Content area
Purpose
Social commerce (SC) is a new genre in electronic commerce (e-commerce) that has great potential. This study proposes a new research framework to address deficiencies in existing social commerce research frameworks (e.g. the information model).
Design/methodology/approach
In the era of Industrial Revolution 4.0 technologies and new social commerce (s-commerce) models, the authors believe that there is an immediate need for a new research framework. The authors analysed the progress of the s-commerce paradigm between 2003 and 2023 by applying longitudinal science mapping. The authors then developed a research framework based on the themes in the strategic diagrams and evolution map.
Findings
From 2003 to 2010, studies on s-commerce mainly focused on social networking sites, virtual communities, social shopping and analytic approaches. From 2011 to 2015, it shifted to s-commerce, consumer behaviour, Web 2.0, artificial intelligence, social technologies, online shopping, user studies, data gathering methods, applications, service-based social commerce constructs, e-commerce and cognitive factors. Social commerce remained the primary research paradigm from 2017 to 2023.
Practical implications
The SC framework may be analogous to popular research frameworks such as technology-organisation-environment (T-O-E) and stimulus-organism-response (S-O-R). Based on this SC framework, researchers may gain a better understanding by determining the factors of the social, commercial, technological and behavioural dimensions.
Originality/value
The authors redefined s-commerce and developed an SC framework. Practical guidelines for the SC framework and an exemplary research model are presented. Overall, this study offers a new research agenda for the extant understanding of s-commerce, with the SC framework as the next frontier of the theoretical advancements and applications of s-commerce.
1. Introduction
The rise of social media has led to the development of social commerce (SC), or s-commerce (Hajli, 2020; Leong et al., 2021; Ooi et al., 2023). In addition, social networking sites (SNSs), such as Meta, WhatsApp, Twitter, WeChat, Instagram and LinkedIn, have contributed to the popularity of s-commerce (Jami Pour et al., 2022; Leung et al., 2022; Lu et al., 2019). Yahoo first coined the term “social commerce” in 2005 to explain how social media is utilised to facilitate business transactions (Cui et al., 2018). T-Commerce on Twitter is an example of s-commerce (Cui et al., 2018). Unlike conventional electronic commerce (e-commerce) in which buyers interact with online vendors individually, s-commerce involves virtual communities and supports user-generated content (UGC) and user interactions (Sheikh et al., 2019). For instance, consumers rely heavily on buying products with low tipping points (Lee et al., 2015). In s-commerce, buyers can build social relationships, communicate, review others' opinions, rate products, recommend products and services, and share experiences (Bazi et al., 2020; Hajli, 2013). Currently, s-commerce is being studied both practically and theoretically (Busalim and Hussin, 2016; Lin and Wang, 2022); however, little effort has been put towards determining its current state and progress (Esmaeili and Hashemi G, 2019). S-commerce is a more sociable, innovative and collaborative way of conducting online business (Gonçalves Curty and Zhang, 2013; Wang et al., 2020). This has evolved into a new phenomenon of universal attention among vendors, marketers, and scholars (Baethge et al., 2016; Ooi et al., 2018). S-commerce research has grown exponentially over the last decade and has become an important emerging research area (Lin et al., 2017). Despite the rapid growth and substantial effects of s-commerce, studies on this phenomenon remain at an early stage and demand further exploration (Han et al., 2018; Huang and Benyoucef, 2013).
S-commerce is anticipated to achieve US$84.2 billion in 2024, contributing 7.8% of US e-commerce retail sales (Tugba Sabanoglu, 2020). Retail s-commerce sales in China are expected to reach US$474.81 billion by 2023 (Influencers MarketingHub, 2021). The S-commerce market revenue is forecast at US$3369.8 billion in 2028 (Grand View Research, 2021). The size of the s-commerce market is expected to grow by US$2051.49 billion between 2020 and 2024 (Technavio, 2021). The enormous potential of s-commerce has attracted considerable interest from practitioners and researchers (Zhou et al., 2013). However, “due to the complexity and innovativeness of s-commerce, it is necessary to have a framework to organise relevant knowledge in a cohesive way that may be used to guide researchers and practitioners” (Liang and Turban, 2011, p. 7).
There are several shortcomings in the existing research frameworks on s-commerce (e.g. Huang and Benyoucef, 2013; Liang and Turban, 2011; Wang and Zhang, 2012; Wu et al., 2015; Zhang and Benjamin, 2007; Zhou et al., 2013). First, the frameworks were introduced between 2007 and 2015. They did not include state-of-the-art technologies such as artificial intelligence, Industry 4.0, blockchain, machine learning, big data analytics (BDA), Internet of things (IoT) and wearable devices. Since 2015, several new s-commerce models have emerged, including meta-verse commerce, conversational commerce, mobile social commerce (ms-commerce), live-streaming commerce, Twitter commerce (t-commerce) and shared commerce (Hew et al., 2019; Koohang et al., 2023; Tan et al., 2023; Theadora et al., 2022). Second, existing frameworks cannot comprehensively explain the nexus between the social and behavioural components of social commerce. For example, the I-model (Zhang and Benjamin, 2007) only entails the components of people, information, technology and organisation/society, while the framework by Liang and Turban (2011, p. 11) does not explain the role of technology in social commerce. Similarly, Wang and Zhang's (2012) model, which consists of the components of management, people, information and technology, cannot explain the role of social and behavioural components in social commerce. Similarly, the framework by Zhou et al. (2013) cannot explain the role of social factors and behaviour as it only comprises the components of people, information, technology and business. Similarly, the frameworks of Huang and Benyoucef (2013) and Wu et al. (2015) are unable to explain the nexus of the components of social factors, behaviour and technology, as these frameworks only provide a set of design principles as guidelines for s-commerce system developers. Third, existing frameworks are developed based on qualitative literature reviews. Thus, these frameworks lack quantitative scientific support. Fourth, few articles were gathered in the reviews, and the existing frameworks were developed based on old definitions of social commerce that are already outdated and thus may not be relevant and valid in the current timeframe. Hence, there are issues of definition accuracy as well as issues of empirical validity, comprehensiveness and coverage of the frameworks that need to be addressed because the existing frameworks are not accurate or comprehensive enough, as they do not cover some components of social commerce. Moreover, they are unsuitable and insufficient for application now because of the rise of new technologies and s-commerce models; thus, there is an urgent need for a refined s-commerce framework. Finally, existing studies do not provide a clear understanding of the evolution of social commerce through the years in terms of research themes, areas and trends and have failed to provide a clear research agenda for social commerce. Hence, this study aims to develop a refined s-commerce research framework called the SC framework. Unlike existing frameworks that were developed through systematic reviews that are qualitative in nature, the current study combined a systematic review with science mapping that is both qualitative and quantitative to provide comprehensive and extensive coverage of the components of social commerce to scientifically develop a new social commerce framework. It also examines the evolution of the s-commerce paradigm by analysing research and publication trends and author performance. To address the shortcomings of the existing frameworks, this study aims to answer the following research questions:
What is the state-of-the-art definition of social commerce?
What are the new dimensions for the social commerce framework?
What is the evolution of social commerce?
What is the research trend of social commerce?
What is the research agenda for social commerce?
This study addresses the shortcomings of existing frameworks in several ways. First, it fills this gap by introducing a new definition of s-commerce and proposing a refined s-commerce research framework, known as the SC framework, for future s-commerce studies. Second, practical guidelines for the application of the SC framework are provided for future theoretical developments. With these practical guidelines, researchers can develop various research models that further extend the extant literature. An exemplary research model is presented to illustrate the application of the SC framework to the context of metaverse commerce. We believe that the SC framework has great potential to emulate the success of other frameworks such as T-O-E and S-O-R. Third, methodologically, this pioneering study applied empirical science-mapping data to develop and refine an s-commerce research framework. Fourth, it identifies the four key components that constitute s-commerce, thus redefining the contextualisation of s-commerce artefacts. Fifth, it provides an evolution map of s-commerce since its emergence in 2003, which may serve as a future research direction or research agenda. Finally, it provides a comprehensive analysis of authors' performance, institutions, countries and publication trends in s-commerce.
The paper begins with an introduction, followed by a literature review of the existing s-commerce frameworks. We then explain the application of longitudinal science mapping and the research methodology. The analysis and results are elaborated, followed by the introduction of the SC framework. Finally, we discuss the research findings in terms of theory and practice before presenting the study's limitations and directions for future research.
2. Literature review
2.1 What is s-commerce?
Until now, there have been inconsistencies in the definition of s-commerce (Zhang and Benyoucef, 2016; Zhang et al., 2020). Liang and Turban (2011) assert that s-commerce has three key attributes: social media technologies, commercial activities and community interactions. Huang and Benyoucef (2013, p. 247) define s-commerce as “an Internet-based commercial application, leveraging social media and Web 2.0 technologies which support social interaction and UGC to assist consumers in their decision-making and acquisition of products and services within online marketplaces and communities”. Zhou et al. (2013, p. 61) define this concept 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 both online and off-line marketplaces, and communities”.
On the other hand, Busalim and Hussin (2016, p. 1077) define s-commerce as “exchange-related activities that take place between and are influenced by social network users in computer mediated social environments, where the activities correspond to the need recognition, pre-purchase, purchase, and post–purchase stages of a focal exchange”. Wang and Zhang (2012, p. 106) refers to s-commerce as “a form of commerce that is mediated by social media and is converging both online and offline environments”. However, Lin et al. (2017, p. 191) define s-commerce as “any commercial activities facilitated by or conducted through the broad social media and Web 2.0 tools in consumers” online shopping process or business' interactions with their customers'. Han et al. (2018, p. 46) assert that s-commerce includes “social media (e.g. SNS), social activities (e.g. WOM, social interactions), e-commerce and Web 2.0”. In contrast, Esmaeili and Hashemi (2019) refer to s-commerce as “an Internet-based commercial application that makes use of Web 2.0 technologies and social media, and it supports user-generated content and social interactions”.
Abdelsalam et al. (2020, p. 89043) define s-commerce as “a new business model of e-commerce, which makes use of Web 2.0 technologies and social media to support social-related exchange activities”. Zhao et al. (2023, p. 2) defined s-commerce as “the marriage of e-commerce and e-word-of-mouth (e-WOM), which brings about the understanding of user-generated content and social interaction among the online community”. However, Hu et al. (2022, p. 120) define s-commerce as “a new form of electronic commerce (e-commerce) that combines e-commerce with social media techniques”. Leung et al. (2022, p. 1132) refer to s-commerce as the “leveraging of online social capital to support commercial transactions and activities on SNSs”. Mou and Benyoucef (2021, p. 2) define s-commerce as “the exchange-related activities that occur in an individual's social network in computer-mediated settings, following a process that includes need recognition, pre-purchase, purchase, and post–purchase stages”. Owing to the inconsistencies in definitions and the emergence of new architectures and technologies (e.g. BDA, IoT, RFID) involved in s-commerce, there is a need for scholars to update the definition of s-commerce (Han et al., 2018).
