Bibliometric and content analysis of viral marketing in marketing literature

Abstract

Viral marketing is one of the most important marketing trends of today as it offers companies the opportunity to reach large masses, low cost, increases brand awareness and has higher conversion rates. In addition, more trust in the content shared by friends or family members and more interaction due to interesting, entertaining or emotional shares are among the features that make viral marketing different. Although it has so many advantages and is used extensively in the field, the lack of a bibliometric analysis on viral marketing and the fact that it does not find enough space in the literature makes the study important. The aim of this study is to identify the leading journals, authors, publications and main research themes in this field using bibliometric and thematic content analyses, to provide an overview of current viral marketing research and to prepare a conceptual framework for future research. Another aim of the study is to identify the missing areas in the viral marketing literature and to create a framework for future research in this field. In this study where quantitative research method was used, bibliometric analysis method was used through VOSviewer 1.6.18 software programme. Between 2003 and 2023, 222 studies were analysed by bibliometric map analysis method with co-citation, author and entity and bibliographic matching analysis. As a result of the content analysis, the studies in the viral marketing literature were divided into three main categories and 10 trends that make up these categories.

Keywords:

• Viral marketing
• eWOM
• bibliometric analysis
• content analysis
• social media
• bibliographic matching

REVIEWING EDITOR:

• Len Tiu Wright, De Montfort University Faculty of Business and Law, United Kingdom of Great Britain and Northern Ireland

SUBJECTS:

• Business, Management and Accounting
• Group Communication
• Consumer Psychology
• Interpersonal Communication

Introduction

It is not for nothing that viral marketing is now referred to as the ‘holy grail of digital marketing’ (Akpinar & Berger, 2017). The emergence of internet-based technologies and platforms has led to major changes in marketing. The emergence of viral marketing can be attributed to the intersection of technological advancements and shifts in cultural dynamics. Additionally, it has evolved to resonate with contemporary market complexities and the ever-changing psychology and behaviors of modern consumers. In a broader approach, viral marketing has emerged with the impact of technological innovation, the impact of participatory culture, changes in the psychographic status and behaviour of existing customers. In addition, the emergence of new marketing trends such as customisation marketing, interaction marketing, relationship marketing and influencer marketing has made viral marketing a much more effective type of marketing (Yang, 2012). Organizations can now reach customers directly using digital platforms. In this way, customers can provide instant feedback about their level of interest, likes or comments on products and services. This is because customers enjoy sharing their opinions about the products and services they purchase on online portals such as e-commerce sites, social media platforms and blogs. Customers’ perspectives and feedback on a product or service also give rise to viral communication (Donthu et al., 2021). Viral marketing is based on the approach of marketing a product or service to these consumers by utilizing the network of relationships between consumers. In short, ‘Viral Marketing’ is a cheap and growing post-modern marketing technique that allows reaching potential consumers through the internet and social networking channels (Yang, 2012). People who know each other are advocates of a product or service they like, whether they are friends, peers or neighbors. Messages shared among individuals in the same social network spread rapidly and create a consumer group with the potential to purchase the product or service. And this group grows like an avalanche with the effect of the spread and enables the participation of new consumers. It is possible to say that viral marketing practices, which are carried out over the Internet and largely using social networks, which are among the important communication tools of today’s world, are an effective marketing method for all generations, especially young people who use social media. It is seen that studies on viral marketing have increased significantly in the last decade. In the ‘Web of Science’ academic studies database, there are 222 studies on viral marketing in the last twenty years, 176 of which were conducted in the last ten years. Although the number of publications has decreased in the last few years, viral marketing practices in the field are increasing day by day.

As mentioned above, although it has been recognized as an important marketing approach in recent years, there are no studies in the literature on viral marketing using bibliometric and content analysis methods. This study was carried out to fill this gap in the literature. Despite the limited number of studies on eWOM, the fact that there are differences between the two concepts as explained in the literature section and that eWOM is perceived as a communication field rather than a marketing field shows that it is not correct to accept the studies conducted in the field of eWOM as viral marketing studies. As a matter of fact, non-marketing words such as ‘mouth’, ‘word’ and ‘electronic word’ were among the most repeated words in eWOM searches in databases. Therefore, bibliometric analyses are far from reflecting the content of viral marketing. On the other hand, some deficiencies were found in these bibliometric studies on eWOM, albeit limited in number. It is thought that it is useful to briefly mention these shortcomings due to their proximity to viral marketing. Donthu et al. (2021) analyzed the literature on electronic word-of-mouth communication (eWOM) using bibliometric analysis and systematic review. Despite its comprehensive content, no co-authorship analysis was found in the study. Mukhopadhyay et al. (2022) conducted a bibliometric analysis on eWOM studies published in hospitality and tourism journals, but did not analyze the top publishing countries and universities. In the bibliometric study conducted by Abbas et al. (2020) on eWOM, the studies in the literature were not systematically evaluated. In addition, no evaluation was made about which analyzes were made in the studies in the literature and what the deficiencies were. In the same study, which areas related to eWOM come to the forefront and which areas should be studied in the future, etc. were not addressed. As can be seen, although there are a limited number of bibliometric studies on eWOM, there is no bibliometric study on viral marketing. Therefore, it can be said that this study is original. In this respect, it is thought that bibliometric and content analysis of viral marketing will make the study unique and contribute to the literature.

The scattered nature of the scientific literature on a particular research discipline or research topic often makes it difficult for researchers to get a good overview of the literature and to see the relationships between different developments. Visualization techniques based on bibliometric data help to get an overview of the literature on complex research topics (Rodrigues et al., 2014). In line with this approach, it is thought that there is a need to examine the studies on viral marketing with bibliometric methods due to the increase in both academic studies and field applications related to viral marketing in the last decade and the gap caused by the lack of bibliometric and content analysis in this field.

Based on these gaps, the subject of this study is the bibliometric and content analysis of academic studies in the viral marketing literature. One of the main factors that make this study important is the lack of a study in this field, as mentioned above, and the other is that viral marketing applications, one of the most important types of digital marketing, are increasing day by day and have become an important marketing channel. Some of the factors that make viral marketing important are the fact that users have become volunteer employees of brands due to their active participation, the exponentially increasing impact potential of the viral cycle and the unique competitive advantages it provides to businesses. The fact that academic studies of such an important type of digital marketing are handled holistically is among the factors that make this study important. Based on all these explanations, the aim of this study is to analyze the existing viral marketing literature and to contribute to future empirical studies in this field. Within the scope of this study, the studies available in the Web of Science database on viral marketing literature were analyzed through bibliometric analysis, the general perspective of the viral marketing literature was tried to be determined and suggestions were made for future research directions. On the other hand, gaps in the viral marketing literature have been identified and detailed in the ‘Potential Research Areas of Viral Marketing’ section. The following research questions were used in this research:

Question 1: What is the global trend in scientific publications in the field of ‘viral marketing’?

Question 2: What are the prominent trends when the studies in this field are categorised?

Question 3: What are the gaps observed when the studies in this field are evaluated with a holistic approach?

Question 4: In which direction can research in this field develop?

The objectives of the bibliometric analysis carried out in this context are as follows:

1. Providing bibliometric information on 222 scientific studies obtained from the Web of Science database;

2. To perform bibliometric analyses through VOSviewer 1.6.18 software software to obtain and record quantitative data on different selected articles, to identify periodically prominent trends and to categorise these trends;

3. To identify the most published and cited authors in this field, the most published countries, universities and journals, and finally the most frequently used keywords in these studies;

Finally, the study is structured as follows: The literature on the main studies in this field is analysed in the second section. The methodology and analysis results of the study are explained in the third section. In the next section, the main trends that viral marketing studies are concentrated on are given. Finally, in the conclusion part of the article, the theoretical and practical results of the research are given and the limitations of the research and the areas recommended for future study are listed.

Literature review

Viral marketing is an effort to quickly and exponentially share the marketing message among consumers. It can be said that viral marketing creates a snowball effect, as the message passes from consumer to consumer and constantly increases the total number of people reached. As soon as a consumer starts to convey his opinions about the product he buys to other people, it means that he has had the opportunity to market to potential consumers (Dasari & Anandakrishnan, 2010). For this reason, viral marketing is called the holy grail of digital marketing. Viral marketing is a marketing process that utilizes the fact that a person is predisposed to share information that attracts them (Bačík & Fedorko, 2017).

Viral marketing stands out for its cost-effectiveness, offering a stark comparison to traditional advertising methods. Once the initial content is crafted and released, its propagation primarily relies on social networks, essentially for free. Moreover, viral content possesses a remarkable capability to swiftly reach a vast audience, surpassing the limitations of conventional advertising channels. Leveraging the interconnected nature of social media and digital platforms, a well-executed viral campaign can effortlessly accumulate millions of views, shares, and engagements. Additionally, viral marketing often features original, engaging content that resonates with audiences on a personal level. When individuals share their content with their networks, it carries implicit endorsement and increases trust and credibility for the brand or product being promoted.

To make a brief evaluation of the origin and development of viral marketing, the following can be said: Shukla (2010) attributes the coining of the term ‘viral marketing’ to Harvard Business School professor Rayport (1996). However, Douglas Rushkoff is recognized as one of the earliest proponents of the concept, having introduced the term ‘media virus’ or ‘viral media’ in his 1994 book, ‘Media Virus: Hidden Agendas in Popular Culture.’ Rushkoff (1996) elucidates the notion that media can function akin to viruses: mobile, easily replicated, and disseminated without perceived threat. In detailing the technique of viral media, Rushkoff (1996) suggests that a message or image is strategically presented to a susceptible audience, effectively infecting them like a virus, and compelling them to spread the message further to others. Viral or viral marketing, which was first introduced by Jurvetson and Draper (1997) to explain Hotmail’s free e-mail service, aimed to spread messages that were suitable for spreading and receiving and had a favourable propagation environment, just like a virus. Juvertson founded Hotmail in 1996. For the promotion of this, he sowed the seeds of viral marketing through every user who sends e-mails with the message ‘You can get your private and free e-mail addresses from http://www. hotmail.com’, which introduces the business and services under each e-mail he sends to the users. Juvertson reached 12 million Hotmail subscribers in 1996 and 1997 (Alakuşu, 2014).

