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A Literature Review of Social Commerce Research from a Systems Thinking Perspective

Xintian Wang

^1,2,*

Hai Wang

and

Caiming Zhang

⁴

Business School, Ningbo University, Ningbo 315211, China

National Academy of Economic Strategy, Chinese Academy of Social Sciences, Beijing 100732, China

Sobey School of Business, Saint Mary’s University, Halifax, NS B3H 3C3, Canada

⁴

China Information Research Center, China University of Labor Relations, Beijing 100089, China

Author to whom correspondence should be addressed.

Systems 2022, 10(3), 56; https://doi.org/10.3390/systems10030056

Submission received: 2 March 2022 / Revised: 9 April 2022 / Accepted: 18 April 2022 / Published: 24 April 2022

(This article belongs to the Special Issue Business Model–the Perspective of Systems Thinking and Innovation)

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Abstract

The paper aims to investigate social commerce systems from a systems thinking perspective. It proposes to model the social commerce process and outlines how Following, Communicating, Purchasing, and Sharing are systematically connected with each other in the social commerce process. The paper describes an exploratory review study using the systematic literature review method, including 384 social commerce research papers, which were published from 2011 to 2021. The data are refined by documentary analysis, including Study Selection Criteria and Quality Assessment processes. The paper systematically develops a conceptual framework for understanding social commerce. Previous research on social commerce mainly focuses on one or more particular key success factors (such as trust) in social commerce, and a few of them investigate social commerce as an integral business system. This review provides a more comprehensive basis for future social commerce research.

Keywords:

social commerce; e-commerce; social media; business model; network steam

1. Introduction

Social commerce has developed rapidly in practice and gained widespread attention in the information systems (IS) discipline. Since it was introduced in 2005 by Yahoo, social commerce has quickly become an effective tool for engaging customers of major e-commerce companies, such as Amazon, Groupon and eBay [1]. It is known that the first academic article that used the “social commerce” term appeared in 2006 [2], while some studies believe that social commerce research can be traced back to the late 1990s [3]. Nevertheless, there is no doubt that social commerce has received widespread attention and the number of social commerce publications has increased tremendously in the past ten years. In practice, the formal adoption of social commerce occurred in 2009 when Flowers.com started the first online store on Facebook [4]. It is predicted that by the end of 2021, a social network sponsored by these internet companies will be able to generate nearly USD 3.5 billion in revenue worldwide [5].

Despite the rapid development and adoption of social commerce, the current understanding of social commerce is still scattered and limited [1,5]. Social commerce research is in the early stages of development since there is little theoretical work on how social commerce operates and little is known about the social commerce business cycle. Furthermore, the current understanding of the distinction of social commerce from e-commerce includes the use of social media as a tool to connect customers, considering social commerce as a new marketing mode of e-commerce. We believe that this understanding of social commerce could be further re-considered.

Systems thinking is a research paradigm that emphasizes the interactions between different components of a system. For a particular problem, systems thinking is a conceptual framework that considers the problem in its entirety [6]. Systems thinking examines the relationships between various components in a system and emphasizes the understanding of the mechanisms among these components. In a social commerce context, system thinking considers social commerce as a system and provides an integral perspective to depict complex components such as trust. This can enhance the understanding of social commerce initiatives to respond to the needs of business organizations.

In order to provide a more comprehensive understanding of social commerce theory for academics, this study conducts a systematic literature review to explore the social commerce business model from a system thinking perspective and puts forward a possible theoretical explanation of why social commerce is essentially different from e-commerce. To achieve the main objective of this review, we propose three major research questions as stated below:

What is social commerce?
What are the key components of social commerce?
What does a social commerce system look like and how do its internal mechanisms make it different from e-commerce?

Overall, the contributions of this research are as follows. First, through the analysis of 300 studies, this review proposes a more detailed social commerce components (SCCs) model compared to previous studies. This will deepen our understanding of the social commerce business model. Secondly, this paper provides a possible explanation of why social commerce is an important transformation of e-commerce. The authors propose that the network stream distribution mechanism is the key difference between social commerce and e-commerce. Lastly, this research develops a framework that includes the entire social commerce business cycle. This can be a cornerstone and starting point for future social commerce research.

The remainder of this research is structured as follows. Section 2 explains the research method used in this review; Section 3 reveals the statistical results; Section 4 reports and analyzes the answers to the research questions; finally, Section 5 presents the three main conclusions of this research.

2. Methodology

In this paper, a systematic literature review is conducted to describe the trend of social commerce in research and explore the social commerce business model. Meanwhile, this review is used to answer the research questions proposed in Section 1 by collecting and analyzing all the previous works in the social commerce research field that fit the pre-specified eligibility criteria.

A systematic literature review is a tool for identifying, evaluating and interpreting all available research relevant to a particular research question, topic area or phenomenon of interest [7]. Systems thinking focuses on the understanding of the mechanisms among different components of a system [6]. In the social commerce context, system thinking considers social commerce as a system and provides an integral perspective to depict complex components such as trust. In this study, we apply systems thinking and use a systematic literature review to examine social commerce with the following objectives:

To propose a conceptual framework of social commerce systems;
To explain why social commerce is a distinct business paradigm from e-commerce by examining previous social commerce studies;
To identify research gaps in current social commerce research for future study.

2.1. Review Protocol

As shown in Figure 1, we design the review protocol of this paper mainly in two stages.

Stage 1 is designed to obtain the raw material of this study, which consists of identifying the research questions and determining the search strategy. We define the search strategy as searching the keywords “social commerce”, “s-commerce”, “social e-commerce” and “social electronic commerce” in Google Scholar/AIS/IEEE and other databases to cover as many social commerce studies as possible.

Stage 2 is designed to refine the data that we collected with multiple criteria, which will be addressed in detail in Section 2.2. At the end of Step 2, we obtain 300 formal published studies as our research database and enter information such as the title, author, publication year, journal name and so on into Zotero software for further processing.

From the systems thinking point of view, we attempt to categorize all components of social commerce systems studied in all publications identified by this review protocol. This enables us to further investigate the relationship among different components of social commerce systems and propose a conceptual framework of social commerce systems.

2.2. Study Selection Process

In this section, we propose our selection criteria according to the research questions and present the quality assessment process of study selection.

2.2.1. Study Selection Criteria

The primary reason for identifying selection criteria is to make sure that the selected articles are relevant and related to the social commerce research field. We develop a set of selection criteria as shown in Table 1. In Stage 2, we filter the studies collected in Stage 1 with these criteria and exclude 22,642 articles. Thus, 300 research papers remain in our database.

2.2.2. Quality Assessment

There is no generally accepted definition of ‘Quality’, but according to [8], a research’s quality relates to the extent to which the study minimizes bias, as well as its validity. Thus, we develop a quality checklist to evaluate the studies:

QA1. To what extent is the article subject associated with social commerce? (−1—low, 0—medium, 1—high)
QA2. Is the research methodology specified in the article? (−1—no, 1—specified)
QA3. Is the data collection described in the article? (−1—no, 1—described)
QA4. Are the results of data analysis explained in the article? (−1—no, 0—yes, but not well explained, 1—well explained)

Each article receives a score from −4 to 4 in this quality assessment. Every study receives a label based on the total score: −4–0 for low quality, 0–2 for medium quality and 2–4 for high quality. Articles filtered by the low-quality label are excluded (735 articles), and 300 articles remain to answer the research questions (86 studies with the high-quality label and 214 studies with the medium-quality label).

3. Data Extraction and Synthesis

The primary goal of this section is to extract the data and analyze the information of the selected studies in each stage. As shown in Table 2, most studies in this field appeared in the context of “social commerce”, and some studies also used the term ”s-commerce”. “Social e-commerce” and “social electronic commerce” are rarely used.

After the selection process, we sum up the publication year distribution of the 300 articles, which is presented in Figure 2. Although we have excluded studies that do not meet the criteria, we can still observe a clear increasing trend of social commerce academic publications. This increasing trend shows no sign of slowing down in 2021 since we do not include all the publications of 2021 (the search date is 25 October 2021). In particular, in 2019 and 2020, the number of social commerce publications is much higher than in previous years. Thus, we believe that social commerce research is still in an early stage and its volume will continue to experience a high level of growth.

Figure 2 also shows the type distribution of the social commerce publications. Almost 80% of them are journal articles and nearly 20% are conference papers. There are also two reports and one book section article in our database. Thus, journal articles and conference papers make up the major part of our research material. While most of the conference papers appeared early, journal articles in social commerce generally appeared after the year of 2016. This shows that social commerce, as an emerging research subject, has been more and more accepted by some major academic journals.

