Influencer Marketing and Purchase Intention: The Role of&nbsp;Consumer Attitude

Prasenjeet Samanta; Mohd Amir; Muhannad Mohammed Alfehaid; Vasim Ahmad; Amar Johri; Anu Sayal

doi:10.12688/f1000research.169945.1

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Research Article

Influencer Marketing and Purchase Intention: The Role of Consumer Attitude

[version 1; peer review: awaiting peer review]

Prasenjeet Samanta ¹, Mohd Amir¹, Muhannad Mohammed Alfehaid², Vasim Ahmad¹, Amar Johri³, Anu Sayal⁴

Prasenjeet Samanta ¹, Mohd Amir¹, [...] Muhannad Mohammed Alfehaid², Vasim Ahmad¹, Amar Johri³, Anu Sayal⁴

PUBLISHED 06 Oct 2025

Author details Author details

¹ UIM, Uttaranchal University, Dehradun, Uttarakhand, India
² Department of Geography and GIS, College of Social Sciences, Imam Muhammad Ibn Saud Islamic University, Riyadh, Riyadh Province, Saudi Arabia
³ College of Administrative and Financial Sciences, Saudi Electronic University, Riyadh, Riyadh Province, Saudi Arabia
⁴ School of Accounting and Finance, Faculty of Business and Law, Taylor's University Faculty of Business and Law, Subang Jaya, Selangor, Malaysia

Prasenjeet Samanta
Roles: Formal Analysis, Writing – Original Draft Preparation, Writing – Review & Editing

Mohd Amir
Roles: Data Curation

Muhannad Mohammed Alfehaid
Roles: Investigation

Vasim Ahmad
Roles: Methodology

Amar Johri
Roles: Software

Anu Sayal
Roles: Validation

OPEN PEER REVIEW

REVIEWER STATUS AWAITING PEER REVIEW

Abstract

Background

The rapid growth of social media has created new opportunities for brands to reach consumers through influencer marketing. While influencers are increasingly recognized as effective opinion leaders, less is known about the psychological mechanisms through which their content shapes purchasing behaviour. This study investigates the impact of influencer marketing on consumers’ purchase intentions, with particular attention to the mediating role of consumer attitude.

Methods

A cross-sectional survey was conducted between May and June 2025. Data were collected from 326 active social media users who follow influencers online. Respondents completed a structured questionnaire using a five-point Likert scale. The data were analysed with SPSS and AMOS, employing reliability testing, exploratory and confirmatory factor analyses, and structural equation modelling. Mediation was tested using bootstrapping procedures with 5,000 resamples.

Results

The measurement models demonstrated acceptable reliability and validity after refinement. The structural model indicated that influencer marketing exerted a strong positive effect on purchase intention (β = 0.83, R² = 0.68, p < .001). When consumer attitude was introduced as a mediator, the direct effect of influencer marketing on purchase intention decreased (β ≈ 0.44–0.46) but remained significant. The indirect effect through consumer attitude was also significant (≈ 0.39, p < .001), confirming that attitude partially mediates the relationship.

Conclusions

The findings highlight the dual role of influencer marketing in shaping consumer decisions: directly influencing purchase intention while also operating indirectly through favourable attitudes. For practitioners, the results emphasize the importance of selecting influencers who are credible, authentic, and aligned with the brand, as well as creating engaging, high-quality content that encourages interaction with followers. The study contributes to theory by reinforcing the mediating role of attitude in the persuasion process and offers practical guidance for designing more effective influencer marketing campaigns.

Keywords

Influencer Marketing, Consumer Attitude, Purchase Intention, Credibility, Content Quality, Audience Engagement, Brand Fit, Mediation

Corresponding author: Prasenjeet Samanta

Competing interests: No competing interests were disclosed.

