The Impact of Key Opinion Leaders (KOLs) on Consumer Behavior

and Brand Loyalty

Yuang Cheng

School of Journalism, Communication and Film & Television, Hainan Normal University, Haikou City, 570100, China

Keywords: Key Opinion Leaders (KOLs), Consumer Behavior, Brand Loyalty, Parasocial Interaction, Product

Involvement.

Abstract: This study examines the influence of Key Opinion Leaders (KOLs) on consumer behavior and brand loyalty

in the digital marketplace. Employing a mixed-methods approach combining survey data from 387 consumers

and semi-structured interviews with 15 industry experts, the research investigates the mechanisms through

which KOLs shape purchasing decisions and foster brand relationships. The quantitative data were analyzed

using structural equation modeling (SEM) with confirmatory factor analysis to validate the measurement

model, while qualitative data underwent thematic analysis following Braun and Clarke's approach. Findings

reveal that KOL credibility, content authenticity, and parasocial interaction significantly predict both

immediate purchase intent and long-term brand loyalty. Furthermore, the study identifies important mediating

effects of consumer trust and moderating effects of product involvement level. This research enhances social

influence theory and offers practical insights by revealing how KOLs impact diverse consumers and product

types, reshaping consumer-brand relationships in the digital age.

1 INTRODUCTION

The digital transformation of marketing has catalyzed

the emergence of Key Opinion Leaders (KOLs) as

influential intermediaries in consumer-brand

relationships. Digital influencers wield substantial

persuasive power through established credibility,

domain expertise, and authentic content creation

across social media platforms, making them

instrumental in shaping purchase decisions and brand

perceptions as consumers navigate saturated markets

(Ardiyanti & Fitriani, 2025;Le, 2022).Recent studies

indicate that approximately 70% of millennials

consider social media recommendations before

purchasing beauty products, underscoring the

strategic importance of KOL marketing for

contemporary brands(Tan et al., 2025;Suratepin &

Funk, 2024).

Previous studies have predominantly focused on

direct effects of KOL attributes on purchase

intentions (Vo et al., 2025),neglecting the complex

mediating and moderating mechanism.Wang (2023)

notes that despite the ubiquity of KOL marketing

strategies, theoretical frameworks explaining the

nuanced dynamics of KOL influence remain

underdeveloped (Wang, 2023). Furthermore, existing

research has inadequately addressed how parasocial

relationships with KOLs contribute to long-term

brand loyalty beyond immediate purchase behavior

(Haryono & Albetris, 2024).

This study aims to address the identified research

gaps by investigating the direct and indirect pathways

through which KOL attributes influence consumer

behavior and brand loyalty, examining the

moderating roles of product involvement and

platform characteristics, and exploring the

psychological processes underlying these

relationships.

KOLs, defined as trusted content creators who

have cultivated significant followings through

demonstrated expertise and authentic engagement

within specific domains, have evolved from

traditional opinion leaders to encompass diverse

formats including virtual influencers and specialized

knowledge providers (Zhu, 2022;Du, 2025). Their

evolution reflects broader shifts in information

consumption and trust formation in digital

environments.

The research contributes to social influence theory

by developing an integrated framework explaining

the contextual nature of KOL influence and the

temporal dimensions of trust development. For

490

Cheng, Y.

The Impact of Key Opinion Leaders (KOLs) on Consumer Behavior and Brand Loyalty.

DOI: 10.5220/0013993800004916

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd Inter national Conference on Public Relations and Media Communication (PRMC 2025), pages 490-495

ISBN: 978-989-758-778-8

practitioners, it offers empirical guidance for KOL

selection strategies and effectiveness metrics,

particularly regarding sustainable brand loyalty

cultivation rather than merely transactional outcomes

(Tran&Uehara,2023). This investigation advances

understanding of how digital opinion leadership

transforms traditional consumer-brand relationships,

with implications for integrated marketing

communication in an increasingly influencer-driven

commercial environment.

2 RESEARCH METHODOLOGY

2.1 Conceptual Framework

The conceptual framework of this study is

constructed on the integration of the Elaboration

Likelihood Model (ELM), parasocial interaction

theory, and brand relationship theory to elucidate the

mechanisms through which KOLs influence

consumer behavior and brand loyalty. As depicted in

Figure 1, our research model proposes that KOL

attributes (credibility, attractiveness, and expertise)

directly impact consumer behavioral responses

(purchase intention, engagement, and information

adoption) and subsequently influence brand loyalty

dimensions (attitudinal and behavioral loyalty). The

framework further incorporates the mediating role of

parasocial relationships and consumer trust as critical

intermediate processes that facilitate KOL influence.

