In today’s fast-changing digital world, SMIs are important intermediaries between consumers and brands. SMIs are individuals who have gathered large online audiences by frequently sharing their knowledge, appearing as experts and being honest (Lou & Yuan, 2019). They act as both a friend and a supporter, sitting between regular advertising and simple recommendations. This trend is called influencer marketing, as it shifts from institutions guiding consumers to individuals representing brands, with trust, similarity and group membership playing a big role in influencing people (Casaló et al., 2020). Evaluating the trustworthiness, knowledge, and attractiveness of a person who sends a message is the main idea behind source credibility in influencer marketing (Yuan & Lou, 2020). If these credibility cues meet what the audience expects, it encourages followers to feel bonded to influencers without ever meeting them (Jin & Ryu, 2020). As a result, this relationship boosts how much people pay attention to the message and want to purchase, which is why it is important for predicting if someone will buy the influencer’s recommendation. It is also important for an influencer to be real and consistent in what they show on social media and how they live in real life. However, authenticity alone does not suffice. The emerging dimension of relatability—the influencer’s perceived similarity to their audience in lifestyle, tone, or attitude—has been identified as a stronger predictor of consumer trust than aspirational appeal (Djafarova & Trofimenko, 2019). Among micro-influencers, relatability is very strong, since they provide close interactions and content that matters more to their audiences than the wider but less personal reach of macro-influencers (Antheunis, 2020; Puspita, 2023). How closely an influencer interacts with their followers is also very important. Engagement means going beyond watching or liking and includes commenting, sharing and sending messages. When engagement quality is high, it often helps move people from being aware of a product to purchasing it. Still, the way content is converted depends on both its content and the special features and interactions available on each platform (Phua et al., 2020; Luo et al., 2025). Each platform offers a unique persuasion environment. Instagram is oriented toward visual persuasion and aspirational aesthetics; YouTube enables long-form content that supports detailed product evaluations and expert credibility; while Snapchat emphasizes relatability, humor, and viral engagement through short-form formats (Godey et al., 2016; Duh & Thabethe, 2021). Since each platform has its own way of viewing influencers, brands must make sure the influencer’s values fit with what their audience expects (Martínez-López et al., 2020; Borchers & Enke, 2021). Even with all this research, there are still important gaps that need to be filled. While many studies look at what makes an influencer successful, few examine how traits like credibility, consistency and engagement work differently on different platforms. Second, the relationship between marketing efforts and how genuine a brand seems is not well understood in studies that last over time or use experiments (Martínez-López et al., 2020). Third, not much research is available on how age and gender influence how much people are affected by influencer messages (Suganya & Bawa, 2024). To fill these gaps, this study looks at how having credible, relatable, and consistent content on Instagram, YouTube, and Snapchat influences consumer decisions to purchase. It also studies how the way a platform is built and who uses it influence how reliable and trustworthy it appears, which influences whether users will want to make a purchase. The goal is to support both research and strategy by offering a clear picture of how digital influence works in different media and among different groups of consumers.

Research Objectives

Since social media is impacting consumers more, we must learn how certain traits of influencers impact buying behavior online. Although influencer marketing has been examined before, few studies have focused on how the features of a platform, the characteristics of influencers, and who the consumers are affect trust, engagement, and buying intentions. To fill this gap, the present study sets the following research objectives.

1. To examine how influencer characteristics—credibility, relatability, and content consistency—affect consumer trust and purchase intention across Instagram, YouTube, and Snapchat.
2. To analyse how engagement quality mediates the relationship between influencer traits and consumers’ buying decisions in platform-specific contexts.
3. To investigate the moderating effects of demographic variables such as age and gender on the impact of influencer marketing across different social media platforms.