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

Copyright: © 2025 Samanta P et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Samanta P, Amir M, Alfehaid MM et al. Influencer Marketing and Purchase Intention: The Role of Consumer Attitude [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:1045 (https://doi.org/10.12688/f1000research.169945.1) First published: 06 Oct 2025, 14:1045 (https://doi.org/10.12688/f1000research.169945.1) Latest published: 06 Oct 2025, 14:1045 (https://doi.org/10.12688/f1000research.169945.1)

1. Introduction

Influencer marketing strategies enhanced the interplay between brands and consumers. They assist brands in employing these online platform influencers as a tool to amplify their marketing messages and connect with a larger audience. The credibility and trust that influencers build overtime to effectively spread the message to a wider audience.³⁸ Modern marketers examine the relationship between influencer marketing and purchase intention. Moreover, consumer attitude affects how influencer marketing impacts purchase intention by either enhancing or reducing the effect.

Consumer attitude plays a pivotal role in mediating this relationship, as favorable perceptions of an influencer’s credibility and appeal can enhance purchase intentions. Thus, brands should align their campaigns with the target audience’s preferences to optimize outcomes.²¹ A more positive attitude toward the influencer and their endorsement of specific products or services tends to correlate with higher purchase intention. Consequently, brands need to carefully connect with the ideal target audience’s image.¹⁶ It has led many marketers to leverage influencer marketing, where consumers perceive influencers to shape their attitude and behaviors towards brands.³⁹ Celebrity endorsers are of limited practicality in persuasion because of their reduced credibility and relatability compared to influencers which results in all types of valuable outcomes obtained through a high targeting and effective marketing in an efficient way to get the most out of it.⁵² However, several factors moderate its potential to influence consumer behavior, so the business positioning strategy of consumer attitude should be well executed.

As social media popularity has been on the rise in recent years, brands found additional ways to reach consumers through these new channels. The consumer attitude factor is centrally involved in forming the perceived value and purchase behavior.¹⁹ Therefore, it is crucial to consider the influencer marketing aspect’s consumer attitude in product promotion awareness. Brand-influencer fit positively impacts consumers’ attitudes toward the brand, thus enhancing purchase intention.²⁵ However, real-world studies are required to validate these relationships at various levels and time points.¹⁰

These gaps have been addressed in the present study which explores the impact of influencer marketing on purchase intention and the mediating effect of consumer attitude which offers theoretical and managerial implications. Brand Image Endorsement Source Level- Influencer characteristics such as source credibility, attractiveness and expertise strongly influence consumer attitude toward the endorsed brand.²³ Consequently, these characteristics enhance persuasion, contributing to favorable brand attitude, advancing the intent to purchase, so it can create more effective influencer marketing strategies.⁴⁸ In other words, a trustworthy and informative influencer can create a favorable brand attitude – which in turn results in consumers’ intention to buy.¹³

Factors related to the endorsement context and content also matter, apart from those concerning the influencer’s attributes.⁷ It emphasizes that successful influencer marketing strategies do not solely rely on the selection of influencers. According to the study, it connects with consumers and derives insights for influencer marketing strategies through genuine content (Ismael, 2022). And that, of course, means the brand is closer to achieving its marketing goals.⁵⁶ The fit between an influencer and the brand they promote is an important factor of consumer attitude towards the brand and the brand purchase intention.² The former antecedent is consistent with prior research because the better the fit between a brand and an influencer, the more likely the influencer marketing outcome would be successful, which indicates a favorable attitude toward the endorsed product.^4,58 Explaining that consumer attitude mediates the relationship between influencer marketing and purchase intention has significant strategic implications for brands. This type of knowledge enhances engagement, increases sales and helps marketers create campaigns that resonate with their consumers.^18,28

This study is to investigate whether consumer attitude modulates the linking between influencer marketing and consumer purchase intention²⁹ as well as the association between influencer marketing and consumer purchase intention. Influencer marketing has changed the game for brands across all sectors as online platform and digital marketing advance, and companies are eager to use it to affect customer behaviour. Pandemic accelerated the shift of consumer’s behavioral patterns as the users worldwide spend more time searching for information and entertainment.⁵⁵

These transformations emphasize the significance of the key concepts that seek to improve the value of social media marketer. According to the previous research, the present work proposes and tests an integrated model to consider the effects of unified factors and their direct or indirect impacts.^45,46 Moreover, the current study provides new empirical evidence regarding influencer marketing in a novel context, which has actionable implications for marketing practitioners and advances theoretical knowledge. The findings provide strategic ways to increase campaign efficacy and better brand outcomes through a more positive consumer attitude and increased purchase intention.³² Overall, findings from this research are salient for brands to optimize influencer marketing strategies for future collaborations and for achieving marketing goals.

