The Power of Social Commerce: Understanding the Role of Social Word-of-Mouth Behaviors and Flow Experience on Social Media Users’ Purchase Intention

Abstract

Social commerce has emerged as a phenomenon that supports social interaction and user-generated content, allowing individuals to make informed purchasing decisions. This study aims to identify the antecedents of the sWOM behaviors present in social network sites (SNSs). Specifically, tie strength, homophily, trust, informational, and normative influence were included as sWOM antecedents. This study provides a deeper understanding of sWOM behavior in SNSs by identifying and analyzing separately three interrelated behaviors with distinct antecedents: opinion-giving, opinion-seeking, and opinion-passing. Additionally, flow experience is proposed as a less studied antecedent that interacts with all sWOM behaviors, and simultaneously, purchase intention. Our results suggest that flow experience, followed by opinion-seeking and opinion-passing, exerts the greatest influence on purchase intention. Additionally, our findings reveal that informative and normative influence are the analyzed antecedents that have the greatest impact on purchase intention.

JEL Classification: M31.

Plain language summary

This study analyzes the sequential process of evaluation and use of social networks regarding the intention to purchase products recommended by other consumers. Three types of internet behaviors are differentiated: searching for product information, sharing this information with one’s contacts in social networks, and/or generating opinions and evaluations about the products. Several factors have been identified that could enhance both the three aforementioned behaviors and the final purchase intention of the product itself: tie strength, homophily, trust, informative influence, normative influence, and flow experience. The results of the study allow us to conclude that flow experience as well as opinion-seeking and opinion-passing behaviors are the factors that enhance product purchase intention the most. Of the remaining factors analyzed, the effect of informative and normative influences is most significant.

Keywords

social commerce opinion-giving opinion-seeking opinion-passing flow experience social network sites

Introduction

Nowadays, the Internet plays a key role as a new information channel in the consumer purchasing process, offering a common space for content creation and opinion sharing (Kozinets et al., 2010). Specifically, it plays an important role as a vehicle for product-related opinion dissemination, that is, in the electronic word-of-mouth or eWOM phenomenon. This widely analyzed concept has traditionally been defined as “positive or negative comments made by potential, current, or former customers about a product, service, brand, or company, which are made available to a multitude of people and institutions through the Internet” (Hennig-Thurau et al., 2004, p. 39).

In this context, e-commerce has evolved into a social phenomenon that supports social interaction and user-generated content through social network sites (SNSs) to support individuals in their purchasing decisions. According to K. Z. Zhang and Benyoucef (2016), social commerce links shopping and social interaction resulting in the diversification of the use of SNSs, which have evolved to become the ideal tools for simultaneously shopping and generating opinions.

Previous research has analyzed the antecedents of social worth-of-mouth (sWOM) and highlighted its influence on consumer behavior by arguing other consumers’ recommendations are perceived as reliable information sources (L. Wang et al., 2020). Thus, as a communication tool, sWOM can reduce product uncertainty and increase an individual’s trust which, in turn, increases purchase intention (Ahmad & Laroche, 2017). It is crucial that companies encourage sWOM participation of SNS users, as these reviews may attract potential customers who give credibility to sWOM and third-party validation (Abrate et al., 2011). Proof of this is that 61% of U.S. users stated that they always consult online reviews before making a purchase (Statista, 2022).

Previous literature has distinguished between two currents of research that analyze sWOM behavior in SNSs. While one current focuses on analyzing overall sWOM behavior, that is, understanding sWOM as a whole (Bilal et al., 2021; Lien & Cao, 2014; T. Wang et al., 2016), other studies analyze the antecedents that form each of the three sWOM behaviors identified: opinion-seeking, opinion-passing, and opinion-giving (Chu & Kim, 2011; Farías, 2017; Ismagilova et al., 2021; Sohaib et al., 2019). When considering sWOM behaviors as a whole, there is an implicit hypothesis that their antecedents and outcomes are homogeneous regardless of the behavior; an advantage derived from disaggregating the three aforementioned behaviors is that it allows us to empirically contrast this implicit hypothesis. Furthermore, while some studies have explored the influence of overall sWOM behavior on purchase intention (Bilal et al., 2021; Vergura et al., 2021), there is little empirical evidence about the differentiated influence of each sWOM behavior on consumer purchase intention (Filieri et al., 2018; Leong et al., 2021). This will enable managers to understand not only how to incentivize SNS user participation in each sWOM behavior, but also which of them translate into higher purchase intention.

Previous studies analyzing opinion-seeking, opinion-passing, and opinion-giving behaviors highlight certain antecedents such as tie strength, homophily, trust, and interpersonal influence that shape such sWOM behaviors (Chu & Kim, 2011; Farzin & Fattahi, 2018). However, the effect of flow experience as an antecedent of the aforementioned behaviors and purchase intention in SNSs has received little attention in sWOM literature. Flow refers to the state of mind that individuals reach when immersed in an activity, leading them to lose track of time and to abstract from other thoughts as a result of the enjoyment and pleasure (Csikszentmihalyi, 1990). The influence of flow experience is relevant for two reasons. First, individuals do not only seek the acquisition of a product but also highly value the experience and emotions derived from the whole purchasing process (H. Zhang et al., 2014). Secondly, business-wise, it is possible to manage the generation of stimuli that lead users into such a flow state that they may share product-related opinions in SNSs. Several studies have proved the influence of flow on both, sWOM behavior and consumer purchase intention, in online environments (e.g., Herrando et al., 2019; Molinillo et al., 2018). However, there is little empirical evidence in the context of SNSs analyzing both concepts simultaneously (Song et al., 2017), being fewer when considering the existence of three individualized sWOM behaviors.

Using a comprehensive model of the entire consumer mental process, this study aims to identify the antecedents that trigger opinion-seeking, opinion-passing, and opinion-giving, and how these behaviors influence purchase intention. For this purpose, tie strength, homophily, trust, informational influence, and normative influence were included as possible predecessors. Additionally, the present research attempts to study the influence of flow experience on the aforementioned sWOM behaviors and, jointly, on purchase intention. This study contributes to the existing literature mainly in two aspects. First, it contemplates not only the existence, but also the compatibility of the different sWOM behaviors and, therefore, the presence of interrelation between them; that is, the different behaviors are not treated to form groups of consumers, as the previous literature suggests. Therefore, it is highlighted the need to disaggregate sWOM into the aforementioned behaviors to understand the different mental processes that trigger each one of them and, consequently, to identify and enhance those that have greater importance in purchase intention. Secondly, flow experience is proposed as a less studied antecedent that interacts with opinion-seeking, opinion-passing, and opinion-giving (H. Zhang et al., 2014). Hence, a non-symmetrical influence of the flow experience on sWOM behaviors is revealed. Specifically, SNS flow experience is the most important antecedent for explaining opinion-giving, being more influential than traditional antecedents discussed by previous literature. In contrast, flow experience has a relatively lower influence on opinion-seeking and opinion-passing. In short, our results contribute to enriching the literature on the state of flow in SNSs, studying the influence of this state of mind on each of the eWOM behaviors and, jointly, on social purchase intention.

The following section outlines the theoretical background that supports the proposed hypotheses. Next, the methodology and the results analysis are detailed. Finally, the main theoretical and business implications are discussed, followed by the study limitations and the future research lines.

Conceptual Background and Hypothesis Development

Antecedents of sWOM Behaviors

Differences in individuals’ social relationships will lead to different sWOM behaviors in SNSs (Chu & Kim, 2011). Consequently, this study focuses on the antecedents of sWOM behavior by developing a theoretical and empirical framework of determinants of consumer engagement in different sWOM behaviors in SNSs. Figure 1 illustrates the theoretical model and the hypotheses put forward that will be developed below.

Figure 1.

Theoretical framework.

