Jurnal Ekonomi Perusahaan ISSN: 0854-8154 .
, 2830-1560 .
The role of micro-influencers in social media marketing: A quantitative study on purchase intent using source credibility theory Yosef Dema1*.
Elisabeth Vita Mutiarawati2.
Yohanes Langgar Billy3 Department of Management.
STIE Tunas Nusantara.
Jl.
Budhi No.
Cawang.
Jakarta.
Indonesia, 13630 Department of Management.
Institut Bisnis dan Informatika Kwik Kian Gie.
Jl.
Yos Sudarso Kav 87.
Sunter.
Jakarta.
Indonesia, 14350.
Department of Communication.
Universitas Multimedia Nusantara.
Jl.
Scientia Boulevard.
Gading Serpong.
Tangerang.
Indonesia, 15810 Email Address: 1yosef@stietn.
id, 2elisabeth.
vita@kwikiangie.
id, 3johnbilly@umn.
*Corresponding author
ARTICLE INFORMATION ABSTRACT
Flow:
Received: June 11, 2025 Reviewed: August 18, 2025 Accepted: August 21, 2025 Published: August 21, 2025 Keywords:
micro-influencer, purchase intent, trustworthiness, attractiveness, source credibility theory How to cite:
Dema.
Mutiarawati.
, & Billy.
The role of micro-influencers in social media marketing: a quantitative study on purchase intent using source credibility theory.
Jurnal Ekonomi Perusahaan, 32.
, 8-26.
https://doi.
org/10.
46806/jep.
Copyright A Jurnal Ekonomi Perusahaan.
All rights reserved In the ever-evolving realm of digital marketing, this study delves into how micro-influencer credibilityAi conceptualized through the dimensions of expertise, trustworthiness, and attractivenessAishapes consumer purchase intent on social media platforms.
Grounded in Source Credibility Theory, the research employs a quantitative design, gathering responses from 270 social media users via a structured online survey.
Employing Structural Equation Modeling (SEM-PLS), the analysis uncovers that while trustworthiness and attractiveness significantly sway consumer behavior, expertise surprisingly falls short of exerting measurable impact.
This departure from prior emphasis on knowledge-based persuasion suggests a shifting consumer landscapeAi where authenticity and visual appeal resonate more than Even more striking is the discovery that variations in age, gender, and social media habits do not meaningfully moderate these relationships.
This uniformity underscores a broad psychological consistency in how consumers evaluate influencers.
Marketers and practitioners, therefore, are encouraged to recalibrate their strategies, prioritizing emotional resonance and personal branding over technical qualifications.
8 | https://doi.
org/10.
46806/jep.
The role of micro-influencers in social media marketing A INTRODUCTION A shocking and important message that needs addressing in this study is that microinfluencers now drive consumer purchase intent more effectively than traditional celebrity endorsements, yet brands still underutilize their potential in digital marketing First, research indicates that micro-influencers generate 60% higher engagement rates than macro-influencers, as their smaller but highly engaged audiences perceive them as more authentic and relatable (Forbes Agency Council, 2.
Second, trustworthiness and perceived expertiseAitwo key dimensions of Source Credibility Theory (Hovland.
Janis, & Kelley, 1.
Aiplay a critical role in influencing consumer behavior, with studies showing that 82% of consumers are more likely to follow a micro-influencerAos recommendation than a celebrity endorsement (Boerman.
Willemsen, & Van der Aa, 2.
Finally, the purchasing power of Gen Z and Millennials, who make up the largest share of social media users, is increasingly shaped by micro-influencer marketing, with 70% of these consumers relying on peer-driven endorsements over traditional advertisements (Lou & Yuan, 2.
Despite these compelling findings, many brands continue to allocate larger budgets to macroinfluencers and celebrities, overlooking the fact that micro-influencers can deliver higher ROI through targeted, trust-based consumer interactions.
This research aims to fill this gap by quantitatively examining how source credibility dimensionsAiexpertise, trustworthiness, and attractivenessAishape consumer purchase intent in microinfluencer marketing.
Despite the growing body of research on influencer marketing, an underexplored area is how micro-influencer credibility interacts with different consumer segments and product categories to shape purchase intent.
While studies confirm that micro-influencers are perceived as more authentic and trustworthy than macro-influencers (Boerman.
Willemsen, & Van der Aa, 2.
, there is limited research on whether this credibility varies by industryAifor instance, whether microinfluencers are more effective in niche markets such as sustainable fashion or fitness compared to mainstream consumer goods.
Furthermore, while Source Credibility Theory (Hovland.
Janis, & Kelley, 1.
emphasizes expertise, trustworthiness, and attractiveness as drivers of persuasion, most studies focus on macro-influencers and celebrities, leaving a gap in understanding how these credibility factors uniquely impact micro-influencers across various platforms like TikTok.
Instagram, and YouTube (Lou & Yuan, 2.
