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. 2025 Jan 27;13:1343. Originally published 2024 Nov 8. [Version 3] doi: 10.12688/f1000research.157553.3

Does Brand Attitude Complement Influencer Credibility in Shaping Purchase Intention of Indian GenZ Consumers?

Pranav Vilas Chavare 1, Smitha Nayak 2,a, Ramona Birau 3, Varalakshmi Alapati 4
PMCID: PMC11795352  PMID: 39911880

Version Changes

Revised. Amendments from Version 2

Few grammatical errors indicated by Reviewer 2 have been incorporated.  In the methodology section, clarity on the 5 point Likert scale and Cronbach alpha is presented and same has been responded to reviewers as well.

Abstract

Introduction

The social media landscape has radically changed and has revolutionized consumer perspectives, purchasing habits, and behaviors. Amidst this emerging trend is the rise of influencer marketing and its impact on the purchase intentions of followers. The objective is to explore the characteristics of influencers that contribute to their credibility. This research aims to explore the role of consumers’ attitude toward brands on their intention to adopt brands endorsed by influencers.

Methods

This cross-sectional research was undertaken among GenZ in the urban landscape of India. Data collected was analyzed using SmartPLS4 software.

Findings

Trust, expertise, and similarity were the significant antecedents of the formation of influencer credibility. Attractiveness did not have a significant influence on influencer credibility. A complementary partial mediation of Attitude towards a brand is observed in the association between influencer credibility and the purchase intention of followers. Attitude towards the video also had a significant positive influence on purchase intention.

Conclusion

The study found that Gen Z places little importance on an influencer’s attractiveness, as it has no significant impact on credibility. However, attitude toward the brand strongly influenced purchase intention and partially mediated the relationship between influencer credibility and purchase intention.

Keywords: influencer credibility, purchase intention, attitude towards brand, India

1. Introduction

The social media landscape has undergone radical changes over the past few years. It has become an important part of people’s everyday routines and significantly influences cultural aspects of society. It has revolutionized consumer perspectives, purchasing habits, and behaviors ( Dwivedi et al., 2021). In today’s technology-driven world, social networking platforms have become an avenue where brands can extend their marketing campaigns to a wider range of consumers. Social media has transformed the way businesses connect with their audience, and India is no exception to this digital revolution social media platforms such as Facebook, YouTube, Instagram, and Twitter have become the grounds for brands to engage with customers, build relationships, and drive sales. India has embraced the Internet, and its digital population has grown rapidly over the past decade, reaching 600 million active Internet users ( www.statista.com/, n.d.). Access to social media has also enhanced the time spent by users on social media daily. A report published by The Indian Institute of Management Ahmedabad on “New Age Digital Media Consumption: A survey of Social Media, OTT Content Online Gaming” reported that Indians, on average, spent 194 minutes daily on social media platforms ( The Esya Centre and IIM Ahmedabad, 2023) and most of them were in the age group of 18 to 34 years of age ( GWI, 2023). This forms a strong foothold for brands to emphasize digital marketing strategies to this specific age group. Amidst this emerging trend is the growth of a concept called marketing influencers. Marketing Influencers are defined as “people who have the power to affect the purchase decisions of others due to their real or perceived authority, knowledge, position, or relationship” ( Mavrck, 2014). A further classification of influencers based on the number of followers they have helps different businesses choose which influencers to partner with. While macro-influencers can claim millions of followers, micro-influencers usually have less than 100,000. Micro-influencers are everyday people who post meaningful content with a significant impact on social media sites such as Facebook, Instagram, and YouTube. Social media organizations and celebrities are examples of macro-influencers. In the present digital landscape, consumers seek opinions from their peers (other consumers) and marketing influencers to make informed purchase decisions. Thus, influencers play a major role in shaping consumer purchase decisions and brand choices ( Anjali Chopra and Jaju, 2020).

Influencer marketing has been used more recently, but there hasn’t been a single scholarly definition of it—especially when it comes to India ( Johansen and Guldvik, n.d.). Based on the number of followers they have, around 4 million influencers in India are divided into four categories: elite, mega, macro, and micro as of June 2023 ( Katya Naydu, 2023). About 118 million Indians have purchased goods or services that influencers have promoted, according to Roshan Behera (2023). The authors decided to look into the influencer marketing scene in India, specifically from the perspective of Generation Z (those born between 1997 and 2012), given the growing importance of influencers in the country and the paucity of scholarly research on the subject. Further, on social media, Gen Z users are more likely to be drawn to content about fashion, travel, and movies. According to a demographic comparison, millennials follow 52% of Instagram influencers, whereas Gen Z members follow about 97.9% of them ( Bandwagon Online, 2023). This highlights the importance of influencer marketing, especially when aimed at the Gen Z demographic in India. Although the influencers themselves have a charm factor, an important question is what makes their content truly appealing. The answer to this question is perhaps the relationship between the influencer’s credibility, the audience’s views towards the brand, and their attitude toward the content. This study aims to understand the connection between these factors and how the audiences’ attitudes mediate the influencers’ credibility and purchase intention. The objective is to analyze the influencer marketing sector in India and find insights into the characteristics of influencers that contribute to their credibility. Furthermore, study the mediating effect of the audiences’ attitude toward the brand on the relationship between influencer credibility and purchase intention. The proposed conceptual framework is presented in Figure 1.

Figure 1. Proposed conceptual framework.

Figure 1.

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Source: Author’s own.

The subsequent sections of the manuscript are structured as follows: the hypothesis development section presents literature related to the context of the present research endeavour and aids in conceptualizing the proposed conceptual model. The methodology section highlights the study setting, sampling procedure, and the research instrument development process. The results section presents the data analysis, followed by the discussion section and theoretical and managerial implications. The last section of the manuscript presents directions for future research and the limitations of the study.

2. Literature review & hypotheses development

2.1 Attractiveness

Research reveals a positive link between the perceived attractiveness of a social media influencer and consumers’ likelihood to purchase the advertised cosmetic product. This attractiveness-purchase intention connection likely stems from several factors: grabbing attention, triggering positive emotions, creating social influence, and boosting perceived trust in the influencer’s opinion. However, the strength of this link can vary. Younger consumers or those who highly value physical appearance might be more susceptible to attractiveness pull. Hence it would be worth exploring this association between the attractiveness of the influencer and

H1:

The attractiveness of social media influencers has a significant positive impact on the credibility of the influencer.

