The Mediated Effect of Social Presence on Social Commerce WOM Behavior

Abstract

Based on expectation disconfirmation theory, this study analyzes how attitudes (satisfaction and loyalty) influence interaction intention (sWOM) and, consequently, active and passive sWOM behavior. It does so by assessing the mediating role of social presence on sWOM intention and behavior. The empirical results provide several contributions. First, knowing how to increase active sWOM contributes to bridging the gap regarding how to enhance interactions between users. Second, fostering active sWOM on social commerce websites will provide companies with more positive user-generated content, since this active sWOM comes from satisfied and loyal users, and it is assumed that they will rate the product positively and report a good experience. Third, companies can benefit more from users if users interact with other users by sharing their experiences. This study sheds light on how social presence can mediate the relationship between intention and behavior, particularly when it comes to increasing active participation and brand promotion.

1. Introduction

In social commerce, platform users can make purchases, share experiences, recommend and rate products, or become informed from information generated by the company and by other users [1,2]. Inherent to this environment, the concept of eWOM turns into sWOM (word-of-mouth in social commerce) to highlight the role of social presence [3], that is, the perception of socialization enabled through social commerce tools such as recommendations and referrals, ratings and reviews, and forums and virtual communities [4,5].

In social commerce contexts, users are increasingly gaining control and power by sharing purchasing experiences with a mass audience; at the same time, they can access an immeasurable amount of user-generated content [6]. Consequently, while users gain control, companies lose it [7]. Therein lies the concern of many companies to boost online consumer reviews of satisfied consumers, avoiding the critical reviews of unsatisfied ones. Nevertheless, not all satisfied and loyal consumers who have intention to actively participate on the website finally do this. Based on this concern, this research investigates why some satisfied consumers show an active sWOM behavior while others show a passive behavior.

The relevance that sWOM has acquired for companies has led to many works published on this topic. Some studies have focused on users’ behavior related to sWOM [8], the effects of sWOM valence [9,10], sWOM credibility [11], etc. However, few investigations have distinguished between active and passive sWOM [12]. While some people actively participate and share their opinions and experiences, others prefer to adopt a more passive stance and listen to others’ experience. Hence, sWOM can be classified as either active (writing reviews and comments) or passive (reading or listening to others’ comments and reviews). Therefore, there is a need for research to ascertain/understand/analyze what drives consumers to participate, whether actively or passively, in sWOM [13].

The context of social commerce makes it reasonable to think that the presence of other users on a website can have an effect on behavior. In the case of social commerce contexts, satisfaction or dissatisfaction is shown through users’ participation when sharing their opinion about products or about a social commerce website. In offline environments, social presence theory [14] suggests that the perception of social presence encourages people to share their experiences. Hence, it is considered interesting to study whether social presence mediates the relation between intention and behavior pertaining to sWOM. Drawing on expectation disconfirmation theory [15,16], we analyze how attitude influences intention and behavior, and what role social presence plays in this relationship. The main contribution of this study is that it sheds light on how social presence can mediate the effect between social commerce users’ intention (sWOM intention) and behavior (sWOM passive and active), as well as on the practical implications, by gaining a deeper understanding of active and passive sWOM behavior. Therefore, this study aims to bridge the gap by analyzing how users’ attitudes (satisfaction and loyalty) affect their sWOM intention and, consequently, their active and passive sWOM behavior mediated by their social presence. Participation can lead to more positive user-generated content because active sWOM comes from satisfied and loyal users who are expected to rate the product positively and to report their good experience.

The structure of this research is as follows. In the next section, we review the literature related to the topic in order to obtain the basis for formulating the hypotheses of the model proposed. We then examine the structural model to test the hypotheses using the statistical software SPSS. The findings show the mediating effect of social presence, with a higher influence on active sWOM than on passive sWOM.