The evolution of viral marketing has been marked by significant shifts in strategy and platforms. Initially reliant on word of mouth and email forwarding, it thrived on humorous jokes and chain emails. However, the advent of social media platforms like Facebook, Twitter, and YouTube sparked a revolution in how content spreads virally. Marketers quickly adapted, crafting content tailored for social sharing, such as videos, witty posts, and interactive challenges. The subsequent rise of influencers further amplified the reach of viral marketing, as their posts became magnets for engagement and shares among their followers. Concurrently, user-generated content gained traction, prompting marketers to leverage customer input and deepen their engagement efforts. In today’s landscape, data-driven approaches take center stage in viral marketing campaigns. Utilizing sophisticated analytics, modern marketers optimize reach and engagement to unprecedented levels. Despite these advancements, viral marketing remains a potent tool for cultivating interest, driving engagement, and enhancing brand visibility in the ever-evolving digital realm.

Although it is such an important member of modern marketing and has a history of over 25 years, there is no common definition of viral marketing that is accepted by everyone (Phelps et al., 2004). Wilson (2000) defines viral marketing as ‘any strategy that encourages’ users to spread content to create ‘the potential for exponential growth’ like a biological virus. Helm (2000) defines viral marketing as a ‘communication and distribution concept’ based on the transmission of digital content among customers who send it via e-mail ‘in their own social sphere’. Welker (2002) states that although viral marketing has significant advantages such as enabling extremely fast and cost-effective dissemination of content, it also has risks such as losing control over the distribution of the message. According to Snyder (2004), viral marketing is a form of word-of-mouth communication over the Internet, the newest platform. Phelps et al. (2004) define viral marketing as ‘as the process of encouraging honest communication among consumer networks.’. In defining viral marketing, Dobele et al. (2005) emphasize the distinction between practical and marketing perspectives. According to them, from a practical perspective, viral marketing is a strategy ‘whereby people forward the message to other people on their e-mail lists or tie advertisements into or at the end of messages’. From a marketing perspective, viral marketing is more of an ‘process of encouraging’ designed to encourage individuals to ‘to pass along favorable or compelling marketing information they receive’. Porter and Golan (2006) use the term ‘viral advertising’ and define it as ‘unpaid peer-to-peer communication of provocative content’. Kotler and Armstrong (2007) defines viral marketing as: ‘an Internet version of marketing, word-of-mouth e-mail, or other marketing actions that are so contagious that the customer wants to share with their friends.’ (Bačík & Fedorko, 2017). According to Cruz and Fill (2008), viral marketing is the transfer of news, information or entertainment to another person via the Internet. Kaplan and Haenlein (2011) argue that two elements play a role in the definition of viral marketing: The first is that there is an increasing trend in the speed of distribution of content to achieve exponential growth, as in an epidemic. The second aspect is the proper use of social media applications to ensure this rapid diffusion. According to Reichstein and Brusch (2019), viral marketing is ‘marketing strategies that permit exponential distribution of content in network-based channels in the shortest time with comparatively little effort and additionally generate measurable added value through the content, which leads to a high cost-benefit effect.’ As can be seen, since the authors emphasize different characteristics of viral marketing, their definitions have also differed. Another differentiated approach among authors is whether viral marketing is the same as e-WOM. Some authors consider viral marketing as e-WOM based on consumers informing other consumers about the product or service. On the other hand, according to another approach (which we also accept), although viral marketing and e-WOM are close concepts, there are some differences between the two concepts (Phelps et al., 2004). The differences believed to exist between e-WOM and viral marketing are as follows:

First of these differences: Companies often conduct viral marketing campaigns to market a product or service. In contrast, eWOM is an informal and inorganic platform where personal opinions are shared voluntarily among consumer groups (Lindgreen et al., 2013). The second difference that distinguishes viral marketing from eWOM is that an action is taken after the viral marketing activity and the marketer prepares the necessary medium (dissemination environment) for the realisation of this action. On the other hand, since viral marketing is driven by the passion of consumers, it cannot be said that the targeted success belongs to the company that initiated the viral cycle. The third difference between the two concepts is controllability. While viral marketing has a controllable structure in terms of the information and content shared, it is very difficult for companies to control eWOM due to the dominance of the consumer. Because there is an ‘echo chamber effect’ in eWOM. The fourth difference between the two concepts stems from the means and purpose approach. In this context, while eWOM is a tool to spread the message among consumers, viral marketing is a goal-oriented activity. The fifth difference is the cause and effect relationship. Viral marketing generates word-of-mouth by creating awareness and buzz through marketing programmes and viral videos. A positive eWOM leads to experimentation and acquisition. The sixth difference is that a strong eWOM usually needs to be based on a customer experience, whereas there is no such requirement in viral marketing. More precisely, viral marketing involves ‘engineering’ to spread a product or service. The seventh difference is that eWOM is more based on the idea of consumers doing others a favour by sharing their opinions and suggestions about a product or service (Hendrayati & Pamungkas, 2020), whereas viral marketing is a way to spread a marketing message quickly and exponentially among consumers. This is usually done through a video or email. At this point, the value of the message spreading like a virus is directly related to the number of other users it attracts. The eighth difference is the speed of feedback. Within the scope of viral marketing activities, the feedback speed in viral marketing is much faster than e’WOM thanks to the motivators such as entertainment, excitement, coercion, financial gain, benefiting from the reputation of influencers, etc. that companies will use to spread the message.

Another issue that draws attention when the literature is examined is that viral marketing studies are concentrated in certain areas. Therefore, it is possible to say that there are still many gaps in the field of viral marketing that have not yet been studied and are felt to be lacking in the literature. Explanations on this issue have been made in the ‘Potential Research Areas of Viral Marketing’ section and the study topics currently in the literature are summarised below: The impact of viral marketing on purchasing behavior (De Bruyn & Lilien, 2008; Leskovec et al., 2007), the role of influencers (Subramani & Rajagopalan, 2003; Yeoh et al., 2013), senders (Ho & Dempsey, 2010; Phelps et al., 2004; Subramani & Rajagopalan, 2003), and social ties (De Bruyn & Lilien, 2008; Leskovec et al., 2007) on viral marketing, seeding strategies (Hinz et al., 2011), impact of social media on viral message dissemination (Bampo et al., 2008; Kaplan & Haenlein, 2011; Schulze et al., 2014), mobile viral marketing applications (Palka et al., 2009), the effect of viral marketing on branding (Moore, 2003), the interaction of viral marketing and traditional marketing (Watts et al., 2007) etc. The contents of these studies are briefly described below:

In some of the studies investigating the effect of viral marketing on purchasing behaviour, model proposals have been made for the effect of recommendations on consumers’ purchasing decisions (De Bruyn & Lilien, 2008; Leskovec et al., 2007). In some studies on purchase behaviour, the relationship between consumers’ infection level and purchase behaviour has been examined. In a study conducted by Leskovec et al. (2007), the following results were obtained; repeated interaction reduces the probability of infection. Also, the probability of purchasing a product increases with the number of recommendations received, but quickly reaches saturation. This result showed that individuals often become desensitised to the recommendations of their friends after a while and resist buying products they do not want. The power of influencers in viral marketing studies is also among the study topics in the field. In a study conducted by Yeoh et al. (2013) on medical tourists coming to Malaysia for treatment, it was concluded that most of the tourists were influenced by the recommendations of friends, family, relatives and doctors. Subramani and Rajagopalan (2003) developed a different approach. They emphasised that for influencers to be successful in viral marketing, they should be perceived as knowledgeable helpers in the social network, rather than as intermediaries of the marketer. One of the most important factors in the spread of a viral message is the motivation, attitude and behaviour of the senders towards sending these messages. Ho and Dempsey (2010) claimed that the following factors are effective on internet users’ online content transmission: (1) the need to be part of a group, (2) the need to be individualistic, (3) the need to be altruistic, and (4) the need for personal development. The study also showed that individuals may be motivated to surf the Internet for additional reasons such as entertainment and socialisation. Subramani and Rajagopalan (2003) found that viral marketing is a powerful tool to capitalise on the innate helpfulness of individuals in social networks. In a study based on the results of three studies examining consumer reactions and motivations for forwarding e-mails, recommendations were made for target selection and message construction for advertisers interested in implementing viral efforts (Phelps et al., 2004). On the other hand, the effect of social ties on the spread of viral messages is also among the issues examined. As a matter of fact, as a result of a study on the subject, it was found that the characteristics of the social bond affect the behaviour of the recipients, but have different effects at different stages (De Bruyn & Lilien, 2008). In another study, a model showing that smaller and closer groups are more favourable to viral marketing was presented (Leskovec et al., 2007). In the study, it was found that extremely highly connected individuals play a critical role in network-based epidemic models, while recommendations exceeding a certain number reduce the chances of success. This means that individuals can influence a few of their friends, but not everyone they know. Another important area of viral marketing that has been academically analysed is seeding strategies. In a study by Hinz et al. (2011), four seeding strategies were compared in a viral marketing campaign involving more than 200,000 customers of a mobile phone service provider. Empirical results showed that the best seeding strategies can be up to eight times more successful than other seeding strategies. The impact of social media, an important digital marketing channel, on the spread of viral messages has also been extensively studied. Kaplan and Haenlein (2011) analysed the relationship between social media and viral marketing and explained the steps to be taken for social media and viral marketing to coexist effectively and the conditions to be met to create a viral cycle. The findings of a study by Bampo et al. (2008) showed that the social structure of digital networks plays a critical role in the spread of a viral message. Schulze et al. (2014) analysed the viral marketing campaigns of 751 products on Facebook and concluded that consumers use Facebook to have fun rather than to do something useful. Although not in large numbers, it is also possible to come across studies on mobile viral marketing applications. For example, Palka et al. (2009) focused on the motivations, attitudes and behaviours of those who receive, use and transmit mobile viral content. Within the scope of the study, a series of determinants affecting behaviours in mobile viral marketing processes were identified and a theory explaining mobile viral effects was presented. The impact of viral marketing on branding is also among the limited number of studies. In this context, Moore (2003) argues that the three phenomena of branding are generic, content branding and viral marketing, thus suggesting that ‘brandable’ things are not only material things but also events, experiences and acts of communication. Finally, some studies involving the interaction of viral marketing and traditional marketing are also found in the literature. Indeed, Watts et al. (2007) argue that despite the recent popularity of viral marketing, companies should not rely solely on viral marketing for their products and brands. Instead, they propose a new model called ‘Big Seed Marketing’ that combines the power of traditional advertising with the extra power of viral propagation.

