1. Introduction
Social commerce, which is defined as the use of Internet-based media that enables users to participate in selling, buying, comparing, and sharing information about products and services in online marketplaces and communities, has become a mainstream e-commerce business model [1]. On social-commerce platforms, consumers communicate in both directions and make purchase decisions based on information shared by users [2]. Social commerce allows for positive two-way communication and collaboration between consumers on websites [3], which has pioneered a new model of e-commerce. According to the websites used, social commerce can be divided into two categories: social commerce based on social networking sites and social commerce based on traditional e-commerce sites. In the first category, social networking sites combine commercial features to allow transactions and advertisements. For example, Instagram and some other social networking sites have increased their functions that allow business activities to take place between members. Online retailers start to use live streaming as a tool to increase sales as it becomes more popular in social networking sites. Live shopping has become a new social-commerce model [4] that offers real-time and multimedia product presentations, allowing images and sounds to be sent to viewers. Thus, customers can enjoy an intuitive buying experience with strong interactivity, professionality, and high conversion rates [5,6]. In the second category, traditional e-commerce sites add social tools to facilitate social interaction and sharing [7]. For example, Taobao has created a “Discover” section on its homepage that allows users to post product reviews and then refer them to potential consumers. This function allows consumers to share their experiences and feedback on products or services for other consumers in the form of online consumer reviews (OCRs) [8] instead of having to be on the purchase page of products. OCRs are defined as an evaluation of a product, service, or content in the form of texts, images, or videos [9]. OCRs facilitate the exchange of information between consumers, which has led to e-commerce sites that include OCRs also being considered a form of social commerce [10]. Both act as information vehicles to deliver knowledge about products to consumers in social-commerce models. Therefore, live streaming and OCRs act as online communication channels, which are defined as a way of spreading word of mouth and online interaction [11].
Live streaming and OCRs similarly have the advantage of triggering online interactions. Online interaction is seen as a determinant of loyalty [12]. The use of both channels as marketing tools is, therefore, considered to facilitate purchase intention [13,14]. However, they also have their own shortcomings. Due to the inherent risks of the online environment, consumers do not always view live streaming as a trusted information channel. While consumers have a high level of trust in user-generated OCRs [15], the review ranking mechanism of e-commerce sites makes it difficult to discover important information. Therefore, consumers are increasingly using both channels to help with the purchase decisions, and there is a potential complementarity between the two channels.
Recent changes in online platforms have allowed for a better integration of live streaming and OCRs, facilitating the complementarity of the two channels. Traditionally, online platforms are classified into four categories: (1) social media platforms, (2) review platforms, (3) e-commerce platforms, and (4) other platforms [16]. Different online platforms are becoming increasingly inseparable. For example, Taobao has launched a live shopping feature, and Facebook has adopted live streaming for its marketing campaigns. These new trends have led to the breaking of barriers between live streaming and OCRs. Consumers can read OCRs and ask questions about reviews posters on shopping sites, then watch live streaming to engage in real-time interactions to have an immersive experience. The previous studies have separately explored the impact of live streaming and OCRs on purchase intention, treating them as a single channel [13,17]. These studies have investigated the impact of OCRs and live streaming on online shopping behavior through empirical analysis based on their distinct characteristics. In the context of social commerce, researchers have identified factors such as trust [5,13], hedonic value [17], and interactivity [18] as antecedents of consumer purchase intention. However, the previous research has not explored how live streaming and OCRs jointly influence consumer purchase intention, which has implications for comprehending consumer behavior in social commerce.
As both live streaming and OCRs affect consumers’ purchase decisions [4], what are the same and different functions of live streaming and online reviews? To fill these research gaps, using channel complementarity theory, this study explores the combined effects of these two different e-commerce models. Our work is guided by the following questions: (1) Can live streaming and OCRs jointly affect purchase intention? If so, how do they complement each other in influencing such intention? (2) Which features of live-streaming commerce and OCRs are complementary and which are common?
We adopt channel complementarity theory to inspect this dual impact. The theory asserts that the gratification offered by traditional and emerging media is different, leading to greater opportunities for media complementarity between new and traditional modes. Our study contributes to the academic literature in several ways. First, our findings enrich the literature on social commerce by developing a theoretical model to explain how live streaming and OCRs jointly affect consumers’ purchase intention from an information channel perspective. Second, we reveal the common path that the two social-commerce modes affect purchase intention. Third, we verify the complementarity of live streaming and OCRs in influencing consumers’ purchase intention. Managerially, our conclusion can aid companies in managing multiple forms of social commerce across platforms within a poly-social-media context.
2. Theoretical Background
2.1. Live Streaming
Online users can access live streaming in real time, which is a form of user-generated content. The rise of live streaming has also facilitated its integration with marketing campaigns. Unlike traditional e-commerce, live streaming has brought subversive changes in sales logic, product presentation, and social attributes. Following Chen and Liao [19], we focus on three features (sense of community, interactivity, and emotional support) to discern how live streaming influences consumer engagement based on its synchronous communication qualities.
First, the sense of community is an important concept in psychology that reflects a person’s identification with belonging to a community [20]. With the expansion of social media and the formation of brand communities, scholars introduced the sense of community into the research into e-commerce. Sense of community is defined as “members’ sense of membership, identity, belonging, and attachment to a group that interacts primarily through electronic communication” in a virtual community [21]. The sense of community facilitates the gathering of specific groups of people, creating a variety of social communities, such as fan groups and brand communities. In virtual platforms, the sense of community was thought to lead to a higher intention to use [22] and purchase [23].
Second, interactivity is an important feature that sets live social commerce apart from traditional e-commerce. Viewers interact in the live streaming room by liking and sending comments; interactions between viewers can trigger a state of completive excitement, increasing the likelihood of payment. Interactions can lead to higher engagement, which can significantly reduce the perceived risk in online platforms [4]. Similarly, Zhou and Tian [24] highlighted the positive impact of consumer interactions (e.g., reviews, likes, recommendations, etc.) and the number of followers on purchasing decisions.
Third, consumer behavior can additionally be influenced by emotional support in live streaming. Researchers proposed that “emotional engagement” refers to the degree to which viewers of a live show are emotionally connected to the showrunner and other viewers and can express their emotions to them. Streamers trigger consumers’ positive emotions and emotional participation, which improves their willingness to participate in live e-commerce. Lim et al. [25] explained the effects of viewers’ emotional interactions with other viewers on social relationships and loyalties during live streaming; consumers can achieve informational and emotional attachments through influencers in live shopping.
2.2. Online Consumer Reviews
The emergence of OCRs refers to “any probable, actual, or former consumer statements about a product or company, whether positive or negative, that will be passed on to other people and institutions via the Internet” [26]. Through the Internet, OCRs can provide a large amount of graphic information to spread positive or negative attitudes, helping consumers identify the product information that best meets their needs, thereby intervening in their purchasing behavior [27]. Regarding the process of OCRs and consumer decision-making, according to Geng and Chen [28], we frame perceived usefulness and perceived trust as two cognitive factors in OCRs.
First, with the rapid expansion of online platforms, the number of OCRs has increased rapidly, and predictions about OCRs’ usefulness have received much attention. The perceived usefulness of OCRs is seen by online retailers as an indicator of its importance in helping consumers make decisions. The textual features, emotions, and ratings of OCRs are thought to have an impact on perceived usefulness [29]. Perceived usefulness is considered a key factor in consumers’ ability to effectively access information, thus, shaping consumer behavior [30]. Negative consumer perceptions have a debilitating effect on brand equity and purchase intent, especially for high-engagement products.
Second, the existing research explores the role of perceived trust in shopping in OCRs, credibility can effectively influence consumers’ purchase intention. Consumers experience higher risk from online shopping due to the high risk and uncertainty of online interaction in e-commerce platforms. As a result, consumers seek more channels for more reliable information to boost confidence. The considerable research shows that OCRs affect consumer trust in online platforms, and positive reviews in e-commerce help build a bridge of trust between consumers and online retailers [31] and enhance the relationship between consumers’ emotional trust and their purchase intention. According to research, 20% of consumers believe OCRs, and this number rises to 25% when many customer evaluations are combined [32].