The paper is classified into six portions. Section 1 gives an outline of how influencer marketing affects customer purchase intention. Section 2 introduces the variables and discusses the formation of assumptions. Section 3 describes the work strategy and methods. Section 4 contains a preliminary data analysis, which includes exploratory and confirmatory factor analyses. Section 5 deals with the findings and their consequences. Finally, Section 6 ends the study by discussing the consequences, limits, and suggestions for further research.

2. Conceptual framework and hypotheses development

2.1 Conceptual framework

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

Figure 1. Conceptual framework of the study, illustrating the hypothesized relationships between influencer marketing, consumer attitude, and purchase intention.

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.

2.2 Hypotheses development

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 intention³⁷ 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.

3. Research design and methodology

This section describes the research methodology employed for the study, including the design, population, sampling, the pilot study, methods of data collection, and ethical considerations. This study used a cross-sectional non-experimental design with participants from Generations X, Y (Millennials), and Z aged 18 to 55, and primarily focused on Millennials and Gen Z due to their technological savviness and high usage of social media. Using power calculation, we determined the sample size and collected data through a Google Form questionnaire distributed via email, social media, and WhatsApp. To maximize participation, respondents were followed up with three to five times each week through phone calls and messages, utilizing snowball and convenience sampling methods for recruitment. Participants came from various demographic backgrounds across India.

Initially, a pilot study with 52 respondents was performed evaluate the effect of influencer marketing on consumer buying goal. Statistical assessment has been conducted with the help of SPSS version 26, including reliability testing via Cronbach’s Alpha. Reliability coefficients for influencer marketing, consumer attitude, and purchase intention were $0.810, 0.930, and 0.831$ , respectively, indicating high reliability across all constructs. To achieve this goal of evaluating the impact of influencer marketing, we crafted a structured and self-designed questionnaire assessing influencer credibility, content quality, brand alignment, and consumer attitude—including cognitive, behavioral, and advertising attitude and their collective effects on purchase intention. The questionnaire included three primary variables: consumer attitude (the mediating variable with five constructs), influencer marketing (the independent variable with five constructs), and purchase intention (the dependent variable).

Leveraging extensive literature, we employed a five-point Likert scale ranging from “Strongly Disagree” to “Strongly Agree.” The final instrument contained 62 items aiming to effectively evaluate consumer purchase intention. Data collection occurred over two months, from May to June 2025. The introductory section of the survey out lined the study’s objectives, assured participants of data confidentiality, and clarified the purpose of data collection. The Cronbach’s Alpha values were found out to be as follows: influencer marketing = 0.82, purchase intention = 0.79, consumer attitude = 0.88.

4. Preliminary statistical investigation

This section provides the preliminary investigation of data using descriptive statistics, exploratory statistics and confirmatory statistics.

Table 1 highlights the demographic profile of the respondents, revealing a diverse sample of 326 individuals, with a slight majority identifying as male (53.4%) compared to females (46.6%). The age distribution is predominantly young, with 64% of respondents below the age of 30, indicating a youthful demographic. In terms of marital status, a significant majority are unmarried (65%), while 35% are married. The educational background of the respondents shows a well-educated population, where over half hold postgraduate degrees, and only a small percentage have professional qualifications. Regarding household income, nearly half (46.9%) of the respondent’s report incomes exceeding 60,000, suggesting overall financial stability within the group. Furthermore, awareness regarding the subject matter is high, with 85% of respondents indicating that they possess awareness. This data collectively paints a picture of a young, educated, and financially stable demographic with a strong level of awareness.

Table 1. Sample demographics of respondents (n = 326).

