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Article

Bibliometric Study on the Social Shopping Concept

by
Branca Barbosa
1 and
José Duarte Santos
2,*
1
ISCAP, Polytechnic of Porto (ISCAP/PP), 4465-004 S. Mamede de Infesta, Portugal
2
CEOS.PP, ISCAP, Polytechnic of Porto (ISCAP/PP), 4465-004 S. Mamede de Infesta, Portugal
*
Author to whom correspondence should be addressed.
Adm. Sci. 2023, 13(10), 213; https://doi.org/10.3390/admsci13100213
Submission received: 10 August 2023 / Revised: 14 September 2023 / Accepted: 21 September 2023 / Published: 25 September 2023
(This article belongs to the Special Issue Change Management and Innovation Strategies for Digital–Quantum Business Transformation)
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Abstract

:
Social shopping has been increasing its impact on e-commerce, motivating companies to rethink their product or service marketing strategies. Also, academics from various countries, aware of this growth, seek, through their scientific publications in various journals, to present studies that allow them to identify elements that contribute to the consolidation of the social shopping concept. Bearing this reality in mind, this study, supported by a bibliometric analysis on social shopping and based on articles published in the last 20 years, seeks to analyze the evolution of scientific research, to identify the most influential scientific publications (for topics related to social shopping) and to detect research opportunities in social shopping, with 39% of scientific publication occurring in the years 2020 to 2022. The study also presents themes that are associated with social shopping, highlighting COVID-19, consumer behavior and online shopping, aspects that companies should consider in their e-commerce strategy. The study also identifies the most influential scientific publications for topics related to social shopping and for detecting research opportunities in social shopping, supported in six clusters.

1. Introduction

Technological advances have significantly changed the way people buy and sell products and services. The growth of e-commerce and media has created new opportunities for businesses and consumers (Nacar and Ozdemir 2022). Social networks are increasingly penetrated and can be a channel for interaction with consumers that contributes to persuasion, conversion and customer loyalty, constituting a growing distribution channel (Pires et al. 2022).
The emergence of the COVID-19 pandemic has also transformed the business-to-consumer relationship, as well as consumer behavior (Basaure et al. 2021; Pantano et al. 2020; Ribeiro et al. 2019), and had implications for academic publications in various areas of knowledge, which can be seen in various bibliometric studies carried out, such as Gatto et al. (2023). This shows the impact that COVID-19 has had on areas of society, such as the economy, religion, politics, technology and medicine. Marketing has not escaped this either and COVID-19 cannot help but be seen as one of the driving forces behind e-commerce in general and social shopping in particular.
Consumer behavior is influenced by internal factors, intrinsic to the individual, and external factors, such as the perceived quality of services when buying online (Panarello and Gatto 2022). By introducing social shopping as another interaction and sales channel, a company boosts its omnichannel strategy (Hüseyinoğlu et al. 2017) and adds value for the customer, influencing their behavior.
Social commerce not only provides a different shopping experience, but also contributes to the discovery of new products (Shen 2012). Social commerce has been broadening its area of operation, no longer centered solely on websites, but also being driven by new technological solutions in social networks, and now has a presence on social networks. Recognizing this trend, social networks have invested in e-commerce features and tools, simplifying the process for companies to sell their products directly through social media applications (Singh and Singh 2018). Shopping posts, digital storefronts and in-feed shopping are some of the features implemented by social networks, allowing users to explore new products, engage with brands and make purchases seamlessly in the social media environment, contributing to the development of e-shopping (Sohn and Kim 2020).
Social shopping, social commerce and e-commerce are terms that describe the way people buy and sell products and services online. Although they have some similarities, each of these terms describes a different approach to e-commerce (Bilgihan et al. 2016; Busalim and Ghabban 2021; Huang and Benyoucef 2015; Jain et al. 2021; Kim and Chan-Olmsted 2022; Liao et al. 2022; Molinillo et al. 2018; Niranjanamurthy et al. 2013; S. Wang 2020).
The decision to buy through e-shopping can be motivated by different value perspectives that the customer may value, such as utilitarian and hedonic value (Overby and Lee 2006). Convenience, available information, social interaction and shopping experience are elements that cannot be overlooked in an online shopping process (Rohm and Swaminathan 2004). Kuşçu and Yozgat (2019) suggested that an online shopping process should provide utilitarian factors (control, convenience, assortment, economy/cost saving, availability of information) and hedonic factors (adventure, social, value/pleasure bargains).
Given the increasing importance of social shopping for the business world from an omnichannel perspective (Hüseyinoğlu et al. 2017), its association with social media communication channels (Anugerak et al. 2022) energized by digital marketing (C. L. Wang 2021) and also the growing interest in academics in the specific nature of social shopping, it was considered pertinent to carry out this study, the results of which will allow future studies to be directed towards elements that contribute in an isolated or aggregated way to the development of the social shopping concept, which herein have been detected and grouped.
There are several bibliometric studies on e-commerce and other areas, such as digital marketing (Guillén-Pujadas et al. 2023), logistics (Cano et al. 2022) or artificial intelligence (Bawack et al. 2022). There are also bibliometric studies on social commerce (Goyal et al. 2021) combined with other topics, such as purchase intent (Dincer and Dincer 2023). However, no bibliometric study applied to social shopping was found. This opportunity, combined with the relevance of the subject, which has already been substantiated, justifies the development of this research.
The article is divided into four parts. The first part reviews the literature on e-commerce, social commerce and social shopping. The next part presents the methodology followed by the analysis of the results obtained from the bibliometric analysis. Finally, the final considerations are presented.

2. Literature Review

2.1. E-Commerce

With the advancement of technology and the creation of the World Wide Web, new forms of trade have emerged (Melo 2021). One of the main forms is the purchase and sale of products and/or services through digital channels, known as e-commerce (Silvestre 2020).
According to Clarke (1999), e-commerce is defined as the trade of goods and services through telecommunications tools. Allison (2013) considers e-commerce as an electronic contract for the exchange of value using information and communication technologies. E-commerce involves the use of the internet, the web, apps and browsers running on mobile devices to conduct digital business transactions (Laudon and Traver 2021).
E-commerce has been evolving since 1995, but it was in 2007 that it was transformed and leveraged with the rapid growth of Web 2.0 (Zhang et al. 2021). Schneider (2016) presented three waves/evolutions: the first from 1995 to 2003, characterized by the rapid growth of e-commerce, called the “boom”; the second from 2004 to 2009, characterized by the increase in broadband connections in the home environment and the third wave, which has occurred from 2010 to the present date, characterized by the growth of the use of digital devices and the growth of high-speed cell phone networks (Yuthayotin 2015). Some authors differ on the dates of the evolution of e-commerce, such as Laudon and Traver (2021).
With the mandatory confinements triggered by the COVID-19 pandemic, there was a reduction in traditional commerce, which, in some cases, even ceased to exist, with new growth opportunities for e-commerce, providing the emergence of the fourth wave, starting in 2020 (Basaure et al. 2021). Companies needed to reinvent themselves, betting on digital media to survive and there was exponential growth in e-commerce like never before (Premebida 2021).

2.2. Social Commerce

Social commerce is a relatively recent term in the commerce spectrum (Busalim et al. 2019). It was at Yahoo! in 2005 that the concept was first used (Rubel 2005). Yahoo!’s Shoposphere created the “Pick List” function that allowed users to give and receive product reviews (Zamrudi et al. 2016).
Baghdadi (2016) stated that social commerce is a new way of doing commerce in a collaborative and participatory way, which involves interactions between all participants in the value chain. Social commerce is considered by some researchers as a new model of commerce (Hajli 2015), with a consensus that it comes from e-commerce (Zhong 2012) and can be seen as a subsection of e-commerce, based on the integration of social networks with e-commerce and the adoption of Web 2.0 functionalities (Huang and Benyoucef 2013; Li et al. 2014; Salvatori and Marcantoni 2015). Some researchers consider social commerce as the update of e-commerce, which seeks to monetize online and social interactions and user-created content, with users actively participating (Cui et al. 2018; Hajli 2015; Han et al. 2018; Huang and Benyoucef 2013).
For Shin (2013), social commerce is limited to a web platform that connects users with other people online and leverages these connected networks for business, education and services, facilitating customer interactions and participation in ways that lead to qualifying outcomes. In turn, Hassan et al. (2016) considered social commerce as online buying and selling activities using social media technologies and digital platforms. Social commerce allows users to sell, buy, compare and share information about products and/or services to help other users purchase them, in marketplaces and online communities (Busalim and Hussin 2016; Esmaeili et al. 2015; Huang and Benyoucef 2013; Liang and Turban 2011; Yu et al. 2020; Zhou et al. 2013).
Indeed, most social commerce concepts refer to some aspects such as social media, social interactions, word-of-mouth, user-generated content, e-commerce and Web 2.0 (Dennison et al. 2009; Han et al. 2018). Driven by social media, social commerce facilitates the buying and selling of various products and/or services (Kim and Park 2013; Wang and Zhang 2012; Zhou et al. 2013).
Social commerce is an interdisciplinary topic that covers social media technology, analytical techniques, business models and strategies, customer and business behaviors, system design, website design and business practice (Zhou et al. 2013). The concept has evolved over the last few years, as social commerce encompasses several disciplines, thus being presented from various perspectives depending on the authors (Goraya et al. 2021). This evolution, according to Wang and Zhang (2012), is supported by four factors: people, management, technology and information.