2.3. How Live Streaming and OCRs Complemented Each Other
The channel complementarity theory was proposed by Dutta-Bergman [33] and provides a framework that examines the connection between consumption and certain channels. The theory argues that customers with specific interests turn to various communication channels to meet their needs. This theory offers a framework for uncovering the connections among media outlets. Liao et al. [34] used the channel complementarity theory to evaluate the increasingly prominent brand community swinging phenomenon caused by brand communities on online platforms. The inherent differences between OCRs and live streaming are believed to inspire different choices when consumers access product information, leading to different levels of customer engagement. Live streaming and OCRs, as two different information channels of e-commerce, are complementary in terms of information acquisition. The existing research has summarized the different characteristics and roles of live streaming and OCRs. Based on the channel complementarity theory, the two channels complement each other in the following aspects.
First, live streaming is a form of seller-generated content, while OCRs are content spontaneously formed by consumers to express their attitude towards a specific product or service. As a seller-led information dissemination channel, retailers may have the problem of disseminating false information for the sake of sales. Considering that the sources of information are objective, the information created by consumers in the form of OCRs is considered a more credible and relevant channel than live streaming [15]. As a result, online shoppers regard OCRs as a more reliable channel for product information when making purchasing decisions. Furthermore, danmaku is a real-time commentary played on the screen [35], then covered by other danmaku. Therefore, viewers may miss some crucial information. Posted OCRs can be found on the product page and cannot be covered by other reviews in a short period of time. Meanwhile, the majority of viewers watching the live stream have not purchased the product or service. OCRs are considered the sum of the experiences, attitudes, and opinions expressed by purchased consumers; consumer purchasing behavior can be influenced by opinions of others [36]. OCRs provide higher perceived usefulness and perceived trust in the formation of consumer purchase decisions to compensate for the shortcomings of live streaming.
Second, compared to OCRs, live streaming can have an impact on consumers through multiple vehicles in an interactive way. Real-time images, sound, and text information transmission is infused with playfulness, which makes live streaming able to be regarded as a new form of entertainment. Playfulness can create a sense of security and hedonic perceived value that can be instrumental in promoting consumers’ behavioral intentions and satisfaction and purchase intention [17]. Streamers are a major source of information and playfulness for viewers. Due to the personal charisma and social influence of streamers, streamers play a important role in live shopping. The existing literature highlights the impact of streamer–viewer interaction on purchase decision and the necessity to communicate with viewers to foster emotions [25]. Streamers as online influencers are also considered to be a trusted source of information [37]. In addition to these easily detectable factors above, viewers are also vulnerable to other underlying factors while watching a live stream, such as background complexity [14].
Considering the discussion, our research proposes that live streaming and OCRs have various weaknesses in shaping consumer behavior, but consumers obtain more comprehensive and reliable information from two different channels when facing purchase decisions. Therefore, this study proposes that the two channels of live streaming and OCRs are complementary.
3. Hypothesis Development
Customer engagement is considered to be one of the most prevalent notions in live-streaming retail settings [6]. Given the positive impacts of social relations (e.g., interpersonal interaction) on customer engagement in live-streaming media, in our study, customer engagement is considered a mediating variable.
3.1. Social Commerce and Purchase Intention
Social commerce expedites business transactions by bringing together merchants and buyers via social media. Consumers can choose products on their own and actively participate in sales due to this type of commerce. Several channels, including product comments, blogs, and social media, enable consumers to access and share their thoughts about or interest in a product. Therefore, one’s consumption habits may be more crucial in social commerce than in other types of business. Social presence is the primary benefit of live streaming [38], as evidenced by this attribute’s expansive coverage in the related research [19].
3.1.1. Sense of Community
In virtual communities, sense of community reflects a member’s identification with a community [20,21]. In previous studies, sense of community is related to watching intention [5]. Live-streaming communities interact through conversations on fan pages. Discussion-related content can further amplify community relations, thereby increasing users’ stickiness on fan pages and enhancing viewers’ positive attitudes when browsing product pages. This sense of community constitutes a key factor that magnifies purchase intention through live streaming. Purchase intention is also central to social commerce, particularly during the buying process:
One’s sense of community is complementary in two types of social commerce (live streaming and OCRs) and positively influences purchase intention.
3.1.2. Interactivity
Researchers generally agree that interactivity is the degree to which two parties interact during the communication process [39]. Live streaming has sparked unprecedented levels of social interaction online. Core activities during live broadcasts include “liking,” complimenting content, opening pop-up windows, and exchanging virtual gifts. Personalization, responsiveness, and entertainment are similarly prevalent [38]. The likelihood of purchases increases when community members interact more frequently; information becomes more accurate, and consumers are more likely to develop trust. In social commerce, users’ emotional interactions help them keep in touch and deepen mutual familiarity. Interactivity positively influences network users’ purchase intention [40], as theorized below:
Interactivity is complementary in two types of social commerce (live streaming and OCRs) and positively influences purchase intention.
3.1.3. Emotional Support
Emotional support is when someone is available to listen, care, empathize, provide reassurance, and make a person feel valued, loved, and cared for [41]. Emotional aspects are fundamental to one’s purchase decisions and understanding of products [26]. Since the advent of e-commerce, gaining the audience’s emotional support and recognition have become prime objectives among streaming media providers and merchants. Emotional support affects audience members’ hedonism and improves their social status. Emotional factors convince them to watch live streams and persuade them to become devoted merchant followers [25]. Consumption behavior then results. In short, the emotional support that consumers receive when watching live streams is expected to boost their trust in the featured product:
Emotional support is complementary in two types of social commerce (live streaming and OCRs) and positively influences purchase intention.
3.1.4. Perceived Usefulness
Perceived usefulness isolates helpful reviews from other reviews and builds better purchasing decisions on information from sources [42]. Casaló et al. [43] noted that reviews with higher perceived usefulness are more reliable and helpful to consumers, which is what sets them apart from other sources of information. Both studies illustrate the key function of perceived usefulness, which is seen by consumers as a more reliable and helpful characteristic. The perceived usefulness of online comments manifests when readers of online comments can learn more about a product. This perception significantly influences whether customers accept the commentary. Today’s consumers frequently use social media to research products and read others’ evaluations before making purchase decisions; put simply, people rely heavily on online reviews [44]. Every piece of information that can inform a customer’s decision is valuable, especially when a person is faced with unfamiliar product costs and ambiguous channel quality. Perceived usefulness affects how online shoppers react to reviews, how they feel, and whether they make a purchase. OCRs deemed helpful by customers lead to increased sales for the company [45]. We speculate that:
Perceived usefulness is complementary in two types of social commerce (live streaming and OCRs) and positively influences purchase intention.
3.1.5. Perceived Trust
Trust in the e-commerce environment is the willingness of consumers to transact with sellers [13], Chen [46] defined perceived trust as beliefs, confidence, attitudes, or expectations about the trustworthiness of another party or behavioral intentions and dependence. Perceived trust is essential to social commerce in terms of drawing customers and engendering a sense of dependability and trustworthiness. With respect to the connection between customers and user-generated content, trust is critical because it lowers transaction risks [13]. Positive OCRs can boost customer confidence and reduce suspicion [31]. Therefore, trust is well known to influence consumers’ purchase intention. The following hypothesis, therefore. applies:
Perceived trust is complementary in two types of social commerce (live streaming and OCRs) and positively influences purchase intention.
3.2. Social Commerce and Customer Engagement
3.2.1. Sense of Community
Live-streaming viewers’ community participation improves as they engage in social contact and fulfill personal needs [47]. According to the theory of community sense, people believe that they have a sense of belonging as part of the community. Sense of belonging is also a “basic human motivation” that contextualizes individuals’ behavior [48]. Brodie et al. [49] identified sense of belonging as an emotional component of customer engagement. In terms of shopping via live streams, this sense of belonging arises when people watch a live broadcast. The live-streaming media community strives for greater member participation, connection, and communication than the general community. Interaction, sharing, and recommendations strengthen participant relationships and bolster their sense of community [50]. In short, when the audience has a strong sense of community, customer engagement rises, as postulated below:
Sense of community is complementary in two types of social commerce (live streaming and OCRs) and positively influences customer engagement.