2. Literature Review and Development of Hypotheses

2.1. Theoretical Framework

Satisfaction has been studied from two different perspectives: transaction-specific satisfaction and cumulative satisfaction [17]. Transaction-specific satisfaction is based on the mere product transaction, while cumulative satisfaction is focused on the evaluation of past, present, and future customer experiences. Apart from users’ satisfaction with previous experiences and transactions [18], satisfaction is also related to the customers’ ideal service [19]. Thus, expectation disconfirmation theory [15,16] has been used to explain satisfaction from the point of view of post-purchase behavior. This study follows the cumulative satisfaction perspective, akin to some previous investigations [16,17,20], in order to study users’ post-purchase behavior in the form of sWOM. According to the Theory of Planned Behavior [21], behavior is preceded by intentions, so sWOM intention is supposed to be a critical antecedent of sWOM behavior. Furthermore, based on the Theory of Reasoned Action [22], attitudes influence intentions, and these intentions are converted into actions. However, in social commerce, not all users who have the intention to spread sWOM end up actively participating. Thus, this study proposes a social commerce attitude–intention–behavior-based model and analyzes the effect of satisfaction and loyalty (considered as users’ attitudes) on sWOM intention and behavior (see Figure 1). Moreover, this study also proposes to test the mediating effect between intentions and behavior of social presence, a perception inherent to this social commerce environment.

2.2. Attitude: Satisfaction and Loyalty

Online satisfaction has been described as “the contentment of the customer with respect to his or her prior purchasing experience with a given electronic commerce firm” [23] (p. 125). Satisfaction has been said to be one of the main determinants of loyalty [18,19,23,24]. In general, online satisfaction is said to positively influence consumers’ experience. Furthermore, it has been confirmed that satisfaction arises from a positive consumer attitude [18] and that it has a positive effect on customer retention regarding online services [19].

In the literature on loyalty (see the literature review of Mellens et al. [25]), brand loyalty has been defined as “the biased, behavioral response, expressed over time, by some decision-making unit, with respect to one or more alternative brands out of a set of such brands, and is a function of psychological (decision-making evaluative) processes” [26] (p. 80). This loyalty definition has been discussed to show a behavioral or an attitudinal measure depending on their purchase or cognitive component, respectively [25]. Loyalty as an attitudinal measure is linked to commitments and intentions, while behavioral loyalty refers to actual loyal behavior [25]. Thus, following the attitude approach of loyalty [27], in this research, loyalty, together with satisfaction, is considered an attitude shaped by users’ cognitive and affective experiences on the social commerce website.

Based on expectation disconfirmation theory [15,28], Toufaily et al. [24] (p. 1439) define loyalty as a “customer’s willingness to maintain a stable relationship in the future and to engage in a repeat behavior of visits and/or purchases of online products/service, using the company’s website as the first choice among alternatives, supported by favorable beliefs and positive emotions toward the online company, despite situational influences and marketing efforts that lead to transfer behavior”. Loyalty refers to the intention to continue to purchase from a specific website rather than changing to another one [29], and to the intention to revisit and repurchase [30]. According to Anderson and Srinivasan [23], electronic loyalty is defined as the customer’s favorable attitude toward an electronic business, resulting in repeat buying behavior. However, apart from showing interest in a product by buying it, users can also show interest by talking about their purchase experience, by stating opinions about the product, by recommending it, by evaluating their purchase, etc. Thus, it is considered interesting to study sWOM intention and behavior from a user loyalty point of view.

According to Oliver [16], loyalty can be divided into four types: cognitive, affective, conative, and action. These are related to brand performance, emotions and brand preference, intention to make a repeat purchase, and the conversion of intentions into action, respectively [20,24]. According to this attitude-based loyalty model, intentions are converted into actions [16]. Some authors have categorized loyalty as an attitude [23]. Therefore, considering satisfaction and loyalty as attitudes, we study how they affect sWOM intention and, consequently, sWOM behavior. Following the consensus in the literature that satisfaction is the main antecedent of loyalty, we start the model by hypothesizing that:

Hypothesis 1 (H1).

Satisfaction has a positive effect on users’ loyalty in social commerce contexts.