2.2 Expertise

The capacity of a communicator to make accurate representations in a certain field of knowledge is referred to as expertise ( Anthony R. Pratkanis, 2011). It includes knowledge, understanding, and experience gained from ongoing work in the same profession. A communicator must have in-depth knowledge, applicable abilities, or a respectable title that establishes credibility in their chosen field to be acknowledged as an expert ( Teresa Borges-Tiago et al., 2023). Consumers are more likely to buy a product endorsed by an influencer they perceive as knowledgeable and experienced, suggesting expertise builds trust and guides informed buying decisions. This knowledge-driven trust stems from several factors: reduced purchase risk due to the influencer’s proven experience, empowered decision-making through clear product explanations, and a boost in overall credibility based on their expertise ( Ashraf et al., 2023; Masuda et al., 2022). However, the strength of this connection can vary depending on factors like product type (expertise’s impact is stronger for complex products), target audience (consumers valuing knowledge are more influenced), and influencer niche (expertise relevant to the product matters most) ( Saima & Khan, 2020). So, expertise strengthens influencer credibility thereby converting likings into purchases. Understanding these dynamics among the GenZ followers would be worth exploring as GenZ is believed to be significantly influenced by peer reviews ( Costa E Silva et al., 2017).

H2:

The expertise of the Influencer has a significant impact on the Credibility of the Influencer.

2.3 Similarity

Similarity implies the extent to which the audience resembles the communicator ( McCracken, 1989). A study was undertaken by Naderer et al. (2021) Documented that similarity with the influencer had a direct positive effect on the purchase intention of the promoted brand. This means that when the influencer showed a brand and a disclosure, it increased the participants’ intention to buy the product compared to not seeing any brand presentation. Furthermore, the researchers found that the trustworthiness of the influencer played a significant role in mediating the relationship between similarity and purchase intention. This is because brand information from someone similar to you is usually regarded as more genuine and credible than, say, a message from the business itself ( Willemsen et al., 2012).

H3:

The similarity of the Influencer with the target audience has a significant positive influence on the credibility of the influencer.

2.4 Trustworthiness

Trust represents an individual’s belief or willingness to depend upon, an attitude-related object or phenomenon ( Ki et al., 2023). Trust in the context of influencer marketing can be defined as the influencer’s audience’s belief that the influencer is honest, transparent and genuinely supports the endorsed brand. An influencer can enhance their trust in the audience by being authentic in their content and interactions. In the absence of physical interaction, trust becomes a cornerstone for customers to establish trust in online activities ( Gibreel et al., 2018; Ventre & Kolbe, 2020). The audience is more likely to engage and show an enhanced purchase intention when they trust the parties involved. This is applicable in social media, e-commerce, online reviews, etc. ( Ventre & Kolbe, 2020). Confirmed the positive relationship between trust and purchase intention in the context of online reviews. Overall, trust is an important factor contributing to the success of influencer marketing ( Sokolova & Kefi, 2020).

H4:

Trust is a significant positive antecedent of Influencer credibility.

2.5 Attitude toward video

Greer (2003) found that people’s opinions about the stories they read are influenced by their perception of the reliability of the material they get online. When it comes to health communication, people’s opinions on online health information are greatly influenced by the websites’ perceived legitimacy ( Wang et al., 2008). In the case of advertising, the literature currently in publication indicates a favorable relationship between customer perceptions of the trustworthiness of endorsers and their endorsements ( Goldsmith et al., 2000). It was established that a favorable correlation between attitudes toward website sponsors and their perceived credibility. According to research by Long and Chiagouris (2006), website users’ opinions of a non-profit organization are greatly influenced by their perception of the website’s legitimacy. Hence in the digital space, it would be worthwhile to explore if the attitude towards the video of the social media influencer influences the purchase intention of their followers.

H5:

Attitude towards the video has a positive influence on purchase intention.

2.6 Attitude towards Brand

An attitude-towards-the-ad model was developed by McCracken (1989). These factors influence consumers’ perceptions of advertisements. The empirical study’s conclusions imply that consumers’ perceptions of advertisers and advertising are positively impacted by their perceived believability ( Aaker et al., 2012). Established that admired brands motivate consumers to purchase more from the brand. According to Roberts (2005), Brands gain the respect of consumers by performing well, which eventually leads to the development of a positive reputation. According to a research project by Trivedi & Sama (2020), consumer-favourite brands enjoy favorable relationships with their customers. Therefore, it may be necessary to investigate the extent to which brand attitude influences followers of social media influencers’ purchase intentions directly and through mediation.

H6:

Influencer credibility has a positive influence on Attitude toward the brand.

H7:

Attitude toward the brand positively influences purchase intention.

H9:

Attitude toward brand mediates the association between influencer credibility and purchase intention.

2.7 Influencer credibility

Influencers on social media are essential for improving marketing campaigns in the digital era. Choosing influencers wisely about the target market can have a big impact on audience purchase intentions and increase brand recognition. Purchase intention, according to J. P. Trivedi (2018) and Shah and Pillai (2012), arises from consumers’ comprehension of the thinking behind the choices they make regarding brands ( Ki et al., 2023). Claim that it illustrates a scenario in which consumers are likely to purchase a product in a given setting. By using influencers in their marketing campaigns, marketers can positively affect the brand by strengthening the image the influencers have established and enhancing product recognition. Customers are more likely to purchase the promoted products when influencers with a strong connection to the target market are used ( Lim et al., 2017). This finding aligns with a study by Singh and Banerjee (2018). Which emphasizes the ability of influencers to attract clients and stimulate purchase intentions through effective communication with their audience. Hence, this research endeavor intends to explore the effect of influencer credibility on the purchase intention of the followers.

H8:

Influencer Credibility has a significant positive influence on the purchase intention of followers.

3. Methods

3.1 Study design

The quantitative research methodology used in this study is called “cross-sectional research design.” “Post-positivism,” which emphasizes the development of empirical tools to interpret and evaluate human behaviour, is the fundamental research philosophy. The goal of the inquiry is to investigate the links that are described in the conceptual framework between independent, dependent, and mediating factors. This marketing study was exempt from submission to the Institutional Ethics Committee of Manipal Academy of Higher Education, by clause 6 of the circular titled “Project Exemption from Submission to IEC,” issued by the Institutional Ethics Committee on 14/01/2021.

