The effect of social commerce attributes on customer engagement: an empirical investigation

Abdelsalam Busalim (Business School, Irish Institute of Digital Business, Dublin City University, Dublin, Ireland)
Linda D. Hollebeek (Department of Marketing, Vilnius University, Vilnius, Lithuania) (Department of Business Administration, Tallinn University of Technology, Tallinn, Estonia)
Theo Lynn (Business School, Irish Institute of Digital Business, Dublin City University, Dublin, Ireland)

Internet Research

ISSN: 1066-2243

Article publication date: 15 September 2023




Social commerce (s-commerce) offers community-based platforms that facilitate customer-to-customer interactions and the development of customers' social shopping-based experience. While prior research has addressed the role of customer engagement (CE) in boosting s-commerce-based sales and performance, insight into the effect of s-commerce attributes on CE remains tenuous. Addressing this gap, this study examines the role of specific s-commerce attributes (i.e. community, collaboration, interactivity and social dynamics) on CE, which is, in turn, proposed to impact customers' repurchase- and electronic word of mouth (eWOM) intention.


A web-based survey was deployed to target users of a popular s-commerce platform, Partial least squares structural equation modeling (PLS-SEM) was, then, used to analyze the survey data collected from 390 users.


The results reveal that the four examined attributes positively affect CE. The findings also demonstrate CE's positive effect on customers' repurchase- and eWOM intention.


Though CE has been identified as a key s-commerce performance indicator, little remains known about the role of specific s-commerce attributes in driving CE, as, therefore, explored in this research. Specifically, the authors examine the role of s-commerce-based community, collaboration, interactivity and social dynamics on CE. Their analyses also corroborate that CE, in turn, drives customers' post-purchase (i.e. repurchase/eWOM) intention. Managerially, our findings can be used to develop more engaging s-commerce platforms.



Busalim, A., Hollebeek, L.D. and Lynn, T. (2023), "The effect of social commerce attributes on customer engagement: an empirical investigation", Internet Research, Vol. ahead-of-print No. ahead-of-print.



Emerald Publishing Limited

Copyright © 2023, Abdelsalam Busalim, Linda D. Hollebeek and Theo Lynn


Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at

1. Introduction

In recent years, the CE concept has emerged as a key business performance metric (Hollebeek et al., 2019). Prior research suggests that engaged customers can dramatically increase business performance by stimulating sales, referrals and customer loyalty (Brodie et al., 2011; Eigenraam et al., 2018). Unsurprisingly, many firms have sought to foster customers' engagement with their products or brands, including through online (e.g. social media) and offline platforms (e.g. retail stores) (De Vries and Carlson, 2014). Of these, social media platforms are particularly beneficial, given their wide (e.g. global) reach and relatively low-cost for content dissemination (Hollebeek et al., 2014).

Social commerce (s-commerce) refers to the addition of social networking tools to traditional e-commerce websites to facilitate social interaction among current and/or prospective customers, and social networking sites integrating commercial features to enable user communications, promotions and transactions (Pöyry et al., 2013; Zhang and Benyoucef, 2016; Hu et al., 2022). While prior research has addressed CE on social media platforms (e.g. Ajiboye et al., 2019), most s-commerce studies have limited their focus to the effect of social factors on consumers' (e.g. purchase) intention (Hajli, 2014a, 2015; Hu et al., 2022; Liang et al., 2011; Lin et al., 2018), behavior (Bai et al., 2015; Huang and Benyoucef, 2017), or s-commerce design issues (Baghdadi, 2013; Gonçalves Curty and Zhang, 2013; Han and Trimi, 2017; Huang and Benyoucef, 2013). As such, important gaps remain in the integrative area of s-commerce/CE. In particular, little remains known regarding the role and effects of s-commerce-based ambience- or environmental characteristics on CE and its downstream consequences (Kang et al., 2021), thus warranting further investigation.

Previous studies also suggest that community, and collaboration, are among the main attributes that distinguish s-commerce from traditional e-commerce (Busalim et al., 2016; Huang and Benyoucef, 2013). However, despite reported s-commerce interaction and communication challenges (Jami Pour et al., 2022), the role of community and collaboration in engaging customers and building positive customer experiences with s-commerce platforms remains nebulous, thus also meriting further scrutiny.

Adopting the stimulus-organism-response (S-O-R) model, the main objective of this study is to investigate the effect of key s-commerce attributes, including collaboration, community, interactivity and social dynamics, on CE (Busalim et al., 2016; Huang and Benyoucef, 2013), which we suggest to, in turn, impact customers' repurchase- and eWOM intention. Specifically, this study seeks answers to the following research questions:


How do key s-commerce attributes affect CE?


What is the effect of s-commerce-based CE on customers' repurchase intention?


What is the effect of s-commerce-based CE on customers' eWOM intention?

This study makes the following primary contribution to s-commerce and CE literature. The ultimate goal for s-commerce is to build, and/or maintain, CE over time, in turn fostering customers' repeat purchases and lifetime value (Hu et al., 2022). Though prior research has shed light on the factors affecting s-commerce-based CE (Busalim et al., 2021; Liu et al., 2021), the role of specific s-commerce attributes on CE, and its downstream effects on repurchase- and eWOM intention, remains unclear. Our findings show that s-commerce attributes, indeed, affect CE, in turn, boosting customers' post-purchase (i.e. repurchase/eWOM) intentions, thus exposing significant insight for s-commerce practitioners. The findings, broadly, extend existing consumer behavior-based s-commerce research on the key role of consumer behavior variables (e.g. trust, subjective norms and perceived value) on s-commerce effectiveness (Nadeem et al., 2021; Wongkitrungrueng and Assarut, 2018; Yu et al., 2020; Liu et al., 2022; Molinillo et al., 2020).

The remainder of the study is organized as follows. Section 2 reviews key literature on s-commerce, CE and the S-O-R model, followed by an overview of the proposed model and the hypotheses in Section 3. Section 4 outlines the deployed methodology, followed by a synthesis of the results in Section 5. Section 6 discusses the findings and the key implications that arise from our analyses. Section 7 highlights key limitations inherent in this study, and Section 8 concludes the study.

2. Literature review

2.1 Social commerce

The interaction between content, community and commerce has revolutionized the online business environment by allowing customers to play a more proactive role in the e-commerce process (Hajli and Sims, 2015). Based on a recent survey, 78% of social community platform (e.g. social networking site) users spend time interacting with other consumers and/or brands (WARC, 2021). Therefore, reaching customers, and engaging with them, on these community-based platforms is expected to boost firms' competitive advantage (Shen et al., 2019).