Although these studies in the Web of Science database, which are the most cited studies in the viral marketing literature, are important, they constitute only a part of the studies in this field. In another classification made with a broader perspective, 222 studies on viral marketing in the Web of Science database between 2003 and 2023 were categorised according to their content and 34 articles were identified. The content analysis of these articles is detailed in the ‘Research Categories and Trends’ sections. As a result, it is possible to find similar fields of study as well as different fields in both classifications (according to the number of citations and content) made on viral marketing literature. On the other hand, there are some exceptional studies that are not included in these two classifications. These studies have been carried out in many different fields ranging from sustainability to private customer practices, from social advertising to promotional tactics for viral marketing campaigns. In addition, the outlines of the researches that are not included in the viral marketing literature and expected to be studied in the future are presented in the ‘Potential Research Areas for Viral Marketing in the Future’ section.

The research methodology

The present study describes an alternative approach to searching, structuring and visualising large amounts of literature based on bibliographic data and uses this approach to analyse the literature on viral marketing. (Rodrigues et al., 2014). In this context, the aim of the study is to determine the main themes in the viral marketing literature and to prepare a conceptual basis for future research directions on this phenomenon in the field of marketing. In the study using quantitative research method, bibliometric and content analysis methods were used for the purpose of the study. VOSviewer 1.6.18 software program was used for bibliometric analysis. All studies on viral marketing in the Web of Science database were included in the research methodology. (The Web of Science database stands out as one of the foremost abstract indexing databases, crucial for preventing the oversight or exclusion of significant articles in research. With its expansive coverage spanning diverse subjects, it provides researchers with sophisticated search capabilities to craft precise search queries, ensuring the retrieval of relevant results, particularly in expansive fields of study.) Based on the search, the keyword combination of the studies was scanned as a ‘title’ in the Web of Science database in the form of ‘viral marketing’. The main reason why the concept of viral marketing is scanned as a ‘title’ is to specifically evaluate viral marketing and prevent loss of focus, especially when performing content analysis. In the research, 222 studies between the years 2003–2023 were analyzed by bibliometric analysis. The main reason why different types of academic studies (articles, proceiding papers, books, book chapters, and review articles, etc.) in the Web of Science database are included in the scope of research is that some studies other than articles have a great impact on the literature. For example, a review article titled ‘The Myth about Viral Marketing’ made a significant contribution to the field with 435 citations. Similarly, a paper titled ‘Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks’ is a Proceedings Paper that makes a significant contribution to the viral marketing literature with 239 citations.

Findings

Bibliometric analysis

Of 222 viral marketing studies published in the Web of Science database between 2003 and 2023, 121 were articles, 87 were proceiding papers, 11 were book and book chapters, 3 review article. In addition, it has been determined that these studies have been published in various categories such as business, economy, management, innovation, data mining, information systems, communication, especially in the field of marketing. The number of publications by years and the total number of citations of the examined studies are presented in Figure 1.

Figure 1. Total number of publications by years and number of citations to these publications.

When examining the graph of the number of publications in the viral marketing literature (Figure 1), it can be seen that the work in this field began in 2003, although there was very little work in the first years, awareness has increased since 2007. There is a slight increase in the number of publications in 2012. In the period of 2013–2020, it can be seen that the number of publications in the field of viral marketing has reached the highest level. In the following years, the number of publications decreased every year: 11 publications in 2021, 5 publications in 2022, and only 3 publications in 2023.

When the number of citations made to the publications in the viral marketing literature is examined, it is seen that 2007 differed positively from other years. The main reason for this is that the publication named ‘Dynamics of Viral Marketing’, which has the most important impact in the field and can be considered as the building block of viral marketing with 2505 citations, was published in the relevant year.

It can be seen that the number of citations of publications related to viral marketing in recent years is quite low. The number of references to publications made in the years after 2018 is decreasing day by day (In 2018, the number of citations to publications was 178, but this number decreased to 152 in 2019, 108 in 2020, 69 in 2021, 20 in 2022, and 2 in 2023). Despite these results, it can be said that the interest in viral marketing has continued in recent years. The main reason for this is that in recent years, the number of references to publications related to viral marketing has been at a very high level. The total number of cites to viral marketing publications by year is presented below:

As seen in Figure 2, the number of citations to publications about viral marketing is still at high levels. In other words, there is still a significant trend towards viral marketing. It is thought that academic studies in the field of viral marketing will continue in the coming years. This interest in viral marketing makes this work important.

Figure 2. Number of citations to viral marketing publications between 2003 and 2023.

In order to make a more comprehensive analysis of the studies in the viral marketing literature, the list of the most cited authors (publications with at least 100 citations) and the journals in which these authors published their studies is presented in Table 1.

Table 1. Most cited authors and journals where their works are published.

Authors	Journals	Number of citations	Average number of citations per year
Leskovec et al. (2007)	ACM Transactions on The WEB	2505	147
De Bruyn and Lilien (2008)	International Journal of Research in Marketing	483	30
Phelps et al. (2004)	Journal of Advertising Research	444	22
Fitzgerald (2013)	Mit Sloan Management Review	435	39
Hinz, Skiera, Barrot … (2011)	Journal of Marketing.	357	27
Ho and Dempsey (2010)	Journal of Business Research	295	21
Nguyen et al. (2016a)	Sigmod’16: Proceedings of The 2016 International Conference on Management of Data	239	30
Kaplan and Haenlein (2011)	Business Horizons	187	14
Bampo et al. (2008)	Information Systems Research	182	11
Subramani and Rajagopalan (2003)	Communications of the ACM	169	8
Schulze et al. (2014)	Journal of Marketing	119	12
Yeoh et al. (2013)	Tourism Management	114	10
Palka et al. (2009)	Journal of Information Technology	106	7

When Table 1 is examined, it can be seen that the publication entitled ‘Dynamics of Viral Marketing’ written by Leskovec et al. (2007) is the most cited publication. The number of citations to this publication published in ‘Proceedings of ACM on the WEB’ is 2505. The second publication with the highest number of citations (483 citations) was carried out by De Bruyn and Lilien (2008) and was published in the International Journal of Research in Marketing. It is seen that the study with the third highest number of citations with 444 citations was carried out by Phelps et al. (2004) and was published in the Journal of Advertising Research. Finally, it will be seen that another study that received 435 citations and made a significant contribution to the literature was carried out by Fitzgerald (2013), and published in the Mit Sloan Management Review.

Co-authorship analysis of viral marketing literature was examined on the basis of the institutions where the publications were made. The results of the co-authorship analysis of the institutions are given in Table 2.

Table 2. Co-authorship analysis (on the basis of universities) on viral marketing literature.

University	Number of publications	Number of citations
University of Florida (USA)	8	375
Virginia Commonwealth University (USA)	5	364
University of Texas Dallas (USA)	4	37
University of Hong Kong (Hong Kong)	4	16
National Yang Ming Chiao Tung University (Taiwan)	4	14
University of Illinois System (USA)	4	9
Deakin University (Australia)	3	192
University of Isfahan (Iran)	3	81
Nanyang Technological University (Singapore)	3	70
University of Szczecin (Poland)	3	48
People’s Security Academy (Vietnam)	3	29
Vietnam National University (Vietnam)	3	29
University of Western Australia (Australia)	3	25
China Renmin University (China)	3	9
Beijing University of Posts and Telecommunications (China)	4	3

As a result of co-authorship analysis (on the basis of universities) of studies in the literature of viral marketing, it can be seen that the following universities stand out: University of Florida (USA) 8 publications and 375 citations; Virginia Commonwealth University (USA) 5 publications and 364 citations; University of Texas Dallas (USA) 4 publications and 37 citations; University of Hong Kong (Hong Kong) 4 publications and 16 citations; National Yang Ming Chiao Tung University (Taiwan) 4 publications and 14 citations; University of Illinois System (USA) 4 publications and 9 citations. On the other hand, despite the fact that there are only 2 publications by the University of Michigan System (USA), a total of 2514 citations have been made to these publications, making this university an important contributor to the literature of viral marketing. A similar comment can be made for Carnegie Mellon University (USA). The reason for this is that Leskovec, one of the authors of the study ‘The Dynamics of Viral Marketing’ with 2505 citations, works at Carnegie Mellon University (USA), and Adamic, another author, works at the University of Michigan System (USA). Also, within the scope of co-authorship analysis, the most co-publishing author couples were analyzed using the VOSviewer program and presented in Figure 3. In this context, it was seen that 222 publications in the field of viral marketing were carried out by 519 authors, and there are 13 authors who have 3 or more authors.

Figure 3. Co-authorization analysis of viral marketing literature.

Thai (8 studies-375 citations), Dinh (5 studies-364 citations) and Nguyen (3 studies-316 citations) had the greatest impact in the literature in terms of the number of studies and citations. It has been observed that these three authors generally work together. When the studies carried out by the relevant authors are examined, the authors generally consider the limited spread of influence and investigate the cost-effective mass viral marketing problem (Dinh et al., 2013), as well as Influence Maximization-IM (Nguyen et al., 2016a), which occurs in the spread of marketing. It has been observed that they also work on Cost-aware Target Viral Marketing (CTVM) (Li et al., 2017; Nguyen et al., 2016b; 2017). Thai has also carried out similar studies with different authors (Pham et al., 2016; 2019) He also developed model proposals for information dissemination in online social networks (OSNs) (Pan et al., 2017).

The citation analysis of the studies discussed in the viral marketing literature was analyzed on the basis of authors, and the results of the citation analysis are presented in Figure 4.

Figure 4. Most cited studies on viral marketing literature.

As can be seen in Figure 4, the most cited study belongs to Leskovec et al. (2007). The weight of the study called ‘Dynamics of Viral Marketing’, which can be considered as the building block of viral marketing with 2505 citations, is remarkable. The study, which presents a model for the effect of 16 million recommendations made by 4 million people over half a million products, on consumers’ purchasing decisions, is among the most effective studies in the field. It is seen that another important study belongs to De Bruyn and Lilien (2008) with 433 citations. In the study, in which a model was developed to help determine the role played by the recipients of viral marketing messages at each stage of the decision-making process, it was concluded that the characteristics of the social tie affect the behavior of the buyers, but they have different effects at different stages. Observing the reactions of 1100 people who received an unsolicited e-mail from their acquaintances and empirically testing the data after the survey are among the important contributions of the study to the literature. Another study with the highest number of citations (423 citations) was conducted by Phelps et al. (2004). Focus group interviews, in-depth interviews, and content analyzes over e-mail messages were carried out in the study, which was carried out in order to better understand the motivations and behaviors of those who sent e-mail messages.