Additionally, existing research largely overlooks the role of consumer demographics, such as age, cultural background, and digital literacy, in moderating the relationship between micro-influencer credibility and purchase decisions (De Veirman.
Cauberghe, & Hudders, 2.
Addressing these gaps will provide a more nuanced understanding of how micro-influencers operate as persuasive marketing agents in an increasingly peer-driven digital landscape.
This research employs Source Credibility Theory (Hovland.
Janis, & Kelley, 1.
as its theoretical lens to examine how micro-influencers impact consumer purchase intent in social media marketing.
Source Credibility Theory posits that the persuasiveness of a communicator is determined by three key dimensions: expertise .
erceived knowledge or competenc.
, trustworthiness .
onesty and integrit.
, and attractiveness .
ikability and relatabilit.
(Ohanian, 1.
In the context of microinfluencer marketing, these credibility factors play a crucial role in shaping consumer attitudes, brand perception, and purchase decisions (Lou & Yuan, 2.
Unlike macroinfluencers, who often maintain celebrity-like status, micro-influencers build engagement through authentic peer-like relationships, making their credibility an .
Management Jurnal Ekonomi Perusahaan.
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March-August 2025 essential determinant of marketing effectiveness (Boerman.
Willemsen, & Van der Aa.
By applying Source Credibility Theory, this study explores how different aspects of credibility influence purchase intent and whether trust and relatability outweigh expertise in consumer decision-making.
Understanding these dynamics provides valuable insights for brands on optimizing influencer partnerships to enhance marketing impact and consumer trust.
The objectives of this research are formulated based on the guiding research questions, focusing on understanding the impact of micro-influencers on consumer purchase intent through the lens of Source Credibility Theory (Hovland.
Janis, & Kelley, 1.
First, this study aims to examine how the three dimensions of source credibilityAiexpertise, trustworthiness, and attractivenessAiinfluence consumer purchase intent in micro-influencer marketing (Ohanian, 1.
By analyzing these credibility factors, this research seeks to determine which dimension plays the most significant role in persuading consumers to make a purchase.
Second, it investigates whether the effectiveness of micro-influencers varies across different social media platforms, such as Instagram.
TikTok, and YouTube, providing insights into platformspecific engagement dynamics (Lou & Yuan, 2.
Third, this study aims to explore how consumer demographics, including age, gender, and social media usage habits, moderate the relationship between micro-influencer credibility and purchase intent, thereby identifying key audience segments that are more responsive to micro-influencer marketing strategies (Boerman.
Willemsen, & Van der Aa, 2.
By addressing these objectives, this research will contribute to both theoretical advancements in influencer marketing and practical strategies for brands to optimize their micro-influencer LITERATURE REVIEW Source Credibility Theory Source Credibility Theory (SCT), first articulated by Hovland.
Janis, and Kelley .
, offers a compelling lens through which to understand the power of persuasionAi anchoring the communicator's perceived credibility as the linchpin in influencing attitudes and behavioral shifts.
Rather than treating messages in isolation.
SCT foregrounds the messenger, asserting that what persuades is not merely what is said, but who says it (Ohanian, 1.
The theory dissects credibility into three interdependent pillars: expertise, trustworthiness, and attractivenessAieach contributing uniquely to the persuasive equation (Hovland et al.
, 1.
Expertise captures the audience's perception of the communicator's knowledge or authority in a specific domain (Eisend, 2.
, while trustworthiness conveys moral integrity, sincerity, and perceived honestyAioften the most decisive factor in forming belief (Lou & Yuan, 2.
Attractiveness, intriguingly, goes beyond mere physical allure to encompass emotional resonance, relatability, and social similarity (Ohanian, 1.
the age of micro-influencersAiwhere everyday individuals wield considerable swayAi SCT becomes especially salient.
Here, influence flows not from celebrity status but from the illusion of proximity, relatability, and peer authenticity (Boerman.
Willemsen, & Van der Aa, 2.
SCT thus provides a robust theoretical scaffold to decode why certain digital voices captivate, convert, and ultimately shape consumer purchase intent in the noisy ecosystem of social media.
Dema et al.
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The role of micro-influencers in social media marketing A Social Media Marketing Social Media Marketing (SMM) refers to the strategic use of social media platforms to create, share, and promote brand messages, engage with audiences, and drive consumer actions (Tuten & Solomon, 2.
It is characterized by two-way communication, enabling brands to build interactive relationships with consumers rather than relying solely on traditional, one-way advertising methods (Kaplan & Haenlein, 2.
SMM
can be categorized into organic marketing .
npaid content strategie.
, paid advertising .
ponsored posts and targeted campaign.
, influencer marketing .
ollaborations with digital personalitie.
, and community-driven engagement .
rand-created online communities and peer interaction.
(Ashley & Tuten, 2.
Additionally, platforms such as Instagram.