3.2 Study setting & eligibility criteria

A study undertaken by BrandWagon Online (2023) indicated that the Indian social media landscape, especially on Instagram, is cluttered by the Gen Z segment of the population, and 97.9 percent of them follow influencers. Hence, respondents for this study are restricted to Gen Z of the Indian Population. Additionally, results reported by Statista on “Use of social media platforms in India” read that 59 percent of the rural population in India did not use social media platforms and the same figure stood at 33 percent for the urban population. It is also revealed that 28 percent of the urban regions accessed at least 6 social media applications. Hence, this study is restricted to respondents from the urban regions of India only. The third inclusion criterion to respond to the questionnaire was that they had to follow influencers on social media platforms and have purchased at least one brand in the last 6 months endorsed by the influencer. These three inclusion criteria ensured that the response was obtained from a well-focused segment. A Google form was created to capture data from respondents. The respondents were contacted directly by the researchers, who additionally on the study’s objectives and guaranteed that their information would only be used for academic purposes. As a result, the participants were given access to the Google form, which they were asked to fill out and submit online. Data collection was undertaken from November 2023 to January 2024.

3.3 Data measurement

A structured questionnaire was designed to collect data from the respondents. Scales to measure the underlying constructs {Purchase Intention ( Mahrous & Abdelmaaboud, 2017); Expertise ( Spake & Megehee, 2010); Attractiveness ( A. Dwivedi et al., 2015); Similarity ( Casaló et al., 2020); Trust ( Mahrous & Abdelmaaboud, 2017); Credibility ( Xiao et al., 2018); Attitude towards Brand ( Spears & Singh, 2004); Attitude Toward Advertisement ( Voss et al., 2003)} were adapted and measured on 5-point Likert, as measured by the source authors. The questionnaire consisted of 29 questions including the inclusion criteria & demographic details. A Google form was generated and circulated to capture their response from the identified segment of respondents. They were informed that the data collected would be kept confidential and would be utilized only for academic purposes.

3.4 Sample size

This study adopted a purposive sampling technique. Data was collected from students of Manipal Academy of Higher Education, and the students are the most representative of the target audience for this research. The sample size was estimated based on the number of indicators in the study by ten ( Sarstedt et al., 2017) i.e.: 29 *10 = 280. It further rounded off to 300. Finally, data was collected from 300 respondents.

3.5 Statistical methods

Microsoft Excel (RRID:SCR_016137 available at https://www.microsoft.com/en-gb/) was used to undertake descriptive analysis and Smart PLS software (SmartPLS, RRID: SCR_022040) was used to analyze the underlying constructs of the study and assess the mediation analysis. The Partial Least Square Structural Equation Modelling technique was used to test the additive and linear models.

3.6 Pilot testing

Before the completed questionnaire was sent out to participants to undertake a pilot study, two experienced researchers reviewed it to ensure face validity. The input gathered in this approach helped to improve the instrument’s readability. Further, to ensure the internal consistency of the scales, a pilot study was undertaken with 40sample response data. Cronbach alpha of all constructs of the study was above o.7 (Attitude towards brand = 0.956; Attitude towards video 0.938; Purchase Intention = 0.845; Attractiveness of Influencer = 0.777; Credibility = 0.934; Influencer Experience = 0.882; trust = 0.826) except of Similarity with the Influencer was below the threshold value of 0.7. In social science research, a construct with a Cronbach alpha above 0.6 in a pilot study could be retained as it is expected the increase with an increment in sample size. Hence the research team decided to retain the construct and explore it further during the final data analysis phase.

4. Results

To investigate the proposed correlations, we used maximum-likelihood estimation in structural equation modeling (SEM). According to Hair (2009), SEM was selected because of its capacity to evaluate the relationships between latent and observable variables directly.

The construct’s reliability and validity were established by assessing Cronbach’s alpha, rhoa, and composite reliability, as outlined in Table 1. The computed rhoa value surpassing the 0.7 cut-offs indicates ( Hair et al., 2010) robust reliability. Composite reliability is adopted to examine the internal consistency of the constructs and all values are above the threshold limit ( Werts et al., 1974). Cronbach alpha estimates reveal reliability of the all the indicators as all figures were above the threshold value of 0.7 ( Ken Kwong-Kay Wong, 2013). Convergent validity was established using Average Variance Extracted (AVE). All constructs have an AVE above the threshold of 0.5 ( Richard P. Bagozzi and Youjae Yi, 1988). Collinearity among the indicators was examined in the VIF estimates of the constructs. All the VIF values were less than 0.5 ( Sarstedt et al., 2017) no collinearity among the constructs of the study. Similarly, the absence of collinearity among the constructs of the study was established where the VIF values of the inner model of all the exogenous constructs were less than 3.3 ( Joseph F. Hair et al., 2017). This also ensures the absence of common method bias impacting the data ( Kock, 2015). Evaluation of the Structure Model is presented in Figure 2. Indicating Discriminant validity was confirmed using the Fornell and Larcker methodology, as depicted in Table 2.

Table 1. Evaluation of structural model.

Construct Indicators Outer loadings AVE Cronbach Alpha rho_a rho_c Outer weights VIF
Attitude towards Brand (AB) AB1 0.842 *** 0.777 0.928 0.930 0.946 0.233 *** 1.746
AB2 0.881 *** 0.210 *** 1.746
AB3 0.886 *** 0.215 *** 2.341
AB4 0.902 *** 0.240 *** 3.180
AB5 0.896 *** 0.236 *** 3.322
Attractive ness (AT) AT1 0.923 *** 0.826 0.791 0.803 0.905 0.491 *** 3.300
AT2 0.894 *** 0.408 *** 3.214
Attitude Towards Video (AV) AV1 0.838 *** 0.752 0.918 0.934 0.938 0.188 *** 1.902
AV2 0.880 *** 0.193 *** 2.713
AV3 0.910 *** 0.281 *** 2.702
AV4 0.845 *** 0.266 *** 2.846
AV5 0.863 *** 0.223 *** 2.954
Credibility (C) C1 0.780 *** 0.732 0.908 0.912 0.932 0.205 *** 1.890
C2 0.873 *** 0.244 *** 2.111
C3 0.866 *** 0.231 *** 1.703
C4 0.879 *** 0.252 *** 1.675
C5 0.875 *** 0.236 *** 1.568
Expertise € E1 0.861 *** 0.739 0.823 0.823 0.895 0.397 *** 1.901
E2 0.881 *** 0.385 *** 2.578
E3 0.836 *** 0.382 *** 3.181
Purchase Intention (PI) PI1 0.870 *** 0.703 0.792 0.826 0.876 0.471 *** 3.158
PI2 0.766 *** 0.295 *** 2.325
PI3 0.874 *** 0.416 *** 2.875
Similarity (S) S1 0.747 *** 0.611 0.683 0.687 0.825 0.391 *** 1.343
S2 0.814 *** 0.413 *** 1.494
S3 0.783 *** 0.475 *** 1.264
Trust (T) T1 0.821 *** 0.656 0.743 0.772 0.851 0.433 *** 1.474
T2 0.752 *** 0.302 *** 1.467
T3 0.852 *** 0.490 *** 1.488
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***

p<0.01.