2.3. sWOM Intention and sWOM Behavior

Social commerce has been defined as a combination of e-commerce, social media, and Web 2.0, where users can participate and interact with the virtual community by spreading WOM [3]. In fact, WOM is so important in social commerce that in these environments rich in social interactions and socialization, some authors specifically call it sWOM. Throughout customers’ journey, companies look for ways to improve customer experience [7] and, since social commerce websites require facilitating many interactions during customers’ purchasing process (from the pre-purchase to the post-purchase phase), sWOM includes not only private communication tools, but also users’ active and public participation tools on the platforms. Hence, sWOM refers to public peer recommendations or posts on discussion boards, where users can share information about products and their experiences, or ask for advice from other users. In our study, we take into account the sWOM generated via social commerce websites—that is, websites that sell products online and contain social commerce features such as recommendation systems, referrals, ratings, discussion forums, etc. Examples of sWOM can be seen on social commerce websites such as Amazon, Booking, and AliExpress.

Theory states that WOM is a source of information (offline) that can help other consumers [31], while sWOM refers to the online exchange of information or experiences to help other users [32]. sWOM can be classified as either active or passive. When users provide their experiences and suggestions, rate products, or make recommendations on social commerce websites, they are spreading active sWOM. On the other hand, drawing on observational social learning theory [33], passive sWOM refers to the tendency of users to just observe and learn through the sWOM shared by other users, without sharing their own experience on the website [34]. Likewise, regarding WOM behavior, users are categorized as WOM receivers and WOM generators [35]. According to an international report published in 2016 [36], 45% of consumers were influenced by reading reviews, comments, and feedback and 22% by writing reviews, comments, and feedback. Thus, interactions with the platform can be either direct or indirect [12], that is, many people actively interact with other users on social commerce websites, sharing their opinions, giving advice, making recommendations, and rating products or services, while others prefer to passively observe those social interactions and information exchanges and learn from them without taking part [34]. Hence, this study classifies sWOM into two main categories: active and passive. Thereby, we distinguish sWOM intention from sWOM behavior (active and passive sWOM behavior). sWOM intention refers to the willingness to make recommendations, provide information, and encourage others to use a social commerce website, whereas sWOM behavior refers to users’ actual behavior.

It is not necessary to be engaged in order to have sWOM intention, but it is mandatory to be engaged in order to actively participate [37]. Hence, it can be assumed that loyal users will have sWOM intention; that is, they will be willing to provide information to other users, make recommendations, and encourage others to consider specific options. Loyalty implies that the consumer gives preference to a particular brand or company, although satisfactory alternatives may exist [38]; in turn, loyalty favors higher-intensity WOM [39]. Therefore, we hypothesize that:

Hypothesis 2 (H2).

Users’ loyalty increases sWOM intention.

Active sWOM is described as users’ willingness to provide constructive feedback and suggestions to other users [37], while passive sWOM is based on observing and learning from others [12,34]. According to social learning theory [33] such “lurking” behavior is based on learning through information shared, without actively interacting. Hence, passive users can develop imitative behavior [40]. According to the Theory of Planned Behavior [21] and the Theory of Reasoned Action [22], intentions determine behavior. However, to the best of our knowledge, the relationship between sWOM intention and the consequent behavior of performing active and passive sWOM behavior has not been tested to date, although some investigations have analyzed how intention affects users’ behavior in virtual communities [41,42]. Taking the above into consideration, we develop the following hypotheses:

Hypothesis 3a (H3a).

Users’ sWOM intention has a positive effect on active sWOM.

Hypothesis 3b (H3b).

Users’ sWOM intention has a positive effect on passive sWOM.

2.4. The Mediating Role of Social Presence

Perceived interaction with another human being in online environments has been called social presence [43], which is defined as the extent to which the feeling of human contact is perceived as similar to that in social relationships in offline environments [43]. Sociability and human contact are absent in e-commerce, which is why websites that increase perceived social presence might encourage positive attitudes towards Internet shopping [44]. This socialization or social presence can influence users’ attitude towards the website through their involvement, committed behavior, co-presence, and affective or cognitive social presence [45]. Social presence is considered an inherent characteristic of social commerce environments perceived through social commerce tools such as recommendations and referrals, ratings and reviews, or forums and virtual communities [4,5]. According to [46], recommendations and reviews that can be found on social commerce websites have both a transactional function—encouraging the individual to make a purchase and, thus, stimulating the utilitarian aspects of the process—and a relational function, through which social presence is increased.