TikTok.
YouTube.
Facebook, and Twitter serve as distinct marketing ecosystems, each with unique audience behaviors, content formats, and algorithm-driven visibility patterns (Voorveld, 2.
The effectiveness of SMM relies on engagement metrics .
ikes, shares, comment.
, consumer-generated content .
eviews, testimonial.
, and trust-building mechanisms .
rand transparency, authenticity, and credibilit.
(Dwivedi et al.
, 2.
With the rise of personalized marketing and AI-driven consumer insights, social media has become a dominant force in digital marketing, shaping brand-consumer interactions and purchase behaviors in real time.
Consumer Demographics Consumer demographics refer to the statistical characteristics of a population that influence consumer behavior, preferences, and purchasing decisions (Schiffman & Kanuk, 2.
These characteristics are essential for market segmentation, targeting, and personalized marketing strategies, as they help businesses understand how different consumer groups respond to products, advertisements, and brand messaging (Kotler & Keller, 2.
Consumer demographics can be categorized into age, gender, income level, education, occupation, geographic location, and digital consumption habits (Solomon, 2.
In the context of social media marketing and influencer-driven commerce, specific demographic factors such as age, gender, and social media usage patterns play a significant role in shaping consumer attitudes toward micro-influencers and purchase intent (Djafarova & Rushworth, 2.
Research suggests that younger consumers (Gen Z and Millennial.
are more likely to trust and engage with influencers than older demographics, highlighting the generational differences in digital marketing responsiveness (Lou & Yuan, 2.
Understanding consumer demographics enables brands to design targeted marketing campaigns, optimize influencer collaborations, and enhance customer engagement strategies based on audience-specific needs and Theoretical Framework and Hypothesis Development Anchored in the foundational logic of Source Credibility Theory (Hovland.
Janis, & Kelley, 1.
, this study interrogates the psychological mechanisms through which a communicatorAos perceived legitimacyAiframed through expertise, trustworthiness, and attractiveness (Ohanian, 1.
Aimodulates consumer persuasion.
In the sphere of micro-influencer marketing, these dimensions take on renewed potency.
Unlike distant celebrities with impersonal glamour, micro-influencers, typically boasting a follower range between 1,000 and 100,000, inhabit a liminal space between peer and authority .
Management Jurnal Ekonomi Perusahaan.
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Boerman.
Willemsen, & Van der Aa, 2.
This study seeks to unravel how these components of credibility converge to shape purchase intent, while simultaneously accounting for the platform-specific ecosystems .
Instagram.
TikTok.
YouTub.
and demographic contours of the digital consumer landscape.
The proposed research model extends Source Credibility Theory into the intricate and dynamic terrain of contemporary digital marketing by conceptualizing credibility dimensionsAiexpertise, trustworthiness, and attractivenessAias independent variables influencing consumer behavioral intention.
Simultaneously, it explores whether the architecture of social media platforms, with their varying algorithmic behaviors, content presentation styles, and interaction cultures, acts as a moderator of this relationship (De Veirman.
Cauberghe, & Hudders, 2.
Additionally, demographic variables such as age, gender, and digital usage patterns are embedded as secondary moderators, grounded in empirical findings suggesting generational divides in media processing and influencer reception (Djafarova & Rushworth, 2.
These moderating factors are not mere background noise but active agents that may recalibrate the way credibility cues are internalized and acted upon by audiences.
To address the three central research objectives, this study formulates three hypotheses grounded in Source Credibility Theory.
The first (H.
posits that each of the three source credibility dimensionsAiexpertise, trustworthiness, and attractivenessAipositively influences consumer purchase intent in the context of microinfluencer marketing (Ohanian, 1.
The second (H.
asserts that the strength of this influence varies across different social media platforms, such as Instagram.
TikTok, and YouTube, suggesting that platform characteristics may condition the effectiveness of influencer traits (Lou & Yuan, 2.
The third (H.
examines the moderating role of consumer demographicsAiincluding age, gender, and social media usage patternsAi on the relationship between influencer credibility and purchase intent, aiming to identify audience segments that are more responsive to specific credibility cues (Boerman.
Willemsen, & Van der Aa, 2.
By empirically testing these hypotheses, the study advances both theoretical refinement in the application of SCT and offers practical insights for optimizing micro-influencer strategies across digital contexts.
METHODS
This research adopted a quantitative, cross-sectional framework to untangle the intricate relationship between the three core dimensions of source credibilityAiexpertise, trustworthiness, and attractivenessAiand their collective sway over consumer purchase intent in the realm of micro-influencer-driven social media marketing.
To rigorously test the theoretical model, the study employed Structural Equation Modeling using the Partial Least Squares (SEM-PLS) technique, renowned for its robustness in handling complex, multi-variable constructs and non-normal data distributions (Hair et al.