S-commerce has been defined as the “use of Internet-based media that allow people to participate in the marketing, selling, comparing, curating, buying, and sharing of products and services in online marketplaces and communities” (Zhou et al., 2013, p. 61). It can, therefore, be used to connect buyers and sellers by allowing them to seek, and share, information about products and services (Xiao et al., 2015). For example, members of s-commerce platforms, such as,, and Facebook are able to join any community of their interest, after which they are able to interact with other members, follow buyers/sellers of interest, seek product-related information and/or share their purchase-related experiences (Huang and Benyoucef, 2013; Xiao et al., 2015). The literature suggests that community represents a key component of s-commerce design, thus distinguishing s-commerce (vs. traditional e-commerce) platforms, and also enabling s-commerce to harness the power of customer-to-customer interactions to a greater extent (Hajli, 2013; Huang and Benyoucef, 2013). The growing importance of s-commerce should, therefore, not be underestimated. According to Accenture, the global s-commerce market is expected to grow three times as fast as that of traditional e-commerce, to US$1.2 trillion by 2025 (Murdoch et al., 2022).

2.2 Social commerce attributes

S-commerce attributes refer to the built-in features of an s-commerce website, or service, that allow customers to rate, review, comment, share and participate in a community (e.g. a forum/blog) to provide, or receive, product-related support, referrals and recommendations (Hajli, 2013; Rashid et al., 2022). Given their social nature, s-commerce attributes can be used to enhance customers' social (e.g. online) experiences (Zhang et al., 2014).

Previous studies suggest that s-commerce attributes (e.g. interactivity, community, social interaction/support, collaboration, information quality, reputation and rewards/recognition) are conducive to fostering enhanced customer-to-customer interactivity and cocreation (Busalim et al., 2016; Kim and Park, 2013; Molinillo et al., 2021). For example, Li (2019) found that s-commerce attributes, including ratings, reviews, recommendations and referrals, positively impact customers' social interactions, in turn significantly affecting their trust in product recommendations. Liu et al. (2021) also examined technical s-commerce attributes (interactivity, stickiness, personalization and sociability) and found that these increase customers' purchase intention by raising their customer-to-customer interactivity and perceived value. Furthermore, several studies have shown that the social aspect of s-commerce platforms is a core characteristic in satisfying customer socialization needs, which is often underserved by, or absent from, traditional e-commerce platforms (Hu et al., 2022; Lin et al., 2018; Liu et al., 2021; Osatuyi and Turel, 2019). Overall, our review suggests that most prior studies highlight the role of customer interactivity, community, collaboration and social dynamics in influencing customer behavior on s-commerce platforms (see Table 1).

2.2.1 Social commerce collaboration

Prior e-commerce, and collaborative shopping, studies also suggest the essential role of collaboration in enhancing customers' shopping experience, online presence and engagement (Kim et al., 2013; Wagner and Majchrzak, 2007). For example, Zhu et al. (2010) reported that collaborative tools (e.g. shared navigation/voice chat) can play a significant role in driving customer responses and shopping experiences. In s-commerce, collaboration refers to the customer's ability to shop at an s-commerce website, or service, with remotely located peers, including friends or family members (Kim et al., 2013). S-commerce, therefore, represents a paradigm-shift in customers' online consumption-related thought processes (i.e. by transitioning from an inefficient individual consumption journey to a collaborative, shared and social shopping experience (Chen and Shen, 2015).

Compared to traditional e-commerce, s-commerce allows customers to generate content, exchange information and build collaborative knowledge (Hu et al., 2022; Huang and Benyoucef, 2017). In this environment, customers are able to acquire valuable information, yielding their improved shopping and decision-making experiences (Hu et al., 2022; Kim and Park, 2013). The richness of the content shared on s-commerce platforms, and customers' interpersonal networks, can also drive more efficient and effective, purchase-related content sharing (Liu et al., 2021). Moreover, the real-time support, detailed product information and other customers' shopping experiences shared on s-commerce platforms can foster customers' heightened engagement with the platform (Hollebeek and Macky, 2019). This can, in turn, boost their repurchase intention (Huang and Benyoucef, 2017; Liu et al., 2021; Seedorf et al., 2014).

2.2.2 Social commerce-based community

S-commerce-based community implies a customer's perceived degree to which an s-commerce platform enables them to connect with, follow, or communicate with other customers during the shopping process (Molinillo et al., 2020). The community aspect, therefore, distinguishes s-commerce from traditional e-commerce (Liang and Turban, 2011; Qin et al., 2023). In e-commerce, customers mainly rely on providers' (sellers') advice to make their purchase decisions (Guo et al., 2021; Hajli and Sims, 2015). However, s-commerce-based communities allow customers to communicate with one another (Molinillo et al., 2020), revealing a greater social aspect. According to Liang and Turban (2011), s-commerce platforms have three major attributes: social media technologies, community and commercial activities. Relatedly, Molinillo et al. (2020) suggested that s-commerce communities can help produce a common identity, and ties, between customers, yielding a sense of belongingness (Lin et al., 2018) and community engagement (Wang et al., 2020). Existing studies also emphasize the role of community attributes in stimulating customers' community-based responses. For example, Islam and Rahman (2017) found that s-commerce communities can stimulate CE if they provide high-quality information, on-demand systems, a virtual interactive environment, and reward systems. Moreover, Qin et al. (2023) highlighted the combined technological/social features of s-commerce that provide dynamic interactive communities in which customers are able to engage with stakeholders including streamers, sellers, or fellow customers (Clark et al., 2020), thus improving their shopping experience.

2.2.3 S-commerce-based interactivity

S-commerce-based interactivity refers to the degree to which an s-commerce site's technical characteristics, and design, facilitate customers' real-time interaction and information exchange (Liu et al., 2021). From a customer perspective, s-commerce-based interactivity refers to “the extent to which consumers participate in social shopping activities, and, as a result, generate and share information to reach a consensus within a social networking environment” (Tajvidi et al., 2021, p. 3). Previous social shopping, and online consumption, research has conceptualized interactivity as a stimulus (Mollen and Wilson, 2010; Ric and Benazić, 2022), acknowledging its capacity to physiologically stimulate customers and, in turn, affect their purchase behavior (Liu et al., 2021). Given the community-based, dynamic nature of s-commerce, interactivity is a key s-commerce metric, like for CE (Hollebeek et al., 2022).

Prior research suggests that interactivity has three core dimensions: perceived responsiveness, perceived user control and two-way communication (McMillan and Hwang, 2002; Mollen and Wilson, 2010; Song and Zinkhan, 2008), which, collectively, articulate users' online interactivity (Tajvidi et al., 2021). Moreover, Ric and Benazić (2022) asserted that elevated virtual community-based interactivity (e.g. on s-commerce websites) can create perceived autonomy, allowing customers to feel in control, in turn raising their engagement and potentially, flow.

2.2.4 Social dynamics

S-commerce (vs. traditional e-commerce) platforms reveal an important social aspect, thus suggesting the expected role of social dynamics on these platforms (Liang et al., 2011; Xue et al., 2020). Extant research suggests that social interaction, and social support, are the most common social facilitators of s-commerce sociability (Hajli, 2014a; Hajli and Sims, 2015; Huang and Benyoucef, 2013; Liang et al., 2011; Liang and Turban, 2011).