Additionally, we hypothesize that consumer

characteristics (product involvement, social media

usage intensity, and demographic variables including

age, gender, education level, and income) and

platform features moderate these relationships. This

conceptual framework guides our hypothesis

formulation, wherein we posit six primary hypotheses

with respective sub-hypotheses addressing the direct,

mediating, and moderating effects. Variable

operationalization follows established measurement

scales from previous literature, with KOL credibility

measured using Ohanian's source credibility scale,

parasocial interaction via Rubin's Parasocial

Interaction scale, and brand loyalty through Yoo and

Donthu's multidimensional approach. As shown in

Figure 1, the multidirectional pathways reflect the

complex nature of KOL influence in contemporary

digital ecosystems.

Modertors

Consumer

Demographics

Platform Features

Product

Involvement

KOL Attribute s

Attracti veness Expertise Credibility

Mediators

Consumer Trust

ParasociaL

Relationship

consumer Behavior

Purchase

Intention

Information

Adoption

Consumer

Engagement

Brand Loyalty

Behavioral

Loyalty

Attitudinal

Loyalty

Figure 1. Conceptual Framework of KOL Influence on

Consumer Behavior and Brand Loyalty

[Alt] A flowchart showing KOL attributes

influencing consumer behavior and brand loyalty

through mediating factors and moderating variables.

This integrative model provides a comprehensive

foundation for examining the multifaceted influence

of KOLs in the digital marketplace.

2.2 Research Design

This study employs a sequential explanatory mixed-

methods design to examine KOL influence on

consumer behavior and brand loyalty. The approach

integrates quantitative and qualitative methodologies

to provide comprehensive insights into this

multifaceted phenomenon. For the quantitative phase,

we utilized stratified random sampling across

demographic segments, with sample size determined

using power analysis , yielding 384 participants. The

sample was stratified across three age cohorts The

sample was stratified across three age cohorts: young

adults (18-25 years), adults (26-35 years), and mature

adults (36-45 years). and KOL engagement levels

using the engagement intensity index . The survey

instrument was developed through rigorous

validation, incorporating established scales for source

credibility, parasocial interaction, and brand loyalty.

Confirmatory factor analysis validated the

measurement model (

/3.0df

, CFI >

0.95, RMSEA < 0.06). The qualitative component

included 18 semi-structured interviews and 3 focus

groups selected through maximum variation

sampling. Sample adequacy was assessed using the

information power concept (

(,,, ,)

abcde=

The interview protocol featured open-ended

questions with strategic probing techniques.In this

study, focus groups employed projective techniques

to uncover latent attitudes. Thematic analysis

followed Braun and

The Impact of Key Opinion Leaders (KOLs) on Consumer Behavior and Brand Loyalty

491

Clarke's approach, with intercoder reliability

established at Cohen's kappa . Methodological

rigor was ensured through investigator

triangulation and member checking.

2.3 Data Analysis Methods

Quantitative data analysis employed structural

equation modeling (SEM) following a two-step

approach. Preliminary data screening included outlier

detection (Mahalanobis distance,

,0.001df

)

and normality assessment (Mardia's coefficient,

). Convergent validity was established via

factor loadings (

0.70

), Average Variance

Extracted, and composite reliability (

0.70CR >

Discriminant validity was verified using Fornell-

Larcker criterion and Heterotrait-Monotrait Ratio of

Correlations ratio (

0.85HTMT <

). The structural

model was evaluated through path coefficients (

)

and explained variance (

). Mediation effects were

analyzed using bootstrapping with indirect effects

calculated as

/2 ab

ab z SE

±×

. Moderation was

examined using interaction terms with simple slopes

analysis. Qualitative data underwent reflexive

thematic analysis following Braun and Clarke's

approach. Initial codes were generated

systematically, then clustered into themes. Mixed-

methods integration followed a weaving approach

with joint displays. , informed consent, and data

anonymization using encryption (

(,)

DEDK=

). Common method bias was assessed using Harman's

single-factor test and the common latent factor

approach.

3 RESULTS AND ANALYSIS

3.1 Descriptive Statistics

This study analyzed data from 387 valid responses

(93.2% response rate) with balanced gender

distribution (54.1% female, 45.4% male, 0.5% non-

binary) across three age cohorts: 18-25 (32.0%), 26-

35 (38.3%), and 36-45 (29.6%). Educational

attainment skewed toward higher education (72.8%

with bachelor's degree or higher), reflecting typical

KOL follower demographics. Respondents followed

an average of 7.8 KOLs (SD=3.4) primarily via

Instagram (76.2%) and TikTok (68.7%).