2.3. Social Shopping

Like social commerce, there are several definitions, some without consensus, for social shopping, which is also known as Sales 2.0. Social shopping emerged in 2008 as a new e-commerce model and provided a new way for companies to access customers (Lee and Lee 2012). Some authors use the term social shopping as a subcategory of social commerce and social media (Curty and Zhang 2013).
Social shopping is a form of e-commerce on social media (Lim and Beatty 2011). It allows users to make purchases directly on social media, such as Instagram, Facebook and TikTok, and to interact with other users and influencers through available possibilities such as product reviews and access to user-created content (Mróz 2021).
The concept of social shopping has been growing strongly in recent years, as consumers increasingly use social media to discover new products (Ahmad et al. 2022). A study developed by Accenture (2022) estimated that the value of social commerce sales in 2025, worldwide, will represent 1.2 trillion dollars. In turn, Insider Intelligence (2023) mentions that social commerce in the US will reach a value of 79.64 billion dollars in 2025, representing only about a tenth of the size of the social commerce market in China.
Social media has also invested in e-commerce features and tools to facilitate businesses to sell their products through social media (Singh and Singh 2018). Social media, in general, has shown steady growth, bringing customers together, who in turn create content, such as product sharing, reviews and comments on social media and on online shopping platforms (Fu et al. 2020).
Social shopping is an approach to online shopping. It is based on interpersonal interactions between social media users, where consumers’ perceptions, attitudes and purchase intentions are influenced by their friends and other users through posts, shares, comments and recommendations (Xu and Lee 2019). In the context of social shopping, people are doing more than shopping online on their own (Hu et al. 2016). Compared to traditional online shopping, social shopping makes it easier and more convenient for users to explore products of their interest and receive buying advice, thus enhancing and personalizing their overall shopping experience (Kim and Park 2013). Moreover, social shopping is more than just buying products; it is also about creating an online community, where people can gain a greater social presence (Zhang et al. 2018).
Social shopping has fundamentally changed the way businesses and consumers engage with each other (Wang et al. 2020). It is a dynamic and engaging experience that replaces lengthy transactions (Mileva 2022). Social media has included tools such as shopping posts and digital storefronts. These features allow users to discover new products, meet new brands and shop without leaving social media. Depending on the social network, consumers can use hashtags, the store tab, tags and other tools to find brands and products. They can also interact with companies and other customers through chats, content created by influencers and public and private groups. Brands create original content to attract customers (Mileva 2022).
Social shopping combines product content, images and videos, stories, directories, consumer reviews and more (Liao et al. 2022). It can be perceived as a journey through commerce, where users research and explore products and engage with brands (Mileva 2022). Thus, commercial transactions supported by brands’ online engagement with customers become personal and relational (Mileva 2022).
With the growth of social media usage and people becoming more comfortable with online shopping, social shopping is expected to take on a more important role in e-commerce (Wu et al. 2023).

2.4. E-Commerce vs. Social Commerce vs. Social Shopping

E-commerce, social commerce and social shopping are all related but have distinct differences (Han et al. 2018). Social commerce is a type of e-commerce, a shopping experience using technology, which allows interaction between sellers and consumers while shopping, providing a mechanism for social shopping activities (Shen 2012; Sturiale and Scuderi 2013; Wu et al. 2018).
To summarize, e-commerce is the broader term that encompasses all commercial transactions that take place online (Chaffey et al. 2019), social shopping presents itself as a subcategory of social commerce (Li 2019) focusing on the social aspect of shopping (Wu et al. 2018), while social commerce integrates social media with e-commerce to create a seamless shopping experience (Liao et al. 2022) (see Figure 1).
The Table 1 summarizes the concepts of e-commerce, social commerce and social shopping by combining different authors’ contributions.

3. Methodology and Research Design

This paper, supported by a bibliometric study, focuses on social shopping and aims to answer the following four research questions:
RQ1: How has the concept of social shopping evolved in academic research over the past 20 years?;
RQ2: Which are the most influential scientifical publications on social shopping?;
RQ3: Which themes involving social shopping are the most popular and emergent among scholars?;
RQ4: What are future research areas in the field of social shopping?
With bibliometric analysis using statistical tools, it is possible to carry out a quantitative analysis of scientific production (Ellegaard and Wallin 2015), through a systematic, transparent and reducible review process (Zupic and Čater 2015). It is also possible to aggregate bibliographic data, using the example of citation, to discover the main themes and research trends. However, it also has limitations unless, for example, certain qualitative elements are considered, such as the impact factor of a journal (Hicks et al. 2015).
Bibliometric analysis methods are used to provide an overview of published scientific articles. This type of analysis is based on the processing of bibliometric data collected in databases such as Scopus, Dimensions, The Lens, PubMed or Web of Science. In recent years, bibliometric methods have been increasing in research works, due to their reliability and, mainly, their efficiency (Mukherjee et al. 2022).
The methodology of this bibliometric analysis was based on the PRISMA method, which presents a set of guidelines for the preparation of systematic reviews and meta-analyses (Moher et al. 2009, 2015). These guidelines consist of a 27-point checklist and a 4-phase flowchart. This list includes the study title, abstract, introduction, methods, results, discussion and funding, as well as points related to search strategy, study selection, data extraction and risk of bias assessment (Page et al. 2021).
The database chosen was The Lens, a free and open data platform with 144.3 million patents and 252.1 million academic scientific articles and research papers in the fields of science, technology, engineering and mathematics for researchers and students (The Lens n.d.).
The keywords ‘social shopping’, ‘online shopping’ and ‘digital shopping’ were used to perform the search from a disjunctive perspective, using the OR operator between the three words. The analysis focused on the last 20 years, that is, the search was restricted to publications between 2003 and 2022. The perspective of technical–scientific quality existing in the type of publication, journal articles, books, book chapters, conference proceedings and conference proceedings was considered. Also, to obtain more specific results within the scope of this study, ‘business’, ‘e-commerce’ and ‘marketing’ were selected as fields of study. A total of 28,520 publications were obtained. In a previous analysis of the results, it was understood that some publications did not focus on the research field. Thus, the search was refined using the fields, title, abstract, keywords and field of study and 15,322 publications were obtained. Finally, for the analysis and visualization of the data obtained, the VOSviewer software version 1.6.19 and the Biblioshiny software version 4 were used. The second software was used to be able to generate graphs that allow for a better graphical representation compared to VOSViewer.
The analysis looks at the distribution of publications over the 20 years analyzed, also identifying the five types of documents considered: book, conference proceedings article, book chapter, journal article and conference proceedings. The top 10 journals that contributed the largest number of publications, the top 10 countries with the largest number of publications, the top 10 authors and the top 10 most-cited articles are identified. Finally, analysis of co-citations, keyword analysis and keywords by cluster analysis are part of step 5 (see Figure 2).
The following figure presents the methodology used in the research, based on the PRISMA method.