Within the scope of the study, bibliographic matching analysis was made on the basis of countries, and the results of the analysis are shown in Table 3.

Table 3. Bibliographic matching analysis in terms of countries related to the literature.

Countries	Number of publications	Number of citations	Total link strength
USA	52	4581	5181
China	35	241	4007
Australia	14	366	3053
Spain	14	244	1731
England	14	151	3402
Germany	13	761	2534
India	12	53	1427
France	8	730	1143
Canada	8	432	1109
Iran	8	114	1060
Indonesia	8	14	355
Brazil	6	13	622
Malaysia	5	123	469
Taiwan	5	15	232

Although bibliographic matching analysis is a citation-based analysis, it is a method that enables the identification of publications that are not directly related to each other but use similar references. In this direction, it is used to reveal the networks of authors, countries or institutions that dominate this technical field, considering that the publications with similar reference lists are related to each other. As shown in Table 3, the first 5 countries with the most publications in the field of viral marketing are USA, China, Australia, Spain and England. When the bibliographic matching analysis and co-authorship analysis results are evaluated together, it has been observed that the USA leads in the current viral marketing literature and there is a scattered concentration on the basis of countries.

Bibliometric matching analysis by countries

In 2014 and 2015, the studies were concentrated in the USA, the UK, Germany and Canada, followed by China, Brazil and Singapore in 2016–2017, and India and Indonesia in the following period. When the distribution of 19 articles published in the last three years (2021, 2022, and 2023) on the basis of countries is analyzed, the USA (7), the UK (4) and the Netherlands (3) stand out as the countries where publications on viral marketing are published. In conclusion, it can be said that the literature on viral marketing has expanded over the years in different countries and cultures, especially in terms of applied studies. The ‘bibliometric match analysis by countries’, which includes the countries with the highest number of studies on viral marketing in all channels including universities, is presented above. As can be seen in Figure 5, while the US leads the viral marketing studies, China is another actor that makes a significant contribution to the field. As a matter of fact, 52 (26%) of the 202 studies conducted in 14 countries with 5 or more publications and 4581 (58%) of the total 7838 citations to these studies were conducted in the USA. China is another country leading the studies in this field with 35 studies (17%). In other words, nearly half (43%) of all studies and 62% of the citations in 14 countries were conducted by these two countries. The two countries with the highest total link strength are the USA (5181) and China (4007).

Figure 5. Bibliographic matching analysis for viral marketing literature by countries.

The journals in which viral marketing studies are most frequently published and the number of publications are presented below;

When the journals with the highest number of publications on viral marketing are analyzed, it is seen that the highest number of studies were conducted in ‘Journal of Interactive Marketing’ and ‘Physica A-Statistical Mechanics and Its Applications’ with 4 publications and in ‘Expert Systems with Applications’, ‘Advances in Economics, Business and Management Research’, ‘IEEE Transactions on Computational Social Systems’, ‘Plos One’ and ‘Social Network Analysis and Mining’ with 3 publications (figüre 6). When the journals with the highest number of publications on viral marketing are analyzed, it is seen that the highest number of studies were conducted in ‘Journal of Interactive Marketing’ and ‘Physica A-Statistical Mechanics and Its Applications’ with 4 publications and in ‘Expert Systems with Applications’, ‘Advances in Economics, Business and Management Research’ and ‘IEEE Transactions on Computational Social Systems’ with 3 publications (Figure 6). As a result of the analysis, it will be seen that the journals in which viral marketing is studied are generally marketing journals, but it is also possible to come across journals that include technical calculations such as statistics, analysis, evaluation, etc. outside the field of marketing. Journals such as ‘International Journal of Advanced Computer Science and Applications’, ‘Knowledge-Based Systems’ and ‘Plos One’ can be considered in this context. This explains the concentration of viral marketing studies in recent years in areas such as impact maximization, approximation algorithms and online social network optimization.

Figure 6. Journals with the most studies in viral marketing.

Keywords are indicators of the most important terms of the article, expressing the intellectual themes and structure of the research fields (Donthu et al., 2021). In the viral marketing studies, 509 keywords were used and the number of keywords with at least 4 or more repetitions was 17. A ‘co-occurrence analysis’ was conducted to reveal the thematic structure of the field. At this point, two different analyzes were conducted from two different points. First, the areas where viral marketing studies are concentrated periodically were mentioned. Then, common categories were created for the related studies by separating the areas of focus of the studies through the keywords in the viral marketing literature. In this context, the results of the co-occurrence analysis of the combined use of keywords in the viral marketing literature on a yearly basis are presented below.

Figure 7 shows the keyword network between 2003 and 2023. The proximity and thickness of the lines connecting two keywords indicates how often they occur together, and the size of a node indicates how often it occurs as a keyword (Donthu et al., 2020). As can be seen from the figure, in the studies conducted between 2012 and 2016 in the viral marketing literature, it is seen that studies on the environments and channels where viral marketing is used such as ‘Word of Mouth’, ‘e-commerce’, ‘viral dynamics’, and ‘e-mail marketing’ come to the fore. In 2016–2020, it is seen that there is an increase in studies on the network structure of viral marketing such as ‘Social Networks’ and ‘Social Media’ and studies on performance measurement and modeling of viral marketing activities such as ‘Influence Maximization’, ‘Data Mining’, and ‘Optimization’ etc. have started. In 2020 and beyond, studies have focused on 2 areas. The first one is the studies on performance measurement and modeling of viral marketing activities such as ‘Analytical Models’, ‘Branching Process’, ‘Referral Programs’, and ‘Heuristic Algorithms’. The second is the studies on increasing the effectiveness of viral marketing on customers such as ‘Customer Behavior’, ‘Dual Incentive’, and ‘Advertising’.

Figure 7. Co-occurrence analysis of keywords related to viral marketing literature by year.

On the other hand, when the keywords in the viral marketing literature are evaluated according to their network connection strength, it is seen that there are 6 different categorizations (Figure 8). Category 1 is related to research on viral marketing tools such as ‘Social Media’, ‘Marketing Message Delivery’ and ‘Digital Marketing’. Category 2 consists of studies on the power of social networks, which are the most effective medium in viral marketing campaigns, and studies that provide practical examples of how to create a viral effect in these channels. Some of the keywords used in this context are ‘Social Networks’, ‘Information Spread’, ‘Referral Programs’ and ‘Data Mining’. Category 3 includes terms such as ‘Influence Maximization’, ‘Influence Measure’ and ‘Profit Maximization’, which are terms related to the evaluation of performance in viral marketing activities. Category 4 consists of terms describing the impact of online social networks in the formation of the viral cycle and possible risks and uncertainties. Some of the keywords used in this context are ‘Online Social Networks’, ‘Information Diffusion’ and ‘Bistability’. Category 5 consists of words related to the optimization and modeling of viral marketing activities such as ‘Optimization’, ‘Mathematical Modeling’ and ‘N-intertwined Model’. Finally, category 6 refers to the economics of viral marketing activities. The keywords used in this context are ‘E-Commerce’, ‘Economics’ and ‘Revenue Maximization’. The map to be created as a result of the analysis of the use of keywords in the viral marketing literature on the basis of categories is presented below.

Figure 8. A collaborative analysis of the keywords in the viral marketing article on the basis of categories.

Context analysis (Research Categories and Trends)

Table 4 presents information on the 34 articles identified as a result of categorizing the studies in the viral marketing literature according to their content. In this context, the studies in the viral marketing literature are divided into three main categories as ‘Critical Success Factors in Viral Marketing Activities’, ‘Viral Marketing Activities and Performance’ and ‘The Power of Social Networks’. In addition, qualitative or quantitative analyzes, methods and theories used in the relevant studies are also explained in the relevant columns.

Table 4. Content analysis of studies in viral marketing literature.

Category	Trend	Articles	Type of analysis	Method	Theories
Viral marketing critical in its operations success factors	Consumer reactions and email forwarding motivations	Phelps et al. (2004).	Qualitative	Focus Group Method and Telephone Intervıew	NO (N/A)
		Ho and Dempsey (2010)	Quantitative	Maximum Likelihood Method	FIRO (Fundamental Interpersonal Relations Orientation) theory
		Rodrigues and Fonseca (2016)	Quantitative	Runge–Kutta method	NO (N/A)
		De Bruyn and Lilien (2008)	Quantitative	Multi-stage response analysis	Social Network Theory
	-Viral marketing / social media marketing	Kaplan and Haenlein (2011)	Qualitative	NO (N/A)	NO (N/A)
		Kaplan and Haenlein (2012)	Qualitative	NO (N/A)	NO (N/A)
		Dinh et al. (2013).	Quantitative and Qualitative	VirAds—Viral Advertising	power-law network theory
		Serrano and Iglesias (2016)	Quantitative	Exploratory data analysis	NO (N/A)
		Schulze et al. (2014)	Quantitative	NO (N/A)	NO (N/A)
	Seeding Strategies	Hinz et al. (2011)	Quantitative	Regression analysis	cognitive dissonance theory
		Shakarian and Paulo (2012)	Quantitative	Regression analysis	NO (N/A)
		Shakarian et al. (2013)	Quantitative	Regression analysis	NO (N/A)
	Mobile viral marketing strategies	Palka et al. (2009)	Quantitative	Focus Groups, In-depth Interviews	Grounded theory
		Hendijani and Marvi (2020)	Quantitative	Factor analysis, Descriptive statistics and Correlations	Theory of reasoned action, information adoption model and technology acceptance model,
		Pescher et al. (2014)	Quantitative	Analysis of psychographic and sociometric consumer characteristics.	NO (N/A)
Viral marketing activities and performance	Viral marketing performance criteria	Cruz and Fill (2008)	Qualitative	Semi-structured interviews	NO (N/A)
		Siri and Thaiupathump (2013)	Quantitative	NO (N/A)	NO (N/A)
		Karczmarczyk et al. (2018)	Quantitative	Sensitivity analysis Uncertainty analysis	NO (N/A)
	Propagation model recommendations	Lu et al. (2013)	Quantitative	NO (N/A)	NO (N/A)
		Sela et al. (2018)	Quantitative	Runtime Complexity Analysis, Sensitivity analysis	NO (N/A)
		Sheikhahmadi and Nematbakhsh (2017).	Quantitative	IMSN (Initial Multi-Spreader Nodes)	NO (N/A)
		Yang et al. (2010).	Quantitative	Simulation model	Complex network theory
	IM (Influence Maximization) and (profit maximization)	Nguyen et al. (2016a)	Quantitative	SSA and D-SSA Analysis	NO (N/A)
		Nguyen et al. (2017)	Quantitative	Cost-award Targeted algorithm (BCT) Linear threshold model,	NO (N/A)
		Tang et al. (2017)	Quantitative	Reverse Influence Sampling (RIS) method	NO (N/A)
The power of social networks	Social and digital networking	Bampo etc. (2008).	Quantitative	Sensitivity analysis	(epidemic theory)
		Abedniya and Mahmouei (2010)	Quantitative	STRUCTURAL equation modeling	NO (N/A)
		Al Abri and Valaee (2020)	Quantitative	Complexity Analysis	NO (N/A)
	Social capital	Camarero and San José (2011)	Quantitative	Exploratory factorial analysis confirmatory factor analysis - path analysis	Social Capital Theory (SCT) Social Network Social Network Theory
		Southwell et al. (2010).	Quantitative	Tobit regression models	NO (N/A)
		Koch and Benlian (2015)	Quantitative	Descriptive statistics & Regression analysis & Mediation analysis.	NO (N/A)
	Discovering the influential users	Zhu (2013)	Quantitative	NO (N/A)	NO (N/A)
		Amnieh and Kaedi (2015)	Quantitative	NO (N/A)	NO (N/A)
		Robles et al. (2020)	Quantitative	NO (N/A)	NO (N/A)

