facebook marketing research paper

Philippine E-Journals

Home ⇛ the paulinian compass ⇛ vol. 2 no. 3 (2012),  facebook as an effective marketing tool.

Noel C. Casquite

Discipline: Marketing , Business

The study employed descriptive and quantitative methodology to evaluate the efficacy of Facebook as a marketing tool among 100 consumer-respondents selected through purposive sampling. The scholastic investigation used the questionnaire as its main research instrument. The research was tailored after the media dependency theory as its theoretical framework. Data collected were described using frequency distribution, mean, and simple ranking. The mean was qualitatively interpreted with the aid of a Likert scale and researcher-constructed statistical limits. The hypothesis of the study was tested using chi-square analysis based on a 0.05 level of significance. 

Findings revealed that the consumer-respondents are predominantly younger females who are either college students, reached the college level or are degree holders, and have an average family income of less than PhP 20,001. Results also showed that as a marketing tool, as provider of relevant consumer information, as a forum for closing sales or business deals, and as a medium of advertisement, Facebook is moderately accepted by the respondents. Moreover, Facebook is highly accepted on account of the business/ consumer-related benefits derived from it. However, grounded on the findings from the chi-square analysis, the respondents significantly differed in their extent of acceptance of Facebook along all the variables considered in the inferential analysis. Based on the great potential of Facebook as a marketing tool for business, it was highly recommended that organizations, particularly small businesses that have not optimized their online profitability prospects, seriously consider polishing their strategies to enhance their Internet presence, particularly in Facebook and other social networking sites.

Note: Kindly Login or Register to gain access to this article.

Share Article:

ISSN 2094-7496 (Online)

ISSN 2094-1838 (Print)

• Citation Generator
• ">Indexing metadata
• Print version

Copyright © 2024  KITE E-Learning Solutions |   Exclusively distributed by CE-Logic | Terms and Conditions

To read this content please select one of the options below:

Please note you do not have access to teaching notes, effective social media marketing strategy: facebook as an opportunity for universities.

International Journal of Retail & Distribution Management

ISSN : 0959-0552

Article publication date: 8 May 2017

The purpose of this paper is to investigate the usage of university Facebook (FB) groups and sites by undergraduate students seeking information about their departments and the ways these pages could be used to acquire students. The factors that can intensify the FB group activities of a university are examined as well as how FB can be used as a marketing tool to improve marketing campaigns.

Design/methodology/approach

The study investigates and compares two universities: the University of Novi Sad of the Republic of Serbia and the Technological Educational Institute of Western Macedonia, Greece. A structured questionnaire was used with samples of 343 and 300 students gathered in this survey.

An enhanced technology acceptance model oriented toward FB is presented and it is the conceptual background of the paper. Student demographics and behavioral characteristics of the FB group they enrolled on were determined. Common behavioral patterns of the usage tension of the FB group are also identified. Additionally, five factors were determined that can be used by university marketers to intensify engagement with the FB group.

Research limitations/implications

Larger samples should be used for future research.

Originality/value

The paper proposes a marketing strategy a higher education institution should follow to more effectively use social networking sites as a marketing tool.

• Universities
• Higher education
• Social networking sites
• Social media marketing
• Greece and Republic of Serbia
• Marketing and Facebook

Assimakopoulos, C. , Antoniadis, I. , Kayas, O.G. and Dvizac, D. (2017), "Effective social media marketing strategy: Facebook as an opportunity for universities", International Journal of Retail & Distribution Management , Vol. 45 No. 5, pp. 532-549. https://doi.org/10.1108/IJRDM-11-2016-0211

Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

Related articles

We’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

• Open access
• Published: 15 February 2024

Emerging trends in social media marketing: a retrospective review using data mining and bibliometric analysis

• Abu Bashar 1 ,
• Mohammad Wasiq 2 ,
• Brighton Nyagadza   ORCID: orcid.org/0000-0001-7226-0635 3 , 4 &
• Eugine Tafadzwa Maziriri 5  

Future Business Journal volume  10 , Article number:  23 ( 2024 ) Cite this article

3589 Accesses

1 Citations

Metrics details

The study conducts a comprehensive retrospective analysis of the social media marketing literature along with text mining and bibliometric analysis using data obtained from the Scopus database. The analysis is conducted for the literature published during 2007–2022 using VOSviewer application and Biblioshiny. The analysis revealed the publication trend and emerging themes in the research landscape of social media marketing. This study has pointed towards important theoretical and practical implications pertaining to the social media marketing. It contributes to the understanding of social media marketing research by identifying and listing the best journal, authors, country, documents, most occurred words, social and intellectual structure, and emerging research trends. The results revealed that social media marketing research is at the focal point of the researchers throughout the word. This study found that there are lack of studies from firm perspective especially small retailers; adoption of disruptive technologies such as AI, ML and block chain and its impact need more exploration.

Introduction

The term social media came in limelight in the early 1990s, and now, it became an inseparable entity of almost every individual having an estimated 2 billion + active users globally [ 24 ]. Social media is a dorm of computer-based programme that allows users to connect, create and share information and exchange views and ideas via specific virtual communities and groups (Aydin 2020). The advancement in technology especially mobile applications and cloud-based analytics had enabled firms to offer and connect to their customers in real time. The proliferation of e-commerce web and mobile applications gives rise to the tremendous growth of social media networks and has transformed the ways of communication between business and consumers who generally shares common interests and demographics [ 144 ]. Social media marketing can be regarded as a form of online marketing, and it has seen manifold growth in the recent past. The marketers are leveraging on social media platform to reach, interact, offer, and transact with their probable customers.

Many firms and brands are relying on the word-of-mouth marketing, and social media had played instrumental role in spreading word of mouth among their customers in a rapid manner that was never before. Additionally, the firms are leveraging social media network platform to expand globally [ 144 ]. Social media has influenced the way consumers were searching for information, evaluating them, and making purchase decision. Moreover, social media became an inseparable integral part of businesses to sustain in this digital disruptive world. The accessibility, ease of use, real-time bound activities and global reach have made social media as a unique marketing tool. Social media enables firm to create a virtual unique platform to mark their online presence, communicate with their target customers and engage with them to increase their revenue [ 90 ]. The increasing importance of social media as a marketing tool has attracted scholars especially researchers in the domain of online buying behaviour in the last decade. Therefore, existing literature on social media marketing is being continuously reviewed by scholars to understand the current trends and suggest the future directions. In recent years, researchers have studied the importance of social media in marketing from various aspects of its application. Few of the important bibliometric studies on social media marketing are mainly focussing on the social networks and platforms. Social media platforms and its role in the evolution and performance of social enterprise conducted by revealed that proper social media strategy is not only helping in increasing revenue and profitability, but also fostering confidence among the consumers. Similarly, bibliometric analysis on the pattern of co-creation , influencers , sentiments and stock market predictions and interactive digital marketing in the context of social media is conducted over the recent few years.

None of the current studies have focussed on the overall role, i.e. integrating and analysing studies focussed on behavioural intentions, impulse purchases, customer engagement, customer loyalty and recommender management of social media in the marketing landscape. The recent advancement in the mobile-based applications had forced the organizations to adopt marketing tools which are readily available to the consumers in real time [ 13 ]. This study is an attempt to gather quality articles pertaining to the marketing applications of social media and analyse its effectiveness as a marketing tool. This article will help the academicians to have a holistic idea about the research trends in social media marketing that will prove conducive to design marketing strategies for industry practitioners. To the best of our knowledge, such comprehensive review of social media as a marketing tool has never been conducted.

The rest of the article is organized as follows. The next section is based on the review of literature. The research methodology adopted for this study is described in section “ methods ”. Section “ Results ” is based on the data analysis and its interpretation, while limitations of this study and future research directions are presented in section “ Discussion ”. Conclusion is made in section “ Conclusions ”.

Review of literature

The current trends of the marketing research in the social media domain predicts that the traditional marketing is going to be entirely disrupted by the adoption of social media-based marketing. The marketing activities such as advertising, promotional programmes and branding seem to be entirely designed and applied using social media tools [ 144 ]. Social media adoption is on rise because of its wide presence in the masses and its easiness of access and operate. Therefore, social media became the first choice of the marketers to promote their products and services to reach to their target audience [ 39 ].

Social media is a specialized software application that connect people in an online environment, where they can interact with each other, share contents and their feedback in the relevant groups about their experiences with a brand or organization [ 137 ]. The marketers realized the importance of social media marketing and started using them as an integral part of their overall marketing strategies [ 89 ]. Social media platforms enable consumers to freely interact with their fellow users on these applications and discuss openly about the advantages or disadvantages of the products [ 20 , 80 ]. So, marketers look at social media as an opportunity to build their brand image and positively position their products in the mind of their target audience [ 123 ]. The word of mouth of the consumers is also of great concern for the firms, as it may harm the brand positioning if not managed in an appropriate manner [ 141 ].

Various social media platforms such as Facebook, Twitter, Instagram, Snapchat and LinkedIn are being used by companies based on their target audience and the products they promote. As noted by [ 74 ], Snapchat is more favourite among youths, LinkedIn is more useful for reaching to mature professionals, so the marketers are selecting the platforms that suits their marketing strategy. The literatures shows that social media users are reacting more on interactive advertising rather than informational one, and it promotes interactions and cultivates the in-group messaging among the users of a particular social media platform [ 11 ].

The synthesis of the recent literatures reveals that opinion leader is playing a crucial role around the online space, so the organizations need to select carefully their leaders who can foster confidence about the firm and positive image of the brands [ 25 ]. Moreover, content is the bone of the social media marketing, and marketers need to carefully select, design and present to their markets. The emotional appeals in the messaging and overall content have been found more influential as customers has responded more often as compared to any other appeals in the social media marketing space [ 92 , 109 ]. In a similar study, it has been found that consumers are finding live videos streaming more trustworthy and authentic as compared to pre-recorded videos [ 23 ].

Therefore, it can be concluded that social media marketing is having a greater impact on the firm, and it can bring variety of positive and negative outcomes. Studies have shown that social media marketing is having positive and substantial impact on the consumer behaviour and especially on consumer retention [ 52 ]. The social media marketing efforts also play an important role in shaping the positive purchase intentions [ 144 ], brand meaning [ 58 ], brand loyalty [ 128 ], brand sustainability (X. [ 142 , 143 ], hotels [ 76 ], luxury brands [ 10 ], educational institutions [ 83 ], brand equity [ 63 ], positive electronic word of mouth [ 87 ], intention to engage online[ 127 ], etc.

Previous review studies on social media marketing and its effectiveness highlighted important aspects of its applications in marketing processes. Review studies have either used a specific database like web of science/Scopus [ 7 , 93 ] or studied a specific relationship such as brand–consumer interaction [ 101 ] in the context of social media marketing. Moreover, previous review studies focussed on specific applications such as evolving trends in Facebook marketing [ 94 ], a comprehensive comparative review of social media and social networks [ 144 ] and rise of social media in sports [ 78 ]. Moreover, previous studies reviewed the influence and effectiveness of social media for a specific sector/industry such as medical [ 90 ], tourism [ 78 ], hospitality and business-to-business applications as a digital mediation [ 68 ]. There is a lack of studies which has comprehensively mapped the marketing applications of social media and measured its effectiveness using bibliometric analysis. This study is an attempt to holistically examine the applications and effectiveness of social media as a marketing tool using state-of-the-art bibliometric analysis.

