This section depicts the variables description for the conceptual Framework shown in Figure 1.

2.1.1 Influencer marketing

In the today’s new era of social media, influencer marketing has been emerging an important factor. It is formed together by addition of the different elements. Most important element among them is influencer credibility, as trust and engagement with the influencers among audiences are high because influencers are recognized as experienced and reliable persons. ^{33, 39, 43} Other than that, the content quality plays another important role as the perception of the audience and interaction with them are strongly affected by it. ^{16, 22, 39}

Along with the measure of audience engagement, which relates to the interaction with the audience and the content serves as a strong indicator for success of campaign. ^{17, 22, 57} Moreover, key role is played by the brand fit, whereby the image of influencer and his values align with those of the brand so that it can produce a more authentic and effective partnership among them. ^{11, 24, 56, 58} Lastly the frequency of exposure shows how frequently is the target audience exposed to the influencer’s content, as this can assist in improving the recall and purchase intentions at the end of consumer. ^{8, 20}

2.1.2 Consumer purchase intention

Consumer purchase intention is primarily the key area of focus in marketing research as it influences the likelihood of consumer purchase of a product or service to a great extent. This intention is determined by many factors, i.e. perceived benefits, perceived risks, customer trust and how effective are the advertisements. ^{34, 61} Perceived benefits mainly refer to the impacts that are positive and what the consumers is expecting to get after purchasing a good or service. These benefits can be either functional and value-based, as well as emotional and social recognition. ^{54, 58} On the other hand, perceived risks, refer to the potential downsides or uncertainties which are associated while buying a product/service. These can be financial risk, performance risk, physical risk, social risk, and psychological risk. ^{9, 31} Customer trust is the confidence that a product or brand will deliver on the promises and meet finally consumer expectations. This trust and relationships among the consumers, in turn, is crucial for establishing long-lasting relationships and promoting repurchase of the product which finally adds to sustained profit. ⁴⁴

Effective Advertising refers to an ad’s ability to capture attention, conveying messages effectively, and convincing the consumers to contemplate or purchase the product. ⁴⁷ In addition to determining an individual’s likelihood of purchasing goods. It determines the buyer’s readiness and ability to make such a decision. ⁶⁰ Likert scale items can be used in the survey to measure the parameters. Researchers must thoroughly comprehend consumers’ purchasing behavior and adequately tailor their marketing endeavors.

2.1.3 Consumer attitude

Consumer attitude is a complex concept that significantly impacts decision-making and purchasing behaviors. ³ These are affective attitude—which refers to the feel about a product, ³⁵ behavioral attitude—which connects to the purpose of purchasing a product, ¹ and cognitive attitude—which relates to the belief in a service or product. ¹² Additionally, brand and advertisement opinions are a vital part of consumers’ daily lives and significantly affect their behavior. ³⁶ Attitude components synergize to moderate the advertising effect on the purchase goal, focusing on an elaborated interconnection between consumer behavior and feelings in outcomes. The components of attitude, when taken holistically, determine the manner in which marketing affects purchase intention.

Hypothesis formulation is based on customer attitude, purchase intention and influencer marketing, as discussed below: It seeks to understand the relationship between endorsement marketing and consumer buying intention (independent and dependent variables, respectively) with customer attitude as a mediator. The framework highlights the critical role that consumer attitude plays in this process by emphasizing the analysis of how various influencer marketing tactics impact consumer purchasing behavior.

2.2.1 Influencer marketing and purchase intention

The relationship between influencer marketing and purchase intention has been transformed in today’s competitive marketing landscape. Brands are now using influencers for marketing their products on the digital channels which indirectly influences consumer perception to drive sales. Influencer marketing is specifically effective for enhancing consumer purchase intention. ²⁷ Since influencers create authenticity and trust, that are the most significant factors that have impact on consumer in their purchase selection, they consequently increase the chance for the purchase of the product. ⁴¹ Influencers significantly affect purchasing behavior by sharing aspirational content that aligns with the desires of consumers. ⁵¹ Moreover, credibility of the influencer impacts purchase intention; endorsed products by them tend to be more trustworthy and beneficial according to consumer perception. ³⁷ The success of marketing campaigns is additionally determined by influencer characteristics like attractiveness and their expertise. ⁴⁰ Engagement metrics (likes, shares, comments) represent complimentary indicators that could positively influence the purchase intention ³⁷ and suggest that a favorable perception around the community enhances the attractiveness of the product. H1:

Influencer marketing is positively related to consumer purchase intention.