4. Analysis

4.1. Descriptive Analysis

This study includes 15,322 documents over a period time of 20 years, i.e., between 2003 and 2022. Figure 3 shows the volume of annual scientific production resulting from the research carried out considering the elements presented in steps 2 and 3 in Figure 2. Thus, it is possible to verify that the annual scientific production on the topic of social shopping shows growth. Considering that, in 2003, 159 documents were published and, in 2022, 2395 documents were published, the growth was 1406%.
The biggest annual increase is seen in 2021, possibly due to the change in consumer behavior because of COVID-19, thus posing challenges for companies in optimizing online shopping, boosting online channels and, at the same time, the concern of researchers to analyze these transformations through online shopping, particularly in social shopping.
The statistics show that 15,322 publications were published in 6384 different journals. Table 2 shows that the top 10 journals accounted for 945 of the publications, with Sustainability magazine having the highest number: 191 publications.
Figure 4 shows the most active countries in the scientific production of the research field, highlighting the USA and China with the most publications.
Complementing Figure 4, Table 3 illustrates the 10 countries with the highest scientific production of the searched terms. This analysis is not intended to highlight the country’s productivity, but rather to identify the countries where there is a greater concern in investigating the issue of social shopping, which could possibly be justified by the geographical origin of two of the largest e-commerce platforms: Amazon.com and Alibaba.com.
Table 4 shows the ten authors who contributed the most to the scientific production. Kyu-Hye Lee is the author who presents the most scientific production, with twelve publications, followed by two authors, Chandra Sekhar Patro and Charles Dennis, with ten publications each. Most of the authors presented are from Asia, thus verifying that it is the continent that presents the greatest scientific production, considering the search carried out with the terms ‘social shopping’, ‘online shopping’ or ‘digital shopping’.

4.2. Analysis of Co-Citations

The number of times two articles are mentioned together in co-citation analyses enables the verification of cited references, authors and publication sources. Typically, publications cited together have similar subjects (Luo et al. 2019). The most prominent articles and journals in the field of study can be located via citation analysis and clusters can be obtained (Hjørland 2013).
A total of 6384 journals were found but, considering only the journals that have at least 5 documents and at least 20 citations per journal, 270 journals were found. Figure 5 shows the connection between these journals, verifying the existence of eight clusters, with the red cluster being the most relevant. This cluster presents the journals with the most citations: the International Journal of Retail & Distribution Management, with 8996 citations; the Management Information System Quarterly, with 6307 citations and the Journal of Interactive Marketing, with 5147 citations.
Table 5 shows the 10 most-cited articles. The articles with the highest number of citations are “Trust and TAM in online shopping: An integrated model” (Gefen et al. 2003), with 5892 citations and “From multi-channel retailing to omni-channel retailing: introduction to the special issue on multi-channel retailing” (Verhoef et al. 2015), with 1351 citations. It is also found that the most-cited article is the one with the highest average annual citations (310.1).

4.3. Keyword Analysis

Analyzing authors’ keywords, which helps researchers identify the most relevant publications in the research field, can help identify research trends as well as gaps in the research field (Wang and Chai 2018). Analyzing Figure 6, which presents the 20 most frequent keywords, it is possible to identify that the most-cited keywords are ‘COVID-19’, ‘consumer behavior’ and ‘online shopping’. With these results, it is noticeable that the COVID-19 pandemic has had significant effects on social shopping research. It was also found that sales through social shopping and online shopping have evolved due to mandatory lockdowns and the impossibility of shopping traditionally, thus creating a modern digital culture, turning shopping into a digital activity (Gao et al. 2022).
Figure 7 presents the co-occurrence of authors’ keywords, which also reflects the impact of COVID-19 on social shopping. A minimum of 4 of occurrences of a keyword were considered and, thus, a total of 39 keywords distributed over 7 clusters were obtained. The largest cluster is the red cluster, with 13 words where the keyword ‘online shopping’ stands out, with 21 occurrences. It is followed by the green and dark blue clusters, both with nine words. In the green cluster, the keyword ‘consumer behavior’ stands out, with 21 occurrences. In the third cluster, with the dark blue color, the keyword ‘COVID-19’ stands out, with 82 occurrences, which is a word in all clusters with the highest number of occurrences. The furthest clusters are the fourth yellow cluster, where the keyword ‘public policy’ stands out; the fifth purple cluster, where the word ‘repurchase intention’ stands out with four occurrences and, finally, the sixth cluster, light blue, consisting of only two words, both with two occurrences.
Table 6 presents the keywords that compose each cluster.

4.3.1. Cluster 1 (Red)

The cluster presents the keywords ‘consumer behavior’, ‘customer satisfaction’, ‘marketing’, ‘e-commerce’, ‘social distancing’ and ‘purchase intention’, which are interconnected and important themes in the context of social shopping (Figure 7 and Table 6).
Social shopping is a model of e-commerce that combines the sale of products with customer engagement only on social media (Lee and Lee 2012). Social shopping involves using social media to create a more personalized and sociable shopping experience for customers (Xu and Lee 2019). Understanding consumer behavior is crucial to the effectiveness of social shopping, as companies need to understand what drives the buying behavior and preferences of their customers (Hsu et al. 2018). By providing a more personalized and interactive shopping experience, social shopping can increase customer satisfaction, which in turn can lead to greater loyalty and more repeat purchases (Li 2019; Xu and Lee 2019).
Purchase intent is a key metric for measuring the success of social shopping actions (Xiao et al. 2019). By monitoring purchase intent, companies can understand how effective their social shopping efforts are and make necessary adjustments to improve the overall customer experience. Customer satisfaction is another important aspect of social shopping, as it reflects the overall experience consumers have with a specific brand or product (Wu et al. 2018).
The cluster also emphasizes social distancing, due to the COVID-19 outbreak that made it impossible to purchase products/services in a traditional way. Thus, it has provided, since 2020, great growth in online shopping and encouraged scientific production on online shopping (Eger et al. 2021; Harris et al. 2017). Also, consumer behavior has changed in regard to their shopping habits, as consumers were forced to adhere to this method and, thus, discovered its benefits (Pantano et al. 2020) because consumer behavior is influenced by several factors, including their context (Ribeiro et al. 2019).
Marketing is also a critical component of social shopping, as it helps businesses reach and engage with potential customers (Chaffey et al. 2019). Marketing today is concerned with adding value and social shopping can contribute. By using more targeted marketing strategies tailored to specific customer segments, businesses can improve the likelihood of attracting and retaining customers (Ballestar et al. 2019).
Overall, all these topics are important to understand how social shopping can help companies improve their social shopping strategies and build stronger relationships with their customers.

4.3.2. Cluster 2 (Green)

Cluster 2 presents the keywords ‘consumer behavior’, ‘COVID-19 pandemic’ and ‘shopping behavior’, which are relevant topics in the current social shopping landscape. In the case of the keyword ‘consumer behavior’, the origin of the authors, especially those from the USA, motivated its appearance in duplicate, with a different form of spelling.
Analyzing the cluster, it appears that consumer behavior plays a crucial role in social shopping, as companies need to understand what motivates their customers to shop online and interact with their content on social media (Hsu et al. 2018). Customers satisfy their needs and want to be influenced by various factors (Ribeiro et al. 2019; Vargas et al. 2019; Wright 2006).
The COVID-19 pandemic has had a significant impact on shopping behavior, with many consumers turning to online shopping to avoid physical stores and minimize the risk of exposure to the virus (Pantano et al. 2020). This shift to e-commerce accelerated the growth of social shopping, as businesses sought new ways to interact with their customers online (Guthrie et al. 2021). With the COVID-19 pandemic, consumers rethought their shopping habits (Eger et al. 2021), increasing online shopping substantially during this period (Pantano et al. 2020) and continuing this habit for the remaining years (Cruz 2021).
As a result, understanding the impact of COVID-19 on shopping behavior has become increasingly important for companies seeking to succeed in the current e-commerce landscape. By understanding how the pandemic has affected consumer behavior and shopping habits, businesses can adapt their social shopping strategies to better meet the needs and preferences of their customers.

4.3.3. Cluster 3 (Dark Blue)

In cluster 3, the keywords ‘COVID-19’, ‘online shopping’ and ‘risk perception’ are interconnected themes that are relevant in the current social shopping landscape. We can see that these keywords are like cluster 1 red and cluster 2 green. However, there is a concern in studying the perception of the risk element, which is a factor that affects the customer’s involvement in the purchase process, such as the risk of not receiving the product ordered online or receiving something that is not in line with the expectations created (Mason et al. 2021).
The COVID-19 pandemic has led to significant changes in consumer behavior, including a shift to online shopping to minimize the risk of exposure to the virus (Pantano et al. 2020). Online shopping has seen slow but steady growth over the past decade (Harris et al. 2017). During the COVID-19 pandemic, online shopping increased exponentially (Pantano et al. 2020). This was due to mandatory lockdowns, which resulted in a reduction of traditional commerce and the adoption of online shopping (Eger et al. 2021). Social shopping has also been contributing to online shopping by providing customers with a more personalized and interactive shopping experience (Li 2019).
However, risk perception is also an important factor to consider in the context of online shopping. Consumers may be concerned about the safety of the products they buy online, as well as the potential risks associated with delivery and contact with delivery services (Mariani and Zappalà 2017). For this reason, companies need to take steps to address these concerns and build trust with their customers (Mou et al. 2017).

