primary constructs and interrelationships in the research model. 3.1. The effects of similarity on perceived usefulness Prior research defines similarity as “the degree to which people who interact are similar in beliefs, education, social status, and the like” (Rogers & Bhowmik, 1970). In this study, similarity is defined as the extent to which users in a social community perceive that they can find members who have names, ages, birthdays or astrological signs, places of residence, interests, values, tastes and preferences that match their own. We believe that external and internal factors will have different effects, so we identify two categories: the former four elements reflect external similarity, while the latter four elements represent internal similarity. Social shopping platform users are more likely to share information with others with whom they share similarities, and more frequent exchanges may feel more enjoyable. Many studies have examined the relationship between similarity and perceived usefulness and contend that consumers’ 90

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Benbasat, & Cenfetelli, 2011) Moreover, Lichtenthal & Tellefsen (2001) indicated that studies that focused on internal characteristics had stronger relationships between similarity and sales performance than studies that focused on observable characteristics. Therefore, we hypothesize that user’s internal similarity will have a stronger influence on users’ perceived enjoyment in the social community based on our prior hypothesis for the relationship between similarity and perceived enjoyment. Thus, we propose the following hypotheses:

recommendations from those who are similar can be more useful. For example, several scholars have shown that information from a similar reviewer is more persuasive than from a dissimilar reviewer (Brock, 1965; Brown & Reingen, 1987). In addition, information that is retrieved from user-generated sources is often perceived as more useful than information that is generated by marketers (Bickart & Schindler, 2001; Bronner & de Hoog, 2010), because consumers are connected to users who are similar to themselves (Simons et al., 1970; Lichtenthal & Tellefsen, 2001). Specifically, individuals tend to be extremely sensitive to incidental similarities between themselves and others (Dholakia & Vianello, 2009). In general, incidental similarity was often recognized as external similarity. In the context of a social shopping website, several website features help consumers find members who have similar interests and lifestyles (viewed as internal similarity in this study), for example, the features “Others Who Also Like It” and “Similar Recommendations.‘A relevant empirical social commerce survey shows that 73% of shoppers agree that “people like me’ are the most trusted sources from whom to seek advice when making a shopping purchase (Marsden, 2009). Users are more likely to highly value information from others who have similarities in the same social community and more frequently exchange their ideas. Because a perception of similarity may compensate for the ambiguity of the information source’s characteristics that are hard to assess in a virtual environment (Smith et al., 2005). Moreover, Lichtenthal and Tellefsen (2001) found that studies that were focused on internal characteristics had stronger relationships between similarity and sales performance than studies that focused on observable characteristics. Therefore, we hypothesize that internal similarities between users will have a stronger influence on users’ perceived usefulness of reviews from other members in the social community based on our prior hypothesis for the relationship between similarity and perceived usefulness. Thus, we propose the following hypotheses:

H2a. External similarity between users is positively associated with users’ perceived enjoyment in the social community. H2b. Internal similarity between users is positively associated with users’ perceived enjoyment in the social community. H2c. Compared with external similarity, internal similarity between users will have a stronger influence on users’ perceived enjoyment in the social community.

3.3. The effects of similarity on trust Digital review processes increase the ‘socialness’ perceptions for opinion exchange (Wang & Yang 2007). Trust is often recognized as an essential element for building a successful relationship (Morgan & Hunt, 1994). With the diversification of information on the Internet, consumers often seek advice from the community and individuals whom they can trust. Consumers will be more likely to share their own information with trusted parties due to privacy concerns (Smith, Dinev, & Xu, 2011). Therefore, what types of features in a social shopping platform lead to trust between users? Similarity may be a possible means through which trust occurs. Research has confirmed the similarity effect in face-to-face sales situations. In the area of web search and data mining, Anderson, Huttenlocher, Kleinberg, & Leskovec (2012) used real data to examine the effect of the similarity between two users on their evaluations of each other. They found that the interaction between similarity and status stimulated emotion; specifically, when a user was similar to the other user, his evaluation was less driven by status. Crandall, Cosley, Huttenlocher, Kleinberg, & Suri (2008) used data from an online community to investigate the interaction between similarity and social relationships, and they found that the similarity between two individuals is the index of their interactions with each other in the future, and this type of similarity and the value of social influence also affected their future behavior. Relying on reviews from similar others serves as a useful heuristic for readers, as these reviewers are more relevant for determining the appropriateness of decisions (Banerjee & Chua, 2016), and reviewers with similar backgrounds or preferences are viewed as more trustworthy and credible (Shan, 2016). Moreover, Lichtenthal and Tellefsen (2001) summarized that studies that focused on internal characteristics had stronger relationships between similarity and sales performance than studies that focused on observable characteristics. Therefore, we hypothesize that internal similarities between users will have a stronger influence on users’ trust in the social community based on our prior hypothesis for the relationship between similarity and trust. As such, we test the following hypotheses:

H1a. External similarity between users is positively associated with users’ perceived usefulness of reviews from other members in the social community. H1b. Internal similarity between users is positively associated with users’ perceived usefulness of reviews from other members in the social community. H1c. Compared to external similarity, internal similarity between users will have a stronger influence on users’ perceived usefulness of reviews from other members in the social community. 3.2. The similarity effect on perceived enjoyment According to similarity extrapolation theory, similarity in preferences generates a positive affective reaction toward the other, which in turn motivates inferences of further similarity (Nisbett & Wilson, 1977). We expect that this positive affection will make consumers feel pleasant and enjoyable. When people interact with those who are similar to themselves, they engage in more emotional connections and feel easier and happier to communicate with each other in the social community. Tauber (1972) found that when someone shops with others, they communicate about the same areas of interest and are better able to assess goods. Similarly, consumers are more likely to accept advice and recommendations from those who are similar to themselves, because similar information is perceived as more relevant (Bearden & Etzel, 1982). Another study provides indirect evidence for the positive influence of the similarity effect on consumers’ perceived enjoyment. For example, Murray, Holmes, Bellavia, Griffin, and Dolderman (2002) found that among married couples, those who were most satisfied in their relationships perceived that they were more similar to their spouses. Because interacting with similar counterparts requires less cognitive effort and yields more hedonism (Al-Natour,

H3a. Users who have external similarities are positively associated with trusting members in the social community. H3b. Users who have internal similarities are positively associated with trusting members in the social community. H3c. Compared to external similarity, internal similarity between users has a stronger influence on trust for members in the social community.

