E-ISSN:2583-0074

Research Article

Social Media

Social Science Journal for Advanced Research

2025 Volume 5 Number 4 July
Publisherwww.singhpublication.com

Influencer Driven Social Media as an Effective Marketing Strategy for Gen Z Consumers: A Bibliometric Review

Bansal N1, Mathur S2*
DOI:10.5281/zenodo.16628919

1 Nidhi Bansal, Associate Professor, Atma Ram Sanatan Dharma College, University of Delhi, Delhi, India.

2* Shruti Mathur, Associate Professor, Department of Commerce, Sri Venkateswara College, University of Delhi, Delhi, India.

The paper performs a bibliometric review of influencer driven social media as an effective marketing strategy for generation Z consumers with the objective of identifying the existing underlying themes of research and finding the underexplored research areas. A dataset of 181 research papers published in the last ten years was extracted from the SCOPUS database. Results show that research in the subject area is limited especially in the Indian context. Five research clusters were identified using co-occurrence analysis. These include relationship between parasocial interaction and influencer’s credibility on trustworthiness; relationship between branding, consumer engagement and consumer’s purchase intention; sustainability driven Marketing and Consumer Engagement; immersive marketing and psychological well-being of the users. It is observed that there is a need for development of a comprehensive conceptual model which relates the direct and moderating variables affecting the Generation Z consumer’s purchase intentions. Gaps also exist in the research on tourism, fashion products, food blogging and investment in financial products in context of social media marketing making them potential areas of research in future.

Keywords: social media, influencer marketing, generation z, bibliometric analysis

Corresponding Author How to Cite this Article To Browse
Shruti Mathur, Associate Professor, Department of Commerce, Sri Venkateswara College, University of Delhi, Delhi, India.
Email: 		Bansal N, Mathur S, Influencer Driven Social Media as an Effective Marketing Strategy for Gen Z Consumers: A Bibliometric Review. Soc Sci J Adv Res. 2025;5(4):49-61.
Available From
https://ssjar.singhpublication.com/index.php/ojs/article/view/273

Manuscript Received Review Round 1 Review Round 2 Review Round 3 Accepted
2025-06-12 2025-06-28 2025-07-17
Conflict of Interest Funding Ethical Approval Plagiarism X-checker Note
None Nil Yes 2.36

© 2025 by Bansal N, Mathur S and Published by Singh Publication. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/ unported [CC BY 4.0].

Soc Sci J Adv Res 2025;5(4)49
Download PDFBack To Article1. Introduction2. Methodology3. Results
and Discussion4. Conclusions
and Directions
for Future
ResearchReferences
Bansal N, et al. Influencer Driven Social Media as an Effective Marketing Strategy

1. Introduction

In this age of digitisation, online networks such as Facebook, Twitter, YouTube, LinkedIn are increasingly becoming popular among people for interaction among each other (Kitsios et al., 2022; Obembe et al., 2021). Individuals are using digital platforms both as producers and consumers. Companies now use social media to reach prospective customers (Buechel & Berger, 2018). Social media is also influencing decision making behaviour of consumers (Sadovykh et al.,2015; Bulut & Karabulut, 2018). It can be used to drive traffic and increase sales (Lee et al., 2015; Chang et al., 2018). To communicate with consumers via social media, influencers act as intermediaries.

‘Social media influencers’ is not a new concept anymore. It has been the focus of much research now. Influencers on digital platforms are a new brand of opinion makers who shape the behaviour of their target audience through social media. (Freberg et al., 2011). Influencers on digital platforms build personal relationships with followers, establish themselves as brands and have the ability to entertain, inform and shape the perception and mindsets behaviour of the subscribers (Dhanesh & Duthler, 2019). In fact, influencers on online networks are regarded as highly trustworthy, create one-way emotional bonding referred to as parasocial interaction similar to celebrity endorsement and are easily relatable (Veerman et al., 2017; Reinikainen et al., 2020; Babu et al., 2024). Traditional advertising is becoming less preferred and has limited reach in this age of digitalization influencers on digital platforms have emerged as a preferred medium to disseminate marketing information (Wiedmann & Von Mettenheim,2020). Consumers feel insecure in making online purchases. However, an influencer because of his / her personal appeal and online customer engagement instills trust and confidence in the customers (Ki et al., 2022). Increasing number of brands are now leveraging online influencers as brand ambassadors to create greater brand engagement and loyalty. (Li, & Feng, 2022; Reinikainen et al., 2020).

Generation Z are those born between late 1990 to 2010 and is referred to as internet generation. These people have used social media since their primary years, so their decision making is influenced by social media. (Djafarova and Bowes, 2021).

Generation Z uses social media for entertainment, and as a significant medium for knowledge accumulation and purchase decisions. (Thangavel et al.,2022). It is observed that as compared to earlier generations, they rely more on recommendations of influencers on digital platforms (Chiu & Ho, 2023). In the current scenario influencer driven social media marketing has evolved as strategic tool to reach Generation Z who are now the prospective customers. It is of interest to explore how this generation of consumers make their purchase decisions and what influences their behaviour. While some work has been done in this area; the field is still new and it is of interest to identify the areas of interest and research gaps.

This paper aims to perform a bibliometric analysis with the following objectives- 1. to observe the patterns in research on Generation Z, social media and influencer marketing in the last 10 years. 2. To conduct performance analysis to identify annual publication trends, prominent journals and contributing countries. 3. To identify the key areas and the underlying themes of existing research. 4. To find the gaps in the literature and potential research avenues especially in the Indian context.