Tie Strength

Tie strength, originally conceptualized by Granovetter (1973, p. 1361), is understood as “the level of intensity of the relationship or bond between members of the same network.” Several studies have analyzed the influence of tie strength in the study of individuals and SNSs, as they are used to satisfy several social needs, such as self-expression and self-presentation (Sohrabi & Akbari, 2016). The intensity of the strength of ties in SNSs can vary considerably; therefore, the literature provides various classifications based on the number and type of resources exchanged, the frequency of these exchanges, the intensity of emotions associated with these exchanges, and the intimacy existing between users. In general, strong ties, such as family and friends, constitute closer relationships and are able to provide material and emotional support. In contrast, weak ties are often found among less personal social relationships that are composed of a broad set of acquaintances and colleagues (Pigg & Crank, 2004). Several authors have concluded that consumers trust online information posted by users with whom they perceive a strong social relationship. Although strong ties exert a more significant impact at the individual level and in small groups, the characteristics of SNSs allow weak ties to extend their influence by extending consumers’ personal networks to external communities or groups.

Previous literature has distinguished between two currents of research that analyze the impact of tie strength on SNS users’ engagement in sWOM behaviors. First, one stream focuses on exploring the impact of tie strength on overall sWOM behavior (Bilal et al., 2021; Luarn et al., 2016; T. Wang et al., 2016). These researches contended that tie strength is positively associated with sWOM behavior, that is, consumers are encouraged to communicate with each other and disseminate product-related information. In contrast, the second stream analyzes tie strength as an antecedent of the three sWOM behaviors identified: opinion-seeking, opinion-passing, and opinion-giving (Chu & Kim, 2011; Farías, 2017; Sohaib et al., 2019). Chu and Kim (2011) found that the closer a consumer feels to the source of information the more likely they are to participate in all three sWOM behaviors in SNSs. Both, Farías (2017) and Sohaib et al. (2019), identified tie strength as an antecedent of opinion-seeking, opinion-passing, and opinion-giving. Lastly, previous studies have also partially analyzed the influence of tie strength on some of the aforementioned sWOM behaviors (Ismagilova et al., 2021; Mahapatra & Mishra, 2017). Mahapatra and Mishra (2017) found tie strength is instrumental in opinion-seeking, which then becomes critical in forwarding online reviews to others. Ismagilova et al. (2021) indicated that tie strength has a positive effect on peer communication, more specifically on opinion-giving and opinion-passing. Therefore, the following hypothesis is proposed:

H₁: Tie strength has a positive and significant influence on (A) opinion-seeking, (B) opinion-passing, and (C) opinion-giving in SNSs.

Homophily

This concept describes a greater intensity in behaviors among members of a network when they present common social attributes or characteristics (Lozares et al., 2014, p. 2658). In SNSs, individuals are more likely to interact with those users who share similar attributes because the possibility of relationship conflict is reduced through greater trust and emotional attachment (Hyun & Kim, 2014). Thus, communication effectiveness may be higher among consumers with similar attributes because individuals with similar lifestyles and social characteristics tend to have similar needs in consumption and, therefore, will provide more relevant information (Sohaib et al., 2019).

In SNSs, the traditional conceptualization of homophily based on similar individual characteristics is inappropriate as users often do not have access to the characteristics that are commonly associated with homophily, such as the demographic and psychological background (Kim et al., 2018). Homophily in this context is driven by shared interests that can be assessed in SNSs, so users are likely to have different perceptions of being similar with respect to other contacts and, consequently, exhibit different levels of homophily. Therefore, it is imperative to understand the differences in perceived homophily levels to fully understand the sWOM behaviors that occur in SNSs.

Reviewing previous literature, the likelihood of information exchange is higher when individuals who exchange opinions share common characteristics (Chu & Kim, 2011; Farías, 2017; Ismagilova et al., 2021; Saleem & Ellahi, 2017; Sohaib et al., 2019; Vergura et al., 2021). Both, Saleem and Ellahi (2017) and Vergura et al. (2021), indicated that when individuals find out that the information provider shares the same attributes, they are more likely to engage in overall sWOM activities. Focusing on the second current of sWOM Research, Chu and Kim (2011) investigated the antecedents of sWOM and found homophily negatively associated with opinion-seeking and opinion-passing behaviors in SNSs. Farías (2017) and Sohaib et al. (2019) discovered a significant, positive, and indirect effect of homophily through tie strength on all three sWOM behaviors: opinion-seeking, opinion-passing, and opinion-giving. Lastly, Ismagilova et al. (2021) found that a higher level of similarity between the members of SNSs encourages individuals’ opinion-giving and opinion-passing. Thus, the following hypothesis is proposed:

H₂: Homophily has a positive and significant influence on (A) opinion-seeking, (B) opinion-passing, and (C) opinion-giving in SNSs.

Additionally, the influence of a reference group is strengthened when group members are similar to each other because their common interests and backgrounds lead them to interact more frequently and establish closer relationships with each other. Therefore, the greater the homophily between the receivers and senders of messages in SNSs, the stronger the strength of the existing ties between them (Farías, 2017; Sohaib et al., 2019). Therefore, the following hypothesis is proposed:

H₃: Homophily has a positive and significant influence on tie strength in SNSs.

Trust

Traditionally, trust focuses on the reliability of the interlocutor’s behavior or the individual’s ability to predict it (Carroll et al., 2007, p. 82). Previous literature has concluded that trust plays a key role in information exchange, as it allows individuals to justify and evaluate their decision to join SNSs to exchange opinions about a product, brand, or company (Chu & Choi, 2011). Thus, consumers perceive social media and, ultimately, SNSs as virtual sWOM platforms as a more reliable source of information than the content generated by companies, which they transmit through their traditional communication mix (Mangold & Faulds, 2009). In addition, the anonymity of the Internet allows consumers to interact freely with others without revealing their true identity, an issue that some authors suggest may increase access to consumer information by removing social barriers (Shah et al., 2001). Previous studies have shown that perceived trust in SNS contacts facilitates information flow increasing the likelihood that consumers will engage in overall sWOM behavior in SNSs (Bilal et al., 2021; Farzin & Fattahi, 2018; Lien & Cao, 2014; Saleem and Ellahi, 2017). Lien and Cao (2014) indicated that trust is significantly associated with positive sWOM. Saleem and Ellahi (2017) found trust to be essential for SNS users to evaluate the value of information and thus, to have a serious effect on sWOM behavior. Farzin and Fattahi (2018) revealed that when consumers trust the source of information, they will be increasingly willing to trust these relationships, and this, in turn, would strengthen the willingness to engage in sWOM behavior in SNSs. In an online survey of Chinese WeChat users, Bilal et al. (2021) found that trust plays a strong positive association with the sWOM intention of fashion products. Chu and Kim (2011) focused on the second current of sWOM research by confirming that the individual’s willingness to rely on the source of information, increases all three sWOM behaviors: opinion-seeking, opinion-passing, and opinion-giving. Likewise, Lee and Choi (2019) analyzed the predictors of sWOM behavior on SNSs in the United States and Korea and found a positive and significant influence of trust on opinion-seeking, opinion-passing, and opinion-giving. Thus, the following hypothesis is proposed:

H₄: Trust has a positive and significant influence on (A) opinion-seeking, (B) opinion-passing, and (C) opinion-giving in SNSs.

Additionally, social interactions become relationships of trust; that is, close and frequent communication between individuals in SNSs allows them to get to know each other, share information, develop a common point of view, and, ultimately, be perceived as more trustworthy. Hence, some authors posit that the greater the strength of the ties between two SNSs users, the greater the existing trust between them (T. Wang et al., 2016). Therefore, the following hypothesis is proposed:

H₅: Tie strength has a positive and significant influence on trust in SNSs.

Interpersonal Influence

Previous literature has identified two types of interpersonal influence with a crucial role in influencing consumer decision-making in SNSs: normative influence and informational influence (Farzin & Fattahi, 2018).