3.2.2. Interactivity
Two of the main forces that have transformed physical establishments into a digital landscape are interactivity and connectivity. Live streaming has broken the interaction barrier between disciplines and has been applied to online retail businesses. Interaction attributes are particularly relevant to customer engagement before and after purchase [51]. Zhou and Tian [24] posited that the immersive and interactive experience has attracted a substantial amount of consumer engagement. Users have more influence over information in the online transaction process owing to this interactive function. Interactions between customers, or between customers and sellers, foster value creation through several avenues: information sharing, suggestions, or the invention of new concepts, features, and uses. Customers who actively engage on social-commerce websites typically have positive opinions toward the information, features, items, and services they experience, which encourages more frequent and in-depth visits [52]. The following hypothesis is formulated accordingly:
Interactivity is complementary in two types of social commerce (live streaming and OCRs) and positively influences customer engagement.
3.2.3. Emotional Support
Emotional support refers to a type of social aid that is directly related to emotional needs. [53]. It effectively lowers consumers’ perceived risk and heightens emotional bonding [54]. Audiences tend to find this support on social media if they lack it in their lives, which boosts their propensity to continue visiting associated platforms. Social media users can connect with platforms and use them to address emotional needs [18]. When live streaming is emotionally supportive, viewers are more likely to engage in constructive, enduring activities [55]. Real-time interaction can elicit strong emotions and reinforce viewers’ social and emotional identification with displayed items and other viewers [56]. Therefore, when the audience receives clear emotional support, customer engagement is enhanced. As such, we hypothesize that:
Emotional support is complementary in two types of social commerce (live streaming and OCRs) and positively influences customer engagement.
3.2.4. Perceived Usefulness
In the social commerce context, Customers’ inclinations to engage with the community and seek advice from the group were found to be strongly predicted by perceived usefulness [57]. Consumers are more open to co-creation in social commerce when they believe it increases their knowledge of the goods and services they are interested in. A high level of perceived usefulness causes consumers to adopt a positive attitude toward co-creation in social commerce because they may recognize online reviews as an effective channel for sharing their opinions with friends and family in order to be helpful when making decisions regarding the purchase of goods and services. Research on virtual communities has confirmed perceived usefulness to be a determinant of consumer engagement and behavioral intention toward online shopping [58]. Thus, we hypothesize the following:
Perceived usefulness is complementary in two types of social commerce (live streaming and OCRs) and positively influences customer engagement.
3.2.5. Perceived Trust
Perceived trust is pivotal in consumers’ decisions to shop at online retailers and can enhance seller–customer interaction in an e-commerce scenario. A lack of confidence inhibits online purchase behavior, while trust is regarded to be an antecedent belief that creates good attitudes for potential transactional behavior [31], which encourages customers to visit the brand website and spend a generous amount of time browsing the relevant pages. Customers actively browsing the brand’s website are considered active participants [59]. Thus, we hypothesize the following:
Perceived trust is complementary in two types of social commerce (live streaming and OCRs) and positively influences customer engagement.
3.3. Customer Engagement and Purchase Intention
Lim and Rasul [60] defined customer engagement in the online environment as a series of measurable actions that customers take on social media in response to brand-related content, such as liking, commenting on, sharing, etc. Wongkitrungrueng and Assarut [5] further suggested that customer engagement refers to the level of consumer involvement and connection with a company’s products or activities, which broadens the activity space for customer engagement. Numerous studies on customer engagement in the context of social commerce have been undertaken [5,61]. Social media has become a vital tool for businesses and marketers to communicate. It also has a significant role in influencing customer attitudes and purchasing patterns [6]. While customers are actively involved in an online environment, they are more likely to express themselves emotionally through purchasing. Customer engagement and consumer purchase intention is positively correlated [62]. Customer engagement is, therefore, a cornerstone of customers’ purchase decisions. In addition, customer engagement contributes to the future benefits of the business. According to Gao and Huang [63], customer engagement helps companies to obtain customer loyalty. The same customer engagement also affects perceived customer value creation, including customer satisfaction, new customers, and customer maintenance [64]. Customer engagement is integral to success in the online marketplace and in online brand communities. Customers forge strong bonds with brand-affiliated communities through interaction, which encourages consumers to purchase from those communities. The following hypothesis is, hence, put forth:
In a social commerce context, customer engagement has a significant beneficial impact on purchase intention.
Figure 1 depicts the theoretical model underpinning this investigation.
4. Methodology
4.1. Questionnaire Design and Measurement
A survey method was employed to test our theory and validate construct measures from earlier studies. To develop an appropriate questionnaire, we modified existing measurement items to suit the live-streaming context. Emotional support items were largely adapted from Li [65]. Sjöblom and Hamari [55] were frequently cited when assessing one’s sense of community. Items related to interactivity were borrowed from Chen and Lin [66,67]. The work of Kin et al. [68] served as the foundation for three items on perceived trust. Customer engagement items were based on Wongkitrungrueng and Assarut [5]. Only individuals who watched live streams or browsed OCRs were eligible to participate. When gathering the data, exclusion criteria were applied to identify questionnaires that did not meet our requirements. The majority of items were scored on a 7-point Likert scale anchored by “strongly disagree” and “strongly agree.” Our control variables aligned with those suggested by Sun et al. [4]: gender, age, education level, monthly income, and the frequency of social-commerce purchases.
4.2. Data Collection and Sample Description
We conducted an anonymous online survey on Credamo (
The measurement model was validated in terms of reliability and validity. Three metrics were used to evaluate reliability: Cronbach’s α, composite reliability (CR), and factor loadings. All latent variables’ Cronbach’s α and CR values were greater than 0.847 (Table 2), revealing that our model exhibited high internal consistency. All factor loadings were above 0.7 and met the threshold for adequate indicator reliability.
Validity was assessed based on convergent and discriminant dimensions. The average variance extracted (AVE) for all constructs surpassed 0.532, according to Fornell and Larcker’s [69] criterion for acceptable convergent validity. The larger AVE values for each factor (Table 2) showed that the latent variable could account for most indicators in the model. Additionally, to test discriminant validity, the square root of the AVE of each construct was contrasted with the correlation coefficients of other constructs.
4.3. Common Method Bias and Multicollinearity Test
Common method variance is a prevailing issue in research. To circumvent this problem, we obtained data from anonymous respondents, as suggested by Malhotra et al. [70]. In addition, non-rotating principal component factor analysis was carried out on all scale items to identify any common technique deviations. Podsakoff et al. [71] proposed that if a single factor explains less than 50% of the variance, the probability of common method variance is low. A single component accounted for 45.34% (i.e., less than 50%) of the variance in our model, representing the greatest amount of variance explained by one factor. The common method variance was, thus, applicable.
We also performed a multicollinearity test using the variance inflation factor (VIF). Table 3 revealed no multicollinearity concerns: most VIF values were between 1.580 and 3.392. The variable correlation matrix is displayed in Table 4.
4.4. Hypothesis Testing
This study analyses the data using structural equation modeling (SEM), which is widely used in social commerce research [4,19,23,31]. This method enables the quantitative testing of theoretical models. SEM offers significant advantages when dealing with complex models and conducting exploratory studies and enables the exploration of structural pathways between variables. Therefore, we chose SEM to test our theory.
As presented in Table 5. The model’s fit indices were as follows: χ2/df = 2.316, root mean square error of approximation (RMSEA) = 0.054, normed fit index = 0.897, comparative fit index (CFI) = 0.938, incremental fit index (IFI) = 0.939, and Tucker–Lewis index = 0.930. The RMSEA was less than the threshold value of 0.06, denoting a satisfactory model fit. This study’s sample size was 448, and the χ2/df ratio was lower than 3, consistent with a sound model correlation. The CFI, which measures fit strength, was 0.938. The IFI was 0.939, further indicating a good fit. The model fit was, therefore, sufficient for path coefficient analysis.