Respondent data were gathered via a digital survey instrument constructed through Google Forms and strategically circulated across multiple WhatsApp groups using the following link: https://forms.
gle/9Jih2RqwzokDuYDt7.
The questionnaire, built on a five-point Likert scale ranging from strong disagreement to strong agreement, drew upon established survey design principles outlined by Sugiyono .
purposive sampling technique guided the recruitment process, ensuring that only individuals who actively engage with social media platforms, follow micro-influencers Dema et al.
| 12
The role of micro-influencers in social media marketing A .
,000Ae100,000 follower.
, and have encountered product endorsements were includedAimaximizing relevance and contextual specificity (Creswell, 2.
Once responses were secured, the dataset underwent rigorous analytical treatment via WarpPLS software.
The measurement model was first scrutinized for internal consistency, convergent validity, and discriminant reliability before proceeding to the evaluation of the structural model.
Through this multi-stage analysis, the study probed not only the direct effects of source credibility traits on purchase behavior but also examined the subtle, possibly divergent moderating effects of user demographics and patterns of social media interaction (Hair et al.
, 2.
RESULTS
Validity and Reliability Test Table 1 presents the results of the validity and reliability tests for the four constructs used in the study: Purchase Intention.
Expertise.
Trustworthiness, and Attractiveness.
The PearsonAos correlation coefficients for all items across these constructs are reported hirgh to be above 0.
7, which exceeds the minimum requirement for construct validity, indicating that each item is appropriately correlated with its corresponding variable.
Furthermore, the CronbachAos Alpha values for Purchase Intention .
Expertise .
Trustworthiness .
, and Attractiveness .
are all well above the accepted threshold of 0.
70, demonstrating a high level of internal consistency and These findings confirm that the measurement instruments used in this research are both statistically valid and reliable for assessing the influence of influencer credibility on consumer purchase intent.
Table 1.
Validity & Reliability Test Descriptions Purchase Intent Expertise Trustworthiness Attrac-tiveness PearsonAos Correlation Coefficients Above 0.
Above 0.
Above 0.
Above 0.
CronbachAos Alpha Descriptions of Respondents The pie chart (Fig.
shows that a vast majority of respondentsAi90.
9%Aifall within the "5" age category, which likely corresponds to the youngest age group .
Gen Z) depending on the coding used in the questionnaire.
Only a small fraction of participants belong to other age categories: "4", "3", "2", and "1", each representing older age brackets or different generational cohorts.
This indicates that the respondent pool is highly concentrated in a single age group, suggesting that the survey results predominantly reflect the perspectives of a younger demographic, with minimal input from older age groups.
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RespondentsAo Age Based on the pie chart (Fig.
, the majority of respondentsAi92.
3%Aiare students ("Pelajar/Mahasiswa"), indicating that the sample is overwhelmingly composed of individuals currently in formal education.
Only a small percentage of participants are from other occupational groups: private employees ("Karyawan swasta"), civil servants ("Pegawai negeri"), entrepreneurs ("Wiraswasta"), and housewives ("Ibu Rumah Tangga").
These categories are represented by thin slices in the chart, suggesting minimal participation from non-student populations.
This demographic concentration implies that the findings of the survey predominantly reflect the views and experiences of the student community.
Figure 2.
RespondentsAo Occupation The pie chart (Fig.
illustrates the gender composition of the 276 survey A majority of the participantsAi56.
1%Aiare female (Perempua.
, while 9% are male (Laki-lak.
This indicates a moderate gender imbalance, with slightly more female than male respondents.
The difference suggests that the survey findings may reflect a stronger female perspective, although both genders are reasonably well represented in the data.
Figure 3.
Gender The pie chart (Fig.
reveals that the vast majority of respondentsAi88.
1%Ai reported using social media several times a day (Beberapa kali sehar.
, indicating a high level of daily engagement with digital platforms.
A smaller proportionAi6.
9%Aiuse social media once a day (Sekali sehar.
, while 3.
5% access it several times a week Dema et al.
| 14
The role of micro-influencers in social media marketing A (Beberapa kali semingg.
Only a very small fraction of respondents reported using social media less than once a week (Jarang kurang dari sekali semingg.
or never (Tidak These findings suggest that the respondent population is highly active on social media, which has implications for marketing strategies, especially those involving micro-influencers and digital engagement.
Figure 4.
Social Media Usage Frequency The pie chart (Fig.
indicates that the largest proportion of respondentsAi 6%Aispend more than 3 hours per day on social media, highlighting a high level of digital engagement.
A further 28.
7% of respondents reported using social media for 2 to 3 hours per day, while 16.
7% use it for 1 to 2 hours daily.
Meanwhile, 11.
indicated spending only 30 minutes to 1 hour, and a very small percentageAi1.
8%Ai reported using social media for less than 30 minutes each day.
These findings suggest that the majority of the sample are heavy social media users, which is relevant for understanding their exposure to online marketing, influencers, and digital content.