Table 3 shows the top 20 journals that publish most studies in social commerce. Most of them are top information management journals, such as the International Journal of Information Management, Electronic Commerce Research and Applications. Some of them are marketing and consumer behavior journals, such as Decision Support Systems and the Journal of Retailing and Consumer Services. It is evident that the social commerce subject was first recognized as a subset of e-commerce and a new tool for marketing.

4. Results

In this section, we address the three research questions and answer them based on the results of Section 3.

4.1. What Is Social Commerce?

Social commerce originated from e-commerce [9]. Online shopping, known as e-commerce today, emerged at the beginning of the 20th century. E-commerce needs to provide not only online business infrastructure, but also tools similar to the traditional offline commercial activities, such as word-of-mouth (WOM) advertising, bargaining, emotional implication, social shopping, etc. [10,11,12,13]. The reason for this is that the offline commercial activities are essentially emotional activities in which buyers are critical because they convey ideas, impressions or feelings about products [14]. Through these ideas or opinions, customers become the link between sellers and other potential buyers and have shown a great influence on this online business ecosystem in recent years. Thus, the customer’s opinion is a central issue in online marketing and serves as an important signal of purchase decision making with previous transaction phenomena, such as repeat purchase and brand loyalty [15,16]. Reports show that people are more willing to make a purchase decision based on other customers’ recommendations, especially when these recommendations come from the people who they are familiar with [17]. Thus, the concept of so-called social commerce becomes more and more popular.

The concept of social commerce mainly has two sources. First, social commerce is based on e-commerce. Without e-commerce, social commerce can only be a concept, which cannot be applied in commercial activities [18]. It is believed that e-commerce provides the fundamental ICT foundation of social commerce [19,20,21]. Secondly, social shopping is the other critical source of social commerce. Social shopping is the prototype of social commerce before social networking based on the Internet became available [11,22,23]. Some studies also conclude that the two sources of social commerce are online shopping and social networking, which is in line with our understanding [24].

In 2005, the first bloggers started to notice the upcoming changes in e-commerce and created the term “social commerce”. Rubel first defined social commerce as follows: “Social commerce can take several forms, but in sum it means creating places where people can collaborate online, get advice from trusted individuals, find goods and services and then purchase them. It shortens the research and purchasing cycle by creating a single destination driven by the power of many” [25]. The first formal definition of social commerce was given by [26]. They believe that social commerce is based on an e-commerce platform that can enable customers to collaborate with each other. Several early studies are found, as shown in Table 4. These definitions mainly focus on three aspects:

E-commerce—emphasizing that social commerce comes from e-commerce and considering social commerce as a new type (application) of e-commerce;
Social media—considering that social media is the basis of social commerce and social media plays an important role in social commerce adoption;
Web 2.0—arguing that it is the Web 2.0 technology that makes social commerce a reality and Web 2.0 is the technology foundation of social commerce [27].

Table 4. Some early definitions related to social commerce.

Definition	Factors			Source
Definition	EC	SM	W2	Source
Social commerce focuses on interpersonal relations (recommendations, feedback, information, etc.) that are influencing a business transaction before, while or after it happens.		*		[26]
A new application in online marketplaces, where business organizations leverage social media or Web 2.0 as a direct marketing tool to support customers’ decision-making processes and buying behavior.		*	*	[28]
Social commerce integrates the customer directly into these processes by using new technologies, applications or functionalities and the existing willingness of the customers to participate.		*	*	[29]
A form of social media, encouraging consumers to actively engage in the marketing and selling of products in online marketplaces and communities.	*	*		[30]
Social commerce is the use of social media, in the context of e-commerce, to assist with buying and selling products and services online.	*	*		[18]
Social commerce can be briefly described as commerce activities mediated by social media. In social commerce, people engage in commerce or intentionally explore commerce opportunities by participating and/or engaging in a collaborative online environment.		*		[31]
A new concept that enables customers to have an active position in cyber space. It is a development in e-commerce based on a network of buyers and sellers. It is more commonly found in social and interactive forms of e-commerce.	*		*	[32]

EC: E-commerce; SM: Social media; W2: Web 2.0.

Since its introduction, the definition of social commerce has constantly evolved. As shown in Table 4 and Table 5, early studies consider social commerce as a subset of e-commerce and believe that social commerce is a new kind of collaborative buying or social shopping [23]. However, as the importance of social media is gradually being recognized, some studies point out that social commerce is more than collaborative buying and social shopping. Social commerce actually is a new form of incorporating “social layers” into e-commerce or linking retail sellers to social media sites [20,31,33]. In general, with the deepening of social commerce study, researchers have incorporated e-commerce, social media and Web 2.0 technology into their studies and focused on specific components of social commerce. Essentially, the following components have been repeatedly examined:

Follow. These studies focus on how eWOMs make potential customers become brand followers/fans or users [34,35].
Trust. These studies try to examine how trust is generated between sellers and buyers or among them in a social commerce context [36].
Share. These articles focus on how eWOMs transfer and spread on a social commerce platform or user communities [37].

Transaction. These studies seek to explain how UGCs on a social commerce platform lead to generating customers’ intention to buy or how customers’ purchase decisions are made in a social commerce context [38].

Table 5. Some representative definitions of social commerce.

Category	Components	Definition	Title	Sources
e-commerce	Share; Exchange	A kind of e-commerce in which users can share and exchange the shopping experience and can make an intelligent business decision.	Social Commerce: A New Electronic Commerce	[21]
social media	Trust	A new online business model incorporating social network sites.	Determinants Influencing Consumers’ Trust and Trust Performance of Social Commerce and Moderating Effect of Experience	[39]
e-commerce	Share	A new form of e-commerce that integrates online shopping and social networking.	Reputation Management in Social Commerce Communities	[40]
e-commerce	Trust; Share	Social commerce encapsulates both seller and buyer networks, as well as the platforms where shopping activities and the related interactions take place.	Website Features that Gave Rise to Social Commerce	[31]
social media/Web 2.0	Transaction; Share	An Internet-based commercial application, leveraging social media and Web 2.0 technologies, which supports social interaction and user-generated content in order to assist consumers in their decision making and acquisition of products and services within online marketplaces and communities.	From E-commerce to Social Commerce: A Close Look at Design Features	[34]
social media/ e-commerce	Follow; Like; Transaction	A subset of electronic commerce that involves using social media to support social interaction and user contributions, to assist in the online buying and selling of products and services.	Social Commerce Emerges as Big Brands Position Themselves to Turn “Follows”, “Likes” and “Pins” into Sales	[35]
social media/ e-commerce	Share	Integrated e-commerce and social media can re-sort the user’s social relationships, and effectively motivate the product spread and form a virtuous circle.	Evolution of Knowledge Sharing Behavior in Social Commerce	[41]
social media/ e-commerce	Share	A new stream in e-commerce where social factors are the determinant of this phenomenon and consumers are empowered to generate content using social media through online communities, forums, ratings, reviews and recommendations.	Social Commerce: The Transfer of Power from Sellers to Buyers	[42]
social media/ e-commerce	Transaction; Share	A new generation of e-commerce that treats social media and social networks as a carrier, promotes online trading and information exchange related to commercial activities.	The Influence of Sharing Evaluation Information on Consumer Buying Behavior in Social Commerce	[38]
social media	Share; Communication	Originated from the idea of knowledge sharing about goods and/or services among customers.	Why Customers Participate in Social Commerce Activities?	[43]
social media/ e-commerce	Transaction; Share; Follow	Refers to the delivery of e-commerce activities and transactions via the social media environment.	Social Presence, Trust and Social Commerce Purchase Intention: An Empirical Research	[44]
social media	Trust	Uses social media to facilitate social interaction and members’ contributions, whose users can share their shopping experiences with other members and seek their opinions and recommendations.	Understanding Social Commerce Acceptance: The Role of Trust, Perceived Risk and Benefit	[36]
social media/ e-commerce	Transaction; Share	A new phenomenon of e-commerce that utilizes social media platforms and applications to conduct e-commerce activities.	The Antecedents of Trust in Social Commerce	[45]
social media/ e-commerce	Share; Trust	An emerging trend where the seller and buyer are connected to the online social media network.	The Influence of WOM on Customer Loyalty to Social Commerce Websites	[46]
social media/ e-commerce	Follow; Like	A subset of electronic commerce that involves social media as a base platform to assist online buying of selling products and services.	Follower’s Quality Factor in Social Commerce	[47]
social media/ e-commerce	Share; Trust	A type of e-commerce platform that enables users to participate in the selling, buying, comparing and sharing of information about products and services in an online marketplace.	An Investigation of the Drivers of Social Commerce and e-WOM Intentions: Elucidating the Role of Social Commerce in E-business	[37]
social media/ e-commerce/ Web 2.0	Follow; Like; Share	An Internet-based commercial application that makes use of Web 2.0 technologies and social media and supports user-generated content and social interactions.	A systematic review on social commerce	[48]

Other components, such as Exchange, Communication and Like, are also examined in previous studies. However, these components can be integrated into the components listed above.