The development and probable future trends of a field of study can be analysed using various review techniques that can fulfil the specific objective of research. A systematic literature review (SLR) is conducted to identify, analyse, evaluate and summarize the overall findings of research in a field; it focusses on the methodological approach, theoretical framework, etc. [ 95 ]. Meta-analysis is an empirical statistical technique which combines the results of multiple studies on a given problem and then estimate the overall effect and direction of the relationship (Hassan) [ 14 , 48 , 86 , 104 , 106 ]. While bibliometric technique is a computer-assisted methodology that helps in measuring performance by identifying the core theme, sub-themes, prolific authors, most influential country, intellectual and social structure of the research [ 6 , 48 ]. For current study, bibliometric research design is adopted to fulfil the objectives of the study which helps in identifying the major trends in social media marketing using network analysis techniques [ 135 ]. It is one of the most used research methods which enables analysis of large volume of data to statistically estimate and visualize the research trends in a particular field of study [ 103 ]. This method is widely employed by other researchers in analysis and predicting the future expansion of research in a particular domain of research [ 12 ], Hassan, [ 49 , 62 , 104 , 106 ].

This review is conducted in two steps; first, the descriptive analysis such as the trend of research publication, best authors and top journals of the social media research is presented and then co-citation and co-occurrence analysis are presented. For descriptive analysis, Biblioshiny applications of R is used; it allows researchers to explore their data and run descriptive analysis and present them in an intuitive tabular and graphic form [ 5 ]. While for co-citation and co-occurrence analysis, VOSviewer software application is employed, it is a tool which produce output in network form—the networks are the combinations of various clusters that enables researchers to find the trending themes and sub-themes in a given area of research [ 126 ].

Scopus database is used for searching and downloading articles based on the applications of social media in marketing. The TITLE-ABS-KEY was searched using most appropriate keywords pertaining to the application of social media in marketing. The keywords such as “Social media marketing”, “Social networking sites”, “Social media platforms”, “Facebook marketing”, “Social network advertising”, “Social media purchasing”, and “digital marketing using social media” were searched using variety of combinations of Boolean operators (AND/OR) syntax. The inclusion of articles is based on certain criteria such as span of publication during (2007–2022), written in English language, must be either research article or reviews, and most importantly, the main theme of the literature must be on the application of social media as a marketing tool.

First search results in 2753 research articles, which are then carefully investigated for the defined inclusion criteria, book chapters, conference papers, short notes, editorial notes, etc., were removed. Literatures published in languages other than English were removed. Then, the researchers looked at tittle and abstract of each article to make sure that the central idea of research is based on the aspects of social media as a marketing tool. The final sample consists of 1232 articles, which then exported in .CSV format for further processing and analysis.

The following table is a snapshot of the data used in this bibliometric analysis.

This dataset consists of 1232 articles out of which 1183 are research articles and 83 reviews articles as presented in Table  1 . There are 58,528 references cited in these studies and the average citations per documents stands at 23.23. These papers were published by 562 sources and written by 2953 authors. It is worth noting that 2994 authors have published on social media marketing, while only 173 documents are single-authored, and all other documents are multi-authored. Documents per author is 0.411, while 2.43 authors are there per document; it shows strong collaborations among authors and collaboration index stands at 2.71.

The following section presents the descriptive analysis of data that is conducted using the Biblioshiny application. The .CVS file of the final data was uploaded on web service provided by Biblioshiny called bibliometrix application for further analysis.

Annual publications

The trends of publication over the years are depicted in Fig.  1 ; it is obvious that this area of research started in the mid of 2000s that signifies the importance of adoption of social media tools for marketing activities. Since then, there have been an exponential increase in the number of publications. From 2015 onwards, there were substantial research for understanding the effectiveness of social media as a marketing tool. As we see that there are already 65 articles published by November 2022—at the time of data extraction for this study. The trend shows that there will be continued research efforts to unveil the various aspects of social media marketing that can help marketers to understand consumer behaviour and make winning marketing strategies.

Annual publication trends in applications of social media in marketing

From thematic perspective, the trend can be further classified into themes which have been identified from the analysis. The early age (2000–2009) of social media marketing can be attributed to its application in advertising on social media platforms and network. This era also fuelled the development of customer groups and community where customers can interact and express their views about brands. Adoption of disruptive technologies defines trends from 2010 to 2017; during this period, smart recommendation systems, automatic feedback analysis and grievance redressal mechanisms introduced on social media. The human machine interaction signifies the trends from 2018 till date. The introduction of social robots, integration of augmented and virtual reality, real-time behavioural intelligence and super personalization can be treated as emerging themes.

Influential sources

Table  2 illustrates the most important journals publishing on the applications of social media in marketing. Top 20 journals based on total number of publications along with their total citations, and indices of h, g and m are presented.

The “Journal of Research in Interactive Marketing” is the most productive journal which has published 56 articles and been cited by 1841 times. This journal is the top-notch source in the production and dissemination of research based on interactive marketing. Few of the important themes of this journal over the years are social media influencers [ 131 ], personalization [ 97 ], adoption of disruptive technologies such as artificial intelligence and machine learning for web personalization [ 50 ] and customer experience [ 4 ], brand–consumer interaction using social media [ 131 ].

The second influential journal publishing on social media marketing is “Journal of Business Research”. This journal published 25 quality articles and been cited 2207 times. This is interesting to note that it has published articles less than half of the “Journal of Research in Interactive Marketing” but cited more often than that. This journal has contributed in the comprehension of the phenomenon of social media marketing by publishing on important aspects such as fake news and social media marketing [ 30 ], social media and brand equity [ 145 ], customer engagement via social media [ 40 ] and use of social media for B2B marketing [ 122 ].

Sustainability (Switzerland) is the third most important source that publishes on social media marketing. It has published 20 articles and attracted only 197 citations since its starting publications. This journal is publishing important aspects of social media-based marketing such as implementation of green marketing using social media [ 85 ], impact of social media on environmental sustainability [ 27 ], digital co-creation [ 22 ] and role of social media in organizational sustainability [ 138 ].

The other journals in the list have also contributed immensely to the growth of social media marketing research and its implications for the businesses.

Most prolific authors

The most prolific authors researching and publishing on social media marketing are presented in Table  3 ; this selection is based on the number of papers published by an author over the period, and their total citations and h-index are also presented for a better comprehension. The first author in the list is Kumar V; he has published six quality articles on the applications of social media tools in marketing. Some of the most influential articles published are “Engaging luxury brand consumers on social media”, “Synergistic effects of social media and traditional marketing on brand sales: capturing the time-varying effects”, “Creating a measurable social media marketing strategy: Increasing the value and ROI of intangibles and tangibles for Hokey Pokey”, “Increasing the ROI of social media marketing” and “An evolutionary road map to winning with social media marketing”. All the above-mentioned articles are focussing on specific marketing applications of social media. The second author in the list is Dwivedi YK with four papers and 718 citations and with an h-index of 4. The important articles published are “Examining the impact of social commerce dimensions on customers’ value cocreation: The mediating effect of social trust”, “Measuring social media influencer index- insights from Facebook, Twitter, and Instagram”, and “Social media marketing: Comparative effect of advertisement sources”. With four publications and 664 citations, Rana NP is the third most influential authors in the research domain of social media marketing. The most influential literature published by Rana NP is “Do Social Media Marketing Activities Improve Brand Loyalty? An Empirical Study on Luxury Fashion Brands”, “Social media marketing: Comparative effect of advertisement sources”, “social media in marketing: A review and analysis of the existing literature”. Rana NP has authored various articles in collaboration with Dwivedi YK as well.

Most important documents

Table 4 presents the top research papers published on the application of social media in marketing. These papers are listed based on their number of citations it has attracted over the years. The top document is written by Kozinets RV and focussed on the importance of the virtual online crowd, where consumers come together to discuss, share their opinion that results in collective innovation. Moreover, this paper emphasizes on the proliferation of networking technology that made online collaboration easy to access and interact; technologies help innovation to take new heights that ultimately impact the consumption patterns of the consumers. This paper has been cited 1077 times with an annual average of 83.

The second influential document found in this dataset is about social media marketing that was published in 2012 and cited 996 times by then. This paper has analysed the importance of social media in marketing from important aspects such as self-expression, socializing, brand interactions and the social communities that have substantial influence on the consumer purchasing intentions and purchasing behaviour. In addition, this paper also touches the importance of the impulsive buying activities on social media platforms; consumers are getting intimated with intuitive advertisements on social media platforms and landing to the e-commerce websites that instigate impulse shopping urges [ 13 ].

The paper published by Journal of Business Research and written by Kim AJ stands third in the list of most influential articles. This paper has investigated the role of social media in enhancing the customer equity with a special focus on luxury fashion brands. This paper was published in 2012 and attracted 904 citations by then. This paper explored the influence of social media marketing activities on customer perceived value, brand equity and customer equity. The findings favour the proposition that customer equity is highly enhanced by social media marketing activities.

The other documents in the list have also been contributed to the understanding and expansion of the area of social media marketing research. Few of the important themes discussed in these papers are measuring ROI of social media, creative social media marketing activities, online reviews, user-generated contents, influencer marketing, etc.

Most productive countries

The countries contributed most to the research stream of social media marketing are illustrated in Table  5 . The countries are selected based on the number of citations of their articles. The greatest number of research articles are contributed by USA; it has produced 222 research papers, and these papers have been cited 7065 times with an average article citation of 31.82. It indicates the adoption of social media as a marketing tool in the USA; the researchers are studying the underlying factors which are crucial to understand consumer behaviour in the space of social media marketing activities.

Moreover, it also indicates that countries having better networking facilities and high bandwidth internet can exploit the advantages of social media marketing more efficiently than the countries not at par in terms of internet and networking facilities. The other two important countries are UK and China with 3195 and 1674 citations, respectively. It can be noticed that there is huge disparity among top 3 countries in terms of publications and number of times it had been cited. Nevertheless, the trend is showing that the proliferation of internet technology and availability of high-quality internet will boost the adoption of social media among users and social media marketing among the business firms.

Citation analysis of the documents

Citation analysis is the method of assessing the quality and impact of an organization, author, source, etc., derived from the quantitative analysis of the citations to references [ 103 ]. VOSviewer application is employed for this purpose, and minimum number of citations of a document was kept 10. The network thus obtained contains four clusters based on the grouping of a specific theme in social media marketing (Fig. 2 ).