Based on the SC framework developed through a scoping review and science mapping of 765 articles published between 2003 and 2023, we define the s-commerce artefact as consisting of four basic dimensions: “Commerce”, “Behaviour”, “Social” and “Technology”. Hence, we define s-commerce as “any commercial activities involving consumer behaviours that happen through social media platforms and facilitated by any state-of-the-art technologies”. We elaborate the content of each dimension of the SC framework in detail in the relevant section.
2.2 Existing s-commerce research frameworks
This research framework is a useful guide for recent social commerce studies (Zhang and Benyoucef, 2016). The framework must be grounded in existing research foundations and key attributes of s-commerce (Liang and Turban, 2011). The first social commerce research framework was introduced in 2007 (Zhang and Benjamin, 2007). The Information Model, or I-Model (Figure 1a), entails four basic dimensions: people, information, technology and organisation/society. The integration and interaction of these basic components may generate exciting and interesting research streams with many potential applications (Zhang and Benjamin, 2007). However, the I-model is not suitable in the current time frame as over the course of 16 years, there have been many advancements in terms of technology and architecture, especially with the introduction of social media and state-of-the-art technologies. In addition, none of the four components represents commercial activities in s-commerce. Therefore, the I-model should be revised to suit the current context. In 2011, Liang and Turban (2011) proposed an s-commerce research framework (Figure 1b) with four example papers in a special issue. The framework comprises six components: social media, theories, commercial activities, research themes, research methods and outcome measures. However, Liang and Turban (2011, p. 11) agree that “the examples described in this introduction are not all-inclusive. Interested readers may extend the framework to fit their study”.
Wang and Zhang (2012) revised the I-model and proposed the dimensions of people, management, information and technology. However, due to the various strategies, policies, processes, opportunities and business models in s-commerce, the term “management” was used instead of “organisation/society” to avoid potential confusion. Nevertheless, in the current context, none of these dimensions represents commercial activities in s-commerce. With state-of-the-art technologies (e.g. the Internet, BDA, blockchain) and new business models (e.g. social group buying, sharing shopping), this model cannot provide an accurate prediction; thus, there is a need to revise it.
In 2013, Zhou et al. (2013) introduced an integrated view of the s-commerce research framework (Figure 2), consisting of the dimensions of people, information, technology and business, which integrate strategic fit, based on a review of 317 papers published between 2003 and 2012. However, the framework does not include any social factors that explain social interactions or UGC in s-commerce. Moreover, the framework is already 10 years old, and there have been many advancements in technologies and architectures within this timeframe. Hence, its parsimony and relevance are more minimal in the current setting.
In 2013, Huang and Benyoucef (2013) proposed a Social Commerce Design Model (Figure 3a) with four layers: individual, conversation, community and commerce. In 2015, Wu et al. (2015) introduced a new s-commerce design model by extending the existing model with another management layer (Figure 3b). However, these models provide only a set of design principles as guidelines for s-commerce website developers and platform designers. Therefore, existing s-commerce frameworks and models have various limitations that warrant an updated framework for future studies. A summary of existing studies in the s-commerce framework is presented in Table 1. This study conducted a scoping review to refine the s-commerce research framework (Cram et al., 2016; Leidner, 2018).
3. Methodology
We conducted a scoping review to gather the articles required for our study based on five stages (Arksey and O'Malley, 2005; Levac et al., 2010).
3.1 Stage 1: identification of the research questions
This study uses the 7 W model (What, When, Where, Who, Why, Which and How) to provide a comprehensive understanding of the progress of s-commerce and the s-commerce framework to answer the following questions (1) What is s-commerce? (2) When did s-commerce arise? (3) Why is there a need to propose an updated s-commerce research framework? (4) How has research on s-commerce progressed over time? (5) Who researches s-commerce? (6) Which research outlets are most receptive to s-commerce studies? (7) Where are the research centres and institutions that examine s-commerce? (8) Which research themes have been published on s-commerce? (9) What are the dimensions of the s-commerce framework? (10) What is the updated definition of s-commerce?
3.2 Stage 2: identification of the relevant studies
Scopus database was chosen for its broad coverage (25,100 titles, 5,000 international publishers, 23,452 peer-review journals, 294 trade articles, 852 book series, 9.8 million conference papers, and 77.8 million records since 1970), quality standards, ease of downloading data and excellent analytical tools; moreover, “it delivers the most comprehensive overview of the world's research output in the fields of science, technology, medicine, social science, and arts and humanities” (Elsevier, 2020, p. 4). Mendeley reference management software was used to manage the references. We extracted articles using the following search keywords: “social commerce”, “social shopping”, “s-commerce”, “Facebook commerce”, “f-commerce” and “mobile social commerce”. We included articles published after 2002, when s-commerce studies began to appear (Cui et al., 2018). The bibliometric analysis involved 1,543 articles.
3.3 Stage 3: selection of studies
Two reviewers applied the inclusion and exclusion criteria outlined in this step. When there were ambiguities in the abstracts of relevant studies, the full articles were reviewed. The reviewers set a deadline on an agreed-upon date after which no more studies were included. Articles not written in English were excluded (Kitsiou et al., 2013; Paré et al., 2007, 2010; Ringeval et al., 2020; Templier and Paré, 2015, 2018). The reviewers filtered the remaining articles and removed all duplicates (Wagner et al., 2021). The final number of articles included in this study after the filtering process for the science mapping analysis was 765, as shown in Figure 4.
3.4 Stage 4: charting the data
To chart the data, we applied longitudinal science mapping (Hu et al., 2022; Zheng et al., 2023). Existing studies have not analysed the roots of s-commerce that can provide information on the dynamic evolution of the field and enable us to understand the origin of s-commerce, its evolution over time, disappearing research topics and the current research paradigm. To address these issues, we applied longitudinal science mapping analysis using the SciMAT software (Cobo et al., 2012). A longitudinal framework allows the progress of the research area to be analysed and traced over several successive sub-periods (Garcia-Buendia et al., 2021). SciMAT was selected because it provides the greatest benefits of current science-mapping tools and offers a state-of-the-art methodology for bibliographic networks and bibliometric indicators (Fouroudi et al., 2020; Moral-Muñoz et al., 2020). This study uses the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) filtering process. The steps involved in science mapping are illustrated in Figure 5.
3.5 Stage 5: collating, summarising and presenting the findings
We first analysed the publication trends in s-commerce studies using the analytical tools in the Scopus database. Overall, there was an exponential publication trend from 2003 to 2022, with the highest number of publications occurring in 2022 (Figure 6). Most articles were in the subject area of computer science, followed by business, management and accounting, and the majority of publications were journal articles or conference papers. Appendix 1 presents the full analysis.
In terms of the publication outlets that are most receptive to s-commerce studies (Appendix 2), the “ACM International Conference Proceeding Series” is at the top of the list, followed by “Lecture Notes in Computer Science”, “International Journal of Information Management”, “Electronic Commerce Research and Applications”, “Journal of Retailing and Consumer Services” and “Information and Management”. We used VOSviewer (van Eck and Waltman, 2010) to generate a networking map of the journals (Figure 7a). Based on a minimum of three articles with zero citations, 60 items and five clusters were obtained.
In terms of the most prolific authors (Appendix 2), Hajli tops the list, followed by Shanmugam, Dwivedi, Benyoucef, Chen, Lin, Sundaram, Davison, Hussin, Liu and Wang. However, in terms of the most influential authors based on citation count (Appendix 5), Liang and Turban are the leaders, followed by Benyoucef, Hajli, P. Zhang, Huang, Kim, Lu, Gupta and H. Zhang. The authors' network map, with a minimum of two articles and zero citations (Figure 7b), indicated that 70 items were spread across 10 clusters.
In terms of the most productive institutions (Appendix 2), the “The City University of Hong Kong” tops the list, followed by the “University of Science and Technology of China”, “Swansea University”, “Universiti Teknologi Malaysia”, “School of Management, the University of Ottawa”, “Universiti Tenaga Nasional” and “Hefei University of Technology”. However, the most influential institutions are the “Department of Information Systems, National Cheng-Chi University”, “University of California (Berkeley)”, “National Sun Yat-Sen University”, “Indian Institute of Management (Raipur)” and “School of Management, Wuhan University”, as shown in Appendix 5. Using a minimum threshold of two articles with 0 citations, 111 items and 78 clusters were obtained (Figure 7c).
In terms of the most productive country/territory (Appendix 3), China tops the list, followed by the U.S., the UK, Malaysia, Taiwan, South Korea, Indonesia, Canada, India, Australia, Hong Kong and Germany. Appendix 6 shows that the most influential country/territory is the U.S., followed by China, Taiwan, the UK, Canada, South Korea, France, Malaysia, India and Hong Kong. Using a minimum of one article with zero citations, 54 items and 14 clusters were obtained (Figure 7d).
In terms of the most cited article (Appendix 4), Liang, Ho, Li and Turban's paper titled “What drives social commerce: The role of social support and relationship quality” tops the list with 828 citations, followed by Huang and Benyoucef (“From e-commerce to social commerce: A close look at design features”), Kim and Park (“Effects of various characteristics of social commerce (s-commerce) on consumers” trust and trust performance’), Liang and Turban (“Introduction to the special issue social commerce: A research framework for social commerce”), Stephen and Toubia (“Deriving value from social commerce networks”) and B. Lu, W. Fan and M. Zhou (“Social presence, trust and social commerce purchase intention: An empirical research”). Moreover, the keywords with the highest occurrences (Appendix 6) are “Social Commerce”, “Social Commerces”, “Commerce”, “Electronic Commerce” and “Social Networking (Online)”. Based on a minimum of five keywords, 211 items and nine clusters were obtained (Figure 7e). Finally, we performed a bibliographic coupling of authors with a minimum of two articles with zero citations and obtained 318 items and eight clusters (Figure 7f).
3.6 Science mapping analysis
We applied the following SciMAT analysis configuration: unit of analysis: “words (author keywords, source keywords)”; type of network: “co-occurrence”; normalisation measure: “equivalence index”; cluster algorithm: “centres simples”; max cluster size: 12; min cluster size: 3; evolution measure: “Jaccard index”; overlapping measure: “inclusion index”. For comparison, we divided the publication years into three stages: 2003 to 2010, 2011 to 2016, and 2017 to 2023. The overlapping map (Figure 8a) indicates the number of articles in each stage (circle), articles that disappeared in the next stage (outgoing arrow), newly entered articles (incoming arrow) and articles that remained in the next stage (connected arrow). The similarity index, which indicates the ratio of shared keywords between successive sub-periods, is shown in parentheses. There are two dimensions (i.e. centrality and density) and four quadrants in the strategic diagram (Figure 8b). Centrality measures the external interactions among networks, whereas density measures a network's internal cohesion (Cobo et al., 2012). The “motor themes” are well developed, important and vital for configuring a research paradigm, while the “basic and transversal themes” are not yet fully developed but are important and relevant to the research field. The “emerging or declining themes” are poorly or marginally developed themes, while the “highly developed and isolated themes” are well developed but of minimal importance, as they are very specific and peripheral. In an evolution map (Figure 8c), the volume of the sphere signifies the number of articles, whereas the width of the line signifies the inclusion index (i.e. the weight of the relationship between themes). The solid line represents a conceptual nexus (i.e. thematic connection), whereas the dotted line represents a component nexus (i.e. keyword connection).