Social interaction denotes any actions users engage in that may affect their product- or service-related decision-making (Godes et al., 2005). S-commerce research shows that customers tend to seek social interaction when they join s-commerce communities to fulfill their social desires and build social ties, in turn shaping their predicted future purchase decision-making behavior (Hajli, 2014a; Wang and Yu, 2017). Prior studies indicate that social interaction represents a core s-commerce element that permits different types of user communication and provides a crucial information source to them (Hu et al., 2022; Huang and Benyoucef, 2013; Liang et al., 2011; Lin et al., 2018). Interactivity is also conducive to the establishment of social support mechanisms (Hajli, 2015). According to Hajli (2013), customers' social interactions on s-commerce platforms are facilitated by ratings and reviews, forums and communities and recommendations and referrals. Collectively, these contribute to building platform trust, while also fostering CE (Busalim et al., 2021; Hajli, 2015).

Social support refers to “socially supportive communication by means of information sharing, so that consumers feel that they are being helped and responded to by others when they are seeking information in a social group” (Wang et al., 2020, p. 4). The literature suggests that the rise of social computing, and Web 2.0 applications (e.g. social media), has revolutionized online environments making by rendering them more conducive to information/knowledge sharing (Hajli, 2014a). Liang et al. (2011) proposed that s-commerce-based social support can bring warmth, thus satisfying the individual's psychological needs, boosting the customer experience and raising perceived relationship quality and loyalty. Prior studies suggest that s-commerce customers, who experience elevated social interaction, and social support, are more likely to engage with the platform (Hajli et al., 2015; Tajvidi et al., 2021; Wang et al., 2020).

2.3 S-commerce-based customer engagement

CE has received substantial attention among academics and practitioners in the last decade (Brodie et al., 2013; Dessart et al., 2016; Gligor et al., 2019). CE has been defined as “the level of an individual customer's motivational, brand-related and context-dependent state of mind characterized by specific levels of cognitive, emotional and behavioral activity in direct brand interactions” (Hollebeek, 2011b, p. 790). As such, CE implies the customer's psychological state that transpires through his/her brand-related interactions (Brodie et al., 2011).

Most CE studies advocate a three-dimensional (i.e. cognitive, emotional and behavioral) perspective of CE (Brodie et al., 2011; Dessart et al., 2016; Hollebeek, 2011a, 2014). However, some authors limit their view of CE to behavioral CE, or engagement behavior. For example, Van Doorn et al. (2010) defined CE as “the customer's behavioral manifestations that have a brand or firm focus, beyond purchase, resulting from motivational drivers.”

In s-commerce, non-transactional CE may help consumers find the required product-related information, yielding expected social and economic benefits (Molinillo et al., 2020). Prior s-commerce research highlights that when customers engage in commercial activities, these can generate positive customer (e.g. co-creation/positive eWOM) responses (Molinillo et al., 2020; Tajvidi et al., 2021; Wang and Hajli, 2014; Zhang et al., 2017a). For example, Molinillo et al. (2020) reported that owing to s-commerce-based community and social support, customers tend to be more willing to engage with the platform, in turn driving desirable (e.g. eWOM) behaviors (Zhang et al., 2017b). Moreover, s-commerce-based CE enables firms to build strong customer relationships by facilitating multiple ways for customers to provide their feedback (Liu et al., 2022). Correspondingly, Wang and Hajli (2014) documented that s-commerce attributes (e.g. online communities/fora, ratings, reviews and recommendations) are likely to strengthen customers' interactions both with other customers, and the brand.

Recently, CE has gained traction in s-commerce research, including studies from different (e.g. platform or customer) perspectives (Algharabat and Rana, 2021; Busalim et al., 2021; Liu et al., 2022; Molinillo et al., 2020; Xue et al., 2020). For example, Kang et al. (2021) examined the effect of s-commerce-based website interactivity on CE. Using real-time data, the authors found that s-commerce responsiveness and personalization, positively affect customer tie strength, in turn raising CE. Molinillo et al. (2021) found that s-commerce website attributes (i.e. information/service quality) are key drivers of perceived value, in turn boosting CE. In an earlier study, Molinillo et al. (2020) suggested that s-commerce community drivenness, identification and trust are key drivers of s-commerce-based CE. From a customer perspective, Nadeem et al. (2021) showed that customers' experiential values (i.e. social, cognitive, ethical and hedonic) have a positive effect on CE. Relatedly, Wang et al. (2020) highlighted that s-commerce customers' social support (i.e. information/emotional support) favorably affects CE, while Algharabat and Rana (2021) showed that s-commerce support increases community members' trust, and as a result boosts CE. Finally, Busalim et al. (2021) proposed a key role of customers' social, technological and motivational factors in driving s-commerce-based CE. An overview of key studies addressing s-commerce-based CE is provided in Table 2.

2.4 Stimulus-organism-response framework

Consumer behavior studies that used the stimulus-response framework are well-documented in the marketing literature (e.g. Jacoby, 2002). However, to address the shortcomings of the stimulus-response framework, Mehrabian and Russell (1974) added the organism concept, yielding the related stimulus-organism-response (S-O-R) model. In these authors' model, individuals' internal cognitive and emotional (affective) states are thought to be influenced by environmental cues (stimuli) that, in turn, impact their behavioral responses. In the consumer behavior context, Bagozzi (1983) noted the high applicability of the S-O-R model, leading to its adoption in this study.

In the S-O-R model, stimuli are viewed as being external to the consumer. Stimuli may include managerially controllable factors (e.g. marketing mix/environmental factors). The organism comprises the consumer's internal processes (Mehrabian and Russell, 1974), including the affective (e.g. arousal/motivation) and cognitive processes (e.g. perceived risk, evaluation, decision rules) that regulate consumer choice (Bagozzi, 1983). Finally, response includes the individual's choice-related behavior, including his/her intentions, or conations, to act, activities leading to choice, actual choices and outcomes and reactions to choice. While the S-O-R model, traditionally, was presented as a process (i.e. Stimulus, Organism and Response), Jacoby (2002) noted that this approach, while not intellectually incorrect, may overlook the nonlinear, interactive nature of consumers' behavioral processes.