In order to make the study more comprehensive, the trends that make up the three main categories were also identified and presented in the relevant table. Considering these trends, it is seen that viral marketing activities are concentrated in 10 different areas. The ‘Critical Success Factors in Viral Marketing Activities’ category includes (1) consumer reactions and motivations for email forwarding, (2) viral marketing and social media, (3) seeding strategies, and (4) mobile viral marketing strategies. The category ‘Viral Marketing Activities and Performance’ includes (1) viral marketing performance criteria, (2) diffusion model recommendation, and (3) impact maximization and profit maximization, while the category ‘The Power of Social Networks’ consists of (1) social and digital networking (2) social capital and (3) discovering influential users. Figure 9 shows the research framework resulting from the ­categorical decomposition.

Figure 9. Figure 8 research framework—prominent categories and trends in viral marketing literature.

Critical success factors in viral marketing activities

The first and perhaps the most important category that constitutes the content of viral marketing is ‘Critical Success Factors in Viral Marketing Activities’. This category consists of consumer reactions and motivations for email forwarding, viral marketing and social media, seeding strategies and mobile viral marketing strategies.

Consumer reactions and motivations for email forwarding

The foundation for the success of viral marketing campaigns is the creation of an online viral loop. This is only possible by motivating internet users to create the online viral loop. In this context, Ho and Dempsey (2010) examined the motivations of internet users to forward online content. Conceptualizing the act of online content forwarding as a special case of a more general communication behavior, they identified four potential motivations. These are (1) the need to be part of a group, (2) the need to be individualistic, (3) the need to be altruistic, and (4) the need for personal growth. The results of the study showed that internet users who internalize individualism and altruism are more likely to forward online content than others. In viral marketing campaigns, it is extremely important to create a small group of high-profile influencers with the potential to have an impact on the target audience in order to achieve an effective spread by involving people in the process. In fact, in a study conducted by Phelps et al. (2004), which has an important place in the literature, focus group discussions, in-depth interviews and content analysis of e-mail messages were conducted and the importance and power of influencers in viral marketing practices were clearly demonstrated. The study also showed that both influencers and other email recipients delete irrelevant emails as soon as they receive them. Therefore, targeting the right people to create a viral effect is extremely important. In addition, messages that are informative or evoke strong emotions such as excitement, fear, sadness, joy, inspiration, etc. can have an impact on people’s motivation to participate in the viral cycle and share these messages with their network. For example, a literature review by Woerdl et al. (2008) concludes that a user’s motivation to share and recommend an incoming message to users in their network depends on whether the message is interesting, entertaining or intriguing. The authors also found that some customers are reluctant to make recommendations unless there is a payoff. Another critical success factor of viral marketing is ensuring sufficient contagion. As a matter of fact, messages that are not contagious do not have an impact on people’s motivation to forward messages. Working in this context, Rodrigues and Fonseca (2016) presented a model covering the viral process of a communication marketing campaign. In this context, the parameters used by the authors are: Contagion and recovery rate. This is because as contagion increases, the proportion of the target audience reached increases and value creation accelerates. De Bruyn and Lilien (2008) also investigated the role of viral messages shared electronically in purchase decisions and developed a model for this. As a result of the study, it was observed that social ties affect the purchasing behavior of buyers at different levels, the strength of the tie facilitates awareness and triggers buyers’ attention. As can be understood from the explanations made, viral posts, which have the quality of activating the transmission motivations of people on the network, have a critical importance in achieving success in viral marketing activities.

Viral marketing and social media

One of the critical success factors for viral marketing is the peer-to-peer information channel. Because the transmission of a message depends on the existence of a common channel by the sender and other users and a combination of leveraged technologies. Peer-to-peer information conduit, incorporates communication channels and technology available, used and leveraged by the message senders (Woerdl et al., 2008). It is possible to say that social media is the most important viral marketing channel because it is a suitable channel for rapid dissemination, allows reaching large masses, and has the visual and content capacity to convey messages in a very good and understandable way. When the literature on the subject is examined, one of the important studies was carried out by Dinh et al. (2013), and the authors investigated the cost-effective mass viral marketing problem, considering the effect spread determined in their study. Kaplan and Haenlein (2011) examined the relationship between social media and viral marketing and listed six steps to be taken for effective social media/viral marketing coexistence. The study also focused on three conditions that must be met in order to create a viral cycle (that is, giving the right message to the right messengers in the right environment - giving the right message to the right messengers in the right environment) and viral marketing campaign groups were created for four different social media channels. (nightmares, luck, homemade issues, and triumphs—(nightmares, strokes-of-luck, homemade issues, and triumphs). In another study by the same authors, Kaplan and Haenlein (2012) analyzed how Britney Spears and his staff utilized social media applications to communicate around this pop icon and to build and maintain a famous brand image. In their study, Serrano and Iglesias (2016) presented a method based on Agent-based Social Simulation research methodology to create viral marketing strategies on Twitter.By modeling a virtual market, the authors were able to design, understand and evaluate their marketing hypotheses before taking them to the real world. In a study conducted on Facebook, another important social media channel (Schulze et al., 2014), it was seen that consumers use Facebook for fun rather than doing something useful. In this respect, the use of entertaining elements in viral messages to be created on Facebook will increase the spread and effectiveness of shared messages.

Seeding strategies

Seeding strategies are also among the critical success factors of viral marketing. In this context, whether the message has an exponential and rapid spread among users and whether the message reaches a wide and accurate audience are among the important factors. Therefore, effective targeting is extremely important. At this point, it is possible to reach different audiences through social connections. In addition, inaccurate message seeding can lead to a lack of control and reluctance to give advice to users who do not receive a return (Woerdl et al., 2008). Studies on seeding strategies generally focus on identifying the first set of ‘seeds’ in a network (Shakarian & Paulo, 2012) and selecting an initial set of ‘seeds’ to enable the entire network to adopt the shared seed (Shakarian et al., 2013). Both studies propose methods for quickly finding seed clusters that scale to very large networks. Another important study by Hinz et al. (2011), with 314 citations, is a large-scale comparison of different seeding strategies. In this study, four seeding strategies were compared in two complementary small-scale field experiments as well as in a real-life viral marketing campaign involving more than 200,000 customers of a cellular service provider. Empirical results showed that the best seeding strategies can be up to eight times more successful than other seeding strategies. This study, which can be characterized as a pioneering work, has made an important contribution to the literature as it is the first study to compare experimental seeding strategies with real-life data.

Mobile viral marketing strategies

Mobile viral marketing allows a user to share their thoughts or promotional messages about a product/service/brand with all the people in their social network over the internet. The costs of such sharing are extremely low and interaction can be achieved very quickly. In addition, mobile viral marketing enables consumers to recognize the brand and develop positive attitudes towards the brand, thus influencing their purchasing decisions (Yang et al., 2012). Studies on mobile viral marketing strategies generally focus on the impact on consumers’ attitudes and behaviors. For example, Hendijani and Marvi (2020) examined the impact of viral marketing on the purchase intentions of mobile application users in Iran, while Palka et al. (2009) focused on the motivations, attitudes and behaviors of those who receive, use and forward mobile viral content to implement effective mobile viral marketing. The study derived a set of determinants that influence behaviors in mobile viral marketing processes and presented a theory explaining mobile viral effects. Yang et al. (2012) conducted a study to determine the attitudes, approaches and behaviors of young people living in China towards mobile viral marketing activities. As a result of the study, it was found that young people have positive attitudes towards mobile viral marketing and are more participatory in spreading fun, useful, purposeful or personal benefit messages. In a study conducted on university students living in the USA, it was concluded that subjective norm, behavioral control and perceived cost are important determinants of young American consumers’ attitudes towards mobile viral marketing. In addition, it was also found that participants developed a more positive attitude if the viral message was useful and entertaining (Yang & Zhou, 2011). Taking a broader approach, Pescher et al. (2014) analyzed a three-stage model of consumer referral behavior via mobile devices in a field study of a mobile viral marketing campaign created by a company. The findings showed that consumers who place high importance on the objective value and entertainment value of a message are more likely to enter the attention and referral stages. According to Pousttchi and Wiedemann (2007), who take a more holistic approach to mobile viral marketing strategies, critical success factors in mobile viral marketing activities can be evaluated under 8 headings: (1) Perceived usefulness by recipient, (2) Reward for Communicator, (3) Perceived ease of use, (4) Free mobile viral content, (5) Initial contacts, (6) First-mover’s advantage, (7) Critical mass, and (8) Scalability.

Viral marketing activities and performance

Another important category that composes the content of viral marketing is the ‘Viral Marketing Activities and Performance’ category. This category consists of viral marketing performance criteria, diffusion model recommendations, and impact maximization and profit maximization categories.