Informative influence is the tendency to accept information from others perceived as experts and to use it as a guide in the search for products and brands, and can manifest itself in two ways: individuals can directly request information from others or they can indirectly make inferences based on the observation of the behavior of others (Deutsch & Gerard, 1955, p. 629). All in all, informative influence is classified as a factor relating to the sender-receiver relationship since the individual (receiver) actively seeks information from experts (sender) to increase his or her knowledge; thus, attributing the perception of product quality, purchase, or endorsement to another individual or opinion group (C. W. Park & Lessig, 1977). Thus, previous literature has confirmed the positive and significant influence of informational influence on consumers’ decision processes regarding product evaluations and overall sWOM behavior (Bilal et al., 2021; Farzin & Fattahi, 2018; Laroche et al., 2005; Saleem & Ellahi, 2017). Laroche et al. (2005) argue that individuals with a higher susceptibility to informative influence focus on the information value and, thus, show a greater need to acquire valuable information from expert contacts to guide their purchases. Both, Saleem and Ellahi (2017) and Farzin and Fattahi (2018) researches indicate that consumer susceptibility to informative influence is an important factor that has a significant and positive impact on product-related information exchange. Bilal et al. (2021) confirmed that consumers requiring information before making an affect purchase decision would join the fashion-connected overall sWOM behavior. On the other hand, in an online survey of Chilean SNS users, Farías (2017) contributed to the second current of sWOM research by identifying informational influence as an antecedent of all three sWOM behaviors: opinion-seeking, opinion-passing, and opinion-giving. Lastly, Chu and Kim (2011) found that the greater the consumer’s susceptibility to informational influence, the greater the use of SNSs will be and, thus, the greater the participation in opinion-seeking and opinion-passing. Therefore, the following hypothesis is proposed:

H₆: Informative influence has a positive and significant influence on (A) opinion-seeking, (B) opinion-passing, and (C) opinion-giving in SNSs.

Normative influence, on the other hand, consists of matching attitudes, beliefs, and behaviors to conform to the expectations of others and thus be accepted (Burnkrant & Cousineau, 1975, p. 207). In this sense, the literature has distinguished between expressive value and utilitarian influence. Thus, the former reflects the individual’s desire to enhance his own image and is motivated by the need for psychological association with a person or group, which implies acceptance of the positions expressed by others. For its part, the utilitarian aspect of normative influence is reflected in the individual’s attempts to comply with the expectations of others to achieve rewards or avoid punishment, and operates through the process of conformity or approval, thus affecting consumers’ decision-making processes (C. W. Park & Lessig, 1977). Normative influence is classified as a consumer-related factor as the consumer is the one who adopts a behavior derived from third parties since, in a way, he/she seeks to improve his/her self-concept (Burnkrant & Cousineau, 1975). Like informational influence, normative influence was identified as a determinant of sWOM-related behaviors in SNSs (Chu & Kim, 2011; Farías, 2017; Hansen & Lee, 2013; Laroche et al., 2005). Laroche et al. (2005) indicated that individuals who are susceptible to normative influence are more likely to seek social approval through the acquisition and use of the same products and brands that are recommended by others in virtual communities. In a survey of users who play games on Facebook, Hansen and Lee (2013) found that with higher normative influence, the more likely the consumer will engage in sWOM and post a message on Facebook. In both researches, Chu and Kim (2011) and Farías (2017) discovered a significant, positive effect of normative influence on all three sWOM behaviors: opinion-seeking, opinion-passing, and opinion-giving. Thus, the following hypothesis is proposed:

H₇: Normative influence has a positive and significant influence on (A) opinion-seeking, (B) opinion-passing, and (C) opinion-giving in SNSs.

Flow

Among the experiences and feelings generated during the information search process, the analysis of the state of flow is key. This concept refers to the mental state reached when the individual is immersed in the activity that occupies him/her, leading him/her to lose track of time and to abstract from other thoughts, as a result of the enjoyment and pleasure that the activity brings him/her (Csikszentmihalyi, 1990). Flow contributes to the creation of optimal experiences that result in positive responses from the consumer, so some authors have highlighted the importance of designing a specific environment in which users can achieve such a state of mind (H. Zhang et al., 2014). In this sense, individuals who reach this optimal experience will seek to repeat it on future occasions, giving rise to positive loyalty responses. Therefore, following previous literature, we posit that reaching the flow state will influence both emotional loyalty and behavioral loyalty (Herrando et al., 2019). In the context of social commerce, emotional loyalty translates into increased engagement in sWOM behaviors. Once the flow experience is achieved, it is considered easy to transform it into positive recommendations; that is, flow is a key enabler for users to tell others about the social commerce site (O’Cass & Carlson, 2010). While numerous authors have tested the influence of flow state on the intention to engage in sWOM behaviors on websites (Herrando et al., 2019; Lu et al., 2010; Molinillo et al., 2018; O’Cass & Carlson, 2010), the is little empirical evidence that analyzes flow experience in SNSs (Song et al., 2017). Additionally, such research focuses on the analysis of sWOM behavior as a whole; that is, without identifying the three sWOM behaviors (opinion-giving, seeking, and passing) and without determining the influence of flow experience on each of the aforementioned behaviors (H. Zhang et al., 2014). Since users who have experienced a state of flow are likely to participate again in SNSs by searching, passing, and/or creating opinions, the following hypothesis is proposed:

H₈: Flow state has a positive and significant influence on (A) opinion-seeking, (B) opinion-passing, and (C) opinion-giving in SNSs.

Concerning behavioral loyalty, previous literature has revealed that reaching a flow state is positively related to the consumer’s attitude towards the social commerce site and the intention to shop there and not switch to another one, as well as the intention to revisit and spend more time there (Flavián et al., 2006; Peña-García et al., 2018). Therefore, the following hypothesis is proposed:

H₉: Flow experience has a positive and significant influence on purchase intention in SNSs.

sWOM Behaviors on SNSs

To thoroughly understand the mechanism of sWOM communication in the consumer purchase decision-making process, it is necessary to analyze the different behaviors associated with this phenomenon: opinion-giving, opinion-seeking, and opinion-passing or dissemination (Chu & Kim, 2011).

Conceptually, in online environments, opinion leaders are those users with knowledge and experience about a product or brand who generate content by posting their opinions or recommendations on the Internet (Kanje et al., 2020, p. 276). Individuals with high levels of opinion leadership tend to possess some ability to influence and motivate information sharing; therefore, they can exert a great impact on the attitudes and behaviors of others. The growth of SNSs offers opinion leaders a unique channel to strengthen their personal characteristics and enhance their self-efficacy to give advice and recommendations to other consumers.

Likewise, opinion-seeking plays an important role as it facilitates the flow of information in the product diffusion process; that is, opinion-seeking is essential since without individuals seeking information, opinion leaders would not exist (Flynn et al., 1996, p. 138). Since opinion seekers possess less knowledge about the product category, they tend to seek information and advice from opinion leaders when making a purchase decision; thus, they reduce the perceived risk of the purchase (Kanje et al., 2020). Opinion-seeking users rely heavily on SNS as a source of information for their purchases, because they consider sWOM recommendations from friends and acquaintances as credible and trustworthy (L. Wang et al., 2020).

It is worth emphasizing the relationship between leadership and opinion-seeking; opinion leaders cannot exist without opinion seekers, and vice versa. However, even though opinion seekers are not necessarily opinion leaders, the latter may at the same time adopt an opinion seeker role because they wish to have more knowledge or expertise (Gharib et al., 2020; Sun et al., 2006). Therefore, the following hypothesis is proposed

H₁₀: Opinion-giving behavior is related to opinion-seeking.