The paper has 4 sections inclusive of this one which introduces the topic and identifies the objectives of research. The second section describes the data and methodology used in the paper. The results are discussed in the third section while conclusions and directions for future research are covered in the last section.

2. Methodology

The paper uses bibliometric analysis technique which is a quantitative approach for examining the existing literature in the area (Pritchard, 1969; Broadus, 1987). The data for the analysis is sourced from Scopus[1][1] database using the PRISMA framework. The paper explores aspects of social media marketing for Generation Z. However, a direct search of the term Social media marketing and Generation Z leads to very few papers after applying the desired filters (less than 200). The number reduces to less than 100 on applying the filters. To overcome this issue; two variations of the keywords “Social Media” and “Influencer Marketing” were used along with the term “Generation Z” which yielded 953 search results.


Soc Sci J Adv Res 2025;5(4)50
Bansal N, et al. Influencer Driven Social Media as an Effective Marketing Strategy

Further filters were applied to restrict the search to ‘final article’ and ‘review papers’ published in English language in the last 10 years. This resulted in 611 records. Further, since the research is being done from the perspective of business and related fields, only business and economics subject areas were included in the search leaving a sample of 237 papers which fulfilled all the criteria. As a next step, the abstract of the papers was carefully analysed for relevance to the research questions and a further 55 papers were excluded from the data set as they were not related to social media marketing. 1 paper was found to be duplicate and was also removed leaving a final data set consisting of 181 papers. The stepwise process is illustrated in Figure 1.

The dataset consisting of 181 papers is used for performance analysis and science mapping which are part of bibliometric analysis (Donthu et al, 2021). A descriptive performance analysis was done through examining the publication related metrics (Donthu, Reinartz, Kumar, & Pattnaik, 2020). VOSviewer software was used to create the network maps (Van Eck & Waltman, 2010) which are used for science mapping through co-authorship (country) analysis and keyword co-occurrence analysis.

ssjar_273_01.JPG
Figure 1: Step-wise Sample Selection Process for Bibliometric Analysis

3. Results and Discussion

3.1 Performance Analysis: Publication Related Metrics

In this sub-section; a performance analysis is conducted by examining the publication related metrics and research interest. The dataset used for bibliometric analysis was summarized for calculating the number of publications over the last 10 years. It may be noted that since 2025 is ongoing, the number of publications in the current year only reflect the number of papers published till May. The results are displayed in figure 2. The figure shows an increasing trend in the number of publications over the last decade. While the total number of publications has increased steadily (from 1 to 20) from 2016 to 2022 (except in 2020 (where it decreased to 7 from 12 in previous year) perhaps due to the disturbance at the onset of COVID pandemic); the number more than doubles in 2023 (44) and remains similar in 2024 (46) as well. In 2025 (current year) there are already 26 publications till May. If the same trend continues; the publications are expected to be more than last year. This indicates growing interest in social media, influencer marketing and Generation Z consumers over the last ten years. The increased volume of publications reiterates the importance of social media and its permeation to all walks of our life.

ssjar_273_02.JPG
Figure 2: Publications in the Research Area in the last 10 years
* Data for 2025 is included only till May


Soc Sci J Adv Res 2025;5(4)51
Bansal N, et al. Influencer Driven Social Media as an Effective Marketing Strategy

Table 1: Top 10 Productive Journals in the field (identified through the volume of publications)

Journal NamePublisher*Volume of publications in the fieldScopus Citescore of the Journal (2024)*
1.Communication TodayUniversity of SS. Cyril and Methodius, Faculty of Mass Media Communication31.5
2.Consumer Behavior in Tourism and HospitalityEmerald Publishing45.3
3.Global Business ReviewSAGE39.5
4.Journal of China Tourism ResearchTaylor & Francis34.7
5.Journal of Fashion Marketing and ManagementEmerald Publishing38.3
6.Journal of Open Innovation: Technology, Market, and ComplexityElsevier414
7.Journal of Product and Brand ManagementEmerald Publishing510.4
8.Journal of Retailing and Consumer ServicesElsevier322.7
9.Tourism ReviewEmerald Publishing318
10.Young ConsumersEmerald Publishing106.4

Table 1 shows the top 10 productive journals for social media, influencer marketing and Generation Z. “Young Consumers”, a journal published by Emerald Publishing with CiteScore 6.4 has published the largest number of papers i.e. 10 (about 5.5 percent of the total publications) in the data set. Further the top two journals are published by Emerald Publishing. The second highest number of publications is 5 in “Journal of Product and Brand Management”. The highest Citescore in 2024 is 22.7 for Journal of Retailing and Consumer Services published by Elsevier making it the most impactful one in the field amongst the listed journals. Taken together the top 10 journals listed in Table 1 account for more than 22 percent of publications in the area (as included in the dataset).

3.2. Co-occurrence Analysis

Co - occurrence analysis is used to identify the keywords which are commonly used in the relevant area by researchers. Words which co-occur may represent an underlying theme, existing links, or research area. This reflects the focus areas in the literature and is also used to identify research gaps, possible links and under examined themes (Emich et al., 2020).

Co-occurrence analysis was performed in VOSviewer using author keywords. After examining the keywords; a thesaurus file was created for merging variations of the same words. General terms were also removed. Keywords with a minimum of 3 occurrences and at least 1 linkage were considered for analysis. Thus, only 30 terms (out of 627) formed the dataset for network visualisation. The minimum cluster size was defined as 4. This resulted in generation of 5 clusters by the software which can be observed in figure 3.