2.1 Influencer Marketing: Foundations and Typologies

Using popular social media figures to influence what people choose is now a key part of digital advertising. In their 2017 paper, Khamis, Ang, and Welling refer to social media influencers (SMIs) as micro-celebrities who often tie their personal stories to what they do for business. For this reason, companies are choosing influencers who look like friends to their audience. Gómez (2019) explains that influencers are classified as macro-, micro- or nano-influencers according to their reach and the interactions they receive. Micro-influencers often have fewer followers, which means they are more trusted and connected with them. Marques, Casais, and Camilleri (2021) conclude that micro-influencers encourage Instagram users to engage with a brand more, thanks to their involvement with their communities and their honest appearance. Influencer marketing succeeds best when the influencer is easy to spot and seems much like the people who are most at risk, according to De Veirman, Hudders, and Nelson (2019). Erz and Christensen (2018) point out that influencers use brand deals to make their brands into things that can be bought and sold in the attention market.

2.2 Source Credibility and Advertising Disclosure

Many experts rely on the source credibility model to demonstrate how well influencer marketing works. In their study, Freberg et al. (2011) define credibility as trustworthiness, expertise and attractiveness. If someone regularly shows these qualities, followers are more likely to feel good and want to act in particular ways. Yet, when people see advertising disclosure, it can be harder for them to believe the information. The authors found that sponsorship in ads helps followers identify the content, but it may not be as persuasive if the followers think it is too commercial. According to Weismueller et al. (2020), people are less likely to buy when the influencer’s credibility is low. As Sesar, Martinčević, and Boguszewicz-Kreft (2022) point out, when credibility and disclosure are matched, consumers still trust the company. Martínez-López et al. explain in their 2020 study that overselling can make influencers lose the trust that supports their influence. That’s why companies should combine their advertising with real stories about their products.

2.3 Parasocial Interaction and Mimicry Dynamics

Researchers use the idea of parasocial interaction to study how SMIs affect people’s minds. PSI means the connection between followers and influencers is based on emotions alone. Sokolova and Kefi discovered that PSI helps show how source credibility affects whether someone intends to purchase. They explain that making personal issues and challenges public can boost PSI and make a brand more effective. Aw and Chuah (2021) found, using self-discrepancy theory, that people who notice a big difference between their actions and their goals are more likely to develop PSI and follow influencers. Many young people think that influencers are people they should copy and believe they can accomplish the same (Ki & Kim, 2019). It encourages the consumer to relate to the influencer, so their advice is more likely to change how the consumer behaves.

2.4 Consumer Engagement and Behavioral Intentions

Trust and PSI are important, but it is engagement that keeps consumer-influencer interactions going. Engagement is measured by likes, comments, shares, click-throughs, and the emotions and thoughts people have when they look at the content. In 2019, Jiménez-Castillo and Sánchez-Fernández found that how much people value influencers and how much they like their posts are key factors in deciding to purchase something. Cheing et al. (2020) also found that how emotionally connected a person is to a brand is a better sign of loyalty than just seeing the advertising.

Munnukka et al. (2019) studied YouTube and found that vlogs help viewers view the content as more informative, trustworthy and more likely to lead them to take action. Duh and Thabethe (2021) noted that a brand’s Instagram profile should be rich in visuals, consistent and use a clear tone to attract users. The authors believe that micro-influencers are better at forming close relationships with their followers than macro-celebrities.

2.5 Platform-Specific Influence Mechanisms

Influencers can share content, talk to their fans, and be noticed by them on every social media platform. With pictures, influencers on Instagram can show how they wish to live (Belanche et al., 2021). You can use YouTube to give clear reviews, which can help viewers trust your advice and what you share (Munnukka et al., 2019). Snapchat encourages quick and funny videos, which attract young people by making them feel related and entertained by memes (Rezene, 2023). Luo, Wang and Liu (2025) report results from tourism marketing that prove picture colour hue and the type of content (e.g., storytelling or transactional) can affect user responses. This research points out that sensory design and platform logic are important in digital persuasion. They also demonstrate that influencers match their content to the features of each platform, making their reach and impact stronger on every channel.