The S-O-R model has been applied in a range of online marketing contexts, including consumer experiential responses to websites (Mollen and Wilson, 2010), e-commerce (Chen and Yao, 2018; Rose et al., 2012), online reviews (Bigne et al., 2020) and social media-based brand communities (Islam and Rahman, 2017; Kamboj et al., 2018), among others. S-commerce has also been explored through an S-O-R lens. In a study of 1,009 RenRen and Sina Weibo users, Zhang et al. (2014) found that s-commerce intention (i.e. response) is determined by consumers' (i.e. the organism's) virtual experiences (i.e. social support, social presence, flow), which are in turn influenced by technological features, or stimuli (e.g. interactivity, stimulus personalization, sociability). Likewise, Zhou (2019) found that support and service quality (i.e. stimuli), affect consumer-perceived (i.e. the organism's) community, consequently affecting their information sharing, participation and intention (i.e. response). Wu and Li (2018) developed an s-commerce marketing mix (SCMM) based on s-commerce needs, risk, convenience, social capital, social identification and social influence. Their findings suggested that all SCMM components (i.e. stimuli) significantly impact consumer-perceived (i.e. the organism's) s-commerce value, in turn influencing s-commerce loyalty (i.e. response). Moreover, Li (2019) explored the impact of s-commerce attributes that connect consumers and enable them to discover, share, recommend, rate and purchase products, on their social shopping intention. Here, the environmental stimuli included ratings, reviews, recommendations and referrals; the customer's experience represented the organism; and the focal response was their social shopping intention.

3. Hypothesis development

This study investigates the effect of s-commerce attributes on CE and its downstream consequences on eWOM and repurchase intention. Based on the literature review, the proposed model incorporates s-commerce-based collaboration, community, interactivity and social dynamics as relevant stimuli, which we expect to impact customers' engagement (i.e. organism). CE, is, in turn, proposed to impact the customer's repurchase- and eWOM intention (i.e. response), as shown in Figure 1.

3.1 Collaboration

With the rise of s-commerce, customers have become more proactive in their shopping behavior, given the former's collaborative environment that permits content creation and sharing (Hajli and Sims, 2015), value co-creation in new product development processes (Wang and Hajli, 2014; Yu et al., 2020), the provision of feedback (Hsiao and Wang, 2015) and the ability to influence other customers' purchase decisions (Huang and Benyoucef, 2017). S-commerce customers are, therefore, exposed to social, collaborative experiences (Huang and Benyoucef, 2017). Previous s-commerce, and social shopping, studies confirm the close relationship of collaboration and CE (Huang and Benyoucef, 2013). For instance, Seedorf et al. (2014) highlighted that while collaborative (e.g. social co-browsing/instant chat) tools are still developing, customers, who have experienced this type of collaboration in social shopping, tend to experience greater social presence and engagement. Accordingly, this study proposes:


The s-commerce collaborative environment has a positive effect on customer engagement.

3.2 Community

S-commerce is a community-based environment (Hajli, 2013; Stephen and Toubia, 2010) that connects buyers/sellers and allows them to share product-related information (Xiao et al., 2015). S-commerce customers are able to freely interact with other customers to seek advice on products before, during and after their purchases (Molinillo et al., 2020), raising their platform-based engagement intensity. Previous s-commerce, and online brand community, research empirically demonstrates the strong relationship between s-commerce community and CE (Brodie et al., 2013; Shen et al., 2019; Molinillo et al., 2020). For example, Shen et al. (2019) corroborated the positive effect of community involvement on users' s-commerce engagement. Pöyry et al. (2013) suggested that customers participate in s-commerce brand communities (e.g. on Facebook) because these provide a sense of enjoyment, fantasy, pleasure and relevant content. Further, Molinillo et al. (2020) identified a significant, positive effect of community factors (e.g. community drivenness, identification and trust) on CE on Facebook fan pages. Thus, the following hypothesis is proposed:


S-commerce community has a positive effect on customer engagement.

3.3 Interactivity

S-commerce-based interactivity has been identified as a crucial facilitator of social interaction (Zhang et al., 2014). Customers engage in s-commerce platforms based on their high perceived interactivity, leading them to feel in control (e.g. by creating/sharing brand-related content) and prolonging their platform engagement. Prior s-commerce research shows that interactivity positively impacts CE (Busalim et al., 2021; Kang et al., 2021), customer experiences (Carlson et al., 2018) and interpersonal interaction (Liu et al., 2021). For example, Kang et al. (2021) found that s-commerce-based interactivity (i.e. responsiveness/personalization), boosts CE. We, therefore, argue that the more customers create, and share, product-related information, the greater their engagement. Thus, the following hypotheses is proposed:


S-commerce-based interactivity has a positive effect on customer engagement.

3.4 Social dynamics

As s-commerce is, by definition, built on specific social media features to support its social functioning, its social dynamics are a key characteristic of these platforms (Liang et al., 2011; Huang and Benyoucef, 2013). Prior s-commerce studies indicate that social support and social interaction, are the most influential social s-commerce attributes (Hajli, 2013, 2014a, b, 2015; Bai et al., 2015; Hajli and Sims, 2015). As discussed, s-commerce platforms provide customers with multiple ways to communicate with one another and form social ties using specific tools and functions (Kim and Park, 2013; Tajvidi et al., 2021). These social functions are defining s-commerce hallmarks that enable customer-to-customer interactions, in turn helping to satisfy customers' social needs and boosting their engagement (Vries and Carlson, 2014). Previous studies demonstrate the intricate association between social interactions and CE (Bitter et al., 2014; Busalim et al., 2021; Jahn and Kunz, 2012). For example, Jahn and Kunz (2012), and Bitter et al. (2014) found that social interaction on Facebook-based brand-fan pages positively influenced user engagement.

Social support comprises two main elements, including information- and emotional support (Tajvidi et al., 2021; Wang et al., 2020). Prior s-commerce research shows that social support positively affects CE on s-commerce platforms (Busalim et al., 2021; Molinillo et al., 2020; Wang et al., 2020; Yusuf et al., 2018). For example, Molinillo et al. (2020) suggested that when s-commerce customers receive social support from another customer, this raises their self-efficacy, in turn boosting their engagement. Thus, this study proposes:


Social s-commerce dynamics have a positive effect on customer engagement.

3.5 Customer engagement

S-commerce-based CE has been associated with several consequences, including increased customer satisfaction, loyalty, cocreation, eWOM, repurchase intention, feedback and collaboration and website stickiness (Carlson et al., 2018; Molinillo et al., 2020; Nadeem et al., 2021; Zhang et al., 2017b). One such positive outcome is repurchase intention, which refers to a customer's intention to rebuy a product/service from an s-commerce supplier (Kim et al., 2013). While repurchase intention directly affects firm performance (Pansari and Kumar, 2017), engaged s-commerce customers are also likely to develop a positive platform-related sentiment, and/or a belief that other customers will help improve their repurchase decisions (Lim et al., 2020). Prior research suggests that CE positively affects s-commerce customers' repurchase intention (Lim et al., 2020; Molinillo et al., 2020).

eWOM is another positive CE consequence. As customers become more engaged with others in real-time dialogue and product-related information sharing, they may feel emotionally attached to and empowered by, the brand (Brodie et al., 2013; Yusuf et al., 2018), contributing to their positive brand-related eWOM. For example, Chu and Kim (2011) reported that social relationship factors, including tie strength, informational influences and trust positively impact eWOM engagement on s-commerce platforms. Previous studies also show that engaged customers are more likely to spread eWOM (Molinillo et al., 2020; Yang et al., 2015; Zhang et al., 2017b). This study, therefore, hypothesizes:


Customer engagement with s-commerce platforms has a positive effect on repurchase intention.