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online consumers enjoy their shopping experience, then they may engage in more exploratory browsing on the web, which leads to more unplanned purchases (Beatty & Ferrell, 1998). Adelaar, Chang, Lancendorfer, Lee, and Morimoto (2003) also found a positive relationship between an individual’s emotional responses and impulsive buying behavior (Adelaar et al., 2003). Therefore, we posit that in the context of online communities, users who feel that using an online community is pleasing are more likely to buy items on impulse. Thus, we propose the following hypotheses:

3.4. The relationship between perceived enjoyment and perceived usefulness The relationship between cognition and affection has been extensively studied (e.g., Shiv & Fedorikhin, 1999), and cognition has been found to positively affect affection (Holbrook & Batra, 1987). Consistent with the SOR model, an individual who is exposed to a stimulus processes and assesses information about the stimulus; assessing information determines the individual’s affective reactions to the stimulus (Berkowitz, 1993). Berbegal-Mirabent, Mas-Machuca, and Marimon (2016) suggested that the hedonic dimension of online quality is important in adding value for customers. Thus, affective reactions only occur after there are cognitive reactions to the environment. Cognitive reactions to the environment can enhance or deter affective reactions. In the context of an online community, the more perceived usefulness of an online community, the more enjoyable it is to use. If the online community can effectively solve problems that are related to online purchase of movie tickets, users may perceive that using the online community is pleasing and may form an affective relationship with the online community. Thus, we propose the following hypothesis:

H6a. Users' perceived usefulness is positively associated with online purchase intention for movie tickets. H6b. Users' perceived enjoyment is positively associated with online purchase intention for movie tickets. 3.7. Trust transfer in the social community and additional effects of trust toward the platform on online purchase intention for movie tickets Drawing on trust transfer theory, trust toward members in a community can be transferred to social shopping platforms via interpersonal trust for two reasons. First, trust among members makes users believe that the provision and transfer of information in the platform is likely to be governed by established principles. Users will recognize this platform as being trustworthy, which results in all members who are in contact with each other abiding by established norms and rules. This helps members be confident that the platform will continue to improve service quality and provide effective management for building a trusted communication environment (Lu et al., 2010; Chen, Zhang, & Xu, 2009). Second, prior studies also indicated that interpersonal trust can provide fertile soil for developing institutional trust (Chen, Zhang, & Xu, 2009). Thus, community members who build strong mutual trust with each other will view the platform as a suitable venue for communication. According to uncertainty reduction theory for the development of relationships, when uncertainty is reduced, attraction and predictability lead to the formation of intimate relationships (Ballantine & Martin, 2005). Furthermore, several researchers have suggested that trust serves a pivotal role in attracting customers to shop (Hoffman, Novak, & Peralta, 1999; Reichheld & Schefter, 2000). According to the user decision-making model that was proposed by Blackwell, Miniard, and Engel (2001), internal information that is retrieved from memory (e.g., familiarity, prior experience) can reduce perceived risk. As such, trust is an essential strategy for reducing perceived risk. In a consumer perceived risk study, Lim (2003) found that consumers disliked engaging with unknown vendors because were afraid of credit card misuse, and many consumers perceived less risk in a reputable business and relied on references from other people rather than individual trial and error. Therefore, a trustworthy social shopping platform may reduce users' perceived risks regarding finances and product performance when buying products that are difficult to evaluate prior to purchase. Therefore, it is reasonable to expect that users' trust toward the platform is positively related to online purchase intention for movie tickets. Hence, we test the following hypotheses:

H4. The perceived usefulness of an online community is positively associated with users’ perceived enjoyment. 3.5. The effect of trust on perceived enjoyment Prior research on trust suggests that enjoyment is a potential outcome of a trust experience and that people develop trusting relationships for enjoyable purposes. When users have a high level of trusting relationships with other users in the same community, they will enjoy their interactions, and they will be more pleasing and enjoyable. In the context of online communities, users are likely to form trusting relationships with other users to acquire or share information in the future. Through interactions with the online community, users may feel intimate with other users, as if they were real friends. Thus, users’ emotional and affective needs are met. Users who have strong trusting relationships with other users in an online community perceive more enjoyment in interactions with the online community. Thus, we propose the following hypothesis: H5. Users’ trust with other users in an online community is positively associated with perceived enjoyment. 3.6. The effect of perceived usefulness and perceived enjoyment on online purchase intention for movie tickets The concept of perceived usefulness (PU) was proposed by Davis (1989) as “the degree to which a person believes that using a technology would enhance his or her performance.” PU has a substantial impact on behavioral intentions. Calisir and Calisir (2004) examined several usability factors that affected end-user satisfaction and found that system capabilities and user guidance (computer factors) are determinants of PU. Similarly, Van der Heijden (2003) supports the role of human factors on PU. Further, website design features, such as menus, icons, and links (computer factors), as well as colors, graphics, and music (human), are specifically intended to enhance usability (Song & Zinkhan, 2003). As such, similar members in a community or platform may also be a type of website feature, which is useful for people who are deciding to purchase online. In other words, well-constructed sites that have positive computer factors may increase the ease of transaction processes, thereby increasing perceived usefulness. Factors that make websites fun, attractive, and appealing (human factors) may also increase the usefulness of the site (Chen & Wells, 1999). The effect of an individual’s affective reactions on his or her responses can be explained with flow research, which indicated that a user’s exploratory behavior can be stimulated with an increase in intrinsic enjoyment (Ghani & Deshpande, 1994). In an online context, if