4.3.4. Cluster 4 (Yellow)

Cluster 4 features the keywords ‘advertising and promotion’, ‘price’, ‘public opinion’ and ‘public policy’, which tend to be interlinked in the context of marketing and social shopping.
Social shopping involves the use of social media to create a more personalized and interactive shopping experience for customers (Xu and Lee 2019). In turn, advertising and promotion are key factors in social shopping, as businesses need to effectively market their products to attract customers and encourage engagement on social media (Liao et al. 2022).
Price is another important factor to consider in social shopping, as customers are often looking for good deals and value for money (Busalim and Ghabban 2021). Thus, companies need to carefully consider their pricing strategies to ensure they are competitive in the market while maintaining profitability (Nagle and Muller 2018).
Public opinion also plays a role in social shopping, as customers are more likely to engage with companies that have a positive reputation and identify with their values (Busalim and Ghabban 2021). For this reason, companies need to be aware of their public image and take steps to build and maintain a strong brand reputation (Devita et al. 2021).

4.3.5. Cluster 5 (Purple)

Cluster 5 incorporates the keywords ‘brand identity’, ‘repurchase intention’ and ‘switching intention’.
Brand identity is important in social shopping, as it helps to differentiate a business from its competitors and build a strong and recognizable brand image (Wang et al. 2020). By effectively communicating their brand identity through social media, businesses can attract and retain customers who share their values and preferences (Bilgin 2018). This can be an effective way to build a brand identity, as companies can use social media to showcase their products and values in a way that matches their target audience (Kumar and Singh 2020).
Repurchase intention is another important factor to consider in social shopping. Customers who have a positive experience with a brand are more likely to repurchase from that brand in the future (Chinomona and Maziriri 2017). Social shopping can help build customer loyalty by creating a more appealing and personalized shopping experience for customers (Xu and Lee 2019).

4.3.6. Cluster 6 (Light Blue)

Cluster 6 groups the keywords ‘motivation’ and ‘theory of planned behavior’. Motivation plays a key role in social shopping, as customers are often motivated by factors such as social influence, personal interest and convenience in making purchasing decisions (Busalim and Ghabban 2021).
The theory of planned behavior is a theoretical framework that helps explain the factors that influence human behavior, including consumer behavior (Ajzen 2020). According to the theory, behavior is influenced by three factors: attitudes, subjective norms and perceived behavioral control (Shin and Hancer 2016). In the context of social shopping, the theory of planned behavior can help explain the factors that influence customers’ purchasing decisions (Li 2019). The social influence of friends and their perceived control over the shopping process can all have an impact on their behavior (Zhao et al. 2020).

5. Findings and Conclusions

With the COVID-19 pandemic in 2020, online shopping, especially on social media, has grown, which has led to a high interest in scientific production on the same topic. Although, in 2003, there were already 159 publications, it was in 2019 that more than half of the volume of scientific publications (54%) that were released between 2003 and 2022 took place. This growth follows the waves of the evolution of e-commerce and, in 2010, a third wave of growth in e-commerce occurred. In addition to mobile devices, social networks are taking on a more important role in business marketing, as they help businesses to advertise, promote and sell their products and services; thus, social networks are also beginning to introduce their e-commerce model.
The importance of social shopping is also reinforced by the emergence of new tools on Facebook and Instagram in 2017. The keyword ‘social media’ stands out. Social media plays a crucial role in e-commerce and social shopping, providing a platform for consumers to engage with and influence each other’s purchasing decisions. Technology adoption was also corroborated, as ‘technology adoption’ is one of the keywords presents.
E-commerce and all the categories it encompasses, including social shopping, has seen a strong evolution in the years following the COVID-19 pandemic. In the last three years of the 20 years analyzed, 39% of scientific production was recorded in the study area. This value can be seen as representative of the importance that social shopping has aroused among academics in the last three years. In turn, the keyword ‘COVID-19’ is also one of the most used, in addition to the words ‘coronavirus’, ‘pandemic’ and ‘SARS-CoV-2’, which demonstrates the concern about the impact of the pandemic on academia.
With the results obtained in this study, we can conclude that the countries and regions with the most scientific production were the USA, the Asian continent (China, Indonesia, India, Taiwan, South Korea, Malaysia), the United Kingdom and Australia. The level of scientific production is aligned with the business reality. In addition, the USA is one of the few countries in the world where Facebook and Instagram allow social shopping, providing special tools and, above all, allowing users to shop directly on these social networks.. The keyword ‘china’ was also highlighted, which is reflected in the Chinese business perspective and business dynamics. Some Asian countries, the US and the UK also already have social shopping on TikTok. This tool is new and still evolving, providing sellers with special tools to sell and users can buy directly from the social network.
In any type of commerce, whether physical or online, the consumer plays an important role, as customers are the main reason for the existence of companies. Consumer behavior is crucial not only in social shopping, but in e-commerce in general because it determines consumers’ purchasing decisions and the success of their platforms. Thus, the keywords, ‘consumer behavior’, ‘consumer satisfaction’, ‘purchase intention’, ‘shopping behavior’, ‘risk perception’ and ‘repurchase intention’ were suggested.
With the bibliometric analysis carried out, it was confirmed that consumer behavior, social networks and marketing are the most common themes related to social shopping.
Marketing also has some relevance in scientific production and the keywords ‘marketing’ and ‘advertising and promotion’ were identified. Marketing is essential in e-commerce and social shopping because it helps companies to reach and engage with their target audience, create brand awareness and drive sales.
Although COVID-19 no longer has the impact on companies that it did at the height of the pandemic, it has changed consumer behavior, created a concern among companies about the need to expand their digital presence, encouraging them to explore new marketing channels that, until now, had seen little investment by organizations. But this strategy, which involves social shopping, implies not only the usual monitoring of consumer behavior and satisfaction, but also an effective response from the technological capacity installed. Technology must be adapted to the needs of customers so that it can be adopted by them to fulfil an omnichannel vision.

5.1. Theoretical and Managerial Implications

This work on social shopping can contribute to both management and academia. By analyzing the existing literature on social shopping, the study provides a comprehensive view of the current state of research, identifying the main themes, trends and research gaps, i.e., non-existent themes, such as, for example, the non-prevalence in the publications studied of a customer relationship management strategy from the perspective of social shopping.
Companies can also benefit, as this study identified elements that should be worked on by the marketing of companies seeking to boost social shopping, such as the risk factor, advertising and promotion, price, brand identity and repurchase intention. It is hoped that companies can create more personalized and engaging shopping experiences for consumers, becoming more competitive in the rapidly evolving world of e-commerce.

5.2. Limitations and Future Research

The results obtained were a consequence of the choices made in steps 1 to 3, as presented in the methodology and research design section, namely, the database used and, especially, the words that supported the search. These choices therefore constitute a limitation of the work carried out.
In future work, it would be relevant to explore the contributions that technology can make to boost social shopping. Therefore, it is suggested to develop a study on the link between social shopping technology and social media marketing.