Customer engagement with s-commerce platforms has a positive effect on eWOM intention.

4. Methodology

4.1 Empirical research context

The empirical context for this study is, one of the most popular s-commerce marketplaces for small businesses (Price and Robinson, 2021). As a dynamic, global marketplace for unique art and craft goods, has over 31.7 million users (, 2022). In the third quarter of 2022, had almost 95 million active buyers, generating US$3 billion in revenue (, 2023). It is a community commerce platform that enables users to connect and engage in social interactions (e.g. by liking content, following other users, or providing product-related feedback (Sun, 2011). Users are also able to create their own profile and join different (e.g. interest) groups. This study selected, given its understudied nature (vs. traditional e-commerce sites) to date, and its ability to generate social interactivity among users (Zhang et al., 2014). While most prior research has focused on s-commerce attributes inherent in social network sites that introduce commercial activities, this study examines consumer behavior dynamics in the less studied s-commerce context (Mamonov and Benbunan-Fich, 2017; Park et al., 2014; Zhang et al., 2014). Therefore, given the social features and functionality of, it is an appropriate s-commerce community to explore our research questions.

4.2 Measurement

This study employed survey methodology to collect the data. The deployed measurement items were acquired from prior research and were adapted to fit the s-commerce context, after which they were validated by an academic expert panel. To ensure item reliability and validity, a pilot test was conducted with 30 users. Based on the feedback from the experts and the pilot test, the survey instrument was refined. The final online survey comprised seven items to measure community, adapted from Gummerus et al. (2012) and Wang and Hajli (2014), and five items gauging interactivity adapted from Wu and Wu (2006), Kim and Park (2013) and Tajvidi et al. (2021). Moreover, three items measuring collaboration were adapted from Seedorf et al. (2014). Social dynamics, comprising social support and social interaction, were measured using items adapted from Hajli (2014a) and Huang and Benyoucef (2015). CE was measured using items adapted from Dessart et al. (2016) and Algharabat and Rana (2021). Repurchase intention was measured using three items adapted from Lin et al. (2017) and Meilatinova (2021) (see Appendix). All the items were measured on seven-point Likert-type scales ranging from “strongly disagree” (1) to “strongly agree” (7).

4.3 Sampling and data collection

This study used non-probability, purposive sampling to collect the data (Wang and Huang, 2023), which is commonly used to determine samples based on particular criteria or similar sets of characteristics (Sekaran and Bougie, 2013). The data was collected from users who have an account and have engaged in several buying/selling activities on the platform. The data was collected using Qualtrics, yielding a total of 410 completed responses. After deleting ineligible responses, including those featuring questionable response patterns (e.g. straight line answers) and outliers, 390 valid responses were retained, as shown in Table 3. The male respondents were 76% (n = 296) and the female were 24% (n = 94). The respondents who are aged 26–35 years were 44% (n = 171), followed by those who are between 18 and 25 (29%, n = 112), 36 and 45 (18%, n = 71) and the remainder (9%, n = 36) were over 45. Most of the respondents had used for 1–three years (58%, n = 226), for 3–5 years (21%, n = 81), and for less than one year (13%, n = 51). The remaining respondents had used for over five years 8% (n = 32). Moreover, the respondents who had shopped at once every few weeks were 55% (n = 215), followed by those who had shopped at the platform several times a month (31%, n = 121). The remaining respondents bought there several times a week (9%, n = 35), or almost every day (5%, n = 19).

4.4 Common method bias

The Harman's one-factor test was used to ensure that the data is free from common method bias (CMB), which is a serious issue if a single factor explains over 50% of the observed variance (Podsakoff et al., 2003). The Harman's one-factor test results revealed that the highest single factor accounted for 34.5% of the observed variance, thus remaining well under the 50% threshold. Therefore, CMB is not a significant issue in our data.

5. Data analysis and results

5.1 Measurement model assessment

Partial least squares structural equation modeling (PLS-SEM) was used to analyze the data, a widely used approach, given its elevated predictive validity (Hair et al., 2016). To estimate the modeled higher-order constructs (i.e. social dynamics), the disjoint two-stage approach was applied (Becker et al., 2012; Hair et al., 2016), which examines the lower-order constructs comprised in the higher-order constructs by linking them to all other constructs that the higher-order constructs are theoretically related to (Sarstedt et al., 2019). To perform the disjoint two-stage approach, the lower-order constructs, social support (SSP) and social interaction (SI) were connected to CE, along with the other constructs (i.e. collaboration, community and interactivity).

Then, the reflective measurement model was examined by testing the reliability and convergent and discriminant, validity of all constructs (Hair et al., 2016). Internal reliability was examined by testing Cronbach's alpha (α) and composite reliability (CR), both of which should exceed 0.7 (Hair et al., 2019). The results in Table 4 show that the Cronbach's alpha of all constructs ranged from 0.700 to 0.873 and the CR results ranged from 0.83 to 0.901, thus demonstrating satisfactory reliability. The convergent validity was also assessed by examining the factor loadings and the Average Variance Extracted (AVE). As shown in Table 4, all factor loadings exceeded 0.50, except for two items, CE1 and CE4. Therefore, following the best PLS-SEM practice, these items were deleted. Furthermore, the AVEs of all items ranged from 0.50 to 0.694, indicating good convergent validity (Benitez et al., 2020; Hair et al., 2016). In Stage Two, the latent variable scores (LVS) of SSP and SI obtained from Stage One were used. The LVS were added to the dataset as new items. Then, the second-order construct (social dynamics) was added to the model and assigned LVS of SSP and SI, as reflective items to it. As shown in Table 4, the results show that the loadings of SSP and SI are 0.904 and 0.921, respectively, with an AVE of 0.832, indicating high indicator reliability. Moreover, the Cronbach's alpha (0.799) and CR (0.908) of the social aspect are at a satisfactory level.

Discriminant validity was examined using Fornell and Larcker's (1981) criterion and the heterotrait-monotrait ratio of correlations (HTMT). The Fornell-Larcker test compares the square root of each construct's AVE values with the other modeled constructs correlations. As shown in Table 5, the square root of the AVE of each construct exceeds its correlation with each of the other constructs. Second, the HTMT was assessed, which is proposed as an alternative discriminant validity measure (Henseler et al., 2015). HTMT is defined as “the average of the correlation indicators across constructs measuring different phenomena, relative to the average of the correlations of indicators within the same construct” (Henseler et al., 2015). The HTMT value of the variables should remain under 0.90 (Benitez et al., 2020; Henseler et al., 2015). Table 6 shows that all HTMT ratios are less than 0.90, indicating adequate discriminant validity.

