Bibliometric and content analysis of viral marketing in marketing literature

Figures & data

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

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

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

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

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

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

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

Figure 4. Most cited studies on viral marketing literature.

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

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

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

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

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

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

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

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

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

Leskovec, J., Adamic, L. A., & Huberman, B. A. (2007). The dynamics of viral marketing. ACM Transactions on the Web (TWEB), 1(1), 5. es https://doi.org/10.1145/1232722.1232727

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Phelps, J. E., Lewis, R., Mobilio, L., Perry, D., & Raman, N. (2004). Viral marketing or electronic word-of-mouth advertising: Examining consumer responses and motivations to pass along email. Journal of Advertising Research, 44(4), 333–348. https://doi.org/10.1017/S0021849904040371

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Data availability statement

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