Citation analysis of documents

The largest cluster (red) of the network is made up of 134 documents and consists of large nodes which specifies the greater number of citations these documents received over the time. The important aspects of social media marketing in this cluster are mainly focussed on the information processing that can be further used to make strategies pertaining to the social media consumers [ 88 ]; analysis of the online conversation among users is of enormous importance because it is crucial in affecting the consumer behaviour either positively or negatively about the firm and its products [ 2 , 31 , 66 ]. B2B semantics is useful for understanding the deep inside thoughts of the firms and their leaders that ultimately shapes their behaviour [ 34 , 128 ]. In addition, the application of big data analytics tools is for processing and analysing the large amount of data to learn pattern of consumer interaction and activities on the social media network platforms [ 55 , 74 , 110 ]. Another important consideration in this cluster is about the use of sentiment analysis and opinion mining for managing the expectations of the consumers and offering them most customized products as per their unique needs.

The green cluster, second major in the network, is made of 93 documents. This cluster of this citation network is found to accumulate documents that addressed the research concerns of consumer behaviour from the perspective of social media marketing. The role of social media-based marketing in shaping the consumer intention to purchase [ 37 , 84 , 123 ], the impact of personalized content and its impact on consumer behaviour [ 19 , 140 ], consumer social media participation and its impact on overall profitability of the firm [ 26 , 29 ], persuasive advertisement and its impact on customer engagement [ 42 , 67 , 113 ].

Third cluster (blue) consists of 69 documents on various important aspects of the social media marketing from the perspective of customer engagement. The documents which have formed the basis of this cluster are essentially addressing the concepts of mutual sustainable relationship between customers and e-retailers are perceived value that a customer assessed about the product of services that meets their unique expectations [ 44 , 82 ]. The service quality on the shopping websites and applications is crucial to persuade customers to revisit and explore which ultimately increase the chances of customer engagement, while poor service quality demoralizes customers and decreases the level of engagement significantly [ 34 , 133 , 134 ]. In addition, customer experience that includes important factors such as personalization, tailoring of offers to match unique expectation of customers, are substantial in the course of customer engagement [ 116 , 123 ]. Customer engagement cannot be achieved if customer satisfaction is not central to a firm, and it must be the prime focus, and marketers needs to make all possible efforts to not only satisfy, but also delight their target customers [ 33 , 41 , 43 ].

Keyword co-occurrence analysis

The keyword co-occurrence analysis can be referred to as a method of analysing the similarities and proximity between knowledge structure that is based on the semantics of the words which are closely related but not exactly the same [ 13 ]. For this analysis, VOSviewer software is employed, and it is among the best tools for scientific data visualization and mapping of co-occurrence of similar keywords to discover the emerging trends in a specific area of research [ 12 ].

The criteria for a keyword to be included in the network was that a keyword must have a frequency of at least 15. The frequency of occurrence is set as 15, to make sure the inclusion of significant keywords that can help in visualizing the scientific landscape. The network thus obtained is based on 235 keywords out of 4061 and presented in Fig.  3 . This network is based on three specific clusters having combined keywords pertaining to a specific aspect of the social media marketing.

Keywords co-occurrence analysis

The largest cluster of the network is represented by red colour and consists of 110 keywords. This cluster is made up of keywords that signifies the importance of technology in social media marketing and social networks. This cluster also explains the importance and adoption of disruptive technologies in the application of social media in marketing activities. The role of artificial intelligence [ 57 , 73 , 130 ], machine learning [ 72 , 74 , 109 ], learning algorithms [ 9 , 31 , 121 ], sentiment analysis [ 109 ], learning systems [ 9 , 130 ], CRM tools [ 29 , 125 ], customer interactions [ 42 , 117 ], customer reviews [ 116 , 124 ] and recommender applications [ 98 , 112 ] has been studied over the period to implement them efficiently for better business outcome. Moreover, this cluster is having important implications for the designers, developers, and implementers of the social media marketing campaigns; it is crucial for the organizations to first collect the large amount of data resulting from the customer exploration of their web portals and process them to learn the trends and expectations of the consumers. This comprehension can further be used to design marketing efforts across the channels to reach to target markets, motivate them to interact over social media and engage into activities that can lead to profitable business transactions.

The second largest cluster (green) consists of 64 keywords; careful analysis of this cluster reveals that this cluster has combined the keyword which is centred around consumer behaviour on social media platforms. The important perspectives of consumer behaviour that can be visualized in this cluster are perceived value [ 79 , 129 ], purchase intentions [ 52 , 99 , 100 ], brand value [ 17 , 115 ], brand loyalty [ 10 , 133 ], brand image [ 107 , 114 ], ethics [ 28 , 91 ], green behaviour [ 10 , 130 ], sustainability aspects [ 99 , 130 ], millennials [ 10 , 34 , 35 ], generation [ 39 , 118 ], and customer engagement [ 99 , 107 , 140 ]. Therefore, it is quite evident that social media tools are being used in almost all facets of consumer behaviour; the above studies also concluded that the use of social media marketing tools has a positive and substantial impact on consumer behaviour.

The third and last cluster (blue) of this network is made of 51 items. This cluster has accumulated keywords which are focussed on the human aspects of social media marketing. As it is obvious from the network that the largest node in this cluster is “human” and “humans”, which specifies the interaction of machine, i.e. computers with human [ 36 , 58 , 139 ]. The human computer interface is a trending research stream in social media marketing, where efforts are made to understand the best practices to interact with computers in a more efficient manner [ 129 , 136 ]. The another important concept in this cluster is about psychology which is quite important for the marketers to understand the cognitive process of consumer when presented with marketing stimuli using social media marketing tools [ 59 , 117 ]. Few other keywords which dominated this cluster are health education and monitoring health using social media applications [ 28 ], young adults [ 81 , 130 ], selection of advertising topics [ 1 , 51 ], etc.

Trends in social media marketing

The network analysis helps in the identifications of emerging trends in the research of social media marketing. The social media networks allow users to interact and share their thoughts and experience with a brand which in turn helps in viral marketing [ 120 ]. The possibility of sharing podcast and video contents has fuelled the interactivity among users [ 15 ]. The reviews and feedback are of enormous importance for the marketers to listen the voice of the customers and adapt accordingly [ 54 ].

One of the major trends is about real-time personalization on the social networks; the recommender system is recommending most sought-after products to the customer in real-time web exploration. The personalization in real time is achieved using technologies such as artificial intelligence, machine learning and predictive analytics that helps in gaining deeper insights into behavioural intentions [ 3 , 96 ].

The introduction of augmented reality and virtual reality is another latest trend that have revolutionized the way marketing was being carried out using social media networks. The augmented reality has enabled the customers to virtually look at desired aspects such as suitability, colour combinations, fittings and virtual trials [ 39 ]. It gives consumers the confidence to immediately decide to purchase and quick gratifications [ 119 ]. The impulse purchasing mechanism is also trending on social media platforms; the firms are appropriately designing and putting across their offerings that creates urge to buy impulsively [ 61 ].

Influencer marketing and brand endorsement are not a new trend, but it is one of the major trends that is going to stay for a while. The brands are associating with influencers who have huge followers and witnessed better results as good as running paid advertisement campaigns [ 39 ].

Live streaming has been adopted by various firms to reach to specific segments of their target markets using webinar or a platform showcase [ 18 ]. It gives them opportunity to socialize and interact with prospective customers and engage with them through Q & A sessions or collaborative contents [ 18 ].

Themes and sub-themes in social media marketing

The detailed analysis of the networks of citations and keyword co-occurrence analysis helps in identifying themes and sub-themes which are emerged from the clusters of the network. Table 6 is representing the main themes and sub-themes along with related studies.

Each research study is having certain limitations and so as this one. One of the major limitations is about citation bases analysis; the selection of articles is based on the number of citations it has received. There might be important studies on social media that may have not been included in the analysis because it did not receive many citations. Moreover, we selected research literature written in English language and either article or review paper; there might be quality articles that left behind.

As far as future direction of research is concerned, it is obvious that social media marketing is evolving at ever high pace and there is a need for deeper investigation into this phenomenon. Careful investigation of the networks unveils important areas of future research expansion. First, the adoption of social media among consumers, what are the factors that hinders the usage of social media and technological barriers that restricts the customers to use social media are needed to be explored further. Studies have shown that one of biggest barrier in the adoption of social networks and platforms is the availability of high speed affordable internet networks [ 132 ]. Therefore, TAM model needed to be revisited from social media perspective, and additional components can be added to understand the adoption of technology and social media applications. A versatile model could be developed that can be used in variety of technology adoption scenarios and can be generalized in various environments. There is a need of study to understand the firm capabilities required and preparedness to adopt social media marketing practices.

The second important area of research is from the firm perspective; still it is not very much clear that how disruptive digital transformation and technologies such as artificial intelligence, Internet of Things, machine learning, deep learning, etc., can be implemented to get maximum ROI and that can persuade consumers to transact with them. Studies have focussed on artificial intelligence applications such as recommendation system which is positively influencing the consumer behaviour especially purchase intention [ 10 , 53 ]. The future studies can help in building a comprehensive model that could be used by the firm to evaluate their investment, ROI, perceived value to customer and overall consumer behaviour.

Thirdly, there is a lack of study on the influence of social media marketing on small retail firms. More studies are warranted to seek clarity about the effectiveness of social media as a marketing tool for smaller firm, which social media tool and tactics will be more advantageous for these firms. Researchers are also encouraged to empirically explore the small retailer’s perspective of adopting social media tools for their marketing activities. There shall be a mechanism to gauge the outcome of social media marketing on their brand awareness, customer growth, sales, and overall profitability of smaller retailers.

Fourth important area of research could be the deliberations of the social content. Content is the core of social media platforms; studies have shown that consumers tend to get attracted and spend more time on the social network where they can create their own content in an easy manner. Content is also crucial in terms of its suitability across platforms and ethical implications.

Therefore, researchers can study and analyse the most appropriate content across the social media platforms, devices and for a specific business. Researchers can explore the critical aspects of social content such as most suitable content for C2C interaction, firm reaction on a specific customer content, content to combat competitions, etc.

Fifth area of future research could be the monitoring of social media, how a firm shall record and acknowledge complaints of the customers. The sub-themes in monitoring can be to find out the best listening mechanisms of consumer activities that can be further analysed using predictive analysis mechanism to develop strategies to engage customers in the way they might be looking for. Another investigation can be done to seek clarity about the mechanism of watching and listening customers, i.e. whether there should be a fully automated process or hybrid one. This is important for the firms to understand the probable capital investment in the implementation of monitoring and responding process. This subject can be further investigated from the CRM perspective. Research can be extended to understand the role of various social media platforms on customer engagement, and what should be the capability of the CRM to exploit maximum from customer created content.

Conclusions

Bibliometric analysis is performed to have a comprehensive understanding of the applications of social media for marketing activities. The aim of the research was to investigate the research trends and emerging themes using the Scopus database. The findings of the research reveal the most important journals, authors, documents, and their intellectual structure. Moreover, it is found that research in the application of social media as a marketing tool is growing at an ever-increasing rate. Scholars around the world are collaborating with each other to comprehend the phenomenon and suggest strategies that can help firms to exploit the power of social media platforms in their marketing tactics. The outcomes of the research shown that there are certain areas of social media marketing landscape which need more scholarly attention; current literature has not considered these essential factors in detail. The findings suggest that current extant literature can be expanded by appending research in the areas such as modelling social media marketing ROI, social content strategies, monitoring and responding social media activities, social media strategies for smaller retailers, etc. This study has enumerated important implications along with the analysis for the businesses. The outcomes would also be helpful for social media enthusiasts and prospective social media marketing researchers.