H7a. Users’ trust toward members of a community is positively associated with their trust toward the social community. H7b. Users’ trust toward the social community is positively associated with their online purchase intention for movie tickets. 4. Research methodology This study is an initial attempt to investigate the effect of similarity on social shopping from the perspectives of users’ perceptions and trust. In this section, we describe the research setting, survey design, data collection, measurement, control variables and common method bias. 92

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inviting our online friends and asking for their help in recommending that their friends complete the questionnaire. This study was introduced as an “opinion survey," and the respondents were asked to recall their recent social shopping experiences with online friends that they frequently interacted with on these websites or apps. Then, they were asked to complete the questionnaire based on their own experiences or perceptions. An Opinion survey is commonly used to understand how respondents feel about target objects (e.g., Bagozzi & Dholakia, 2006; Shen, Lee, & Cheung, 2014), i.e., social shopping activities in the community for this study. As such, a screening question asked if potential respondents had read reviews and purchased movie tickets in social community platforms, such as douban.com, mtime.com and dianying.fm, to ensure that all successful respondents had prior experience with social shopping activities. This method is consistent with previous studies that adopted screening questions to identify the most appropriate respondents (e.g., Cheung & Lee, 2009). Thus, we collected a total of 394 responses. We designed the final question to determine whether the respondents had carefully read and completed the questionnaire, which resulted in 8 respondents who chose “no." Thus, we ultimately obtained 386 usable responses. Note that a possible concern for our study is non-response bias, which occurs when there is bias from significant differences between non-respondents and respondents. Because it was not possible to compare these two groups of users in this study, we followed prior research (e.g., Al-Qirim 2007) and compared the demographics for the early (the first 50) and late (the last 50) respondents. This approach views late respondents as representative of non-respondents (Karahanna, Straub, & Chervany, 1999). The results showed that there were no significant differences. Hence, non-response bias is not a critical concern in this study. As shown in Table 1, the sample had 45.1% male and 54.9% female respondents. Most of the respondents (81.1%) were aged 18–35, and

4.1. Research setting This paper focuses on similarities between users who are recognized as members of a social community. We chose users in douban.com, mtime.com and dianying.fm (similar to Internet Movie Database and Rotten Tomatoes in America), the typical websites for movie reviews based on social communities in Mainland China, as the participants. These sites are social networking-based rating websites and were developed based on the idea that purchasing decisions are often influenced by friends' recommendations. Thus, there is a direct path or a link for purchasing online for all platform users. Therefore, it is common knowledge among users that these websites are online communities where people can easily share and find opinions about products from their online peers. Importantly, in contrast to other transaction-oriented websites, such as Amazon, these websites are constructed on specific interests and shared connections, which allow its users to connect and get to know each other. As such, users can create unique profiles and share their comments with online friends. In addition, the links are helpful for transferring user’s online interactions to online purchases, that is, users’ behaviors move from communication to commerce. Therefore, we believe that these websites provide an adequate sample for investigating social community-based commercial activities. 4.2. Survey design We conducted a survey to test our research model. We chose to administer a survey because this quantitative research method predicts behavior and examines the relations between variables and constructs (Newsted, Huff & Munro, 1998). In addition, the survey method has been widely employed in investigating behaviors in social shopping platforms (Huang & Benyoucef, 2014). To collect our survey data, the present study used an online survey. Our target population included online users of a specific website. Using an online survey can maintain consistency between the research and data collection contexts. Moreover, an online survey has many advantages, such as a broad reach and easy access to target users. In addition, this study integrates the model and includes many social variables that are difficult to measure with other methods, such as case studies or experiments. Thus, we believe that the survey is an appropriate method for the current study (Cheung & Lee, 2009).

Table 1 Demographics of respondents. Profile of respondents (n = 386)

93

Percentage

Gender

Male Female

174 212

45.1 54.9

Age

≤18 18–25 26–35 36–45 > 45

20 152 161 46 7

5.2 39.4 41.7 11.9 1.8

Educational background

High school or below College Bachelors Masters or above

7 52 242 85

6.8 18.5 62.7 12.0

Income (RMB/Month)

≤2000 2001–4000 4001–8000 8001–12000 > 12000

129 64 109 60 24

33.4 16.6 28.2 15.5 6.2

Occupation

Student Office staff Self-employed Others

139 184 22 41

36.0 47.7 5.7 10.6

Internet experience

≤2 years 3–4 years 5–6 years 7–8 years > 8 years

16 29 65 57 219

4.1 7.5 16.8 14.8 64.5

Frequency of daily Internet use

1 h or below 1–2 h 2–3 h 3 h above

14 78 105 189

3.6 20.2 27.2 49.0

4.3. Data collection Data were collected with an online survey on douban.com, mtime. com and dianying.fm. Because this study was conducted in Mainland China, the scales in the questionnaire went through a translation and back translation process with the assistance of two doctoral students. First, one student translated the instruments from English to Chinese, and then, the other student translated them from Chinese to English. The two English versions of the scales were compared, and all inconsistencies were resolved to improve the quality of the questionnaire. Further, we invited experts, including professors and doctoral students who majored in communications, psychology and marketing, to review the questionnaire to examine the face validity of the survey instruments, refine the questionnaire wordings, assess logical consistencies, judge the ease of understanding, and identify areas for improvement. Overall, the questionnaire was viewed as concise and easy to complete. They also proposed several suggestions for the formatting and wording of the questions, which were incorporated in the revised version of the questionnaire. To maximize the number of responses, we posted invitation messages in several popular communities in the websites that had different interests, and people who visited these communities had an opportunity to access our online questionnaire. In addition, we registered an account on these platforms and employed the “snowball effect” by

Frequency

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74.7% had a bachelor’s degree, master’s degree or above. The demographic characteristics for our sample were consistent with the results from a recent survey, “Demographics of Social Media Users," by the Pew Research Center (Duggan, 2015).