Figure 2. Structural Model.

Figure 2.

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Table 2. Discriminant validity was confirmed using the Fornell and Larcker method.

Constructs AB AT AV C E PI S T
AB 0.882
AT 0.337 0.909
AV 0.515 0.396 0.867
C 0.458 0.362 0.386 0.856
E 0.301 0.288 0.319 0.567 0.860
PI 0.464 0.430 0.417 0.530 0.383 0.838
S 0.341 0.439 0.401 0.487 0.419 0.536 0.782
T 0.512 0.420 0.404 0.598 0.443 0.607 0.551 0.810
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Subsequently, the structural model was assessed by the bootstrapping technique and the hypothesis was tested. The structural wall was further evaluated with the help of the path coefficients ( Table 3). R 2 and Q 2 were obtained from the structural model. R 2 indicates that a model’s prediction ability should be more than 0.1 ( Falk and Miller, 1992). Trust, Expertise, Attractiveness, and similarity explained 48.7 percent of the variance for Influencer credibility. The credibility of the influencer explained a 20.9 percent variance of Attitude towards Brand and both these constructs explained a 36.6 percent variance of the outcome construct Purchase Intention. Furthermore, Q 2, which assesses the model’s predictive usefulness, ought to be greater than 0. The efficiency of the model was confirmed in the current investigation when both R 2 and Q 2 values were above the designated thresholds, thus the model’s predictive relevance was justified.

Table 3. Testing direct relationships.

Hypothesis Path coefficient Standard deviation (STDEV) t value (bootstrap) P values F 2 BI Status
H1: At -> C 0.062 0.054 1.135 0.256 0.006 0.043, 0.169 Not supported
H2:E -> C 0.342 *** 0.049 7.007 0.000 0.172 0.245, 0.437 Supported
H3: S -> C 0.123 * 0.059 2.095 0.036 0.018 0.011, 0.239 Supported
H4:T -> C 0.353 *** 0.054 6.549 0.000 0.149 0.245, 0.453 Supported
H5:ATV->PI 0.167 *** 0.060 2.771 0.006 0.031 0.052, 0.286 Supported
H6:C-> ATB 0.458 *** 0.050 9.063 0.000 0.265 0.357, 0.554 Supported
H7:ATB-> PI 0.208 *** 0.056 3.697 0.000 0.045 0.099, 0.320 Supported
H8: C ->PI 0.370 *** 0.060 6.134 0.000 0.164 0.250, 0.484 Supported
R 2 AB = 0.209 Q 2 AB = 0.208
R 2 C = 0.487 Q 2 C = 0.465
R 2 PI = 0.363 Q 2 PI = 0.353
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Abbreviation: BI = bias-corrected.

***

p<0.01.

**

p<0.05.

*

p<0.1.

The results revealed that two exogenous constructs, Expertise (β = 0.342, t = 7.007 p = 0.000) & Trust (β = 0.353, t = 6.549, p = 0.000) had a significant positive impact on the credibility of the influencer. However, Similarity (β = 0.123, t = 2.095, p = 0.036) partially influenced the influencer credibility. Hypothesis 5 (Attitude towards Brand on Purchase Intention) and Credibility of Influencer on Attitude towards Brand were supported revealing two dimensions. Firstly, Attitude towards the Video (β = 0.167, t = 2.771, p = 0.006) has a significant positive association with to Purchase Intention of the followers, and secondly, Credibility of the Influencer (β = 0.458, t = 9.063, p = 0.000) had a significant positive influence on the Followers Attitude towards the Brand. Attitude towards the Brand (β = 0.208, t = 3.697, p = 0.000) and Credibility of the Influencer (β = 0.370, t = 6.134, p = 0.000) had a significant positive impact on the Purchase intention of the follower. Thus, H7 and H8 were also supported.

4.1 Mediating role of attitude towards the brand

A mediation analysis was performed to assess the mediating effect of ATB on the linkage between Credibility and PI of the followers. The results ( Table 4) revealed that the total effect of Credibility on PI was significant (β = 0.524, t = 10.305, p = 0.000). With the inclusion of the mediating variable.

Table 4. Mediation analysis.

Path Direct effect, p-value Total effect, p-value Specific indirect effect SD T Value p Value BI (2.5%, 97.5%) Mediation
H9: Credibility>Attitude towards Brand-> Purchase Intention 0.395, 0.000 0.524, 0.000 0.129, 0.000 0.031 4.201 0.000 0.077, 0.198 Complementary Partial
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The Standard Root Mean Square Residual (SRMR), with a suggested threshold of <0.8, was used to assess model fitness. ( Sarstedt et al., 2017). The model’s SRMR rating of 0.055 indicated that it suited the data quite well. Additionally, Q 2 was calculated to determine predictive relevance when the endogenous construct was greater than zero.

4.2 Importance Performance Matrix Analysis (IPMA)

IPMA’s result is presented as bootstrapping with 5,000 subsamples, demonstrating that all requirements for applying the IPMA approach are met and that some path coefficients are statistically significant. IPMA clarifies the comparative importance and effectiveness of exogenous (Trust, Expertise, Similarity, Attractiveness, Attitude towards Brand, Attitude towards the video, Influencers Credibility) on the endogenous construct (Purchase Intention). Their significance and efficacy are shown by their aggregate impacts and index values. The impact on the endogenous variables is significant. The intrinsic potential of the latent variable scores is demonstrated through effectiveness ( Table 5, Figure 3).