Finally, and less analyzed, we find opinion passing. Regarding this behavior, authors such as Sun et al. (2006, p. 1111) have considered the transmission of opinion, materialized in the forwarding, online chatting of opinions and recommendations from third parties, as an inherent consequence of leadership and opinion-seeking. Additionally, in this study, it will also be considered as one of the three behaviors that make up sWOM. In the digital context, the unique characteristics of the Internet can facilitate the dissemination of information and, therefore, we can consider that transmission is a natural sWOM behavior that takes place in SNSs (Norman & Russell, 2006).

These types of consumers are motivated by a desire to help others, thus gaining pleasurable feelings caused by disseminating or sharing information with others (Walsh et al., 2004). Due to the interactive and sometimes anonymous platform that SNSs represent, the traditional line between opinion-giving and opinion-seeking is increasingly blurred. The absence of social pressure may make it possible for opinion seekers to adopt a more assertive behavior and, therefore, to share information through actions such as forwarding opinions. Therefore, it is reasonable to state that both behaviors are related to opinion-passing:

H₁₁: Opinion-giving behavior is related to opinion-passing.

H₁₂: Opinion-seeking behavior is related to opinion-passing.

sWOM Behaviors and Purchase Intention

The online socialization and collaboration that SNSs facilitate present great opportunities for consumers to actively participate in product recommendations, that is, sWOM behaviors increase. By accessing the reviews of other consumers online, the individual can form his or her own opinion on some aspects related to the product, such as its quality or performance, reducing uncertainty about the product and increasing his or her confidence, which in turn increases his or her purchase intention. The relationship between sWOM behavior and consumer purchase intention has been widely analyzed in the literature, concluding the existence of a positive and significant influence (Erkan & Evans, 2016; D. H. Park et al., 2007). Given the influence of recommendations in the purchase process and the purchase intention of consumers, there is a need for companies to encourage the consultation and generation of information. To this end, it will be essential to analyze the behaviors that consumers may adopt when interacting with others (Filieri et al., 2018; Leong et al., 2021). Therefore, the following hypothesis is proposed:

H₁₃: (A) Opinion-seeking, (B) opinion-passing, and (C) opinion-giving in SNSs positively and significantly influence consumers’ purchase intention.

Methodology

To address the proposed hypotheses, a survey was carried out with a target population of individuals residing in Spain who buy fashion-related products (clothing, footwear, and accessories) and who, in turn, participate in SNSs. The sample included a total of 1,493 individuals (Table 1).

Table 1.

Sample Distribution.

Characteristics	Percentage (%)
Gender
Male	49.8
Female	50.2
Age (years)
Under 30	66.2
31–45	12.5
46–65	19.8
65 and above	1.5
Educational attainment
No formal education	5.1
Primary school	5.4
Secondary school	40.1
College	49.4

For the evaluation of the variables under study, a questionnaire was created and used based on scales validated in previous literature to guarantee their reliability and validity as shown in Table 2. All of them have been adapted to the context of the study and measured on a 7-level Likert.

Table 2.

Individual Reliability of the Indicators.

Construct/associated items	Loadings	Construct/associated	Loadings
Tie strength (TS) (Chu & Kim, 2011)		Flow experience (FE) (O’Cass & Carlson, 2010)
TS1	.800	FE1	.822
TS2	.915	FE2	.819
TS3	.922	FE3	.564
Homophily (H) (Chu & Kim, 2011)		FE4	.824
H1	.897	Opinion seeking (OS) (Flynn et al., 1996)
H2	.941	OS1	.907
H3	.906	OS2	.936
Trust (T) (Chu & Kim, 2011)		OS3	.857
T1	.928	Opinion passing (OP) (Sun et al., 2006)
T2	.907	OP1	.891
T3	.954	OP2	.923
Informative influence (II) (Chu & Kim, 2011)		OP3	.920
ii1	.889	Opinion gassing (OG) (Flynn et al., 1996)
II2	.880	OG1	.919
II3	.839	OG2	.931
Normative influence (NI) (Chu & Kim, 2011)		OG3	.924
NI1	.890	Purchase intention (PI) (Alhidari et al., 2015)
NI2	.921	PI1	.770
NI3	.875	PI2	.872
		PI3	.874
		PI4	.818

Testing of the research model uses the partial least squares (PLS) technique, a variance-based structural equation model (SEM) method. PLS is suitable owing to the following reasons (J. F. Hair et al., 2019): (1) the focus of the study is the prediction of the dependent variables; (2) the research model is highly complex according to the type of the relationships in the hypotheses; (3) this study uses latent variables scores in subsequent analysis of predictive relevance; and (4) it is not necessary to assume a normal distribution of the data. The PLS-SEM analysis was conducted using SmartPLS 3.3.9 software.

Results

Two phases comprise the analysis and interpretation of a PLS model. First, the assessment of the reliability and validity of the measurement model; second, the evaluation of the structural model.

Measurement Model

The sequential procedure detailed by J. F. Hair et al. (2019) has been followed. First, examining that the indicator loadings are above .708 (Carmines & Zeller, 1979); all items met these conditions (Table 2), except for the FE3 indicator (λ = .564). It was decided to keep this item, as the aforementioned criteria are flexible in cases in which the indicators contribute to the factor’s content validity (Chin, 1998).

The second step is to assess internal consistency reliability; the model satisfies the prerequisite of construct reliability because Cronbach’s alpha >.70 and composite reliability >.708 were met for all constructs (Carmines & Zeller, 1979), as can be seen in columns 1 and 2 of Table 3.

Table 3.

Construct Reliability, Convergent Validity, and Discriminant Validity.

Note. CA = Cronbach’s Alfa; CR = Composite Reliability; AVE = Average variance extracted. Bolded numbers are the square root of the average variance extracted (AVE). Figures below the AVE line are the interconstruct correlations; above is the HTMT ratio (in green color).

The third step is to assess the convergent validity of each construct measure, that is, that each set of indicators represents a single underlying construct (Henseler et al., 2009). The scores for average variance extracted (AVE) surpass the threshold of .5 (Fornell & Larcker, 1981) as column 3 of Table 3 shows.

Lastly, the fourth step is to assess discriminant validity. Confirmation of this last requirement comes from: (1) the square root of the AVE is higher than the correlations with other constructs; and (2) the Heterotrait–Monotrait (HTMT) ratio of correlations <.85 in all cases shown in Table 3 (Clark & Watson, 1995).

Structural Model

After confirming the adequacy of the measurement model, the next step is assessing the structural model. First, the predictive power of the model is evaluated using G-power 3.1.9.7 software (Faul et al., 2007); the statistical power obtained for the model is .987, which exceeds the minimum of .80 required (Cohen, 1988).

Second, the explanatory capacity of the model was examined. For this purpose, the coefficient of determination (R²) was used. Figure 2 shows the model’s overall results which are considered acceptable since the values obtained for the coefficient of determination are greater than .20, a threshold considered high for disciplines such as consumer behavior, as is the case (J. Hair et al., 2023).

Figure 2.

Hypothesis testing.

Third, Geiser and Stone’s Q² is obtained using the blindfolding procedure to measure the predictive accuracy of the model (J. F. Hair et al., 2019). All the values were positive, so it can be affirmed that the theoretical model has relevance for predicting consumer behavior (Geisser, 1975; Stone, 1974). To assess the intensity of the predictive ability, Q² values higher than 0, .25, and .5 depict small, medium, and large predictive relevance of the PLS-path model (J. F. Hair et al., 2019, p. 19). The values obtained for opinion-seeking, opinion-giving, and purchase intention were .477, .271, and .301, respectively, which means medium intensity levels. As for opinion-passing, the value obtained was .533, which is described as a large predictive relevance. Concerning purchasing intention, the value obtained was .301.