3.6.1 Stage 1: 2003 to 2010 (Figure 9a)
The motor themes are social networking, commercial studies and virtual community, while the emerging themes are communication studies and analytic approach; social shopping is the basic and transversal theme, and community studies is an isolated and highly developed theme.
3.6.2 Stage 2: 2011 to 2016 (Figure 9b)
Social commerce emerged as the top motor theme, followed by artificial intelligence, online shopping and social technologies. The emerging themes include f-commerce, mobile applications and service-based. Consumer behaviour, Web 2.0 and data-gathering methods are the basic and transversal themes, while the isolated and highly developed themes are user studies, social commerce constructs, shared commerce and cognitive factors.
3.6.3 Stage 3: 2017 to 2023 (Figure 9c)
During this stage, social commerce and data-gathering methods remain the top motor themes, followed by innovation studies, product recommendation and human-computer-interaction. The basic and transverse themes were replaced by social factors, quality-based and impulse buying behaviour. Perceived social presence, consumption behaviour and continuance intentions are isolated and highly developed themes. The emerging theme is consumer engagement, and competition and social commerce constructs have transformed into declining themes from the highly developed and isolated themes in the previous stage.
Looking more closely, the subthemes for social commerce (Figure 10a) include commercial studies, social networking sites, theories or models, e-commerce, purchase intention, trust-based, social media, sales, analytic approach, information systems and economic studies. On the other hand, the subthemes for innovation studies (Figure 10b) include technological factors, perception-based, risk assessment, mobile applications, m-commerce, value co-creation, adoption studies, privacy concerns, security, health and sustainability. The subthemes for data gathering methods (Figure 10c) are consumer decision-making, websites, research frameworks, social interactions, purchase decision, business studies, literature reviews, the internet, advertising, developing countries and food studies. The subthemes for continuance intentions (Figure 10d) include marketing studies, consumer satisfaction, gratifications, mobile social commerce, social commerce sites, personalisation, personality, collaborative behaviours, information technology, communication studies and agricultural. Conversely, the subthemes for human-computer-interaction (Figure 10e) are behavioural research, consumer studies, empirical studies, relationship studies, social influence, user studies, blockchain studies, cloud computing, cost factors, social design and interface design. Figure 10f shows that the subthemes for consumption behaviour are purchasing behaviours, e-tailing, business models, consumer-generated content, social network services, information sharing behaviour, utilitarian, participatory behaviour, serendipity, virtual community and uncertainty. The subthemes for product recommendation (Figure 10g) consist of machine learning approaches, big data, the fuzzy logic approach, swift guanxi, community studies, online systems, C2C commerce, service-based, social relationships, cognitive factors and affective factors.
The subthemes for quality-based (Figure 10h) include artificial intelligence, online consumer review, research methodologies, online platforms, intention to buy, social referrals, information quality, crowdsourcing, corporate social responsibilities, environmental studies and cultural factors. For perceived social presence, the subthemes (Figure 10i) consist of social commerce platform, social support, Web 2.0, interaction factors, computer applications, e-loyalty, online communities, product-based, information seeking, social technologies and emerging markets.
Figure 10j illustrates the subthemes for social factors, including consumer behaviour, f-commerce, social shopping, attitude, social identity, emotional factors, psychological studies, push-pull-mooring, relational model, Industry 4.0 and behavioural intention. Figure 10k shows the subthemes for shopping value, including consumer engagement electronic word of mouths, social media marketing, informational support, brand, live stream shopping, IT affordance, collective buying, perceived risk, experimental studies and organisational perspective. For impulse buying behaviour (Figure 10l), the subthemes are liability, online shopping, social aspects, hedonic value, customer value, usability, sentiment analysis, digital business studies, systematic literature review, price-based and demographic factors. Figure 10m depicts the competition's subthemes, including motivation, small and medium-sized enterprise, knowledge-based systems and event studies. Finally, the subthemes for social commerce constructs (Figure 10n) are social commerce acceptance and shared commerce.
3.7 The s-commerce paradigm evolution map
Figure 11 presents the evolution map and performance analysis of the entire s-commerce paradigm during these three stages. From 2003 to 2010, studies on s-commerce mainly focused on social networking sites, commercial studies, social shopping, community studies, virtual communities, analytical approaches and communication studies. However, the focus from 2011 to 2015 shifted to social commerce, Web 2.0, F-commerce, consumer behaviour, user studies, artificial intelligence, social technologies, data gathering methods, online shopping, mobile applications, shared commerce, service-based, social commerce constructs and cognitive factors. From 2017 to 2023, social commerce remained the research paradigm, with a focus on data-gathering methods, impulse buying behaviour, perceived social presence, human-computer-interactions, consumer engagement, quality-based, consumption behaviour, innovation studies, product recommendation, continuance intentions, social commerce constructs and competition.
There is a strong connection between social networking sites and social commerce, which constitutes one of the bases of the s-commerce field. Other strong connections include virtual community-artificial intelligence-competition, community studies-shared commerce-social commerce constructs, community studies-consumer behaviour-impulse buying behaviour, community studies-e-commerce-social factors, commercial studies-social commerce-social commerce, social shopping-user studies-human-computer interactions, mobile applications-innovation studies, cognitive factors-product recommendation, shared commerce-social commerce constructs, shared commerce-consumption behaviour, and social commerce constructs-social commerce constructs.
4. Discussion
Following the approach used by Santana and Cobo (2020), we classified the research themes into five dimensions: social, commerce, technology, behaviour and research.
4.1 Social dimension
The research themes for this dimension consist of virtual community, community studies, social networking sites, perceived social presence and social factors. Moreover, s-commerce has other social elements, including social media marketing, social advertising, social CRM, social group buying, social shopping, social support, social influence, social financing, social recommendations and social reviews. Because S-commerce is conducted using social media, where the virtual community can create UGC and social interactions that allow the sharing of reviews, ratings, opinions, recommendations, information, experiences, etc., the social theme is indeed one of the backbones of s-commerce.
Social factors are the major pull factors of the s-commerce phenomenon as buyers are authorised to create user content on social media (Huang and Benyoucef, 2013). Liang and Turban (2011) argue that social media is a key component of s-commerce. On social platforms, buyers can harness social experiences and knowledge to better understand online purchase decision-making in a socially centred and user-driven s-commerce marketplace. Based on peer consumer-generated content, buyers can obtain product evaluations from others which influence their purchase decisions (Lin et al., 2017). S-commerce is also related to the application of Internet-based social communities by e-commerce vendors from the perspective of sociology and is mostly focused on the social influence that leads to consumer interactions (Esmaeili and Hashemi, 2019).
Social factors such as social support can build close relationships among s-commerce users while fortifying their well-being in organisations (Bai et al., 2015). On s-commerce platforms, users can receive and share information with others, and sharing supportive information can strengthen friendships and trust among them (Hajli, 2014). Social support can be classified into user, UGC and platform support. In s-commerce, user support refers to user relationships, whereas UGC support refers to reviews, recommendations and ratings. Platform support refers to the tools that support s-commerce activities (Liang et al., 2011). Generally, studies have shown that social theories such as social capital theory, social cognitive theory, social exchange theory, social influence theory, social response theory, social identity, social bonding, social interaction, social presence and social support theory play a significant role in s-commerce consumer behaviour (Busalim et al., 2019; Han et al., 2018; Zhang and Benyoucef, 2016).
4.2 Commerce dimension
The commerce dimension includes the research themes of social commerce, online shopping, shared commerce, commercial studies, quality-based, social shopping, e-commerce, service-based, competition and social commerce constructs. In addition, s-commerce involves various commercial activities, including marketing, advertising, transactions, ratings, reviews, word-of-mouth, customer service (CRM), business collaboration, HRM, referrals and recommendations (Liang and Turban, 2011). Commercial s-commerce activities may assist consumers in their pre-purchase product assessment, purchase decision-making and post-purchase behaviours (Lin et al., 2017). Commerce is the fourth layer of the s-commerce design model (Huang and Benyoucef, 2013). The commerce layer provides the opportunity to conduct commercial activities within a community.
To summarise, there are many perspectives on the commercial facets of s-commerce, including business strategies, business forecasting, branding, marketing, advertising, deals, discounts, promotions, one-stop shopping, group buying, fixed-price group buying, venture capital, cash back, competitive advantage and smart partnerships (Zhou et al., 2013). Studies have shown the significant effects of social interactions among online social network users. For example, strong ties among family and friends of the users can influence their purchase decisions (Baethge et al., 2016). In addition, s-commerce is dynamic and continues to evolve according to extant technological advancements. For example, with the emergence of metaverse commerce, a new form of s-commerce, also known as social metaverse commerce, has emerged (Chen and Yang, 2022; Zvarikova et al., 2022).
4.3 Technology dimension
The technology dimension encompasses the research themes of social technologies, innovation studies, artificial intelligence, mobile applications and Web 2.0. Additionally, many emerging technologies may alter the manner in which s-commerce is conducted. These include artificial chatbots, BDA, blockchain technology, machine learning, IoT, virtual reality, quantum computing, smart systems, expert systems, robotics and other IR4.0 emerging technologies. Technological advancements strongly facilitate s-commerce (Wang and Zhang, 2012). For instance, Facebook is used by eBay as its s-commerce platform, Google+ is used as a platform for g-commerce, and SellSimply is used by Twitter as the t-commerce platform. Facebook is the main platform for s-commerce and is more popularly known as f-commerce. Recently, mobile technology has pushed s-commerce to unite physical stores with online social networks in what is known as ms–commerce. Software-as-a-service (SaaS) capabilities also facilitate s-commerce implementation (Zhou et al., 2013). S-commerce is expected to evolve from a single IT platform into multiple platforms, channels and social networks (Wang and Zhang, 2012).
In short, technology plays a vital role in providing the best user experience for s-commerce. For example, collaborative shopping technologies, such as virtual advisors, avatars and artificial intelligence-assisted social recommender systems, can be applied to support communication, navigation and customer shopping value (Baethge et al., 2016). In addition, through social recommender systems, users' social relationship data and profiles can be used to filter information and create meaningful recommendations.
4.4 Behaviour dimension
This dimension consists of the research themes of continuance intention, human-computer interaction, user studies, consumer behaviour, product recommendation, consumer behaviour, consumer engagement and cognitive factors. As S-commerce encompasses selling and purchasing products and services within virtual communities, it also involves various consumer behaviours such as customer loyalty, attitude, satisfaction, intentions, acceptance, rejection, trust and distrust. Zhang and Benyoucef (2016) classified consumer decision-making in s-commerce into five stages, namely, “need recognition”, “search, evaluation”, “purchase” and “post-purchase”. The consumer behaviours within these stages include “attention” “attraction”, “information seeking”, “browsing”, “attitude”, “purchase behaviour”, “information disclosure”, “s-commerce intention”, “website usage”, “participation”, “brand loyalty” and “information sharing”. Yadav et al. (2013) classified the outcomes of s-commerce activities into four categories, namely “need recognition”, “pre-purchase activities”, “purchase decision” and “post–purchase activities”. Consumer behaviours include the stimulation of demand, awareness of alternatives, direct attention, information search, identifying salient attributes, assessing reviews, sharing consumption experiences and addressing post-purchase issues.