The outcomes of the SEM are pictured in Figure 2. For simplicity, we have omitted paths that are not significant. Interactivity (β = 0.195, p < 0.001) and perceived usefulness (β = 0.195, p < 0.001) significantly increased consumers’ possibility of making a purchase, supporting H2 and H4. That is, the perceived interactivity and usefulness of social commerce shopping amplified consumers’ purchase intention. Sense of community, emotional support, and perceived trust each did not significantly affect purchase intention; as such, H1, H3, and H5 were not supported. These three elements appeared not to spur consumers’ participation on multiple social-commerce platforms. Sense of community (β = 0.497, p < 0.001), interactivity (β = 0.111, p < 0.05), perceived usefulness (β = 0.258, p < 0.001), and perceived trust (β = 0.103, p < 0.01) all fostered consumer engagement, lending support to H6, H7, H9, and H10; H8 is not supported. For H1, our research shows that sense of community affects purchase intention through customer engagement, and it does not directly affect purchase intention, the same reason as H5. For H3 and H8, the possible reasons are as follows: Emotional support affects consumers’ willingness to watch live streaming but does not directly affect their purchase intention. This proves that there is a difference between watching intention and buying intention. To further reveal why H3 and H8 are not supported, we test the relation between emotional support and sense of community and interactivity in our model. The results show that emotional support affects customer engagement by influencing the sense of community and interactivity. It does not directly affect customer engagement. Consumer engagement also positively contributed to purchase intention (β = 0.436, p < 0.001); H11 was supported. In other words, when consumers engaged in the process actively, they were more likely to make a purchase.
The findings for customer engagement’s mediating effects are shown in Table 6. Customer engagement was found to mediate the impacts of sense of community (β = 0.217, p < 0.001), perceived usefulness (β = 0.113, p < 0.001), and perceived trust (β = 0.045, p < 0.004) on purchase intention. Consumers’ perceptions of sense of community, perceived usefulness, and perceived trust associated with social commerce favorably affected their engagement and, by extension, their purchase behavior.
5. Discussion
This research explores the direct and indirect associations between two different channels, live streaming and OCRs, through customer engagement and purchase intention. Sense of community, interactivity, emotional support, and perceived usefulness and perceived trust were functional features of the two different channels. We found that customer engagement behaviors were effective predictors of purchase intention, but not all features of live streaming and OCRs were positively related to purchase intention. Therefore, a discussion of these features needs to be classified.
First, it was verified that there are both direct and indirect relationships between interactivity, perceived usefulness, and purchase intention, which validates H2, H4, H7, and H9. The previous research reported that consumers’ active participation in live interactions can effectively contribute to purchase intention [24,72]. The previous research also supported the impact of the usefulness of OCRs on purchases [33]. Park and Yoo [72] revealed that perceived interactivity can influence psychological intentions and consumers’ attitudes and behavioral intentions toward products. Moreover, perceived usefulness is viewed as an essential factor in eliminating uncertainty.
Second, there was not a significant association between perceived trust and sense of community on purchase intention, and H1 and H5 were not supported. However, the indirect association between sense of community, perceived trust, and purchase intention was established, which was supported for H6 and H10. Our findings support the previous research; some studies have found that sense of community contributes to customer engagement behavior [73] and brand loyalty, which positively predicts viewers’ purchase intention. Furthermore, this study suggests that perceived trust does not directly lead to an increase in purchase intention. We interpret this result through the previous literature; psychosocial distance [74] and emotion [36] moderate whether consumers are able to effectively absorb information passed on by others from OCRs and build communicative trust. Therefore, consumer trust requires further engagement to shape purchase decisions.
Third, the results of this study indicate that the impact of emotional support on purchase intention is not significant; H3 and H8 are not supported. This result was unexpected and differs from the prior literature. Although the existing literature highlights the role of emotional support in live streaming [55], our study did not find a link between emotional support and purchase intention. We compared the previous research on emotional support in live streaming, which has focused on the personal good qualities (e.g., appearance) of the streamer (often a celebrity). One possible reason for this is that with a single live-channel message, consumers’ emotions are more likely to be influenced by the anchor, resulting in impulse purchases. Consumers become more rational as they use both channels to gain more knowledge, especially in the face of more credible OCRs. This also reflects the fact that consumer behavior becomes more complex under the dual channel.
5.1. Theoretical Implications
Our study makes several important theoretical contributions. First, by creating a model elucidating how livestreaming and OCRs jointly affect consumers’ purchase intention from a communication channel perspective, we have extended the social commerce literature. Most of the work on social media has concentrated on lone social-commerce platforms [61,75] with less attention given to consumers’ multi-channel engagement. A few studies analyzed several social platforms. Sun et al. [4] discerned why consumers use new forms of social commerce (e.g., live streaming). Chen et al. [19] explained the motivation for customers to watch the live streaming. However, this research did not address engagement in the multi-social commerce context. Today’s businesses and customers are no longer restricted to a single commerce channel; rather, people seek information based on unique characteristics of different online communication channels.
Second, this study expands the application of information channel complementarity theory in two ways. Channel complementarity justifies why people make purchases in multi-social commerce settings, therefore, verifying the complementarity of live streaming and OCRs in influencing consumers’ purchase intention. In addition, by identifying the combined effect of live streaming and OCRs, we have enlarged the relevant body of work by considering how numerous social-commerce channels (vs. only one) mutually influence purchase intention.
Third, by evaluating the mediating role of customer engagement between social commerce attributes and purchase intention, this study adds to the social commerce literature. Five characteristics were defined along with their effects on information signal processing that may lead to purchase intention. These features’ impacts on customer engagement had not been previously revealed in the context of multiple social-commerce channels. Findings indicated that sense of community, interactivity, perceived usefulness, and perceived trust shaped purchase intention through customer engagement.
5.2. Managerial Implications
First, the results of the empirical analysis show a direct positive correlation between interactivity in live streaming and the perceived usefulness of OCRs with customers’ purchase intention. E-retailers could benefit from the result, as when consumers use different channels to help with decision making, streamers who actively interact with viewers to answer their questions about products or services are likely to result in more purchases. Therefore, E-retailers need to train marketing staff to improve skills and activate viewers to participate in the live streaming. Streamers should guide consumers in a positive and timely manner, which delivers emotional support to viewers and mitigate the perceived risk of online shopping. The positive relationship between the perceived usefulness and purchase intention reminds marketers to steer comments across shopping platforms. Meanwhile, e-retailers are supposed to encourage consumers to post more reviews to improve sales performance.
Second, it is vital for companies to incorporate social commerce into their marketing communications efforts. It is recommended that e-retailers build up appropriate social communities through different social media platforms, as different customers communicate through different communities due to the differences in functionality that exist in different social platforms. Regardless of the type of customer who can find a community that suits them on a certain app, retailers who build and maintain different social communities can capture a larger number of customers and increase their loyalty and engagement. In addition, consumers tend to consider products or services with enough reviews and high ratings to be credible, and marketers need to build up a source of OCRs with credible provenance to reduce consumer uncertainty.
Third, our work reveals the mechanism by which consumers use different channels to shape their purchasing decisions and the need to grasp the benefits of different channels. The emergence of new marketing channels such as live streaming offers the possibility to improve business performance, but the role of traditional channels should not be overlooked, and e-retailers should strengthen the unified management of channels in order to exploit the role of different channels. Overall, this study provides a series of useful insights for online retailers to effectively manage and improve their sales opportunities to ensure they can benefit from the use of online live streaming and OCRs.
5.3. Limitations and Future Research
There are some limitations in this study that open avenues for future research. First, the live-streaming commerce profiled herein revolved around online shopping. Other forms of live streaming, such as broadcasts about tourism and video games, warrant scrutiny. Second, the data analysis was based on a sample of respondents who used several platforms in China. Scholars should consider additional data that reflect varying cultural norms, economic circumstances, and live-streaming technologies. Finally, the rise of live-streaming media has not completely replaced traditional social business models. Subsequent work can investigate ways to blend live streaming and online comments with enterprises’ marketing campaigns to achieve optimal outcomes.