Author Contributions

Conceptualization, B.B. and J.D.S.; methodology, B.B. and J.D.S.; software, B.B.; validation, B.B. and J.D.S.; formal analysis, B.B.; investigation, B.B. and J.D.S.; resources, J.D.S.; data curation, B.B.; writing—original draft preparation, B.B.; writing—review and editing, J.D.S.; visualization, J.D.S.; supervision, J.D.S.; project administration, J.D.S.; funding acquisition, J.D.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work is financed by Portuguese national funds through FCT—Fundação para a Ciência e Tecnologia, under the project UIDP/05422/2020.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Accenture. 2022. Shopping on Social Media Platforms Expected to Reach $1.2 Trillion Globally by 2025, New Accenture Study Finds. Available online: https://newsroom.accenture.com/news/shopping-on-social-media-platforms-expected-to-reach-1-2-trillion-globally-by-2025-new-accenture-study-finds.htm (accessed on 5 April 2023).
  2. Ahmad, Zulaiha, Farah L. Azizan, Nor A. M. Shuib, Mohamad N. M. Nor, and Ahmad N. M. Noor. 2022. Examining the Determinants of Social Shopping Behaviour among Malaysian Social Media Users. Advances in Business Research International Journal 8: 10–20. [Google Scholar]
  3. Ajzen, Icek. 2020. The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies 2: 314–24. [Google Scholar] [CrossRef]
  4. Allison, Jerry. 2013. E-Commerce: Exactly What Is It? Available online: www.drjerryallison.hubpages.com (accessed on 15 April 2023).
  5. Anugerah, Adhe Rizky, Prafajar Suksessanno Muttaqin, and Wahyu Trinarningsih. 2022. Social network analysis in business and management research: A bibliometric analysis of the research trend and performance from 2001 to 2020. Heliyon 8: e09270. [Google Scholar] [CrossRef]
  6. Baghdadi, Youcef. 2016. A framework for social commerce design. Information Systems 60: 95–113. [Google Scholar] [CrossRef]
  7. Ballestar, María T., Pilar Grau-Carles, and Jorge Sainz. 2019. Predicting customer quality in e-commerce social networks: A machine learning approach. Review of Managerial Science 13: 589–603. [Google Scholar] [CrossRef]
  8. Basaure, Mauro, Alfredo Joignant, and Aldo Mascareño. 2021. Between distancing and interdependence: The conflict of solidarities in the COVID-19 pandemic. Sociological Perspectives 64: 706–25. [Google Scholar] [CrossRef]
  9. Bawack, Ransome Epie, Samuel Fosso Wamba, Kevin Daniel André Carillo, and Shahriar Akter. 2022. Artificial intelligence in E-Commerce: A bibliometric study and literature review. Electronic Markets 32: 297–338. [Google Scholar] [CrossRef]
  10. Bilgihan, Anil, Jay Kandampully, and Tingting Zhang. 2016. Towards a unified customer experience in online shopping environments: Antecedents and outcomes. International Journal of Quality and Service Sciences 8: 102–19. [Google Scholar] [CrossRef]
  11. Bilgin, Yusuf. 2018. The effect of social media marketing activities on brand awareness, brand image and brand loyalty. Business & Management Studies: An International Journal 6: 128–48. [Google Scholar]
  12. Busalim, Abdelsalam H., Ab R. C. Hussin, and Noorminshah A. Iahad. 2019. Factors influencing customer engagement in social commerce websites: A systematic literature review. Journal of Theoretical and Applied Electronic Commerce Research 14: 1–14. [Google Scholar] [CrossRef]
  13. Busalim, Abdelsalam H., and Fahad Ghabban. 2021. Customer engagement behaviour on social commerce platforms: An empirical study. Technology in Society 64: 101437. [Google Scholar] [CrossRef]
  14. Busalim, Abdelsalam H., and Ab Razak Che Hussin. 2016. Understanding social commerce: A systematic literature review and directions for further research. International Journal of Information Management 36: 1075–88. [Google Scholar] [CrossRef]
  15. Cano, Jose Alejandro, Abraham Londoño-Pineda, and Carolina Rodas. 2022. Sustainable logistics for e-commerce: A literature review and bibliometric analysis. Sustainability 14: 12247. [Google Scholar] [CrossRef]
  16. Chaffey, Dave, David Edmundson-Bird, and Tanya Hemphill. 2019. Digital Business and E-Commerce Management. Oxford: Pearson UK. [Google Scholar]
  17. Chinomona, Richard, and Eugine T. Maziriri. 2017. The influence of brand awareness, brand association and product quality on brand loyalty and repurchase intention: A case of male consumers for cosmetic brands in South Africa. Journal of Business and Retail Management Research 12: 143–54. [Google Scholar] [CrossRef]
  18. Clarke, Roger. 1999. Electronic Commerce Definitions. Roger Clarke’s Web-Site. Available online: http://www.rogerclarke.com/EC/ECDefns.html (accessed on 20 March 2023).
  19. Cruz, Catarina A. 2021. Alterações no comportamento do consumidor online: Antes versus durante a pandemia [Dissertation Master]. Instituto Superior de Contabilidade do Porto, Politécnico do Porto. Available online: https://recipp.ipp.pt/bitstream/10400.22/19926/1/Catarina_Cruz_MMKD_2021.pdf (accessed on 19 March 2023).
  20. Cui, Yi, Jian Mou, and Yanping Liu. 2018. Knowledge mapping of social commerce research: A visual analysis using CiteSpace. Electronic Commerce Research 18: 837–68. [Google Scholar] [CrossRef]
  21. Curty, Renata Gonçalves, and Ping Zhang. 2013. Website features that gave rise to social commerce: A historical analysis. Electronic Commerce Research and Applications 12: 260–79. [Google Scholar] [CrossRef]
  22. Dennison, Gabe, Stephanie Bourdage-Brau, and Madhu Chetuparambil. 2009. Social Commerce Defined. White Paper, 23747. Available online: https://digitalwellbeing.org/documents/IBM2009.pdf (accessed on 2 March 2023).
  23. Devita, Mela, Zuhrinal M. Nawawi, and Nuri Aslami. 2021. Shopee’s E-Commerce Marketing Strategy in International Business. Journal of Social Research 1: 27–31. [Google Scholar] [CrossRef]
  24. Dincer, Caner, and Banu Dincer. 2023. Social Commerce and Purchase Intention: A Brief Look at the Last Decade by Bibliometrics. Sustainability 15: 846. [Google Scholar] [CrossRef]
  25. Eger, Ludvík, Lenka Komárková, Dana Egerová, and Michal Mičík. 2021. The effect of COVID-19 on consumer shopping behaviour: Generational cohort perspective. Journal of Retailing and Consumer Services 61: 102542. [Google Scholar] [CrossRef]
  26. Ellegaard, Ole, and Johan A. Wallin. 2015. The Bibliometric Analysis of Scholarly Production: How Great Is the Impact? Scientometrics 105: 1809–31. [Google Scholar] [CrossRef]
  27. Esmaeili, Leila, Muhamad Mutallebi, Shahla Mardani, and Seyyed A. H. Golpayegani. 2015. Studying the affecting factors on trust in social commerce. International Journal of Advanced Studies in Computers, Science and Engineering 4: 41–47. [Google Scholar]
  28. Fu, Jen-Ruei, I-Wei Lu, Jessica H. F. Chen, and Cheng-Kiang Farn. 2020. Investigating consumers’ online social shopping intention: An information processing perspective. International Journal of Information Management 54: 102189. [Google Scholar] [CrossRef]
  29. Gao, Yunqi, Nasser Bagheri, and Luis Furuya-Kanamori. 2022. Has the COVID-19 pandemic lockdown worsened eating disorders symptoms among patients with eating disorders? A systematic review. Journal of Public Health 30: 2743–52. [Google Scholar] [CrossRef] [PubMed]
  30. Gatto, Andrea, Carlo Drago, and Matteo Ruggeri. 2023. On the frontline—A bibliometric study on sustainability, development, coronaviruses, and COVID-19. Environmental Science and Pollution Research 30: 42983–99. [Google Scholar] [CrossRef] [PubMed]
  31. Gefen, David, Elena Karahanna, and Detmar W. Straub. 2003. Trust and TAM in online shopping: An integrated model. MIS Quarterly 27: 51–90. [Google Scholar] [CrossRef]
  32. Goraya, M. Awais Shakir, Zhu Jing, Mahmud Akhter, Shareef Muhammad Imran, Aneela Malik, and M. Shakaib Akram. 2021. An investigation of the drivers of social commerce and e-word-of-mouth intentions: Elucidating the role of social commerce in E-business. Electronic Markets 31: 181–95. [Google Scholar] [CrossRef]
  33. Goyal, Sandeep, Chihua Hu, Sumedha Chauhan, Parul Gupta, Amit Kumar Bhardwaj, and Ankit Mahindroo. 2021. Social commerce: A bibliometric analysis and future research directions. Journal of Global Information Management (JGIM) 29: 1–33. [Google Scholar] [CrossRef]
  34. Guillén-Pujadas, Miguel, David Alaminos, Emili Vizuete-Luciano, Ana María Argila-Irurita, and María Luisa Solé-Moro. 2023. Digital Marketing and E-Commerce a Bibliometric Analysis. In Digital Marketing & eCommerce Conference. Cham: Springer Nature Switzerland, pp. 253–69. [Google Scholar]