Most researchers concur that user participation behaviour is of utmost importance for s-commerce success (Baethge et al., 2016). There are two types of participation behaviours: active and passive. Active participation involves contributing to content and relationships on s-commerce platforms by commenting on posts, whereas passive participation entails browsing content without contributing to content generation or relationship building. In addition to user participation behaviour, consumers' purchase intention for s-commerce is popular. It has been found that consumers' personal and machinery interactivity can influence online purchase intention; machinery interactivity can affect attitudes, physical telepresence, perceived behavioural control and trust. Consumer behaviour in generating user content (e.g. “likes”) and disseminating information via electronic word-of-mouth (eWoM) contributes to online purchase intention (Baethge et al., 2016). Moreover, consumers' behaviours also include loyalty in the form of repeat purchase behaviours. We found that utilitarian shopping value (e.g. monetary savings) and hedonic shopping value (e.g. exciting shopping experiences) may trigger repeat purchase intentions.
4.5 Research dimension
This dimension consists of the themes of research methodology, data gathering methods and analytical approach. Research methodology is an imperative tool for differentiating research projects (Liang and Turban, 2011). Existing s-commerce studies use various research methodologies to offer empirical evidence on consumer behaviour (Zhang and Benyoucef, 2016). Generally, empirical studies can be classified as quantitative (e.g. surveys, observations and experiments) or qualitative (e.g. focus group interviews, narrative analysis and ethnographic studies). From an s-commerce perspective, the panel data approach can be applied to gather qualitative (e.g. the content of messages) and quantitative (e.g. the total number of messages) data using web crawlers with the application of SNS APIs (Zhang and Benyoucef, 2016). In addition, the experimental method can be applied to create experimental brand pages on SNS and investigate consumer behaviour. We believe that by applying new research methods, new discoveries can be obtained.
4.6 The SC framework and its applications
Based on these four themes, we developed an updated research framework for s-commerce studies. We call this framework the SC framework (Figure 12). The framework consists of social, commerce, technology and behaviour dimensions. These dimensions may interact with one another. Various research methods can be applied to these dimensions to obtain a better understanding of the context of the studies. The framework is just a basic model for s-commerce and is not exhaustive or static; we encourage researchers to further extend the framework by incorporating other external variables, such as moderators, mediators and other socio-demographic variables.
To date, no studies have used this newly developed framework. However, we found studies that used some of the dimensions of this framework. Ideally, to apply the framework, researchers should integrate social, technological, business, or behavioural theories into all four dimensions. However, researchers may use only some of these dimensions in certain contexts. To illustrate this further, we refer to related studies that used some of the dimensions. For example, Horng and Wu (2020) integrate social capital theory with the dimensions of behaviour (i.e. participating and browsing) and commerce (s-commerce intention of giving and receiving). Molinillo et al. (2020) integrate the social support theory with the dimensions of social (i.e. community drivenness, trust and identification) and behaviour (i.e. customer engagement, repurchase intention, stickiness intention, willingness to co-create and positive eWoM). Osatuyi et al. (2020) integrate expectation-confirmation theory with the dimensions of technology (i.e. perceived usefulness), intention (i.e. continuance intention) and behaviour (i.e. confirmation, satisfaction). Loh et al. (2022a) used the component of social (referent network size), commerce (price savings), technology (mobile usefulness, technostress) and behaviour (satisfaction, inertia, continuance intention) to study continuance intention to use mobile payment during the Covid-19 pandemic. Loh et al. (2022b) used a multidimensional nomological network consisting of the social (reference network size), technology (mobile usefulness, perceived complementarity, technostress), commerce (price savings) and behaviour (satisfaction, inertia, continuance intention) dimensions to understand continuance intention regarding mobile payment. By using different combinations of dimensions, the SC framework has great potential for researchers. To demonstrate the application of the SC framework using the same approach as that used by Leong et al. (2022), an example model is illustrated in Figure 13. The proposed model is based on the social ties theory (Li et al., 2023) in the context of metaverses (Dwivedi et al., 2022) assisted by AI technology (Balakrishnan and Dwivedi, 2021). This model can be further extended using new internal, external, moderating, contextual, control and outcome mechanisms to enhance the predictive power of the SC framework.
5. Conclusions
This study successfully answered the research questions through a comprehensive scoping review and science mapping analysis. The study provided a new definition of s-commerce artefacts and proposed a refined s-commerce research framework for future research and theory development. Below are several important and significant contributions of this study.
5.1 Theoretical contributions
This study makes several important theoretical contributions to the literature. The most important theoretical contribution is the development of an SC framework. This addresses the shortcomings of the existing s-commerce framework. The quadruplet SC framework has closed the gaps in the previous framework as it incorporates the most fundamental dimensions of s-commerce in the era of Industrial Revolution (IR) 4.0. For example, state-of-the-art technologies such as the meta-verse, virtual reality, augmented reality, artificial intelligence and IoT can be integrated into the SC framework's technology dimension, whereas contemporary s-commerce enablers such as chatbots, virtual agents and artificial intelligence assistants can be included in the social dimension of the SC framework. In the commerce dimension, new business models, such as conversational commerce, can be incorporated, and in the behavioural dimension, unexplored behaviours such as stickiness, distrust and disloyalty can be inserted into the SC framework. Using the SC framework provides researchers with clear guidelines for conducting their studies. More importantly, the SC framework successfully addresses the shortcomings of existing frameworks by addressing issues of consistency, extensiveness, completeness, accuracy and parsimony. It provides a theoretical foundation to strengthen our understanding of the key dimensions that define s-commerce.
Second, this study addresses the issues of inconsistency and ambiguity in defining s-commerce. Previously, the definitions of s-commerce were derived from qualitative systematic reviews. Unlike these studies, this systematic review combines science mapping, which is quantitative, as a complementary approach. Thus, the definition derived from this approach will be more scientific and empirical and can provide a more comprehensive, definite and accurate definition for s-commerce compared to existing definitions. With a new definition of s-commerce, this study makes an important theoretical contribution to the extant s-commerce literature.
Third, this study provides a holistic understanding of the evolution of the s-commerce paradigm right from its birth. With the knowledge of the evolution of the s-commerce paradigm, researchers can avoid “re-inventing the wheel” as they will not replicate research themes that have declined or disappeared. Furthermore, the evolution map can provide guidelines for researchers to revisit areas deemed relevant and important in the current context. By revisiting these areas, researchers can address the limitations and research lacunae that were not addressed previously owing to technological constraints.
Fourth, researchers can derive various research models for theoretical development based on the dimensions of the SC framework. For example, they could examine the effects of social factors, blockchain technology and s-commerce service quality on consumer engagement. Researchers can also integrate socio-demographic variables, research theories or frameworks, and moderating and mediating variables. The SC framework provides a fundamental framework for researchers to explore the variability of research models that can mitigate the weaknesses and limitations of existing models.
Fifth, based on the motor themes of the last stage (2017–2023), researchers can position their papers and research focus relative to the current s-commerce paradigm. This will assist researchers in obtaining desk rejections while increasing the possibility of paper acceptance. In addition, it can also help narrow the scope and area of study because researchers can identify research lacunae based on strategic diagrams and evolution maps. More importantly, this study provides a research agenda based on the s-commerce framework to guide researchers towards further advancing the s-commerce literature in terms of theoretical development. Details of the research agenda are presented in Section 5.3.
Sixth, based on the list of the most receptive journals for s-commerce studies, researchers can decide the best outlets for their research. Finally, using a scoping review combined with a bibliometric science mapping approach to develop a research framework provides a new methodological contribution to future work.
5.2 Practical contributions
The SC framework may be analogous to the T-O-E framework (Tornatzky and Fleischer, 1990). Based on this SC framework, researchers may determine the factors of social, commercial, technological and behavioural dimensions. Figure 14 shows an application of the SC framework that can be altered according to the specific needs of researchers. This framework provides a base model for researchers to extend in the future. By applying a practical guide, researchers and scholars can develop meaningful research models that are highly relevant to the s-commerce paradigm.
Based on a list of the most influential authors and institutions, s-commerce practitioners can establish smart partnerships and collaborations to gain the best researchers and institutions and further upgrade the quality of their products and services. Second, s-commerce service providers can use the framework as a guideline for research and development, marketing strategies, decision-making policies and strategic management. For example, in the social dimension, they may focus on applying social factors to virtual communities to ensure that the s-commerce platform is socially friendly. In the commerce dimension, they may upgrade consumer services and ensure that the platform is business-friendly. In the technology dimension, efforts may be diverted to utilising state-of-the-art technologies to ensure that s-commerce transactions can be performed securely, quickly, effectively and reliably.
Furthermore, universities can recruit the best academics in the field of s-commerce or collaborate with the most influential institutions to further develop their research centres. Third, academics in the field of s-commerce can identify potential co-researchers and co-authors and establish more networks based on network maps. In addition, they can identify potential external examiners among postgraduate students. Potential students can identify institutions at which to further their postgraduate studies.
5.3 Research agenda for SC framework
Based on the SC framework and practical guidance, we propose the following research agenda:
To further extend the theoretically meaningful predictors by incorporating external factors beyond the framework
To expand the levels of predictors, including individuals, dyads, teams, groups, organisations, manufacturers, suppliers, advertisers, marketers and vendors.
To investigate linear, nonlinear, or curvilinear effects using contemporary and state-of-the-art statistical analyses, such as necessary condition analysis (NCA), artificial neural networks (ANNs), fuzzy-set comparative qualitative analysis (fs-QCA), data mining, machine learning, multi-level modelling, predictive analysis and latent growth modelling.
To expand research methodologies, including quasi-experimental, longitudinal, mixed-method, action research, case studies, grounded theory and comparative studies.
To integrate the SC framework with other research frameworks such as technology-organisation-environment (T-O-E), task-technology-fit (T-T-F) and stimulus-organism-response (S-O-R).
To assess the temporal effects in the SC framework, such as pre-adopters, adopters, post-adopters, experienced and non-experienced.
To expand the categories of moderators and mediators, such as cross-national differences; types of platforms; customer, brand, product, service, or system attributes; religion; ethnicity; cultural or linguistic diversity; social status; digital divide or disparity; usage frequency; actual spending; incentive; and promotion.
To extend the context of the commercial dimension to newly minted business models such as meta-verse commerce, NFT commerce, conversational commerce and virtual goods.