2.2.2 Mediating role of consumer attitude

Consumer attitude facilitates the link between influencer marketing and consumer buying intention. Previous studies confirm the positive impact of influencer marketing on purchase intention, although limited knowledge has been provided to identify the specific strategies that can help to improve consumer decision-making. The objective of the study is to examine the mediation role of consumer attitude between influencer marketing and purchase behavior relationship. Consumer attitude has a contribution in the success of influencer marketing by creating trusts and engagement. ¹⁶ A credible influencer can change the perceptions of the consumer and, hence, their receptive to marketing messages. ³³ When there is a strong fit between the influencer and the brand, consumers tend to have a more favorable attitude towards the products which is being endorsed by the influencer, and therefore, the consumers are likely to have a higher intention to buy those endorsed products. ²⁶ Moreover, previous research suggests that influencers are acting as mediators between their audiences and brands as the emotional attachments which they build up with their audiences which ultimately increases the purchase intent as a result of consumer attitude. ³⁸ H2:

Consumer attitude positively mediates the relationship between influencer marketing and purchase intention.

The results of the Exploratory Factor Analysis (EFA) provide valuable insights into the constructs associated with influencer marketing, consumer attitude, and purchase intention.

Beginning with influencer marketing, the KMO measure of 0.686 indicates moderate sampling adequacy, while Bartlett’s test of sphericity was significant (p < 0.05), verifying that the variables were satisfactorily linked to conduct factor analysis. The total variance examined by the extracted factors was found to be 59.48%, suggesting a moderate level of explanatory power within the model. The analysis identified several key components: credibility, content quality, audience engagement, brand fit, and frequency of exposure. Notably, the items associated with influencer credibility demonstrated strong loadings varying from 0.676 to 0.749, underscoring the significance of perceptions of credibility in influencer marketing effectiveness. Content quality also showed high loadings, indicating its essential role in capturing consumer attention. Furthermore, audience engagement, brand fit, and frequency of exposure were identified as critical factors influencing marketing outcomes, emphasizing the multi-dimensional nature of influencer marketing.

In terms of consumer attitude, the analysis yielded a higher KMO value of 0.716, suggesting good sampling adequacy. Bartlett’s test was again significant (p ¡ 0.05), affirming the data’s feasibility for factor analysis, with the total variance evaluated by the factors reaching 72.21%. This robust explanation indicates that the customer attitude variables are well-structured. The analysis categorized consumer attitude into cognitive, affective, behavioral, ad-related, and brand-related components. The strong loadings for cognitive attitude items indicate that the rational evaluations significantly shape consumer attitude, while the affective components highlight the emotional responses to influencer marketing, illustrating their impact on overall attitude formation.

Regarding purchase intention, the analysis produced an excellent KMO value of 0.862, with Bartlett’s test also significant (p < 0.05). This indicates a high suitability for analysis, and the total variance explained was 68.55%, reflecting a strong explanatory capacity. Importantly, the purchase intention construct was represented by a single robust component, suggesting that motivations to purchase are coherently linked with the previously analyzed influencer marketing factors and consumer attitude.

The extraction method used for this analysis was Principal Component Analysis (PCA), which helped in identifying the underlying structure of the data effectively. The rotation method employed was Varimax with Kaiser normalization, which maximizes the variance of the squared loadings of a factor across variables to achieve a simpler and more interpretable structure. Overall, the distinct grouping of factors across all constructs signifies high data quality and sets a solid foundation for further exploration of causal relationships within marketing communication strategies.

The item reliability analysis across influencer marketing, consumer attitudes, and purchase intentions reveals important insights into internal consistency. For influencer marketing factors, the Cronbach’s alpha is 0.666, denoting acceptable but improvable consistency. Content quality is notably lower at 0.648, suggesting inconsistency. Audience engagement, with a Cronbach’s alpha of 0.727, highlights a relatively consistent measure. Brand fit shows a lower reliability at 0.633, while frequency of exposure has a strong reliability coefficient of 0.83, indicating high consistency for this construct.