  35. Guthrie, Cameron, Samuel Fosso-Wamba, and Jean Brice Arnaud. 2021. Online consumer resilience during a pandemic: An exploratory study of e-commerce behavior before, during and after a COVID-19 lockdown. Journal of Retailing and Consumer Services 61: 102570. [Google Scholar] [CrossRef]
  36. Hajli, Nick. 2015. Social commerce constructs and consumer’s intention to buy. International Journal of Information Management 35: 183–91. [Google Scholar] [CrossRef]
  37. Han, Hui, Hongyi Xu, and Hongquan Chen. 2018. Social commerce: A systematic review and data synthesis. Electronic Commerce Research and Applications 30: 38–50. [Google Scholar] [CrossRef]
  38. Harris, Patricia, Francesca Dall’Olmo Riley, Debra Riley, and Chris Hand. 2017. Online and store patronage: A typology of grocery shoppers. International Journal of Retail & Distribution Management 45: 419–45. [Google Scholar]
  39. Hassan, Syahida, Janet Toland, and Mary Tate. 2016. From blogosphere to social commerce: A laddering analysis of sellers’ motivation. Paper presented at 2016 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, USA, January 5–8; pp. 2146–55. [Google Scholar]
  40. Hicks, Diana, Paul Wouters, Ludo Waltman, Sarah de Rijcke, and Ismael Rafols. 2015. Bibliometrics: The Leiden Manifesto for research metrics. Nature 520: 429–31. [Google Scholar] [CrossRef] [PubMed]
  41. Hjørland, Birger. 2013. Facet Analysis: The Logical Approach to Knowledge Organization. Information Processing & Management 49: 545–57. [Google Scholar]
  42. Hsu, Chia-Lin, Mu-Chen Chen, and Vikas Kumar. 2018. How social shopping retains customers? Capturing the essence of website quality and relationship quality. Total Quality Management & Business Excellence 29: 161–84. [Google Scholar]
  43. Hu, Xi, Qian Huang, Xuepan Zhong, Robert M. Davison, and Dingtao Zhao. 2016. The influence of peer characteristics and technical features of a social shopping website on a consumer’s purchase intention. International Journal of Information Management 36: 1218–30. [Google Scholar] [CrossRef]
  44. Huang, Zhao, and Morad Benyoucef. 2013. From e-commerce to social commerce: A close look at design features. Electronic Commerce Research and Applications 12: 246–59. [Google Scholar] [CrossRef]
  45. Huang, Zhao, and Morad Benyoucef. 2015. User preferences of social features on social commerce websites: An empirical study. Technological Forecasting and Social Change 95: 57–72. [Google Scholar] [CrossRef]
  46. Hüseyinoğlu, Işık Özge Yumurtacı, Erdem Galipoğlu, and Herbert Kotzab. 2017. Social, local and mobile commerce practices in omni-channel retailing: Insights from Germany and Turkey. International Journal of Retail & Distribution Management 45: 711–29. [Google Scholar]
  47. Insider Intelligence. 2023. Social Commerce 2023: Social Media and E-Commerce Convergence Trends Bring Growth Opportunity for Brands. Insider Intelligence. Available online: https://www.insiderintelligence.com/insights/social-commerce-brand-trends-marketing-strategies/ (accessed on 3 April 2023).
  48. Jain, Vipin, Bindoo Malviya, and Satyendra Arya. 2021. An overview of electronic commerce (e-Commerce). Journal of Contemporary Issues in Business and Government 27: 665–70. [Google Scholar]
  49. Kim, Hyehyun J., and Sylvia Chan-Olmsted. 2022. Influencer Marketing and Social Commerce: Exploring the Role of Influencer Communities in Predicting Usage Intent. Journal of Interactive Advertising 22: 249–68. [Google Scholar] [CrossRef]
  50. Kim, Sanghyun, and Hyunsun Park. 2013. Effects of various characteristics of social commerce (s-commerce) on consumers’ trust and trust performance. International Journal of Information Management 33: 318–32. [Google Scholar] [CrossRef]
  51. Kumar, Pawan, and Gursimranjit Singh. 2020. Using social media and digital marketing tools and techniques for developing brand equity with connected consumers. In Handbook of Research on Innovations in Technology and Marketing for the Connected Consumer. Hershey: IGI Global, pp. 336–55. [Google Scholar]
  52. Kuşçu, Aslı, and Uğur Yozgat. 2019. What drives consumers to buy online? A study on exploring online consumer behavior. Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 10: 74–103. [Google Scholar]
  53. Laudon, Kenneth C., and Carol G. Traver. 2021. E-Commerce 2020–2021. Business. Technology. Society. Oxford: Pearson Higher. ISBN 13: 978-1-292-34321-1. [Google Scholar]
  54. Lee, In, and Kyoochun Lee. 2012. Social shopping promotions from a social merchant’s perspective. Business Horizons 55: 441–51. [Google Scholar] [CrossRef]
  55. Li, Chia-Ying. 2019. How social commerce constructs influence customers’ social shopping intention? An empirical study of a social commerce website. Technological Forecasting and Social Change 144: 282–94. [Google Scholar] [CrossRef]
  56. Li, Hongxiu, Yong Liu, and Pia Tukkinen. 2014. Social Commerce in Retaling-Why You Use IT? Paper presented at 27th BLED eConference, Bled, Slovenia, June 1–5; pp. 158–68. [Google Scholar]
  57. Liang, Ting-Peng, and Efraim Turban. 2011. Introduction to the special issue social commerce: A research framework for social commerce. International Journal of Electronic Commerce 16: 5–14. [Google Scholar] [CrossRef]
  58. Liao, Shu-Hsien, Retno Widowati, and Chieh-Ju Cheng. 2022. Investigating Taiwan Instagram users’ behaviors for social media and social commerce development. Entertainment Computing 40: 100461. [Google Scholar] [CrossRef]
  59. Lim, Junsang, and Sharon E. Beatty. 2011. Factors affecting couples’ decisions to jointly shop. Journal of Business Research 64: 774–81. [Google Scholar] [CrossRef]
  60. Luo, Jian Ming, Ka Yin Chau, and Chi Fung Lam. 2019. The Relationship of Student’s Motivation, Program Evaluation, Career Attitudes and Career Aspirations in University-Industry Cooperation Program. Cogent Education 6: 1608686. [Google Scholar] [CrossRef]
  61. Mariani, Marco G., and Salvatore Zappalà. 2017. Risk perception in online shopping. In Impact of e-Commerce on Consumers and Small Firms. Oxford: Routledge, pp. 207–21. [Google Scholar]
  62. Mason, Andrew N., John Narcum, and Kevin Mason. 2021. Social media marketing gains importance after Covid-19. Cogent Business & Management 8: 1870797. [Google Scholar]
  63. Melo, Max Y. A. 2021. Empreendedorismo digital: Evolução do e-commerce. Repositório Institucional da Associação Educativa Evangélica. Available online: http://45.4.96.19/bitstream/aee/18124/1/MAX%20YAN.pdf (accessed on 16 April 2023).
  64. Mileva, Geri. 2022. The State of Social Shopping in 2022. Influencer Marketing Hub. Available online: https://influencermarketinghub.com/social-shopping/ (accessed on 16 April 2023).
  65. Moher, David, Alessandro Liberati, Jennifer Tetzlaff, Douglas G. Altman, and The PRISMA Group. 2009. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine 151: 264269. [Google Scholar] [CrossRef]
  66. Moher, David, Larissa Shamseer, Mike Clarke, Davina Ghersi, Alessandro Liberati, Mark Petticrew, Paul Shkelle, Lesley A. Stewart, and Prisma-P Group. 2015. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews 4: 1–9. [Google Scholar] [CrossRef]
  67. Molinillo, Sebastiàn, Francisco Liébana-Cabanillas, and Rafael Anaya-Sánchez. 2018. A social commerce intention model for traditional e-commerce sites. Journal of Theoretical and Applied Electronic Commerce Research 13: 80–93. [Google Scholar] [CrossRef]
  68. Mou, Jian, Dong-Hee Shin, and Jason F. Cohen. 2017. Trust and risk in consumer acceptance of e-services. Electronic Commerce Research 17: 255–88. [Google Scholar] [CrossRef]
  69. Mróz, Bogdan. 2021. Consumer shopping behaviours on social media platforms. In Disruptive Platforms: Markets, Ecosystems, and Monopolists. Oxford: Routledge, pp. 113–29. [Google Scholar]
  70. Mukherjee, Debmalya, Weng M. Lim, Satish Kumar, and Naveen Donthu. 2022. Guidelines for advancing theory and practice through bibliometric research. Journal of Business Research 148: 101–15. [Google Scholar] [CrossRef]
  71. Nacar, Ramazan, and Kadir Ozdemir. 2022. From commerce to E-commerce and social commerce: How global? How local? In Industry 4.0 and Global Businesses. Bingley: Emerald Publishing Limited, pp. 95–109. [Google Scholar]