Table 2 Results of the confirmatory factor analysis.

4.4. Measures All constructs in the research model were measured with multipleitem scales that were validated in prior studies. Minor modifications were made to the measures to ensure that they had face validity in the current research context (see Appendix A). The items used a 7-point Likert scale that ranged from “1 = strongly disagree” to “7 = strongly agree”. Because the survey was administered in China, we used the translation-back-translation approach to ensure the consistency between the original English and the Chinese instruments.

Construct

Items

Cronbach’s Alpha

Composite Reliability

AVE

External similarity (ES) Internal similarity (IS) Perceived usefulness (PU) Perceived enjoyment (PE) Trust toward members (TTM) Trust toward community (TTC) purchase intention (PI)

3 4 5 3 3

0.816 0.923 0.860 0.920 0.887

0.880 0.945 0.902 0.949 0.930

0.651 0.813 0.652 0.861 0.816

3

0.842

0.905

0.761

3

0.871

0.921

0.794

5.1. Measurement model In the first step, the construct of validity was assessed with confirmatory factor analysis (CFA). We used individual item loadings and the Average Variance Extracted (AVE) to test the convergent validity. As shown in Appendix A, all of the measurement items demonstrated adequate convergent validity. The results indicated that all standardized item loadings were above the desired value of 0.7 (Carmines & Zeller, 1979) (See Appendix A). In addition, Table 2 shows that the AVEs for all constructs ranged from 0.651 to 0.861, which were above the recommended value of 0.5 (Fornell & Larcker, 1981). Composite reliability and Cronbach’s Alpha were used to test the construct reliability, as suggested by Fornell and Larcker (1981). The values for composite reliability ranged from 0.880 to 0.949, which were higher than the benchmark value of 0.7. The Cronbach’s Alpha values ranged from 0.816 to 0.920, which were above the threshold of 0.7 (Fornell & Larcker, 1981). The results implied that our measurement model had adequate reliability. Furthermore, the discriminant validity was assessed by comparing the square root of AVE with the correlations among the constructs (Fornell & Larcker, 1981). As shown in Table 3, the square roots of the AVEs for all constructs in the diagonal row were larger than the correlations between constructs. To further access the validity of our measurement instruments, we constructed a cross-loadings table (see Appendix C), as suggested by Gefen, Straub, and Boudreau (2000). Each item loading in the table is much higher for its assigned construct than the other constructs, which supports adequate convergent and discriminant validity. As shown in Table 3, the two inter-construct correlations values were above 0.6, which indicates that there may be multicollinearity. Thus, we tested for multicollinearity by analyzing the Variance Inflation Factors (VIFs) and the tolerance values. Testing for multicollinearity requires identifying whether there are VIF values that are above 10 or have a tolerance value that is less than 0.1 (Mason & Perreault, 1991). The results indicated that the highest VIF was 2.417. Thus, multicollinearity is not a significant issue.

4.5. Control variables Without randomly assigning participants, an online survey may increase the likelihood for systematic individual differences, which could influence the results. Therefore, this study included several control variables that measured users' characteristics on community-based social shopping platforms, such as gender, education, occupation and income. Furthermore, we also included computer experience. 4.6. Common method bias All answers were collected from one questionnaire from the same respondent; thus, common method bias could be a threat to the validity of this study (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). First, Harman's single factor test (Podsakoff & Organ, 1986) was conducted for the seven conceptually critical variables in our theoretical model, including ES, IS, PU, PE, TTM, TTP and PI. The results from this test showed that no single factor explained most of the variance, and the most variance that was explained by one factor was 34.5%, which suggests that common method bias was not a serious concern in the present study. Second, consistent with Liang, Saraf, Hu, and Xue (2007), we included a common method factor in our hypothesized model and allowed the constructs' indicators to reflectively associate with this factor. As shown in Appendix B, the results indicated that the substantive factor loadings were significant and high (average 0.828 and lowest 0.721), and the method factor loadings were low and nonsignificant (average 0.054 and highest 0.265). The average substantively explained variance for the indicator was 0.692, while the average common method based variance was 0.006. The ratio of substantive variance to method variance was approximately 115:1. In addition, most of the method factor loadings were not significant, which suggests that common method bias is unlikely to be a serious concern. 5. Data analysis and results This study employed Smart PLS (partial least squares) 3.0 to analyze the data. PLS is a component-based structural equation modeling approach that has been widely used in the existing literature (e.g., Ahuja & Thatcher, 2005; Xiang, Zheng, Lee, & Zhao, 2016). Compared to covariance-based structural equation modeling methods, such as LISREL, PLS does not require a normal distribution (Chin, 1998), and it is the preferred method when the research objective is theory development and prediction (Hair, Ringle, & Sarstedt, 2011). Tamjidyamcholo, Gholipour, Baba, and Yamchello (2013) posited that Smart PLS can formulate a formative model for latent constructs and has fewer requirements for model verification. Due to the data sample and the non-normality of the data as well as the predictive nature of this study, we chose the PLS method rather than other SEM methods. The research model was validated with two-step analytical procedures: the measurement and structural model (Hair et al., 1998).

Table 3 Means, standard deviations and correlations. Variable

Mean

S.D.

1

2

3

4

5

6

7

1. 2. 3. 4. 5. 6. 7.

4.553 5.034 4.545 4.867 3.978 4.440 4.878

1.462 1.428 1.480 1.213 1.414 1.344 1.452

0.793 0.656 0.512 0.491 0.347 0.384 0.342

0.901 0.681 0.561 0.367 0.509 0.497

0.774 0.607 0.566 0.574 0.569

0.834 0.440 0.571 0.552

0.903 0.707 0.530

0.872 0.648

0.875

ES IS PU PE TTM TTC PI

Note: Diagonal values in bold are the square root of the AVEs.