It has been said that consumers are more likely to engage in WOM than in sWOM because they perceive a level of social risk inherent in the online context [47]. Therefore, if websites increase social presence, they could create an atmosphere that is more similar to those of offline environments, such that sWOM behavior could increase. Social presence has been considered an intrinsic part of social commerce contexts, enhancing users’ engagement [5]. Hence, it is reasonable to think that this social interaction perception that drives engagement will be also one of the originators of users’ participation in the form of WOM. Research on social presence highlights its role for enhancing trust [48,49,50] and several investigations confirm the positive relationship between trust and WOM [32], but they do not consider the difference between active and passive WOM behavior. Distinguishing between WOM use and WOM behavior, Wang et al. [35] studied the influence of social presence, concluding that the perception of social presence on a website positively affects the generation of WOM, but not the use of WOM. Taking this idea into account, this study expects social presence to mediate the relationship between the intention to sWOM and active or passive sWOM behavior, see in Figure 1.

Hypothesis 4 (H4).

Social presence mediates the effect between sWOM intention and (a) active sWOM behavior and (b) passive sWOM behavior.

3. Materials and Methods

The data used for this analysis were collected through an online survey in Spain. The sample comprised 715 users of social commerce websites, of which 49.9% were male and 50.1% were female, with ages ranging from 16 to 55 (see

Table 1. Detailed demographics of the participants.

Age	Data Collected
16–24	135	18.9%
25–34	262	36.6%
35–55	318	44.5%
Total	715	100%
Genre
Men	357	49.9%
Women	358	50.1%
Total	715	100%

). Participants were recruited through a market research agency to fit socio-demographically with the distribution of Spanish online social commerce users according to Telecommunications and Information Society Spanish Watch [51]. All respondents were online buyers who had recently purchased products or services via social commerce websites such as Amazon, AliExpress, Booking, etc.

At the beginning of the questionnaire, after they were given an explanation of the concept of social commerce, participants were asked whether they had recently purchased using a website with the characteristics of a social commerce platform. If they answered yes, they carried on answering the survey and were asked to name the social commerce website from which they had purchased. Among their answers were Amazon, AliExpress, and Booking. Throughout the questionnaire, the respondents were continuously asked to recall their experience on the website they had chosen.

In order to ensure content validity, we thoroughly reviewed literature about the variables included in our model, adapting them to the social commerce context. The survey was checked by several experts and pretested by twenty social commerce users, with the aim to ensure that all the questions and text were understandable, apart from assessing its length and ease of completion. Satisfaction was adapted from the scales of Flavián et al. [18] and Gustafsson et al. [19]. The variable of loyalty was based on the scales of Zeithaml et al. [52] and Cyr et al. [30]. Intention to sWOM was adapted from the scale of Liang et al. [3]. The active and passive sWOM variables were created from the scale of social commerce intention proposed by Liang et al. [3]. The mediating variable, social presence, was adapted from the scales of Gefen and Straub [43] and Hassanein and Head [41] (see

Table 2. Scales.

Satisfaction—adapted from [18,19]: The experience that I have had with this social commerce website has been satisfactory In general terms, I am satisfied with the way that this social commerce website has carried out transactions In general, the service received from this web is close to the ideal

Loyalty—adapted from [30,52]: I would consider this social commerce website my first choice to buy I would recommend this social commerce website to someone who seeks your advice I would use this social commerce website again

sWOM intention—adapted from [3]: I am likely to provide others with positive information on this social commerce website I am likely to make recommendations on this social commerce website I am likely to encourage others to consider this social commerce website

Active sWOM behavior—adapted from [3]: I have provided my experiences and suggestions when other users need advice on buying something I have recommended a product that is worth buying

Passive sWOM behavior—adapted from [3]: I have read other users’ experiences to provide me with their suggestions before I go shopping I have bought the products recommended by others

Social presence—adapted from [43,44]: There is a sense of human contact on this social commerce website There is a sense of sociability on this social commerce website There is a sense of human warmth on this social commerce website

). All survey variables were measured on a seven-point Likert scale, with the lowest score being 1, strongly disagree, and the highest being 7, strongly agree. In the next section, exploratory factor analysis and confirmatory factor analysis are carried out in order to guarantee the validity of the items in the context of social commerce.