Although there is no standard definition of social commerce, the opinions among researchers have actually tended to be consistent: in other words, social commerce is a subset of e-commerce activities that incorporates or is realized by social media and social networks. In this study, we follow this definition temporarily and examine other questions listed at the beginning.

4.2. What Are the Key Components of Social Commerce?

As shown in Section 4.1, recent research in this field focuses on particular aspects or components of social commerce, such as trust, purchase decisions, etc. We count the keywords of social commerce in the database and display them in Figure 3. Previous studies can be classified into four main categories or components.

4.2.1. Following

Following is one of the key components of social commerce and can be considered as the starting point of the social commerce eco-system [49]. This component is also examined as Like, which indicates that a person has become a potential customer and is beginning to pay attention to the products and services that a company provides [34]. As shown in Figure 4, previous studies indicate that social commerce users start following a particular product or service after being exposed to WOM or UGCs on social media; then, they tend to either communicate with others (become UGC creator) or go directly to purchase the products or services (become customers).

Several studies focus on this particular component [35,47,49,50]. In general, Following is an intermediate component between Sharing and Communication (Transaction). It is Following (Like) that causes a WOM become a purchase intention [51]. Early studies consider Facebook and Twitter as social media or SNS, which draw people’s attention by UGCs (including WOM) and transform this attention into purchase intention by leading them to an e-commerce platform such as eBay or Amazon [47]. However, some rising content media such as TikTok have not been fully examined in a social commerce context, while businesses based on these content platforms have already become an important tool for companies to gain customers in the real business world [52]. The mechanism of Following can be summarized as follows:

Sharing—Following: Studies analyze how UGCs (including WOM) affect potential customers’ behavior, especially how to attract potential customers’ attention to particular products and services. Hairudin et al. (2019) analyze how a follower’s quality affects followers’ behavior in a social commerce context and identify five key factors, including social sharing, that affect customers’ behavior [52].
Following—Communication: Studies in this field focus on how followers communicate with each other and generate content that can be used by companies to promote products and services [34]. In particular, Hofer and Aubert (2013) used data collected on Twitter to analyze how bridging and bonding social capital affect communication among followers and these social capitals can be used by companies to generate UGCs in order to build brand loyalty [53].
Following—Transaction: Studies tend to examine how purchasing intention is generated among followers/fans [50,54]. Jung (2014) conducted empirical research on how social commerce website design affects followers’ purchase intention and results show that the information characteristic generally has a more significant impact on purchase intention than the visual property [49]. However, few studies can integrate other components such as Trust to exclude influences caused by factors outside the model.

Figure 4. The mechanism of Following in social commerce [34,50,52].

4.2.2. Communication

This study combines Trust and Communication, which frequently appear in previous research as one component. The reason for this is that it is widely believed that trust comes from regular communications among users in a social commerce context [46] and communication is also the prerequisite of creating trust either among users or between buyers and sellers [43].

There are plenty of studies that focus on Communication and Trust. In general, the following questions are frequently examined in previous research. The first is how trust is generated or how communication among users can be promoted [39,54,55,56,57]. This question is considered to be important because communication and trust are critical factors that have a great influence on purchase decisions [58]. The second is to what extent this trust affects users’ purchase intention [59,60]. The last is how WOMs and UGCs affect the formation of trust [61,62]. Based on these research topics of previous studies, we can construct the mechanism of Communication (Trust) as shown in Figure 5.

A few studies have compared the role of social e-commerce and traditional e-commerce in promoting sales. Taking Facebook as an example, Wongkitrungrueng and Assarut (2020) discuss the role of social commerce in promoting sales through streaming video. Their conclusion points out that, unlike traditional e-commerce, social commerce has important advantages in building trust and improving user participation.

The main importance of the mechanism of communication can be summarized as follows.

Following—Communication: As mentioned in Section 4.2.1, studies in this field focus on how communication and trust are built among followers. Moreover, several studies examine the factors that affect the formation of trust among followers. Alhulail et al. (2018b) conducted empirical research and point out that reputation, satisfaction, WOM and social presence have a positive effect on trust [63]. Yahia et al. (2018) also examined a similar topic and concluded that the social habits, reputation and price advantage of users have positive impacts on trust formation, while product differentiation generally weakens the formation of trust and communication [64].
Sharing—Communication: Studies focusing on this topic seek to explain how UGCs such as customers’ reviews influence users’ behavior (Trust/Communication). Patrick et al. (2017a) examine the relationship between content shared among users (as well as that between social commerce vendors and users) and find that the perceived security and general credibility of the content have a more significant positive impact on users’ trust than susceptibility to reviews and persuasiveness [61]. Similar studies are conducted by [65,66].
Communication—Transaction: These studies seek to examine how communication and trust are transformed into a purchase decision. WOM [67], informational support and community commitment [68,69] are the main factors that transform trust and communication into a purchase decision. Moreover, Makmor et al. (2018) further confirmed that trust acts as an intermediate variable that connects social supports (emotional and informational) and purchase intention (transaction) [70].

4.2.3. Transaction

Transaction or Purchase is the core component of the social commerce system. Studies in this field generally focus on the following aspects. The first is how a transaction is realized in the social commerce context [71,72]. This topic is also examined in terms of how a customer’s purchase decision is made under the effects of social media and WOM [73,74]. The second is which factors affect customers’ purchase intention [75,76,77]. This includes customers’ online behavior [78], social presence [60,63] and self-identification [79]. Lastly, some studies also focus on how customers behave after their purchase [80,81]. This kind of research generally examines customers’ sharing behavior, which can lead to more attention among users [82]. According to these previous studies, we can construct the mechanism of Transaction (Purchase) shown in Figure 6.

As shown in Figure 6, the main mechanisms of the Transaction component can be summarized as follows.

Communication—Transaction: As mentioned in Section 4.2.2, research on this topic examines how trust and communication among users are turned into a transaction. Moreover, from the perspective of transaction, several studies conclude that peer influence [83], brand relationship [84], perceived ease of use [85,86,87] and IT affordance [88] are also important factors that promote transaction.
Following—Transaction: As examined in Section 4.2.1, studies in this field seek to explain how a purchase is generated among users. Moreover, some studies examine this question from the perspective of the culture dimension. For example, Yin et al. (2014) pointed out that intimacy among followers contributes to trust-building and both of their positive impacts on purchase intention show distinct effects in different cultures [89].
Transaction—Sharing: Studies focusing on this question attempt to examine how customers’ sharing behavior is determined [90]. For example, Ko (2018) points out that commercial desire is more influential for social sharing intentions on SNS [91]. Generally, brand co-creation [92], technical support [93] and social relations (such as guanxi) [94] are the key factors that promote customers’ sharing behavior after a purchase in a social commerce environment.

4.2.4. Sharing

Sharing is the last key component found in social commerce research. In these studies, we integrate the concepts of share, recommend, spread, review and rate as the Sharing component because research in this field generally focuses on customers’ behavior after a purchase, which can still be utilized in a social commerce system.

Studies that focus on Sharing mainly try to explain the following four questions. The first is how customers’ rate/review/recommend (known as the “3Rs”) behavior is determined after a transaction [92,93,94,95]. The second is how the WOM and UGCs created by customers’ sharing behavior affect other users’ purchase intention [96,97]. The third is how social commerce users decide to create or share content based on Trust or Communication with other users [90,98]. The last is how potential users are turned into followers or fans by UGCs or WOM generated by sharing behavior [47,89]. According to the analysis above, we can construct the mechanism of Sharing (Review) shown in Figure 7.

Some studies use qualitative and quantitative methods to examine the impact of KOLs’ sharing behavior on sales volume in the social commerce context [99,100]. The results show that KOLs, as social media influencers, communicators and innovators, can promote innovative behavior and further promote an increase in sales volume. However, some research also showed that the moderation effects of celebrities’ authenticity are insignificant [101].

The main mechanisms of the Sharing component can be summarized as follows.