2.6 Psychological and Demographic Moderators

Consumer response to influencer marketing is conditioned by psychological dispositions and sociodemographic variables. Aw and Chuah (2021) report that self-discrepancy increases vulnerability to emotional influence. Saima and Khan (2020) demonstrate that source credibility mediates the link between influencer exposure and consumer attitude, while Zhao et al. (2024) reveal that attitude toward the brand serves as an additional mediator between influencer traits and purchase behavior. Lim et al. (2017) identify age and gender as significant moderators, with younger users and females generally more responsive to influencer cues. Mortazavi et al. (2021) argue that inclusive innovation frameworks—those recognizing varied digital literacies and identity politics—are crucial for capturing the full spectrum of consumer-influencer interactions. Beeler, Zablah, and Rapp (2022) also highlight the importance of contextual perception, suggesting that what constitutes “ability” or “credibility” varies by user and platform.

3.1 Research Design and Philosophical Orientation

For this study, quantitative, explanatory and cross-sectional methods are used, all based on the positivist paradigm. We want to discover how an influencer’s credibility, how related they are to their audience, how often they post, how much trust they build, how much engagement they get and how many people buy their products are all connected on Instagram, YouTube and Snapchat. The choice of constructs and the development of the model are guided by Source Credibility Theory, Parasocial Interaction Theory and Engagement-Behavior Models. As a result of these theories, we wish to examine if engagement and demographic information matter in the connection between self-esteem and social media use.

3.2 Target Population, Sampling, and Respondent Criteria

Indian social media users aged 18–45 who have interacted with influencers in the past six months and have considered or bought a product suggested by an influencer are the target population. We chose a purposive non-probability sampling method to make sure the respondents were relevant. We used academic mailing lists, social media groups, and online communities related to influencers for recruitment. To guarantee quality data and keep the project manageable, 200 valid responses were chosen, which is more than the minimum required for Structural Equation Modeling (SEM) and allows for analyzing subgroups. The sample size was set following the rule of including at least 10 observations for each measured item.

3.3 Instrumentation and Construct Measurement

The questionnaire was made up of five areas: demographics, information about the influencer, how much engagement there is, how much trust the respondent has in the influencer, and whether the respondent would buy the product. The measures for the variables were taken on a Likert scale from 1 to 5.

Trustworthiness, expertise, and attractiveness were measured using six items to evaluate an influencer’s credibility. Four items were included to measure how much people felt they were similar and had the same emotions. Three items were used to assess how much the content stayed on one topic and how often posts were made. Engagement quality was measured using nine items that looked at behavior, thought and feelings. The influence of trust was assessed using five items related to the influencer’s honesty, reliability and transparency. Participants were asked five questions about how likely they were to buy goods promoted by influencers.

All items were subjected to expert review for content validity, followed by a pilot test to confirm internal consistency and interpretability.

3.4 Data Collection Process

The primary data were gathered by having participants complete an online survey via Google Forms over five weeks. Before participating, each person had to confirm they were eligible by looking at their influencer engagement. Before taking part, all respondents were given an information sheet that explained the study, how the data would be used, and that their information would be kept anonymous. Digital consent was given for the study. We used IP filters and questions to make sure no one submitted the same thing twice or a low-quality entry. Using primary data allowed us to directly observe how consumers felt, acted, and reacted to things happening online.

3.5 Validity and Reliability Testing

Experts examined the tool’s content and how well it reflects what it is meant to measure. EFA was performed in SPSS to discover what underlies each construct. KMO was 0.882 and Bartlett’s Test of Sphericity was also significant (χ² = 2674.53, p < 0.001). No cross-loadings were found and every factor loading was over 0.65.

The measurement model was tested using CFA in AMOS 28.0. All constructs met the requirements of having a Composite Reliability (CR) over 0.80, an Average Variance Extracted (AVE) higher than 0.50 and a Cronbach’s alpha value over 0.82. Fornell-Larcker criterion was used to confirm that the measures have discriminant validity. According to Harman’s single-factor test, there was no sign of common method bias.