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Fig. 2. Results from the PLS analysis.

and perceived enjoyment had positive effects on users' online purchase intention for movie tickets, with path coefficients of 0.221 and 0.178, respectively (H6a and H6b were supported). In addition, trust toward members had a significant effect on users' trust toward the social community platform and positively affects users' online purchase intention for movie tickets, with path coefficients of 0.707 and 0.419 (Both H7a and H7b were supported). All control variables were not significant, including gender (β = 0.04, p > .1), education (β = 0.01, p > .1), occupation (β = −0.02, p > .1), income (β = 0.05, p > .1), and computer experience (β = −0.05, p > .1). As a post hoc analysis, based on the procedure for analyzing mediating effects, which was proposed by Zhao, Lynch and Chen (2010), we employed a Bootstrap Method to test multiple mediator models (Preacher & Hayes, 2008) to understand the mediating role of perceived usefulness (PU) and perceived enjoyment (PE) in the research model. This procedure is appropriate for small sample sizes (Hayes, 2013). To test the robustness of our proposed multiple mediation model, we used a 95% bias-corrected bootstrap confidence interval, based on 5000 bootstrapped samples (Hayes, 2013, model 4). The results of the meditational analysis indicated that the total indirect effect of external similarity on the purchasing intention for movie tickets through both PU and PE was statistically significant (95%CI:LL = 0.294, UL = 0.454), with an effect size of 0.368. A closer examination showed that there were indirect effects for external similarity on purchase intention through PU (95%CI:LL = 0.128, UL = 0.281), with an effect size of 0.194, and similarity on purchase intention through PE (95%CI:LL = 0.113, UL = 0.250), with an effect size of 0.174. The total indirect effect for internal similarity on the purchasing intention of movie tickets through both PU and PE was also statistically significant (95%CI:LL = 0.255, UL = 0.420), with an effect size of 0.334. Specifically, there were indirect effects for internal similarity on purchase intention through PU (95%CI:LL = 0.098, UL = 0.258), with an effect size of 0.175, and through PE (95%CI:LL = 0.092, UL = 0.235), with an effect size of 0.160. These results confirmed that PU and PE played a full mediating role in the research model.

5.2. Structural model Fig. 2 (Results from the PLS analysis) presents the results from our research model with overall explanatory power, estimated path coefficients (all significant paths are indicated with asterisks) and the associated t-values for each path. Path significance tests were performed using a bootstrap re-sampling procedure. As shown in Fig. 2, the results demonstrate that the exogenous variables (external similarity; internal similarity) in the current research model effectively explain the variance in the endogenous variables (perceived usefulness; perceived enjoyment; trust toward members; trust toward the social community platform). Specifically, the integrated model accounted for 49.6% of the variance in the online purchase intention for movie tickets. All hypotheses were significantly supported. As hypothesized, the results also demonstrated that all path coefficients were statistically significant. Compared to external similarity for members in a social community, internal similarity had a greater significant impact on perceived usefulness, with a path coefficient of 0.584 (0.149) (H1a and H1b were supported). External similarity and internal similarity exert significant effects on perceived enjoyment, with path coefficients of 0.143 and 0.202, respectively (H2a and H2b were supported). External similarity and internal similarity had significant effects on trust toward members, with path coefficients of 0.187 and 0.245, respectively (H3a and H3b were supported). We further tested the statistically significant difference between the comparisons using the standardized coefficients. The results supported our hypothesis that internal similarities between users will have a stronger influence on users’ perceived usefulness of reviews from other members in the social community, with the standardized coefficients of βinternal = 0.640 and βexternal = 0.506. The hypotheses that internal similarity between users will have a stronger influence on users’ trust with members in the social community was also supported, with standardized coefficients of βinternal = 0.416 and βexternal = 0.343. Finally, the hypotheses that internal similarity between users will have a stronger influence on users’ perceived enjoyment in the social community was supported, with standardized coefficients of βinternal = 0.556 and βexternal = 0.448 (H1c, H2c and H3c were supported). Similarly, perceived usefulness had a positive influence on perceived enjoyment, and trust toward members had a positive influence on perceived enjoyment, with path coefficients of 0.316 and 0.138, respectively (H4 and H5 were supported). Both perceived usefulness

5.3. Competing model analysis Information obtained from credible sources is usually regarded as more useful, and thus will be used as decision aid (Sussman & Siegal, 2003). And theoretically speaking, both trust towards member and trust towards community could be regarded as credible sources. In this 95

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Second, there are differences between external and internal similarity effects. Bloch, Sherrel, and Ridgway (1986) refer to practical reasons for information searches, as well as emotional motivation. That is, users’ information searches are often attributed to the need to make better decisions or because it is pleasurable for individuals to engage in information-seeking behavior. In the context of a social shopping platform, external similarities function to satisfy users' needs to make better decisions, while internal similarities provide pleasure for users. Furthermore, internal similarities between users exert an influence on their perceived usefulness (p = 11.478), perceived enjoyment (p = 3.763) and trust toward members (p = 4.640) that is greater than the impact of external similarities on perceived usefulness (p = 1.212), perceived enjoyment (p = 1.234) and trust toward members (p = 1.354). Compared to limited face-to-face communication, it is easier for people to find someone who has external similarities on the Internet, such as the same first name, age, birthday or astrological sign, or residence. As such, in surface-level homogeneous groups, group members are likely to assume that they possess the same information about the task (Phillips & Loyd, 2006). When demographic information is ambiguous, deep-level similarities can compensate for the lack of information in the online environment and provide useful cues for consumers’ decision-making processes. For purchasing movie tickets online, users' internal similarities, such as similar interests, attitudes, values and preferences toward movies, are more helpful than external similarities for users who are deciding whether they want to purchase and view a movie. Third, perceived usefulness, perceived enjoyment and trust toward members mediate the relationship between similarities and users' purchase intention for movie tickets. However, the mediating effects for perceived usefulness and perceived enjoyment are more profound than trust, as the results show that perceived usefulness and perceived enjoyment can explain 44.7% and 43.6% of the variance, while trust toward members explains 15.5% of the variance. Trust among users is a type of relationship between people, while perceived usefulness and perceived enjoyment occur through the relationship between people and objects (the social community platform). Further, the relation between perceived usefulness and perceived enjoyment reflects the changes from users' cognition to affection. As the Internet has replaced modes of face-to-face communication, building and maintaining relationships online now depends on website interfaces (Rayport & Jaworski, 2001). If the interface fails to connect with the consumer on a personal level, users are more likely to search for products on other websites. Without personal contact, trust becomes an issue. Finally, the mechanism that drives the similarity effect is also a type of expression of similarity extrapolation, as users experience the psychological transfer of similar preferences between the self and others from a member-to-member domain to a member-to-platform domain. This mechanism can also be viewed as a specific embodiment of the Elaboration Likelihood Model (ELM) and Stimuli-Organism-Response Model (S-O-R).