Transaction—Sharing: As mentioned in Section 4.2.3, studies in this field focus on the influential factors that determine users’ sharing behavior after a transaction.
Communication—Sharing: Studies in this topic try to explain how UGCs and WOM generated from Communication and Trust affect users’ post-purchase behavior. It is believed that perceived trustworthiness [85], social capital bonding [96,102] and individual capital (such as reputation and the enjoyment of helping others) [103] are the key factors that promote users’ sharing behavior.
Sharing—Following: As mentioned in Section 4.2.1, studies in this field analyze how to attract potential customers’ attention to particular products and services by UGCs (including WOM). Moreover, from the perspective of Sharing, several studies focus on explaining the relationship commitment, which has a positive impact on users’ following behavior, such as customer loyalty [97,104,105,106,107,108,109,110,111,112,113,114,115].
Sharing—Transaction: Studies focusing on this question seek to examine how sharing behavior promotes other users’ purchase intention. For example, Chen et al. (2019) conducted empirical research to explain how product recommendations on social media affect users’ urge to buy impulsively [106]. Results indicate that purchase intention influenced by recommendations is determined by affective trust in the recommender and affection toward the recommended product. This conclusion is also supported by other previous research, such as [107]. However, the results presented by [97] showed that online consumer reviews do not have a direct influence on users’ intention to buy. Thus, the question of whether there is a direct relationship between Sharing and Transaction still needs further investigation.

4.3. The Conceptual Framework of Social Commerce Systems

Thus far, we can answer the third question proposed in this research. Based on the discussion in Section 4.2, a concrete conceptual framework of social commerce can be built. Figure 8 integrates Figure 4, Figure 5, Figure 6 and Figure 7 as a typical social commerce business cycle and the sub-graphs I–IV represent Figure 4, Figure 5, Figure 6 and Figure 7, respectively.

A typical social commerce business cycle (closed loop) consists of four components: Following, Communication, Transaction and Sharing. Social commerce completes the connection between buyers and sellers through social media and can be summarized as a closed loop as follows: social commerce users, including potential buyers and sellers, generate an online community based on the same value identification driven by social media; continuous and in-depth interactions in SNS build long-lasting relationships among users; trust attached to these social relationship can be turned into purchase intention according to previous studies; social commerce transactions are realized by a mutually accepted payment system, which is built by the e-commerce platform; buyers share the “3Rs” on social media and sellers improve products and services based on buyer’s feedback. Based on the experience after the transaction, WOM is formed and continues to attract new potential followers (network stream).

We can draw some internal mechanisms of social commerce from Figure 8.

First, as shown in sub-graph I–II, new followers generate an online community in a social network, which can be considered as the starting point of social commerce. Interactions among users, including potential buyers and sellers, promote the formation of customer loyalty to particular brands. Moreover, this process also can be induced by company players and its feedback is an important source of product and service improvement.

Secondly, as shown in sub-graph II–III, communication on a social network generates trust among users and trust can strengthen the herd mentality before a purchasing decision is made. As shown in previous research, an online community formed by social media can amplify the conformity among users, which has already been used by many brands as an effective marketing tool.

Thirdly, as shown in sub-graph III–IV, transactions led by trust will be realized by a payment system built by the e-commerce platform and the “3Rs” will be more actively produced by social commerce users compared to traditional e-commerce transactions. This is mainly because, in the social commerce context, the transaction is a kind of social activity rather than a purely business activity. Once a transaction or a brand becomes the subject of an online community, the “3Rs” will continuously be created until the end of the brand life cycle.

Lastly, as shown in sub-graph IV–I, the e-WOM generated by the “3Rs” will attract more potential followers and continue this social commerce business cycle. The potential new online followers are also called a network stream or traffic in some previous studies [110,111]. Streaming is a concept in physics that indicates the amount of fluid flowing through a section of a closed pipe or open channel per unit time. Internet economics and e-commerce theory use this concept to refer to the online views or clicks of a specific network channel per unit time. In this study, we use this concept to refer to the number of page views (PVs) and unique visitors (UVs) on a social commerce website.

Sub-graph I–IV constructs the complete social commerce system. In this model, Following, Communication, Transaction and Sharing are the key components of social commerce, which are also the main steps that social commerce users perform. Social media, the online community and the e-commerce platform are the main supporting components, which are the infrastructure of the social commerce system. Moreover, followers, fans, customers, KOLs, cyberstars and companies are the main players on the social commerce platform.

We can thus obtain the answer regarding the difference between social commerce and e-commerce. The main difference between social commerce and e-commerce is shown in Figure 9. In the traditional e-commerce context, an e-commerce platform has decisive power in allocating a network stream through the search engines that they develop. This centralized mode of network stream allocation has caused e-commerce platforms to become huge, such as Alibaba and Amazon, which benefit from these “economies of scale” [108,109]. Although e-commerce platforms and social media still play a critical role in the social commerce context, they have no decisive power to allocate a network stream, i.e., social commerce users (buyers and sellers) connect with each other on their own. This fundamental difference is an important sign of social commerce as a new kind of online business.

5. Conclusions

As shown in the Results section, in the social commerce context, systems thinking considers social commerce as a system and provides an integral perspective to depict complex components. From the systems thinking point of view, we categorize all components of social commerce systems identified in previous publications to further investigate the relationships among different components of social commerce systems and propose a conceptual framework of social commerce systems. Some interesting conclusions can be drawn from this study. First, social commerce is an important evolution from e-commerce. Many previous studies consider social commerce as a subset of traditional e-commerce [21,110,112,116]. However, in this study, we believe that social commerce is a new form of online business that is essentially different from the traditional e-commerce that we are familiar with. Future research can be conducted from the perspective of social commerce as a new form of online business rather than a subset of traditional e- commerce.

Secondly, social commerce is changing the foundation of online marketing. As has already been widely accepted, e-commerce represents a paradigm shift as a “disruptive” innovation that is radically changing the traditional ways of doing business [113]. Social commerce may be a “disruptive” innovation that is changing the traditional e-commerce methods of doing business, rather than an alternative means of online marketing. Social commerce is an emerging trend in which sellers are connected by online social networks [114], and this has changed the core of marketing from brand recognition to community recognition and from brand management to relationship management [84,114]. This shift in social commerce has turned traditional online marketing into e-marketing or digital marketing, which is completely data-driven. Further marketing studies should not ignore this fundamental change brought about by social commerce.

Lastly, social commerce will promote the implementation of a C2B model. The most important aspect for companies engaging in business activities in the social commerce context is relationship operation. The core of relationship operation is to achieve continuous and in-depth interaction with customers in order to establish emotional relationships and value recognition. In this process, users’ in-depth involvement in product development, design and improvement is the prototype of a demand-driven production and operation model (C2B). In this model, companies need to be “closer” to their users, and start the initial integration of crowd-funding, crowd-creating and crowd-sourcing. The distinction between producers, communicators and consumers in the traditional economy will become blurred in social commerce, in which online community participants are “active producers”, “faithful consumers” and “enthusiastic communicators” at the same time. It can be seen that in the social commerce context, consumers and their communities are the promoters of business activities, and the C2B model is easier to be implemented under the influence of SNS and social media. Future research should start from the specific mechanisms of how social commerce promotes the C2B model.

6. Discussions

This study proposes a social commerce business model framework by conducting a systematic literature review. The main contributions of this paper are as follows.

Compared to research focusing on social commerce constructs, this study proposes a more detailed SCC model. Previous studies generally consider social commerce as composed of a social component and commercial component [117,118,119] and try to establish a social commerce model by examining how a particular factor, such as trust, is affected by social support [120]. Based on previous studies, this paper integrates current key factors that are involved in social commerce and builds a more comprehensive model that includes Following, Communicating, Purchasing and Sharing. This model can deepen the current understanding of social commerce systems.

Compared to previous literature review studies of social commerce, this paper focuses on explaining why social commerce is an important transformation from e-commerce. Previous studies focus on social commerce adoption [121,122], social commerce characteristics/topics [2,4,48,80] and consumers’ behavior [123,124,125]. Based on these studies, this paper further proposes a network traffic distribution mechanism as the key difference between e-commerce and social commerce, and this makes social commerce an important evolution of e-commerce.

Compared to other social commerce frameworks or conceptual model research, this study develops a theoretical framework that includes the entire social commerce business cycle. Previous studies are relatively scattered and mainly focus on one or two particular components of social commerce, such as trust and social commerce adoption [110,126,127], the influencing factors of online marketing [114], customer satisfaction [128], social commerce website design [129,130,131] and C2C social commerce [132,133,134]. Based on previous works, this study constructs a more integral social commerce theoretical framework that contains all social commerce activities examined before. This provides a more comprehensive basis for future social commerce research.

The main limitation of this study is that the transformation of social commerce has not fully been examined through real business cases. In reality, this transformation process may be unclear, as some traditional e-commerce platforms are also integrating social elements, such as Alibaba’s live-broadcast shopping. However, as mentioned in this paper, the network traffic distribution mechanism is fundamentally different between social commerce and e-commerce. Based on this limitation, more in-depth research in the future can be started by conducting more detailed social commerce case studies to provide more concrete real business evidence.