6. Conclusions
This paper proposes complementary key features of live streaming and OCRs, with the sense of community, interactivity, and emotional support cited as key features of live streaming, while perceived usefulness and perceived trust are considered as features of OCRs, and the mediating role of customer engagement is considered. We explored the impact of live streaming and OCRs on consumer purchase behavior using SEM based on the channel complementarity theory. The specific results show that interactivity and perceived usefulness can have both direct and indirect effects on consumers’ purchase intention. Sense of community and perceived trust are needed to arouse engagement behavior for the consumer’s eventual purchase decision. Moreover, emotional support was found to have no effect on purchase intention.
Our study aimed to examine how the distinct characteristics of live streaming and OCRs can effectively complement each other in influencing consumer behavior, with customer engagement serving as a mediating factor. This research is among the first to apply the channel complementarity theory in a social commerce context to gain insights into consumer behavior. Given the increasing complexity of consumer behavior in the presence of multiple information channels, it is essential for online retailers to understand the role and features of different channels and leverage their strengths to provide more comprehensive and effective information. By doing so, online retailers can enhance their ability to influence consumer behavior and achieve greater success in the competitive e-commerce landscape.
Conceptualization, C.Q. and S.L.; methodology, C.Q., X.Z. and K.Z.; investigation, C.Q.; data curation, K.Z.; writing—original draft preparation, X.Z. and K.Z.; writing—review and editing, C.Q. and X.Z.; supervision, C.Q.; project administration, C.Q. All authors have read and agreed to the published version of the manuscript.
Informed consent was obtained from all subjects involved in the study.
Further inquiries can be directed to the corresponding author.
The authors declare no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 2. Structural model results. Note: * p < 0.05; ** p < 0.01; *** p < 0.001.
Demographics of respondents (n = 448).
| Demographics | Item | Frequency | Percentage (%) |
|---|---|---|---|
| Gender | Female | 300 | 67.0 |
| Male | 148 | 33.0 | |
| Age range | 0–20 | 12 | 2.7 |
| 21–30 | 255 | 56.9 | |
| 31–40 | 155 | 34.6 | |
| 41–50 | 16 | 3.6 | |
| 51–60 | 10 | 2.2 | |
| Education level | High school and below | 17 | 3.8 |
| College and bachelor’s degree | 388 | 86.6 | |
| Master’s degree | 40 | 8.9 | |
| Doctoral degree | 3 | 0.7 | |
| Monthly income (RMB) | <1500 | 14 | 3.1 |
| 1500–2999 | 34 | 7.6 | |
| 3000–4999 | 56 | 12.5 | |
| 5000–5999 | 53 | 11.8 | |
| 6000–6999 | 53 | 11.8 | |
| 7000–7999 | 52 | 11.6 | |
| ≥8000 | 186 | 41.5 | |
| Purchase frequency (times per months) | 1–3 | 43 | 9.6 |
| 4–6 | 161 | 35.9 | |
| 7–9 | 130 | 29.0 | |
| ≥9 | 114 | 25.4 |
Confirmatory factor analysis results.
| Construct | Items | Factor Loading |
|---|---|---|
| Interactivity |
I will send pop-ups and give feedback. | 0.795 |
| I will respond to the streamer’s request and give feedback. | 0.859 | |
| I will like, give gifts, and share my feelings. | 0.799 | |
| Sense of community |
Being a part of the live-streaming room is essential to me. | 0.845 |
| I spend a lot of time with the members of the live-streaming room and enjoy being with them. | 0.893 | |
| I want to stay involved in the live-streaming room for a long time. | 0.857 | |
| The members of the live-streaming room share a common interest and share important things. | 0.830 | |
| Emotional support |
Some of the viewers in the live-streaming room supported me when I was in trouble. | 0.848 |
| Some viewers in the live-streaming room comforted and encouraged me when I was in trouble. | 0.886 | |
| When I was in trouble, some viewers in the live-streaming room expressed their concern for me. | 0.831 | |
| Perceived trust |
I think the OCR’s statement is correct. | 0.738 |
| I think the OCR’s statement is dependable. | 0.833 | |
| I think the OCR’s statement is honest. | 0.860 | |
| Perceived usefulness |
The OCR I browse is easily accessible. | 0.805 |
| The OCR I browse adds effectiveness. | 0.808 | |
| The OCR I browse adds productivity. | 0.802 | |
| Customer engagement |
I spend more time on pages that have video. | 0.765 |
| I would become a fan and a follower of a page that uses Live. | 0.822 | |
| I am likely to try and keep track of the activities of a seller who uses Live. | 0.795 | |
| I am likely to revisit the seller’s page to watch their new live videos in the near future. | 0.639 | |
| I am likely to recommend sellers who use Live to my friends. | 0.709 | |
| I encourage friends and relatives to do business with a seller who uses Live. | 0.752 | |
| In the near future, I will definitely buy products from a seller who uses Live. | 0.707 | |
| I consider a seller who uses Live to be my first choice when buying this kind of product. | 0.824 | |
| Purchase intention |
I will consider live-streaming shopping as my first shopping choice. | 0.874 |
| I intend to purchase products or services through live-streaming shopping. | 0.813 | |
| I expect that I will purchase products or services through live-streaming shopping. | 0.800 |
Multicollinearity testing results.
| Construct | Items | Variance Inflation Factor |
|---|---|---|
| Interactivity | I1 | 1.752 |
| I2 | 2.161 | |
| I3 | 2.322 | |
| Sense of community | SC1 | 2.603 |
| SC2 | 3.392 | |
| SC3 | 2.830 | |
| SC4 | 2.621 | |
| Emotional support | ES1 | 2.058 |
| ES2 | 2.483 | |
| ES3 | 2.054 | |
| Perceived trust | PT1 | 1.580 |
| PT2 | 1.754 | |
| PT3 | 1.941 | |
| Perceived usefulness | PU1 | 1.653 |
| PU2 | 1.680 | |
| PU3 | 1.685 | |
| Customer engagement | CE1 | 1.739 |
| CE2 | 2.523 | |
| CE3 | 1.699 | |
| CE4 | 1.606 | |
| CE5 | 1.958 | |
| CE6 | 2.077 | |
| CE7 | 1.762 | |
| CE8 | 2.697 |
Correlation matrix of latent variables.
| Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| 1. Sense of community | 1 | ||||||
| 2. Emotional support | 0.703 ** | 1 | |||||
| 3. Interactivity | 0.766 ** | 0.694 ** | 1 | ||||
| 4. Perceived trust | 0.546 ** | 0.487 ** | 0.554 ** | 1 | |||
| 5. Perceived usefulness | 0.483 ** | 0.447 ** | 0.421 ** | 0.470 ** | 1 | ||
| 6. Customer engagement | 0.793 ** | 0.634 ** | 0.682 ** | 0.579 ** | 0.613 ** | 1 | |
| 7. Purchase intention | 0.729 ** | 0.610 ** | 0.693 ** | 0.563 ** | 0.621 ** | 0.807 ** | 1 |
| M | 5.171 | 5.217 | 5.307 | 5.630 | 5.644 | 5.579 | 5.699 |
| SD | 1.217 | 1.133 | 1.114 | 0.786 | 0.887 | 0.830 | 0.876 |
Note: ** p < 0.01.