Availability of data and materials

Not applicable.

Abbreviations

Customer-to-customer

Customer relationship management

Return on investment

Technology acceptance model

Abuljadail M, Ha L (2019) Engagement and brand loyalty through social capital in social media. Int J Internet Mark Advert 13(3):197–217. https://doi.org/10.1504/IJIMA.2019.102557

Article   Google Scholar  

Aghdam SM, Jafari Navimipour N (2016) Opinion leaders selection in the social networks based on trust relationships propagation. Karbala Int J Mod Sci 2(2):88–97. https://doi.org/10.1016/j.kijoms.2016.02.002

Aknine S, Slodzian A, G. Q. I. T. for W, et al (2003) Web personalisation for users protection: a multi-agent method. Springer

Aljukhadar M, Bériault Poirier A, Senecal S (2020) Imagery makes social media captivating! Aesthetic value in a consumer-as-value-maximizer framework. J Res Interact Mark 14(3):285–303. https://doi.org/10.1108/JRIM-10-2018-0136

Alshater MM, Hassan MK, Khan A, Saba I (2020) Influential and intellectual structure of Islamic finance: a bibliometric review. Int J Islam Middle East Financ Manag. https://doi.org/10.1108/IMEFM-08-2020-0419

Alshater MM, Saba I, Supriani I, Rabbani MR (2022) Fintech in islamic finance literature: a review. Heliyon, Cambridge

Google Scholar  

Alves H, Fernandes C, Raposo M (2016) Social media marketing: a literature review and implications. Psychol Mark 33(12):1029–1038. https://doi.org/10.1002/mar.20936

Ameur K, Benblidia N, Khouas SO (2016) Dimensions reordering for visual mining of association rules using parallel set. Int J Data Anal Tech Strat 8(4):296–315. https://doi.org/10.1504/IJDATS.2016.081362

Aswani R, Kar AK, Vigneswara Ilavarasan P (2018) Detection of Spammers in Twitter marketing: a hybrid approach using social media analytics and bio inspired computing. Inf Syst Front 20(3):515–530. https://doi.org/10.1007/s10796-017-9805-8

Bali S, Bélanger CH (2019) Exploring the use of Facebook as a marketing and branding tool by hospital foundations. Int J Nonprofit Volunt Sect Mark. https://doi.org/10.1002/nvsm.1641

Bashar A, Ahmad I, Wasiq M (2012) Effectiveness of social media as a marketing tool: an empirical study. Int J Mark Financ Serv Manag Res 2(11):88–99

Bashar A, Rabbani MR, Khan S, Ali MAMd (2021) Data driven finance: a bibliometric review and scientific mapping. 2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021, pp 161–166. https://doi.org/10.1109/ICDABI53623.2021.9655898

Bashar A, Singh S (2022) Impulsive buying on social media platforms: a bibliometric review. J Contemp Issues Bus Govern 28(3):386–420

Bashar A, Jreisat A, Kaur J, Al-Mohamad S, Rabbani MR (2022) Looking into corporate boardrooms through the lens of gender diversity: a bibliometric review and META analysis. Planning 17(5):1593–1603

Bello-Orgaz G, Mesas RM, Zarco C, Rodriguez V, Cordón O, Camacho D (2020) Marketing analysis of wineries using social collective behavior from users’ temporal activity on Twitter. Inform Process Manag. https://doi.org/10.1016/j.ipm.2020.102220

Bello MJG (2019) Cloud-based conversational agents for user acquisition and engagement. In M VM, F D, H M, P C (eds) 9th International Conference on Cloud Computing and Services Science, CLOSER 2019. SciTePress, pp 528–534. https://doi.org/10.5220/0007766105280534

Beneke J, Blampied S, Miszczak S, Parker P (2014) Social networking the brand-an exploration of the drivers of brand image in the South African beer market. J Food Prod Mark 20(4):362–389. https://doi.org/10.1080/10454446.2013.807402

Bharadwaj N, Ballings M, Naik PA, Moore M, Arat MM (2022) A new livestream retail analytics framework to assess the sales impact of emotional displays. J Mark 86(1):27–47. https://doi.org/10.1177/00222429211013042

Bhardwaj P, Adhikari RS, Ahuja V (2018) An analytical study of the facebook content management strategies of dominos India. In: Digital marketing and consumer engagement: concepts, methodologies, tools, and applications, pp 1091–1105. IGI Global. https://doi.org/10.4018/978-1-5225-5187-4.ch055

Bilkova R, Zelenka Z (2015) Social network marketing: An examination of marketing behavior of small businesses. In S KS (ed) 26th International Business Information Management Association Conference - Innovation Management and Sustainable Economic Competitive Advantage: From Regional Development to Global Growth, IBIMA 2015. International Business Information Management Association, IBIMA, pp 2171–2180

Buckley S, Ettl M, Jain P, Luss R, Petrik M, Ravi RK, Venkatramani C (2014) Social media and customer behavior analytics for personalized customer engagements. IBM J Res Develop. https://doi.org/10.1147/JRD.2014.2344515

Calcagni F, Maia AA, James C-S et al (2019) Digital co-construction of relational values: understanding the role of social media for sustainability. Springer 14(5):1309–1321. https://doi.org/10.1007/s11625-019-00672-1

Chang H-L, Chou Y-C, Wu D-Y, Wu S-C (2018) Will firm’s marketing efforts on owned social media payoff? A quasi-experimental analysis of tourism products. Decis Support Syst 107:13–25. https://doi.org/10.1016/j.dss.2017.12.011

Chawla Y, Chodak G (2021) Social media marketing for businesses: organic promotions of web-links on Facebook. J Bus Res 135:49–65. https://doi.org/10.1016/j.jbusres.2021.06.020

Chellam A, Chaturvedi A, Ramanathan L (2020) Data visualization: visualization of social media marketing analysis data to generate effective business revenue model. In: Data visualization: trends and challenges toward multidisciplinary perception. Springer, Singapore, pp 75–92. https://doi.org/10.1007/978-981-15-2282-6_5

Cheung TY, Ye Z, Chiu DKW (2020) Value chain analysis of information services for visually impaired people: a case study of contemporary technological solutions. Library Hi Tech 39(2):625–642. https://doi.org/10.1108/LHT-08-2020-0185

Chuang HM, Liao YD (2021) Sustainability of the benefits of social media on socializing and learning: An empirical case of facebook. Sustainability (Switzerland). https://doi.org/10.3390/su13126731

Cox T, Park JH (2014) Facebook marketing in contemporary orthodontic practice: a consumer report. J World Federation Orthod 3(2):e43–e47. https://doi.org/10.1016/j.ejwf.2014.02.003

Doligalski T (2013) Social network marketing: customer value, CRM, and competitive actions. In: Marketing in the Cyber Era: strategies and emerging trends. IGI Global, pp 96–113. https://doi.org/10.4018/978-1-4666-4864-7.ch007

Domenico GD, Sit J, Ishizaka A, Nunan D (2021) Fake news, social media and marketing: a systematic review. J Bus Res 124:329–341. https://doi.org/10.1016/j.jbusres.2020.11.037

Domeniconi G, Semertzidis K, Moro G, Lopez V, Kotoulas S, Daly EM (2017) Identifying conversational message threads by integrating classification and data clustering. In: H M, F C (eds) 5th International Conference on Data Management Technologies and Applications, DATA 2016. Springer Verlag, vol 737, pp 25–46. https://doi.org/10.1007/978-3-319-62911-7_2

DSouza S, Rabbani MR, Hawaldar IT, Kumar AJ (2022) Impact of bank efficiency on the profitability of the Banks in India: an empirical analysis using panel data approach. Int J Financ Stud (Forthcoming)

Duffett RG (2015) Effect of Gen Y’s affective attitudes towards facebook marketing communications in South Africa. Electron J Inform Syst Develop Ctries 68:1–27. https://doi.org/10.1002/j.1681-4835.2015.tb00488.x

Ebrahimi P, Khajeheian D, Fekete-Farkas M (2021) A sem-nca approach towards social networks marketing: evaluating consumers’ sustainable purchase behavior with the moderating role of eco-friendly attitude. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph182413276

Article   PubMed   PubMed Central   Google Scholar  

Ebrahimi P, Salamzadeh A, Gholampour A, Fekete-Farkas M (2021) Social networks marketing and Hungarian online consumer purchase behavior: the microeconomics strategic view based on IPMA matrix. Acad Strateg Manag J 20(4):1–7

Fagerstrøm A, Ghinea G (2011) Co-creation of value through social network marketing: a field experiment using a facebook campaign to increase conversion rate. In Human Interface and the Management of Information: Interacting with Information - Symposium on Human Interface 2011, Held as Part of HCI International 2011: Vol. 6772 LNCS (Issue PART 2, pp 229–235). https://doi.org/10.1007/978-3-642-21669-5_27

Faiers A, Cook M, Neame C (2007) Towards a contemporary approach for understanding consumer behaviour in the context of domestic energy use. Energy Policy 35(8):4381–4390. https://doi.org/10.1016/j.enpol.2007.01.003

Fusté-Forné F, Filimon N (2021) Using social media to preserve consumers’ awareness on food identity in times of crisis: the case of bakeries. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph18126251

Ghouse SM, Duffett RG, Chaudhary M (2022) How Twitter advertising influences the purchase intentions and purchase attitudes of Indian millennial consumers? Int J Internet Mark Advert 16(1–2):142–164. https://doi.org/10.1504/IJIMA.2022.120973

Gligor D, Bozkurt S, Russo I (2019) Achieving customer engagement with social media: a qualitative comparative analysis approach. J Bus Res 101:59–69. https://doi.org/10.1016/j.jbusres.2019.04.006

Groothuis D, Spil TAM, Effing R (2020) Facebook marketing intelligence. In: B TX (ed) 53rd Annual Hawaii International Conference on System Sciences, HICSS 2020 (vols. 2020-Janua). IEEE Computer Society, pp 2559–2568

Gulay Ozturk R (2014) Friendvertising: a new advertising strategy in social network marketing. In: Digital arts and entertainment: concepts, methodologies, tools, and applications. IGI Global, vol 3, pp 1575–1600. https://doi.org/10.4018/978-1-4666-6114-1.ch078

Gulay Ozturk R (2015) FRIENDVERTISING: A new advertising strategy in social network marketing. In: Social Media and Networking: Concepts, Methodologies, Tools, and Applications (Vols. 4–4, pp 2051–2075). IGI Global. https://doi.org/10.4018/978-1-4666-8614-4.ch094

Han K-S (2016) Study of structural relationship between the value proposition of facebook brand fan pages and commitment and engagement. Indian J Sci Technol. https://doi.org/10.17485/ijst/2016/v9i41/103927

Hassan MK, Bashar A, Rabbani MR, Choudhury T (2022) An insight into the Fintech and Islamic Finance Literature: a bibliometric and visual analysis. In: FinTech in Islamic Financial Institutions: Scope, Challenges, and Implications in Islamic Finance. Springer International Publishing, Cham, pp 131–156