5.2 Structural model assessment

The structural model was assessed based on the path coefficients and the variance explained (R2) by the dependent variables. PLS-SEM was, again, used to generate the estimates using bootstrapping with 5,000 subsamples and a two-tailed test (Hair et al., 2016). The hypothesis results are shown in Table 7 and Figure 2. The R2 values in Figure 2 show that the explained variance of CE, repurchase intention and eWOM intention are 64.1%, 18.9% and 38.3%, respectively, demonstrating satisfactory predictive power (Hair et al., 2019). Moreover, as shown in Figure 2, the hypotheses are supported. According to the path coefficients, collaboration (β = 0.115, t = 2.217), community (β = 0.221, t = 4.604), interactivity (β = 0.134, t = 2.225) and social aspects (β = 0.532, t = 8.926) positively affect customer engagement, supporting H1, H2, H3 and H4. Furthermore, as hypothesized, the results reveal that CE positively impacts repurchase intention (β = 0.435, t = 8.987) and eWOM intention (β = 0.618, t = 15.471), supporting H5 and H6.

6. Discussion and implications

This study examined the role of particular s-commerce attributes in fostering CE. Specifically, the findings provide empirical support for the impact of s-commerce attributes on CE and its outcomes of repurchase- and eWOM intention. Specifically, based on the attained R2 result, a substantial amount of the variance (64.1%) observed in CE is explained by the four studied s-commerce attributes.

This study also unveils additional findings that are of scholarly, and practitioner, interest. First, the results reveal that the s-commerce-based collaborative environment has a significant positive effect on customer engagement. When a customer interacts with others on an s-commerce platform, (s)he tends to feel connected to those others, thus adding value to sellers and customers, in line with prior collaborative online shopping studies (Zhu et al., 2010; Kim et al., 2013; Seedorf et al., 2014).

S-commerce platforms offer collaborative environments that enable customers to share information, experience, or knowledge, in turn affecting other customers' purchase decision-making processes (Huang and Benyoucef, 2013). Boon et al. (2015) noted that, unlike traditional e-commerce platforms, offers its members multiple ways to communicate and collaborate (e.g. including through blog posts, video seminars, webinars, or the ability to set up, and communicate in, relevant (e.g. interest) groups), thus stimulating their interactivity and CE (Hollebeek and Macky, 2019).

Second, the results reveal a positive effect of s-commerce community attributes on CE, consistent with prior studies that highlight the role of s-commerce-based community and its effect on customer behavior (Algharabat and Rana, 2021; Molinillo et al., 2020; Shen et al., 2019). This finding is of particular relevance in a (post-)pandemic environment, in which consumers are expected to display elevated health/safety concerns, while maintaining their social needs (Hollebeek et al., 2022). Therefore, the more consumers feel comfortable with, trust in and engage with s-commerce platforms (Qin et al., 2023), the greater the expected uptake and continued use, of these platforms, offering important insight for s-commerce providers.

Third, consistent with Kang et al. (2021), our results show that s-commerce interactivity has a positive influence on CE. Similarly, Zhang et al. (2014) suggested that interactivity is one of the technological features that positively affect customers' virtual s-commerce experience, implying that customer-to-customer and customer-to-seller, interactions significantly shape CE on For example, with over 250,000 followers, CaitlynMinimalist is among's top ten most popular sellers. This seller provides several customer interaction avenues, including detailed product descriptions, order procedures, payments and return policies. Customers are also easily able to view the latest product reviews and ratings on its homepage and to reply to other customers' reviews, demonstrating the close link between s-commerce-based interactivity and CE. While it is important to note that the majority of the deployed sample were male consumers, the attained finding is consistent with prior research, suggesting that male consumers primarily respond to functional, or utilitarian, features (Chen et al., 2018).

Fourth, the findings show that social s-commerce dynamics, including social support and social interaction, positively affect CE, consistent with prior s-commerce studies (Bai et al., 2015; Hu et al., 2022; Lin et al., 2018; Zhang et al., 2014). Our findings support Ling and Husain's (2013) rationale that when a customer is able to access other customers' comments and reviews, and to interact with them before they buy a product, they will tend to exhibit elevated engagement and more informed decision-making. Moreover, once s-commerce customers receive information and emotional support, from other customers, this supportive climate encourages them to continue their relationship with fellow customers, and with the platform (Busalim et al., 2021; Liang et al., 2011), in turn also raising their engagement (Molinillo et al., 2020; Wang et al., 2020). Prior studies highlight the role of gender in consumers' social interaction on s-commerce. The results of this study are consistent with those of prior research in that the impact of social dynamics on s-commerce is significantly stronger for male (vs. female) consumers (Bitter et al., 2014). The outcomes of H1, H2, H3 and H4 offer an empirical answer to RQ1 of this study.

Finally, our results reveal CE as a positive predictor of customers' repurchase- and eWOM intention, emphasizing CE's key role in building customers' transactional and non-transactional behavior. These findings are in line with previous literature (Doha et al., 2019; Molinillo et al., 2020; Rahman et al., 2018; Zhang et al., 2017b). For example, Molinillo et al. (2020) suggested that CE is a central s-commerce factor and a key predictor of customer loyalty. In addition, the results of H5 and H6 provide a response RQ2 and RQ3 of this study.

6.1 Theoretical implications

This study raises important implications for future s-commerce and CE research. First, despite the growing popularity of CE in s-commerce, little remains known regarding the role of specific s-commerce attributes on CE in the s-commerce context. This study finds that the four studied social s-commerce attributes (i.e. community, collaboration, interactivity and social dynamics), indeed, positively shape customers' s-commerce-based engagement. These findings have pertinent implications for further theory building, and development, in this topic area.

First, debate surrounds the nature, and role, of specific dimensions characterizing social interactions (e.g. Polley, 1987). For example, some authors propose a key role of micro (vs. macro)-interactions (e.g. Kemper and Collins, 1990). Tse and Bond (2001) proposed the social interaction dimensions of active participation, active non-participation, passive participation and passive non-participation. While this study deployed the s-commerce interactivity facets of social interaction and social support, alternate models may be used that may yield novel, or different, insights into s-commerce-based dynamics, yielding important theory verification or refinement opportunities. For example, to what extent do s-commerce platforms boost CE for different social interaction models?

Second, this study investigated the role of social s-commerce attributes on CE, other (i.e. non-social) attributes may also impact CE and/or other consumer behavior variables (e.g. consumer involvement, brand love, brand attachment, or self-brand connection; e.g. Hollebeek et al., 2014). For example, high-involvement products may see a greater relevance of cognitive (vs. social) CE (Brodie et al., 2013), or hedonic (vs. utilitarian) offerings may be more prominently characterized by emotional CE. As another example, for some product categories (e.g. sensitive purchases), consumers may be more reserved in terms of engaging in social purchase-related interactions, or purchases (e.g. financial, legal, or counseling services; adult products). In these cases, s-commerce may offer a sub-optimal selling platform, thus warranting further investigation. For example, how might the addition, or removal, of specific social interaction features affect CE with offerings in these categories? Should, or can, s-commerce platforms be used to sell products in these categories, or are marketers advised to abstain from using these platforms for these products? To what extent, and how, do social s-commerce-based attributes interact with other s-commerce attributes to produce, and sustain, CE for specific categories?