Author Contributions

Conceptualization, H.W. and C.Z.; methodology, X.W.; investigation, X.W.; data curation, H.W. and X.W.; writing—original draft preparation, X.W.; writing—review and editing, X.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by “The Fundamental Research Funds for the Provincial University of Zhejiang”(grant number SJWY2022007, offered by Ningbo University), “The key project of Beijing Higher Education undergraduate Teaching Reform and Innovation Project in 2021” and “The Construction and Practice of Human Resource Management Simulation Training System for the Construction of National First-class Undergraduate Major” (grant number 202112453003).

Institutional Review Board Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

Wang, C.; Zhang, P. The evolution of social commerce: The people, management, technology, and information dimensions. Commun. Inf. Syst. 2012, 31, 105–127. [Google Scholar] [CrossRef]
Han, H.; Xu, H.; Chen, H. Social commerce: A systematic review and data synthesis. Electron. Commer. Res. Appl. 2018, 30, 38–50. [Google Scholar] [CrossRef]
Lin, X.; Li, Y.; Wang, X. Social commerce research: Definition, research themes and the trends. Int. J. Inf. Manag. 2017, 37, 190–201. [Google Scholar] [CrossRef]
Busalim, A.H. Understanding social commerce: A systematic literature review and directions for further research. Int. J. Inf. Manag. 2016, 36, 1075–1088. [Google Scholar] [CrossRef]
Statista. Revenue from Enterprise Social Networks Worldwide from 2010 to 2021 (in Million U.S. Dollars). 2015. Available online: https://www.statista.com/statistics/503514/worldwide-enterprise-social-networks-revenue/ (accessed on 7 December 2021).
Rubenstein-Montano, B.; Liebowitz, J.; Buchwalter, J.; McCaw, D.; Newman, B.; Rebeck, K.; Team, T.K. A systems thinking framework for knowledge management. Decis. Support Syst. 2001, 31, 5–16. [Google Scholar] [CrossRef]
Kitchenham, B. Procedures for Performing Systematic Reviews; Keele University: Keele, UK, 2004; Volume 33, pp. 1–26. [Google Scholar]
Kitchenham, B. Guidelines for Performing Systematic Literature Reviews in Software Engineering; Technical Report No. 2.3; IEEE: London, UK, 2007; pp. 1–65. [Google Scholar]
Liang, T.P.; Ho, Y.T.; Li, Y.W.; Turban, E. What Drives Social Commerce: The Role of Social Support and Relationship Quality. Int. J. Electron. Commer. 2011, 16, 69–90. [Google Scholar] [CrossRef] [Green Version]
Zhu, L.; Benbasat, I.; Jiang, Z. Investigating the Role of Presence in Collaborative Online Shopping. In Proceedings of the 12th Systems Americans Conference on Information Systems (AMCIS), Acapulco, Mexico, 4–6 August 2006; p. 11. [Google Scholar]
Jascanu, N.; Jascanu, V.; Nicolau, F. A new approach to E-commerce multi-agent systems. Ann. Dunarea de Jos Univ. Galati Fascicle III Electrotech. Electron. Autom. Control Inform. 2007, 30, 8–11. [Google Scholar]
Leitner, P.; Grechenig, T. Community Driven Commerce: Design of an Integrated Framework for Social Shopping. In Proceedings of the IADIS International Conference e-Commerce, Algarve, Portugal, 5–6 March 2007; p. 4. [Google Scholar]
Kang, Y.R.; Park, C. Acceptance Factors of Social Shopping. In Proceedings of the 11th International Conference on Advanced Communication Technology, Phoenix Park, Korea, 8–10 May 2009; p. 5. [Google Scholar]
Militaru, D. Consumer Behavior in Electronic Commerce Environments and Fashion Effect; ICEC: Minneapolis, MN, USA, 2007. [Google Scholar]
Fornell, C. A national customer satisfaction barometer: The Swedish Experience. Int. J. Mark. 1992, 56, 6–21. [Google Scholar]
Trusov, M.; Bucklin, R.E.; Pauwels, K. Effects of word-of-mouth versus traditional marketing: Findings from an Internet social networking site. J. Mark. 2009, 73, 90–102. [Google Scholar] [CrossRef] [Green Version]
Marsden, P. Top Social Commerce Survey Findings. Socialcommercetoday.com. 2009. Available online: http://socialcommercetoday.com/top-socialcommerce-survey-findings-ripple6/ (accessed on 7 December 2021).
Linda, S.L. Social commerce–e-commerce in social media context. World Acad. Sci. Eng. Technol. 2010, 72, 39–44. [Google Scholar]
Wang, C. Linking shopping and social networking: Approaches to social shopping. In Proceedings of the 15th Americans Conference on Information Systems (AMCIS), San Diego, CA, USA, 6–9 August 2009. [Google Scholar]
Turban, E.; Liang, T.P.; Wu, S. A framework for adopting collaboration 2.0 tools for virtual group decision making. Group Decis. Negot. 2011, 20, 137–154. [Google Scholar] [CrossRef]
Zhong, Y. Social Commerce: A New Electronic Commerce. In Proceedings of the Eleventh Wuhan International Conference on e-Business, Wuhan, China, 21 September 2012; p. 49. Available online: http://aisel.aisnet.org/whiceb2011/49 (accessed on 7 December 2021).
Tedeschi, B. Like Shopping? Social Networking? Try Social Shopping. The New York Times. 11 September 2006. Available online: http://www.nytimes.com/2006/09/11/technology/11ecom.html (accessed on 17 November 2009).
Hsiao, K.L.; Chuan-Chuan Lin, J.; Wang, X.Y.; Lu, H.P.; Yu, H. Antecedents and consequences of trust in online product recommendations: An empirical study in social shopping. Online Inf. Rev. 2010, 34, 935–953. [Google Scholar] [CrossRef]
Stephen, A.T.; Toubia, O. Deriving value from social commerce networks. J. Mark. Res. 2010, 47, 215–228. [Google Scholar] [CrossRef] [Green Version]
Rubel, S. “2006 Trends to Watch Part II: Social Commerce”, Micro Persuasion. 2005. Available online: https://www.micropersuasion.com/2005/12/2006_trends_to_html (accessed on 7 December 2021).
Richter, A.; Koch, M.; Krisch, J. Social Commerce–Eine Analyse des Wandels im ECommerce, Technischer Bericht Nr. 2007-03; Fakultät für Informatik, Universität der Bundeswehr München: Munich, Germany, 2007. [Google Scholar]
Huang, Z.; Benyoucef, M. From e-commerce to social commerce: A close look at design features. Electron. Commer. Res. Appl. 2013, 12, 246–259. [Google Scholar] [CrossRef]
Constantinides, E.; Fountain, S.J. Web 2.0: Conceptual foundations and marketing issues. J. Direct Data Digit. Mark. Pract. 2008, 10, 231–244. [Google Scholar] [CrossRef]
Ickler, H.; Schülke, S.; Wilfling, S.; Baumöl, U. New Challenges in E-Commerce: How Social Commerce Influences the Customer Process. In Proceedings of the 5th National Conference on Computing and Information Technology, Hagen, Germany, 3 May 2019; pp. 51–57. [Google Scholar]
Stephen, A.T.; Toubia, O. Explaining the power-law degree distribution in a social commerce network. Soc. Netw. 2009, 31, 262–270. [Google Scholar] [CrossRef]
Curty, R.G.; Zhang, P. Website features that gave rise to social commerce: A historical analysis. Electron. Commer. Res. Appl. 2013, 12, 260–279. [Google Scholar] [CrossRef] [Green Version]
Hajli, M. Social Commerce Adoption Model. In Proceedings of the UK Academy of Information Systems Conference, Oxford, UK, 4 October 2012. [Google Scholar]
Curty, R.G.; Zhang, P. Social Commerce: Looking Back and Forward. In Proceedings of the American Society for Information Science and Technology, New Orleans, LA, USA, 9–13 October 2011. [Google Scholar]
Barnes, N.G. Social commerce emerges as big brands position themselves to turn” follows”,” likes” and” pins” into sales. Am. J. Manag. 2014, 14, 11. [Google Scholar]
Li, Y.M.; Chou, C.L.; Lin, L.F. A social recommender mechanism for location-based group commerce. Inf. Sci. 2014, 274, 125–142. [Google Scholar] [CrossRef]
Farivar, S.; Yuan, Y.; Turel, O. Understanding social commerce acceptance: The role of trust, perceived risk, and benefit. In Proceedings of the Twenty-second Americas Conference on Information Systems, San Diego, CA, USA, 11–14 August 2016; pp. 1–10. [Google Scholar]
Goraya MA, S.; Jing, Z.; Shareef, M.A.; Imran, M.; Malik, A.; Akram, M.S. An investigation of the drivers of social commerce and e-word-of-mouth intentions: Elucidating the role of social commerce in E-business. Electron. Mark. 2019, 31, 181–195. [Google Scholar] [CrossRef]
Shen, B.; Tai, L.; Liu, D. The influence of sharing evaluation information on consumer buying behavior in social commerce. Int. J. U-E-Serv. Sci. Technol. 2015, 8, 117–134. [Google Scholar]
Kim, S.; Noh, M.J. Determinants influencing consumers’ trust and trust performance of social commerce and moderating effect of experience. Inf. Technol. J. 2012, 11, 1369–1380. [Google Scholar] [CrossRef] [Green Version]
Yang, X.; Liu, L.; Davison, R. Reputation Management in Social Commerce Communities. In Proceedings of the AMCIS 2012 Proceedings, New Orleans, LA, USA, 3 April 2012; p. 23. Available online: http://aisel.aisnet.org/amcis2012/proceedings/AdoptionDiffusionIT/23 (accessed on 7 December 2021).
Jiang, G.; Ma, F.; Shang, J.; Chau, P.Y. Evolution of knowledge sharing behavior in social commerce: An agent-based computational approach. Inf. Sci. 2014, 278, 250–266. [Google Scholar] [CrossRef]
Hajli, N.; Sims, J. Social commerce: The transfer of power from sellers to buyers. Technol. Forecast. Soc. Change 2015, 94, 350–358. [Google Scholar] [CrossRef] [Green Version]