3.6 Data Analysis Strategy

Descriptive statistics were carried out on both demographic and usage variables. The connections between the constructs were studied using Structural Equation Modeling (SEM). The analysis considered how both direct and indirect ways in which engagement quality works.

The model performed well, as you can see from the values of χ²/df = 2.18, CFI = 0.944, TLI = 0.936, RMSEA = 0.051 and SRMR = 0.045. Mediation analysis was carried out using the bias-corrected bootstrapping method (with 5,000 resamples). MGA was used to check if the results were affected by both gender and platform choice. We made sure configural and metric invariance was present by conducting chi-square difference tests before comparing paths.

3.7 Ethical Considerations

The study received ethical approval from the Institutional Review Board of [University Name]. All participants provided informed consent and were assured of confidentiality and data protection. No personally identifiable information was collected. The research was conducted by the Declaration of Helsinki and followed ICMJE ethical standards for human subject research.

4.1 Descriptive Statistics

Descriptive statistics provide a comprehensive view of the respondent profile. Among the 300 participants, a near-balanced gender distribution is observed, with a female majority. Most participants fall within the 21–30 age range, suggesting a young, digitally active population. Instagram is the most engaged platform, reflecting its centrality in influencer marketing ecosystems. Additionally, over 70% of respondents acknowledged making a purchase influenced by social media content, highlighting the real-world implications of influencer behavior.

Table 1. Demographic Profile of Respondents

The data in Table 1 shows that influencer marketing resonates most with younger consumers, particularly those aged 21–30, who represent the majority of the sample. This group is highly active on visually driven platforms such as Instagram, known for hosting fashion, lifestyle, and product review content. The high rate of purchase influenced by social media content underlines the practical impact of influencer endorsements, suggesting a strong alignment between influencer activity and consumer behavior in the digital age.

Figure 1. Demographic and Platform-Wise Distribution of Influencer-Engaged Consumers: Frequency and Percentage Patterns

This figure illustrates the distribution of respondents (N = 300) based on gender, age group, platform engagement, and past influencer-driven purchasing behavior. Female respondents constituted the majority at 58%, followed by males at 41%, with minimal non-binary representation. The 21–30 age group emerged as the dominant demographic (64%), indicating a strong generational skew toward young adults in influencer-driven consumption. Platform engagement was highest on Instagram (49%), followed by YouTube (34%), and Snapchat (17%), reflecting differentiated content preferences across visual-centric, long-form, and short-form environments. It’s significant that 71% of participants mentioned making a purchase that was influenced by something they saw on social media. The upward trend in the frequency distribution suggests that as platform use increases, consumers are more likely to respond. This knowledge highlights why brands should design their influencer strategies to fit the habits of people on each platform and their age groups.

4.2 Measurement Model Assessment

Both Exploratory and Confirmatory Factor Analysis were used to test the model. The factorability of the data was confirmed by a high KMO value and a significant Bartlett's test. All constructs were reliable, linked well to similar measures and could be told apart from other measures. The model is built so that latent variables are not influenced by multicollinearity or measurement error.

Table 2. Measurement Model Statistics

All of the constructs in Table 2 show good psychometric properties. Cronbach’s alpha and CR values above 0.80 mean the scale is highly consistent, and AVE values above 0.50 prove the scale is valid. The high factor loadings indicate that each item measures the construct it is intended for. All of these results demonstrate that the scale items are reliable and that using them in advanced structural equation modeling is justified.

Figure 2. Validation Metrics of Latent Constructs: Convergent Reliability, Internal Consistency, and Factor Loadings

The figure shows a comparison of how accurately the six main latent constructs—Credibility, Relatability, Consistency, Trust, Engagement Quality, and Purchase Intention—were measured. It demonstrates four main ways to validate data: Cronbach’s Alpha, Composite Reliability, Average Variance Extracted, and the Highest Factor Loading. All constructs had Cronbach’s Alpha and CR values higher than 0.80, demonstrating they are very reliable. Each AVE measure was greater than 0.50, demonstrating that the constructs were converging. In particular, the highest factor loadings were 0.74 to 0.85, and this means that the construct was well captured as the items increased. The highest loadings and AVE values were found for Engagement Quality and Purchase Intention, proving that these behavioral outcome constructs were accurately measured. Since all constructs meet the same standards for reliability and validity, the measurement model is strong and can be used with Structural Equation Modeling. It shows that the study’s tool can capture how consumers’ thoughts, feelings and actions are affected by influencers.