6. Limitations and future research directions
First, the articles were limited to those written in English. Future studies should include articles published in other languages after translation. Second, because of the different numbers of fields among the various databases, this study used only one database, Scopus. Therefore, future studies should consider using other databases (e.g. Web of Science) to conduct comparative studies. A promising future direction would be to empirically validate various research models derived from the practical guide to further extend the existing literature in various fields of study.
Funding: This work was supported by the UCSI University under the UCSI World's Top 2% Scientist Research Grant under the project number T2S-2023/003.
Since acceptance of this article, the following author(s) have updated their affiliation(s): Keng-Boon Ooi is at the FORE School of Management, New Delhi, India and Faculty of Business, Design, and Arts, Swinburne University of Technology Sarawak Campus, Kuching, Malaysia. Nick Hajli is at the Loughborough Business School, Loughborough University, Loughborough, UK. Garry Wei-Han Tan is at the FORE School of Management, New Delhi, India, Faculty of Business, Design, and Arts, Swinburne University of Technology Sarawak Campus, Kuching, Malaysia, College of Business Administration, Adamson University, Manila, Philippines, and Department of Business Administration, IQRA University, Karachi, Pakistan.
Figure 1
S-commerce research models
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Figure 2
An integrated view of the s-commerce framework
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Figure 3
S-commerce design models
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Figure 4
The selection protocol
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Figure 5
The science mapping protocol
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Figure 6
Yearly publications in s-commerce studies as of 6 April 2023
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Figure 7
Matrix of network maps
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Figure 8
Components in science mapping analysis
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Figure 9
Strategic diagrams of the s-commerce paradigm
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Figure 10
Matrix of network diagrams
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Figure 11
The s-commerce paradigm evolution map (2003–2023) and performance analysis
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Figure 12
The SC framework
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Figure 13
An exemplary research model using the SC framework
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Figure 14
A practical guideline for the application of the SC framework
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Summary of existing s-commerce frameworks in comparison to the SC framework
| Year | Study | Title of paper | Journal | Period of articles | No of articles | Research method | Model/Framework | Elements/components/layers | Evolution map | Performance analysis | Research agenda |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2007 | Zhang and Benjamin (2007) | Understanding Information Related Fields: A Conceptual Framework | Journal of The American Society for Information Science and Technology | N/S | N/S | N/S | Information Model (I-Model) | People, Information, Technology, and Organisation/Society | No | No | No |
| 2011 | Liang and Turban (2011) | Introduction to the Special Issue Social Commerce: A Research Framework for Social Commerce | International Journal of Electronic Commerce | 2011–2012 | 4 | Literature Review | Social Commerce Research Framework | Research theme, Social media, Commercial activities, Underlying theories, Outcomes, and Research methods | No | No | No |
| 2012 | Wang and Zhang (2012) | The Evolution of Social Commerce: The People, Management, Technology, and Information Dimensions | Communications of the Association for Information Systems | 2005–2011 | N/S | Literature Review | The People, Management, Technology, and Information Dimensions | People, Information, Technology, and Management | No | No | No |
| 2013 | Zhou et al. (2013) | Social Commerce Research: An Integrated View | Electronic Commerce Research and Applications | 2003–2012 | 317 | Literature Review | An Integrated View of Social Commerce Research | People, Information, Technology, and Business | No | No | No |
| 2013 | Huang and Benyoucef (2013) | From e-Commerce to Social Commerce: A Close Look at Design Features | Electronic Commerce Research and Applications | N/S | N/S | N/S | Social Commerce Design Model | Individual, Community, Conversation, and Commerce | No | No | No |
| 2015 | Wu et al. (2015) | The Research of Design Based on Social Commerce | International Journal of Social Science Studies | N/S | N/S | N/S | New Social Commerce Design Model | Individual, Community, Conversation, Commerce, and Management | No | No | No |
| 2023 | This study | Revisiting the Social Commerce Paradigm: The SC Framework and a Research Agenda | Internet Research | 2003–2023 | 765 | Scoping Review and Science Mapping | SC Framework | Social, Commerce, Technology, and Behaviour | Yes | Yes | Yes |
Note(s): N/S = Not specified
Source(s): Table by authors
Publication year, subject area and document type
| Publication year | No. of papers | Subject area | No. of papers | Document type | No. of papers |
|---|---|---|---|---|---|
| 2003 | 1 | Computer science | 939 | Journal article | 916 |
| 2004 | 4 | Business, management and accounting | 648 | Conference paper | 440 |
| 2005 | 7 | Social sciences | 326 | Book chapter | 69 |
| 2007 | 2 | Engineering | 221 | Conference review | 44 |
| 2008 | 8 | Decision sciences | 197 | Review | 40 |
| 2009 | 14 | Economics, econometrics and finance | 189 | Editorial | 16 |
| 2010 | 20 | Mathematics | 125 | Book | 11 |
| 2011 | 31 | Psychology | 99 | Note | 3 |
| 2012 | 51 | Arts and humanities | 78 | Data paper | 1 |
| 2013 | 72 | Environmental science | 46 | Erratum | 1 |
| 2014 | 79 | Energy | 37 | Short survey | 1 |
| 2015 | 84 | Agricultural and biological sciences | 19 | Retracted | 1 |
| 2016 | 101 | Medicine | 19 | ||
| 2017 | 108 | Physics and astronomy | 12 | ||
| 2018 | 133 | Materials science | 11 | ||
| 2019 | 198 | Multidisciplinary | 8 | ||
| 2020 | 187 | Chemical engineering | 4 | ||
| 2021 | 179 | Biochemistry, genetics and molecular biology | 3 | ||
| 2022 | 203 | Neuroscience | 3 | ||
| 2023 | 61* | Nursing | 2 |
Note(s): *The number of publications for 2023 is taken up to 6 April 2023 only
Source(s): Appendix by authors
Top 50 ranking by journal, author and institution
| Journal | No. of papers | Author | No. of papers | University | No. of papers |
|---|---|---|---|---|---|
| ACM International Conference Proceeding Series | 43 | Hajli, N. | 32 | City University of Hong Kong | 34 |
| Lecture Notes in Computer Science including subseries lecture notes in Artificial Intelligence and Lecture Notes in Bioinformatics | 43 | Shanmugam, M. | 15 | University of Science and Technology of China | 28 |
| International Journal of Information Management | 37 | Dwivedi, Y.K. | 11 | Swansea University | 27 |
| Electronic Commerce Research and Applications | 31 | Benyoucef, M. | 10 | Universiti Teknologi Malaysia | 24 |
| Journal of Retailing and Consumer Services | 27 | Chen, X. | 10 | School of Management, University of Ottawa | 24 |
| Information and Management | 24 | Lin, X. | 10 | Universiti Tenaga Nasional | 22 |
| Computers in Human Behavior | 22 | Sundaram, D. | 10 | Hefei University of Technology | 18 |
| Internet Research | 21 | Davison, R.M. | 9 | Newcastle University Business School, United Kingdom | 17 |
| Frontiers in Psychology | 19 | Hussin, A.R.C. | 9 | Newcastle University | 16 |
| Sustainability Switzerland | 19 | Liu, L. | 9 | Dalian University of Technology | 15 |
| Information Technology and People | 18 | Wang, Y. | 9 | Hong Kong Baptist University | 15 |
| Journal of Theoretical and Applied Electronic Commerce Research | 16 | Cheung, C.M.K. | 8 | Universitas Indonesia | 15 |
| International Journal of Electronic Commerce | 14 | Herrando, C. | 8 | The University of Manchester | 14 |
| Journal of Business Research | 14 | Islam, T. | 8 | Huazhong University of Science and Technology | 14 |
| Journal of Electronic Commerce Research | 14 | Lee, I. | 8 | Xi'an Jiaotong University | 13 |
| Technological Forecasting and Social Change | 13 | Leong, L.Y. | 8 | Chaoyang University of Technology | 13 |
| Behaviour and Information Technology | 12 | Liébana-Cabanillas, F. | 8 | Universidad de Granada | 13 |
| Asia Pacific Journal of Marketing and Logistics | 11 | Lu, Y. | 8 | Wuhan University | 13 |
| Electronic Commerce Research | 11 | Rana, N.P. | 8 | Bina Nusantara University | 13 |
| Industrial Management and Data Systems | 11 | Shen, J. | 8 | Princess Nourah Bint Abdulrahman University | 13 |
| International Journal of E Business Research | 11 | Turel, O. | 8 | Swinburne University of Technology | 12 |
| Journal of Computer Information Systems | 11 | Yang, X. | 8 | Zhejiang University of Finance and Economics | 12 |
| Information Systems Frontiers | 10 | Bakar, A.A. | 7 | The University of Auckland | 11 |
| Decision Support Systems | 9 | Benbasat, I. | 7 | Birkbeck, University of London | 11 |
| Advances in Intelligent Systems and Computing | 8 | Choi, Y. | 7 | Zhejiang Gongshang University | 11 |
| British Food Journal | 8 | Gupta, S. | 7 | Renmin University of China | 11 |
| International Journal of Electronic Commerce Studies | 8 | Henninger, C.E. | 7 | Harbin Institute of Technology | 11 |
| Journal of Fashion Marketing and Management | 8 | Huang, Z. | 7 | Universiti Putra Malaysia | 11 |
| Journal of Internet Commerce | 8 | Mikalef, P. | 7 | National Chengchi University | 11 |
| Communications in Computer and Information Science | 7 | Pappas, I.O. | 7 | University of Ottawa | 11 |
| Developments in Marketing Science Proceedings of The Academy of Marketing Science | 7 | Peko, G. | 7 | École de Gestion Telfer (Telfer School of Management) | 11 |
| Electronic Markets | 7 | Sarker, P. | 7 | Azman Hashim International Business School | 11 |