The larger circles in the figure 3 reflect a stronger link with the other keywords. Being a keyword for searching Scopus database; the term “Gen Z” occurred the largest number of times followed by “Social Media” and “Influencer marketing”. The terms “Social media platforms”, “Social media marketing” and “purchase intentions” also find place in the top 5 keywords. Social media platforms influence consumer behaviour especially for the younger consumers (Chiu & Ho, 2023). Similarly, many studies also examine the factors affecting the intention to purchase in case of SMM (Vijaya et al 2023; Ghaleb & Alawad 2024).

Table 2: List of Keywords used in Co-occurrence analysis

KeywordOccurrenceTotal Link StrengthCluster
Online Brand Advocacy351
Parasocial Interaction461
Social Media Influencers11221
Social Media Platform19341
Source Credibility481
Technology351
Trust8151
Trustworthiness3101
Brand9192
Customer Engagement7162
Gen X392
Purchase Intentions15222
Social Media Marketing15292
Structural Equation Model352

Soc Sci J Adv Res 2025;5(4)52
Bansal N, et al. Influencer Driven Social Media as an Effective Marketing Strategy

Tourism7112
User-Generated Content332
Advertising363
Consumer Behavior13253
Marketing4123
Millennials12303
Social Media55793
Sustainability463
Artificial Intelligence384
Augmented Reality354
Influencer Marketing24484
Marketing Communication394
Gen Y585
Gen Z851275
Information Overload355
Mental Health355

ssjar_273_03.JPG
Figure 3: Keyword Co-occurrence Analysis

Cluster 1: Relationship between Parasocial Interaction and Influencer’s Credibility on Trustworthiness

Cluster 1 consists of 8 items as indicated in figure 3. Out of the 8 items, keyword social media platform has the link strength of 34 indicating highest linkage with other key words. The cluster explores the relationship between social media influencers (occurring 11 times), credibility (occurring 4 times) and parasocial social interaction in building trust among the consumers. The studies in the cluster depicts positive moderating influence of parasocial interaction on perceived trustworthiness and willingness to buy of Gen Z end users. (Babu et al., 2024). One of the main predictors of parasocial interaction is likeability which further improves the efficacy of marketing initiatives (Copeland et al.,2023). Influencer marketing aids the companies in engaging with the customers more actively through Instagram live streaming,

Chatbot and other interactive technologies helps in building brand trust and increases users’ satisfaction and creates an environment of trust. (Joshi, 2025; Maghraoui et al, 2025). However virtual influencers are not as effective in creating a bond with consumers as human influencers (Kholkina et al.,2025). Greater brand engagement, identification and brand trust create brand love which creates a higher value for the organisation in the form of premium price (Wallace et al.,2022; Kim et al., 2023).

Cluster 2: Relationship between Branding, Consumer Engagement and Purchase Intentions

Cluster 2 seen in green colour in Figure 3 consists of 8 keywords which are listed in table 2. Most studies in this cluster explore the relationship between ‘branding’, ‘consumer engagement’ and ‘purchase intentions’ in ‘social media marketing’ scenario (Alrwashdeh et al 2019; Florenthal B. 2019; Hazzam 2022; Molina-Prados et al 2022; Singh and Dagur 2022; Wallace et al 2022; Ngo et al 2023; Ortiz et al 2023; Sesar et al 2023; Ghaleb and Alawad 2024). The terms “social media marketing” and “purchase intentions” have the highest number of occurrences (i.e. 15) in this cluster. The two terms are also found to be linked together with some papers comparing the purchase intentions of different generations in the context of SMM (Vijaya et al 2023; Huwaida et al 2024; Vernuccio et al 2025). ‘Structural equation model’ is used as the methodology to identify the mediating role of variables in many papers (Huwaida et al 2024; Wang et al 2024). A number of papers also focus on ‘tourism’ (Iványi 2021; Iványi & Bíró-Szigeti 2021; Lusianingrum & Pertiwi 2023; Fong et al 2024). Some studies also observe the effect of ‘user-generated content’ on tourism (Correia et al 2025; Jiang et al 2025; Susanto et al 2024). A few studies also compare the responses of ‘Generation X’ with other generations of social media users (Bratina and Faganel 2024 and Olajide et al 2024)

Cluster 3: Relationship between Sustainability Driven Marketing and Consumer Interaction

In the present cluster, ‘social media’ is the highest occurring term in all the studies quoted in the cluster, then comes consumer behaviour at 13 occurrences. The cluster elaborates the relationship between consumer behaviour and sustainable and political consumerism on social media platforms.


Soc Sci J Adv Res 2025;5(4)53
Bansal N, et al. Influencer Driven Social Media as an Effective Marketing Strategy

There is the relatively higher concern among Gen Z and Y towards sustainable and political consumerism[2] (Seyfi et al., 2023). Many studies highlight that social media influencers are great opinion makers. Younger generation both Gen Z and Y irrespective of nationality are more likely to adapt to sustainable consumption when recommended by a reliable influencer on social platforms (Confetto et al., 2023; Sethuraman et al., 2023; Caratù et al., 2024)). Millennials together with Gen Z have preference for authentic and reliable social media influencers. So, the brands should carefully select the influencer who matches with their values to leverage influencer marketing and enhance consumer engagement (Sharma & Sanu, 2025).