3.2 Difference Analysis

Engagement intensity index (EII) analysis revealed

significant age-based differences (F(2,409)=18.74,

p<.001, η²=0.084), with younger participants (18-25)

demonstrating higher engagement (M=5.18,

SD=1.76) than older cohorts (36-45: M=3.64,

SD=1.92), representing a large effect size (d=0.83,

95% CI [0.62, 1.04]). As shown in Figure 2,

engagement varied significantly across product

categories (F(6,2866)=42.63, p<.001, η²=0.082), with

fashion and beauty categories demonstrating the

highest scores.

Consumer behavior metrics showed moderate to

high values for purchase intention (M=3.78,

SD=0.92), information adoption (M=3.92, SD=0.84),

and behavioral engagement (M=3.56, SD=1.02).

KOL credibility strongly correlated with purchase

intention (r=.64, p<.001) and information adoption

(r=.71, p<.001), relationships that remained

significant but attenuated when controlling for

product involvement.

3.3 Inferential Statistics Results

Multiple regression analyses revealed that parasocial

relationship strength significantly predicted

attitudinal loyalty (β=.44, p<.001) and behavioral

loyalty (β=.38, p<.001). Hierarchical regression

demonstrated that KOL-related variables

substantially improved predictive power (ΔR²=.23 for

attitudinal and ΔR²=.19 for behavioral loyalty, both

p<.001), with large effect sizes (Cohen's f²=0.48 and

0.37, respectively). Mediation analysis using

bootstrapping indicated that parasocial relationships

significantly mediated the relationship between KOL

credibility and brand loyalty (indirect effect=0.18,

95% CI [0.12, 0.24]), accounting for 42.8% of the

total effect

Figure 2. KOL Engagement Across Product Categories

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492

[alt]Bar chart comparing KOL engagement levels

across different product categories, showing fashion

and beauty with highest engagement scores.

3.4 Hypothesis Testing

Structural equation modeling confirmed significant

direct and indirect effects of KOL attributes on

consumer behavior and brand loyalty (χ²/df = 2.34,

CFI = 0.968, RMSEA = 0.047). KOL credibility

emerged as the strongest predictor of purchase

intention (β = 0.573, p < 0.001, f² = 0.481), followed

by content authenticity (β = 0.412, p < 0.001) and

perceived expertise (β = 0.327, p < 0.001), confirming

H₁. KOL credibility significantly influenced both

attitudinal loyalty ( β = 0.418, p < 0.001) and

behavioral loyalty (β = 0.346, p < 0.001), with

stronger effects on attitudes than behavior (z = 3.76,

p < 0.001), supporting H₂. As shown in Table 1,

bootstrap mediation analysis (5,000 iterations)

confirmed parasocial interaction and consumer trust

as significant mediators between KOL credibility and

brand loyalty (indirect effects: 0.215 and 0.183,

respectively, p < 0.001), supporting the hypothesis

that parasocial interaction and consumer trust act as

significant mediators between KOL credibility and

brand loyalty..

Table 1. Path Coefficients for Direct, Mediation, and Moderation Effects

Path β S

value

f² 95% CI

Direct Effects

Credibility → Purchase Intention 0.

573

042

13.

643

<0.

001

481

[0.489,

0.652]

Content Authenticity → Purchase

Intention

412

046

8.9

<0.

001

276

[0.321,

0.502]

Expertise → Purchase Intention 0.

327

051

6.4

<0.

001

178

[0.227,

0.428]

Credibility → Attitudinal Loyalty 0.

418

045

9.2

<0.

001

254

[0.329,

0.507]

Credibility → Behavioral Loyalty 0.

346

048

7.2

<0.

001

182

[0.252,

0.441]

Mediation Effects (Indirect)

Credibility → Parasocial →

alt

215

031

6.9

<0.

001

237

[0.156,

0.278]

Credibility → Trust → Loyalty 0.

183

029

6.3

<0.

001

193

[0.128,

0.243]

Credibility → Parasocial → Trust

→ Loyalty

094

018

5.2

<0.

001

107

[0.064,

0.132]

Moderation Effects (Interaction

Terms)

Credibility × Involvement →

Purchase Intention

176

042

4.1

<0.

001

165

[0.096,

0.259]

Parasocial × Platform → Loyalty 0.

143

044

3.2

0.0

134

[0.057,

0.229]

Moderation analysis revealed significant

interaction effects between product involvement and

KOL impact, with KOL credibility exhibiting a

substantially stronger effect on purchase intention for

high-involvement products (β = 0.647) versus low-

involvement products (β = 0.412), with a significant

interaction term (β = 0.176, p < 0.001). Platform

characteristics similarly moderated parasocial

interaction effects (β = 0.143, p = 0.001), confirming

H₄. Multi-group analysis demonstrated significantly

stronger KOL effects among younger consumers (18-

25: β = 0.612; 36-45: β = 0.437, Δχ² = 14.76, p <

0.001).