| International Journal of Data and Network Science | 7 | Tajvidi, M. | 7 | Universiti Utara Malaysia | 10 |
| Journal of Theoretical and Applied Information Technology | 7 | Wang, X. | 7 | Beijing University of Posts and Telecommunications | 10 |
| Information Japan | 6 | Zhang, P. | 7 | Tamkang University | 10 |
| Information Resources Management Journal | 6 | Al-Adwan, A.S. | 6 | Universiti Malaya | 10 |
| International Journal of Business Information Systems | 6 | Attar, R.W. | 6 | McMaster University | 10 |
| International Journal of Electronic Marketing and Retailing | 6 | Boardman, R. | 6 | Qatar University | 10 |
| Journal of Research in Interactive Marketing | 6 | Dahlan, H.M. | 6 | Universiti Teknologi MARA | 9 |
| Journal of Strategic Marketing | 6 | Friedrich, T. | 6 | National Sun Yat-Sen University | 9 |
| Kybernetes | 6 | Grange, C. | 6 | Norges Teknisk-Naturvitenskapelige Universitet | 9 |
| Lecture Notes in Networks and Systems | 6 | Huang, Q. | 6 | University of International Business and Economics | 9 |
| Lecture Notes of The Institute for Computer Sciences Social Informatics and Telecommunications Engineering LNICST | 6 | Lee, M.K.O. | 6 | Beihang University | 9 |
| CEUR Workshop Proceedings | 5 | Liu, I.L.B. | 6 | Southwestern University of Finance and Economics | 9 |
| Information Development | 5 | Ooi, K.B. | 6 | Tsinghua University | 9 |
| Information Switzerland | 5 | Yuan, Y. | 6 | Universiti Tunku Abdul Rahman | 9 |
| International Journal of Electronic Business | 5 | Zhang, K.Z.K. | 6 | UCSI University | 9 |
| Online Information Review | 5 | Abareshi, A. | 5 | California State University, Fullerton | 8 |
| Conference on Human Factors in Computing Systems Proceedings | 4 | Abed, S.S. | 5 | Ministry of Education China | 8 |
| Expert Systems with Applications | 4 | Akram, U. | 5 | Kyung Hee University | 8 |
Source(s): Appendix by authors
Top 30 by country/territory and funder
| Country/territory | No. of papers | Funder | No. of papers |
|---|---|---|---|
| China | 342 | National Natural Science Foundation of China | 148 |
| United States | 278 | Fundamental Research Funds for the Central Universities, China | 32 |
| United Kingdom | 132 | Ministry of Science and Technology, Taiwan | 23 |
| Malaysia | 126 | National Office for Philosophy and Social Sciences, China | 15 |
| Taiwan | 109 | European Regional Development Fund | 13 |
| South Korea | 105 | Ministry of Education of the People's Republic of China | 13 |
| Indonesia | 63 | Ministry of Higher Education, Malaysia | 11 |
| Canada | 59 | Natural Science Foundation of Guangdong Province | 11 |
| India | 56 | National Research Foundation of Korea | 10 |
| Australia | 54 | European Social Fund | 7 |
| Hong Kong | 54 | Ministry of Education, Taiwan | 7 |
| Germany | 42 | Natural Science Foundation of Beijing Municipality | 7 |
| Spain | 41 | Universitas Indonesia | 7 |
| Saudi Arabia | 40 | Foundation for Innovative Research Groups of the National Natural Science Foundation of China | 6 |
| Thailand | 33 | Horizon 2020 Framework Programme, European Union | 6 |
| France | 31 | National Key Research and Development Program of China | 6 |
| Iran | 31 | China Postdoctoral Science Foundation | 5 |
| Pakistan | 30 | China Scholarship Council | 5 |
| Italy | 29 | Ministerio de Economía y Competitividad, Spain | 5 |
| Jordan | 24 | Natural Science Foundation of Anhui Province | 5 |
| Finland | 19 | Natural Sciences and Engineering Research Council of Canada | 5 |
| New Zealand | 18 | Research Grants Council, University Grants Committee | 5 |
| Qatar | 16 | Universiti Teknologi Malaysia | 5 |
| Turkey | 16 | Academy of Finland | 4 |
| Norway | 12 | City University of Hong Kong | 4 |
| Switzerland | 12 | Federación Española de Enfermedades Raras, Spain | 4 |
| Viet Nam | 12 | Humanities and Social Science Fund of Ministry of Education of China | 4 |
| Bangladesh | 11 | Lembaga Pengelola Dana Pendidikan, Indonesia | 4 |
| Singapore | 11 | Ministerio de Ciencia, Innovación y Universidades, Spain | 4 |
| United Arab Emirates | 11 | Ministry of Science, ICT, and Future Planning, South Korea | 4 |
Source(s): Appendix by authors
Top 50 most cited publications
| No. | Year | Document title | Authors | Journal title | Volume | Issue | Citation |
|---|---|---|---|---|---|---|---|
| 1 | 2011 | What drives social commerce: the role of social support and relationship quality | Liang T.-P., Ho Y.-T., Li Y.-W., Turban E. | International Journal of Electronic Commerce | 16 | 2 | 828 |
| 2 | 2013 | From e-commerce to social commerce: a close look at design features | Huang Z., Benyoucef M. | Electronic Commerce Research and Applications | 12 | 4 | 754 |
| 3 | 2013 | Effects of various characteristics of social commerce (s-commerce) on consumers' trust and trust performance | Kim S., Park H. | International Journal of Information Management | 33 | 2 | 601 |
| 4 | 2011 | Introduction to the special issue social commerce: a research framework for social commerce | Liang T.-P., Turban E. | International Journal of Electronic Commerce | 16 | 2 | 584 |
| 5 | 2010 | Deriving value from social commerce networks | Stephen A.T., Toubia O. | Journal of Marketing Research | 47 | 2 | 574 |
| 6 | 2016 | Social presence, trust, and social commerce purchase intention: an empirical research | Lu B., Fan W., Zhou M. | Computers in Human Behavior | 56 | 535 | |
| 7 | 2014 | What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences | Zhang H., Lu Y., Gupta S., Zhao L. | Information and Management | 51 | 8 | 472 |
| 8 | 2013 | Transforming homo economicus into homo ludens: a field experiment on gamification in a utilitarian peer-to-peer trading service | Hamari J. | Electronic Commerce Research and Applications | 12 | 4 | 470 |
| 9 | 2015 | Social commerce constructs and consumer's intention to buy | Hajli N. | International Journal of Information Management | 35 | 2 | 440 |
| 10 | 2012 | The evolution of social commerce: the people, management, technology, and information dimensions | Wang C., Zhang P. | Communications of the Association for Information Systems | 31 | 1 | 395 |
| 11 | 2014 | The role of social support on relationship quality and social commerce | Hajli M.N. | Technological Forecasting and Social Change | 87 | 330 | |
| 12 | 2017 | A social commerce investigation of the role of trust in a social networking site on purchase intentions | Hajli N., Sims J., Zadeh A.H., Richard M.-O. | Journal of Business Research | 71 | 322 | |
| 13 | 2011 | Harnessing the influence of social proof in online shopping: the effect of electronic word of mouth on sales of digital microproducts | Amblee N., Bui T. | International Journal of Electronic Commerce | 16 | 2 | 322 |
| 14 | 2016 | Consumer behavior in social commerce: a literature review | Zhang K.Z.K., Benyoucef M. | Decision Support Systems | 86 | 317 | |
| 15 | 2015 | Consumers' decisions in social commerce context: an empirical investigation | Chen J., Shen X.-L. | Decision Support Systems | 79 | 303 | |
| 16 | 2013 | Intention to purchase on social commerce websites across cultures: a cross-regional study | Ng C.S.-P. | Information and Management | 50 | 8 | 303 |
| 17 | 2013 | Social commerce: a contingency framework for assessing marketing potential | Yadav M.S., de Valck K., Hennig-Thurau T., Hoffman D.L., Spann M. | Journal of Interactive Marketing | 27 | 4 | 300 |
| 18 | 2013 | Social commerce research: an integrated view | Zhou L., Zhang P., Zimmermann H.-D. | Electronic Commerce Research and Applications | 12 | 2 | 297 |
| 19 | 2016 | Exploring consumers' impulse buying behavior on social commerce platform: the role of parasocial interaction | Xiang L., Zheng X., Lee M.K.O., Zhao D. | International Journal of Information Management | 36 | 3 | 263 |
| 20 | 2020 | The role of live streaming in building consumer trust and engagement with social commerce sellers | Wongkitrungrueng A., Assarut N. | Journal of Business Research | 117 | 257 | |
| 21 | 2014 | Understanding the paradigm shift to computational social science in the presence of big data | Chang R.M., Kauffman R.J., Kwon Y. | Decision Support Systems | 63 | 248 | |
| 22 | 2013 | User experience in social commerce: in friends we trust | Shin D.-H. | Behaviour and Information Technology | 32 | 1 | 234 |
| 23 | 2014 | Do actions speak louder than voices? The signaling role of social information cues in influencing consumer purchase decisions | Cheung C.M.K., Xiao B.S., Liu I.L.B. | Decision Support Systems | 65 | C | 229 |
| 24 | 2019 | How live streaming influences purchase intentions in social commerce: an IT affordance perspective | Sun Y., Shao X., Li X., Guo Y., Nie K. | Electronic Commerce Research and Applications | 37 | 227 | |
| 25 | 2013 | Can we get from liking to buying? Behavioral differences in hedonic and utilitarian Facebook usage | Pöyry E., Parvinen P., Malmivaara T. | Electronic Commerce Research and Applications | 12 | 4 | 205 |
| 26 | 2011 | Modeling consumer purchasing behavior in social shopping communities with clickstream data | Olbrich R., Holsing C. | International Journal of Electronic Commerce | 16 | 2 | 201 |
| 27 | 2010 | Antecedents and consequences of trust in online product recommendations an empirical study in social shopping | Hsiao K.-L., Lin J.C.-C., Wang X.-Y., Lu H.-P., Yu H. | Online Information Review | 34 | 6 | 200 |
| 28 | 2016 | Understanding social commerce: a systematic literature review and directions for further research | Busalim A.H., Hussin A.R.C. | International Journal of Information Management | 36 | 6 | 195 |
| 29 | 2015 | Social commerce: the transfer of power from sellers to buyers | Hajli N., Sims J. | Technological Forecasting and Social Change | 94 | 195 | |
| 30 | 2017 | Social interaction-based consumer decision-making model in social commerce: the role of word of mouth and observational learning | Wang Y., Yu C. | International Journal of Information Management | 37 | 3 | 194 |
| 31 | 2016 | Enhancing the flow experience of consumers in China through interpersonal interaction in social commerce | Liu H., Chu H., Huang Q., Chen X. | Computers in Human Behavior | 58 | 193 | |
| 32 | 2013 | A research framework for social commerce adoption | Hajli M. | Information Management and Computer Security | 21 | 3 | 189 |
| 33 | 1997 | Hope: an individual motive for social commerce | Snyder C.R., Cheavens J., Sympson S.C. | Group Dynamics | 1 | 2 | 189 |