Figure 5.
Daily Time Spent on SocialMedia The chart (Fig.
shows that Instagram is the most frequently used social media platform among respondents, with 242 individuals .
6%) indicating it as their primary This is followed by TikTok, used by 211 respondents .
1%), and YouTube, selected by 160 respondents .
3%).
Other platforms show significantly lower usage:
Twitter/X with 63 users .
3%).
Facebook with 20 users .
4%), and LinkedIn with 13 users .
8%).
A small group of 10 respondents .
7%) mentioned using other These results suggest that visual and short-form content platforms dominate among the respondents, which may have significant implications for influencer marketing strategies and brand outreach targeting this demographic.
Management Jurnal Ekonomi Perusahaan.
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The Most Often Used SocialMedia Platforms The chart (Fig.
reveals that the majority of respondentsAi215 out of 270 .
6%)Aiinteract passively with influencers or brand content by only watching without engaging.
A smaller groupAi65 respondents .
1%)Aireported that they share influencer or brand content with friends or on their profiles.
Meanwhile, 53 respondents .
6%) stated they like or react to influencer posts, and 25 respondents .
indicated that they comment on influencer content.
A very small portionAi11 individuals .
1%)Aisaid they directly message influencers, and 6 respondents .
claimed to participate in influencer polls or questions.
These results suggest that while exposure to influencer content is high, active engagement remains relatively low, indicating that most interactions are observational rather than participatory.
Figure 7.
How Respondents Interacts with Influencers or Brand Content The chart (Fig.
shows that engagement with influencer-generated content varies among respondents, with a significant portion showing low to moderate Out of 270 responses, 73 respondents .
%) reported never engaging with influencer content .
, while 67 .
8%) said they rarely engage .
The highest concentrationAi85 respondents .
5%)Aiindicated a moderate level of engagement .
In contrast, fewer respondents are highly involved, with only 31 .
5%) selecting scale 4, and just 14 .
2%) indicating very frequent engagement .
These results suggest that while most respondents are aware of and occasionally interact with influencer content, only a small minority engage with it frequently, indicating a passive or selective engagement trend among the majority.
Dema et al.
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The role of micro-influencers in social media marketing A Figure 8.
Level of Engagement The chart (Fig.
indicates that among the 270 respondents, the most frequently followed content on social media is food and cooking (Makanan dan masaka.
, selected by 171 participants .
3%), followed closely by entertainment and gaming (Permainan dan hibura.
with 163 selections .
4%).
Other popular categories include fashion and beauty (Mode dan kecantika.
with 130 responses .
1%), travel and lifestyle with 107 .
6%), and technology and gadgets with 100 responses .
%).
Health and fitness attracted 85 followers .
5%), while finance and business (Keuangan dan bisni.
was followed by 91 respondents .
7%).
A small numberAi7 respondents .
6%)Aichose "Other" and specified their interests.
These findings suggest that lifestyle-related content dominates user preferences, with food, entertainment, and fashion being the leading areas of interest among social media users in the sample.
Figure 9.
Types of Content Mostly Followed on SocialMedia The chart (Fig.
shows that a majority of respondentsAi67.
8%Aireported following micro-influencers .
hose with 1,000 to 100,000 follower.
to obtain product In contrast, 32.
2% stated that they do not follow micro-influencers for this purpose.
This indicates that micro-influencers play a significant role in shaping consumer awareness and influencing purchase decisions among the surveyed The relatively high percentage of followers suggests that trust in peer-level content creators is prevalent, making micro-influencers an effective channel for brand promotion and consumer engagement.
Management Jurnal Ekonomi Perusahaan.
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March-August 2025 Figure 10.
Whether respondents follow micro-influencers for product The chart (Fig.
reveals that influencer recommendations have a notable impact on purchasing behavior.
Among the 270 respondents, 47.
4% stated they occasionally buy products based on influencer recommendations, making it the most common response.
5% reported rarely doing so, while 11.
1% indicated they often make purchases influenced by such endorsements.
A smaller segment, 9.
3%, claimed they never purchase products recommended by influencers.
These results suggest that while not all respondents frequently act on influencer suggestions, the majority do consider them to some degree, confirming the relevance of influencersAiespecially micro-influencersAias credible marketing agents in shaping consumer decisions.
Figure 11.
Frequency of purchasing products based on influencer recommendations The chart (Fig.
shows that the most influential factor in respondentsAo purchasing decisions is discounts and promotions, selected by 189 respondents .
%).
This is followed closely by reviews from other users, which influenced 153 respondents .
6%), and the authenticity and credibility of the influencer, cited by 113 respondents .
9%).
Meanwhile, personal recommendations from friends or family were considered important by 88 respondents .
6%), and visual appeal of the content influenced 77 respondents .
5%).
These results suggest that while influencer credibility and visual content play a role, financial incentives and peer reviews remain the most persuasive factors in driving consumer decisions on social media platforms.