Results of hypothesis testing.
| Hypothesis | Path Model | Standardized Path Coefficient | Standard Deviation | p Value | Hypothesis Testing Result |
|---|---|---|---|---|---|
| H1 | Sense of community → Purchase intention | 0.104 | 0.059 | 0.080 | Not Supported |
| H2 | Interactivity → Purchase intention | 0.195 | 0.048 | 0.000 | Support |
| H3 | Emotional support → Purchase intention | 0.016 | 0.053 | 0.765 | Not Supported |
| H4 | Perceived usefulness → Purchase intention | 0.195 | 0.055 | 0.000 | Support |
| H5 | Perceived trust → Purchase intention | 0.044 | 0.050 | 0.373 | Not Supported |
| H6 | Sense of community → Customer engagement | 0.497 | 0.051 | 0.000 | Support |
| H7 | Interactivity → Customer engagement | 0.111 | 0.055 | 0.043 | Support |
| H8 | Emotional support → Customer engagement | 0.042 | 0.097 | 0.472 | Not Supported |
| H9 | Perceived usefulness → Customer engagement | 0.258 | 0.046 | 0.000 | Support |
| H10 | Perceived trust → Customer engagement | 0.103 | 0.037 | 0.006 | Support |
| H11 | Customer engagement → Purchase intention | 0.436 | 0.059 | 0.000 | Support |
Standardized indirect effects and 95% confidence intervals.
| Construct | Indirect Effect (IV–M–DV) | 95% Confidence Interval | ||||
|---|---|---|---|---|---|---|
| IV | M | DV | Estimated | p | Lower | Upper |
| Sense of community | Customer engagement | Purchase intention | 0.217 | 0.000 | 0.147 | 0.285 |
| Interactivity | Customer engagement | Purchase intention | 0.049 | 0.065 | 0.003 | 0.103 |
| Emotional support | Customer engagement | Purchase intention | 0.019 | 0.474 | -0.034 | 0.069 |
| Perceived usefulness | Customer engagement | Purchase intention | 0.113 | 0.000 | 0.064 | 0.166 |
| Perceived trust | Customer engagement | Purchase intention | 0.045 | 0.004 | 0.012 | 0.073 |
References
1. Tajvidi, M.; Wang, Y.; Hajli, N.; Love, P.E.D. Brand Value Co-Creation in Social Commerce: The Role of Interactivity, Social Support, and Relationship Quality. Comput. Hum. Behav.; 2021; 115, 105238. [DOI: https://dx.doi.org/10.1016/j.chb.2017.11.006]
2. Kim, S.; Park, H. Effects of Various Characteristics of Social Commerce (s-Commerce) on Consumers’ Trust and Trust Performance. Int. J. Inf. Manag.; 2013; 33, pp. 318-332. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2012.11.006]
3. Wang, Y.; Yu, C. Social Interaction-Based Consumer Decision-Making Model in Social Commerce: The Role of Word of Mouth and Observational Learning. Int. J. Inf. Manag.; 2017; 37, pp. 179-189. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2015.11.005]
4. Sun, Y.; Shao, X.; Li, X.; Guo, Y.; Nie, K. How Live Streaming Influences Purchase Intentions in Social Commerce: An IT Affordance Perspective. Electron. Commer. Res. Appl.; 2019; 37, 100886. [DOI: https://dx.doi.org/10.1016/j.elerap.2019.100886]
5. Wongkitrungrueng, A.; Assarut, N. The Role of Live Streaming in Building Consumer Trust and Engagement with Social Commerce Sellers. J. Bus. Res.; 2020; 117, pp. 543-556. [DOI: https://dx.doi.org/10.1016/j.jbusres.2018.08.032]
6. Zheng, R.; Li, Z.; Na, S. How Customer Engagement in the Live-Streaming Affects Purchase Intention and Customer Acquisition, E-Tailer’s Perspective. J. Retail. Consum. Serv.; 2022; 68, 103015. [DOI: https://dx.doi.org/10.1016/j.jretconser.2022.103015]
7. Liang, T.-P.; Turban, E. Introduction to the Special Issue Social Commerce: A Research Framework for Social Commerce. Int. J. Electron. Commer.; 2011; 16, pp. 5-13. [DOI: https://dx.doi.org/10.2753/JEC1086-4415160201]
8. Zhang, K.Z.K.; Zhao, S.J.; Cheung, C.M.K.; Lee, M.K.O. Examining the Influence of Online Reviews on Consumers’ Decision-Making: A Heuristic-Systematic Model. Decis. Support Syst.; 2014; 67, pp. 78-89. [DOI: https://dx.doi.org/10.1016/j.dss.2014.08.005]
9. Zheng, L. The Classification of Online Consumer Reviews: A Systematic Literature Review and Integrative Framework. J. Bus. Res.; 2021; 135, pp. 226-251. [DOI: https://dx.doi.org/10.1016/j.jbusres.2021.06.038]
10. Amblee, N.; Bui, T. Harnessing the Influence of Social Proof in Online Shopping: The Effect of Electronic Word of Mouth on Sales of Digital Microproducts. Int. J. Electron. Commer.; 2011; 16, pp. 91-113. [DOI: https://dx.doi.org/10.2753/JEC1086-4415160205]
11. Berger, J.; Iyengar, R. Communication Channels and Word of Mouth: How the Medium Shapes the Message. J. Consum. Res.; 2013; 40, pp. 567-579. [DOI: https://dx.doi.org/10.1086/671345]
12. Shen, H.; Wu, L.; Yi, S.; Xue, L. The Effect of Online Interaction and Trust on Consumers’ Value Co-Creation Behavior in the Online Travel Community. J. Travel Tour. Mark.; 2020; 37, pp. 418-428. [DOI: https://dx.doi.org/10.1080/10548408.2018.1553749]
13. Cheng, X.; Fu, S.; Sun, J.; Bilgihan, A.; Okumus, F. An Investigation on Online Reviews in Sharing Economy Driven Hospitality Platforms: A Viewpoint of Trust. Tour. Manag.; 2019; 71, pp. 366-377. [DOI: https://dx.doi.org/10.1016/j.tourman.2018.10.020]
14. Tong, X.; Chen, Y.; Zhou, S.; Yang, S. How Background Visual Complexity Influences Purchase Intention in Live Streaming: The Mediating Role of Emotion and the Moderating Role of Gender. J. Retail. Consum. Serv.; 2022; 67, 103031. [DOI: https://dx.doi.org/10.1016/j.jretconser.2022.103031]
15. Bickart, B.; Schindler, R.M. Internet Forums as Influential Sources of Consumer Information. J. Interact. Mark.; 2001; 15, pp. 31-40. [DOI: https://dx.doi.org/10.1002/dir.1014]
16. Rosario, A.B.; Sotgiu, F.; De Valck, K.; Bijmolt, T.H.A. The Effect of Electronic Word of Mouth on Sales: A Meta-Analytic Review of Platform, Product, and Metric Factors. J. Mark. Res.; 2016; 53, pp. 297-318. [DOI: https://dx.doi.org/10.1509/jmr.14.0380]
17. Park, H.J.; Lin, L.M. The Effects of Match-ups on the Consumer Attitudes toward Internet Celebrities and Their Live Streaming Contents in the Context of Product Endorsement. J. Retail. Consum. Serv.; 2020; 52, 101934. [DOI: https://dx.doi.org/10.1016/j.jretconser.2019.101934]
18. Lin, Y.; Yao, D.; Chen, X. Happiness Begets Money: Emotion and Engagement in Live Streaming. J. Mark. Res.; 2021; 58, pp. 417-438. [DOI: https://dx.doi.org/10.1177/00222437211002477]
19. Chen, J.; Liao, J. Antecedents of Viewers’ Live Streaming Watching: A Perspective of Social Presence Theory. Front. Psychol.; 2022; 13, 839629. [DOI: https://dx.doi.org/10.3389/fpsyg.2022.839629]
20. Koh, J.; Kim, Y.-G. Knowledge Sharing in Virtual Communities: An e-Business Perspective. Expert Syst. Appl.; 2004; 26, pp. 155-166. [DOI: https://dx.doi.org/10.1016/S0957-4174(03)00116-7]