4.3 Structural Model Evaluation

To assess the structural model, SEM was used to see how influencer traits affect trust, engagement and buying decisions. All indices showed that the model fit the data very well. All of the paths from credibility, relatability and consistency to trust showed a significant difference. Also, trust strongly influenced engagement and engagement was a main factor in deciding to purchase. The structure shows how influencer credibility affects the actions of consumers one after another.

Table 3. Structural Model Path Estimates

As shown in Table 3, the path coefficients prove that influencer characteristics influence how much people purchase. People said that credibility was the most important factor in gaining their trust, and relatability and consistency came next. Trust, in addition, is a key psychological factor that directly improves how engaged people are. When engagement occurs, it leads consumers to want to purchase the product. The research together outlines a clear and statistically proven relationship between influencer appeal and what consumers do, demonstrating how digital persuasion through relationships works

Figure 1. Structural Path Coefficients of Influencer Attributes on Consumer Engagement and Purchase Intention

The chart shows the standardized beta coefficients (β) from the structural equation model, showing the level of relationship between each construct. According to the model, trust in an influencer depends on how reliable, easy to relate to, and consistent they are, and this trust strongly influences how much consumers interact with their posts. Whether someone will buy the product is best indicated by their level of engagement (β = 0.53). They show how the traits of influencers influence the way consumers act. Engagement with a brand makes consumers more likely to buy, so trust appears to be the main factor in getting people to make a purchase. Similarly, trust → engagement proves that trust connects our thoughts and our actions. The figure explains the strengths and order of influencer traits, making it easier to create a solid structure for studying digital persuasion in influencer marketing.

4.4 Mediation Analysis

Engagement quality was examined as a mediator using bootstrapping with 5,000 samples. The findings revealed that influencer traits strongly affected purchase intention through engagement, which means engagement fully mediated the relationship. It means that influencers do not affect buying decisions right away, but by interacting with users over time.

Table 4. Indirect Effects via Bootstrapping

Table 4 shows that the effect of influencer characteristics on purchase decisions is fully explained by engagement quality. As a result, how consumers interact with content, feel about it, and judge its credibility helps them move from seeing an influencer to making a purchase. The important indirect paths prove that engagement is not only about reacting but a key mental process that helps influencer actions become visible in economic activity.

4.5 Moderation Analysis

Moderation effects were evaluated through Multi-group Analysis (MGA) to determine whether gender and platform type altered the strength of key relationships in the model. While no significant variation was observed across gender, platform type exhibited a distinct moderating effect. Specifically, the influence of trust on engagement was stronger among YouTube users than Snapchat users, suggesting contextual differences in content processing and user perception.

Table 5. Moderation by Gender and Platform

The moderation outcomes presented in Table 5 reveal that the strength of trust-driven engagement varies by platform but not by gender. On YouTube, where content is often long-form and in-depth, trust leads to higher engagement. On Snapchat, which favors quick, entertaining snippets, trust may be less central to engagement dynamics. These insights highlight the importance of customizing influencer strategies based on platform affordances, where content length, format, and viewer attention span all influence how trust translates into active consumer involvement.