Figure 12.
Drivers of Purchase Decisions on social media The chart (Fig.
shows that among the 270 respondents, a majority of 54.
indicated they have never used a discount code or affiliate link provided by a microinfluencer.
Meanwhile, 45.
6% reported having used such codes or links.
This finding suggests that while nearly half of the respondents are open to or have already engaged in influencer-driven promotions, more than half remain passive or uninterested in transactional interactions with influencer content.
It reflects a moderate level of conversion from micro-influencer campaigns, indicating potential for growth if trust and perceived value are strengthened.
Dema et al.
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The role of micro-influencers in social media marketing A Figure 13.
Level of Conversion Descriptive Statistics Table 2 presents the descriptive statistics for the key variables in the study: Purchase Intention.
Expertise.
Trustworthiness, and Attractiveness.
The results show that the average scores for all variables fall within the moderately high range on a 5-point Likert Attractiveness has the highest mean score (M = 3.
, suggesting that respondents generally perceive influencers as visually appealing.
This is followed closely by Expertise (M = 3.
and Trustworthiness (M = 3.
, indicating positive perceptions of influencers' credibility.
Purchase Intention has a slightly lower mean (M = 3.
, reflecting a moderate tendency to consider buying products recommended by The standard deviations for all variables range from 0.
845 to 0.
indicating a moderate level of variation in respondentsAo perceptions and behavioral intentions across the sample.
Table 2.
Descriptive Statistics Variables Mean Std.
Deviation Purchase Intent Expertise Trust-worthiness Attractiveness Goodness of Fit Test Table 3 presents the results of the Goodness of Fit Test for the structural model.
The Average Path Coefficient (APC) is 0.
138 with p = 0.
003, indicating a statistically significant average strength of the relationships among variables in the model.
The Average R-Squared (ARS) is 0.
902 and the Average Adjusted R-Squared (AARS) is 899, both with p-values less than 0.
001, suggesting that the model explains a very high proportion of variance in the dependent variables and is statistically robust.
terms of multicollinearity, the Average Block VIF (AVIF) is 4.
356, which is within the acceptable threshold of O5, indicating adequate collinearity within predictor blocks.
However, the Average Full Collinearity VIF (AFVIF) is 5.
085, slightly above the ideal threshold of 5, which may indicate a mild concern regarding collinearity across the entire model, though still close enough to be considered acceptable.
Overall, these results confirm that the model demonstrates a strong fit, high explanatory power, and acceptable collinearity diagnostics.
Table 3.
Goodness of Fit Test Criterion Average Path Coefficients (APC) Average R-Squared (ARS) Average Adjusted R-Squared (AARS) Average block VIF (AVIF) Average full collinearity VIF (AFVIF) R-square & Q-squared Test .
Management Coefficients Criterion P=0.
P<0.
P<0.
<=5
<=5
Jurnal Ekonomi Perusahaan.
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Trustworthiness, and AttractivenessAiserve as predictors and thus do not have RA or QA values reported.
The R-squared value of 0.
905 indicates that the model explains 90.
5% of the variance in Purchase Intent, signifying an excellent level of explanatory power.
Additionally, the Q-squared value of 0.
766 reflects strong predictive relevance, confirming that the model not only fits the sample data well but also performs well in predicting new or unseen data.
These results validate the structural modelAos robustness in capturing the impact of influencer credibility on consumer purchase decisions.
Table 4.
R-squared & Q-squared Variables Expertise Trustworthiness Attractiveness Purchase Intent R-squared Q-squared Path Coefficients Test The structural equation modeling analysis reveals a nuanced picture of how microinfluencer credibility influences consumer purchase intent and how this relationship may vary by social media platform and consumer demographics.
As shown in Table 7 and illustrated in Figure 1, the model evaluates both direct effects of source credibility dimensionsAiexpertise, trustworthiness, and attractivenessAiand their moderated effects through respondent characteristics (RESC: age and gende.
and social media usage patterns (SMPT).
In line with H1, the findings affirm that two of the three credibility dimensionsAitrustworthiness and attractivenessAiexert a significant positive influence on purchase intent.
Among these, trustworthiness emerges as the most powerful predictor, underscoring the central role of perceived honesty and reliability in shaping consumer decisions within influencer marketing.
This dimension significantly influences consumer intent, suggesting that authenticity remains a cornerstone of digital Attractiveness, defined by visual appeal and emotional resonance, also has a strong, statistically significant impact, confirming its persuasive power.
However, expertise, often assumed to be a cornerstone of source credibility, fails to reach This suggests that perceived knowledge alone does not necessarily compel consumers to act, especially in the peer-driven, visually curated world of microinfluencers.
Addressing H2, the analysis explored whether the effectiveness of source credibility differs across platforms.