regard, we verify the causal direction between TTM(trust towards members in a community) and TTC(trust towards social community) by setting up competing models(as shown in Fig. 3) based on the methods proposed by Cohen et al. (Cohen et al., 1993), which also has been validated by a number of previous studies (Sun & Zhang 2006). Then we calculated the total squared error of these two models by using Cohen's path analysis method (see the bold text in Table 4). We first checked error changes from Model 1 to Model 2. The total squared error (TSE) is changed by12.05% (=(0.0437-0.0390)/0.0390). The effect size is 0.1935.1 Then, we checked error changes in reverse order: from Model 2 to Model 1. The TSE is changed by −10.76% (= (0.0390–0.0437)/0.0437). The effect size is −0.1935. The positive sign of effect size means the TSE is actually increased (or improved in Cohen’s terminology) from Model 1 to Model 2 and the negative sign means that when we change the causal direction from Model 2 to Model 1, the TSE is actually reduced. However, according to Cohen (1988), effect size of 0.2 is defined as small, 0.5 as medium, and 0.8 and above as large. Since the effect size is below 2.0 in our research, we do not think Model 1 is better than Model 2. Furthermore, we calculated the T statistics of these two models, as shown in Table 5 in bold text. When we use Model 1, the coefficients of two paths would be no longer significant(See the bold text in Table 5), suggesting the relationship between the two variables changed in the whole research model when we changed the direction of TTM and TTC. Because we have our logical hypotheses and theoretical support. Therefore, from the perspective of statistics, Model 2 (our model)is better than Model 1(the competing model). 6. Discussion and conclusion Research reports that 92% of users no longer trust the information that they receive from traditional sources, such as television, and are increasingly turning to online sources, such as blogs and review sites or social communities for product information (Penn & Zalesne, 2007). Research has also shown that users seek out external information about products and services from personal sources, such as friends or social community members. A study by GroupM Next and Compete (2016) provided interesting insight into the modern digital consumer journey and found that 48% percent of purchases were heavily influenced by digital media and technology. Therefore, the types of functions or features that a social community platform should have for influencing users’ behaviors, such as users’ online purchase intention of movie tickets, will be increasingly important for marketers. This paper explored this problem from the perspective of perception and trust transfer and specifically focused on the effects of similarities between users, including external and internal similarities on users’ online purchase intention. 6.1. Discussion of key findings

6.2. Theoretical implications

First, there is a similarity effect between users in the context of social shopping, which has positive effects on users' online purchase intention. According the results from the PLS analysis, the similarity effect can explain 49.6% of the variance in users' online purchase intention. Although users' decision-making is relatively stable, social purchase intention within the context of a social community are a situation-specific attribute that may be influenced by interpersonal relationships (Kachersky et al., 2014; Sun & Wu, 2011). The finding for the similarity effect is consistent with the previous research (Brack & Benkenstein, 2012; Jiang et al., 2010; Martin et al., 2013).

This study focuses on users' online purchase intention for movie tickets, which were initiated by users' similarities on social shopping websites. Specifically, we examined how users' external and internal similarities change users' perceptions and the transfer of trust and in turn affect the purchase intention for movie tickets in the context of digital communication. Overall, this study enriches the existing literature on similarity effects on users' social shopping behaviors in several ways. First, we conduct this study from a new theoretical framework that incorporated similarities between users. Prior studies on users' purchase intention through social shopping often concentrated on movie attributes, such as actors and reviews, and production and promotion but lacked the perspective of interpersonal relationships, such as

1 Note: Here we use Cohen's d to calculate the effect size, which defines the effect size M − M1 as f 2 = 2

σ12 + σ22 2

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Fig. 3. The Competing Models of the Relationship between TTM and TTC.

Table 4 Path analysis of two models. Path

Squared Error

Model 1: TTC → TTM Total Squared Error 0.0390 ES → PE 0.00306916 ES → PU 0.003481 ES → TTC 0.00425104 IS → PE 0.00432964 IS → PU 0.00299209 IS → TTC 0.004356 PE → PI 0.00306916 PU → PE 0.00418609 PU → PI 0.003364 TTC → PE 0.00256036 TTC → TTM 0.00091204 TTM → PI 0.00247009

Table 5 T Statistics of two models. Path

Squared Error

Path

Model 2: TTM → TTC Total Squared Error 0.0437 ES → PE 0.00335241 ES → PU 0.00341056 ES → TTM 0.00416025 IS → PE 0.00499849 IS → PU 0.00303601 IS → TTM 0.00502681 PE → PI 0.00356409 PU → PE 0.0049 PU → PI 0.00349281 TTC → PI 0.00351649 TTM → PE 0.003364 TTM → TTC 0.00083521