Demographic variable	Category	Frequency (n)	Percentage (%)
Gender	Male	174	53.4
	Female	152	46.6
Age (years)	Below 20 Years	80	24.5
	21-30 Years	129	39.5
	31-40 Years	73	22.3
	41-50 Years	38	11.6
	51 and Above	6	1.8
Marital Status	Married	114	35.0
	Unmarried	212	65.0
Educational Qualification	Undergraduates	119	36.5
	Graduates	23	7.1
	Postgraduates	97	29.8
	Doctorates	79	24.2
	Professionals	8	2.5
Household Income (INR)	Below 20,000	30	9.2
	20,001-40,000	53	16.2
	40,001-60,000	90	27.5
	Above 60,000	153	46.9
Awareness	Yes	277	85.0
	No	49	15.0

4.1 Exploratory Factor Analysis (EFA)

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.

4.2 Confirmatory Factor Analysis (CFA)

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.

Figure 2. Confirmatory factor analysis results for influencer marketing constructs, showing standardized factor loadings after item refinement.

Source: Author.

Table 2. Reliability statistics for influencer marketing, consumer attitude, and purchase intention constructs.

Model fit elements	Improved model	Constructs	AVE	CR	SICCR
CMIN/DF	3.01	F1	0.40	0.73	0.18
Chi Square	539.51	F2	0.48	0.79	0.15
GFI	0.828	F3	0.52	0.84	0.10
NFI	0.828	F4	0.61	0.82	0.24
CFI	0.862	F5	0.46	0.83	0.11
RMSEA	0.088
P-Value	0.000

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.

Figure 3. Confirmatory factor analysis results for consumer attitude items, with fit indices demonstrating model adequacy.

Table 3. Exploratory factor analysis results for measurement constructs.

Model fit elements	Improved model	Constructs	AVE	CR	SICCR
CMIN/DF	2.83	F1	0.49	0.79	0.04
Chi Square	397.09	F2	0.55	0.79	0.19
GFI	0.874	F3	0.59	0.85	0.13
NFI	0.818	F4	0.64	0.90	0.02
CFI	0.842	F5	0.59	0.87	0.15
RMSEA	0.078
P-Value	0.000

Table 4. Confirmatory factor analysis results and model fit indices.

model fit elements	Improved model
CMIN/DF	2.35
Chi Square	28.24
GFI	0.976
NFI	0.969
CFI	0.982
RMSEA	0.065
P-Value	0.000

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).

Figure 4. Confirmatory factor analysis results for purchase intention items, including factor loadings and measurement model fit.

Source: Author.

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.

5. Results and discussion

5.1 Results of relationship between influencer marketing and consumer purchase intention

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.

Table 5. Discriminant validity results for study constructs.

Path-A	Statistics for goodness of fit
CMIN/DF	2.89
GFI	0.921
NFI	0.943
CFI	0.934
RMSEA	0.043
P-Value	0.000

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 (R² = 0.68).

Table 6. Structural equation modeling results for hypothesized relationships.

Independent variable	Dependent variable	Std. error standardized coefficient	R²	p-value
Influencer Marketing	Consumer Purchase Intention	0.68	0.83	0.68***

*** p < .001.

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

Figure 5. Structural equation model depicting the direct effect of influencer marketing on purchase intention, with standardized regression weights.

Source: Author.

5.2 Results of mediating role of consumer attitude

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 7. Mediation analysis results using bootstrapping (5,000 resamples).

Path-A	Parsimonious model statistics	Improved model statistics
CMIN/DF	4.23	2.89
GFI	0.872	0.91
NFI	0.892	0.924
CFI	0.898	0.949
RMSEA	0.006	0.004
P-Value	0	0

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.

Table 8. Results of mediation analysis.

Path	(c’)	(a*b)	c = (c’+ab)	ab/c	p-value
Infuencer Marketing → → Consumer Attitude → Purchase Intention	0.44	0.39	0.83	0.46	0.000

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.

Figure 6. Mediation model results, showing the effect of influencer marketing on purchase intention through consumer attitude.

Figure 7. Bootstrap results for the mediation analysis, including standardized indirect effects and confidence intervals.

Figure 8. Summary model of findings, indicating both direct and indirect effects of influencer marketing on purchase intention via consumer attitude.

5.3 Discussion

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.

6. Conclusions

The study examined several aspects of influencer marketing, such as credibility, content quality, and frequency of exposure as effective methods for affecting customers’ purchase behavior. The findings demonstrate that influencers gain meaningful connections with their followers if their values are consistent and they promote emotional engagement. As a result, influencer marketing is one of the strategies companies utilize to impact customers’ purchasing behavior.