Hassan MK, Rabbani MR, Ali MA (2020) Challenges for the Islamic Finance and banking in post COVID era and the role of Fintech. J Econ Coop Develop 43(3):93–116

Hassan MK, Rabbani MR, Brodmann J, Bashar A, Grewal H (2022) Bibliometric and Scientometric analysis on CSR practices in the banking sector. Rev Financ Econ

Hassan MK, Raza Rabbani M (2022) Sharia governance standards and the role of AAOIFI: a comprehensive literature review and future research agenda. J Islamic Account Bus Res. https://doi.org/10.1108/JIABR-04-2022-0111

Hindley C, Smith MK (2017) Cross-cultural issues of consumer behaviour in hospitality and tourism. In: The Routledge Handbook of Consumer Behaviour in Hospitality and Tourism. Taylor and Francis, pp 86–96. https://doi.org/10.4324/9781315659657

Huang R, Ha S, Kim SH (2018) Narrative persuasion in social media: an empirical study of luxury brand advertising. J Res Interact Mark 12(3):274–292. https://doi.org/10.1108/JRIM-07-2017-0059/FULL/HTML

Iannelli L, Giglietto F, Rossi L, Zurovac E (2020) Facebook digital traces for survey research: assessing the efficiency and effectiveness of a facebook ad–based procedure for recruiting online survey respondents in niche and difficult-to-reach populations. Soc Sci Comput Rev 38(4):462–476. https://doi.org/10.1177/0894439318816638

Imtiaz R, Ul Ain Kazmi SQ, Amjad M, Aziz A (2019) The impact of social network marketing on consumer purchase intention in Pakistan: a study on female apparel. Manag Sci Lett. https://doi.org/10.5267/j.msl.2019.3.015

Ioanid A, Deselnicu DC, Militaru G (2017) Branding in the age of social media: How entrepreneurs use social networks to boost their service-based businesses. Balkan Region conference on engineering and business education 3(1):79–85. https://doi.org/10.1515/cplbu-2017-0011

Jaakonmäki R, Müller O, vom Brocke J (2017) The impact of content, context, and creator on user engagement in social media marketing. In: B TX, S R (eds) 50th Annual Hawaii International Conference on System Sciences, HICSS 2017. IEEE Computer Society, vols 2017-Janua, pp 1152–1160

Jami Pour M, Hosseinzadeh M, Amoozad Mahdiraji H (2021) Exploring and evaluating success factors of social media marketing strategy: a multi-dimensional-multi-criteria framework. Foresight 23(6):655–678. https://doi.org/10.1108/FS-01-2021-0005

Jiang L, Erdem M (2017) Twitter-marketing in multi-unit restaurants: Is it a viable marketing tool? J Foodserv Bus Res 20(5):568–578. https://doi.org/10.1080/15378020.2016.1222746

Jung YJ, Kim J (2016) Facebook marketing for fashion apparel brands: Effect of other consumers’ postings and type of brand comment on brand trust and purchase intention. J Glob Fash Market 7(3):196–210. https://doi.org/10.1080/20932685.2016.1162665

Jussila J, Madhala P (2019) Cognitive computing approaches for human activity recognition from tweets—A case study of twitter marketing campaign. In V A, L MD (eds) Research and Innovation Forum, Rii Forum 2019. Springer, pp 153–170. https://doi.org/10.1007/978-3-030-30809-4_15

Kachniewska M (2015) Gamification and social media as tools for tourism promotion. In: Handbook of research on effective advertising strategies in the Social Media Age. IGI Global, pp 17–51. https://doi.org/10.4018/978-1-4666-8125-5.ch002

Kao L-J, Huang Y-P (2014) Mining implict outlier purchasing behaviors from fan group marketing data. In: 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014, pp 1048–1053. https://doi.org/10.1109/SCIS-ISIS.2014.7044662

Karim W, Abdul M, Chowdhury M, Al Masud A, Arifuzzaman M (2021) Analysis of Factors influencing Impulse Buying behavior towards e-tailing sites: An application of SOR model. Cmr-JournalOrg. https://doi.org/10.7903/cmr.20457

Kaur G, Singh M, Singh S (2021) Mapping the literature on financial well-being: A systematic literature review and bibliometric analysis. Int Soc Sci J 71(241–242):217–241. https://doi.org/10.1111/issj.12278

Kawaf F, Istanbulluoglu D (2019) Online fashion shopping paradox: the role of customer reviews and facebook marketing. J Retail Consum Serv 48:144–153. https://doi.org/10.1016/j.jretconser.2019.02.017

Khan S, Rabbani MR (2020) Chatbot as Islamic Finance Expert (CaIFE): when finance meets artificial intelligence. In: 2020 International Conference on Computational Linguistics and Natural Language Processing (CLNLP 2020), Seoul, South Korea, pp 1–5

Khan S, Rabbani MR (2021) Artificial intelligence and NLP based chatbot as islamic banking and finance expert. Int J Inform Retr Res (IJIRR) 11(3):65–77

Kim YS, Tran VL (2013) Assessing the ripple effects of online opinion leaders with trust and distrust metrics. Expert Syst Appl 40(9):3500–3511. https://doi.org/10.1016/j.eswa.2012.12.058

Kovco A, Vranesic P, Aleksic-Maslac K (2018) Advantages of WCA facebook advertising with analysis and comparison of efficiency to classic facebook advertising. WSEAS Trans Bus Econ 15:73–79

Kumar B, Sharma A, Vatavwala S, Kumar P (2020) Digital mediation in business-to-business marketing: a bibliometric analysis. Ind Mark Manage 85:126–140. https://doi.org/10.1016/j.indmarman.2019.10.002

Leung XY, Bai B, Stahura KA (2015) The marketing effectiveness of social media in the hotel industry: a comparison of facebook and twitter. J Hosp Tourism Res 39(2):147–169. https://doi.org/10.1177/1096348012471381

Leung XY, Baloglu S (2015) Hotel facebook marketing: an integrated model. Worldwide Hosp Tour Themes 7(3):266–282. https://doi.org/10.1108/WHATT-03-2015-0011

Leung XY, Jiang L (2018) How do destination Facebook pages work? An extended TPB model of fans’ visit intention. J Hosp Tour Technol 9(3):397–416. https://doi.org/10.1108/JHTT-09-2017-0088

Li X, Guo Y, Sheng Y, Chen Y (2020) Characterizing social marketing behavior of e-commerce celebrities and predicting their value. In: 2020 IEEE INFOCOM conference on computer communications workshops, INFOCOM WKSHPS 2020, pp 1166–1171. https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162757

Liao S-H, Hsian P-Y, Wu G-L (2014) Mining user knowledge for investigating the facebook business model: the case of Taiwan users. Appl Artif Intell 28(7):712–736. https://doi.org/10.1080/08839514.2014.927695

Liao S-H, Yang C-A (2021) Big data analytics of social network marketing and personalized recommendations. Soc Network Anal Min. https://doi.org/10.1007/s13278-021-00729-z

Liu Y, Wan H, Yang X (2010). Social network based marketing in mobile phone users’ community. In: 2010 international conference on machine vision and human-machine interface, MVHI 2010, pp 669–672. https://doi.org/10.1109/MVHI.2010.11

Lo YC, Fang C-Y (2018) Facebook marketing campaign benchmarking for a franchised hotel. Int J Contemp Hosp Manag 30(3):1705–1723. https://doi.org/10.1108/IJCHM-04-2017-0206

Lokshina I, Lanting CJM (n.d.) A qualitative evaluation of IoT-driven eHealth: knowledge management, business models and opportunities, deployment and evolution

López-Carril S, Escamilla-Fajardo P, González-Serrano MH, Ratten V, González-García RJ (2020) The rise of social media in sport: a bibliometric analysis. Int J Innov Technol Manag. https://doi.org/10.1142/S0219877020500418

Lupa-Wójcik I (2020) Emotions aroused by the most popular content on Facebook and their virality on the example of selected industries. In: K C, V C (eds) 7th European Conference on Social Media, ECSM 2020. Academic Conferences International, pp 154–162. https://doi.org/10.34190/ESM.20.022

Maziriri ET, Nyagadza B, Mapuranga M, Maramura TC (2022) Habitual Facebook use as a prognosticator for life satisfaction and psychological well-being: Social safeness as a moderator. Arab Gulf J Sci Res (AGJSR) 40(2):153–179. https://doi.org/10.1108/AGJSR-04-2022-0011

Mejova Y, Weber I, Fernandez-Luque L (2018) Online health monitoring using facebook advertisement audience estimates in the United States: evaluation study. JMIR Public Health Surveill. https://doi.org/10.2196/publichealth.7217

Moyer C, Griffin RJ, Pokrywczynski J (2018) Take me out to the facebook page. J Digit Soc Media Mark 6(4):357–370

Muangmee C (2021) Effects of Facebook advertising on sustainable brand loyalty and growth: case of Thai start-up businesses. Transnatl Corp Rev. https://doi.org/10.1080/19186444.2021.1986340

Mulero O, Adeyeye M, Ajibesin AA (2012) Determinants of user acceptance of online social networks marketing. International conference on communication, internet, and information technology, CIIT 2012:338–345. https://doi.org/10.2316/P.2012.773-013

Nabivi E (2020) Implementation of green marketing concept through social media activities: a systematic literature review. J Mark Consum Behav Emerg Mark 2/2020(11):55–67. https://doi.org/10.7172/2449-6634.jmcbem.2020.2.4

Naeem MA, Karim S, Rabbani MR, Bashar A, Kumar S (2022) Current state and future directions of green and sustainable finance: a bibliometric analysis. Qualit Res Financ Mark (ahead-of-p)

Nassar MA (2012) Harnessing the power of social networks for branding hotel services: evidence from the egyptian hotel sector. Innov Mark 8(2):58–66

Nastisin L, Fedorko R, Vavrecka V, Bacík R, Rigelský M (2019) Quantitative study of selected Facebook marketing communication engagement factors in the optics of different post types. Innov Mark 15(3):16–25. https://doi.org/10.21511/im.15(3).2019.02

Nobre H, Silva D (2014) Social network marketing strategy and SME strategy benefits. J Transnatl Manag 19(2):138–151. https://doi.org/10.1080/15475778.2014.904658

Nusair K, Butt I, Nikhashemi SR (2019) A bibliometric analysis of social media in hospitality and tourism research. Int J Contemp Hosp Manag 31(7):2691–2719. https://doi.org/10.1108/IJCHM-06-2018-0489

Nuseir MT, AlShawabkeh A, Leibfried EL (2021) Factors affecting the use of social networks as a customer relationship management tool. Int J Bus Inform Syst 38(2):179–199. https://doi.org/10.1504/IJBIS.2021.119182

Nyagadza B, Mazuruse G, Simango K, Chikazhe L, Tsokota T, Macheka L (2023) Examining the influence of social media eWOM on consumers’ purchase intentions of commercialised indigenous fruits (IFs) products in FMCGs retailers, Sustainable Technology & Entrepreneurship (STE). Elsevier España. https://doi.org/10.1016/j.stae.2023.100040