| 34 | 2018 | Investigating the drivers for social commerce in social media platforms: importance of trust, social support and the platform perceived usage | Yahia I.B., Al-Neama N., Kerbache L. | Journal of Retailing and Consumer Services | 41 | 183 | |
| 35 | 2012 | Social comparison, social presence, and enjoyment in the acceptance of social shopping websites | Shen J. | Journal of Electronic Commerce Research | 13 | 3 | 174 |
| 36 | 2010 | Markets, morals, and practices of trade: jurisdictional disputes in the U.S. commerce in cadavers | Anteby M. | Administrative Science Quarterly | 55 | 4 | 174 |
| 37 | 2017 | Collaborative commerce in tourism: implications for research and industry | Sigala M. | Current Issues in Tourism | 20 | 4 | 170 |
| 38 | 2015 | Effect of social commerce factors on user purchase behavior: an empirical investigation from renren.com | Bai Y., Yao Z., Dou Y.-F. | International Journal of Information Management | 35 | 5 | 166 |
| 39 | 2011 | The influence of personal and social-interactive engagement in social TV websites | Pagani M., Mirabello A. | International Journal of Electronic Commerce | 16 | 2 | 164 |
| 40 | 2016 | Facebook C2C social commerce: a study of online impulse buying | Chen J.V., Su B.-C., Widjaja A.E. | Decision Support Systems | 83 | 160 | |
| 41 | 2020 | Consumers' decision-making process on social commerce platforms: online trust, perceived risk, and purchase intentions | Lăzăroiu G., Neguriţă O., Grecu I., Grecu G., Mitran P.C. | Frontiers in Psychology | 11 | 158 | |
| 42 | 2017 | Customers' purchase decision-making process in social commerce: a social learning perspective | Chen A., Lu Y., Wang B. | International Journal of Information Management | 37 | 6 | 157 |
| 43 | 2017 | The influence of perceived value on purchase intention in social commerce context | Gan C., Wang W. | Internet Research | 27 | 4 | 153 |
| 44 | 2011 | Social commerce: looking back and forward | Curty R.G., Zhang P. | Proceedings of the ASIST Annual Meeting | 48 | 153 | |
| 45 | 2017 | Social commerce research: Definition, research themes, and the trends | Lin X., Li Y., Wang X. | International Journal of Information Management | 37 | 3 | 147 |
| 46 | 2015 | User preferences of social features on social commerce websites: an empirical study | Huang Z., Benyoucef M. | Technological Forecasting and Social Change | 95 | 143 | |
| 47 | 2018 | Marketing mix, customer value, and customer loyalty in social commerce: a stimulus-organism-response perspective | Wu Y.-L., Li E.Y. | Internet Research | 28 | 1 | 139 |
| 48 | 2013 | Website features that gave rise to social commerce: a historical analysis | Gonçalves Curty R., Zhang P. | Electronic Commerce Research and Applications | 12 | 4 | 135 |
| 49 | 2012 | How consumer shopping orientation influences perceived crowding, excitement, and stress at the mall | Baker J., Wakefield K.L. | Journal of the Academy of Marketing Science | 40 | 6 | 132 |
| 50 | 2010 | Seniors' online communities: a quantitative content analysis | Nimrod G. | Gerontologist | 50 | 3 | 132 |
| Total | 14006 | ||||||
Source(s): Appendix by authors
Science network mappings of the top 50 by authors and institutions
| No. | Author | No. of papers | Citations | Institution | No. of papers | Citations |
|---|---|---|---|---|---|---|
| 1 | Liang T.-P. | 4 | 1,006 | Department of Information Systems, National Cheng-Chi University, Taiwan | 2 | 973 |
| 2 | Turban E. | 3 | 1,006 | University of California, Berkeley, United States | 2 | 973 |
| 3 | Benyoucef M. | 8 | 954 | National Sun Yat-Sen University, Taiwan | 2 | 571 |
| 4 | Hajli N. | 21 | 737 | Indian Institute of Management, Raipur, 492051, India | 2 | 315 |
| 5 | Zhang P. | 7 | 726 | School of Management, Wuhan University of Science and Technology, Wuhan, 430081, China | 2 | 315 |
| 6 | Huang Z. | 6 | 690 | School of Management, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui, 230026, China | 2 | 251 |
| 7 | Kim S. | 4 | 428 | Telfer School of Management, University of Ottawa, 55 Laurier East, Ottawa, ON K1N 6N5, Canada | 2 | 251 |
| 8 | Lu Y. | 7 | 418 | Newcastle University Business School, United Kingdom | 3 | 250 |
| 9 | Gupta S. | 6 | 365 | FHS St. Gallen, University of Applied Sciences, Switzerland | 2 | 225 |
| 10 | Zhang H. | 4 | 331 | Birkbeck, University of London, United Kingdom | 4 | 193 |
| 11 | Wang C. | 5 | 308 | Department of Interaction Science, Sungkyunkwan University, Seoul, South Korea | 2 | 184 |
| 12 | Fan W. | 3 | 296 | Faculty of Business and Finance, Universiti Tunku Abdul Rahman, Kampar, Malaysia | 4 | 150 |
| 13 | Lu B. | 3 | 296 | Department of Information Management, Shu-Te University, Kaohsiung, Taiwan | 2 | 148 |
| 14 | Wang Y. | 11 | 272 | Department of Information Systems, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong | 2 | 111 |
| 15 | Hajli M.N. | 3 | 249 | School of Economics and Management, Beihang University, Beijing, 100191, China | 2 | 111 |
| 16 | Lee M.K.O. | 6 | 244 | Faculty of Business and Information Science, UCSI University, Kuala Lumpur, Malaysia | 2 | 102 |
| 17 | Zheng X. | 5 | 214 | Graduate Institute of Technology, Innovation and Intellectual Property Management, National Chengchi University, Taiwan | 2 | 100 |
| 18 | Shen X.-L. | 3 | 209 | School of Management, Huazhong University of Science and Technology, Wuhan, 430074, China | 3 | 99 |
| 19 | Hajli M. | 5 | 197 | Department of Aviation and Supply Chain Management, Raymond J. Harbert College of Business, Auburn University, Auburn, al 36849, United States | 2 | 89 |
| 20 | Shen J. | 9 | 195 | College of Business and Entrepreneurship, University of Texas Rio Grande Valley, Edinburg, TX 78539, United States | 2 | 80 |
| 21 | Zhao D. | 3 | 190 | Department of Information Systems, City University of Hong Kong, Hong Kong | 5 | 74 |
| 22 | Holsing C. | 4 | 164 | Department of Information Systems, City University of Hong Kong, Hong Kong | 3 | 69 |
| 23 | Huang Q. | 4 | 154 | Department of Business Administration, National Taichung University of Science and Technology, Taichung, Taiwan | 2 | 67 |
| 24 | Lin X. | 6 | 142 | College of Economics and Management, South China Agricultural University, Guangzhou, 510642, China | 2 | 61 |
| 25 | Xiang L. | 3 | 141 | Department of Operations and Management Information Systems, Faculty of Business and Accountancy, University of Malaya, Kuala Lumpur, 50603, Malaysia | 2 | 60 |
| 26 | Shanmugam M. | 7 | 137 | School of Business, Kyung Hee University, Hoegi-Dong 1, Dongdaemoon-Gu, Seoul 130–701, South Korea | 2 | 57 |
| 27 | Wang X. | 5 | 136 | Newcastle University Business School, Newcastle University, United Kingdom | 3 | 55 |
| 28 | Leong L.-Y. | 6 | 132 | Faculty of Business and Accountancy, University of Malaya, Kuala Lumpur, 50603, Malaysia | 2 | 52 |
| 29 | Lee K. | 5 | 129 | Faculty of Business and Finance, Universiti Tunku Abdul Rahman, JAlan Universiti, Bandar Barat, Kampar, Perak 31900, Malaysia | 2 | 52 |
| 30 | Busalim A.H. | 4 | 128 | College of Hotel and Tourism Management, Kyung Hee University, Seoul, South Korea | 2 | 52 |
| 31 | Chen X. | 8 | 125 | School of Management, Huazhong University of Science and Technology, Wuhan, China | 3 | 50 |
| 32 | Ooi K.-B. | 4 | 122 | Indian Institute of Management, Raipur, India | 2 | 48 |
| 33 | Turel O. | 7 | 118 | University of British Columbia, Canada | 2 | 44 |
| 34 | Cheung C.M.K. | 6 | 118 | Telfer School of Management, University of Ottawa, Canada | 2 | 41 |
| 35 | Yao Z. | 4 | 115 | Degroote School of Business, Mcmaster University, Hamilton, Canada | 2 | 40 |
| 36 | Hew J.-J. | 3 | 114 | Department of Family and Consumer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States | 2 | 40 |
| 37 | Liu L. | 8 | 112 | School of Business Administration, Southwestern University of Finance And Economics, Chengdu, China | 2 | 39 |
| 38 | Lin J. | 4 | 108 | School of Business, Monash University, Selangor Darul Ehsan, Malaysia | 2 | 39 |
| 39 | Hussin A.R.C. | 9 | 106 | Department of International Business Administration, Chinese Culture University, 55, Hwa-Kang Road, Yang-Ming-Shan, Taipei, 11114, Taiwan | 2 | 38 |
| 40 | Li Y. | 10 | 105 | Department of Transportation and Logistics Management, National Chiao Tung University, 4 F, No. 118, Section 1, Chung Hsiao W. Road, Taipei, 100, Taiwan | 2 | 38 |
| 41 | Jaafar N.I. | 4 | 100 | School of Management, University of Science and Technology of China, Hefei, China | 6 | 35 |
| 42 | Khani F. | 3 | 92 | Economics and Management School, Wuhan University, China | 2 | 33 |
| 43 | Hu X. | 3 | 86 | School of Information Management, Wuhan University, China | 2 | 33 |
| 44 | Yen D.C. | 3 | 83 | School of Management and Economics, Beijing Institute of Technology, China | 2 | 33 |
| 45 | Li X. | 7 | 82 | Allame Tabatabee University, Iran | 2 | 29 |
| 46 | Wang B. | 3 | 80 | Azad University, Iran | 2 | 29 |
| 47 | Tajvidi M. | 5 | 79 | School of Management, Hefei University of Technology, Hefei, China | 3 | 29 |
| 48 | Li L. | 4 | 79 | Department of Information Systems, City University of Hong Kong, Kowloon, Hong Kong | 3 | 26 |
| 49 | Han H. | 3 | 78 | School of Management, Swansea University, Swansea, SA1 8EN, United Kingdom | 2 | 24 |
| 50 | Liana-Cabanillas F. | 4 | 77 | School of Economics and Management, Tongji University, Shanghai, China | 2 | 23 |
Source(s): Appendix by authors
Science network mappings of the top 50 by countries and keywords
| No | Country | No. of papers | Citations | Keyword | Occurrences | Cited reference | Citations |
|---|---|---|---|---|---|---|---|
| 1 | United States | 162 | 5,463 | Social commerce | 415 | Huang, Z., Benyoucef, M., From e-commerce to social commerce: a close look at design features (2013) Electronic Commerce Research and Applications, 12 (4), pp. 246–259 | 113 |
| 2 | China | 145 | 2,902 | Social commerces | 279 | Stephen, A.T., Toubia, O., Deriving value from social commerce networks (2010) Journal of Marketing Research, 47 (2), pp. 215–228 | 79 |
| 3 | Taiwan | 52 | 2,029 | Commerce | 246 | Liang, T.P., Ho, Y.T., Li, Y.W., Turban, E., What drives social commerce: the role of social support and relationship quality (2011) International Journal of Electronic Commerce, 16 (2), pp. 69–90 | 68 |