Third, this study deployed the S-O-R model to investigate the research objectives, which assumes that a particular stimulus (e.g. s-commerce website) will generate a focal organism's (i.e. customer's) particular (e.g. purchase) response (e.g. Zhang et al., 2014; Li, 2019). While the S-O-R model has been shown to have elevated validity, it is worthwhile ascertaining the sequence of the predicted s-commerce events in further studies conducted in (a) different context(s). Specifically, through (e.g. selection) bias, a customer may seek out particular stimuli while discounting, overlooking, or ignoring others. In such cases, a revised sequence of the traditional S-O-R model may apply, meriting further scrutiny.

6.2 Practical implications

This study also offers important implications for s-commerce suppliers, sellers and managers. First, the pandemic has changed the face of retailing, including on s-commerce platforms, while also raising customers' caution regarding being physically present in service environments. Here, s-commerce, which is still at an early stage, offers an important growth opportunity in the coming years. Our results show that s-commerce-based community, collaboration, interactivity and social dynamics significantly shape CE, offering a concrete starting point for sellers in building, or improving, their s-commerce platforms. Overall, the findings indicate that managers should incorporate social features in their customers' s-commerce experiences (e.g. by allowing customers to interact, share with, and offer support to each other). This may be accomplished by enabling customers to like, comment on, or share relevant product-related content (e.g. product reviews) online, or by establishing relevant interest groups.

Second, the results show that s-commerce-based interactivity boosts CE. The results, therefore, suggest that firms should prioritize customer interactivity by making it center stage in their s-commerce operations. Correspondingly, managers are advised to pay particular attention to interactive s-commerce attributes, including personalization and customer commenting facilities, fora and/or blogs that enable customers to generate, and share, content, in turn encouraging real-time customer-to-customer interactivity.

7. Limitations and future research

This study has several limitations that offer additional avenues for further research. First, the data was collected from a single s-commerce website,, representing merely one s-commerce category. Future research may, thus, focus on other s-commerce types, including social media platforms, group buying marketplaces, multi-user gaming platforms, or virtual reality environments. An exploration of what factors, and functions, encourage customers to engage with these platforms may be a fruitful area of research. Further research may also account for other relevant consumer, platform, brand-related, or situational factors that may influence CE (Hollebeek et al., 2019).

Second and relatedly, this study examined only two positive outcomes of s-commerce-based CE (i.e. repurchase intention/eWOM). Therefore, future research could examine other outcomes (e.g. brand performance or loyalty). Relatedly, while this study assessed customers' intent to repurchase the brand and to disseminate positive brand-related word of mouth, intentions do not lead to behavior per se. Therefore, future research may wish to examine consumers' actual purchase data, whether cross-sectionally or longitudinally. Overall, further insight into CE's s-commerce-based nomological network is expected to be of value.

Third, the deployed sample comprised of predominantly male customers. As women, likewise, represent a significant s-commerce-based user segment, future research could consider the role of gender and examine the existence of potential differences across (e.g. male/female, or non-binary) s-commerce customers. Moreover, examination of other demographic customer variables (e.g. age, income, or education level) could further enhance scholarly understanding of s-commerce-based CE.

8. Conclusion

Despite the recent rise of s-commerce, the dynamics characterizing customers' engagement with, or on, s-commerce platforms (e.g. based on specific s-commerce attributes) remains nebulous, exposing an important literature-based gap. Drawing on the S-O-R model, this study developed a model proposing that s-commerce-based collaboration, community, interactivity and social dynamics drive CE, which we, in turn, predict to raise customers' repurchase- and eWOM intent. To explore the hypothesized associations, survey data was collected from 390 users. The PLS-SEM results indicate that the four studied s-commerce attributes significantly boost CE. In other words, when these s-commerce attributes are well-developed and -implemented on the platform, customers are more likely to actively engage with the platform. The results also corroborate that CE, in turn, raise customers' repurchase- and eWOM intent. Overall, the results shed much-needed light on the importance of s-commerce attributes in driving CE. By fostering collaboration, building strong communities, encouraging interactivity and providing social support and interaction opportunities, s-commerce providers are able to strategically raise their customers' engagement, in turn boosting platform performance.


Conceptual model

Figure 1

Conceptual model

Structural model results

Figure 2

Structural model results

Social commerce attributes addressed in prior research

S-commerce attributes
StudyCommunityCollaborationInteractivitySocial dynamics
Hajli (2013)
Huang and Benyoucef (2013)
Kim and Park (2013)
Pöyry et al. (2013)
Hajli (2014b)
Hajli et al. (2014)
Seedorf et al. (2014)
Zhang et al. (2014)
Hajli and Sims (2015)
Hajli (2015)
Bryant and Thompson (2016)
Friedrich et al. (2016)
Zhang and Benyoucef (2016)
Huang and Benyoucef (2017)
Carlson et al. (2018)
Li (2019)
Alalwan et al. (2019)
Molinillo et al. (2020)
Kang et al. (2021)
Liu et al. (2021)
Hu et al. (2022)
Rashid et al. (2022)

Source(s): Author's own creation/work

Selected s-commerce-based customer engagement research

StudiesCE dimensionAntecedents to customer engagement in s-commerceConsequences of customer engagementKey findings
Shen et al. (2019)Unidimensional: BehavioralTechnology attractiveness and community involvementTechnology attractiveness (i.e. task, social and physical attractiveness) has a positive effect on community involvement, which has a positive influence on s-commerce engagement
Molinillo et al. (2020)Multidimensional: Vigor, dedication and absorptionSocial support, community drivenness, community identification and community trustWillingness to co-create, stickiness Intention, positive eWOM intention, repurchase intentionCommunity factors including community trust and community identification, along with social support, have a positive effect on CE. Thus, CE can lead to positive outcomes such as customer stickiness and repurchase intention
Wang et al. (2020)Unidimensional: BehavioralSocial support (i.e. emotional and informational support)Social support has a positive effect on s-commerce engagement
Algharabat and Rana (2021)Multidimensional: Cognitive, affective and behavioralSocial support, social presence, trust in community members and flowS-commerce constructs and social factors have a positive effect on community trust, which has a positive influence on customer engagement within s-commerce communities
Guo et al. (2021)Unidimensional: BehavioralTrust in community members, broadcasters and productsTrust in live streaming (i.e. trust in product, trust in community members and broadcasters) has a positive effect on CE
Kang et al. (2021)Unidimensional: BehavioralInteractivity and tie strengthBoth interactivity and tie strength have a positive effect on CE
Nadeem et al. (2021)Multidimensional: Cognitive, affective and activationExperiential value (i.e. cognitive, hedonic, social and ethical values)Brand relationship performance (i.e. brand loyalty, satisfaction) and co-creationExperiential value has a positive impact on CE, which in-turn has a positive effect on brand performance and value co-creation