Nyagadza, B. (2022). Search engine marketing and social media marketing predictive trends, Journal of Digital & Media Policy (JDMP), Vol.13 [Issue 3] pp. 407–425, Intellect Publishers, Bristol, United Kingdom (UK), (DHET/SCOPUS). DOI: https://doi.org/10.1386/jdmp_00036_1

Othman N, Mohd Suki N, Mohd Suki N (2021) Evolution trends of facebook marketing in digital economics growth: a bibliometric analysis. Int J Interact Mobile Technol 15(20):68–82. https://doi.org/10.3991/ijim.v15i20.23741

Paul J, Lim WM, O’Cass A, Hao AW, Bresciani S (2021) Scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR). Int J Consum Stud. https://doi.org/10.1111/IJCS.12695

Pearson A (2019) Personalisation the artificial intelligence way. J Digit Soc Media Mark 7(3):245–269

Pelletier MJ, Krallman A, Adams FG, Hancock T (2020) One size doesn’t fit all: a uses and gratifications analysis of social media platforms. J Res Interact Mark 14(2):269–284. https://doi.org/10.1108/JRIM-10-2019-0159/FULL/HTML

Phelan KV, Chen H-T, Haney M (2013) “Like” and “Check-in”: How hotels utilize Facebook as an effective marketing tool. J Hosp Tour Technol 4(2):134–154. https://doi.org/10.1108/JHTT-Jul-2012-0020

Poolperm P, Thongmak M (2021) The influence of facebook marketing using gamification on consumers purchase intention. In: 27th Annual Americas Conference on Information Systems, AMCIS 2021

Prabowo H, Bramulya R, Yuniarty (2020) Student purchase intention in higher education sector: the role of social network marketing and student engagement. Manag Sci Lett 10(1):103–110. https://doi.org/10.5267/j.msl.2019.8.012

Qin YS (2020) Fostering brand–consumer interactions in social media: the role of social media uses and gratifications. J Res Interact Mark 14(3):337–354. https://doi.org/10.1108/JRIM-08-2019-0138/FULL/HTML

Rabbani MR (2020) The competitive structure and strategic positioning of commercial banks in Saudi Arabia. Int J Emerg Technol 11(3):43–46

Rabbani MR, Ali MAM, Rahiman H, Atif M, Zulfikar Z, Naseem Y (2021) The response of Islamic financial service to Covid-19 pandemic: the social open innovation of financial system. J Open Innov: Technol Market Complex

Rabbani MR, Bashar A, Hawaldar IT, Shaik M, Selim M (2022). What do we know about crowdfunding and P2P lending research? A bibliometric review and meta-analysis. J Risk Financ Manag. https://doi.org/10.3390/jrfm15100451

Rabbani MR et al (2021) Text mining and visual analytics in research: Exploring the innovative tools. International conference on decision aid sciences and application (DASA) 2021:1087–1091

Rabbani MR, Kayani U, Bawazir HS, Hawaldar IT (2022) A commentary on emerging markets banking sector spillovers: Covid-19 vs GFC pattern analysis. Heliyon 8(3):e09074

Article   CAS   PubMed   PubMed Central   Google Scholar  

Radpour R, Honarvar AR (2018) Impact of social networks on brand value based on customer behavior using structural equations. Int J Cust Relations Mark Manag 9(3):50–67. https://doi.org/10.4018/IJCRMM.2018070104

Raveendirarasa V, Amalraj CRJ (2020) Sentiment analysis of Tamil-English code-switched text on social media using sub-word level LSTM. In: 5th international conference on information technology research, ICITR 2020. https://doi.org/10.1109/ICITR51448.2020.9310817

Romero Moreno FY, Sanchez Martelo CA, Alfonso Corredor BY, Sanchez Cifuentes JF, Ospina López JP (2020) Sentiment analysis to the opinions generated in the social network twitter: political marketing. RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao 2020(E35):187–203

Rozas L, Castronuovo L, Busse P, Mus S, Barnoya J, Garrón A, Tiscornia MV, Guanieri L (2021) Data on the Facebook marketing strategies used by fast-food chains in four Latin American countries during the COVID-19 lockdowns. BMC Res Notes. https://doi.org/10.1186/s13104-021-05870-8

Samala N, Katkam BS, Bellamkonda RS, Rodriguez RV (2020) Impact of AI and robotics in the tourism sector: a critical insight. J Tour Futures. https://doi.org/10.1108/JTF-07-2019-0065

Sevli O, Küçüksille EU (2017) Advertising recommendation system based on dynamic data analysis on Turkish speaking Twitter users. Tehnicki Vjesnik 24(2):571–578. https://doi.org/10.17559/TV-20151020205558

Shareef MA, Mukerji B, Alryalat MAA, Wright A, Dwivedi YK (2018) Advertisements on Facebook: Identifying the persuasive elements in the development of positive attitudes in consumers. J Retail Consum Serv 43:258–268. https://doi.org/10.1016/j.jretconser.2018.04.006

Sharma R, Alavi S, Ahuja V (2017) Generating trust using Facebook-A study of 5 online apparel brands. In: S Y, A V, D D, S Y, B D, T Y, T JM, A N (eds) 5th international conference on information technology and quantitative management, ITQM 2017. Elsevier B.V, vol 122, pp 42–49. https://doi.org/10.1016/j.procs.2017.11.339

Sharma R, Alavi S, Ahuja V (2019) Generation of trust using social networking sites: a comparative analysis of online apparel brands across social media platforms. Int J Manag Pract 12(4):405–425. https://doi.org/10.1504/IJMP.2019.102532

Sijabat DCS, Saputra FD, Ikhsan RB, Yuniarty (2020) The impact of social network marketing and customer engagement on purchase intentions in wedding service business. In: 5th International Conference on Information Management and Technology, ICIMTech 2020, pp 97–102. https://doi.org/10.1109/ICIMTech50083.2020.9211285

Smith D, Hernández-García A, Agudo Peregrina AF, Hair Jr JF (2016) Social network marketing: A segmentation approach to understanding purchase intention. In: G. J., J. J., R. E., M. P., & M. D. (Eds.), 7th International Conference on Social Media and Society, SMSociety 2016. Association for Computing Machinery. DOI: https://doi.org/10.1145/2930971.2930992

Spackman JS, Larsen R (2017) Evaluating the impact of social media marketing on online course registration. J Contin Higher Educ 65(3):151–165. https://doi.org/10.1080/07377363.2017.1368774

Sugawara E, Nikaido H (2014) Properties of AdeABC and AdeIJK efflux systems of Acinetobacter baumannii compared with those of the AcrAB-TolC system of Escherichia coli . Antimicrob Agents Chemother 58(12):7250–7257. https://doi.org/10.1128/AAC.03728-14

Tang S (2018) When social advertising meets viral marketing: sequencing social advertisements for influence maximization. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, pp 176–183

Thingom C, Yeon G (2017) An integration of big data and cloud computing. In B V, S SC, J A (eds), 1st International Conference on Data Engineering and Communication Technology, ICDECT 2016. Springer Verlag, vol 469, pp 729–737. https://doi.org/10.1007/978-981-10-1678-3_70

Tiwary NK, Kumar RK, Sarraf S, Kumar P, Rana NP (2021) Impact assessment of social media usage in B2B marketing: a review of the literature and a way forward. J Bus Res 131:121–139. https://doi.org/10.1016/j.jbusres.2021.03.028

Toor A, Husnain M, Hussain T (2017) The impact of social network marketing on consumer purchase intention in Pakistan: Consumer engagement as a mediator. Asian J Bus Account 10(1):167–199

Tu Y, Zhao M, Jones C (2014). Insights into social media and online digital music. In: Digital Arts and Entertainment: Concepts, Methodologies, Tools, and Applications. IGI Global, vol 2, pp 684–710. https://doi.org/10.4018/978-1-4666-6114-1.ch032

Tussyadiah IP (2012) A concept of location-based social network marketing. J Travel Tour Mark 29(3):205–220. https://doi.org/10.1080/10548408.2012.666168

Van Eck NJ, Waltman L (2019) VOSviwer Manual version 1.6.10. In: CWTS Meaningful metrics

Varma M, Dhakane N, Pawar A (2020) Evaluation of impact of instagram on customer preferences: the significance of online marketing. Int J Sci Technol Res 9(2):548–554

Vasudevan S, Kumar FJP (2018) Social media and B2B brands: an Indian perspective. Int J Mech Eng Technol 9(9):767–775

Vasudevan S, Peter Kumar FJ (2018) Brand social engagement: learnings from Indian real estate websites. Int J Civil Eng Technol 9(7):1861–1870

Wajid A, Awan MJ, Ferooz F, Shoukat S, Anwar M, Mazhar M (2021) Facebook marketing analytics. In: 4th International conference on innovative computing, ICIC 2021. https://doi.org/10.1109/ICIC53490.2021.9693023

Wang CL (2021) New frontiers and future directions in interactive marketing: Inaugural Editorial. J Res Interact Mark 15(1):1–9. https://doi.org/10.1108/JRIM-03-2021-270/FULL/HTML

Wang G, Tan GW-H, Yuan Y, Ooi K-B, Dwivedi YK (2021) Revisiting TAM2 in behavioral targeting advertising: a deep learning-based dual-stage SEM-ANN analysis. Technol Forecast Soc Chang. https://doi.org/10.1016/j.techfore.2021.121345

Wang SS, Lin Y-C, Liang T-P (2018) Posts that attract millions of fans: the effect of brand-post congruence. Electron Commer Res Appl 28:73–85. https://doi.org/10.1016/j.elerap.2017.12.010

Wang Z, Zhao H, Zhang G, Zhang J (2017) An ACP-based approach for complex social network marketing system. Xitong Gongcheng Lilun yu Shijian/Syst Eng Theory Pract 37(11):2897–2907. https://doi.org/10.12011/1000-6788(2017)11-2897-11

Article   CAS   Google Scholar  

Wasiq M, Bashar A, Akmal S, Rabbani MR, Saifi MA, Nawaz N, Nasef YT (2023) Adoption and applications of blockchain technology in marketing: a retrospective overview and bibliometric analysis. Sustainability 15(4):3279

Wright LT, Gaber H, Robin R, Cai H (2018) Content strategies for facebook marketing: a case study of a leading fast-food brand page. In: Developments in Marketing Science: Proceedings of the Academy of Marketing Science. Springer Nature, pp 779–791. https://doi.org/10.1007/978-3-319-66023-3_246

Wu Y, Stewart M, Liu R (2015) Social networking sites and marketing strategies. In Handbook of Research on Integrating Social Media into Strategic Marketing. IGI Global, pp 207–239. https://doi.org/10.4018/978-1-4666-8353-2.ch013

Yang S, Zeng X (2018) Sustainability of government social media: A multi-analytic approach to predict citizens’ mobile government microblog continuance. Sustainability (Switzerland). https://doi.org/10.3390/su10124849