| 4 | United Kingdom | 70 | 1,678 | Electronic commerce | 182 | Fornell, C., Larcker, D.F., Evaluating structural equation models with unobservable variables and measurement error (1981) Journal of Marketing Research, 18 (1), pp. 39–50 | 63 |
| 5 | Canada | 32 | 1,159 | Social networking (online) | 176 | Kim, S., Park, H., Effects of various characteristics of social commerce (s-commerce) on consumers' trust and trust performance (2013) International Journal of Information Management, 33 (2), pp. 318–332 | 53 |
| 6 | South Korea | 71 | 1,064 | Sales | 144 | Liang, T.P., Turban, E., Introduction to the special issue social commerce: a research framework for social commerce (2011) International Journal of Electronic Commerce, 16 (2), pp. 5–14 | 53 |
| 7 | France | 11 | 783 | Social media | 136 | Liang, T.-P., Turban, E., introduction to the special issue social commerce: a research framework for social commerce (2011) International Journal of Electronic Commerce, 16 (2), pp. 5–14 | 53 |
| 8 | Malaysia | 77 | 663 | Trust | 93 | Liang, T.-P., Ho, Y.-T., Li, Y.-W., Turban, E., What drives social commerce: the role of social support and relationship quality (2011) International Journal of Electronic Commerce, 16 (2), pp. 69–90 | 52 |
| 9 | India | 25 | 624 | Information Systems | 79 | Gefen, D., Karahanna, E., Straub, D.W., Trust, and TAM in online shopping: an integrated model (2003) MIS Quarterly, 27 (1), pp. 51–90 | 46 |
| 10 | Hong Kong | 31 | 551 | Economic and social effects | 73 | Zhang, H., Lu, Y., Gupta, S., Zhao, L., What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences (2014) Information and Management, 51 (8), pp. 1017–1030 | 46 |
| 11 | Germany | 24 | 501 | E-commerce | 70 | Hajli, N., Social commerce constructs and consumer's intention to buy (2015) International Journal of Information Management, 35 (2), pp. 183–191 | 46 |
| 12 | Australia | 27 | 473 | Purchase intention | 62 | Zhou, L., Zhang, P., Zimmermann, H.D., Social commerce research: an integrated view (2013) Electronic Commerce Research and Applications, 12 (2), pp. 61–68 | 44 |
| 13 | Iran | 17 | 317 | Social shopping | 58 | Lu, B., Fan, W., Zhou, M., Social presence, trust, and social commerce purchase intention: an empirical research (2016) Computers In Human Behavior, 56, pp. 225–237 | 43 |
| 14 | Spain | 16 | 239 | Purchasing | 51 | Olbrich, R., Holsing, C., Modeling consumer purchasing behavior in social shopping communities with clickstream data (2011) International Journal of Electronic Commerce, 16 (2), pp. 15–40 | 40 |
| 15 | Switzerland | 4 | 231 | Consumer behavior | 49 | Kaplan, A.M., Haenlein, M., Users of the world, unite! the challenges and opportunities of social media (2010) Business Horizons, 53 (1), pp. 59–68 | 39 |
| 16 | Indonesia | 22 | 125 | Social support | 39 | Hajli, N., Social commerce constructs and consumer's intention to buy (2015) International Journal of Information Management, 35 (2), pp. 183–191 | 38 |
| 17 | Qatar | 7 | 97 | Decision making | 38 | Hajli, M.N., The role of social support on relationship quality and social commerce (2014) Technological Forecasting and Social Change, 87, pp. 17–27 | 37 |
| 18 | Turkey | 6 | 97 | Surveys | 36 | Zhou, L., Zhang, P., Zimmermann, H.-D., Social commerce research: an integrated view (2013) Electronic Commerce Research and Applications, 12 (2), pp. 61–68 | 35 |
| 19 | Norway | 8 | 91 | Websites | 35 | Gefen, D., Straub, D.W., Consumer trust in b2c e-commerce and the importance of social presence: experiments in e-products and e-services (2004) Omega, 32 (6), pp. 407–424 | 35 |
| 20 | Saudi Arabia | 19 | 89 | Social networking sites | 35 | Kim, S., Park, H., Effects of various characteristics of social commerce (s-commerce) on consumers' trust and trust performance (2013) International Journal of Information Management, 33 (2), pp. 318–332 | 35 |
| 21 | Greece | 4 | 87 | 35 | Yadav, M.S., De Valck, K., Hennig-Thurau, T., Hoffman, D.L., Spann, M., Social commerce: a contingency framework for assessing marketing potential (2013) Journal of Interactive Marketing, 27 (4), pp. 311–323 | 33 | |
| 22 | Tunisia | 2 | 78 | Behavioral research | 32 | Hajli, N., Sims, J., Social commerce: the transfer of power from sellers to buyers (2015) Technological Forecasting and Social Change, 94, pp. 350–358 | 32 |
| 23 | Sweden | 3 | 74 | S-commerce | 32 | Pavlou, P.A., Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model (2003) International Journal of Electronic Commerce, 7 (3), pp. 101–134 | 30 |
| 24 | Thailand | 18 | 74 | Information use | 30 | Hajli, M., A Research framework for social commerce adoption (2013) Information Management and Computer Security, 21 (3), pp. 144–154 | 29 |
| 25 | Oman | 3 | 70 | Social interactions | 28 | Curty, R.G., Zhang, P., Social commerce: looking back and forward (2011) Proceedings of The American Society for Information Science and Technology, 48 (1), pp. 1–10 | 27 |
| 26 | Pakistan | 8 | 63 | Social networks | 26 | Amblee, N., Bui, T., Harnessing the influence of social proof in online shopping: the effect of electronic word of mouth on sales of digital micro products (2011) International Journal of Electronic Commerce, 16 (2), pp. 91–114 | 26 |
| 27 | Kuwait | 2 | 60 | WEB 2.0 | 26 | Lin, X., Li, Y., Wang, X., Social commerce research: definition, research themes, and the trends (2017) International Journal of Information Management, 37 (3), pp. 190–201 | 26 |
| 28 | Chile | 4 | 51 | Human | 25 | Davis, F.D., Perceived usefulness, perceived ease of use, and user acceptance of information technology (1989) MIS Quarterly, 13 (3), pp. 319–340 | 26 |
| 29 | Portugal | 3 | 49 | Online Shopping | 25 | Curty, R.G., Zhang, P., Website features that gave rise to social commerce: a historical analysis (2013) Electronic Commerce Research and Applications, 12 (4), pp. 260–279 | 25 |
| 30 | Romania | 4 | 43 | Social presence | 24 | Wang, C., Zhang, P., The evolution of social commerce: the people, management, technology, and information dimensions (2012) Communications of The Association for Information Systems, 31 (5), pp. 105–127 | 25 |
| 31 | Iceland | 1 | 39 | Marketing | 23 | Gefen, D., E-commerce: the role of familiarity and trust (2000) Omega, 28 (6), pp. 725–737 | 25 |
| 32 | Jordan | 10 | 36 | Internet | 20 | Kim, D.J., Ferrin, D.L., Rao, H.R., A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents (2008) Decision Support Systems, 44 (2), pp. 544–564 | 24 |
| 33 | Austria | 4 | 35 | Social aspects | 18 | Zhang, K.Z., Benyoucef, M., Consumer behavior in social commerce: a literature review (2016) Decision Support Systems, 86, pp. 95–108 | 23 |
| 34 | Italy | 8 | 32 | Article | 17 | Parboteeah, D.V., Valacich, J.S., Wells, J.D., The influence of website characteristics on a consumer's urge to buy impulsively (2009) Information Systems Research, 20 (1), pp. 60–78 | 22 |
| 35 | Macau | 2 | 31 | Purchase decision | 17 | Mcknight, D.H., Choudhury, V., Kacmar, C., Developing and validating trust measures for e-commerce: an integrative typology (2002) Information Systems Research, 13 (3), pp. 334–359 | 22 |
| 36 | Japan | 2 | 28 | Technology Acceptance Model | 17 | Shen, J., Social comparison, social presence, and enjoyment in the acceptance of social shopping websites (2012) Journal of Electronic Commerce Research, 13 (3), pp. 198–212 | 22 |
| 37 | Finland | 6 | 25 | Perceived usefulness | 16 | Ng, C.S.P., Intention to purchase on social commerce websites across cultures: a cross-regional study (2013) Information and Management, 50 (8), pp. 609–620 | 21 |
| 38 | Ecuador | 1 | 18 | Social network | 15 | Hassanein, K., Head, M., Manipulating perceived social presence through the web interface and its impact on attitude towards online shopping (2007) International Journal of Human-Computer Studies, 65 (8), pp. 689–708 | 21 |
| 39 | Netherlands | 2 | 14 | World Wide Web | 15 | Zhang, K.Z.K., Benyoucef, M., Consumer behavior in social commerce: a literature review (2016) Decision Support Systems, 86, pp. 95–108 | 21 |
| 40 | South Africa | 2 | 14 | eWoM | 15 | Podsakoff, P.M., Mackenzie, S.B., Lee, J.Y., Podsakoff, N.P., Common method biases in behavioral research: a critical review of the literature and recommended remedies (2003) Journal of Applied Psychology, 88 (5), pp. 879–903 | 20 |
| 41 | New Zealand | 9 | 12 | Least squares approximations | 14 | 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 (2017) Journal of Business Research, 71, pp. 133–141 | 20 |
| 42 | Iraq | 2 | 9 | Research models | 14 | Wang, Y., Yu, C., Social interaction-based consumer decision-making model in social commerce: the role of word of mouth and observational learning (2017) International Journal of Information Management, 37 (3), pp. 179–189 | 20 |
| 43 | Bangladesh | 4 | 6 | Word of mouth | 14 | Arnold, M.J., Reynolds, K.E., Hedonic shopping motivations (2003) Journal of Retailing, 79 (2), pp. 77–95 | 20 |
| 44 | Singapore | 3 | 6 | TAM | 14 | Anderson, J.C., Gerbing, D.W., Structural equation modeling in practice: a review and recommended two-step approach (1988) Psychological Bulletin, 103 (3), pp. 411–423 | 19 |
| 45 | Denmark | 1 | 6 | Information management | 14 | Mayer, R.C., Davis, J.H., Schoorman, F.D., An integrative model of organizational trust (1995) Academy of Management Review, 20 (3), pp. 709–734 | 19 |
| 46 | Israel | 1 | 5 | Human computer interaction | 14 | Shin, D.-H., User experience in social commerce: in friends we trust (2013) Behaviour and Information Technology, 32 (1), pp. 52–67 | 18 |
| 47 | Russian Federation | 1 | 4 | Consumption behavior | 14 | Stewart, K.J., Trust transfer on the World Wide Web (2003) Organization Science, 14 (1), pp. 5–17 | 18 |
| 48 | Nigeria | 3 | 3 | Motivation | 14 | Morgan, R.M., Hunt, S.D., The commitment-trust theory of relationship marketing (1994) Journal of Marketing, 58 (3), pp. 20–38 | 18 |
| 49 | Sri Lanka | 1 | 3 | Social capital | 14 | Busalim, A.H., Hussin, A.R.C., Understanding social commerce: a systematic literature review and directions for further research (2016) International Journal of Information Management, 36 (6), pp. 1075–1088 | 18 |
| 50 | Mexico | 1 | 2 | Structural equation modeling | 13 | Ng, C.S.P., Intention to purchase on social commerce websites across cultures: a cross-regional study (2013) Information and Management, 50 (8), pp. 609–620 | 18 |
Source(s): Appendix by authors
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