In consumer attitudes, all factors exhibit stronger reliability. Cognitive attitude shows an alpha of 0.772, and affective attitude is 0.75, both indicating good consistency. Behavioral attitude is reported at 0.758. Ad attitude stands out with an impressive alpha of 0.893, reflecting excellent reliability, and brand attitude also shows robust consistency at 0.89. For purchase intention, a single item achieved a reliable score of 0.842.

This section provides the confirmatory factors along with the model fit measures for each construct. Convergent validity was evaluated using Average Variance Extracted (AVE) and Construct Reliability (CR), while discriminant validity was determined using Squared Inter-Construct Correlations (SICCR).

Figure 2 shows confirmatory factor structure of influencer marketing. Initially, model indices showed poor fit with CMIN/DF at 6.60, Chi-square at 2801.71 (p = 0.000), GFI at 0.606, NFI at 0.550, CFI at 0.588, and RMSEA at 0.131, all exceeding recommended thresholds. To address these issues, ten items were removed from the influencer marketing model due to failure to meet threshold criteria. The revised indices presented in Table 2 indicate a significant improvement, with CMIN/DF at 3.01, Chi-square at 539.51 (p = 0.000), GFI at 0.828, NFI at 0.828, CFI at 0.862, and RMSEA at 0.088, closely aligning with acceptable thresholds.

The AVE values for the five constructs range from 0.40 to 0.61, with F1 and F5 falling below the 0.50 threshold, indicating that they require additional development. However, the CR values (0.73 to 0.84) are satisfactory, indicating a valid measurement. The SICCR values (0.10 to 0.24) are adequately low, indicating high discriminant validity. Overall, the model has a good fit and acceptable validity, while F1 and F5 may require changes.

Similarly, factor structure of consumer attitude showed in Figure 3 exhibited inadequate fit with CMIN/DF at 6.70, Chi-square at 1200.3 (p = 0.000), GFI at 0.723, NFI at 0.781, CFI at 0.806, and RMSEA at 0.132, further underscoring the need for model refinement. Four items were excluded from the consumer attitude model. The revised indices showed in Table 3 demonstrated improved fit with CMIN/DF at 2.83, Chi-square at 397.09 (p = 0.000), GFI at 0.874, NFI at 0.818, CFI at 0.842, and RMSEA at 0.078.

Table 4 Above depicts the model fitness values for Confirmatory factor analysis.

Regarding convergent validity, the AVE values for the five constructs range from 0.49 to 0.64, all of which surpasses the recommended threshold of 0.50, suggesting high convergent validity. The CR values vary from 0.79 to 0.90, further demonstrating the constructions’ reliability. For discriminant validity, the SICCR values range from 0.02 to 0.19, which is low enough to indicate that the constructs are unique and do not overlap significantly. Thus, the improved model has strong convergent and discriminant validity.

The factor structure of consumer purchase intention showed in ( Figure 4) exhibited excellent fit without modifications, with CMIN/DF at 2.35, Chi-square at 28.24 (p = 0.000), GFI at 0.976, NFI at 0.969, CFI at 0.982, and RMSEA at 0.065. The third model, which examined purchase intention, was represented by a single factor (F1) that exhibited an AVE value of 0.443 and a CR of 0.838 (Table).

These results indicate that the revised models provide an adequate representation of the constructs, achieving improved goodness of fit indices and ensuring the robustness of the assessment.

The structural model demonstrates an acceptable fit, as shown in Table 5, validating its suitability for examining the relationship between influencer marketing and consumer purchase intention. The CMIN/DF (2.89) falls within the acceptable range (<3), indicating a reasonable model fit. GFI (0.921), NFI (0.943), and CFI (0.934) exceed the recommended threshold of 0.9, signifying a well-fitting model. Additionally, RMSEA (0.043) is below the cutoff of 0.05, reflecting minimal approximation error. ¹⁴ While the p-value (0.000) indicates statistical significance, the overall fit indices confirm the practical adequacy of the model.

The hypothesis ( H1) posits that influencer marketing is positively related to consumer purchase intention. The results presented in Table 6 support this hypothesis (p < 0.001). The standardized coefficient for influencer marketing is 0.83 indicating a strong positive relationship with customer purchase intention. The model explains 68% of the variance in consumer purchase intention (R ² = 0.68).

Figure 5 illustrates a structural model that depicts the direct influence of influencer marketing on consumer purchase intention.