Article   PubMed Central   Google Scholar  

Yarahmadi F, Yarahmadi F, Nader BS (2022) Investigating the impact of social network marketing on the bank customers’ profitability. In: Contributions to Management Science. Springer Science and Business Media Deutschland GmbH, pp 119–134. https://doi.org/10.1007/978-3-030-86028-8_7

Yoo K-H, Lee W (2017) Facebook marketing by hotel groups: Impacts of post content and media type on fan engagement. In: Advances in social media for travel, tourism and hospitality: new perspectives, practice and cases. Taylor and Francis, pp 131–146. https://doi.org/10.4324/9781315565736

Yoon Y, Deng R, Joo J (2022) The effect of marketing activities on Web Search Volume: an empirical analysis of Chinese Film Industry Data. Appl Sci (Switzerland). https://doi.org/10.3390/app12042143

Zhang B, Mildenberger M, Howe PD, Marlon J, Rosenthal SA, Leiserowitz A (2020) Quota sampling using Facebook advertisements. Polit Sci Res Methods 8(3):558–564. https://doi.org/10.1017/psrm.2018.49

Zhang X, Gong Y, Peng L (2020) The impact of interdependence on behavioral engagement in online communities. Mark Intell Plan 38(4):417–431. https://doi.org/10.1108/MIP-05-2019-0285

Zhao H, Huang Y, Wang Z (2020) Comparison between social media and social networks in marketing research: a bibliometric view. Nankai Bus Rev Int 12(1):122–151. https://doi.org/10.1108/NBRI-12-2019-0072

Zollo L, Filieri R, Rialti R, Yoon S (2020) Unpacking the relationship between social media marketing and brand equity: The mediating role of consumers’ benefits and experience. J Bus Res 117:256–267. https://doi.org/10.1016/j.jbusres.2020.05.001

Download references

Acknowledgements

The researchers express unwavering gratitude to the authors of many sampled articles included in this current data mining and bibliometric analysis. Their invaluable efforts cannot be ignored.

Declaration of generative AI in scientific writing

The chapter was scientifically prepared without the writing assistance of any enhanced generative artificial intelligence (AI) software(s).

The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.

This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

Author information

Authors and affiliations.

School of Management, IMS Unison University, Dehradun, India

College of Administration and Financial Sciences, Saudi Electronic University, Riyadh, Saudi Arabia

Mohammad Wasiq

Department of Marketing, Marondera University of Agricultural Sciences and Technology (MUAST), Marondera, Zimbabwe

Brighton Nyagadza

Institute for the Future Knowledge (IFK), University of Johannesburg (UJ), Johannesburg, South Africa

Department of Management and Entrepreneurship, University of the Western Cape , Bellville, South Africa

Eugine Tafadzwa Maziriri

You can also search for this author in PubMed   Google Scholar

Contributions

Authors contributed equally in the development of the article.

Corresponding author

Correspondence to Brighton Nyagadza .

Ethics declarations

Conflict of interests.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethics approval and consent to participate

Consent for publication.

The authors consent publication of the article with Future Business Journal .

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Bashar, A., Wasiq, M., Nyagadza, B. et al. Emerging trends in social media marketing: a retrospective review using data mining and bibliometric analysis. Futur Bus J 10 , 23 (2024). https://doi.org/10.1186/s43093-024-00308-6

Download citation

Received : 26 August 2023

Accepted : 22 January 2024

Published : 15 February 2024

DOI : https://doi.org/10.1186/s43093-024-00308-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Social media marketing
• Bibliometrics
• Biblioshiny
• Social media networks
• Social media platforms
• Marketing tools

Analyzing influence operations on Facebook: an exploratory study

• Exploratory Article
• Published: 29 May 2024

Cite this article

• Craig Douglas Albert   ORCID: orcid.org/0000-0003-3225-9386 1 ,
• Lance Y. Hunter 1 ,
• Samantha Mullaney 1 &
• Meagan Mays 1  

30 Accesses

6 Altmetric

Explore all metrics

Recently, there have been groundbreaking studies that seek to create unique cybersecurity datasets used to empirically test theories related to strategic cybersecurity. To date, however, this research has neglected cyber-enabled information operations (CEIO). With the remarkable amount of information operations being reported on social media platforms such as Facebook, Twitter, and Instagram, there is a substantial gap in the literature regarding empirical studies on CEIO using cross-national datasets. This exploratory, descriptive study seeks to remedy this dilemma. To do so, this paper investigates the question, “What are the political and economic characteristics of states that are most likely to be targeted by CEIO over social media on Facebook?” To investigate, this exploratory, descriptive study utilizes a unique Information Operations Threat Report Dataset (2020) based on Facebook’s release of 2020 influence operations information that captures CEIO on its platform from 2017 to 2020. A descriptive data analysis reveals that mixed regimes (i.e., states that are partially authoritarian and democratic) and slightly wealthier states are more likely to be targeted in CEIO on Facebook. These exploratory findings provide useful insights into what types of states may be more susceptible to CEIO attacks on Facebook.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Sexist Slurs: Reinforcing Feminine Stereotypes Online

Mainstreaming the Blackpill: Understanding the Incel Community on TikTok

Indian government initiatives on cyberbullying: A case study on cyberbullying in Indian higher education institutions

This material is partially based upon research supported by the Office of Naval Research under Award Number N00014-22-1-2549.

Initiatives were defined as focusing on the analysis or study of influence operations, or the countering of influence operations (Smith 2020 ).

IWIO refers to “the deliberate use of information to confuse, mislead, and affect the choices and decisions that the adversary makes” (National Academies 2017 , 4).

Adamsky, Dimitry. 2015. Cross-Domain Coercion: The Current Russian Art of Strategy. Proliferation Papers 54: 26–27.

Google Scholar  

Argaw, Salem T., Nefti-Eboni. Bempong, Bruce Eshaya-Chauvin, and Antoine Flahault. 2019. The State of Research on Cyberattacks Against Hospitals and Available Best Practice Recommendations: A Scoping Review. BMC Medical Informatics and Decision Making 19(1): 1–11.

Article   Google Scholar  

Bateman, Jon, Elonnai Hickok, Laura Courchesne, Isra Thange, and Jacob N. Shapiro. 2021. Measuring the Effects of Influence Operations: Key Findings and Gaps From Empirical Research. Carnegie Endowment for International Peace . https://carnegieendowment.org/2021/06/28/measuring-effects-of-influence-operations-key-findings-and-gaps-from-empirical-research-pub-84824#effects/?view_52_page=3 .

Benson, M., and J. Kugler. 1998. Power Parity, Democracy, and the Severity of Internal Violence. Journal of Conflict Resolution 42(2): 196–209.

Bergh, A. 2020. Understanding Influence Operations on Social Media. Journal of Information Warfare 19(4): 110–131.

Boyd, Bradley, and Herbert Lin. 2019. Affecting the Cognitive Dimension of the Information Environment through Cyber-Enabled Information Operations. Journal of Information Warfare 18(3): 49–66.

Bradshaw, Samantha and Philip N. Howard. 2018. The Global Organization of Social Media Disinformation Campaigns. Journal of International Affairs 71(1.5): 23–31.

Brangetto, Pascal and Matthijs A. Veenendaal. 2016. Influence Cyber Operations: The Use of Cyberattacks in Support of Influence Operations. In: 2016 8th International Conference on Cyber Conflict and Cyber Power. N. Pissanidis, H. Rõigas, M. Veenendaal (Eds.) 2016 © NATO CCD COE Publications, Tallinn.

Buchanon, Ben and Michael Sulmeyer. 2016. Russia and Cyber Operations: Challenges and Opportunities for the Next U.S. Administration. Carnegie Endowment for International Peace .

D’Agostino, Giorgio, John Paul Dunne, and Luca Pieroni. 2012. Corruption, Military Spending and Growth. Defence and Peace Economics 23(6): 591–604.

Cohen, Hillel. 2022. Marketing the Occupation to the Palestinians of the West Bank: Shabak Facebook Pages in Historical Context. Israel Studies 27(1): 1–34.

Colligan, Christopher. Might or Byte(s): On the Safeguarding of Democracy in the Digital Age. In, Information Warfare in the Age of Cyber Conflict , Christopher Whyte, A. Trevor Thrall, and Brian M. Mazanec, Eds. 2021. London: Routledge Press.

Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, Agnes Cornell, M. Steven Fish, Lisa Gastaldi, Haakon Gjerløw, Adam Glynn, Allen Hicken, Anna Lührmann, Seraphine F. Maerz, Kyle L. Marquardt, Kelly McMann, Valeriya Mechkova, Pamela Paxton, Daniel Pemstein, Johannes von Römer, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Aksel Sundtröm, Eitan Tzelgov, Luca Uberti, Yi-ting Wang, Tore Wig, and Daniel Ziblatt. 2021. V-Dem Codebook v11.1. Varieties of Democracy (V-Dem) Project.

Cyber Defense Project (CDP)/Center for Security Studies (CSS). 2019 Cyber Influence Operations: An Overview and Comparative Analysis. https://css.ethz.ch/content/dam/ethz/special-interest/gess/cis/center-for-securities-studies/pdfs/Cyber-Reports-2019-10-CyberInfluence.pdf .

Dube, Rohit. Understanding the Communist Party of China’s Information Operations, Working Paper, 2021, arXiv.2107.05602

Facebook Threat Report: The State of Influence Operations 2017–2020.

International Monetary Fund (IMF). 2022. GDP Per Capita: Current Prices. https://www.imf.org/external/datamapper/NGDPDPC@WEO/OEMDC/ADVEC/WEOWORLD .

Farrell, H.J. and B Schneier. 2018. Common-Knowledge Attacks on Democracy. Berkman Klein Research Publication No. 2018-7.

Fearon, James D. 2005. Primary Commodity Exports and Civil War. Journal of Conflict Resolution 49(4): 483–507.

Fearon, James D. 2008. Economic Development, Insurgency, and Civil War. In Institutions and Economic Performance Ed. Elhanan Helpman. Cambridge: Harvard University Press: 292–328.

Foote, Colin and Ryan C. Maness, Benjamin Jensen, and Brandon Valeriano. 2021. Cyber Conflict at the Intersection of Information Operations. In Information Warfare in the Age of Cyber Conflict (pp. 54–69).

Francois, Camille, and Herb Lin. 2021. The Strategic Surprise of Russian Information Operations on Social Media in 2016 in the United States: Mapping a Blind Spot. Journal of Cyber Policy 6(1): 9–30.

Garfinkel, Ben, and Allan Dafoe. 2019. How Does the Offense-Defense Balance Scale? Journal of Strategic Studies 42(6): 736–763.

Goel, Sanjay, and Brian Nussbaum. 2021. Attribution Across Cyber Attack Types: Network Intrusion and Information Operations. IEEE Open Journal of the Communications Society 2: 1082–1093.

Hartmann, Kim and Keir Giles. 2020. The Next Generation of Cyber-Enabled Information Warfare. In 12 th International Conference on Cyber Conflict 1300 (May): 233–250.

Heim, Jacob L., and Benjamin Miller. 2020. Measuring Power, Power Cycles, and the Risk of Great-Power War in the 21st Century . RAND.