To ensure the validity and reliability of the measurement scale, construct validity was analyzed using partial least squares (PLS) with the statistical software Smart PLS 3 [53]. Construct validity relates to whether there are high correlations between measures of the same construct (convergent validity) and low correlations between measures of constructs that are expected to differ (discriminant validity) [54].

The validity of the model was assessed by analyzing the structural path coefficients and the percentage of variance explained, since PLS does not generate an overall goodness-of-fit index as does structural equation modeling. We performed bootstrapping with 5000 sub-samples to test the statistical significance.

4. Results

4.1. Measurement Model and Validation

Based on Fornell and Larcker [55], to assess the convergent validity, we examined the reliability of each item, with internal consistency considered acceptable when Cronbach’s alpha values were higher than 0.70 [56,57], the composite reliability values of each construct were greater than 0.60 [55,58], and the average variance extracted exceeding the values of 0.50 [55] and 0.70 [59].

We tested the discriminant validity to confirm that constructs differed from each other. Firstly, we analyzed the cross-loadings [60]. Secondly, in a symmetric matrix, we corroborated that the AVE on the diagonal is larger than its corresponding squared correlation coefficients in its rows and columns [55,60]. The measurement model results are shown in

Table 3. Reliability and convergent validity of the measurement model.

Variable	Item	Loading	t-Value	CA	CR	AVE
Satisfaction	SA1	0.936	144.427 ***	0.919	0.949	0.861
	SA2	0.942	151.102 ***
	SA3	0.905	92.056 ***
Loyalty	LOY1	0.855	63.895 ***	0.881	0.927	0.808
	LOY2	0.944	187.332 ***
	LOY3	0.896	91.052 ***
sWOM Intention	Intention sWOM1	0.934	121.453 ***	0.925	0.952	0.869
	Intention sWOM2	0.937	111.645 ***
	Intention sWOM3	0.927	101.328 ***
Active sWOM Behavior	Active sWOM1	0.928	90.377 ***	0.835	0.924	0.859
	Active sWOM2	0.925	91.722 ***
Passive sWOM Behavior	Passive sWOM1	0.923	103.562 ***	0.796	0.907	0.830
	Passive sWOM2	0.899	69.514 ***
Social Presence	SP1	0.932	110.240 ***	0.925	0.953	0.870
	SP2	0.925	104.389 ***
	SP3	0.941	171.194 ***

Note: CA = Cronbach alpha; CR = composite reliability; AVE = average variance explained. *** p < 0.01.

and

Table 4. Discriminant validity.

	Satisfaction	Loyalty	sWOM Intention	Active sWOM Behavior	Passive sWOM Behavior	Social Presence
Satisfaction	0.928
Loyalty	0.812	0.899
sWOM Intention	0.783	0.792	0.932
Active sWOM Behavior	0.355	0.361	0.433	0.927
Passive sWOM Behavior	0.427	0.433	0.504	0.578	0.911
Social Presence	0.348	0.309	0.389	0.381	0.372	0.933

Note: Diagonal values are AVE squared roots. Below the diagonal: correlations among factors.

.

4.2. Hypotheses Tests

The empirical results, as shown in

Table 5. Hypothesis tests.