Viral marketing performance criteria

Achieving set targets is crucial to the success of a viral campaign. Therefore, it is extremely important to set defined and measurable goals for the campaign. Otherwise, the number of users participating in the campaign, user sessions and even the emails sent will be meaningless. The generally accepted basic criteria for the success of a marketing campaign are reaching the targeted sales volume and strengthening brand awareness. However, it is difficult to directly associate the increase in sales with the campaign. Although there are some studies in the literature, there is no standardized approach to measure the success of a viral marketing campaign (Cruz & Fill, 2008). Studies in this field generally focus on determining viral marketing performance criteria or evaluating the performance of viral marketing activities based on predetermined criteria. Karczmarczyk et al. (2018) presented assumptions for a decision support system for multi-criteria campaign planning and evaluation with inputs from agent-based simulations (Multi-criteria decision support for evaluating the performance of viral marketing campaigns in social networks). In a study of performance evaluation of viral marketing activities, the effectiveness of information dissemination in a dynamic behavioral environment was measured and the actual performance of viral marketing campaigns was analyzed with the model used here. The result of the study showed that the level of connection between customers in social networks has a significant impact on the performance of marketing programs (Siri & Thaiupathump, 2013). Cruz and Fill (2008) categorized viral marketing into two main areas. Virals that develop randomly, without any intervention by the marketer, and virals that are deliberately placed to achieve goals set by the marketer. The study presents a viral marketing evaluation framework that identifies three key objectives and their specific evaluation criteria (Cognitive, Behavioral, and Financial). Cruz and Fill (2008) categorized the approaches used to measure and evaluate the success of viral marketing as follows: Number of new users or loyalty levels, attitude and behavior changes, reach, frequency, penetration, speed of transmission, and content of conversations, etc. Helm (2000) suggested that the goal of viral marketers is to maximize reach. Welker (2002) stated that a virus of ideas can be measured in the following dimensions: Speed (i.e., speed of transmission), persistence (i.e. how long it circulates), ease of transmission (simplicity: no mental barriers, low costs, little processing).

Diffusion model recommendation

Viral marketing first targets a limited number of users (seeds) in the social network by providing incentives, and these targeted users then start the process of creating awareness by spreading the information to their friends through their social relationships. In other words, the main objective of viral marketing is to create a reason for the message to be forwarded by selected influencers (Long & Wong, 2014). This is only possible through the methods to be developed in this field. Therefore, this section discusses studies that have an important place in the literature on diffusion model proposals. In one study, the problem of diffusion, which is among the important problems of competitive viral marketing, was evaluated from the perspective of social network platform owners and a new diffusion model that captures the competitive nature of viral marketing was proposed (Lu et al., 2013). Sela et al. (2018) also proposed a new diffusion model suitable for real-world marketing scenarios. In this model, diffusion is based on the marketer’s ongoing active seeding efforts. The proposed model emphasizes that the success of a marketing attempt to influence a potential customer depends on the number of friends of that user. Sheikhahmadi and Nematbakhsh (2017) ranked social network connections according to their diffusion power. The authors proposed a method called IMSN (Initial Multi-Spreader Nodes), which tries to optimize the spreading and select a group of networks to start the spreading process. A model has also been proposed by Yang et al. (2010). In this work, a new model for the study of large-scale complex systems is developed using recent advances in complex network theory, graph theory and computational techniques. In the study carried out by Leskovec et al. (2007), a diffusion model was proposed for the effect of 16 million recommendations made by 4 million people over half a million products on consumers’ purchasing decisions. Within the scope of the study, the diffusion and cascade dimensions of recommendations described by a simple stochastic model are observed. In addition, it is analyzed how user behavior changes in user communities defined by a recommendation network.

Impact maximization and profit maximization

As mentioned in the related section, in recent years, viral marketing studies have focused on impact maximization, approximation algorithms and online social network optimization. In other words, the increase in measurement, evaluation, statistical analysis and engineering studies in the field is also reflected in the media. The influence maximization problem defines the subset of influential users in the network to provide solutions to real-world problems such as epidemic detection, viral marketing, etc. Therefore, influence maximization is an important problem to tackle some real-world problems and activities (Singh et al., 2022). In this context, Nguyen et al. (2016a) developed two new sampling frameworks for IM (Influence Maximization) based viral marketing problems, namely SSA (Stop-and-Stare Algorithm) and its dynamic version D-SSA. Similarly, Nguyen et al. (2017) proposed a new model called cost-aware targeted viral marketing (CTVM) to find the most cost-effective seed users that can attract the most relevant users to the advertisement. Tang et al. (2017) proposed a profit maximization proposal for viral marketing in online social networks. In doing so, they tried to identify initial seed networks that maximize the total profit by considering the cost of seed selection as well as the benefit of influence propagation. Zhu and Li (2018) developed a limited scalable approximation algorithm for competitive profit maximization called AlgCP (Algorithm for Competitive Profit) that works on billion-scale networks. They claimed that this algorithm can identify the best seeds for the host in a network with 1.5 billion edges in just a few minutes. A recent study by Singh et al. (2022) provides a comparative review of state-of-the-art approaches for impact maximization algorithms.

The power of social networks in viral marketing

The final category of viral marketing content is ‘The power of social networks in viral marketing’, which consists of social and digital networking, social capital and the discovery of influential users.

Social and digital network formations

The social nature of digital networks is critical in spreading a viral message. Today, everything is in place to ensure that viral marketing and social networking are incorporated into an integrated marketing and communication strategy. This strategy provides an opportunity to increase brand awareness and utilize the most effective marketing strategy. In this context, Abedniya and Mahmouei (2010) investigated the role of social networks in influencing viral marketing and the characteristics of the most effective users in sharing viral content. The study concluded that viral content is more likely to be shared and spread on highly community-oriented social networking sites. It was also found that potential users of social networks with high levels of critical mass have higher levels of belief in and engagement in viral activity. Bampo et al. (2008) examined the formation of an active digital network. Another research area of the authors is the impact of the social structure of digital networks and the transmission behavior of individuals on campaign performance. The authors also conducted a series of simulation experiments to predict the spread of a viral message within different social network structures under different assumptions and scenarios. In another study in this area, a new model for information flow in online social networks was created that captures the sharing behavior of users when they transfer information from one online social network to their social circles in another network (Al Abri & Valaee, 2020). In conclusion, social networking websites play an important role in the effectiveness of viral marketing. Therefore, it can be argued that the characteristics of social networking websites have a potentially strong influence on user’s viral content sharing. A social networking site is based on network effects that increase the probability of a message reaching the right people (Abedniya & Mahmouei, 2010).

Social capital

New information technologies have enabled individuals to create social networks by increasing their connections with others through e-mail, mobile or online networks. When individuals are involved in these social networks, they create social capital. Social capital refers to the network of relationships in which an individual is involved and the resources that this network contains. Social capital is measured in three dimensions: The structural dimension (the connections between individuals of a social group), the relational dimension (the willingness of people to act together) and the cognitive dimension (the degree to which individuals have a shared vision and language) (Camarero & San José, 2011). An important and highly cited study in the literature investigated the effects of two classic promotional practices, scarcity and personalization, on actual referral behavior in a field experiment of an online fashion service provider called StyleCrowd (Koch & Benlian, 2015). Camarero and San José (2011) proposed a causal model in which viral dynamics are determined by an individual’s social capital and prior attitudes. As a result of the study, it was found that an individual’s social capital and prior attitudes determine viral dynamics, integration into the e-mail network facilitates the receipt and forwarding of messages, and close relationships encourage the opening and forwarding of messages. Attitudes towards viral messages were found to be critical for message opening and forwarding. Another important study in the literature was conducted by Southwell et al. (2010). The study investigated the potential effects of community ties on the diffusion of publicly funded breast cancer screening in the United States.

Discovery of ınfluential users

The goal of viral marketing on the popular online social networking platform is to spread marketing information quickly at a lower cost and increase sales. The key issue here is how exactly to discover the most influential users in the information dissemination process. Zhu (2013) has conducted the most influential study in this field in terms of number of citations. He proposed a model for discovering the most influential users in viral marketing. First, the user trust network for viral marketing and the combined interest level of users in the network are comprehensively defined. Then, a model considering the time factor is built and a dynamic algorithm definition is proposed to simulate the information diffusion process in viral marketing. Finally, experiments are conducted with a real dataset from Epinions, a famous SNS (social networking service) website. Amnieh and Kaedi (2015) used the graph structure of social networks to predict two personality traits, openness and extraversion, for network members. They then evaluated these two predictive traits along with other characteristics of social networks and treated them as selection criteria to select influential people who will have the greatest impact on diffusion. They used a real-coded genetic algorithm to perform this process. Robles et al. (2020) proposed a multi-objective approach to the influence maximization problem to reduce the costs and increase the revenues of viral marketing campaigns. In this context, they used local social network metrics to find influential people.