The final step is to evaluate the statistical significance and relevance of the path coefficients, using bootstrapping procedure (5,000 subsamples). The results in Figure 2 and Table 4 show that, of all the proposed hypotheses, hypotheses H_1B, H_2A, H_4B, and H_4C are not statistically significant and are therefore rejected. In other words, tie strength has no significant effect on opinion-passing. Homophily also shows no significant influence on opinion-seeking and, similarly, trust has no significant effect on either opinion-passing or opinion-giving. Furthermore, it can be highlighted that informational influence, normative influence, and flow have a positive and significant effect on the three sWOM behaviors in SNSs, as expected given the literature reviewed. In turn, the positive and significant relationship between homophily and tie strength, as well as the influence of homophily on trust, is evident. In addition, none of the hypotheses put forward to explain the relationship between seeking, passing, and opinion-giving is rejected. Finally, both flow and each of the sWOM behaviors in SNSs show a positive and significant influence on consumer purchase intention.

Table 4.

Structural Model Results (Hypothesis Testing).

Hypothesis	Path coefficient (p-value)	Percentile bootstrap 95% confidence interval		Decision
Hypothesis	Path coefficient (p-value)	Lower	Upper	Decision
H_1A: TS → (+)OS	.113 (.000)	0.085	0.186	Support
H_1B: TS → (+)OP	.028 (.115)	−0.009	0.067	Reject
H_1C: TS → (+)OG	.054 (.043)	0.021	0.109	Support
H_2A: H → (+)OS	−.013 (.243)	−0.004	0.095	Reject
H_2B: H → (+)OP	.068 (.002)	0.026	0.117	Support
H_2C: H → (+)OG	.130 (.032)	0.059	0.152	Support
H₃: H → (+) TS	.475 (.000)	0.432	0.518	Support
H_4A: T → (+)OS	.068 (.026)	0.009	0.122	Support
H_4B: T → (+)OP	−.001 (.992)	−0.042	0.039	Reject
H_4C: T → (+)OG	−.009 (.874)	−0.090	0.005	Reject
H₅: TS → (+)T	.582 (.000)	0.543	0.620	Support
H_6A: II → (+)OS	.231 (.000)	0.189	0.286	Support
H_6B: II → (+)OP	.046 (.015)	0.012	0.088	Support
H_6C: II → (+)OG	.118 (.000)	0.071	0.157	Support
H_7A: NI → (+)OS	.116 (.000)	0.087	0.203	Support
H_7B: NI → (+)OP	.055 (.012)	0.011	0.101	Support
H_7C: NI → (+)OG	.318 (.000)	0.245	0.379	Support
H_8A: FE → (+)OS	.048 (.021)	0.013	0.072	Support
H_8B: FE → (+)OP	.048 (.004)	0.015	0.083	Support
H_8C: FE → (+)OG	.182 (.000)	0.127	0.234	Support
H₉: FE → (+)PI	.248 (.000)	0.205	0.297	Support
H₁₀: OS → (+)OG	.472 (.000)	0.428	0.579	Support
H₁₁: OG → (+)OP	.411 (.000)	0.366	0.472	Support
H₁₂: OS → (+)OP	.344 (.000)	0.278	0.386	Support
H_13A: OS → (+)PI	.263 (.000)	0.180	0.315	Support
H_13B: OP → (+)PI	.230 (.000)	0.155	0.302	Support
H_13C: OG → (+)PI	.089 (.004)	0.028	0.159	Support

Importance-Performance Map Analysis (IPMA)

As discussed by Ringle and Sarstedt (2016), the IPMA is particularly useful for generating additional findings by combining the analysis of the importance and performance dimensions in practical PLS-SEM applications. In our research, purchase intention (PI) represents the key target construct. Thus, the importance and performance values of PI’s predecessor constructs allow creating the importance-performance map of PI as shown in Figure 3. While the x-axis represents the importance of a target construct, the y-axis denotes its performance. The IPMA findings indicate that per unit increase in flow experience, opinion-seeking, opinion-passing, informative influence, normative influence, opinion-giving, homophily, tie strength, and trust, the performance of consumers’ purchase intention increased by .311, .263, .230, .121, .116, .089, .072, .053, and .026, respectively. Thus, the IPMA allows us to know the intensity and order of importance of each construct on the endogenous variable, purchase intention.

Figure 3.

IPMA for consumers’ social commerce-based purchase intention.

Discussion

As SNSs have become a popular phenomenon worldwide, online socialization presents great opportunities for consumers to actively participate in product recommendations. This study addressed the importance of understanding sWOM behavior as three distinct and interrelated behaviors (opinion-seeking, opinion-passing, and opinion-giving), as opposed to previous research currents that either contemplated sWOM behavior as a whole (e.g., Bilal et al., 2021; Vergura et al., 2021) or that considered each one of the aforementioned behaviors to be independent of one another (see Farías, 2017; Sohaib et al., 2019). Additionally, while previous research has mainly focused on some antecedents such as tie strength, homophily, trust, and interpersonal influence (e.g., Chu & Kim, 2011; Farías, 2017), this study also includes flow experience, as a less studied predecessor of opinion-seeking, opinion-passing, and opinion-giving. Flow experience is also examined as a catalyst for purchase intention in the context of SNSs.

To understand opinion-seeking, opinion-passing, and opinion-giving as three distinct behaviors, we first analyze the antecedents that configure each of these behaviors. Opinion-seeking is positively influenced by informative and normative influence, followed by tie strength, trust, and flow experience. These results align somewhat with previous literature as individuals seek third parties’ recommendations to reduce the perceived risk in the purchase process (Pigg & Crank, 2004). Contrary to expectations, our results show that homophily does not have a direct and significant effect on opinion-seeking behavior as previously hypothesized. However, homophily exerts an indirect and positive influence through two ways, tie strength and trust, being the former the most intense (in line with Farías, 2017 and Sohaib et al., 2019).

Opinion-passing is positively influenced by homophily, followed by normative influence, flow, and to a lesser extent, informative influence. These results contrast Chu and Kim (2011) and Sohaib et al. (2019), which found tie strength and trust positively impacting opinion-passing in SNSs. One reason could be that consumers focus on sharing feedback with all the contact circles, which include a large number of acquaintances rather than simply sharing information with the closest contacts (Bilal et al., 2021). The immediacy of the Internet has contributed to the rise of a culture of instantaneity, where communication options are abundant and third-party recommendations are quickly and effortlessly available (Tomlinson, 2007). Moreover, the impulsiveness associated with SNSs opinion-passing may explain this finding as it encompasses various characteristics, including impatience, carelessness, seeking excitement, and lack of deep thought (Chamberlain & Sahakian, 2007). Therefore, users may find themselves forwarding third-party opinions in just one click—that is, easily, quickly, and massively—, often without even thinking carefully about the level of trust or closeness with each of their hundreds of contacts.

Lastly, opinion-giving is positively influenced by normative influence, followed by flow, homophily, informative influence, and to a lesser extent, tie strength. These results align with Hansen and Lee (2013) and Farías (2017), who found trust is not an antecedent of opinion-giving behavior. Yet, historically, trust motivated overall sWOM (Chu & Kim, 2011). A possible explanation regarding why trust only affects opinion-seeking behavior, but not the other two behaviors, may come from signaling theory. It is very common for consumers to find themselves in a situation of information asymmetry when evaluating various purchase options. Against the certainty of each purchasing alternative’s acquisition price, there is the uncertainty of each alternative’s associated quality level. Specifically, a problem of adverse selection emerges when the seller’s unobservable quality is fixed and does not change from one transaction to the next (Kirmani & Rao, 2000); adverse selection problems may be resolved by quality signals. When a quality signal is unclear and is neither consistent over time nor believed, empirical studies have shown that consumer’s perceived risk associated with the purchase increases. Thus, they develop opinion-seeking strategies to reduce their level of uncertainty and make their purchase decision (Erdem et al., 2006). Consumers use different cognitive heuristics when determining which information sources to trust online (Metzger & Flanagin, 2006); when the aim is to predict purchase intention, there is empirical evidence proving that whenever high involvement or motivations for opinion-seeking are present, consumers tend to look for additional information from different websites and SNSs (Go et al., 2016; Hansen & Lee, 2013). Consequently, adverse selection problems cause consumers to strengthen their opinion-seeking sWOM behavior to the detriment of other observed behaviors.