Hypotheses	Standardized Coefficients (ß)	t-Value (Bootstrapping)	R2	Q2
H1: Satisfaction → Loyalty	0.812	45.927 ***	65.8%	0.530
H2: Loyalty → sWOM Intention	0.792	40.746 ***	62.7%	0.544
H3a: sWOM Int. → Active sWOM	0.433	11.790 ***	18.6%	0.160
H3b: sWOM Int. → Passive sWOM	0.505	13.465 ***	25.4%	0.207

Level of significance: *** p < 0.01.

and Figure 2, confirm that none of our hypotheses had to be rejected. The blindfolding analysis, through cross-validated redundancy [59], confirmed that the model has predictive relevance. The findings show that satisfaction increases users’ loyalty and, as a result, their sWOM intention; the effect is positive for both active and passive sWOM. Nevertheless, it must be highlighted that the relationship is stronger between sWOM intention and passive sWOM behavior than between sWOM intention and active sWOM behavior. Therefore, for companies seeking to enhance active behaviors, it was considered interesting to study whether social presence can mediate the effect of sWOM intention on active and passive sWOM behavior.

4.3. The Mediating Role of Social Presence

We also analyzed the mediating role of social presence in sWOM intention and active and passive sWOM behavior. According to [61], new causal models need to be analyzed for mediating effects. In our case, we applied the effects of social presence as a mediating variable on sWOM intention and active and passive sWOM to the entire proposed model. Figure 3 shows the overall effects of the two direct links.

As can be seen from Figure 4, we analyzed the significance of the indirect effects using the bootstrapping method [62,63]. First, the overall effect of sWOM intention on active sWOM behavior can be expressed as the sum of the direct (d) and indirect (a*c) effects; that is, d = d’ + a* [64]. Second, the total effect of sWOM intention on passive sWOM can be expressed as the sum of the direct (e) and indirect (b*c) effects; that is, e = e’ + b*c [61]. The advantage of this perspective is that it enables us to study the indirect effects (a*c and b*c) separately.

Using PLS, we performed bootstrapping with 5000 subsamples.

Table 6. Path coefficient and indirect effects for the mediation model.

Relationship	Total Effect β (t-Value)	Direct Effect β (t-Value)	Indirect Effect Path	Indirect Effect β (t-Value)	Hmed	Variance Accounted For (VAF)	Indirect Effect Confidence Interval
sWOM Int. → Active sWOM	0.433 (11.790 ***)	0.336 (8.009 ***)	sWOM Int. → SP → Active sWOM	0.097 (5.453 ***)	H4a	22.40%	(0.064; 0.135)
sWOM Int. → Passive sWOM	0.505 (13.465 ***)	0.423 (9.406 ***)	sWOM Int. → SP → Passive sWOM	0.081 (4.511 ***)	H4b	16.03%	(0.047; 0.117)

*** p < 0.01 (t = 2.6012). When the t value obtained using the bootstrap method is greater than Student’s t value, the hypothesis is confirmed with a significance of 99%.

shows the overall, direct, and indirect effects, the variance accounted for (VAF), and the confidence interval for social presence as the mediator variable. When the confidence interval does not contain zero, the indirect effect is significantly different from zero with a 95% confidence level [65]. As can be seen in Table 6, the paths of sWOM intention → social presence → active sWOM behavior and sWOM intention → social presence→ passive sWOM behavior do not contain the value zero; thus, the indirect effect for these relationships is statistically significant. However, only in the path sWOM intention → social presence → active sWOM behavior is the VAF greater than 20%; thus, there is a partial mediation effect [59]. Therefore, social presence only mediates the effect of sWOM intention on active sWOM behavior. When social presence is introduced in the model as a mediator variable, sWOM intention decreases its direct effect on active sWOM, although it remains significant (e’ = 0.336; t = 8.009), while its indirect effect via social presence is 0.250. Thus, social presence partially mediates the effect of sWOM intention on active sWOM behavior.