Hunter, Lance Y., Craig Douglas Albert, and Eric Garrett. 2021. Factors that Motivate State-Sponsored Cyberattacks. Cyber Defense Review 7(1): 72–94.

Hunter, Lance Y., Craig Douglas Albert, Eric Garrett, and Josh Rutland. 2022. Democracy and Cyberconflict: How Regime Type Affects State-Sponsored Cyberattacks. Journal of Cyber Policy 7(1): 72–94.

Hunter, Lance Y., Craig D. Albert, Josh Rutland, Kristen Topping, and Christopher Hennigan. 2024. Artificial intelligence and information warfare in major power states: how the US, China, and Russia are using artificial intelligence in their information warfare and influence operations. Defense & Security Analysis : 1–35. https://www.tandfonline.com/doi/abs/ https://doi.org/10.1080/14751798.2024.2321736

Innes, H., and M. Innes. 2023. De-platforming Disinformation: Conspiracy Theories and Their Control. Information, Communication, & Society 26(6): 1262–1280.

Jennings, Jay, and Natalie Jomini Stroud. 2021. Asymmetric Adjustment: Partisanship and Correcting Misinformation on Facebook. New Media & Society 25(7): 1501–1521.

Kania, Elsa B. 2021. The Ideological Battlefield: China’s Approach to Political Warfare and Propaganda in an Age of Cyber Conflict. Information Warfare in the Age of Cyber Conflict Routledge.

Knerler, Kathryn, Ingrid Parker, and Carson Zimmerman. 2022. 11 Strategies of a World-Class Cybersecurity Operations Center . MITRE.

Lacina, B. 2006. Explaining the Severity of Civil Wars. Journal of Conflict Resolution 50(2): 276–289.

Lin, H., and J. Kerr. 2018. On cyber-enabled information/influence warfare and manipulation . New York: Oxford University Press.

Lin, Herbert. On the Organization of the U.S. government for Responding to Adversarial Information Warfare and Influence Operations. In Information Warfare in the Age of Cyber Conflict , Christopher Whyte, A. Trevor Thrall, and Brian M. Mazanec, Eds. 2021. London: Routledge Press.

Lin, Herbert, and Jaclyn Kerr. 2021. On cyber-enabled information warfare and information operations. The Oxford Handbook of Cyber Security , p. 251.

MacDonald, Scot. 2006. Propaganda and Information Warfare in the Twenty-First Century . London: Routledge.

Book   Google Scholar  

Marcoux, Thomas and Nitin Agarwal, Obadimu Adewale, Muhammad Nihal Hussain, Katrin Kania Galeano, Samer Al-Khateeb, Understanding Information Operations using YouTubeTracker, IEEE/WIC/ACM International Conference On Web Intelligence, October 14, 2019.

META. 2021. October 2021 Coordinated Inauthentic Behavior Report. https://about.fb.com/news/2021/11/october-2021-coordinated-inauthentic-behavior-report/ (accessed 11/1/22).

Moga, Horatiu, Mircea Boscoianu, Delia A. Ungureanu, Florin D. Sandu, and Razvan Boboc. 2017. Refined Concepts of Massive and Flexible Cyber Attacks with Information Warfare Strategies. Journal of Communications 12(6): 364–370.

Mueller, Milton, and Karl Grindal. 2022. Information as Power: Evolving US Military Information Operations and Their Implications for Global Internet Governance. The Cyber Defense Review 7(2): 79–98.

Nakayama, Bryan. 2022. Democracies and the Future of Offensive (Cyber-Enabled) Information Operations. Cyber Defense Review 7(3): 49–65.

National Academies. 2017. On Cyber-Enabled IWIO (Information Warfare and Influence Operations). Decadal Survey of Social and Behavioral Sciences for Applications to National Security. Board on Behavioral, Cognitive, and Sensory Sciences.

Organski, A.F.K., and Jacek Kugler. 1980. The War Ledger . Chicago: The University of Chicago.

Paul, C., and M. Matthews. 2016. The Russian “Firehose of Falsehood” Propaganda Model: Why it Might Work and Options to Counter It . Santa Monica: Rand Corporation.

Prier, Jarred. 2020. Commanding the trend: Social media as information warfare. In Information warfare in the age of cyber conflict (pp. 88–113). Routledge .

Pierri, Francesco, Luca Luceri, Nikhil Jindal, and Emilio Ferrara. 2023. Propaganda and Misinformation on Facebook and Twitter During the Russian Invasion of Ukraine. In Proceedings of the 15th ACM Web Science Conference (pp. 65–74).

Pavlovsky, Gleb. 2016. Russian Politics under Putin. Foreign Affairs 95(3): 10–17.

Rosenberger, Laura, and Lindsay Gorman. 2020. How Democracies can Win the Information Contest. The Washington Quarterly 43(2): 75–96.

Saltzman, Ilai. 2013. Cyber Posturing and the Offense-Defense Balance. Contemporary Security Policy 34(1): 40–63.

Singer, J. David., Stuart Bremer, and John Stuckey. 1972. Capability Distribution, Uncertainty, and Major Power War, 1820–1965. In Peace, War, and Numbers , ed. Bruce Russett, 19–48. Beverly Hills: Sage.

Slayton, Rebecca. 2016. What Is the Cyber Offense-Defense Balance?: Conceptions, Causes, and Assessment. International Security 41(3): 72–109.

Sloss, David L. Information Warfare and Democratic Decay: Tyrants on Twitter: Protecting Democracies from Chinese and Russian Information Warfare (Stanford University Press, Forthcoming). Posted 14 January 2021.

Smeets, M. and H.S. Lin. 2018. Offensive cyber capabilities: to what ends? In 2018 10th International Conference on Cyber Conflict (CyCon) Tallinn, Estonia: IEEE. 55-7.

Smith, Victoria. 2020. Mapping Worldwide Initiatives to Counter Influence Operations. Carnegie Endowment for International Peace . https://carnegieendowment.org/2020/12/14/mapping-worldwide-initiatives-to-counter-influence-operations-pub-83435 .

Stockholm International Peace Research Institute (SIPRI). 2021. World military spending rises to almost $2 trillion in 2020. https://www.sipri.org/media/press-release/2021/world-military-spending-rises-almost-2-trillion-2020 (accesses 11/2/22).

Theohary, Catherine A. 2018. Information Warfare: Issues for Congress. Congressional Research Service Report .

Thomas, Timothy. 2017. Kremlin Kontrol: Russia's Political-Military Reality Fort Leavenworth: Foreign Military Studies Office.

Unver, Akin, and Arhan S. Ertan. 2022. The Strategic Logic of Digital Disinformation: Offense, Defence and Deterrence in Information Warfare. Defence and Deterrence in Information Warfare. SSRN:  https://ssrn.com/abstract=4098988  or  https://doi.org/10.2139/ssrn.4098988

Valeriano, Brandon. 2022. The Failure of Offense/Defense Balance in Cybersecurity. The Cyber Defense Review 7(3): 91–1012.

Valeriano, Brandon, Benjamin Jensen, and Ryan C. Maness. 2018. Cyber Strategy: The Evolving Character of Power and Coercion . New York: Oxford University Press.

Vreeland, James Raymond. 2008. The Effect of Political Regime on Civil War: Unpacking Anocracy. Journal of Conflict Resolution 52(3): 401–425.

Warren, T. Camber. 2014. Not by the Sword Alone: Soft Power, Mass Media, and the Production of State Sovereignty. International Organization 68(1): 111–141.

Whyte, Christopher. 2020. Cyber Conflict or Democracy “Hacked”? How Cyber Operations Enhance Information Warfare. Journal of Cybersecurity 6(1): 1–17.

Wilson, Tom and Kate Starbird. 2021. Cross-Platform Information Operations: Mobilizing Narratives and Building Resilience Through both ‘Big’ and ‘Alt’ Tech. In PACM on Human Computer Interaction 5(CSCW2): Article 345: 1–32.

Download references

Author information

Authors and affiliations.

Intelligence and Security Studies, Augusta University, Augusta, GA, USA

Craig Douglas Albert, Lance Y. Hunter, Samantha Mullaney & Meagan Mays

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Craig Douglas Albert .

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Albert, C.D., Hunter, L.Y., Mullaney, S. et al. Analyzing influence operations on Facebook: an exploratory study. Digi War (2024). https://doi.org/10.1057/s42984-024-00093-0

Download citation

Published : 29 May 2024

DOI : https://doi.org/10.1057/s42984-024-00093-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Information warfare
• Social media
• Information operations
• Cyber-enabled influence operations
• Find a journal
• Publish with us
• Track your research

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Table of Contents

Which social media platforms are most common, who uses each social media platform, find out more, social media fact sheet.

Many Americans use social media to connect with one another, engage with news content, share information and entertain themselves. Explore the patterns and trends shaping the social media landscape.

To better understand Americans’ social media use, Pew Research Center surveyed 5,733 U.S. adults from May 19 to Sept. 5, 2023. Ipsos conducted this National Public Opinion Reference Survey (NPORS) for the Center using address-based sampling and a multimode protocol that included both web and mail. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, education and other categories.

Polls from 2000 to 2021 were conducted via phone. For more on this mode shift, read our Q&A.

Here are the questions used for this analysis , along with responses, and  its methodology ­­­.

A note on terminology: Our May-September 2023 survey was already in the field when Twitter changed its name to “X.” The terms  Twitter  and  X  are both used in this report to refer to the same platform.

YouTube and Facebook are the most-widely used online platforms. About half of U.S. adults say they use Instagram, and smaller shares use sites or apps such as TikTok, LinkedIn, Twitter (X) and BeReal.

Note: The vertical line indicates a change in mode. Polls from 2012-2021 were conducted via phone. In 2023, the poll was conducted via web and mail. For more details on this shift, please read our Q&A . Refer to the topline for more information on how question wording varied over the years. Pre-2018 data is not available for YouTube, Snapchat or WhatsApp; pre-2019 data is not available for Reddit; pre-2021 data is not available for TikTok; pre-2023 data is not available for BeReal. Respondents who did not give an answer are not shown.

Source: Surveys of U.S. adults conducted 2012-2023.

Usage of the major online platforms varies by factors such as age, gender and level of formal education.

% of U.S. adults who say they ever use __ by …

• RACE & ETHNICITY
• POLITICAL AFFILIATION

This fact sheet was compiled by Research Assistant  Olivia Sidoti , with help from Research Analyst  Risa Gelles-Watnick , Research Analyst  Michelle Faverio , Digital Producer  Sara Atske , Associate Information Graphics Designer Kaitlyn Radde and Temporary Researcher  Eugenie Park .

Follow these links for more in-depth analysis of the impact of social media on American life.

• Americans’ Social Media Use  Jan. 31, 2024
• Americans’ Use of Mobile Technology and Home Broadband  Jan. 31 2024
• Q&A: How and why we’re changing the way we study tech adoption  Jan. 31, 2024

Find more reports and blog posts related to  internet and technology .

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

© 2024 Pew Research Center

The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

Explore a career with us

Related articles.

Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale

A generative AI reset: Rewiring to turn potential into value in 2024

Implementing generative AI with speed and safety