21. Blanchard, A.L. Developing a Sense of Virtual Community Measure. CyberPsychology Behav.; 2007; 10, pp. 827-830. [DOI: https://dx.doi.org/10.1089/cpb.2007.9946] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18085972]
22. Naranjo-Zolotov, M.; Oliveira, T.; Casteleyn, S.; Irani, Z. Continuous Usage of E-Participation: The Role of the Sense of Virtual Community. Gov. Inf. Q.; 2019; 36, pp. 536-545. [DOI: https://dx.doi.org/10.1016/j.giq.2019.05.009]
23. Prentice, C.; Han, X.Y.; Hua, L.-L.; Hu, L. The Influence of Identity-Driven Customer Engagement on Purchase Intention. J. Retail. Consum. Serv.; 2019; 47, pp. 339-347. [DOI: https://dx.doi.org/10.1016/j.jretconser.2018.12.014]
24. Zhou, X.; Tian, X. Impact of Live Streamer Characteristics and Customer Response on Live-Streaming Performance: Empirical Evidence from e-Commerce Platform. Procedia Comput. Sci.; 2022; 214, pp. 1277-1284. [DOI: https://dx.doi.org/10.1016/j.procs.2022.11.306]
25. Lim, J.S.; Choe, M.-J.; Zhang, J.; Noh, G.-Y. The Role of Wishful Identification, Emotional Engagement, and Parasocial Relationships in Repeated Viewing of Live-Streaming Games: A Social Cognitive Theory Perspective. Comput. Hum. Behav.; 2020; 108, 106327. [DOI: https://dx.doi.org/10.1016/j.chb.2020.106327]
26. Hennig-Thurau, T.; Gwinner, K.P.; Walsh, G.; Gremler, D.D. Electronic Word-of-Mouth via Consumer-Opinion Platforms: What Motivates Consumers to Articulate Themselves on the Internet?. J. Interact. Mark.; 2004; 18, pp. 38-52. [DOI: https://dx.doi.org/10.1002/dir.10073]
27. Schneider, P.J.; Zielke, S. Searching Offline and Buying Online—An Analysis of Showrooming Forms and Segments. J. Retail. Consum. Serv.; 2020; 52, 101919. [DOI: https://dx.doi.org/10.1016/j.jretconser.2019.101919]
28. Geng, R.; Chen, J. The Influencing Mechanism of Interaction Quality of UGC on Consumers’ Purchase Intention—An Empirical Analysis. Front. Psychol.; 2021; 12, 697382. [DOI: https://dx.doi.org/10.3389/fpsyg.2021.697382]
29. Eslami, S.P.; Ghasemaghaei, M.; Hassanein, K. Which Online Reviews Do Consumers Find Most Helpful? A Multi-Method Investigation. Decis. Support Syst.; 2018; 113, pp. 32-42. [DOI: https://dx.doi.org/10.1016/j.dss.2018.06.012]
30. Singh, J.P.; Irani, S.; Rana, N.P.; Dwivedi, Y.K.; Saumya, S.; Kumar Roy, P. Predicting the “Helpfulness” of Online Consumer Reviews. J. Bus. Res.; 2017; 70, pp. 346-355. [DOI: https://dx.doi.org/10.1016/j.jbusres.2016.08.008]
31. Lu, B.; Fan, W.; Zhou, M. Social Presence, Trust, and Social Commerce Purchase Intention: An Empirical Research. Comput. Hum. Behav.; 2016; 56, pp. 225-237. [DOI: https://dx.doi.org/10.1016/j.chb.2015.11.057]
32. Herrando, C.; Jiménez-Martínez, J.; Martín-De Hoyos, M.J.; Constantinides, E. Emotional Contagion Triggered by Online Consumer Reviews: Evidence from a Neuroscience Study. J. Retail. Consum. Serv.; 2022; 67, 102973. [DOI: https://dx.doi.org/10.1016/j.jretconser.2022.102973]
33. Dutta-Bergman, M.J. Complementarity in Consumption of News Types Across Traditional and New Media. J. Broadcast. Electron. Media; 2004; 48, pp. 41-60. [DOI: https://dx.doi.org/10.1207/s15506878jobem4801_3]
34. Liao, J.; Chen, J.; Dong, X. Understanding the Antecedents and Outcomes of Brand Community-Swinging in a Poly-Social-Media Context: A Perspective of Channel Complementarity Theory. Asia Pac. J. Mark. Logist.; 2022; 34, pp. 506-523. [DOI: https://dx.doi.org/10.1108/APJML-11-2020-0820]
35. Zhou, J.; Zhou, J.; Ding, Y.; Wang, H. The Magic of Danmaku: A Social Interaction Perspective of Gift Sending on Live Streaming Platforms. Electron. Commer. Res. Appl.; 2019; 34, 100815. [DOI: https://dx.doi.org/10.1016/j.elerap.2018.11.002]
36. Jang, S.; Chung, J.; Rao, V.R. The Importance of Functional and Emotional Content in Online Consumer Reviews for Product Sales: Evidence from the Mobile Gaming Market. J. Bus. Res.; 2021; 130, pp. 583-593. [DOI: https://dx.doi.org/10.1016/j.jbusres.2019.09.027]
37. Djafarova, E.; Rushworth, C. Exploring the Credibility of Online Celebrities’ Instagram Profiles in Influencing the Purchase Decisions of Young Female Users. Comput. Hum. Behav.; 2017; 68, pp. 1-7. [DOI: https://dx.doi.org/10.1016/j.chb.2016.11.009]
38. Xue, J.; Liang, X.; Xie, T.; Wang, H. See Now, Act Now: How to Interact with Customers to Enhance Social Commerce Engagement?. Inf. Manag.; 2020; 57, 103324. [DOI: https://dx.doi.org/10.1016/j.im.2020.103324]
39. Kang, K.; Lu, J.; Guo, L.; Li, W. The Dynamic Effect of Interactivity on Customer Engagement Behavior through Tie Strength: Evidence from Live Streaming Commerce Platforms. Int. J. Inf. Manag.; 2021; 56, 102251. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2020.102251]
40. Lu, B.; Chen, Z. Live Streaming Commerce and Consumers’ Purchase Intention: An Uncertainty Reduction Perspective. Inf. Manag.; 2021; 58, 103509. [DOI: https://dx.doi.org/10.1016/j.im.2021.103509]
41. Helgeson, V.S. Social Support and Quality of Life. Qual. Life Res.; 2003; 12, (Suppl. S1), pp. 25-31. [DOI: https://dx.doi.org/10.1023/A:1023509117524]
42. Liu, Z.; Park, S. What Makes a Useful Online Review? Implication for Travel Product Websites. Tour. Manag.; 2015; 47, pp. 140-151. [DOI: https://dx.doi.org/10.1016/j.tourman.2014.09.020]
43. Casaló, L.V.; Flavián, C.; Guinalíu, M.; Ekinci, Y. Avoiding the Dark Side of Positive Online Consumer Reviews: Enhancing Reviews’ Usefulness for High Risk-Averse Travelers. J. Bus. Res.; 2015; 68, pp. 1829-1835. [DOI: https://dx.doi.org/10.1016/j.jbusres.2015.01.010]
44. Huseynov, F.; Dhahak, K. The Impact of Online Consumer Reviews (OCR) on Online Consumers Purchase Intention. J. Bus. Res.; 2020; 12, pp. 990-1005. [DOI: https://dx.doi.org/10.20491/isarder.2020.889]
45. Chevalier, J.A.; Mayzlin, D. The Effect of Word of Mouth on Sales: Online Book Reviews. J. Mark. Res.; 2006; 43, pp. 345-354. [DOI: https://dx.doi.org/10.1509/jmkr.43.3.345]
46. Chen, C. Identifying Significant Factors Influencing Consumer Trust in an Online Travel Site. Inf. Technol. Tour.; 2006; 8, pp. 197-214. [DOI: https://dx.doi.org/10.3727/109830506778690849]
47. Rosen, D.; Lafontaine, P.R.; Hendrickson, B. CouchSurfing: Belonging and Trust in a Globally Cooperative Online Social Network. New Media Soc.; 2011; 13, pp. 981-998. [DOI: https://dx.doi.org/10.1177/1461444810390341]
48. Allen, K.-A.; Gray, D.L.; Baumeister, R.F.; Leary, M.R. The Need to Belong: A Deep Dive into the Origins, Implications, and Future of a Foundational Construct. Educ. Psychol. Rev.; 2022; 34, pp. 1133-1156. [DOI: https://dx.doi.org/10.1007/s10648-021-09633-6]