Importantly, these results indicate that homophily is the antecedent that most differently influences each of the three sWOM behaviors in SNSs. Specifically, homophily has the greatest impact on opinion-giving, followed by opinion-passing. However, opinion-seeking is indirectly influenced by homophily. This fact could be explained based on the individualist or collectivist predominant character of every society. Whereas homophily’s influence is the opposite in societies with a predominantly individualistic mindset, our finding is common in relatively collectivistic societies, such as Spain, where cooperation and social integration are emphasized (Farías, 2017; Helfrich, 2023). Furthermore, prior research emphasized that members of collectivist societies are more likely to listen to others in sWOM via SNSs than members of individualist cultures (Al-Omoush et al., 2022; Chu & Choi, 2011).

Regarding the relationship between all three behaviors, we first conclude that opinion-giving is related to opinion-seeking. This relationship can be explained by the fact that, in social environments, individuals are connected to diverse communities and are exposed to new information from multiple sources daily. Therefore, it is crucial that opinion-seeking users actively search for messages from various sources to update or expand their knowledge about products or brands. In other words, opinion-seeking has become an essential condition for being an opinion leader in today’s communication (Sun et al., 2006). Similarly, both opinion-giving and opinion-seeking are positively related to opinion-passing. This finding highlights that opinion-passing, embodied in the forwarding of third-party opinions, is an inherent consequence of opinion-giving and opinion-seeking behaviors. Therefore, opinion-passing is considered a differentiated sWOM behavior related to the previous ones. Finally, as expected, all three sWOM behaviors play a key role in consumers’ purchase intention, with opinion-seeking being the most influential behavior, followed by opinion-passing and opinion-giving.

For an in-depth discussion of the most important factors affecting purchase intention, the IPMA is applied as it enables an enriched interpretation of the results. For a better orientation, as shown in Figure 3, it is also drawn two additional lines in the importance-performance map: the mean importance value (0.142) and the mean performance value (36.50) of the displayed constructs. Generally, constructs in the fourth quadrant are of highest interest for improving purchase intention, followed—in decreasing order of importance—by the first, third, and second quadrants (Ringle & Sarstedt, 2016). Constructs mapped in the third quadrant—that is, normative influence and opinion-giving—(low importance-low performance) have less influence on purchase intention than the other constructs analyzed. Variables in the second quadrant (tie strength, trust, homophily, and informative influence) are close to saturating their influence on purchase intention. On the contrary, the constructs that allow purchase intention to be maximized are those situated in the fourth quadrant (with more room for improvement) and the first quadrant since their global importance is higher. Hence, to improve purchase intention, the constructs that must be prioritized are—in decreasing order of importance—, opinion-seeking, opinion-passing behaviors, and next, flow experience.

From a theoretical point of view, our research yields two main contributions. Firstly, in contrast to the first current of work that analyzed sWOM in SNSs as a whole (see T. Wang et al., 2016, among others), our research focuses on explaining three differentiated but interrelated behaviors. Therefore, our study contributes to the existing literature by emphasizing the importance of disaggregating sWOM into opinion-seeking, opinion-passing, and opinion-giving. This finding is key for two reasons: (1) to understand the different mental processes that trigger each of the three aforementioned behaviors; and (2) to identify those behaviors that are most important in purchase intention. Specifically, in a comprehensive model that outlines the entire consumer process until reaching their purchase decision, opinion-seeking is identified as the most influential behavior. Secondly, the inclusion of flow experience as an antecedent of sWOM behaviors constitutes the second contribution of the study. Flow influences each of the behaviors asymmetrically, with this influence being almost four times more intense for opinion-giving than for opinion-seeking and opinion-passing. Finally, flow has been identified as the most important antecedent when explaining SNS users’ purchase intention.

From a business perspective, the findings of this study can yield several insights for the development of Internet marketing strategies. Marketers should consider the antecedents that influence each of the sWOM behaviors to develop customized marketing communication strategies. Specifically, action should be prioritized for those that encourage opinion-seeking and opinion-passing, since these behaviors translate into a higher purchase intention. When aiming to encourage opinion-seeking behavior, managers should design messages that incite users to seek other consumers’ advice regarding products. Additionally, given that interpersonal influence has been shown to impact opinion-seeking, it is important to recognize that users are highly attentive to social cues. Consequently, promotional messages should refrain from emphasizing that seeking advice will make them more unique but rather suggest that doing so may better align with their social context. Additionally, to encourage opinion-passing, our research reveals that managers should focus on those users who share similar views on SNSs. Hence, companies could derive benefits from identifying clusters of similar SNS users by analyzing their demographics, attitudes, and interests. Moreover, managers should implement analytical data to identify the most influential reviews by matching reviewer profiles with those seeking reviews. Enabling users to filter recommendations according to their preferences would further enhance reviews effectiveness and, in turn, purchase intention (Vergura et al., 2021). Finally, our findings show that SNS platforms that incorporate video content, virtual reality, and/or augmented reality can boost flow experience. Thus, companies should strive to enhance immersive experiences through SNS content curation that may contribute to generating positive user experiences. Such SNS content should be prioritized to encourage not only the three sWOM behaviors but also overall purchase intention.

However, the results show that there are some not included variables that could be interesting as future research lines in terms of improving the proposed model. First, the quality of the content—understood as the value perceived by the user of the different information resources offered by a social network—has been shown to be a source of flow status and the generation of positive online recommendations (Hsu et al., 2012). Second, the aesthetics and presentation of content have also been detected as an important antecedent of sWOM and flow state (Shobeiri et al., 2014). Finally, the inclusion of the variable “collectivism” could be useful to obtain different results based on a culture’s individualism/collectivism level, resulting in cross-cultural outcomes (Leonhardt et al., 2020).

Footnotes

Authors’ Contributions

The three co-authors have participated in all stages of work, including the conception and design of the research, the revision of intellectual content and drafting the work.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Institutional Review Board Statement

Ethical review and approval were waived for this study, due to the fact that research questionnaire was anonymous and no personal information was collected.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

ORCID iD

Coloma Álvarez Santamaría

Data Availability Statement

Data available on request from the corresponding author.

References

Abrate

Capriello

Fraquelli

(2011). When quality signals talk: Evidence from the Turin hotel industry. Tourism Management, 32(4), 912–921. https://doi.org/10.1016/j.tourman.2010.08.006

Ahmad

S. N.

Laroche

(2017). Analyzing electronic word of mouth: A social commerce construct. International Journal of Information Management, 37(3), 202–213. https://doi.org/10.1016/j.ijinfomgt.2016.08.004

Alhidari

Iyer

Paswan

(2015). Personal level antecedents of sWOM and purchase intention, on social networking sites. Journal of Customer Behaviour, 14(2), 107–125. https://doi.org/10.1362/147539215X14373846805707

Al-Omoush

K. S.

de Lucas Ancillo

Gavrila

S. G.

(2022). The role of cultural values in social commerce adoption in the Arab world: An empirical study. Technological Forecasting and Social Change, 176, 121440. https://doi.org/10.1016/j.techfore.2021.121440

Bilal

Jianqiu

Dukhaykh

Fan

Trunk

(2021). Understanding the effects of sWOM antecedents on online purchase intention in China. Information, 12(5), 192. https://doi.org/10.3390/info12050192

Burnkrant

R. E.

Cousineau

(1975). Informational and normative social influence in buyer behavior. Journal of Consumer Research, 2(3), 206. https://doi.org/10.1086/208633

Carmines

Zeller

(1979). Reliability and validity assessment (Vol. 17). Sage.