Cluster 4: Immersive Marketing and Customer Experience

The studies in the cluster highlights the role of immersive marketing in enhancing customers experience and satisfaction. Keyword ‘Influencer marketing’ not only appears 24 times but also has high link strength of 48 indicating high linkage with other keywords. Keywords ‘augmented reality’ and ‘artificial intelligence’ both appear 4 times, indicating importance of AI blended influencer marketing. Immersive Marketing which involves the use of interactive content, virtual reality and 360 - degree experience is more effective in engaging Gen Z, particularly female consumers in the beauty industry, tend to have positive influence on self image and purchase intention (Ameen et al.,2022; Sinha, 2024; Bratina & Faganel, 2024). Studies also show in this context, that augmented reality driven marketing and influencer credibility enhances active consumer engagement, leading to a more positive brand image (Bratina, 2024; Sinha & Srivastava, 2025). Furthermore, interactive influencer marketing has been identified as a important factor in creating brand trust and image among Gen Z consumers (Wright & Cherry, 2023; Sesar et al., 2023),

Cluster 5: Social Media Marketing and Psychological Well-being

The cluster explores an important theme of mental health and psychological well-being among social media users. The cluster comprises 4 terms – ‘Generation Z’, ‘Generation Y’, ‘Information Overload’ and ‘Mental Health’.

The term ‘Generation Z’ is the most occurring keyword in the dataset with 85 occurrences and total link strength of 127. As already mentioned at the beginning of this section; the frequency is in line with expectations as it was one of the keywords used in Scopus database for the search of papers. Further this term has a link strength of 5 with another keyword ‘Generation Y’ in the database. A number of studies compare the social media marketing behaviour of Generation Y and Z (Florenthal 2019; Agrawal 2022; Damanik et al 2022 and Mude & Undale 2023). Specifically, the cluster focuses on the psychological well-being and mental health issues of the users as indicated by the keywords ‘Information Overload’ and ‘Mental Health’. Coker et al 2025 investigation issues like social media addiction and stress in Generation Y and Z while Liu et al (2021), Sharma et al (2023) and Sao et al (2024) study fear of missing out, anxiety, depression, stress and information overload in Generation Z.

3.3 Co-authorship-Country Analysis

For the next analysis, type and unit selected is co-authorship and countries. A threshold of at least one document was applied per country, with zero as limit was applied to minimum number of citations per country. This gives the number of countries selected as 55. Out of the 55 countries selected, many items are not connected to one another, the widest range set of connected items comprises of 36 items.

The analysis of these 36 countries is done on the basis of link strength (table 3). Link strength indicates collaborative (co-authorship) research work undertaken with other countries on the selected topic. Analysis reveals Malaysia is doing a great work in this field with 16 documents, 271 citations and maximum collaborations with the highest link strength of 20. It is followed by the United Kingdom at link strength of 19 and then United States is also not far behind with 34 documents and link strength of 11. Looking at India’s position, India is also also making considerable contribution in the field with 24 documents and link strength of 9. Link strength is also as low as 1 for some countries like Kuwait, Slovakia, Tunisia and others indicating minimal collaboration on part of authors of these countries.


Soc Sci J Adv Res 2025;5(4)54
Bansal N, et al. Influencer Driven Social Media as an Effective Marketing Strategy

Table 3: Co-authorship Country Analysis with Link Strength

CountryDocumentsCitationsTotal Link
Strength		Cluster
France5339151
South Korea4147141
Finland498131
New Zealand289121
Sweden3135121
Iran13771
South Africa325271
Bangladesh11621
Lebanon1011
United Kingdom131033192
Australia884172
Canada44192
Poland8742
Spain76742
Norway1622
Tunisia1112
United States34766113
Czech Republic3723
Kuwait1013
Latvia4723
Macao11113
Slovakia513013
China939564
Malaysia16271204
Philippines2624
United Arab Emirates32814
Singapore22634
Greece412445
Portugal510545
Viet nam99135
Lithuania23015
Ireland17915
India2435496
Fiji1426
Afghanistan1426
Saudi Arabia3016

ssjar_273_04.JPG
Figure 4: Co-authorship Countries Analysis

When these 36 items are grouped into clusters, 6 clusters are formed (Figure 4). A Cluster indicates the countries in the cluster tend to collaborate more often with each other. In cluster 1, there are 9 counties, with very high collaboration among authors of these countries, like France, Sweden, New Zealand and Finland. Cluster 2 also fairs well with Australia and United Kingdom taking the lead. Indian authors collaborate more with Afghanistan, Fiji and Saudi Arabia, though is India taking the lead among these countries.

4. Conclusions and Directions for Future Research

Generation Z is the first set of consumers that have used smartphones and social media from an early age. Their comfort with digitalization and technology makes them more likely to respond to social media marketing efforts. Consequently, it is important to observe how this generation interacts with social media and influencers and what factors shape their behaviour as consumers. However, the area of social media marketing is a recent one and Generation Z (having been born in late 90s) are the new focus group as consumers. Therefore, there is limited research in the area in the last 10 years as indicated by the small dataset used in the current research. This paper aims to identify the focus areas where research has been conducted and the potential areas which should be explored by the future researchers through bibliometric analysis.


Soc Sci J Adv Res 2025;5(4)55
Bansal N, et al. Influencer Driven Social Media as an Effective Marketing Strategy

Results indicate that many researchers focus on parasocial interaction and how it enhances influencer’s credibility. Parasocial interaction is also a factor which influences purchase intention. Another area where research is concentrated is branding and consumer engagement and their impact on purchase decision. Amongst the products/ services explored by researchers, sustainable and political consumerism are popular research areas. Gen Z’s tourism behaviour in the social media marketing framework is also found to be an area of marketer interest. Another cluster which emerges is the immersive marketing and the use of augmented reality and artificial intelligence in improving customer experience. A comparison of the different generations of consumers in their social media marketing behaviour is also highlighted as a key area. Some research also identifies the undesirable aspects of SMM especially on consumers’ mental well-being.