Hassan, S.; Toland, J.; Tate, M. Why customers participate in social commerce activities?—A laddering analysis. arXiv 2016, arXiv:1606.02502. [Google Scholar]
Lu, B.; Fan, W.; Zhou, M. Social presence, trust, and social commerce purchase intention: An empirical research. Comput. Hum. Behav. 2016, 56, 225–237. [Google Scholar] [CrossRef] [Green Version]
Hammouri, Q.; Abu-Shanab, E. The Antecedents of Trust in Social Commerce (Review). In Proceedings of the 8th International Conference on Information Technology, Stockholm, Sweden, 8 February 2017. [Google Scholar]
Alhulail, H.; Dick, M.; Abareshi, A. The Influence of Word-of-Mouth on Customer Loyalty to Social Commerce Websites. In Proceedings of the International Conference on Information Resources Management (CONF-IRM); Springer: Cham, Switzerland, 2018. [Google Scholar]
Hairudin, H.; Dahlan, H.M.; Selamat, M.H. Follower’s Quality Factor in Social Commerce. In Journal of Physics: Conference Series; IOP Publishing: Pittsburgh, PA, USA, 2019; Volume 1196, p. 012002. [Google Scholar]
Esmaeili, L.; Hashemi, G.S.A. A systematic review on social commerce. J. Strateg. Mark. 2019, 27, 317–355. [Google Scholar] [CrossRef]
Jung, L.S. A study of affecting the purchasing intention of social commerce. Int. J. Softw. Eng. Its Appl. 2014, 8, 73–84. [Google Scholar]
Anderson, M.; Sims, J.; Price, J.; Brusa, J. Turning “Like” to “Buy” Social Media Emerges as a Commerce Channel; Booz & Company Inc.: Washington, DC, USA, 2011; Volume 2, pp. 102–128. [Google Scholar]
Xiang, L.; Zheng, X.; Lee, M.K.; Zhao, D. Exploring consumers’ impulse buying behavior on social commerce platform: The role of parasocial interaction. Int. J. Inf. Manag. 2016, 36, 333–347. [Google Scholar] [CrossRef]
Hannah Murphy. TikTok Takes on Facebook in US Advertising Push, Financial Times, 2019-12-10. 2019. Available online: https://www.ftchinese.com/story/001085467/en?ccode=LanguageSwitch&exclusive (accessed on 7 December 2021).
Hofer, M.; Aubert, V. Perceived bridging and bonding social capital on Twitter: Differentiating between followers and followees. Comput. Hum. Behav. 2013, 29, 2134–2142. [Google Scholar] [CrossRef]
Lee, K.; Lee, B.; Oh, W. Thumbs up, Sales up? The contingent effect of Facebook likes on sales performance in social commerce. J. Manag. Inf. Syst. 2015, 32, 109–143. [Google Scholar] [CrossRef]
Lee, J.Y. Trust and social commerce. Univ. Pittsburgh Law Rev. 2015, 77, 137–181. [Google Scholar] [CrossRef] [Green Version]
Wongkitrungrueng, A.; Assarut, N. The role of live streaming in building consumer trust and engagement with social commerce sellers. J. Bus. Res. 2018, 11, 223–231. [Google Scholar] [CrossRef]
Lee, C.C.; Cho, Y.S.; Bae, B.B. Factos Affecting Trust in Social Commerce: A Structural Equation Model. Issues Inf. Syst. 2017, 18, 70–90. [Google Scholar]
Hajli, N.; Sims, J.; Zadeh, A.H.; Richard, M.O. A social commerce investigation of the role of trust in a social networking site on purchase intentions. J. Bus. Res. 2017, 71, 133–141. [Google Scholar] [CrossRef] [Green Version]
Zamrudi, Z.; Suyadi, I.; Abdillah, Y. The Effect of Social Commerce Construct and Brand Image on Consumer Trust and Purchase Intention. Profit J. Adm. Bisnis 2017, 10, 1–13. [Google Scholar] [CrossRef] [Green Version]
Hassan, M.; Iqbal, Z.; Khanum, B. The role of trust and social presence in social commerce purchase intention. Pak. J. Commer. Soc. Sci. 2018, 12, 111–135. [Google Scholar]
Mikalef, P.; Pappas, I.O.; Giannakos, M.N.; Sharma, K. Determining Consumer Engagement in Word-of-Mouth: Trust and Network Ties in a Social Commerce Setting. In Conference on e-Business, e-Services and e-Society; Springer: Cham, Switzerland, 2017; pp. 351–362. [Google Scholar]
Liu, X.L.; Xiang, G.P.; Zhang, L. Social support and social commerce purchase intention: The mediating role of social trust. Soc. Behav. Personal. 2021, 49, 7. [Google Scholar] [CrossRef]
Alhulail, H.; Dick, M.; Abareshi, A. Factors that Impact Customers’ Loyalty to Social Commerce Websites. In Proceedings of the International Conference on Information Resources Management, New York, NY, USA, 3–4 May 2018; Volume 6, pp. 1–13. [Google Scholar]
Yahia, I.B.; Al-Neama, N.; Kerbache, L. Investigating the drivers for social commerce in social media platforms: Importance of trust, social support and the platform perceived usage. J. Retail. Consum. Serv. 2018, 41, 11–19. [Google Scholar] [CrossRef]
Porntrakoon, P. Factors Influencing a Thai Individual’s Trust and Distrust in Social Commerce. Humanit. Arts Soc. Sci. Stud. 2018, 18, 757–808. [Google Scholar]
Rosa, R.P.; Qomariah, N.; Tyas, W.M. Impact of Social Commerce Characteristics on Consumer Trust on Online Shop in Instagram. Pros. CELSciTech 2018, 3, 80–88. [Google Scholar]
Mikalef, P.; Pappas, I.; Giannakos, M. Value co-creation and purchase intention in social commerce: The enabling role of word-of-mouth and trust. In Proceedings of the Twenty-third Americas Conference on Information Systems, Boston, MA, USA, 10–12 August 2017. [Google Scholar]
Ahmad, R.; Rashin, G. The Effect of Social Commerce on Customer’s Intention to Buy The Case Study: Online Shoppers. Palma J. 2017, 16, 155–165. [Google Scholar]
Lal, P. Analyzing determinants influencing an individual’s intention to use social commerce website. Future Bus. J. 2017, 3, 70–85. [Google Scholar] [CrossRef]
Makmor, N.; Alam, S.S.; Aziz, N.A. Social Support, Trust and Purchase Intention in Social Commerce Era. Int. J. Supply Chain Manag. 2018, 7, 572–581. [Google Scholar]
Chung, N.; Song, H.G.; Lee, H. Consumers’ impulsive buying behavior of restaurant products in social commerce. Int. J. Contemp. Hosp. Manag. 2017, 29, 709–731. [Google Scholar] [CrossRef]
Cho, E.; Son, J. The effect of social connectedness on consumer adoption of social commerce in apparel shopping. Fash. Text. 2019, 6, 14. [Google Scholar] [CrossRef]
Huang, Z.; Benyoucef, M. The effects of social commerce design on consumer purchase decision-making: An empirical study. Electron. Commer. Res. Appl. 2017, 25, 40–58. [Google Scholar] [CrossRef]
Hussain, S.; Li, Y.; Li, W.; Ali, M. Psychological Contracts, Antecedents and Consequences: A New Roadmap to Enhance Purchase Intentions in Social Commerce. In Proceedings of the Australasian Conference on Information Systems, Hobart, Australia, 15–16 March 2017. [Google Scholar]
Akram, U.; Hui, P.; Khan, M.; Yan, C.; Akram, Z. Factors affecting online impulse buying: Evidence from Chinese social commerce environment. Sustainability 2018, 10, 352. [Google Scholar] [CrossRef] [Green Version]
Athapaththu, J.C.; Kulathunga, D.; Mawatha, B. Factors Affecting Online Purchase Intention: Effects of Technology and Social Commerce. Int. Bus. Res. 2018, 11, 111–128. [Google Scholar] [CrossRef]
Al-Adwan, A.S. Revealing the influential factors driving social commerce adoption. Interdiscip. J. Inf. Knowl. Manag. 2019, 14, 295–324. [Google Scholar]
Farivar, S.; Yuan, Y. Understanding Consumers’ Impulsive Buying Behavior in Social Commerce Platforms. In Proceedings of the Twenty-third Americas Conference on Information Systems, Boston, MA, USA, 10–12 August 2017. [Google Scholar]
Farivar, S.; Turel, O.; Yuan, Y. Skewing users’ rational risk considerations in social commerce: An empirical examination of the role of social identification. Inf. Manag. 2018, 55, 1038–1048. [Google Scholar] [CrossRef]
Lin, J.; Yan, Y.; Chen, S. Understanding the impact of social commerce website technical features on repurchase intention: A Chinese guanxi perspective. J. Electron. Commer. Res. 2017, 18, 225. [Google Scholar]
Wu, Y.L.; Li, E.Y. Marketing mix, customer value, and customer loyalty in social commerce: A stimulus-organism-response perspective. Internet Res. 2018, 28, 74–104. [Google Scholar] [CrossRef] [Green Version]
Ghahtarani, A.; Sheikhmohammady, M.; Rostami, M. The Impact of Social Capital and Social Interaction on Customers’ Purchase Intention, Considering Knowledge Sharing in Social Commerce Context. J. Innov. Knowl. 2020, 5, 191–199. Available online: https://www.sciencedirect.com/science/article/pii/S2444569X19300447 (accessed on 7 December 2021). [CrossRef]
Hu, X.; Chen, X.; Davison, R.M. Social support, source credibility, social influence, and impulsive purchase behavior in social commerce. Int. J. Electron. Commer. 2019, 23, 297–327. [Google Scholar] [CrossRef]
Hsu, L.C. Building brand-fan relationships in social commerce contexts: Mediators of online brand relationships. J. Theor. Appl. Electron. Commer. Res. 2019, 14, 106–123. [Google Scholar] [CrossRef] [Green Version]
Qin, L.; Kong, S. Why do People Seek Shopping Recommendations in Social Commerce Sites? In Proceedings of the Twenty-First Americas Conference on Information Systems, San Juan, PR, USA, 13–15 August 2015. [Google Scholar]
Makmor, N.; Aziz, N.A.; Alam, S.S. Social Commerce an Extended Technology Acceptance Model: The Mediating Effect of Perceived Ease of Use and Perceived Usefulness. Malays. J. Consum. Fam. Econ. 2019, 22, 119–136. [Google Scholar]
Othman, A.K.; Hassan LF, A.; Hamzah, M.I.; Razali, A.R.; Saim MA, S.; Ramli, M.S.; Azhar, M.A. The Influence of Social Commerce Factors on Customer Intention to Purchase. Asian Themes Soc. Sci. Res. 2019, 3, 1–10. [Google Scholar] [CrossRef]