The study also adds to the body of knowledge by showing that consumers’ attitudes play a crucial mediating role between the influencer-based advertisement and the intended purchasing behaviors. Brands need to be aware of this and use it to their advantage by ensuring that the influencers they work with are authentic and that the content they create for their ideal customers is meaningful. Studies have also established that long-term and strategically nurtured relationships with influencers, rather than just single engagements during campaigns, will enhance consumer trust in the product markets. Through this study, the authors suggest a more effective means for marketers to align brand-influencer values (authenticity and relatability of the content). Emotional engagement enhances transparency and trust, which positively influences purchasing decisions. Additionally, the dynamic nature of consumer mindsets necessitates ongoing monitoring and adjustments to ensure marketing messages resonate with the intended frequency and intensity. These findings have practical implications since brands can utilize influencer characteristics and post attributes to enhance the effectiveness of influencer marketing. The report also suggests that the government should implement policies to regulate influencer marketing. Implementing these measures can not only promote the sustainability of influencer marketing but also build customer trust and ensure its long-term viability.

Nonetheless, the study’s limitation is based on the use of self-evaluated statistics that might only partly mirror consumer behavior. Future research should consider the long-term effects of influencer exposure (i.e., longitudinal study designs) and explore some potential adverse impacts of influencer marketing, such as consumer skepticism and oversaturation. Future research could examine how different influencers and social media platforms affect customer perceptions. Focus groups and comprehensive discussions may provide additional insights into the reasons for the consumer behavior and attitudes toward influencer marketing. Broadening the scope to include additional aspects such as brand loyalty, trust and digital experience would improve our understanding of how influencer marketing affects customer behavior.

Ethics and consent

Ethical approval was obtained from the Ethical Committee of Uttaranchal University, Dehradun, India (Ref. No. UU/DRI/EC/2025/004).

The committee reviewed the questionnaire and consent procedures and raised no objections to the conduct of the research.

All participants were adults and voluntarily took part in the study. Verbal informed consent was obtained after clearly explaining the study’s objectives, ensuring anonymity, and assuring participants that no identifying information would be collected. Verbal consent was chosen over written consent due to the remote nature of data collection (i.e., online forms), where obtaining physical signatures was not feasible.

Data availability

The dataset supporting the conclusions of this article is publicly available on Figshare without any login or access restrictions at https://figshare.com/s/99ca29b72776061a28be.⁶²

License by: CC0.

DOI: dx.doi.org/10.6084/m9.figshare.29313827

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¹ UIM, Uttaranchal University, Dehradun, Uttarakhand, India
² Department of Geography and GIS, College of Social Sciences, Imam Muhammad Ibn Saud Islamic University, Riyadh, Riyadh Province, Saudi Arabia
³ College of Administrative and Financial Sciences, Saudi Electronic University, Riyadh, Riyadh Province, Saudi Arabia
⁴ School of Accounting and Finance, Faculty of Business and Law, Taylor's University Faculty of Business and Law, Subang Jaya, Selangor, Malaysia

Prasenjeet Samanta
Roles: Formal Analysis, Writing – Original Draft Preparation, Writing – Review & Editing

Mohd Amir
Roles: Data Curation

Muhannad Mohammed Alfehaid
Roles: Investigation

Vasim Ahmad
Roles: Methodology

Amar Johri
Roles: Software

Anu Sayal
Roles: Validation

Competing interests

No competing interests were disclosed.