Table 5. Important-Performance Matrix Analysis of Behavior Intention.

Latent construct Importance (Total effect) Performance (Index value)
Att towards Brand 0.208 57.862
Attractiveness 0.029 62.887
Attitude towards Video 0.167 62.067
Credibility 0.466 52.412
Experience 0.159 61.272
Similarity 0.057 53.226
Trust 0.164 49.631
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Figure 3. Importance performance matrix.

Figure 3.

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5. Discussion

Influencer marketing has become a potent catalyst propelling brands’ social media investment in the ever-changing world of digital marketing. Influencers provide organizations with a means of accessing pre-existing communities and fostering genuine interactions with their target audiences. Influencers are frequently people with sizable and devoted online followings. Upholding the model proposed by Hovland et al. (1953) on Communication and persuasion, trust in the influencer significantly influences the credibility of the influencer. Similarity with the influencer and Expertise of the influencer is explored to have a significant role in determining the influencer’s Credibility. These findings are in line with the previously published literature on the construct of trust in the marketing influencer ( Johnsson, 2023; Saima & Khan, 2020; Trivedi & Sama, 2020; Patmawati & Miswanto, 2022). Digital media platforms provide internet users with substantial control over the information they share, which fosters a climate even conducive to the spread of misleading information, such as fake news. Internet users now place more emphasis on original information and reliable sources, highlighting the significance of real and honest information sources in the digital sphere.

Further Naderer et al., (2021) documented that similarity with the influencer had a direct positive effect on the purchase intention of the promoted brand. Additionally, a qualitative study undertaken by Johnsson (2023). Among GenZ of Bangladesh, it was documented that most of the followers compared themselves with celebrity influencers and tried to imitate them. This is not justified in our research context as attractiveness did not significantly influence the credibility of the influencer. Another study undertaken by Trivedi (2018) revealed a higher efficacy of attractiveness the social media influencers. Although the findings contradict the research findings of Johnsson (2023) and Trivedi (2018), it is noteworthy to observe that these studies are undertaken in different contexts. Trivedi (2018) documented research in the context of fashion influencers. However, it is also documented by Masuda et al. (2022) that the strength of this association can vary depending on the age group of the followers and the product category under consideration. Younger consumers or those who highly value physical appearance might be more susceptible to attractiveness pull. Similarly, luxury or aspirational products can have a stronger effect.

Attitude towards the Video content is documented to have a significant direct positive influence on purchase intention by the followers, leading to acceptance of H5. This finding is also affirmed by Trivedi (2018) that attitude towards the brand endorsed plays a significant role in determining the consumers’ purchase intention. This finding was significantly endorsed in the fashion marketing landscape. This strongly affirms the Theory of Reasoned Action ( Icek Ajzen, 1980).

Attitude towards the brand significantly influenced Purchase Intention and it partially mediated the association between influencer credibility and purchase intention leading to acceptance of H7 and H9. The result is consistent with previous research, supporting the important role that attitudes are shaped by perceived information credibility, as has been shown in studies by Masuda et al. (2022); Naderer et al. (2021) and Wang et al. (2008). This finding also raises questions about the possible value of using YouTube videos’ perceived legitimacy to forecast viewers’ purchase intentions for the products they showcase. The substantial correlation between attitudes and behavioral intentions is a link that Ajzen and Fishbein (1977). Thoroughly examined gives rise to this assumption.

6. Theoretical and managerial implications

This study is meaningful in its input to the theoretical understanding of using the Theory of Reasoned Action and the Tri Component Attitude Model in explaining the impact of relevant cues on influencer credibility and purchase intention. This research attempts to deepen knowledge of how GenZ influencer marketing in the context of the Indian market. This study closes a gap in the literature by examining the influence of social media influencers on purchase intentions in India, a topic that has not been well studied. By providing a thorough framework that clarifies the role of influencer qualities in influencing customer purchase intentions through the mediation of credibility, it advances our understanding of the subject. The importance of antecedents of the credibility of the influencer, namely expertise, trust, and similarity, has emerged in this research endeavor. The impact of influencer credibility on purchase intention is well documented and resonates outcomes of previous researchers. This study establishes the significance of the truthfulness and resemblance of the influencer to be effective with his or her followers. It is observed that the target segment (GenZ) gives less importance to the Attractiveness of the influencer and it was insignificant in influencing the credibility. In the backdrop of the Tri Component Attitude Model ( Rosenberg et al., 1960), this finding emphasizes the cognitive component of attitude formation. The Tripartite model, also recognized as the three components of attitude, encompasses affective, behavioural, and cognitive elements. Collectively, these elements create attitudes, with one element usually predominating over the others at any one time. The findings revealed that Attitude towards the brand has a significant positive influence on Purchase intention. The IPMA results also indicate that Attitude towards the brand is the second most important factor determining the purchase intention of followers. Similar previous studies ( J. P. Trivedi, 2018; J. Trivedi & Sama, 2020) have also highlighted the importance of attitude towards the brand in followers’ purchase intention. This strengthens our understanding of the need to build brands and consider influencers as mere enablers of decision-making. This is further validated by the mediation analysis performed in this study. A partial mediation is proved ( Table 4) which indicates that both the antecedents (attitude towards brand and influencer Credibility) have an important role to play in shaping followers’ purchase intention. The significant and positive impact of trustworthiness, similarity, and expertise on influencer credibility should inform advertisers or marketers to use trustworthiness as a criterion to evaluate the power of a social media influencer as a potential advertising partner. This project is also among one of the few studies to analyze the follower’s attitude towards video content on purchase intention. It indicates marketers to take special cognizance of this fact and encourages them to create defined standard operating procedures to be necessarily followed by the influencers. If social media influencer intends to continue their collaborations with advertisers, or wish to expand their followers they must connect with the followers.

7. Future directions and limitations

There are certain drawbacks to this study. It doesn’t distinguish between different kinds of videos or brands/products; instead, it concentrates on social media influencers and brands/products in a general way. Subsequent investigations ought to improve the accuracy of influencer marketing theories by examining particular video categories (e.g., office supplies, alcoholic beverages, gaming videos), alternative content formats (e.g., blog posts, audio podcasts, Instagram/Facebook photos), or product genres (e.g., product reviews, how-to tutorials, gaming videos). Further research could also be extended to influencer marketing across specific product categories. Furthermore, examining particular groups of micro or macro influencers may reveal differences in the ways that trustworthiness is influenced. Additionally, this study uses a single survey instrument and depends on a single source of consumers, which could introduce a single-source bias. To get over this restriction, future research should think about selecting participants from a variety of sources and using a range of techniques to look more deeply into the dynamics of social media influencer marketing and how consumers perceive the reliability of information.