  72. Nagle, Thomas T., and Gerog Muller. 2018. The Strategy and Tactics of Pricing. Oxford: Routledge. ISBN 978-1-315-18530-9. [Google Scholar]
  73. Niranjanamurthy, M, N. Kavyashree, S. Jagannath, and Dharmendra Chahar. 2013. Analysis of e-commerce and m-commerce: Advantages, limitations and security issues. International Journal of Advanced Research in Computer and Communication Engineering 2: 2360–70. [Google Scholar]
  74. Overby, Jeffrey W., and Eun-Ju Lee. 2006. The effects of utilitarian and hedonic online shopping value on consumer preference and intentions. Journal of Business Research 59: 1160–66. [Google Scholar] [CrossRef]
  75. Page, Matthew J., Joanne E. McKenzie, Patrick M. Bossuyt, Isabelle Boutron, Tammy C. Hoffmann, Cynthia D. Mulrow, Larissa Shamseer, Jennifer M. Tetzalff, Elie A. Akl, Sue E. Brennan, and et al. 2021. Declaración PRISMA 2020: Una guía actualizada para la publicación de revisiones sistemáticas. Revista Española de Cardiología 74: 790–99. [Google Scholar] [CrossRef] [PubMed]
  76. Panarello, Demetrio, and Andrea Gatto. 2022. Connecting Perceived Service Quality, Value and Shopping Behavior: An Analysis on Chinese College Students Traveling Overseas. Knowledge 2: 557–71. [Google Scholar] [CrossRef]
  77. Pantano, Eleanora, Gabriele Pizzi, Daniele Scarpi, and Charles Dennis. 2020. Competing during a pandemic? Retailers’ ups and downs during the COVID-19 outbreak. Journal of Business Research 116: 209–13. [Google Scholar] [CrossRef]
  78. Pires, Paulo Botelho, José Duarte Santos, Pedro Quelhas de Brito, and David Nunes Marques. 2022. Connecting Digital Channels to Consumers’ Purchase Decision-Making Process in Online Stores. Sustainability 14: 14392. [Google Scholar] [CrossRef]
  79. Premebida, Eduardo A. 2021. E-commerce em 2020, um cenário de oportunidades em meio a pandemia. Research, Society and Development 10: e59210212984. [Google Scholar] [CrossRef]
  80. Ribeiro, Ana P., Robert Harmsen, Jesús R. Carreón, and Ernst Worrell. 2019. What influences consumption? Consumers and beyond: Purposes, contexts, agents and history. Journal of Cleaner Production 209: 200–15. [Google Scholar] [CrossRef]
  81. Rohm, Andrew J., and Vanitha Swaminathan. 2004. A typology of online shoppers based on shopping motivations. Journal of Business Research 57: 748–57. [Google Scholar] [CrossRef]
  82. Rubel, Steve. 2005. Trends to Watch. Part II: Social Commerce–Micro Persuasion. Available online: https://digitalwellbeing.org/steve-rubels-original-2005-social-commerce-post/ (accessed on 1 July 2009).
  83. Salvatori, Luca, and Fausto Marcantoni. 2015. SmartSocialMarket: A social commerce architecture. Paper presented at the Third International Conference on Building and Exploring Web Based Environments, Rome, Italy, May 24–29. [Google Scholar]
  84. Schneider, Gary P. 2016. Electronic Commerce. Boston: Cengage Learning. ISBN 978-1-305-86781-9. [Google Scholar]
  85. Shen, Jia. 2012. Social comparison, social presence, and enjoyment in the acceptance of social shopping websites. Journal of Electronic Commerce Research 13: 198. [Google Scholar]
  86. Shin, Dong-Hee. 2013. User experience in social commerce: In friends we trust. Behaviour & Information Technology 32: 52–67. [Google Scholar]
  87. Shin, Yeon H., and Murat Hancer. 2016. The role of attitude, subjective norm, perceived behavioral control, and moral norm in the intention to purchase local food products. Journal of Foodservice Business Research 19: 338–51. [Google Scholar] [CrossRef]
  88. Silvestre, Luane. 2020. O que é e-commerce: Para que serve e como funciona. Nuvemshop Blog. Available online: https://www.nuvemshop.com.br/blog/o-que-e-ecommerce/ (accessed on 1 March 2023).
  89. Singh, Manohar, and Gobindbir Singh. 2018. Impact of social media on e-commerce. International Journal of Engineering & Technology 2: 21–26. [Google Scholar]
  90. Sohn, JeongWoong, and Jin Ki Kim. 2020. Factors that influence purchase intentions in social commerce. Technology in Society 63: 101365. [Google Scholar] [CrossRef]
  91. Sturiale, Luisa, and Alessandro Scuderi. 2013. Evaluation of social media actions for the agrifood system. Procedia Technology 8: 200–8. [Google Scholar] [CrossRef]
  92. The Lens. n.d. Explore Global Science and Technology Knowledge: Aggregated Metadata. Lens.org. Available online: https://www.lens.org/ (accessed on 5 April 2023).
  93. Vargas, Katiuscia S., Kristina K. Figueira, and Janaína Marchi. 2019. A influência das dimensões de confiança e risco no comportamento de compra online. Revista de Administração Unimep 17: 1–23. [Google Scholar]
  94. Verhoef, Peter C., Pallassana K. Kannan, and J. Jeffrey Inman. 2015. From multi-channel retailing to omni-channel retailing: Introduction to the special issue on multi-channel retailing. Journal of Retailing 91: 174–81. [Google Scholar] [CrossRef]
  95. Wang, Mengyang, and Lihe Chai. 2018. Three new bibliometric indicators/approaches derived from keyword analysis. Scientometrics 116: 721–75. [Google Scholar] [CrossRef]
  96. Wang, Shuyu. 2020. A Chinese Lesson in Combatting Online Counterfeits: The Need to Place Greater Obligations on Social Media as They Transform to E-Commerce Platforms. Federal Communications Law Journal 73: 339. [Google Scholar]
  97. Wang, Cheng Lu. 2021. New frontiers and future directions in interactive marketing: Inaugural Editorial. Journal of Research in Interactive Marketing 15: 1–9. [Google Scholar] [CrossRef]
  98. Wang, Chingning, and Ping Zhang. 2012. The evolution of social commerce: The people, management, technology, and information dimensions. Communications of the Association for Information Systems 31: 5. [Google Scholar] [CrossRef]
  99. Wang, Xuequn, Mina Tajvidi, Xiaolin Lin, and Nick Hajli. 2020. Towards an ethical and trustworthy social commerce community for brand value co-creation: A trust-commitment perspective. Journal of Business Ethics 167: 137–52. [Google Scholar] [CrossRef]
  100. Wright, Ray. 2006. Consumer Behaviour. Boston: Cengage Learning EMEA. ISBN 13: 978-1844801381. [Google Scholar]
  101. Wu, Wei, Sihang Wang, Guanqi Ding, and Jinfei Mo. 2023. Elucidating trust-building sources in social shopping: A consumer cognitive and emotional trust perspective. Journal of Retailing and Consumer Services 71: 103217. [Google Scholar] [CrossRef]
  102. Wu, Wei, Vivian Huang, Xiayu Chen, Robert M. Davison, and Zhongsheng Hua. 2018. Social value and online social shopping intention: The moderating role of experience. Information Technology & People 31: 688–711. [Google Scholar]
  103. Xiao, Liang, Feipeng Guo, Fumao Yu, and Shengnan Liu. 2019. The effects of online shopping context cues on consumers’ purchase intention for cross-border E-Commerce sustainability. Sustainability 11: 2777. [Google Scholar] [CrossRef]
  104. Xu, Yu, and Michael J. Lee. 2019. Personalizing User Interactions in a Social Shopping Context and Open Challenges. Paper presented at IUI Workshops, Los Angeles, CA, USA, March 20. [Google Scholar]
  105. Yu, Chia-Hui, Chia-Chang Tsai, Yichuan Wang, Kuei-Kuei Lai, and Mina Tajvidi. 2020. Towards building a value co-creation circle in social commerce. Computers in Human Behavior 108: 105476. [Google Scholar] [CrossRef]
  106. Yuthayotin, Sutatip. 2015. Access to Justice in Transnational B2C E-Commerce. Cham: Springer. ISBN 978-3-319-11131-5. [Google Scholar]
  107. Zamrudi, Zakky, Imam Suyadi, and Yusri Abdillah. 2016. The effect of social commerce construct and brand image on consumer trust and purchase intention. Profit: Jurnal Administrasi Bisnis 10: 1–13. [Google Scholar] [CrossRef]
  108. Zhang, Hong, Ling Zhao, and Sumeet Gupta. 2018. The role of online product recommendations on customer decision making and loyalty in social shopping communities. International Journal of Information Management 38: 150–66. [Google Scholar] [CrossRef]
  109. Zhang, Xi, Hongda Liu, and Pinbi Yao. 2021. Research jungle on online consumer behaviour in the context of web 2.0: Traceability, frontiers and perspectives in the post-pandemic era. Journal of Theoretical and Applied Electronic Commerce Research 16: 1740–67. [Google Scholar] [CrossRef]
  110. Zhao, Taiyang, Wei Song, Xiaotong Jin, Hongjing Cui, and Yang Li. 2020. Hedonism or self-growth? The influence of perceived control on individual product preferences for individuals under self-threat. Asia Pacific Journal of Marketing and Logistics 32: 1343–61. [Google Scholar] [CrossRef]