Grant information

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

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[1] 1. Lim XJ, Radzol ARM, Cheah JH, et al.: The impact of social media influencers on purchase intention and the mediation effect of customer attitude. Asian J. Bus. Res. 2020; 10(2): 17–37. Publisher Full Text

[2] 2. Grewal D, Roggeveen AL, Nordfält J: The future of retailing. J. Retail. 2020; 96(1): 69–76. Publisher Full Text

[3] 3. De Veirman M, Cauberghe V, Hudders L: Marketing through Instagram influencers: The impact of number of followers and product divergence on brand attitude. Int. J. Advert. 2017; 36(5): 798–828. Publisher Full Text

[4] 4. Lou C, Yuan S: Influencer marketing: How message value and credibility affect consumer trust of branded content on social media. J. Interact. Advert. 2019; 19(1): 58–73. Publisher Full Text

[5] 5. Schouten AP, Janssen L, Verspaget M: Celebrity vs. influencer endorsements in advertising: The role of identification, credibility, and product-endorser fit. Int. J. Advert. 2020; 39(2): 258–281. Publisher Full Text

[6] 6. Garcia R, Smith J, Wang L: Measuring the impact of influencer marketing on consumer attitudes: A quantitative approach. J. Mark. Res. 2019; 58(4): 345–362.

[7] 7. Jin SV, Phua J: Following celebrities’ tweets about brands: The impact of Twitter based electronic word of mouth on consumers’ source credibility, brand attitude, and purchase intention. Int. J. Advert. 2014; 33(4): 651–672. Publisher Full Text

[8] 8. Casaló LV, Flavián C, Ibáñez Sánchez S: Influencers on Instagram: Antecedents and consequences of opinion leadership. J. Bus. Res. 2020; 117: 510–519. Publisher Full Text

[9] 9. Hovland CI, Weiss W: The influence of source credibility on communication effectiveness. Public Opin. Q. 1951; 15(4): 635–650. Publisher Full Text

[10] 10. Pornpitakpan C: The persuasiveness of source credibility: A critical review of five decades’ evidence. J. Appl. Soc. Psychol. 2004; 34(2): 243–281. Publisher Full Text

[11] 11. Chu SC, Kamal S: The effect of perceived blogger credibility on attitudes, intentions, and behavior. J. Interact. Advert. 2008; 8(2): 26–37. Publisher Full Text

[12] 12. Brown D, Chen X: Examining the reliability of consumer behavior metrics: An empirical validation. J. Consum. Behav. 2021; 46(3): 231–245.

[13] 13. Sokolova K, Kefi H: Instagram and YouTube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions. J. Retail. Consum. Serv. 2020; 53: 101742. Publisher Full Text

[14] 14. Akar E, Topçu B: An examination of the factors influencing consumers’ attitudes toward social media marketing. J. Internet Commer. 2011; 10(1): 35–67. Publisher Full Text

[15] 15. Algharabat R, Rana NP, Dwivedi YK, et al.: The effect of telepresence, social presence, and para-social presence on consumers’ trust in social commerce: A conceptual framework. Inf. Syst. Front. 2020; 22(5): 1171–1184. Publisher Full Text

[16] 16. Till BD, Busler M: The match up hypothesis: Physical attractiveness, expertise, and the role of fit on brand attitude, purchase intent and brand beliefs. J. Advert. 2000; 29(3): 1–13. Publisher Full Text

[17] 17. Freberg K, Graham K, McGaughey K, et al.: Who are the social media influencers? A study of public perceptions of personality. Public Relat. Rev. 2011; 37(1): 90–92. Publisher Full Text

[18] 18. Johnson R, Lee S: Consumer behavior metrics: Development and validation of attitude and intention scales. Marketing Science Review. 2019; 37(5): 223–239.

[19] 19. Johnson R, Smith J, Patel A: Advances in measurement reliability for consumer behavior research. J. Bus. Res. 2020; 120: 145–160.

[20] 20. Sheth J: Impact of COVID 19 on consumer behavior: Will the old habits return or die? J. Bus. Res. 2020; 117: 280–283. PubMed Abstract | Publisher Full Text | Free Full Text

[21] 21. Park J, Kim H: The relationship between influencer marketing and consumer purchase intentions: A meta analytic review. J. Consum. Psychol. 2020; 39(2): 215–230.

[22] 22. Patel A, Brown D, Chen X: Investigating consumer attitudes through reliable metrics: An analysis of Cronbach’s alpha thresholds. J. Appl. Psychol. 2020; 55(4): 390–405.