Carroll

Barnes

S. J.

Scornavacca

Fletcher

(2007). Consumer perceptions and attitudes towards SMS advertising: Recent evidence from New Zealand. International Journal of Advertising, 26(1), 79–98. https://doi.org/10.1080/02650487.2007.11072997

Chamberlain

S. R.

Sahakian

B. J.

(2007). The neuropsychiatry of impulsivity. Current Opinion in Psychiatry, 20(3), 255–261. https://doi.org/10.1097/YCO.0b013e3280ba4989

10.

Chin

W. W.

(1998). The partial least squares approach to structural equation modeling. In Marcoulides

G. A.

(Ed.), Modern methods for business research (pp. 295–358). Lawrence Erlbaum.

11.

Chu

S.-C.

Choi

S. M.

(2011). Electronic word-of-mouth in social networking sites: A cross-cultural study of the United States and China. Journal of Global Marketing, 24(3), 263–281. https://doi.org/10.1080/08911762.2011.592461

12.

Chu

S.-C.

Kim

(2011). Determinants of consumer engagement in electronic word-of-mouth (sWOM) in social networking sites. International Journal of Advertising, 30(1), 47–75. https://doi.org/10.2501/IJA-30-1-047-075

13.

Clark

Watson

(1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319. https://doi.org/10.1037/14805-012

14.

Cohen

(1988). Statistical power analysis for the behavioral sciences (2nd ed.). Erlbaum. https://doi.org/10.1111/1467-8721.ep10768783

15.

Csikszentmihalyi

(1990). Flow. The psychology of optimal experience. Harpers Perennial.

16.

Deutsch

Gerard

H. B.

(1955). A study of normative and informational social influences upon individual judgment. The Journal of Abnormal and Social Psychology, 51(3), 629. https://doi.org/10.1037/h0046408

17.

Erdem

Swait

Valenzuela

(2006). Brands as signals: A cross-country validation study. Journal of Marketing, 70(1), 34–49. https://doi.org/10.1509/jmkg.70.1.034.qxd

18.

Erkan

Evans

(2016). The influence of SWOM in social media on consumers’ purchase intentions: An extended approach to information adoption. Computers in Human Behavior, 61, 47–55. https://doi.org/10.1016/j.chb.2016.03.003

19.

Farías

(2017). Identifying the factors that influence EWOM in SNSs: The case of Chile. International Journal of Advertising, 36(6), 852–869. https://doi.org/10.1080/02650487.2017.1364033

20.

Farzin

Fattahi

(2018). EWOM through social networking sites and impact on purchase intention and brand image in Iran. Journal of Advances in Management Research, 15(2), 161–183. https://doi.org/10.1108/JAMR-05-2017-0062

21.

Faul

Erdfelder

Lang

A. G.

Buchner

(2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146

22.

Filieri

McLeay

Tsui

Lin

(2018). Consumer perceptions of information helpfulness and determinants of purchase intention in online consumer reviews of services. Information & Management, 55(8), 956–970. https://doi.org/10.1016/j.im.2018.04.010

23.

Flavián

Guinalíu

Gurrea

(2006). The role played by perceived usability, satisfaction and consumer trust on website loyalty. Information & Management, 43(1), 1–14. https://doi.org/10.1016/j.im.2005.01.002

24.

Flynn

L. R.

Goldsmith

R. E.

Eastman

J. K.

(1996). Opinion leaders and opinion seekers: Two new measurement scales. Journal of the Academy of Marketing Science, 24(2), 137–147. https://doi.org/10.1177/0092070396242004

25.

Fornell

Larcker

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

26.

Geisser

(1975). The predictive sample reuse method with applications. Journal of the American Statistical Association, 70(350), 320–328. https://doi.org/10.1080/01621459.1975.10479865

27.

Gharib

R. K.

Garcia-Perez

Dibb

Iskoujina

(2020). Trust and reciprocity effect on electronic word-of-mouth in online review communities. Journal of Enterprise Information Management, 33(1), 120–138. https://doi.org/10.1108/JEIM-03-2019-0079

28.

You

K. H.

Jung

Shim

(2016). Why do we use different types of websites and assign them different levels of credibility? Structural relations among users’ motives, types of websites, information credibility, and trust in the press. Computers in Human Behavior, 54, 231–239. https://doi.org/10.1016/j.chb.2015.07.046

29.

Granovetter

M. S.

(1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380. https://www-jstor-org.web.bisu.edu.cn/stable/2776392

30.

Hair

J. F.

Jr Sarstedt

Ringle

C. M.

Gudergan

S. P.

(2023). Advanced issues in partial least squares structural equation modeling (2nd ed.). Sage

31.

Hair

J. F.

Risher

J. J.

Sarstedt

Ringle

C. M.

(2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

32.

Hansen

S. S.

Lee

J. K.

(2013). What drives consumers to pass along marketer-generated eWOM in social network games? Social and game factors in play. Journal of Theoretical and Applied Electronic Commerce Research, 8(1), 53–68. https://doi.org/10.4067/S0718-18762013000100005

33.

Helfrich

(2023). Description and classification of cultures. In Helfrich

(Ed.), Cross-cultural psychology (pp. 53–66). Springer. https://doi.org/10.1007/978-3-662-67558-8_1

34.

Hennig-Thurau

Gwinner

K. P.

Walsh

Gremler

D. D.

(2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38–52. https://doi.org/10.1002/dir.10073

35.

Henseler

Ringle

C. M.

Sinkovics

R. R.

(2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277–319. https://doi.org/10.1108/S1474-7979(2009)0000020014

36.

Herrando

Jiménez-Martínez

Martín-De Hoyos

M. J.

(2019). Social commerce users’ optimal experience: Stimuli, response and culture. Journal of Electronic Commerce Research, 20(4), 123439.

37.

Hsu

C. L.

Chang

K. C.

Chen

M. C.

(2012). The impact of website quality on customer satisfaction and purchase intention. Perceived playfulness and perceived flow as mediators. Information Systems and e-Business Management, 10(4), 549–570. https://doi.org/10.1007/s10257-011-0181-5

38.

Hyun

S. S.

Kim

(2014). Identifying optimal rapport-building behaviors in inducing patrons’ emotional attachment in luxury restaurants. Journal of Hospitality & Tourism Research, 38(2), 162–198. https://doi.org/10.1177/1096348012451458

39.

Ismagilova

Rana

N. P.

Slade

E. L.

Dwivedi

Y. K.

(2021). A meta-analysis of the factors affecting eWOM providing behaviour. European Journal of Marketing, 55(4), 1067–1102. https://doi.org/10.1108/EJM-07-2018-0472

40.

Kanje

Charles

Tumsifu

Mossberg

Andersson

(2020). Customer engagement and sWOM in tourism. Journal of Hospitality and Tourism Insights. https://doi.org/10.1108/JHTI-04-2019-0074

41.

Kim

Kandampully

Bilgihan

(2018). The influence of eWOM communications: An application of online social network framework. Computers in Human Behavior, 80, 243–254. https://doi.org/10.1016/j.chb.2017.11.015

42.

Kirmani

Rao

A. R.

(2000). No pain, no gain: A critical review of the literature on signaling unobservable product quality. Journal of Marketing, 64(2), 66–79. https://doi.org/10.1177/0276146707311515

43.

Kozinets

R. V.

De Valck

Wojnicki

A. C.

Wilner

S. J. S.

(2010). Networked narratives: Understanding word-of-mouth marketing in online communities. Journal of Marketing, 74, 71–89. https://doi.org/10.1509/jmkg.74.2.71

44.

Laroche

Kalamas

Cleveland

(2005). “I” versus “we”: How individualists and collectivists use information sources to formulate their service expectations. International Marketing Review, 22(3), 279–308. https://doi.org/10.1108/02651330510602213

45.