To evaluate the mediating effect of consumer attitude, we follow the guidelines of Ref. [ 6]. Mediation occurs if the direct relationship between the independent variable (X) and the dependent variable (Y) either disappears (full mediation, c’ = 0) or diminishes (partial mediation, c’ < c) with the introduction of a mediator.

The mediating role of customer attitude has been evaluated with three independent steps. First, the direct connection between influencer marketing and consumer buying intention was investigated. Second, the direct relationship between consumer attitudes and purchase intentions was investigated. Finally, consumer attitudes were evaluated as a moderating link between influencer marketing and consumer buying intention.

Table 7 presents the model fit measures of mediation analysis. The improved model outperforms the parsimonious model across all fit indices, demonstrating acceptable alignment with the observed data. It satisfies key goodness-of-fit criteria (e.g., lower CMIN/DF, higher GFI, NFI, and CFI, and a reduced RMSEA), confirming its validity and effectiveness for the mediation analysis.

Table 8 presents the outcomes of mediation study. In our study, the direct result of influencer marketing on purchase intention diminished when consumer attitude was included, confirming its mediating role. However, as the reduction in the direct effect did not meet the threshold for full mediation (0.46), the results indicate partial mediation. The mediating effect of consumer attitude was quantified at 0.39.

Further analysis revealed that consumer attitude accounted for a total effect of 0.83 in the link between influencer marketing and consumer buying intention. This value reduced to 0.39 upon incorporating consumer attitude as a mediator, substantiating its partial mediation effect.

For clarity, the breakdown of direct and indirect effects, illustrated in Figures 6, 7, 8, shows that all coefficients are statistically significant. Thus, H1 and H2 are supported for direct effect and indirect effect respectively.

The present study is guided by two primary questions: “How does consumer attitude mediate the impact of influencer marketing on purchase intention?” and “What factors enhance the effectiveness of endorsement marketing through consumer attitude?” This investigation delves into consumer perceptions and behaviors regarding endorsement marketing strategies and their consequential effects on purchasing behavior. Critical factors identified include influencer credibility, content quality, audience engagement, brand fit, and exposure frequency, which collectively shape consumer attitudes and subsequent buying intentions. The outcomes shows considerable positive connection between influencer marketing and purchase intention, suggesting that effectively executed strategies directly enhance buying intention ( See Figure 6). Furthermore, a direct link between consumer attitude and purchase intention establishes consumer attitude as a mediator ( See Figure 7). Path analysis results from the mediating model reveal significant relationships among the constructs studied, highlighting that influencer marketing strongly correlates with consumer attitude, which in turn influences purchase intention ( see Figure 8). These results emphasize that buyer’s attitude acts as a crucial moderator role, under- scoring the importance of trust and relatability in maximizing the efficacy of influencer marketing. Iinfluencer credibility significantly impacts consumer purchase intentions, reinforcing the important function of trust and attitudes in shaping buyer behavior. ⁴⁹ The important role of consumer attitudes which acts as a mediator between social media marketing and buyer intentions, particularly among Generation Z, which aligns with our findings. ⁴² In the cosmetic industry, while explore the effect of credible digital feedback on attitudes and subsequent buyer’s intentions, which further demonstrates that authentic feedback enhances consumer perceptions and finally purchase intentions. ¹⁵ Moreover, the findings are consistent which focusses the function of trust and attitude acting as moderators in the effectiveness of endorsements of celebrity and online reviews in influencing Generation Z’s purchase decisions. ⁵⁰

Electronic word-of-mouth (E-WOM) and social media marketing in the food industry promote trust, which serves as a crucial bridge in influencing purchase intentions. ⁵ Influencer marketing affects customer attitudes towards environmentally responsible purchase, particularly with regard to refurbished furniture, lend further credence to this notion. ³⁰

The relationship between brand image and green marketing tactics, highlighting the indirect impact of influencer campaigns on brand perception and pinpointing purchase intention as a mediator. ⁵³ Social self-congruity shows how influencers can improve consumer attitudes and behaviors by matching marketing tactics with their audience’s self-image. ⁵⁹ These findings highlight the importance of consumer attitude in determining the relationship between influencer marketing and purchase intentions. This study is the first to assess consumer attitude as a mediating factor in influencer marketing.