The interaction terms involving social media patterns (SMPT)Aiincluding frequency of use, platform type, and user engagement styleAido not yield statistically significant results for any of the three credibility In other words, whether a consumer frequently uses Instagram, casually browses TikTok, or engages deeply with YouTube content, their evaluation of an influencerAos trustworthiness or attractiveness remains consistent.
This finding suggests that platform-based variance does not moderate the relationship between influencer credibility and purchase behavior, thereby refuting H2.
Turning to H3, the model assesses whether demographic characteristics (RESC) moderate the credibilityAeintent relationship.
Interestingly, only one interaction achieves significance: RESC y Expertise reveals a negative moderation effect, indicating that for some groupsAiparticularly younger or more digitally literate usersAithe influence of Dema et al.
| 20
The role of micro-influencers in social media marketing A expertise diminishes.
This finding suggests that conventional authority signals, such as technical knowledge or credentials, may hold less sway in younger audiences compared to emotional and visual cues.
However, no significant moderation was found for RESC y Trustworthiness or RESC y Attractiveness, indicating that these traits maintain a stable persuasive effect across demographic lines.
Table 5.
Summary of Path Coefficients & Interaction Effects Predictor Ie Outcome Expertise Ie Purchase Intent Trustworthiness Ie Purchase Intent Attractiveness Ie Purchase Intent RESC y Expertise RESC y Trustworthiness RESC y Attractiveness SMPT y Expertise SMPT y Trustworthiness SMPT y Attractiveness Path Coefficient () Ae0.
Ae0.
Ae0.
Significance Not significant Significant Significant Significant Not significant Not significant Not significant Not significant Not significant Figure 14.
Structural Model In summary, the results lend strong support for H1, as trustworthiness and attractiveness significantly predict purchase intent.
However.
H2 and H3 are not supported, as neither platform type nor most demographic variables significantly moderate these relationshipsAiexcept in the case of expertise.
The novelty of these findings lies in the diminished role of expertise in driving purchase behavior and the uniform power of trust and visual appeal across platforms and demographics.
These insights are critical for marketers looking to fine-tune influencer campaigns: building relatable, trustworthy personas may prove far more effective than relying solely on perceived knowledge or platform mechanics.
Management Jurnal Ekonomi Perusahaan.
Volume 32.
Issue 01.
March-August 2025
DISCUSSION
The results provide partial support for the proposed hypotheses.
The study confirms that trustworthiness and attractiveness play a crucial role in shaping consumer purchase intent in the context of micro-influencer marketing, highlighting the importance of perceived sincerity and visual appeal in influencing buying decisions.
However, perceived expertise does not appear to be a strong factor, suggesting that knowledge alone may not be as persuasive as trust and appearance when consumers evaluate Consequently, the second hypothesis is not supported, as trustworthiness stands out as the most influential dimension, followed by attractiveness, with expertise having the least impact.
Furthermore, the study finds no meaningful differences in how social media usage patternsAisuch as frequency of use, platform preference, and interaction styleAialter the relationship between influencer credibility and purchase Similarly, demographic factors such as age and gender do not significantly change this relationship.
These findings suggest that the influence of credible microinfluencers is broadly consistent across various user profiles and usage behaviors.
The findings of this study partially affirm the core assumptions of Source Credibility Theory, which proposes that a communicatorAos expertise, trustworthiness, and attractiveness influence the persuasiveness of their message (Hovland.
Janis, & Kelley, 1953.
Ohanian, 1.
In the context of micro-influencer marketing, trustworthiness and attractiveness emerged as significant predictors of purchase intent, suggesting that perceived integrity and visual relatability are more persuasive than professional competence.
This deviates from traditional expectations that expertise is a primary driver of credibility, as expertise showed no significant impact, particularly among certain demographic groups, where its effect was negatively moderated.
These results reflect a shift in consumer behavior toward valuing authenticity and emotional resonance over informational authority (Lou & Yuan, 2.
Moreover, the absence of significant moderating effects from social media usage patterns underscores the broad applicability and stability of credibility dimensions across different levels of platform Thus, the study reinforces the notion that in todayAos digital environments, trust and relatabilityAinot just knowledgeAiserve as the most effective levers of The findings of this study can be understood through the conceptual framework grounded in Source Credibility Theory, which posits that a communicator's trustworthiness, expertise, and attractiveness shape persuasive effectiveness (Hovland.
Janis, & Kelley, 1953.
Ohanian, 1.
In the context of micro-influencer marketing, the strong effects of trustworthiness and attractiveness on purchase intent suggest that consumers are primarily driven by influencers they perceive as sincere, relatable, and emotionally engaging, rather than solely by informational authority.
This departs from earlier assumptions that expertise is the most influential dimension, as the current study found it to be statistically non-significant, and further moderated negatively by demographic factorsAiindicating that younger or digitally fluent users may be more skeptical of authority and more responsive to perceived authenticity (De Veirman.