T Statistics

Model 1 ES → PE ES → PU ES → TTC IS → PE IS → PU IS → TTC PE → PI PU → PE PU → PI TTC → PE TTC → TTM TTM → PI

Path

T Statistics

Model 2 2.7214 2.5305 1.3774 (0.0898) 1.9503 (0.1284) 10.6587 6.8196 5.1645 4.1917 4.2698 5.7894 23.4417 5.309

ES → PE ES → PU ES → TTM IS → PE IS → PU IS → TTM PE → PI PU → PE PU → PI TTC → PI TTM → PE TTM → TTC

2.4707 2.5054 2.9021 2.8809 10.6512 3.5103 2.8992 4.2623 3.6882 7.1091 2.3774 23.8362

Note: the significance of bold values (indicated in parentheses)

similarities between users. Although Jiang et al. (2010) and Lichtenthal & Tellefsen (2001) examined the similarity effect, these studies were not conducted in the social shopping context or focused on the buyerseller similarities (different from our buyer–buyer similarity). This study found that similarity is an important peripheral cue for users who are deciding to buy tickets and see films. Second, we re-conceptualize similarity by dividing similarity into

two categories based on external and internal factors, which often have different influences on the same objects. This is the first attempt to do so in the literature on the similarity effect. This study found that internal similarity had a more profound effect on users’ social shopping behaviors than external similarity. Our findings suggested that internal similarity could increase users’ willingness to trust members of the social community and the social shopping platform and further increase 97

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interactions and induce bonding among users. Moreover, websites can also remind users about who has purchased the same product or service as well as similar goods. Practitioners should pay close attention to the formation and trends of users’ internal similarities, such as values, interests, attitudes and consumption preferences, to construct and adapt to users’ similarities to improve the effectiveness of marketing activities. Targeting users based on demographic information or so-called external similarities is no longer sufficient because users may defect due to intense competition. Social shopping websites should build more congenial online communities to increase user loyalty based on users’ internal similarities. In sum, the online community is an important venue in which users can generate trust and bonding with other users, which can lead to trust in the social shopping platform. Therefore, practitioners should attend to building and effectively maintaining their online communities based on internal similarities between users.

the intentions for purchasing movie tickets on these social shopping platforms. Third, we provided a new theoretical explanation for how similarity affects user's decision-making processes. Few studies have examined the types of social shopping websites that attract users to purchase movie tickets online and psychological mechanisms that underlie these transactions. We explored the working mechanism of the similarity effect by examining users’ perceptions and trust transfer. Very few, if any, studies have studied the effects of social relationships on users’ online behaviors (Mattila & Wirtz, 2008), so we addressed this gap and included the concepts of “perceived enjoyment,” ‘perceived usefulness’ and “trust transfer” from the fields of information technology and organizational behavior in this study. Moreover, our study is the first to demonstrate that the theories of trust transfer and similarity extrapolation are consistent in explaining users’ social shopping behaviors. As such, the similarity effect is consistent with the homophily effect in social network analyses.

7. Limitations and future research directions 6.3. Practical implications The present study uses cross-sectional data. In the future, longitudinal studies should explore users’ behaviors in social shopping and examine actual, rather than self-reported data on social shopping and social sharing. Longitudinal studies and experiments can provide a strong inference of causality and improve our understanding of the direction of causality (Dillon, Dillon, & Goldstein, 1984). However, given the limitations in time and resources, cross-sectional studies are used as exploratory vehicles that can determine the relationships among variables. Next, we only examined similarity from the perspective of interpersonal interactions; however, there are other factors that affect users’ social shopping behaviors, such as familiarity, expertise or liking. Thus, we will further explore the effects of other factors on users’ social shopping behaviors from the perspective of interpersonal interactions. In addition, the generalizability of the similarity effect that was produced in our study is limited by the methodology and the specific characteristics of our study of movie ticket purchase intention. Specifically, this research uses the Chinese social community platforms (douban.com, mtime.com and dianying.fm) based on users’ shared interests or attitudes toward an event. However, we do believe that the similarity effect in our research project provides useful guidelines for all e-commerce platforms based on social communities. Additional research is needed to validate or improve the research model.

What drives users' social shopping behavior is one yet-to-be resolved question in social shopping research. Our research developed a conceptual model from the perspective of perception and trust to address this research gap and further to help practitioners to address this issue. Practitioners should pay attention to the formation of trends in users’ internal similarities, such as values, interests, attitudes and consumption preferences, to build and adapt to users’ similarities to further improve the effectiveness of marketing. For social shopping website design, Hasan (2016) and Wu, Quyen, and Rivas (2017) Lightner, Yenisey, Ozok, and Salvendy (2002) held that the site’s design can affect users’ decisions to purchase online. The elements of a site that are enjoyable, engaging and interactive are more likely to lead to positive attitudes toward the online store brand (Huang, Lurie, & Mitra, 2009), which may lead to an increased likelihood of purchasing (Chen, Zhang, & Xu, 2009). Moreover, Al-Qeisi, Dennis, Alamanos, and Jayawardhena (2014) showed that a halo effect may influence the overall evaluation of a website because the dimensions of quality website design are interrelated. This suggests that improving the appearance of a website should enhance the overall evaluation of the site, which in turn will lead to greater intentions of use. Therefore, according to our study, the design of social shopping websites that are based on similarities between users should facilitate Appendix A. Measurement items

Constructs

Items

ES1 The community of this platform have features by which I find members with whom I share the same birthday or constellation. ES2 The community of this platform have features by which I recognize members with whom I share a similar ages or the same period of life span. ES3 The community of this platform have features by which I identify members with whom I a share similar place of residence. Internal similarity (IS) IS1 The community of this platform have features by which I find members with whom I share similar values. IS2 The community of this platform have features by which I identify members with whom I share similar opinions or attitudes. IS3 The community of this platform have features by which I recognize members with whom I share similar interests. IS4 The community of this platform have features by which I identify members with whom I share similar viewing preferences. External similarity (ES)

98

References

Loading

Modified from Shen, Huang, Chu, and Liao (2010).