Lee

K. Y.

Choi

(2019). Predictors of electronic word-of-mouth behavior on social networking sites in the United States and Korea: Cultural and social relationship variables. Computers in Human Behavior, 94, 9–18. https://doi.org/10.1016/j.chb.2018.12.025

46.

Leong

C. M.

Loi

A. M. W.

Woon

(2021). The influence of social media eWOM information on purchase intention. Journal of Marketing Analytics, 10(2), 1–13. https://doi.org/10.1057/s41270-021-00132-9

47.

Leonhardt

J. M.

Pezzuti

Namkoong

J.-E.

(2020). We’re not so different: Collectivism increases perceived homophily, trust, and seeking user-generated product information. Journal of Business Research, 112, 160–169. https://doi.org/10.1016/j.jbusres.2020.03.017

48.

Lien

C. H.

Cao

(2014). Examining WeChat users’ motivations, trust, attitudes, and positive word-of-mouth: Evidence from China. Computers in Human Behavior, 41, 104–111. https://doi.org/10.1016/j.chb.2014.08.013

49.

Lozares

Verd

J. M.

Cruz

Barranco

(2014). Homophily and heterophily in personal networks. From mutual acquaintance to relationship intensity. Quality and Quantity, 48(5), 2657–2670. https://doi.org/10.1007/s11135-013-9915-4

50.

H. P.

Lin

J. C. C.

Hsiao

K. L.

Cheng

L. T.

(2010). Information sharing behaviour on blogs in Taiwan: Effects of interactivities and gender differences. Journal of Information Science, 36(3), 401–416. https://doi.org/10.1177/0165551510363631

51.

Luarn

Huang

Chiu

Y. P.

Chen

I. J.

(2016). Motivations to engage in word-of-mouth behavior on social network sites. Information Development, 32(4), 1253–1265. https://doi.org/10.1177/0266666915596804

52.

Mahapatra

Mishra

(2017). Acceptance and forwarding of electronic word of mouth. Marketing Intelligence & Planning, 35(5), 594–610. https://doi.org/10.1108/MIP-01-2017-0007

53.

Mangold

W. G.

Faulds

D. J.

(2009). Social media: The new hybrid element of the promotion mix. Business Horizons, 52(4), 357–365. https://doi.org/10.1016/j.bushor.2009.03.002

54.

Metzger

M. J.

Flanagin

A. J.

(2013). Credibility and trust of information in online environments: The use of cognitive heuristics. Journal of Pragmatics, 59, 210–220. https://doi.org/10.1016/j.pragma.2013.07.012

55.

Molinillo

Liébana-Cabanillas

Anaya-Sánchez

(2018). A social commerce intention model for traditional e-commerce sites. Journal of Theoretical and Applied Electronic Commerce Research, 13(2), 80–93. https://doi.org/10.4067/S0718-18762018000200107

56.

Norman

A. T.

Russell

C. A.

(2006). The pass-along effect: Investigating word-of-mouth effects on online survey procedures. Journal of Computer-Mediated Communication, 11(4), 1085–1103. https://doi.org/10.1111/j.1083-6101.2006.00309.x

57.

O’Cass

Carlson

(2010). Examining the effects of website-induced flow in professional sporting team websites. Internet Research, 20(2), 115–134. https://doi.org/10.1108/10662241011032209

58.

Park

C. W.

Lessig

V. P.

(1977). Students and housewives: Differences in susceptibility to reference group influence. Journal of Consumer Research, 4(2), 102–110. https://doi.org/10.1086/208685

59.

Park

D. H.

Lee

Han

(2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125–148. https://doi.org/10.2753/JEC1086-4415110405

60.

Peña-García

Gil-Saura

Rodríguez-Orejuela

(2018). E-loyalty formation: A cross-cultural comparison of Spain and Colombia. Journal of Electronic Commerce Research, 19(4), 336–356.

61.

Pigg

Crank

(2004). Building community social capital: The potential and promise of information and communications technologies. Journal of Community Informatics, 1(1), 58–73. https://doi.org/10.15353/joci.v1i1.2062

62.

Ringle

C. M.

Sarstedt

(2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems, 116(9), 1865–1886. https://doi.org/10.1108/IMDS-10-2015-0449

63.

Saleem

Ellahi

(2017). Influence of electronic word of mouth on purchase intention of fashion products in social networking websites. Pakistan Journal of Commerce and Social Sciences (PJCSS), 11(2), 597–622.

64.

Shah

D. V.

Kwak

Holbert

R. L.

(2001). “Connecting” and “disconnecting” with civic life: Patterns of internet use and the production of social capital. Political Communication, 18, 141–162. https://doi.org/10.1080/105846001750322952

65.

Shobeiri

Mazaheri

Laroche

(2014). Improving customer website involvement through experiential marketing. The Service Industries Journal, 34(11), 885–900. https://doi.org/10.1080/02642069.2014.915953

66.

Sohaib

Hui

Akram

Majeed

Tariq

(2019, August). How social factors drive electronic word-of-mouth on social networking sites? In International Conference on Management Science and Engineering Management (pp. 574–585). Springer. https://doi.org/10.1007/978-3-030-21255-1_44

67.

Sohrabi

M. K.

Akbari

(2016). A comprehensive study on the effects of using data mining techniques to predict tie strength. Computers in Human Behavior, 60, 534–541. https://doi.org/10.1016/j.chb.2016.02.092

68.

Song

S. Y.

Cho

Kim

Y.-K.

(2017). Personality factors and flow affecting opinion leadership in social media. Personality and Individual Differences, 114, 16–23. https://doi.org/10.1016/j.paid.2017.03.058

69.

Statista. (2022). Online reviews in e-commerce—Statistics & facts. https://www.statista.com/topics/9736/online-reviews-in-e-commerce/#topicOverview

70.

Stone

(1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B (Methodological), 36(2), 111–147. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x

71.

Sun

Youn

Kuntaraporn

(2006). Online word-of-mouth (or mouse): An exploration of its antecedents and consequences. Journal of Computer-Mediated Communication, 11(4), 1104–1127. https://doi.org/10.1111/j.1083-6101.2006.00310.x

72.

Tomlinson

(2007). The culture of speed: The coming of immediacy. Sage.

73.

Vergura

D. T.

Luceri

Zerbini

(2021). The effect of social EWOM on consumers’ behaviour patterns in the fashion sector. In Ozuem

Ranfagni

(Eds.), The art of digital marketing for fashion and luxury brands: Marketspaces and marketplaces (pp. 221–242). Palgrave Macmillan. https://doi.org/10.1007/978-3-030-70324-0_10

74.

Walsh

Gwinner

K. P.

Swanson

S. R.

(2004). What makes mavens tick? Exploring the motives of market mavens’ initiation of information diffusion. Journal of Consumer Marketing, 21(2), 109–122. https://doi.org/10.1108/07363760410525678

75.

Wang

Ren

Wan

Yan

(2020). Managerial responses to online reviews under budget constraints: Whom to target and how. Information & Management, 57(8), 103382. https://doi.org/10.1016/j.im.2020.103382

76.

Wang

Yeh

R. K.-J.

Chen

Tsydypov

(2016). What drives electronic word-of-mouth on social networking sites? Perspectives of social capital and self-determination. Telematics and Informatics, 33(4), 1034–1047. https://doi.org/10.1016/j.tele.2016.03.005

77.

Zhang

Gupta

Zhao

(2014). What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences. Information & Management, 51(8), 1017–1030. https://doi.org/10.1016/j.im.2014.07.005

78.

Zhang

K. Z.

Benyoucef

(2016). Consumer behavior in social commerce: A literature review. Decision Support Systems, 86, 95–108. https://doi.org/10.1016/j.dss.2016.04.001