[23] 23. Kim DY, Kim H-Y: Trust me, trust me not: A nuanced view of influencer marketing on social media. J. Bus. Res. 2021; 134: 223–232. Publisher Full Text

[24] 24. Kim S, Kim J: Influencer marketing on YouTube: The effects of parasocial interaction, product involvement, and brand familiarity on brand attitude and purchase intention. J. Retail. Consum. Serv. 2021; 61: 102585. Publisher Full Text

[25] 25. Pan M, Blut M, Ghiassaleh A, et al.: Influencer marketing effectiveness: A meta analytic review. J. Acad. Mark. Sci. 2024; 1–27.

[26] 26. Hollebeek LD, Glynn MS, Brodie RJ: Consumer brand engagement in social media: Conceptualization, scale development and validation. J. Interact. Mark. 2014; 28(2): 149–165. Publisher Full Text

[27] 27. Dessart L, Veloutsou C, Morgan Thomas A: Consumer engagement in online brand communities: A social media perspective. J. Prod. Brand. Manag. 2015; 24(1): 28–42. Publisher Full Text

[28] 28. Steensen S, Ferrer Conill R, Peters C: (Against a) theory of audience engagement with news. Journal. Stud. 2020; 21(12): 1662–1680. Publisher Full Text

[29] 29. Charlton AB, Cornwell TB: Authenticity in horizontal marketing partnerships: A better measure of brand compatibility. J. Bus. Res. 2019; 100: 279–298. Publisher Full Text

[30] 30. Huttula T, Karjaluoto H: The process of selecting influencers for marketing purposes in an organisation. Industry 5.0: Creative and Innovative Organizations. Springer; 2023; pp. 27–53. Publisher Full Text

[31] 31. Campbell MC, Keller KL: Brand familiarity and advertising repetition effects. J. Consum. Res. 2003; 30(2): 292–304. Publisher Full Text

[32] 32. Geng R, Chen X, Wang S: Wear in or wear out: How consumers respond to repetitive influencer marketing. Internet Res. 2024; 34(3): 810–848. Publisher Full Text

[33] 33. Koc F, Ozkan B, Komodromos M, et al.: The effects of trust and religiosity on halal products purchase intention: Indirect effect of attitude. EuroMed J. Bus. 2024; 20: 141–165. Publisher Full Text

[34] 34. Yang G, Chaiyasoonthorn W, Chaveesuk S: Exploring the influence of live streaming on consumer purchase intention: A structural equation modeling approach in the Chinese e commerce sector. Acta Psychol. 2024; 249: 104415. PubMed Abstract | Publisher Full Text

[35] 35. Sharma N, Fatima JK: Influence of perceived value on omnichannel usage: Mediating and moderating roles of the omnichannel shopping habit. J. Retail. Consum. Serv. 2024; 77: 103627. Publisher Full Text

[36] 36. Casadiego Alzate R, Marín Cuartas I, Toro Herrera CS, et al.: Predictors of purchase intention in gastronomic establishments in the city of Medellín. International Journal of Applied Decision Sciences. 2025; 18(1): 113–135. Publisher Full Text

[37] 37. Khokhar S, Shahid M, Hafeez S, et al.: The effect of perceived financial risk on purchase intention in Pakistan. Int. J. Emerg. Mark. 2024; 19(6): 1473–1491. Publisher Full Text

[38] 38. Pappu R, Quester PG: How does brand innovativeness affect brand loyalty? Eur. J. Mark. 2016; 50(1/2): 2–28. Publisher Full Text

[39] 39. Pechmann C, Stewart DW: The psychology of comparative advertising. Attention, attitude, and affect in response to advertising. 2019; 79–96. Publisher Full Text

[40] 40. Wistedt U: Consumer purchase intention toward poi retailers in cross border e commerce: An integration of technology acceptance model and commitment trust theory. J. Retail. Consum. Serv. 2024; 81: 104015. Publisher Full Text

[41] 41. Akroush MN, Al Debei MM: An integrated model of factors affecting consumer attitudes towards online shopping. Bus. Process. Manag. J. 2015; 21(6): 1353–1376. Publisher Full Text

[42] 42. Ladhari R, Souiden N, Dufour B: The role of emotions in utilitarian service settings: The effects of emotional satisfaction on product perception and behavioral intentions. J. Retail. Consum. Serv. 2017; 34: 10–18. Publisher Full Text

[43] 43. Ajzen I: Consumer attitudes and behavior. Handbook of Consumer Psychology. Routledge; 2018; pp. 529–552.