Ethical approval and consent

This marketing study was exempt from submission to the Institutional Ethics Committee of Manipal Academy of Higher Education, by clause 6 of the circular titled “Project Exemption from Submission to IEC,” issued by the Institutional Ethics Committee on 14/01/2021.

Consent statement

No minors were included in the study. It clearly states that while collecting data, respondents were briefed, and then handed over the Google form. The first section of the Google form briefed the respondent again and only if they consent, they clicked the link and presented their consent.

Consent was taken Verbally and while filling questionnaire the following text was displayed first:

Dear Respondent, Marketing Influencers have become a part of our digital ecosystem.

In light of this pervasive influencer content that you actively follow, please address the following questions, reflecting on your exposure to these influencers and their messages. Your responses will be kept confidential. If you consent to provide data on your experience with marketing influencer content, click on the link below to proceed. Thank You"

The respondent has to consent before clicking on the questionnaire link. This is now having to be how consent was captured from each respondent.

Author contributions

  • •

    Study conception and design: Prof Smitha Nayak, Pranav Chavare, Dr Ramon Birau

  • •

    Data collection: Prof Smitha Nayak, Pranav Chavare,

  • •

    Analysis and interpretation of results: Prof Smitha Nayak, Pranav Chavare, Dr Varalakshmi Alapati

  • •

    Manuscript preparation. Prof Smitha Nayak, Pranav Chavare, Dr Ramona Birau, Dr Varalakshmi Alapati

Funding Statement

The author(s) declared that no grants were involved in supporting this work.

[version 3; peer review: 3 approved]

Data availability

Underlying data

Figshare: Influencer Marketing https://doi.org/10.6084/m9.figshare.27094330.v1 ( Nayak, 2024).

The project contains the following underlying data:

  • •

    Impact of Marketing Influencers Dataset

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

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F1000Res. 2025 Feb 18. doi: 10.5256/f1000research.177412.r367236

Reviewer response for version 3

Harjinder Kaur Balbir Singh 1

  1. Introduction

  • Strengths: The introduction provides a comprehensive overview of the topic and sets the stage for the research. It effectively highlights the importance of social media and influencer marketing in India.

  • Areas for Improvement: The introduction is somewhat lengthy. Consider condensing it to focus on the most critical points. Additionally, the transition between different ideas could be smoother to enhance readability. Suggest adding in the specific industry of the study in the title.

2.Literature Review and Hypotheses Development

  • Strengths: The literature review is thorough and covers a wide range of relevant studies. The hypotheses are clearly stated and logically derived from the literature.

  • Areas for Improvement: Some sections of the literature review read more like summaries rather than critical evaluations. Aim to provide more critical analysis of the studies reviewed. Additionally, ensure that each hypothesis is explicitly linked to the literature discussed. Better to have latest scholarly journals of 5 years data.

3. Methodology

     Study Design:

  • Strengths: The cross-sectional research design is appropriate for the study. The explanation of the research philosophy is clear.

  • Areas for Improvement: The explanation of the research philosophy could be more concise. Some sections are overly detailed and could be streamlined.

    Study Setting and Eligibility Criteria:

  • Strengths: The criteria for selecting respondents are well-defined and justified.

  • Areas for Improvement: The justification for focusing solely on urban Gen Z could be stronger. Discuss potential biases this might introduce and how they were mitigated.

 

    Data Measurement:

  • Strengths: The use of established scales for measuring constructs is commendable. The questionnaire design and data collection process are well-explained.

  • Areas for Improvement: Consider discussing potential biases in the questionnaire design. Were there any leading questions or ambiguities?

 

    Sample Size:

  • Strengths: The sample size calculation is appropriate, and the use of a purposive sampling technique is justified.

  • Areas for Improvement: The rationale for using a purposive sampling technique could be more critically discussed. Consider potential limitations of this approach.

    Statistical Methods:

  • Strengths: The use of SEM and other statistical techniques is appropriate for the analysis. The explanation of the methods is clear and detailed.

  • Areas for Improvement: Some sections could benefit from more detail on why specific methods were chosen. Discuss any assumptions made and how they were addressed.

4. Results

    Reliability and Validity:

  • Strengths: The assessment of reliability and validity is thorough. The use of Cronbach's alpha, rhoa, and composite reliability is appropriate.

  • Areas for Improvement: The discussion of reliability measures could be more critical. Were there any limitations in these measures?

Structural Model:

  • Strengths: The evaluation of the structural model is well-explained. The use of bootstrapping and the reporting of path coefficients, R2, and Q2 values are commendable.

  • Areas for Improvement: The presentation of results could be more concise. Some sections are overly detailed and could be streamlined.

Mediation Analysis:

  • Strengths: The mediation analysis is well-conducted and clearly presented.

  • Areas for Improvement: The interpretation of results could be more critical. Were there any limitations in the mediation analysis?

Importance Performance Matrix Analysis (IPMA):

  • Strengths: The IPMA results are well-explained and provide valuable insights.

  • Areas for Improvement: The discussion could be more critical. How do these results compare to existing literature?

5. Discussion

Interpretation of Results:

  • Strengths: The discussion section effectively interprets the results in the context of existing literature. The findings are well-explained and supported by relevant studies.

  • Areas for Improvement: The discussion could be more critical. Some interpretations feel overly positive without sufficient critical analysis.

Theoretical and Managerial Implications:

  • Strengths: The implications are clearly stated and provide valuable insights for both theory and practice.

  • Areas for Improvement: The discussion could be more critical. Are there any potential limitations or biases in these implications?

 Future Directions and Limitations

Future Research:

  • Strengths: The suggestions for future research are relevant and well-justified.

  • Areas for Improvement: The suggestions could be more detailed. Consider discussing specific research questions or methodologies for future studies.

Limitations:

  • Strengths: The limitations are clearly stated and appropriately acknowledged.

  • Areas for Improvement: The discussion of limitations could be more critical. Consider discussing potential biases and how they might impact the results.