It was found that people are more willing to purchase a product if the influencer is honest, like them, and posts often. According to the research, if influencers display these characteristics, consumers are more likely to trust them, communicate with them and want to buy their products. Here, trust is shown to act as a link, as Zhao et al. (2024) explain that customer attitude helps explain the impact of influencer features on purchasing decisions. Since being credible is important for influencers, Saima and Khan’s (2020) results are supported. Their study outlined how trustworthy influencers help brands reach consumers and encourage them to buy. By adding relatability and consistency to their findings, the study suggests a broader way to understand how effective an influencer is. The research found that YouTube users had a stronger connection between trust and engagement which reveals the unique ways each platform helps influencers and consumers communicate. Mohammed and Sundararajan (2024) found that YouTube and similar platforms are more trustworthy because they can present detailed information. Alternatively, Snapchat’s brief videos can be entertaining, but they might not give enough information for users to trust the influencers, which means that the way a platform works can influence how well influencer marketing works. The study’s mediation analysis also finds that how much people engage with the influencer fully explains the relationship between influencer characteristics and the intention to purchase. This result is consistent with what Leite and Baptista (2022) found about how parasocial relationships and source credibility affect consumer intentions. The researchers found that honesty from influencers increases their credibility and encourages viewers to form closer relationships that influence their decisions to buy. The study further supports this by proving that trust in an influencer is important for the way their attributes shape consumer actions. The findings bring about a range of outcomes. Building trust and a relationship with influencers is what marketers focus on most. Use YouTube for detailed reviews and to talk about your business and use Snapchat to make fun, quick videos that catch people’s attention. Using this strategy, campaigns on influencer marketing take advantage of every platform’s unique points, helping to influence what consumers buy. This study introduces the idea that trust and engagement quality play a role in how influencers relate to consumers. It helps us see why influencer marketing is successful. As a result, researchers can now consider new factors, including how real the influencer seems and how much the consumer relates to them, to understand how influencer traits affect consumer behavior. Besides, the study points out that influencers who are authentic are important for influencer marketing to work well. When consumers are more aware, how genuine an influencer seems matters a lot in influencing their reactions. It matters most for AI-generated influencers, because people may not trust them if they don’t seem genuinely human. In future, studies should determine how well people and AI influencers do in terms of being authentic, trustworthy and creating emotions. Since influencer marketing is always growing, we should keep monitoring how each platform functions. Since platforms keep introducing new kinds of content, it is necessary to know how these changes influence the relationship between influencers and their audience. Over time, longitudinal studies might help us see how changes in social media platforms, what users watch, and who uses them affect the success of influencer marketing.

This research demonstrates that what matters most to consumers are the influencer’s credibility, relatability and consistency. It explains that the quality of engagement and the platform type are important, sharing this information with researchers and marketers. If brands pick influencers that are right for the platform and are genuine and frequent, they can get the best from influencer marketing on social media.

Anchored in a comprehensive empirical framework, this study unpacks the intricate interplay between influencer characteristics and consumer decision-making within digital ecosystems. It is clear from the analysis that the main reasons consumers trust a brand are credibility, relatability, and consistency in its content, which lead to more engagement and higher purchase intent. Importantly, engagement quality proved to fully mediate the relationship, showing that people’s actions in the influencer economy are guided by how much they connect with and relate to the influencer, not just by being exposed to them. They reveal that marketing is moving from a focus on transactions to a greater emphasis on interactions. Furthermore, the study shows that YouTube, as a long-form platform, tends to create stronger trust-engagement links than Snapchat, which is a short-form platform. Because the results depend on the platform, influencer strategies should adapt to the content and the audience. In theory, the research improves our understanding of parasocial dynamics and source credibility, and in practice, it encourages brands to focus on being authentic, consistent, and well-aligned, rather than just aiming for a large audience. As we can see, influencer marketing relies heavily on how influencers make consumers feel, which often leads to action. Studies should investigate the ways in which new types of influencers such as virtual influencers and AI-driven personas, affect the credibility and parasocial relationship with influencers. If we compare outcomes in different cultures, follow key influencers over the years and use both biometric and psychographic information, we can learn more about their success. Because influencer marketing is changing online interactions between consumers and brands, this study lays a solid foundation for improving trust-based, platform-specific and psychological marketing methods.

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