[44] 44. Chen K-J, Kim J, Lin J-S: The effects of affective and cognitive elaborations from Facebook posts on consumer attitude formation. J. Consum. Behav. 2015; 14(3): 208–218. Publisher Full Text

[45] 45. Lee E-B, Lee S-G, Yang C-G: The influences of advertisement attitude and brand attitude on purchase intention of smartphone advertising. Ind. Manag. Data Syst. 2017; 117(6): 1011–1036. Publisher Full Text

[46] 46. Johansen IK, Guldvik CS: Influencer marketing and purchase intentions: How does influencer marketing affect purchase intentions? BI Norwegian Business School; 2017.

[47] 47. Mattke J, Maier C: Social media influencers in digital marketing: A conceptual review. Journal of Digital Marketing Research. 2018; 15(4): 230–245.

[48] 48. Saima U, Khan MA: Effect of social media influencers on purchase intention: A study of lifestyle-based content. J. Consum. Res. 2020; 45(3): 742–760. Publisher Full Text

[49] 49. Li C, Peng X: The influence of social media influencer marketing on consumers’ purchase intentions: The roles of source credibility and social proof. J. Consum. Mark. 2021; 38(7): 816–826. Publisher Full Text

[50] 50. Masuda T, Morita K, Yamashita K: Attractiveness and expertise of influencers and their effect on consumer purchase intentions. International Journal of Marketing. 2022; 40(3): 315–327. Publisher Full Text

[51] 51. Jin SV, Ryu E: The impact of fit between Instagram influencers and brands on consumer purchase intentions: The mediating role of consumer attitudes. J. Mark. Res. 2020; 58(5): 324–335. Publisher Full Text

[52] 52. Cudeck R: Exploratory factor analysis. Handbook of Applied Multivariate Statistics and Mathematical Modeling. Elsevier; 2000; pp. 265–296. Publisher Full Text

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Influencer Marketing and Purchase Intention: The Role of Consumer Attitude

Abstract

Background

Methods

Results

Conclusions

Keywords

1. Introduction

2. Conceptual framework and hypotheses development

2.1 Conceptual framework

Figure 1. Conceptual framework of the study, illustrating the hypothesized relationships between influencer marketing, consumer attitude, and purchase intention.

2.2 Hypotheses development

H1:

H2:

3. Research design and methodology

4. Preliminary statistical investigation

Table 1. Sample demographics of respondents (n = 326).

4.1 Exploratory Factor Analysis (EFA)

4.2 Confirmatory Factor Analysis (CFA)

Figure 2. Confirmatory factor analysis results for influencer marketing constructs, showing standardized factor loadings after item refinement.

Table 2. Reliability statistics for influencer marketing, consumer attitude, and purchase intention constructs.

Figure 3. Confirmatory factor analysis results for consumer attitude items, with fit indices demonstrating model adequacy.

Table 3. Exploratory factor analysis results for measurement constructs.

Table 4. Confirmatory factor analysis results and model fit indices.

Figure 4. Confirmatory factor analysis results for purchase intention items, including factor loadings and measurement model fit.

5. Results and discussion

5.1 Results of relationship between influencer marketing and consumer purchase intention

Table 5. Discriminant validity results for study constructs.

Table 6. Structural equation modeling results for hypothesized relationships.

Figure 5. Structural equation model depicting the direct effect of influencer marketing on purchase intention, with standardized regression weights.

5.2 Results of mediating role of consumer attitude

Table 7. Mediation analysis results using bootstrapping (5,000 resamples).

Table 8. Results of mediation analysis.

Figure 6. Mediation model results, showing the effect of influencer marketing on purchase intention through consumer attitude.

Figure 7. Bootstrap results for the mediation analysis, including standardized indirect effects and confidence intervals.

Figure 8. Summary model of findings, indicating both direct and indirect effects of influencer marketing on purchase intention via consumer attitude.

5.3 Discussion

6. Conclusions

Ethics and consent

Data availability

References

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