Source(s): Author's own creation/work

Sample's demographic characteristics

Profile of respondents (n = 390)Frequency Percentage
Length of time using Etsy.comLess than a year5113%
1–3 years22658%
3–5 years8121%
>5 years328%
Frequency of shopping on Etsy.comOnce every few weeks21555%
Several times a month12131%
Several times a week359%
Almost every day195%

Source(s): Author's own creation/work

Measurement model results

ConstructItemsLoadingCronbach's alphaComposite reliabilityAVE
Repurchase intentionRPI10.7880.7800.8720.694
Customer engagementCE20.7060.8660.8940.500
Social interactionSI10.7930.7600.8470.581
Social supportSSP10.7080.8360.8800.551
eWOM intentioneWOM10.7120.7950.8590.549
Higher-order construct
Social aspectSocial support0.9040.7990.9080.832
Social interaction0.921

Source(s): Author's own creation/work

Fornell–Larcker criterion results

eWOM Intention0.6010.4550.6190.6660.6190.6240.5300.740

Note(s): COL=Collaboration; COM= Community; INT= Interactivity; RPI= Repurchase intention; CE= Customer engagement; SI= Social interaction; SSP= Social support; eWOM = Electronic word of mouth

Italic values = the square root of construct's AVE

Source(s): Author's own creation/work

HTMT results ratio of first-order constructs

eWOM intention0.8110.5310.7790.8540.7220.7960.640

Note(s): COL=Collaboration; COM= Community; INT= Interactivity; RPI= Repurchase intention; CE= Customer engagement; SI= Social interaction; SSP= Social support; eWOM = Electronic word of mouth

Source(s): Author's own creation/work

Hypothesis testing results

COL → CE0.1152.2170.027
COM → CE0.2214.6040.000
INT → CE0.1342.2250.026
SA → CE0.5328.9260.000
CE → RPI0.4358.9870.000
CE → eWOM intention0.61815.4710.000

Note(s): COL=Collaboration; COM= Community; INT= Interactivity; SA= Social aspect; CE= Customer engagement; RPI= Repurchase intention; eWOM = Electronic word of mouth

Source(s): Author's own creation/work

ConstructItemContents of items
CollaborationCOL1I am willing to recommend a product that is worth buying to other customers on Etsy
COL2I found the collaboration with other customers is useful in achieving my shopping tasks on Etsy
COL3Etsy provides a collaborative environment
Community I join Etsy forums/community to
COM1Get to know other Etsy customers/sellers
COM2To ask Etsy members to provide me with their suggestions before I buy product/service
COM3To get information about sales and coupons
COM4To get fast responses for my inquiries
COM5To be informed about new products and services
COM6Using Etsy forums/community, I could start a discussion and share my ideas about products to other community members
COM7Using Etsy forums/community, I could express my opinion about product/seller
InteractivityINT1On Etsy, I could send an instant message to seller to ask for further inquiries about the product I want to buy
INT2On Etsy, I could share my own shopping experience with other customers through ratings and reviews
INT3Etsy keeps me informed of new developments
INT4Etsy listens to my feedback on its service
INT5Etsy provides me with timely information
Repurchase intentionRPI1I would consider buying products from Etsy in the near future
RPI2I will repurchase other products at Etsy
RPI3Given the opportunity, I intend to place an order from Etsy again
Customer engagementCE1Etsy makes me feel enthusiastic
CE2Interacting with Etsy community gives me a treat
CE3Etsy makes me feel happy when I interact with other customers
CE4I found interesting
CE5Time flies when I am interacting with Etsy community members/customers
CE6When I am interacting with Etsy I get carried away
CE7I spend time thinking about
CE8I share my thoughts with Etsy community
CE9I share exciting content (reviews, product/service experience) with Etsy community
CE10I seek ideas or information from Etsy community
CE11I try to get other interested in
CE12I am an active member in
Social interactionSI1Etsy allows me to respond to comments made by other customers
SI2On Etsy, I could follow sellers to see their products offers
SI3On Etsy, I could follow other customers and see their favorite items
SI4Etsy allows me to chat with other customers/sellers
Social supportSSP1When faced with difficulties, some customers on Etsy comforted and encouraged me
SSP2When faced with difficulties, some customers on Etsy listened to me talk about my feelings
SSP3When faced with difficulties, some customers on Etsy expressed interest and concern in my well-being
SSP4On Etsy, some customers would offer suggestions when I needed help
SSP5When I encountered a problem, some customers on Etsy would give me information to help me overcome the problem
SSP6When faced with difficulties, some customers on Etsy would help me discover the cause and provide me with suggestions
eWOMeWOM1I would provide others with information on Etsy
eWOM2I will tell others the positive aspects of Etsy
eWOM3I am likely to encourage others to consider Etsy
eWOM4I will recommend Etsy to friends/someone who seeks my advice
eWOM5I will speak favorably about Etsy to others

Source(s): Author's own creation/work


Table A1


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Funding: This work was supported by DCU Business School Internal Seed Funding.

Corresponding author

Abdelsalam Busalim is the corresponding author and can be contacted at:

About the authors

Dr Abdelsalam Busalim is Assistant Professor in Digital Business at DCU Business School. He received his PhD in Information systems from Universiti Teknologi Malaysia. His research focuses on social commerce, social media, customer behavior analytics and data analytics. He has published several papers in peer-review journals such as Computers and Education, International Journal of Information Management, Journal services marketing. Currently, he is working on multiple research projects related to; consumer behavior in sustainable fashion, social commerce engagement design features, social commerce in tourism services.

Linda D. Hollebeek, PhD, is Professor of Marketing at IPAG Business School, Vilnius University, and Tallinn University of Technology. Her research on customer, and stakeholder engagement has published in the Journal of the Academy of Marketing Science, Journal of Service Research and International Journal of Research in Marketing, among others. She has been named a Clarivate Highly Cited Researcher (2021, 2020), is included on Stanford University's Top 2% of Researchers (2021, 2020), and is listed on's Top Business and Management Scientists (2021). Linda serves as Associate Editor of the Journal of Service Research, is the recipient of the 2020 SERVSIG Emerging Service Scholar Award, and is co-editor of The Handbook of Research on Customer Engagement.

Theo Lynn is Professor of Digital Business at DCU Business School. Professor Lynn specializes in the role of digital technologies in transforming business processes. His main teaching areas are strategy and digital marketing. He has been published widely including the European Journal of Marketing, Information, Communications and Society, the Journal of Distributed and Parallel Computing, the International Journal of Advertising, European Planning Studies and others. He is the Series Editor on the Palgrave Studies in Digital Business and Enabling Technologies. He is a regular speaker at both academic and industry conferences.

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