Overall Comments

  • Strengths: The report is well-structured and comprehensive. It demonstrates a strong understanding of the topic and employs appropriate research methods. The writing is clear and concise, making the report easy to follow. The use of figures and tables enhances the presentation of the results.

  • Areas for Improvement: The report could benefit from more critical analysis in several sections. Some areas are overly detailed and could be streamlined to enhance readability.

  • Suggest relooking into each section above on the areas for improvement which will enhance better the report.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Is the study design appropriate and is the work technically sound?

No

Are the conclusions drawn adequately supported by the results?

Yes

Reviewer Expertise:

Marketing, Digital Marketing , Consumer Behaviour, Entrepreneurship

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

F1000Res. 2024 Dec 17. doi: 10.5256/f1000research.173012.r339089

Reviewer response for version 1

Meenakshi Sharma 1

  1. Detailed Explanation: The narrative style of the paper is scientific. The paper has a high degree of clarity and is focused. It contributes to the body of knowledge in the marketing domain, especially in the context of GenZ. It has been clearly articulated.

  2. Figures and Tables: All figures and tables are in order and have an intext mentioned at appropriate places. Consistent with the paper's objective, the tables provide an overview of major findings.

  3. Methodology: The methodology section is well drafted and captures the process appropriately. However, the authors are advised to explain the sample size estimation process in detail. This will strengthen the methodology section. There appears to be an error in the data measurement section. Relook at the “5-5 point likert scale”. In the pilot testing section “Similarity with the Influencer was below the threshold value of 0.7. In social science research, a construct with a Cronbach alpha above 0.6 in a pilot study could be retained as it is expected the increase with an increment in sample size. Hence the research team decided to retain the construct and explore it further during the final data analysis phase.” Authors are advised to include a  citation for this.

  4. Data sharing: data is shared on Figshare and incorporated in the manuscript.

  5. Discussion and Directions for future direction: The discussion section is well drafted and provides a comparison to previously published literature at appropriate places. A few grammatical errors are observed, Authors are advised to relook and incorporate corrections. Eg.. A sentence starts with “To address ……..”; Typo errors like 3.6….Cronbach alpha of all constructs of the study was above o.7 etc.

Authors are advised to take a relook at the paper and incorporate the suggestions presented above.

The paper maybe accepted for indexing as it is written well and contributes to the body of knowledge of management science.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Marketing, Green banking, Sensory marketing

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

F1000Res. 2025 Jan 22.
smitha nayak 1

Dear Reviewer, 

Thank you for your observations and feedback on the manuscript. 

I acknowledge the typo errors in the document and have included the corrections in the new version of the manuscript. 

As far as the Cronbach Alpha goes, in social science research if the values are in the range of 0.5 to 0.7, it is still recommended to proceed with the scale as the assumption is that a bigger sample size will normalize this when the final data collection is undertaken. 

I am uploading a new version of the manuscript after refining it with your suggestions. 

Thank you again. 

F1000Res. 2024 Nov 27. doi: 10.5256/f1000research.173012.r339092

Reviewer response for version 1

Yoesoep Edhie Rachmad 1,2

Submission ID     : 2024_IJWOE-237645

Title                     :  Does Brand Attitude Complement Influencer Credibility in Shaping Purchase Intention of Indian GenZ Consumers?

Authors               :  Pranav Vilas Chavare, Smitha Nayak, Ramona Birau, Varalakshmi Alapati.

Journal                 : F1000Research 2024, 13:1343 Last updated: 13 NOV 2024

Dear Authors,

 Thank you for your submission of the manuscript titled "Does Brand Attitude Complement Influencer Credibility in Shaping Purchase Intention of Indian GenZ Consumers?" This study offers a valuable and timely contribution to the field of influencer marketing, particularly in the context of Generation Z consumers in India. The research methodology, findings, and theoretical implications are well-articulated. However, I would like to suggest a few areas for improvement to further enhance the manuscript’s clarity, impact, and contribution.

Feedback and Suggestions

Clarity in Presentation

  1. Detailed Explanations:

While the methodology section is robust, additional explanations regarding the choice of a five-point Likert scale and its appropriateness in this context would enhance understanding. Consider including a justification for its use over other scales.

  1. Figures and Tables:

Some figures and tables, such as the structural model and hypothesis testing results, could benefit from clearer captions or legends. For example, explicitly defining significance levels (e.g., p values) directly in the table or figure notes would improve readability.

Data Accessibility and Reproducibility

  1. Data Sharing:

It is commendable that the dataset is available on Figshare under a Creative Commons license. However, it may be beneficial to provide an appendix or a supplementary section with detailed instructions on how to access and use the dataset for replication studies.

Discussion and Implications

  1. Comparative Context:

The discussion section could be expanded to include comparisons with similar studies in other regions or demographic groups. This would contextualize the findings and highlight their generalizability.

  1. Practical Applications:

Including examples of how brands or marketers can apply these findings to develop effective influencer strategies would increase the manuscript's practical utility.

Future Research Directions

  1. Content and Product Categories:

The study would benefit from a clearer acknowledgment of its limitations regarding generalizations across different content types or product categories. Suggestions for future research in these areas could provide valuable guidance for subsequent studies.

Minor Corrections

  1. Consistency in Terms:

Ensure all technical terms are consistently defined throughout the manuscript. For instance, maintaining uniform terminology for constructs like “trust,” “expertise,” and “credibility” will aid comprehension.

  1. Grammar and Typographical Errors:

A few minor typographical errors were noted. A careful review of the manuscript will help ensure overall accuracy and readability.

Overall, this manuscript represents a significant contribution to the literature on influencer marketing and consumer behavior. The innovative approach and detailed analysis are commendable, and I am confident that with these refinements, the paper will achieve even greater impact.

Thank you for the opportunity to review this work. I look forward to the revised version.

Best regards,

Prof. Dr. Yoesoep Edhie Rachmad

Reviewer

Submission ID     : 2024_IJWOE-237645

Title                     :  Does Brand Attitude Complement Influencer Credibility in Shaping Purchase Intention of Indian GenZ Consumers?

Authors               :  Pranav Vilas Chavare, Smitha Nayak, Ramona Birau, Varalakshmi Alapati.