  111. Zhong, Yao. 2012. Social Commerce: A New Electronic Commerce. Paper presented at WHICEB 2012 Proceedings, Wuhan, China, May 26–27; pp. 164–69. [Google Scholar]
  112. Zhou, Lina, Ping Zhang, and Hans-Dieter Zimmermann. 2013. Social commerce research: An integrated view. Electronic Commerce Research and Applications 12: 61–68. [Google Scholar] [CrossRef]
  113. Zupic, Ivan, and Tomaz Čater. 2015. Bibliometric methods in management and organization. Organizational Research Methods 18: 429–72. [Google Scholar] [CrossRef]
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Figure 1. Relationship between social shopping, social commerce and e-commerce.
Figure 1. Relationship between social shopping, social commerce and e-commerce.
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Figure 2. Methodology.
Figure 2. Methodology.
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Figure 3. Evolution of the number of publications by type of document.
Figure 3. Evolution of the number of publications by type of document.
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Figure 4. Scientific production by country.
Figure 4. Scientific production by country.
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Figure 5. Co-citations per journal.
Figure 5. Co-citations per journal.
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Figure 6. Treemap of keyword co-occurrence.
Figure 6. Treemap of keyword co-occurrence.
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Figure 7. Keyword co-occurrence network.
Figure 7. Keyword co-occurrence network.
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Table 1. Concepts of e-commerce, social commerce and social shopping.
Table 1. Concepts of e-commerce, social commerce and social shopping.
ConceptDescriptionAuthors
E-commerceE-commerce refers to the buying and selling of products and services online via a website or mobile app. E-commerce allows consumers to buy products from anywhere, at any time and from a wide range of merchants.Bilgihan et al. (2016);
Jain et al. (2021);
Niranjanamurthy et al. (2013)
Social commerceSocial commerce refers to the use of social media platforms for commercial transactions. It combines social media with e-commerce to create a seamless shopping experience for consumers. Social commerce allows consumers to search for products, interact with brands and influencers and use social media be able to head directly to the product on the website to purchase.Busalim and Ghabban (2021);
Huang and Benyoucef (2015);
Molinillo et al. (2018)
Social shoppingSocial shopping refers to the use of social media platforms, such as Facebook, Instagram or TikTok, to shop and purchase products. Social shopping allows consumers to search for products, connect with friends and influencers and shop directly within a social network.Kim and Chan-Olmsted (2022);
Liao et al. (2022);
S. Wang (2020)
Table 2. Top 10 journals by number of publications.
Table 2. Top 10 journals by number of publications.
JournalFieldNumber of Publications
SustainabilityEnergy, Environmental Science, Social Sciences191
SSRN Electronic JournalSocial Sciences, including Economics, Law, Corporate Governance and Humanities159
International Journal of Retail & Distribution ManagementBusiness, Management and Accounting143
Journal of Emerging Technologies and Innovative ResearchTechnological88
Journal of Retailing and Consumer ServicesBusiness, Management and Accounting81
Journal of the Korean Society of Clothing and TextilesArts and Humanities, Engineering, Materials
Science, Social Sciences		66
IOP Conference Series: Materials Science and EngineeringEngineering, Materials Science56
Journal of Physics: Conference SeriesPhysics and Astronomy55
Frontiers in PsychologyPsychology54
European Journal of Business and ManagementBusiness, Management and Accounting,
Economics, Econometrics and Finance		52
Table 3. Top 10 countries by number of publications.
Table 3. Top 10 countries by number of publications.
CountryNumber of Publications
USA1511
China1238
Indonesia626
United Kingdom531
India466
Taiwan271
Germany260
South Korea234
Malaysia232
Australia219
Table 4. Number of articles written by author.
Table 4. Number of articles written by author.
AuthorNumber of ApplicationAffiliation
Kyu-Hye Lee12Da Vinci College of General Education, Chung-Ang University, Seoul, Korea
Chandra Sekhar Patro10Gayatri Vidya Parishad College of Engineering, Visakhapatnam, India
Charles Dennis10Middlesex University, UK
Abdul R. Ashraf9NEOMA Business School, Reims, France
Ali Khatibi8School of Management, Management & Science University, Malaysia
Gary Mortimer8Business School, Queensland University of Technology, Brisbane, Australia
Ronald E. Goldsmith8University of Southern Mississippi, United States
Sanjeev Prashar8Indian Institute of Management Raipur, Chhattisgarh, India
Young-Kyung Kim8College of Medicine, Yonsei University, Seoul, Korea
Alan D. Smith7Robert Morris University, Pennsylvania, USA
Table 5. Top 10 cited articles.
Table 5. Top 10 cited articles.
ArticlePublication YearCitation FrequencyAverage *
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90.20035892310.1
Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From multi-channel retailing to omni-channel retailing: introduction to the special issue on multi-channel retailing. Journal of Retailing, 91(2), 174–181.20151351193
Lee, G. G., & Lin, H.-F. (2005). Customer perceptions of e-service
quality in online shopping. International Journal of Retail & Distribution Management, 33(2), 161–176.		200597257.2
Koufaris, M., & Hampton-Sosa, W. (2004). The development of initial trust in an online company by new customers. Information &
Management, 41(3), 377–397.		200487748.7
Liang, T. P., & Turban, E. (2011). Introduction to the special issue
social commerce: a research framework for social commerce.
International Journal of Electronic Commerce, 16(2), 5–14.		201185677.8
Perea y Monsuwé, T., Dellaert, B. G., & De Ruyter, K. (2004). What drives consumers to shop online? A literature review. International Journal of Service Industry Management, 15(1), 102–121.200477743.2
Rohm, A. J., & Swaminathan, V. (2004). A typology of online shoppers based on shopping motivations. Journal of Business Research, 57(7), 748–757.200482245.7
Overby, J. W., & Lee, E.-J. (2006). The effects of utilitarian and hedonic online shopping value on consumer preference and intentions. Journal of Business Research, 59(10–11), 1160–1166.200671644.8
Stewart, K. J. (2003). Trust transfer on the world wide web.
Organization Science, 14(1), 5–17.		200371437.6
Stephen, A. T., & Toubia, O. (2010). Deriving value from social commerce networks. Journal of Marketing Research, 47(2), 215–228.201070959.1
* The average was calculated based on the time interval between the year of publication and the year 2022.
Table 6. Keywords by cluster.
Table 6. Keywords by cluster.
ClusterKeywords
1—Red‘consumer behaviour’, ‘customer satisfaction’, ‘e-commerce’, ‘marketing’, ‘omnichannel’, ‘online shopping’, ‘purchase intention’, ‘qualitative research’, ‘social distancing’, ‘social media’, ‘south Africa’, ‘technology adoption’ and ‘trust’
2—Green‘china’, ‘consumer behavior’, ‘COVID-19 pandemic’, ‘COVID-19’, ‘online grocery’, ‘policy’, ‘qualitative’, ‘retail’ and ‘shopping behavior’
3—Dark blue‘coronavirus’, ‘COVID-19’, ‘online’, ‘online grocery shopping’, ‘pandemic’, ‘risk perception’, ‘SARS-CoV-2’ and ‘shopping’
4—Yellow‘advertising and promotion’, ‘price’, ‘public opinion’ and ‘public policy’
5—Purple‘brand identity’, ‘repurchase intention’ and ‘switching intention’
6—Light blue‘motivation’ and ‘theory of planned behavior’
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Barbosa, B.; Santos, J.D. Bibliometric Study on the Social Shopping Concept. Adm. Sci. 2023, 13, 213. https://doi.org/10.3390/admsci13100213

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Barbosa B, Santos JD. Bibliometric Study on the Social Shopping Concept. Administrative Sciences. 2023; 13(10):213. https://doi.org/10.3390/admsci13100213

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Barbosa, Branca, and José Duarte Santos. 2023. "Bibliometric Study on the Social Shopping Concept" Administrative Sciences 13, no. 10: 213. https://doi.org/10.3390/admsci13100213

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