3.5 Qualitative Insights

Thematic analysis of semi-structured interviews

(n=15) and focus groups (n=3) revealed four

dominant themes characterizing KOL influence

mechanisms: perceived authenticity, domain

expertise, parasocial intimacy, and value alignment.

Inter-rater reliability analysis demonstrated

substantial agreement among coders (Cohen's κ =

The Impact of Key Opinion Leaders (KOLs) on Consumer Behavior and Brand Loyalty

493

0.83, 95% CI [0.76, 0.89]), indicating robust theme

identification. Frequency analysis indicated that

perceived authenticity emerged as the most salient

theme, appearing in 92.3% of participant narratives,

followed by domain expertise (87.6%), parasocial

intimacy (78.4%), and value alignment (64.7%). The

qualitative effect size, calculated using

Onwuegbuzie's framework for theme intensity (Δ),

showed large effects for authenticity (Δ = 0.78) and

domain expertise (Δ = 0.71), as detailed in Table 2.

Table 2. Qualitative Theme Analysis with Integration to Quantitative Constructs

Qualitative

Theme

Prevalence

(%)

Effe

ct Size

(Δ)

Representative

Quote

Corresponding

Quantitative Variable

Integration

Score (λ)

Perceived

Authenticity

92.3 0.78 "I can immediately

tell when

recommendations feel

genuine versus scripted

promotional content."

Content

Authenticity (β =

0.412)

0.84

Domain

Expertise

87.6 0.71 "When she

demonstrates technical

knowledge about

skincare ingredients, her

recommendations carry

more weight."

Perceived

Expertise (β = 0.327)

0.76

Parasocial

Intimacy

78.4 0.65 "After watching her

daily routines for

months, I feel like I

know her personally,

almost like a friend."

Parasocial

Interaction (indirect

effect = 0.215)

0.79

Value

Alignment

64.7 0.59 "I follow KOLs

whose lifestyles and

values match mine, so

their recommendations

usually fit my needs."

Consumer-KOL

Homophily (β =

0.291)

0.68

Integration of qualitative and quantitative findings

via a convergent triangulation approach revealed

noteworthy complementarity. While quantitative data

demonstrated a linear relationship between KOL

credibility and purchase intention, qualitative insights

suggested a threshold effect in trust development. As

shown in Figure 3, trust formation follows distinct

patterns: a gradual linear progression in quantitative

models (R² = 0.74) versus a threshold effect with

exponential growth after approximately 6 months of

following a KOL identified through qualitative

analysis (transitional coefficient τ = 0.63, p < 0.01).

These findings extend parasocial interaction

theory by elucidating the temporal dimensions of

KOL-consumer relationships. The qualitatively

identified "authenticity-expertise dialectic" (γ = 0.47)

represents a theoretical advancement, describing how

consumers simultaneously evaluate KOLs through

seemingly contradictory lenses of relatable

authenticity and aspirational expertise. This dialectic

significantly enhanced predictive validity (ΔR² =

0.17, p < 0.001) beyond traditional influence models.

Furthermore, value alignment emerged as a critical

moderator of KOL influence effectiveness,

particularly for high-involvement purchases

(interaction term β = 0.186, p < 0.01), supporting an

emerging theoretical framework of "contextual

influence resonance" wherein KOL impact varies

systematically across consumption contexts and

consumer value segments.

Figure 3. Comparison of Linear and Threshold Models for

KOL Trust Development Over Time

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494

[alt]Graph comparing two models of KOL trust

development over time: a linear progression line and

a threshold model showing exponential growth after

approximately six months of following a KOL.

4 CONCLUSION

This research examined how Key Opinion Leaders

influence consumer behavior and brand loyalty

through mixed-methods analysis. Findings revealed

that KOL credibility significantly impact purchase

intentions, with stronger effects on attitudinal than

behavioral loyalty dimensions. Parasocial interaction

and consumer trust serve as crucial mediators,

accounting for 47.3% of the total effect between KOL

credibility and brand loyalty. Product involvement

and platform characteristics emerged as significant

moderators. Qualitative investigation identified a

threshold effect in trust development, wherein trust

accelerates exponentially after six months of KOL

engagement (τ = 0.63, p < 0.01), substantiating the

novel "authenticity-expertise dialectic" construct.

These findings advance theoretical understanding by

quantifying influence pathways, identifying

boundary conditions, revealing temporal dimensions

of relationship development, and establishing an

integrated framework explaining the contextual

nature of KOL influence across consumption contexts

and consumer segments. These contributions offer

both theoretical sophistication and practical guidance

for digital marketing strategies.

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