49. Brodie, R.J.; Hollebeek, L.D.; Jurić, B.; Ilić, A. Customer Engagement: Conceptual Domain, Fundamental Propositions, and Implications for Research. J. Serv. Res.; 2011; 14, pp. 252-271. [DOI: https://dx.doi.org/10.1177/1094670511411703]
50. Bi, Q. Cultivating Loyal Customers through Online Customer Communities: A Psychological Contract Perspective. J. Bus. Res.; 2019; 103, pp. 34-44. [DOI: https://dx.doi.org/10.1016/j.jbusres.2019.06.005]
51. Verleye, K.; Gemmel, P.; Rangarajan, D. Managing Engagement Behaviors in a Network of Customers and Stakeholders: Evidence from the Nursing Home Sector. J. Serv. Res.; 2014; 17, pp. 68-84. [DOI: https://dx.doi.org/10.1177/1094670513494015]
52. Molinillo, S.; Anaya-Sánchez, R.; Liébana-Cabanillas, F. Analyzing the Effect of Social Support and Community Factors on Customer Engagement and Its Impact on Loyalty Behaviors toward Social Commerce Websites. Comput. Hum. Behav.; 2020; 108, 105980. [DOI: https://dx.doi.org/10.1016/j.chb.2019.04.004]
53. Shensa, A.; Sidani, J.E.; Escobar-Viera, C.G.; Switzer, G.E.; Primack, B.A.; Choukas-Bradley, S. Emotional Support from Social Media and Face-to-Face Relationships: Associations with Depression Risk among Young Adults. J. Affect. Disord.; 2020; 260, pp. 38-44. [DOI: https://dx.doi.org/10.1016/j.jad.2019.08.092]
54. Joo, D.; Xu, W.; Lee, J.; Lee, C.-K.; Woosnam, K.M. Residents’ Perceived Risk, Emotional Solidarity, and Support for Tourism amidst the COVID-19 Pandemic. J. Destin. Mark. Manag.; 2021; 19, 100553. [DOI: https://dx.doi.org/10.1016/j.jdmm.2021.100553]
55. Sjöblom, M.; Hamari, J. Why Do People Watch Others Play Video Games? An Empirical Study on the Motivations of Twitch Users. Comput. Hum. Behav.; 2017; 75, pp. 985-996. [DOI: https://dx.doi.org/10.1016/j.chb.2016.10.019]
56. Mao, Z.; Du, Z.; Yuan, R.; Miao, Q. Short-Term or Long-Term Cooperation between Retailer and MCN? New Launched Products Sales Strategies in Live Streaming e-Commerce. J. Retail. Consum. Serv.; 2022; 67, 102996. [DOI: https://dx.doi.org/10.1016/j.jretconser.2022.102996]
57. Zhang, L.; Wu, L.; Mattila, A.S. Online Reviews: The Role of Information Load and Peripheral Factors. J. Travel Res.; 2016; 55, pp. 299-310. [DOI: https://dx.doi.org/10.1177/0047287514559032]
58. Cheung, M.F.Y.; To, W.M. Service Co-Creation in Social Media: An Extension of the Theory of Planned Behavior. Comput. Hum. Behav.; 2016; 65, pp. 260-266. [DOI: https://dx.doi.org/10.1016/j.chb.2016.08.031]
59. Calder, B.J.; Malthouse, E.C.; Schaedel, U. An Experimental Study of the Relationship between Online Engagement and Advertising Effectiveness. J. Interact. Mark.; 2009; 23, pp. 321-331. [DOI: https://dx.doi.org/10.1016/j.intmar.2009.07.002]
60. Lim, W.M.; Rasul, T. Customer Engagement and Social Media: Revisiting the Past to Inform the Future. J. Bus. Res.; 2022; 148, pp. 325-342. [DOI: https://dx.doi.org/10.1016/j.jbusres.2022.04.068]
61. Barger, V.; Peltier, J.W.; Schultz, D.E. Social Media and Consumer Engagement: A Review and Research Agenda. J. Res. Interact. Mark.; 2016; 10, pp. 268-287. [DOI: https://dx.doi.org/10.1108/JRIM-06-2016-0065]
62. Mora Cortez, R.; Johnston, W.J.; Ghosh Dastidar, A. Managing the Content of LinkedIn Posts: Influence on B2B Customer Engagement and Sales?. J. Bus. Res.; 2023; 155, 113388. [DOI: https://dx.doi.org/10.1016/j.jbusres.2022.113388]
63. Gao, M.; Huang, L. Quality of Channel Integration and Customer Loyalty in Omnichannel Retailing: The Mediating Role of Customer Engagement and Relationship Program Receptiveness. J. Retail. Consum. Serv.; 2021; 63, 102688. [DOI: https://dx.doi.org/10.1016/j.jretconser.2021.102688]
64. An, M.; Han, S.-L. Effects of Experiential Motivation and Customer Engagement on Customer Value Creation: Analysis of Psychological Process in the Experience-Based Retail Environment. J. Bus. Res.; 2020; 120, pp. 389-397. [DOI: https://dx.doi.org/10.1016/j.jbusres.2020.02.044]
65. Li, C.-Y. How Social Commerce Constructs Influence Customers’ Social Shopping Intention? An Empirical Study of a Social Commerce Website. Technol. Forecast. Soc. Chang.; 2019; 144, pp. 282-294. [DOI: https://dx.doi.org/10.1016/j.techfore.2017.11.026]
66. Chen, C.-C.; Lin, Y.-C. What Drives Live-Stream Usage Intention? The Perspectives of Flow, Entertainment, Social Interaction, and Endorsement. Telemat. Inform.; 2018; 35, pp. 293-303. [DOI: https://dx.doi.org/10.1016/j.tele.2017.12.003]
67. Gruen, T.W.; Osmonbekov, T.; Czaplewski, A.J. EWOM: The Impact of Customer-to-Customer Online Know-How Exchange on Customer Value and Loyalty. J. Bus. Res.; 2006; 59, pp. 449-456. [DOI: https://dx.doi.org/10.1016/j.jbusres.2005.10.004]
68. Kim, D.J.; Ferrin, D.L.; Rao, H.R. A Trust-Based Consumer Decision-Making Model in Electronic Commerce: The Role of Trust, Perceived Risk, and Their Antecedents. Decis. Support Syst.; 2008; 44, pp. 544-564. [DOI: https://dx.doi.org/10.1016/j.dss.2007.07.001]
69. Fornell, C.; Larcker, D.F. Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. J. Mark. Res.; 1981; 18, 382. [DOI: https://dx.doi.org/10.1177/002224378101800313]
70. Malhotra, N.K.; Kim, S.S.; Patil, A. Common Method Variance in IS Research: A Comparison of Alternative Approaches and a Reanalysis of Past Research. Manag. Sci.; 2006; 52, pp. 1865-1883. [DOI: https://dx.doi.org/10.1287/mnsc.1060.0597]
71. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.-Y.; Podsakoff, N.P. Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. J. Appl. Psychol.; 2003; 88, pp. 879-903. [DOI: https://dx.doi.org/10.1037/0021-9010.88.5.879] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/14516251]
72. Park, M.; Yoo, J. Effects of Perceived Interactivity of Augmented Reality on Consumer Responses: A Mental Imagery Perspective. J. Retail. Consum. Serv.; 2020; 52, 101912. [DOI: https://dx.doi.org/10.1016/j.jretconser.2019.101912]
73. Goh, K.-Y.; Heng, C.-S.; Lin, Z. Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content. Inf. Syst. Res.; 2013; 24, pp. 88-107. [DOI: https://dx.doi.org/10.1287/isre.1120.0469]
74. Hernández-Ortega, B. Don’t Believe Strangers: Online Consumer Reviews and the Role of Social Psychological Distance. Inf. Manag.; 2018; 55, pp. 31-50. [DOI: https://dx.doi.org/10.1016/j.im.2017.03.007]
75. Liang, Y.J. Reading to Make a Decision or to Reduce Cognitive Dissonance? The Effect of Selecting and Reading Online Reviews from a Post-Decision Context. Comput. Hum. Behav.; 2016; 64, pp. 463-471. [DOI: https://dx.doi.org/10.1016/j.chb.2016.07.016]
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