0.810 0.723

0.843 Modified from Shen, Huang, Chu, and Liao (2010)

0.869 0.923 0.926 0.886

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Trust toward members (TTM)

TTM1 Members in the community of this platform will always try and help me out if I get into difficulties. TTM2 Members in the community of this platform will always keep the promises that they make to one another. TTM3 Members in the community of this platform are truthful in dealing with one another. Trust toward social TTC1 The performance of this platform always meets my expectations. community (TTC) TTC2 This platform can be counted on as a good social networking site. TTC3 This is a reliable e-commerce platform. Perceived usefulness PU1 Visiting the community of this platform helps me acquire (PU) information. PU2 The community of this platform are useful for information exchange. PU3 The community of this platform are useful for relationship development. PU4 The community of this platform are useful for relationship maintenance. PU5 The community platform are usefulness for obtaining social and emotional support. Perceived Enjoyment PE1 I feel the reviews are enjoyable in the community of this platform. (PE) PE2 I feel the reviews are pleasant in the community of this platform. PE3 I feel the reviews are funny in the community of this platform. PI1 I will consider the movie reviews of other members on this platform Online purchase intention of movie when I want to purchase a movie ticket online. tickets (PI) PI2 I will ask other members on this platform to provide me with their suggestions before I purchase a movie ticket online. PI3 I am willing to purchase a movie ticket online recommended by other members on this platform.

Modified from Chang & Chuang, (2011); Chow & Chan (2008)

0.867 0.913 0.929

Modified from Liang, Ho, Li, and Turban (2011) Modified from Chung, Park, Wang, Fulk, & McLaughlin (2010)

0.903 0.893 0.818 0.712 0.862 0.837 0.835 0.791

Modified from (Davis et al., 1992; Venkatesh, Morris, Davis, & Davis, 2003) Modified from Chen & Shen (2015)

0.871 0.776 0.851 0.856 0.863 0.905

Appendix B. Common method bias analysis.

Construct

Indicator

Substantive factor loading (R1)

R12

Method factor loading (R2)

R22

External similarity

ES1 ES2 ES3 IS1 IS2 IS3 IS4 PU1 PU2 PU3 PU4 PU5 PE1 PE2 PE3 TTM1 TTM2 TTM3 TTC1 TTC2 TTC3 PI1 PI2 PI3

0.851*** 0.734*** 0.767*** 0.825*** 0.832*** 0.817*** 0.853*** 0.721*** 0.830*** 0.814*** 0.823*** 0.784*** 0.797*** 0.863*** 0.897*** 0.816*** 0.897*** 0.846*** 0.842*** 0.834*** 0.888*** 0.901*** 0.856*** 0.795*** 0.828

0.724 0.539 0.588 0.681 0.692 0.667 0.728 0.520 0.689 0.663 0.677 0.615 0.635 0.745 0.805 0.666 0.8805 0.716 0.709 0.696 0.789 0.812 0.733 0.632 0.692

−0.064 0.265** 0.079 −0.037 −0.069 0.027 0.004 −0.027 0.041 0.042 0.009 −0.110 0.006 0.018 −0.016 −0.031 0.048 0.000 0.138* 0.049 −0.021 −0.070 0.025 0.020 0.054

0.004 0.070 0.006 0.001 0.005 0.001 0.000 0.001 0.010 0.002 0.000 0.012 0.000 0.000 0.000 0.001 0.002 0.000 0.019 0.002 0.000 0.005 0.001 0.000 0.006

Internal similarity

Perceived usefulness

Perceived enjoyment

Trust toward members

Trust toward community

Purchase intention

Average *

p < .01. p < .05. *** p < .001. **

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Appendix C. Item loading and cross loadings.

ES1 ES2 ES3 IS1 IS2 IS3 IS4 PE1 PE2 PE3 PI1 PI2 PI3 PU1 PU2 PU3 PU4 PU5 TTC1 TTC2 TTC3 TTM1 TTM2 TTM3

ES 0.810 0.723 0.843 0.576 0.593 0.598 0.596 0.431 0.371 0.422 0.246 0.293 0.355 0.412 0.460 0.427 0.421 0.273 0.323 0.322 0.367 0.233 0.355 0.346

IS 0.515 0.474 0.564 0.869 0.923 0.926 0.886 0.483 0.387 0.518 0.413 0.403 0.483 0.565 0.599 0.463 0.451 0.334 0.447 0.410 0.483 0.327 0.318 0.349

PE 0.303 0.409 0.442 0.480 0.518 0.510 0.515 0.871 0.776 0.851 0.482 0.415 0.540 0.566 0.464 0.452 0.434 0.355 0.537 0.529 0.421 0.325 0.415 0.445

PI 0.253 0.243 0.310 0.449 0.460 0.489 0.389 0.485 0.332 0.536 0.856 0.863 0.905 0.480 0.500 0.367 0.442 0.345 0.569 0.565 0.565 0.418 0.504 0.510

PU 0.393 0.393 0.470 0.549 0.596 0.543 0.564 0.558 0.442 0.509 0.463 0.459 0.565 0.712 0.862 0.837 0.835 0.791 0.472 0.552 0.476 0.480 0.509 0.541

TTC 0.281 0.313 0.318 0.489 0.449 0.495 0.398 0.518 0.359 0.528 0.522 0.488 0.579 0.398 0.468 0.394 0.496 0.444 0.903 0.893 0.818 0.524 0.597 0.591

TTM 0.281 0.236 0.306 0.398 0.314 0.327 0.279 0.427 0.278 0.378 0.456 0.421 0.507 0.186 0.497 0.459 0.583 0.517 0.563 0.570 0.505 0.867 0.913 0.929

Note: all the constructs with bold values were above 0.7.

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