Sun, Y.; Shao, X.; Li, X.; Guo, Y.; Nie, K. How live streaming influences purchase intentions in social commerce: An IT affordance perspective. Electron. Commer. Res. Appl. 2019, 37, 100886. [Google Scholar] [CrossRef]
Yin, C.; Liu, L.; Yang, J.; Mirkovski, K.; Zhao, D. Information sharing behavior in social commerce sites: The differences between sellers and non-sellers. In Proceedings of the Thirty Fifth International Conference on Information Systems, Auckland, New Zealand, 14 October 2014. [Google Scholar]
Hsieh, Y.H.; Lo, Y.T.; Chang, L.H. The Influence of Customer’s Sharing Behavior in Social Commerce. In Proceedings of the Twenty-Third Americas Conference on Information Systems, Boston, MA, USA, 10–12 August 2017. [Google Scholar]
Ko, H.C. Social desire or commercial desire? The factors driving social sharing and shopping intentions on social commerce platforms. Electron. Commer. Res. Appl. 2018, 28, 1–15. [Google Scholar] [CrossRef]
Tajvidi, M.; Richard, M.O.; Wang, Y.; Hajli, N. Brand co-creation through social commerce information sharing: The role of social media. J. Bus. Res. 2018, 121, 476–485. [Google Scholar] [CrossRef] [Green Version]
Wang, H.; Fang, H.; Tang, Q. Exploring the Role of Technical Features in Promoting the Intention to Continue Sharing Contents in Social Commerce Platform. J. Serv. Sci. Manag. 2019, 12, 214. [Google Scholar] [CrossRef] [Green Version]
Yang, X. Consumers’ decisions in social commerce: The role of guanxi elements. Asia Pac. J. Mark. Logist. 2019, 31, 759–772. [Google Scholar] [CrossRef]
Qin, L.; Kong, S. Perceived helpfulness, perceived trustworthiness, and their impact upon social commerce users’ intention to seek shopping recommendations. J. Internet Commer. 2015, 14, 492–508. [Google Scholar] [CrossRef]
Horng, S.M.; Wu, C.L.; Liang, T.P. How Behaviors on Social Network sites and Online Social Capital Influence Social Commerce: The Case of Facebook. PACIS 2016, 6, 295. [Google Scholar]
Muslimah, N.; Mursid, M.C. The Effect of Online Consumer Review on the Intention of Buying Products on Social Commerce. Aptisi Trans. Manag. 2019, 3, 22–28. [Google Scholar] [CrossRef]
Zhang, Y.; Liu, L.; Ho, S.Y. How do interruptions affect user contributions on social commerce? Inf. Syst. J. 2019, 30, 535–565. [Google Scholar] [CrossRef]
Wu, Y.; Nambisan, S.; Xiao, J.H.; Xie, K. Consumer resource integration and service innovation in social commerce: The role of social media influencers. J. Acad. Mark. Sci. 2022, 50, 429–459. [Google Scholar] [CrossRef]
Meilatinova, N. Social commerce: Factors affecting customer repurchase and word-of-mouth intentions. Int. J. Inf. Manag. 2020, 57, 102300. [Google Scholar] [CrossRef]
Zafar, A.U.; Qiu, J.N.; Li, Y.; Wang, J.G.; Shahzad, M. The Impact of Social Media Celebrities’ Posts and Contextual Interactions on Impulse Buying in Social Commerce. Comput. Hum. Behav. 2021, 115, 106178. [Google Scholar] [CrossRef]
Horng, S.M.; Wu, C.L. How behaviors on social network sites and online social capital influence social commerce intentions. Inf. Manag. 2019, 57, 103176. [Google Scholar] [CrossRef]
Liu, L.; Cheung, C.M.; Lee, M.K. An empirical investigation of information sharing behavior on social commerce sites. Int. J. Inf. Manag. 2016, 36, 686–699. [Google Scholar] [CrossRef]
Hajli, M.N. The role of social support on relationship quality and social commerce. Technol. Forecast. Soc. Change 2014, 87, 17–27. [Google Scholar] [CrossRef]
Noori, A.S.; Hashim, K.F.; Yusof SA, M. The Conceptual Relation of Electronic Word-of-mouth, Commitment and Trust in Influencing Continuous Usage of Social Commerce. Int. Rev. Manag. Mark. 2016, 6, 226–230. [Google Scholar]
Chen, Y.; Lu, Y.; Wang, B.; Pan, Z. How do product recommendations affect impulse buying? An empirical study on WeChat social commerce. Inf. Manag. 2019, 56, 236–248. [Google Scholar] [CrossRef]
Wang, Y.; Yu, C. Social interaction-based consumer decision-making model in social commerce: The role of word of mouth and observational learning. Int. J. Inf. Manag. 2017, 37, 179–189. [Google Scholar] [CrossRef]
Pei, Y.; Zhang, M.; Zhang, Y.; Wang, S. Convert Traffic to Purchase: The Impact of Social Network Information on Trust and Purchase Intention in Social Commerce. In Proceedings of the 17th Wuhan International Conference on E-Business, Wuhan, China, 21 June 2018. [Google Scholar]
Xintian, W.; Xiangdong, W. Socialization, traffic disturibution and E-commerce trends: An interpretation of the “Pingduoduo” phenomenon. China Econ. 2019, 14, 56–72. [Google Scholar]
Hajli, M. A research framework for social commerce adoption. Inf. Manag. Comput. Secur. 2013, 21, 144–154. [Google Scholar] [CrossRef]
Sharma, S.; Crossler, R.E. Disclosing too much? Situational factors affecting information disclosure in social commerce environment. Electron. Commer. Res. Appl. 2014, 13, 305–319. [Google Scholar] [CrossRef]
Han, M.C.; Kim, Y. Can Social Networking Sites Be E-commerce Platforms? Pan-Pac. J. Bus. Res. 2016, 7, 24. [Google Scholar]
Lee, C.S. An analytical framework for evaluating e-commerce business models and strategies. Internet Res. 2001, 11, 349–359. [Google Scholar] [CrossRef] [Green Version]
Yadav, M.S.; De Valck, K.; Hennig-Thurau, T.; Hoffman, D.L.; Spann, M. Social commerce: A contingency framework for assessing marketing potential. J. Interact. Mark. 2013, 27, 311–323. [Google Scholar] [CrossRef]
Riaz, M.U.; Guang, L.X.; Zafar, M.; Shahzad, F.; Shahbaz, M.; Lateef, M. Consumers’ purchase intention and decision-making process through social networking sites: A social commerce construct. Behav. Inf. Technol. 2021, 40, 99–115. [Google Scholar] [CrossRef]
Yesmin, M.; Hossain, M.; Kim, M. A Review of Theoretical Models of Social Commerce. Asia-Pac. J. Bus. Commer. 2020, 12, 89–107. [Google Scholar] [CrossRef]
Ng, C.S.P. Intention to purchase on social commerce websites across cultures: A cross-regional study. Inf. Manag. 2013, 50, 609–620. [Google Scholar] [CrossRef]
Diao, Y.; He, Y.; Yuan, Y. Framework for understanding the business model of social commerce. Int. J. Manag. Sci. 2015, 26, 112–118. [Google Scholar]
Chen, A.; Lu, Y.; Wang, B. Customers’ purchase decision-making process in social commerce: A social learning perspective. Int. J. Inform. Manag. 2017, 376, 627–638. [Google Scholar] [CrossRef]
Shanmugam, M.; Sun, S.; Amidi, A.; Khani, F.; Khani, F. The applications of social commerce constructs. Int. J. Inform. Manag. 2016, 363, 425–432. [Google Scholar] [CrossRef]
Salvatori, L.; Marcantoni, F. Social commerce: A literature review. In Proceedings of the 2015 Science and Information Conference SAI, London, UK, 28–30 July 2015; pp. 257–262. [Google Scholar]
Friedrich, T. Analyzing the factors that influence consumers’ adoption of social commerce: A literature review. In Proceedings of the Twenty-First Americas Conference on Information Systems, Fajardo, PR, USA, 9 April 2015. [Google Scholar]
Baethge, C.; Klier, J.; Klier, M. Social commerce—State-of-the-art and future research directions. Electron. Mark. 2016, 26, 269–290. [Google Scholar] [CrossRef] [Green Version]
Zhang, K.Z.; Benyoucef, M. Consumer behavior in social commerce: A literature review. Decis. Support Syst. 2016, 86, 95–108. [Google Scholar] [CrossRef]
Busalim, A.H.; Hussin, A.R.C.; Iahad, N.A. Factors influencing customer engagement in social commerce websites: A systematic literature review. J. Theor. Appl. Electron. Commer. Res. 2019, 142, 1–14. [Google Scholar] [CrossRef] [Green Version]
Sheikh, Z.; Islam, T.; Rana, S.; Hameed, Z.; Saeed, U. Acceptance of social commerce framework in Saudi Arabia. Telemat. Inform. 2017, 348, 1693–1708. [Google Scholar] [CrossRef]
Al-Adwan Ahmad, S. Novel research framework for social commerce purchase intentions. J. Theor. Appl. Inf. Technol. 2018, 96, 14. [Google Scholar]
Beyari, H.; Abareshi, A. The conceptual framework of the factors influencing consumer satisfaction in social commerce. J. Dev. Areas 2016, 506, 365–376. [Google Scholar] [CrossRef]
Baghdadi, Y. A framework for social commerce design. Inf. Syst. 2016, 60, 95–113. [Google Scholar] [CrossRef]
Han, H.; Trimi, S. Social commerce design: A framework and application. J. Theor. Appl. Electron. Commer. Res. 2017, 123, 50–68. [Google Scholar] [CrossRef] [Green Version]
Beyari, H.; Ghouth, A. Customer experience in social commerce websites: Toward an integrated conceptual framework. J. Manag. Res. 2018, 103, 52–62. [Google Scholar] [CrossRef] [Green Version]
Sukrat, S.; Papasratorn, B. An architectural framework for developing a recommendation system to enhance vendors’ capability in C2C social commerce. Soc. Netw. Anal. Min. 2018, 81, 22. [Google Scholar] [CrossRef]
Sukrat, S.; Mahatanankoon, P.; Papasratorn, B. The driving forces of C2C social commerce in Thailand: A developing framework. Knowl. Soc. Sci. 2018, 31, 108–118. [Google Scholar] [CrossRef] [Green Version]
Mekki, A.B. A conceptual framework for the evolution of C2C social commerce business models in Tunisia. Int. J. Commer. Financ. 2019, 51, 51–59. [Google Scholar]