Potential research areas of viral marketing

Identifying future research areas in bibliometric studies is extremely important to understand the direction in which the field will develop (Ferreira et al., 2016; Lopes et al., 2019; 2021). In this section, based on the classification of the 34 studies used in the content analysis, potential research areas of viral marketing are identified and proposed research areas are mentioned:

Within the category of ‘Critical Success Factors in Viral Marketing Activities’; viral participant types can be profiled (Fard & Marvi, 2020; Phelps et al., 2004), research can be conducted to examine the effects of source characteristics and reliability on the transmission of online content (Ho & Dempsey, 2010; Phelps et al., 2004), online content characteristics that have the potential to go viral can be identified (Ho & Dempsey, 2010), ethical issues and boundaries in viral marketing can be investigated (Schulze et al., 2014), and the best incentives to activate ‘send to a friend’ behaviours in the formation of the viral cycle can be investigated (De Bruyn & Lilien, 2008). As a matter of fact, Van der Lans and van Bruggen (2010) emphasised in their study that firms allocate a portion of their marketing communication budgets to viral marketing and that they expect the marketing budgets allocated for viral marketing, which is one of the fastest growing marketing trends, to increase in the coming period. The main factors that increase the popularity of viral marketing are the decreasing effectiveness of traditional marketing communication tools and the increasing ability of consumers to exchange information on the Internet. Furthermore, how consumers who are not willing to receive messages respond to unsolicited mobile advertising messages and which products or services are more suitable for mobile viral marketing campaigns can be investigated (Pescher et al., 2014), the role of mobile viral application features in the formation of purchase intention can be examined (Fard & Marvi, 2020). It can also be investigated whether consumers of old and reputable companies use their personal networks more effectively than consumers of new and less well-known companies (De Bruyn & Lilien, 2008). On the other hand, it can also be investigated which design aspects of the message create a more positive attitude towards the viral message, which users show the closest relational connection over the internet, in other words, who will be more prone to viral marketing (Camarero & San José, 2011). Indeed, Birke (2013) identified critical success factors for viral marketing, including the design aspects of the message to be shared, as follows: Proposition excellence, Observability of the product or its use, Designing the campaign with a good understanding of the special role of viral communication in product dissemination, Viral communication studies for economic benefit, Utilising storytelling and capturing the spirit of the time, Utilisation of influential expert users, Engaging in attractive exchanges with relevant communities. Motoki et al. (2020) concluded that the combination of self-reported data and socially relevant neural measures plays an important role in predicting viral marketing success in social media. The topics that can be researched under the category of ‘Viral Marketing Activities and Performance’ are as follows Types of performance-enhancing incentives that can be used to ensure message delivery (Camarero & San José, 2011), the impact of source credibility on the delivery performance of online content (Ho & Dempsey, 2010), and the impact of big data technologies & viral marketing integration on performance (Serrano & Iglesias, 2016). Also, an experiment can be conducted to compare the adoption rate of the scheduled seeding approach and the unscheduled seeding approach (Sela et al., 2018). Ewing et al. (2014) developed and tested a mathematical model that addresses the problems of evaluating viral marketing campaign performance. In this way, they have provided the viral marketing literature with a more accurate and valid tool for evaluating campaign performance. In addition, the impact of QR Code applications, virtual reality applications and augmented reality applications on viral marketing performance can be investigated. As a matter of fact, Sung (2021) investigated consumer reactions to AR mobile application advertisements and concluded that immersive new brand experiences made possible by AR positively affect consumer reactions. Finally, under the category of ‘Power of Social Networks in Viral Marketing’; a classification can be made on the strength of social ties through interaction data (Serrano & Iglesias, 2016) or effective nodes can be identified through personality traits (Amnieh & Kaedi, 2015). As a matter of fact, Zhang et al. (2023) concluded in their study that the sharing of the content created by the company is affected by the personality traits of the consumers such as extraversion and suggested that future studies should be conducted to determine the effect of the personality traits of the participants on these shares. Aggarwal and Arora (2023) found that the proposed optimisation DBATM (BAT-modified) algorithm for detecting influential users is effective and convincing for viral marketing. In addition, viral marketing applications can be tested on social media platforms that have not yet been studied, especially LinkedIn and Instagram (Schulze et al., 2014), alternative models of member behaviour can be examined and a parallel can be drawn between the effect of these models and the effect of member character in viral marketing practices on social networking websites (Abedniya & Mahmouei, 2010), users’ opinions about the advertised product can be analysed based on sentiment analysis and how to dynamically update the seed set of the viral marketing campaign according to the results of this analysis can be investigated (Al Abri & Valaee, 2020), how scarcity affects referrals at different diffusion stages can be examined (Koch & Benlian, 2015). What incentives can be used to encourage the sharing of viral messages (Camarero & San José, 2011). When defining the viral marketing problem, trust characteristics can be included in the social network to enrich the social peer effect (Robles et al., 2020).

Given the evolving nature of modern marketing approaches, it is useful to address the possible impact of emerging trends such as artificial intelligence, blockchain or virtual reality on viral marketing. These trends can be taken into consideration in future academic studies. For example, artificial intelligence can more accurately identify and segment the target audience using big data analytics and machine learning. In this way, marketers can create more effective and personalised content. In addition, artificial intelligence can make content recommendations based on users’ interests. For example, it can recommend relevant content based on a user’s previous interactions. This makes the content more personalised and effective and increases the likelihood of it being shared. On the other hand, artificial intelligence can analyse user emotions on social media and other platforms. It can identify positive or negative feedback and adjust marketing strategies based on this feedback. Content that encourages positive emotions is more likely to go viral. AI can also interact with users and automatically respond to questions or comments. This allows brands to interact with users more quickly and improves the user experience. By analysing data from social media and other platforms, AI can also identify trends and predict future viral content. In this way, brands can get more shares by creating content in line with trends. Finally, artificial intelligence can continuously monitor and analyse the performance of marketing campaigns. In this way, it can make the necessary changes to optimise and improve campaigns.

The use of blockchain in viral marketing strategies can be realised in the following ways: A company can incentivise certain actions by offering incentives such as rewarding users who share their products on social media with cryptocurrencies. In addition, a company can organise user loyalty programmes by giving each customer who makes a purchase a certain amount of cryptocurrency or offering discounts with this currency. These programmes can have a viral effect as they encourage users to recommend the product or service to their friends. Companies can incentivise the increase in user-generated content by rewarding users for producing content, for example with cryptocurrency. As blockchain enables secure payments, even for very small amounts, companies can reward users with small amounts of cryptocurrency for performing certain actions or producing content. These micropayments can increase user engagement and strengthen the viral effect.

Using VR for viral marketing can be realised in the following ways: For example, an automobile manufacturer can offer potential customers the chance to experience their vehicles virtually, enabling users to learn more in-depth information about the product and share the content. In other words, interactive experiences can be provided to customers through VR content. Companies can also use VR technology to offer 360-degree videos or virtual tours to potential customers, and after experiencing this experience, users can share the content and recommend it to the people around them. Companies developing fun and shareable content can also attract users’ attention and encourage them to share the content. Social media platforms, which have the potential to deliver content to a wider audience and increase viral impact, can also be considered as important channels for sharing VR content and social media integration can be achieved.

Conclusions and recommendations

Main conclusion

The aim of this study is to identify the leading journals, authors, publications and main research themes in this field using bibliometric and thematic content analyses, to provide an overview of existing viral marketing research, to identify research gaps and to provide a conceptual framework for future research. Within the scope of the study conducted for this purpose, 222 viral marketing studies published in the Web of Science database between 2003 and 2023 were examined through VOSviewer 1.6.18 software programme. In the study, bibliometric analysis method, co-existence, co-citation, co-author, bibliographic matching analyses and thematic content analyses were performed.

As a result of the study, it was observed that the number of publications on viral marketing reached its peak with 24 studies conducted in 2016. The most cited study belongs to Leskovec et al. (2007) with 2505 citations. It was observed that the USA (26%) led the studies and China (17%) was another country that made a significant contribution to the field. Thai (8 studies-375 citations), Dinh (5 studies-364 citations) and Nguyen (3 studies-316 citations) are the authors with the greatest impact on the literature in terms of the number of studies and citations. It was observed that these three authors usually work together. When we look at the journals in which the most studies were published, it was seen that the most studies were published in ‘Journal of Interactive Marketing’ and ‘Physica A-Statistical Mechanics and Its Applications’ with 4 publications and in ‘Expert Systems with Applications’, ‘Advances in Economics, Business and Management Research’, ‘IEEE Transactions on Computational Social Systems’, ‘Plos One’ and ‘Social Network Analysis and Mining’ with 3 publications. As a result of the analysis, it will be seen that the journals in which viral marketing studies are published are generally marketing journals, but it is also possible to come across studies in journals that include technical calculations such as statistics, analysis, evaluation, etc. outside the field of marketing. When the results of the common asset analysis were evaluated, it was seen that the studies in the viral marketing literature were concentrated between 2013 and 2020. In the studies conducted between 2012 and 2016, it was observed that studies on the environment of viral marketing and the channels used in viral marketing came to the fore. In 2016–2020, studies on the network structure of viral marketing, and after 2020, studies on performance measurement and modelling of viral marketing activities have come to the fore. In this period, it is also possible to come across studies aimed at increasing the effectiveness of viral marketing on customers. In the content analysis, information was given about 34 articles determined as a result of the separation of the studies in the viral marketing literature according to their content. In this context, the studies in the viral marketing literature are divided into three main categories.

Critical success factors in viral marketing activities

(1) Consumer reactions and motivations for email forwarding: The foundation for the success of viral marketing campaigns is the creation of an online viral loop. This is only possible by motivating internet users to create the online viral loop (Ho & Dempsey, 2010). In viral marketing campaigns, it is extremely important to create a small group of high-profile influencers with the potential to have an impact on the target audience in order to achieve an effective spread by involving people in the process (Phelps et al., 2004). Therefore, targeting the right people to create a viral effect is extremely important. In addition, messages that are informative or evoke strong emotions such as excitement, fear, sadness, joy, inspiration, etc. can have an impact on people’s motivation to participate in the viral cycle and share these messages with their network (Woerdl et al., 2008). Another critical success factor of viral marketing is ensuring sufficient contagion. As a matter of fact, messages that are not contagious do not have an impact on people’s motivation to forward messages (Rodrigues & Fonseca, 2016). (2) Viral marketing and social media: One of the critical success factors for viral marketing is the peer-to-peer information channel. Because the transmission of a message depends on the existence of a common channel by the sender and other users and a combination of leveraged technologies. Peer-to-peer information conduit, incorporates communication channels and technology available, used and leveraged by the message senders (Woerdl et al., 2008). It is possible to say that social media is the most important viral marketing channel because it is a suitable channel for rapid dissemination, allows reaching large masses, and has the visual and content capacity to convey messages in a very good and understandable way. It is possible to find many studies in the literature that support this conclusion (Dinh et al., 2013; Kaplan & Haenlein, 2011; 2012; Schulze et al., 2014; Serrano & Iglesias, 2016). (3) Seeding strategies: Seeding strategies are also among the critical success factors of viral marketing. In this context, whether the message has an exponential and rapid spread among users and whether the message reaches a wide and accurate audience are among the important factors. Therefore, effective targeting is extremely important. At this point, it is possible to reach different audiences through social connections. In addition, inaccurate message seeding can lead to a lack of control and reluctance to give advice to users who do not receive a return (Woerdl et al., 2008). Studies on seeding strategies generally focus on identifying the first set of ‘seeds’ in a network (Shakarian & Paulo, 2012) and selecting an initial set of ‘seeds’ to enable the entire network to adopt the shared seed (Shakarian et al., 2013). (4) Mobile viral marketing strategies: Mobile viral marketing allows a user to share their thoughts or promotional messages about a product/service/brand with all the people in their social network over the internet. The costs of such sharing are extremely low and interaction can be achieved very quickly. In addition, mobile viral marketing enables consumers to recognize the brand and develop positive attitudes towards the brand, thus influencing their purchasing decisions (Yang et al., 2012). Studies on mobile viral marketing strategies generally focus on the impact on consumers’ attitudes and behaviors. It is possible to come across many studies in the literature on the effectiveness of different functions included in mobile viral marketing (Hendijani & Marvi, 2020; Palka et al., 2009; Pescher et al., 2014; Pousttchi & Wiedemann, 2007; Yang et al., 2012; Yang & Zhou, 2011).