The present paper contributes by identifying the research gaps. Firstly, most of the studies have tried to identify the direct and moderating variables which affect the Generation Z consumer’s purchase intentions in marketing through social media (Alrwashdeh et al 2019; Hazari & Sethna 2022; Susanto et al 2024). However, a comprehensive conceptual model including all the relevant variables is still lacking. Secondly, there is limited research in the subject area especially in the Indian context. Though second in terms of contribution of volume of papers; India has contributed about only 24 papers in the last decade. Though some Indian studies explore the effect of social media and influencers on consumer behaviour (Vijaya et al 2023; Sharma & Sanu 2025), further research is required to develop a comprehensive model. Another gap that can be identified is that the past research has concentrated on consumer behaviour in few areas (tourism, sustainable and political consumerism etc). But the efficacy of influencer marketing may also be taken up in the context of fashion products, online gaming, food bloggers, investment in financial products etc. The effect of social media on consuming these products/ services is under-researched. Even in the case of tourism which turns up as a popular keyword in the results of co-occurrence analysis; the research papers are restricted to studies conducted outside India; so researchers may study the travel behaviour in the Indian context.

Thus, the study makes significant contribution by identifying the popular themes of research and also the research gaps which may be explored by the researchers.

References

1. Agrawal, D. K. (2022). Determining behavioural differences of Y and Z generational cohorts in online shopping. International Journal of Retail & Distribution Management, 50(7), 880-895. https://doi.org/10.1108/IJRDM-12-2020-0527.

2. Alrwashdeh, M., Emeagwali, O. L., & Aljuhmani, H. Y. (2019). The effect of electronic word of mouth communication on purchase intention and brand image: An applicant smartphone brands in North Cyprus. Management Science Letters, 9(4), 505-518. https://doi.org/10.5267/j.msl.2019.1.011.

3. Ameen, N., Cheah, J. H., & Kumar, S. (2022). It's all part of the customer journey: The impact of augmented reality, chatbots, and social media on the body image and self‐esteem of Generation Z female consumers. Psychology & Marketing, 39(11), 2110–2129. https://doi.org/10.1002/mar.21715.

4. Babu, E., Joseph, N. M., & Aboobaker, N. (2024). Unveiling the Impact of Influencer Attributes on Purchase Intention of Gen Z: The Moderating Role of Parasocial Interaction. Global Business Review, 09721509241280996. https://doi.org/10.1177/09721509241280996.

5. Bratina, D. (2024). An Investigation of Celebrities’ and Influencers’ Credibility Dimensions in Different Generations. Market-Tržište, 36(2), 191–211. https://doi.org/10.22598/mt/2024.36.2.191.

6. Bratina, D., & Faganel, A. (2024). Understanding Gen Z and Gen X responses to influencer communications. Administrative Sciences, 14(2), 33. https://doi.org/10.3390/admsci14020033.

7. Broadus, R. N. (1987). Toward a definition of “bibliometrics”. Scientometrics, 12(5–6), 373–379. https://doi.org/10.1007/bf02016680.

8. Buechel, E. C., & Berger, J. (2018). Microblogging and the value of undirected communication. Journal of Consumer Psychology, 28(1), 40–55. https://doi.org/10.1002/jcpy.1013.


Soc Sci J Adv Res 2025;5(4)56
Bansal N, et al. Influencer Driven Social Media as an Effective Marketing Strategy

9. Bulut, Z. A., & Karabulut, A. N. (2018). Examining the role of two aspects of eWOM in online repurchase intention: An integrated trust–loyalty perspective. Journal of Consumer Behaviour, 17(4), 407–417. https://doi.org/10.1002/cb.1721.

10. Caratù, M., Cherubino, P., Menicocci, S., & Martinez-Levy, A. C. (2024). Does sustainable communication have an impact on international social media audiences? A neuromarketing explorative study between Finland and Italy. Italian Journal of Marketing, 2024(1), 21–54. https://doi.org/10.1007/s43039-023-00086-z.

11. 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. Decision Support Systems, 107, 13–25. https://doi.org/10.1016/j.dss.2017.12.011.

12. Chiu, D. K., & Ho, K. K. (2023). Special selections on contemporary social media and social network research. Library Hi Tech, 41(3), 685–693. https://doi.org/10.1108/LHT-04-2023-586.

13. Confetto, M. G., Covucci, C., Addeo, F., & Normando, M. (2023). Sustainability advocacy antecedents: how social media content influences sustainable behaviours among Generation Z. Journal of Consumer Marketing, 40(6), 758–774. https://doi.org/10.1108/JCM-11-2021-5038.

14. Coker, K. K., Hale, D., AlSaleh, D. A., & Thakur, R. (2025). Social media addiction and stress: insights from US Facebook and TikTok consumers. Journal of Consumer Marketing, 42(3), 349-364. https://doi.org/10.1108/JCM-02-2024-6597.

15. Copeland, L., Lyu, J., & Han, J. (2023). Does familiarity matter? Examining model familiarity in Instagram advertisements. Journal of Internet Commerce, 22(1), 97–121. https://doi.org/10.1080/15332861.2021.2011600.