5. Discussion

Following the suggestion of Oliver [14,15] regarding how attitudes drive intentions and how these are converted into actions, the aim of this study was to analyze how satisfaction and loyalty affect sWOM intention and, consequently, active and passive sWOM behavior. The motivation of this study stemmed from companies’ concern regarding how to enhance active participation on their websites. Hence, based on the proposal of Wang et al. [35] that the perception of social presence on a website positively affects the generation of WOM, the originality of our research lies in its study of the mediating role that social presence plays between sWOM intention and behavior. Therefore, the main contribution of the present research, after confirming the social commerce attitude–intention–behavior-based model, is that social presence mediates between WOM intention and active behavior. This means that companies wanting to increase the participation of satisfied and loyal users need to boost the perception of social presence on their social commerce websites.

The results of our study allow us to draw the following conclusions. First, the study supports the usage of expectation disconfirmation theory [14,15] as a way of analyzing post-purchase behaviors [66,67], and introduces sWOM intention and active and passive sWOM behavior as part of the model in order to show that the process of people confirming or disconfirming their beliefs is expressed as sWOM behavior. Likewise, the attitude-based loyalty model [16] has been expanded to the attitude–intention–behavior-based model in the specific context of social commerce, where intentions are converted into actions in the form of active and passive sWOM behavior. Thereby, although marketers have traditionally focused on loyalty as a consequence of consumer behavior-based models, this research goes further, analyzing the consequences of loyalty on users’ sWOM intention and behavior. Moreover, Theory of Planned Behavior [21] and Theory of Reasoned Action [22] allow understanding how intentions precede behavior, although sometimes something or someone in the environment alters the final response. In the present research, due to the inherent characteristics of the context of study, social presence needed to be analyzed as the mediating effect between intentions and behavior in social commerce.

Second, we have corroborated that attitudes drive intentions and behavior. We support the idea demonstrated in the literature [15,24] that satisfied users are more loyal, and that this loyalty turns into an increase in sWOM intention. That is, as stated in the introduction, satisfied consumers exhibit loyalty and are prone to report their satisfaction through sWOM. However, although their expectances have been confirmed, there are several aspects that can favor (or not) whether people actively participate by reporting their experiences. In fact, the empirical findings show that sWOM intention has a higher impact on passive sWOM than on active sWOM. This means that social commerce users take into account user-generated content, which introduces a bridge to determine what mediates the relationship between intention and final behavior.

Third, arising from the need to study how companies can foster active participation, the mediating role of social presence on the sWOM intention and behavior was analyzed. According to the results, social presence contributes to increase active sWOM, while it has no effect on passive sWOM. Therefore, we contribute to bridging the gap regarding how to enhance active participation. This finding shows that active sWOM can be enhanced by boosting socialization on a website through the perception of social presence. Likewise, it must be highlighted that the mediating effect of social presence, which turns intention into active sWOM behavior, is very important because social interactions are key for these websites to be successful [3]. However, additional research opportunities remain. For example, it would be useful to analyze the different types of social interactions that can foster this social presence and, likewise, to test the level of familiarity with other users on the social commerce website—that is, whether they are friends, acquaintances, strangers, influencers, etc. Likewise, it is interesting to discuss the non-significant effect of social presence on passive sWOM behavior. According to this finding, users’ passive behavior is not affected by the presence of others. This result can drive us to think that users do not feel intimidated or shy to spread sWOM, so there must be other effects that discourage them to actively participate. For instance, based on WOM literature reviews [68,69], it could be interesting to study how credibility of sWOM and information usefulness affect users, as well as the reputation or the familiarity or expertise of the source.

Finally, recent works [70] have classified the different factors influencing positive sWOM in four groups: personal conditions, social conditions, perceptual conditions and consumption-based conditions. We consider social presence as one of the factors included in the social conditions group. Others have also addressed the importance of distinguishing between active WOM and passive WOM [13]. Some of them conclude that emotions derived from the experience with the product affect the post-purchase behavior of active users more than that of passive users, further encouraging them to engage in sWOM [71]. Lee et al. [72] corroborates that students who share positive reviews about their university on social networks tend to have better psychological health than passive ones. In line with these studies, our work contributes to close this gap by highlighting that social presence has a mediating effect in the case of active sWOM and not in the case of passive sWOM. Other works [73] confirm the importance of passive sWOM as a way to foster loyalty. This result raises the need for future research about the common influence between loyalty and passive sWOM.