Viral marketing activities and performance

(1) Viral marketing performance criteria: Achieving set targets is crucial to the success of a viral campaign. Therefore, it is extremely important to set defined and measurable goals for the campaign. Otherwise, the number of users participating in the campaign, user sessions and even the emails sent will be meaningless. The generally accepted basic criteria for the success of a marketing campaign are reaching the targeted sales volume and strengthening brand awareness. However, it is difficult to directly associate the increase in sales with the campaign. Although there are some studies in the literature, there is no standardized approach to measure the success of a viral marketing campaign (Cruz & Fill, 2008). Studies in this field generally focus on determining viral marketing performance criteria or evaluating the performance of viral marketing activities based on predetermined criteria (Cruz & Fill, 2008; Helm, 2000; Karczmarczyk et al., 2018; Siri & Thaiupathump, 2013; Welker, 2002). (2) Diffusion Model Recommendation: Viral marketing first targets a limited number of users (seeds) in the social network by providing incentives, and these targeted users then start the process of creating awareness by spreading the information to their friends through their social relationships. In other words, the main objective of viral marketing is to create a reason for the message to be forwarded by selected influencers (Long & Wong, 2014). This is only possible through the methods to be developed in this field. It is possible to come across various studies in the literature in which different diffusion model recommendation have been developed (Leskovec et al., 2007; Lu et al., 2013; Sela et al., 2018; Sheikhahmadi & Nematbakhsh, 2017; Yang et al., 2010). (3) Impact maximization and profit maximization: As mentioned in the related section, in recent years, viral marketing studies have focused on impact maximization, approximation algorithms and online social network optimization. In other words, the increase in measurement, evaluation, statistical analysis and engineering studies in the field is also reflected in the media. The influence maximization problem defines the subset of influential users in the network to provide solutions to real-world problems such as epidemic detection, viral marketing, etc. Therefore, influence maximization is an important problem to tackle some real-world problems and activities (Singh et al., 2022). It is seen in the literature that various model suggestions have been developed for impact maximization and profit maximization (Nguyen et al., 2016a; 2017; Singh et al., 2022; Tang et al., 2017; Zhu & Li, 2018).

The power of social networks in viral marketing

(1) Social and digital network formations: The social nature of digital networks is critical in spreading a viral message. Today, everything is in place to ensure that viral marketing and social networking are incorporated into an integrated marketing and communication strategy. This strategy provides an opportunity to increase brand awareness and utilize the most effective marketing strategy. Various studies can be seen in the literature on social and digital network formations (Abedniya & Mahmouei, 2010; Al Abri & Valaee, 2020; Bampo et al., 2008). In conclusion, social networking websites play an important role in the effectiveness of viral marketing. Therefore, it can be argued that the characteristics of social networking websites have a potentially strong influence on user’s viral content sharing. A social networking site is based on network effects that increase the probability of a message reaching the right people (Abedniya & Mahmouei, 2010). (2) Social capital: New information technologies have enabled individuals to create social networks by increasing their connections with others through e-mail, mobile or online networks. When individuals are involved in these social networks, they create social capital. Social capital refers to the network of relationships in which an individual is involved and the resources that this network contains. Social capital is measured in three dimensions: The structural dimension (the connections between individuals of a social group), the relational dimension (the willingness of people to act together) and the cognitive dimension (the degree to which individuals have a shared vision and language) (Camarero & San José, 2011). It can be seen in the literature that various studies have been conducted on social capital formations (Camarero & San José, 2011; Koch & Benlian, 2015; Southwell et al., 2010). (3) Discovery of ınfluential users: The goal of viral marketing on the popular online social networking platform is to spread marketing information quickly at a lower cost and increase sales. The key issue here is how exactly to discover the most influential users in the information dissemination process. It can be seen in the literature that many studies have been conducted on the discovery of effective users Zhu, 2013; Amnieh & Kaedi, 2015; Robles et al., 2020).

Practical and theoretical significance

As explained in the methodology section of the study, bibliometric research is research that provides a systematic review of the knowledge accumulated in a particular field by comprehensively analysing the existing literature. This study contributes to the development of the article by analysing the knowledge accumulated in the existing literature on viral marketing. Viral interaction, which enables sharing electronically and largely through social media channels, has the potential to provide competitive advantage in different sectors and market segments in the coming period. In this respect, continuing to carry out viral marketing practices in different sectors will especially contribute to marketing practitioners. Although viral marketing has been recognised as an important marketing approach in recent years, no study on viral marketing using bibliometric and content analysis methods has been found in the literature. This study was carried out to fill this gap (although there are a limited number of studies on eWOM, there are many differences between viral marketing and eWOM as mentioned in the literature section). On the other hand, the limited number of studies in the field of viral marketing brings along the need for theoretical and empirical studies in this field. For this reason, it is recommended that empirical studies as well as theoretical studies be conducted for both marketing practitioners and theoreticians of the viral marketing concept in future research directions. The main reasons for making this recommendation are: Theoretical and empirical studies on the effectiveness and cost-effectiveness of viral marketing can examine how quickly a viral marketing campaign spreads, the cost of reaching potential customers and conversion rates. Although there are some studies in this area, there is no generally accepted formulation. The design and optimisation of viral marketing campaigns requires an understanding of users’ behaviour and reactions. Theoretical studies can analyse social sharing and interaction patterns to understand which types of content are more likely to go viral. Empirical studies can validate or optimise the effectiveness of strategies through experiments on real-world data. For a viral marketing campaign to be effective, it is important to correctly identify the target audience. Theoretical and empirical studies can identify which demographic groups or market segments are more likely to share and interact with viral content. This information can help marketers better target their campaigns. The speed and impact of viral marketing campaigns can sometimes raise social or ethical issues. Theoretical studies can assess the potential ethical risks and social impacts of such campaigns. Empirical studies can examine how these effects play out in real-world data. New trends and technologies are constantly emerging in the field of marketing. Theoretical studies can analyse these trends and technologies and investigate how they can be integrated into viral marketing strategies. Empirical studies can evaluate the real-world effects of these new approaches. Moreover, the study areas that are observed to be lacking in the literature are detailed in ‘Potential Research Areas of Viral Marketing’.

Limitations and future research suggestions

This study, in which viral marketing literature is evaluated by bibliometric and content analysis methods, has some limitations. The publications analysed in this study were obtained from the Web of Science database. Therefore, the fact that the publications obtained only from a certain database constitutes a limitation. In this respect, future research can be carried out with data obtained from different databases such as Scopus and Google. On the other hand, VOSviewer programme was preferred in this study because it uses bibliometric mapping technique in bibliometric analyses and can evaluate a large number of data at the same time. Programs such as Cite Space and Bibexcel, which are based on bibliometric mapping technique, can be used in future researches since they allow different types of analyses besides basic bibliometric analyses. Another limitation of the current study is the time period in which the data were obtained. In addition, considering the fact that the viral marketing literature will develop over time, it is recommended to repeat the studies with the bibliometric method that offers the possibility of longitudinal analysis based on the fact that the results of the study will differ in different time periods. Specific recommendations that are believed to be necessary in the future are as follows: Due to the growing popularity of mobile apps, companies can aim to create a viral effect by sharing users’ apps or encouraging in-app interactions to achieve a specific goal. In the future, responsive and socially relevant content is expected to play a more prominent role in viral marketing strategies. This content can help brands create emotional connections that touch people, which can increase the shareability of the content. Focusing on specific niche audiences rather than broad audiences may become more important in viral marketing strategies. This can help brands build a smaller but more engaged and interactive following with specific interests. Encouraging users to create their own content and integrating this content into the brand’s marketing strategies could play an important role in viral marketing in the future. In addition, collaborations with influencers can also help brands deliver their content to a wider audience. In the future, the use of technologies such as artificial intelligence and data analytics can help better understand and optimise viral content. This can help brands spread their content more effectively and increase shareability. Connecting with communities, both locally and globally, allows brands to create greater diversity and impact in their viral marketing strategies. On the other hand, all the topics described in the ‘Potential Research Areas of Viral Marketing’ section are suggested for future studies.

Author contributions

Conceptualization, M.Ç; methodology, M.Ç.; formal analysis, M.Ç.; resources, M.Ç.; writing—original draft preparation, M.Ç.; writing—review and editing, M.Ç. and Ö.A.A.; visualization, M.Ç.; supervision, Ö.A.A. and M.Ç. All authors have read and agreed to the published version of the manuscript.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Disclosure statement

We have no competing of interest or additional sources of funding to disclose. The authors have no relevant financial or non-financial interests to disclose.

Data availability statement

The datasets generated during and/or analysed during the current study are available in the Web of Science database repository, https://www.webofscience.com/wos/woscc/summary/4b0318fc-b7f1-4cce-b118-a2809999cadd 5edd628c/relevance/1

Additional information

Funding

This work was not supported by any funding agency.

Notes on contributors

Murat Çakirkaya

Murat Çakirkaya, he graduated from Anadolu University, Faculty of Economics and Administrative Sciences, Department of Economics. He completed his master’s degree at Karamanoğlu Mehmet Bey University, Institute of Social Sciences, Department of Business Administration. He completed his PhD at Selcuk University, Institute of Social Sciences, Department of Business Administration. Associate Professor of Marketing. He continues his studies in the fields of consumer behavior, digital marketing and relational marketing etc.

Önder Aytaç Afşar

Önder Aytaç Afar, he graduated from Selçuk University, Faculty of Economics and Administrative Sciences, Department of International Relations. He completed his master’s degree at Selçuk University, Social Sciences Institute, Department of International Relations. He completed his PhD at Marmara University, Institute of Social Sciences, Department of Politics and Social Sciences. Associate Professor of International Relations.