16. Correia, R., Aksionova, E., Venciute, D., Sousa, J., & Fontes, R. (2025). User-generated content’s influence on tourist destination image: a generational perspective. Consumer Behavior in Tourism and Hospitality. https://doi.org/10.1108/CBTH-11-2023-0208.

17. Damanik, J., Priyambodo, T. K., Wibowo, M. E., Pitanatri, P. D. S., & Wachyuni, S. S. (2022). Travel behaviour differences among Indonesian youth in Generations Y and Z: pre-, during and post-travel.

Consumer Behavior in Tourism and Hospitality, 18(1), 35-48. https://doi.org/10.1108/CBTH-07-2021-0184.

18. De Veirman, M., Cauberghe, V., & Hudders, L. (2017). Marketing through Instagram influencers: The impact of number of followers and product divergence on brand attitude. International Journal of Advertising, 36(5), 798–828. https://doi.org/10.1080/02650487.2017.1348035.

19. Dhanesh, G. S., & Duthler, G. (2019). Relationship management through social media influencers: Effects of followers’ awareness of paid endorsement. Public Relations Review, 45(3), 101765. https://doi.org/10.1016/j.pubrev.2019.03.002.

20. Djafarova, E., & Bowes, T. (2021). ‘Instagram made me buy it’: Generation Z impulse purchases in fashion industry. Journal of Retailing and Consumer Services, 59, 102345. https://doi.org/10.1016/j.jretconser.2020.102345.

21. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of business research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070.

22. Donthu, N., Reinartz, W., Kumar, S., & Pattnaik, D. (2020). A retrospective review of the first 35 years of the International Journal of Research in Marketing. International Journal of Research in Marketing. 38. https://doi.org/10.1016/j.ijresmar.2020.10.006.

23. Emich, K. J., Kumar, S., Lu, L., Norder, K., & Pandey, N. (2020). Mapping 50 Years of Small Group Research Through Small Group Research. Small Group Research, 51(6), 659–699. https://doi.org/10.1177/1046496420934541.

24. Florenthal, B. (2019). Young consumers’ motivational drivers of brand engagement behavior on social media sites: A synthesized U&G and TAM framework. Journal of Research in Interactive Marketing, 13(3), 351-391. https://doi.org/10.1108/JRIM-05-2018-0064.

25. Fong, S. F., Ong, Y. X., Ating, R., & Besa, M. S. (2025). Exploring the intermediary effects of TikTok on Generation Z’s visit intention. Journal of Vacation Marketing, 31(3), 736-752. https://doi.org/10.1177/13567667241229449.


Soc Sci J Adv Res 2025;5(4)57
Bansal N, et al. Influencer Driven Social Media as an Effective Marketing Strategy

26. Freberg, K., Graham, K., McGaughey, K., & Freberg, L. A. (2011). Who are the social media influencers? A study of public perceptions of personality. Public Relations Review, 37(1), 90–92. https://doi.org/10.1016/j.pubrev.2010.11.001.

27. Ghaleb, M. M. S., & Alawad, W. A. (2024). Social Media and Purchase Intentions of Gen Z: Role of Social Media Influencers. Transnational Marketing Journal, 12(1), 86-105. DOI: https://doi.org/10.58262/tmj.v12i1.1006.

28. Hazari, S., & Sethna, B. N. (2022). A Comparison of Lifestyle Marketing and Brand Influencer Advertising for Generation Z Instagram Users. Journal of Promotion Management, 29(4), 491–534. https://doi.org/10.1080/10496491.2022.2163033.

29. Hazzam, J. (2022). The moderating role of age on social media marketing activities and customer brand engagement on Instagram social network. Young Consumers, 23(2), 197-212. https://doi.org/10.1108/YC-03-2021-1296.

30. Huwaida, L. A., Yusuf, A., Satria, A. N., Darmawan, M. A., Ammar, M. F., Yanuar, M. W., Hidayanto A.N & Yaiprasert, C. (2024). Generation Z and Indonesian social commerce: Unraveling key drivers of their shopping decisions. Journal of Open Innovation: Technology, Market, and Complexity, 10(2), 100256. https://doi.org/10.1016/j.joitmc.2024.100256.

31. Iványi, T. (2021). Analysis of sources for information gathering before visiting a festival in the case of Generation Z. Periodica Polytechnica. Social and Management Sciences, 29(2), 196. https://doi.org/10.3311/PPso.15947.

32. Iványi, T., & Bíró-Szigeti, S. (2021). Understanding visitors’ motivations of sharing experiences related to Hungarian tourism events and festivals. Argumenta Oeconomica, 47(2), 81-106. https://doi.org/10.15611/aoe.2021.2.05.

33. Joshi, H. (2025). Integrating trust and satisfaction into the UTAUT model to predict Chatbot adoption–A comparison between Gen-Z and Millennials. International Journal of Information Management Data Insights, 5(1), 100332. https://doi.org/10.1016/j.jjimei.2025.100332.

34. Jiang, Y., Lyu, C., Chen, W., & Wen, J. (2025). Following mega sports events on social media impacts Gen Z travelers’ sports tourism intentions: the case of the 2022 Winter Olympic Games. Tourism Recreation Research, 50(2), 282-296. https://doi.org/10.1080/02508281.2023.2274155.

35. Ki, C. W. C., Park, S., & Kim, Y. K. (2022). Investigating the mechanism through which consumers are “inspired by” social media influencers and “inspired to” adopt influencers’ exemplars as social defaults. Journal of Business Research, 144, 264–277. https://doi.org/10.1016/j.jbusres.2022.01.071.