Therefore, there is still a need to advance the study of the reasons and consequences of passive sWOM. The main reason is that comments of other users on the network can be of great help for passive users both in making purchasing decisions and creating affective links with brands [74].

6. Conclusions

This study opens new horizons for both marketers and researchers, providing three main managerial contributions. First, knowing how to increase active sWOM contributes to bridging the gap regarding how to enhance active participation. Not all of those who report sWOM intention finally actively participate on the social commerce website. Some of them tend to exercise more passive behavior. One of the factors that can mediate in the relationship between the intention and the active participation is social presence. Based on the empirical findings, this study suggests companies that run social commerce business to enhance the perception of social presence in order to turn intentions into actions, that is, to drive users to actively participate on the platform. This study recommends taking care of the social interactive tools—such as recommendations and referrals, ratings and reviews, or forums and virtual communities—to encourage social presence perception. Social commerce companies should study what makes users perceive social presence through these tools. Likewise, it would be advisable that companies motivate the usage of these social tools by displaying them properly to increase the perception of social presence. For instance, it could be helpful if users who have participated on the website have the option to be informed when someone finds their review useful.

Second, our study has implications for business, since knowing how to foster active sWOM on social commerce websites will enable companies to facilitate more positive user-generated content, since this active sWOM comes from satisfied and loyal users, who can be expected to rate a specific product positively and report on their good experience. Nevertheless, this study investigated the online consumer experience as a whole, disregarding the different steps of the purchasing process. That is, for instance, users may be satisfied with the pre-purchase process because they were able to access useful information from the company and from other users in order to make their purchase decision, but yet find the purchase process unclear or difficult due to problems with the website; alternatively, the post-purchase process may be catastrophic due to severe delivery problems.

Third, the results contribute to understanding how satisfaction and loyalty can be externalized. That is, while it is important to have satisfied and loyal customers, companies can benefit more from this if customers evangelize their experience on the company website and act as brand promoters. Hence, marketers should focus on generating this perception of social presence, since it does not only depend on the company’s management. Therefore, it would be interesting for future research to consider which social commerce tools increase the perception of social presence. We suggest that companies not only focus on gaining loyal users, but also boosting loyal behaviors, such as sWOM, derived from satisfaction and loyalty. As a way to increase active sWOM, firms should study also the barriers that dissuade users from active behavior.

Finally, the main theoretical contribution of this study lies in its identification of the effect of the mediating role of social presence on active behavior. We showed that the perception of social presence related to social commerce websites is one way to increase active sWOM; coming from satisfied and loyal users, this active sWOM would be in terms of positive sWOM. The proposed model shows parsimony, since its focus is on testing the intention–behavior sWOM relationship and the influence of social presence without studying the possible effect of additional variables. Hence, it would be interesting to broaden the model in the future. Furthermore, the research should be extended to identify ideal ways to generate the perception of social presence. Likewise, it would be interesting to study which users generate the perception of social presence—e.g., friends, acquaintances, or strangers—since the degree of familiarity with other users on a social commerce website could impact the perception of social presence in different ways. On the other side, this study goes beyond the extant literature on loyalty, which, although broad, has not extensively studied the consequences of loyalty. In this study, sWOM intention and active and passive sWOM behavior were analyzed as such outcomes.

This study is not without limitations. This study does not consider sWOM during the different stages of the purchasing process (pre-purchase, purchase, and post-purchase); thus, it would be pertinent to study how the different purchase steps can affect the social commerce attitude–intention–behavior-based model. It is reasonable to think that passive sWOM behavior occurs when users are looking for, checking alternatives, and collecting details about a product information before making a purchasing decision. Therefore, it would be interesting to know when users actively participate and with what purpose, e.g., if they write questions during the pre-purchase phase regarding product quality, other consumer experiences, or delivery details, or if they write during the post-purchase phase to share their experiences. On this matter, a follow-up study with a focus group could be helpful to further explore users’ participation and usage of online consumer reviews.