Journal                 : F1000Research 2024, 13:1343 Last updated: 13 NOV 2024

Dear Editor,

 The manuscript titled "Does Brand Attitude Complement Influencer Credibility in Shaping Purchase Intention of Indian GenZ Consumers?" presents a compelling study that contributes significantly to the growing field of influencer marketing and its impact on consumer behavior, specifically within the context of Generation Z in India. While the research demonstrates robust methodology and meaningful findings, there are a few areas that I would recommend addressing to enhance the overall clarity and impact of the manuscript:

Key Points for Consideration

  1. Clarity and Accessibility in Methodology:

The study employs PLS-SEM to test hypotheses effectively. However, for a broader audience, providing additional context or illustrative examples regarding the structural model and key metrics (e.g., significance thresholds) would increase clarity and accessibility.

 

  1. Data Availability and Reproducibility:

While the authors commendably provide access to the dataset on Figshare, it may be beneficial to suggest including explicit guidance or links in an appendix to facilitate easier replication by future researchers.

 

  1. Discussion of Findings and Generalizability:

The discussion section could be expanded to compare the findings with similar research across different demographics or industries. Highlighting potential applications of the study's insights in other regions or sectors would underscore its broader relevance.

 

  1. Practical Implications:

The paper makes significant theoretical contributions. To balance this, elaborating on how brands can implement these findings to design effective influencer campaigns could enhance the manuscript’s practical utility.

 

  1. Future Research Directions:

The limitations section briefly mentions opportunities for future work. However, more detailed suggestions, such as examining content types or distinguishing between macro- and micro-influencers, would provide valuable guidance for subsequent research in this field.

Overall Recommendation

The manuscript offers a valuable addition to the literature on influencer marketing and consumer behavior. Addressing the points above through minor revisions would further strengthen its impact and readability.

Thank you for the opportunity to review this manuscript. I look forward to seeing its refined version.

Best regards,

Prof. Dr. Yoesoep Edhie Rachmad

Reviewer

Submission ID     : 2024_IJWOE-237645

Title                     :  Does Brand Attitude Complement Influencer Credibility in Shaping Purchase Intention of Indian GenZ Consumers?

Authors               :  Pranav Vilas Chavare, Smitha Nayak, Ramona Birau, Varalakshmi Alapati.

Journal                 : F1000Research 2024, 13:1343 Last updated: 13 NOV 2024

 I recommend Minor Revisions for the manuscript titled " Does Brand Attitude Complement Influencer Credibility in Shaping Purchase Intention of Indian GenZ Consumers?".

Justification for Recommendation:

 

  1. Innovative Approach:

The manuscript provides an insightful examination of influencer credibility and its impact on purchase intention, particularly in the context of Generation Z in India. The integration of trust, expertise, and similarity in assessing influencer credibility adds a significant contribution to the literature on consumer behavior.

 

  1. Clarity and Detail:

The study uses advanced methodologies such as PLS-SEM, which are well-documented. However, the manuscript would benefit from additional intuitive examples or explanations for readers less familiar with this approach. Adding more visuals or diagrams, particularly in the methodology section, could enhance clarity.

 

  1. Data Accessibility:

While the dataset is made publicly available through Figshare, the authors should provide additional guidance or references for accessing and utilizing the data to support reproducibility.

 

  1. Discussion and Practical Implications:

The discussion section is thorough but could be expanded with a stronger emphasis on practical applications. For instance, the authors could elaborate on how brands can leverage the findings to design effective influencer marketing campaigns.

 

  1. Minor Corrections:

Some typographical errors and inconsistencies in the use of technical terms were noted. Ensuring uniformity in terminology and addressing these minor errors will improve the manuscript's readability.

Conclusion:

This manuscript is a valuable contribution to the understanding of influencer marketing and consumer behavior. The proposed revisions are minor and intended to enhance the manuscript's clarity, practical relevance, and broader impact.

I look forward to reviewing the revised version.

Best regards,

Prof. Dr. Yoesoep Edhie Rachmad

Reviewer

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Yes, I consider aspects of this article to be of major significance.Justification:The study provides critical insights into the mechanisms by which influencer credibility and brand attitude shape purchase intentions, particularly among Gen Z consumers in a rapidly digitizing market like India. This demographic and geographical focus offers unique value in understanding emerging consumer behaviors.Why It Is Significant:Practical Implications:The findings can guide brands and marketers in designing more effective influencer strategies, especially in leveraging trust and expertise over attractiveness.Theoretical Contribution:By identifying the mediating role of brand attitude, the study advances existing frameworks like the Theory of Reasoned Action, adding depth to literature on influencer marketing and consumer behavior.Emerging Market Focus:The focus on Gen Z in India highlights evolving trends in an underrepresented region, providing valuable insights that could inform both academic and industry strategies globally.The work bridges theoretical and practical gaps, making it a substantial addition to the field of marketing research.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

F1000Res. 2024 Dec 2.
smitha nayak 1

1. While the methodology section is robust, additional explanations regarding the choice of a five point likert scale.

Response : Changes have been incorporated. As these scales were originally developed and designed to be measured on 5 point likert scale, the same has been adopted in the manuscript.

2. Data Sharing:

It is commendable that the dataset is available on Figshare under a Creative Commons license. However, it may be beneficial to provide an appendix or a supplementary section with detailed instructions on how to access and use the dataset for replication studies.

Response: This statement has been included in the “Underlying Data“ section of the. The underlying data of the manuscript can be accessed on Figshare by clicking on this link

3. Including examples of how brands or marketers can apply these findings to develop effective influencer strategies would increase the manuscript's practical utility.

Response: The same has already been addressed in the theoretical and managerial implication sections of the manuscript.

4. Content and Product Categories:

The study would benefit from a clearer acknowledgment of its limitations regarding generalizations across different content types or product categories. Suggestions for future research in these areas could provide valuable guidance for subsequent studies. 

Response has been incorporated.

5. Manuscript has been rechecked for Consistency, Typographical errors and Grammatical errors.

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Data Citations

    1. Nayak S: Data Set _Influencer Marketing. figshare.[Dataset].2024. 10.6084/m9.figshare.27094330.v1 [DOI]

    Data Availability Statement

    Underlying data

    Figshare: Influencer Marketing https://doi.org/10.6084/m9.figshare.27094330.v1 ( Nayak, 2024).

    The project contains the following underlying data:

    • •

      Impact of Marketing Influencers Dataset

    Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

    Articles from F1000Research are provided here courtesy of F1000 Research Ltd

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