36. Kholkina, V., Chesnokova, E., & Zelenskaya, E. (2025). Virtual or human? The impact of the influencer type on Gen Z consumer outcomes. Journal of Product & Brand Management, 34(1), 104–118. https://doi.org/10.1108/JPBM-12-2023-4885.

37. Kim, D. Y., & Kim, H. Y. (2022). Social media influencers as human brands: an interactive marketing perspective. Journal of Research in Interactive Marketing, 17(1), 94–109. https://doi.org/10.1108/JRIM-08-2021-0200.

38. Kitsios, F., Mitsopoulou, E., Moustaka, E., & Kamariotou, M. (2022). User-Generated Content behavior and digital tourism services: A SEM-neural network model for information trust in social networking sites. International Journal of Information Management Data Insights, 2(1), 100056. https://doi.org/10.1016/j.jjimei.2021.100056.

39. Kyroglou, G., & Henn, M. (2022). On measuring political consumerism: An exploratory study among young people in the UK and in Greece. Social Indicators Research, 163(3), 1191–1220. https://doi.org/10.1007/s11205-022-02931-8.

40. Lee, K., Lee, B., & Oh, W. (2015). Thumbs up, sales up? The contingent effect of Facebook likes on sales performance in social commerce. Journal of Management Information Systems, 32(4), 109–143. https://doi.org/10.1080/07421222.2015.1138372.

41. Li, X., & Feng, J. (2022). Engaging social media influencers in nation branding through the lens of authenticity. Global Media and China, 7(2), 219–240. https://doi.org/10.1177/20594364221094668.


Soc Sci J Adv Res 2025;5(4)58
Bansal N, et al. Influencer Driven Social Media as an Effective Marketing Strategy

42. Liu, H., Liu, W., Yoganathan, V., & Osburg, V. S. (2021). COVID-19 information overload and generation Z's social media discontinuance intention during the pandemic lockdown. Technological Forecasting and Social Change, 166, 120600. https://doi.org/10.1016/j.techfore.2021.120600.

43. Lusianingrum, F. P. W., & Pertiwi, W. N. B. (2023). Applying Stimulus-Organism-Response (SOR) Adoption for Predicting Generation Z’s Intention to Visit Tourism in Indonesia. Qubahan Academic Journal, 3(4), 277-288. https://doi.org/10.58429/qaj.v3n4a180.

44. Molina-Prados, A., Munoz-Leiva, F., & Prados-Peña, M. B. (2022). The role of customer brand engagement in the use of Instagram as a “shop window” for fashion-industry social commerce. Journal of Fashion Marketing and Management: An International Journal, 26(3), 495-515. https://doi.org/10.1108/JFMM-12-2020-0275.

45. Maghraoui, S., & Khrouf, L. (2025). Instagram live-streamings: how does influencer–follower congruence affect Gen Z trust, attitudes and intentions?. Young Consumers, 26(1), 150–169. https://doi.org/10.1108/YC-03-2024-2045.

46. Mude, G., & Undale, S. (2023). Social media usage: A comparison between Generation Y and Generation Z in India. International Journal of E-Business Research (IJEBR), 19(1), 1-20. DOI: https://doi.org/10.4018/IJEBR.317889.

47. Ngo, T. T. A., Bui, C. T., Chau, H. K. L., & Tran, N. P. N. (2023). The effects of social media live streaming commerce on Vietnamese Generation Z consumers’ purchase intention. Innovative Marketing, 19(4), 269-283. doi: https://doi.org/10.21511/im.19(4).2023.22.

48. Obembe, D., Kolade, O., Obembe, F., Owoseni, A., & Mafimisebi, O. (2021). Covid-19 and the tourism industry: An early stage sentiment analysis of the impact of social media and stakeholder communication. International Journal of Information Management Data Insights, 1(2), 100040. https://doi.org/10.1016/j.jjimei.2021.100040.

49. Olajide, O., Pandey, S., & Pandey, I. (2024). Social media for investment advice and financial satisfaction: Does generation matter?. Journal of Risk and Financial Management, 17(9), 410. https://doi.org/10.3390/jrfm17090410.

50. Ortiz, J. A. F., De Los M. Santos Corrada, M., Lopez, E., Dones, V., & Lugo, V. F. (2023). Don't make ads, make TikTok’s: media and brand engagement through Gen Z's use of TikTok and its significance in purchase intent. Journal of Brand Management, 30(6), 535-549. https://doi.org/10.1057/s41262-023-00330-z.

51. Pritchard, A. (1969). Statistical bibliography or bibliometrics?. Journal of Documentation, 25(4), 348–349. https://doi.org/10.1108/eb026482.

52. Popşa, R. E. (2024). Exploring the Generation Z Travel Trends and Behavior. Studies in Business and Economics, 19(1), 189-189. https://doi.org/10.2478/sbe-2024-0010.

53. Reinikainen, H., Munnukka, J., Maity, D., & Luoma-Aho, V. (2020). ‘You really are a great big sister’ – Parasocial relationships, credibility, and the moderating role of audience comments in influencer marketing. Journal of Marketing Management, 36(3–4), 279–298. https://doi.org/10.1080/0267257X.2019.1708781.

54. Sadovykh, V., Sundaram, D., & Piramuthu, S. (2015). Do online social networks support decision-making?. Decision Support Systems, 70, 15–30. https://doi.org/10.1016/j.dss.2014.11.011.

55. Sao, R., Chandak, S., Barhate, B., & Mondal, S. (2024). Social Media and Gen Z's Mental Well-Being: Impact of Excessive Usage on Anxiety, Stress, and Depression Levels Analysis. PURUSHARTHA-A journal of Management, Ethics and Spirituality, 17(1), 23-38. https://doi.org/10.21844/16202117102.