Figure 1. The review protocol.

Figure 2. Social commerce contribution trend from 2011 to 2021.

Figure 3. Distribution of some of the most frequent key components. Notes: The vertical axis shows how often these components appear in titles, abstracts and keywords.

Figure 5. The mechanism of Communication in social commerce [39,43,56,61].

Figure 6. The mechanism of Transaction in social commerce [71,73,75].

Figure 7. The mechanism of Sharing in social commerce [89,90,92,93].

Figure 8. A systems thinking view of social commerce systems.

Figure 9. The transformation from e-commerce to social commerce.

Table 1. Inclusion and exclusion criteria.

Inclusion Criteria	Exclusion Criteria
Full text	Full text is not available by current database
English studies	Non-English studies
Published within the selected period of time (2009–2019)	Outside the selected time
In the domain of social commerce	Duplicated studies
Research articles	Research in progress Editorials Short communications News

Table 2. Search results.

Key Words	S-Commerce	Social Commerce	Social E-Commerce	Social Electronic Commerce
search in title	45	1110	53	1
search in all areas	7940	13,100	674	19

Search date 25 October 2021 by Google Scholar.

Table 3. Journals related to social commerce.

Journal	Articles
International Journal of Information Management	20
Electronic Commerce Research and Applications	16
Information and Management	14
Computers in Human Behavior	11
Journal of Theoretical and Applied Electronic Commerce Research	6
Technological Forecasting and Social Change	6
Decision Support Systems	5
Electronic Commerce Research	5
International Journal of Electronic Commerce	4
Internet Research	4
Journal of Retailing and Consumer Services	4
Electronic Markets	3
Information Sciences	3
Journal of Business Research	3
Journal of Internet Commerce	3
Journal of Strategic Marketing	3
Modern Applied Science	3
Pakistan Journal of Commerce and Social Sciences	3
Sustainability	3
Asia Pacific Journal of Marketing and Logistics	2

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Wang, X.; Wang, H.; Zhang, C. A Literature Review of Social Commerce Research from a Systems Thinking Perspective. Systems 2022, 10, 56. https://doi.org/10.3390/systems10030056

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Wang X, Wang H, Zhang C. A Literature Review of Social Commerce Research from a Systems Thinking Perspective. Systems. 2022; 10(3):56. https://doi.org/10.3390/systems10030056

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Wang, Xintian, Hai Wang, and Caiming Zhang. 2022. "A Literature Review of Social Commerce Research from a Systems Thinking Perspective" Systems 10, no. 3: 56. https://doi.org/10.3390/systems10030056

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Wang, X., Wang, H., & Zhang, C. (2022). A Literature Review of Social Commerce Research from a Systems Thinking Perspective. Systems, 10(3), 56. https://doi.org/10.3390/systems10030056

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