56. Sethuraman, P. (2023). Social Media's effect on millennials and generation Z's green purchasing habits. International Journal of Professional Business Review, 8(5), 16. https://doi.org/10.26668/businessreview/2023.v8i5.1470.

57. Sesar, V., Hunjet, A., & Martinčević, I. (2023). Generation Z Purchase Intentions: Does Sponsorship Disclosure Matter?. Business Systems Research: International journal of the Society for Advancing Innovation and Research in Economy, 14(2), 158-172. https://doi.org/10.2478/bsrj-2023-0017.


Soc Sci J Adv Res 2025;5(4)59
Bansal N, et al. Influencer Driven Social Media as an Effective Marketing Strategy

58. Seyfi, S., Hall, C. M., Vo-Thanh, T., & Zaman, M. (2023). How does digital media engagement influence sustainability-driven political consumerism among Gen Z tourists?. Journal of Sustainable Tourism, 31(11), 2441–2459. https://doi.org/10.1080/09669582.2022.2112588.

59. Sharma, R., & Sanu, K. (2025). Impact of influencer marketing on buying behaviour of millennials and generation Z. International Journal of Electronic Customer Relationship Management, 15(1-2), 1-23. https://doi.org/10.1504/IJECRM.2025.145710.

60. Sharma, M., Kaushal, D., & Joshi, S. (2023). Adverse effect of social media on generation Z user's behavior: Government information support as a moderating variable. Journal of Retailing and Consumer Services, 72, 103256. https://doi.org/10.1016/j.jretconser.2023.103256.

61. Singh, B., & Dagur, A. (2022). Understanding influence of social media marketing of masstige fashion brands on generation Z female Indian consumers. Indian Journal of Marketing, 52(5), 8-24.

62. Sinha, J. I. (2024). Tapping Generation Z micro-influencers for marketing and branding. Journal of Brand Strategy, 13(4), 391–401. https://doi.org/10.69554/MKSO7650.

63. Sinha, M., & Srivastava, M. (2025). Driving customer brand engagement through augmented reality-induced influencer content among Gen Z: The moderating role of credibility. Journal of Global Fashion Marketing, 1–18. https://doi.org/10.1080/20932685.2024.2441768.

64. Susanto, E., Gaffar, V., Disman, D., & Furqon, C. (2024). Influencing Travel Decisions: Social Media's Role in Destination Perception and Visit Intentions Ek. African Journal of Hospitality, Tourism and Leisure, 13(3), 531-539. https://doi.org/10.46222/ajhtl.19770720.537.

65. Thangavel, P., Pathak, P., & Chandra, B. (2022). Consumer decision-making style of Gen Z: A generational cohort analysis. Global Business Review, 23(3), 710–728. https://doi.org/10.1177/0972150919880128.

66. Van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. scientometrics, 84(2), 523-538. DOI https://doi.org/10.1007/s11192-009-0146-3.

67. Vernuccio, M., Boccalini, S., & Patrizi, M. (2025). “Being” with the brand in the metaverse: Strengthening brand anthropomorphism to foster brand love. Journal of Retailing and Consumer Services, 84, 104204. https://doi.org/10.1016/j.jretconser.2024.104204.

68. Vijaya, M. Thirumagal, Yamuna, S. M. and Chitra, B. (2023). Gen Z versus Millennials, purchase intentions: A comparative study based on social media marketing strategies in India. In the Applied Marketing Analytics: The Peer-Reviewed Journal, 9 (1). https://doi.org/10.69554/JCGH3759.

69. Wang, G., Musa, R., & Kou, S. (2024). Research on the Green Purchase Intentions and Driving Factors of Generation Z in Developing Countries. Malaysian Journal of Consumer and Family Economics, 33, 62-92. https://doi.org/10.60016/majcafe.v33.02.

70. Wallace, E., Torres, P., Augusto, M., & Stefuryn, M. (2022). Do brand relationships on social media motivate young consumers’ value co-creation and willingness to pay? The role of brand love. Journal of Product & Brand Management, 31(2), 189-205. https://doi.org/10.1108/JPBM-06-2020-2937.

71. Wiedmann, K. P., & von Mettenheim, W. (2020). The interaction of consumer, endorser, and brand personality in social influencer marketing: An abstract. In S. Stieler (Ed.), Marketing Opportunities and Challenges in a Changing Global Marketplace: Proceedings of the 2019 Academy of Marketing Science (AMS) Annual Conference (pp. 359–360). Springer. https://doi.org/10.1007/978-3-030-46046-8_124.

72. Wright, L., & Cherry, M. (2023). How does influencer marketing on TikTok impact brand trust among generation Z?. Journal of Digital & Social Media Marketing, 10(4), 326–339. https://doi.org/10.69554/DKNY2861.

Footnotes:

[1] Chadegani et al., 2013 observed that Scopus is the largest database for multidisciplinary scientific works of literature. Therefore, it is used for drawing the data for analysis.

[2] “Political consumerism refers to the conscious and deliberate promotion of ethically and environmentally sustainable products , alos taking into account political cpnsiderations (Kyroglou & Henn, 2022).


Soc Sci J Adv Res 2025;5(4)60
Bansal N, et al. Influencer Driven Social Media as an Effective Marketing Strategy
Disclaimer / Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of Journals and/or the editor(s). Journals and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Soc Sci J Adv Res 2025;5(4)61