Cauberghe, & Hudders, 2017.
Lou & Yuan, 2.
The lack of significant moderation by social media usage patterns further suggests that the persuasive impact of influencer credibility is stable across different engagement behaviors, emphasizing that emotional trust and visual relatability now outweigh technical knowledge as primary drivers of consumer intention in digital marketplaces.
The novelty of this study lies in its discovery that, unlike earlier research which emphasized expertise as the most influential factor in shaping consumer behavior Dema et al.
| 22
The role of micro-influencers in social media marketing A (Ohanian, 1990.
Lou & Yuan, 2.
, expertise does not appear to play a dominant role in driving purchase intent.
Instead, trustworthiness and attractiveness emerge as the primary drivers of consumer decisions in micro-influencer marketing.
This finding departs from traditional perspectives that often treated attractiveness as a secondary or superficial trait (Djafarova & Rushworth, 2.
, revealing its persuasive power when combined with authenticity and relatability.
Additionally, the study uncovers that consumer demographics, particularly age and gender, may reduce the relevance of expertise, suggesting that younger or more digitally native audiences respond less to authority-based cues and more to perceived sincerity and appeal.
This contrasts with earlier findings that viewed demographic and platform-based segmentation as stable determinants of marketing outcomes (De Veirman et al.
, 2017.
Lim et al.
, 2.
Overall, these results challenge established credibility hierarchies and highlight the growing influence of emotional and visual engagement over technical knowledge in digital marketing contexts.
In response to these findings, brands and marketers should prioritize partnerships with micro-influencers who exhibit strong trustworthiness and relatable appeal, as these dimensions have proven to significantly influence consumer purchase intent (Ohanian, 1990.
Lou & Yuan, 2.
While expertise has traditionally been emphasized, its non-significant impactAiparticularly among certain demographic segmentsAisuggests that influencer campaigns should shift away from authority-based messaging and instead focus on building emotional connection and authenticity.
Since trustworthiness remains the most robust predictor and attractiveness also plays a persuasive role, marketers are encouraged to select influencers who are perceived as genuine and visually engaging, rather than strictly knowledgeable.
Furthermore, given that demographics such as age and gender moderate the influence of expertise, marketers should tailor messaging styles or platform choices when targeting diverse consumer groups (De Veirman et al.
, 2.
Influencer development programs should reinforce skills in honest communication, visual storytelling, and community engagement rather than relying solely on informational authority (Lim et al.
, 2.
These insights provide a foundation for more adaptive, evidence-based influencer strategies that reflect evolving digital consumer preferences.
CONCLUSION
One of the most surprising findings of this study is that expertiseAilong regarded as a cornerstone of influencer credibilityAidoes not significantly influence consumer purchase intent, especially among certain demographic groups.
While traditional frameworks such as Source Credibility Theory (Hovland.
Janis, & Kelley, 1953.
Ohanian, 1.
emphasize the persuasive power of a communicatorAos knowledge and competence, this research reveals a critical shift: trustworthiness and attractiveness have become more influential drivers, with expertise showing a non-significant effect and even being negatively moderated by respondentsAo characteristics.
This suggests that younger or more digitally savvy consumers may be less persuaded by authority-based cues and more responsive to authenticity and relatability.
The finding challenges longstanding assumptions in influencer marketing and signals a paradigm shift in digital persuasion, where emotional resonance and visual engagement now trump technical The application of Source Credibility Theory (Hovland.
Janis, & Kelley, 1953.
Ohanian, 1.
combined with a quantitative research method has proven effective in addressing the research questions concerning how micro-influencer credibility impacts .
Management Jurnal Ekonomi Perusahaan.
Volume 32.
Issue 01.
March-August 2025 consumer purchase intent.
This theory, which conceptualizes source credibility through the dimensions of expertise, trustworthiness, and attractiveness, provided a structured lens through which to measure the persuasive power of micro-influencers.
The findings reveal that trustworthiness and attractiveness significantly influence purchase intent, while expertise does notAia surprising result that suggests consumers may value emotional relatability and visual appeal more than perceived competence in social media contexts.
The quantitative approach, utilizing structured surveys and statistical path analysis, enabled a systematic evaluation of these relationships and the testing of demographic and behavioral moderators.
Although moderating effects from age, gender, and social media usage were not significant, the theory and method together effectively captured the key drivers of consumer behavior in influencer marketing.
Thus, the conceptual framework and empirical strategy were both appropriate and capable of generating robust and insightful answers to the research problems.
Despite offering rich insights into the persuasive power of micro-influencer credibility, this study is bounded by several methodological and conceptual constraints.
Reliance on self-reported online survey data invites classic response biases, such as social desirability and flawed recall (Podsakoff et al.
, 2.
Moreover, the theoretical frameworkAifocused solely on expertise, trustworthiness, and attractivenessAi sidesteps emerging attributes like authenticity or transparency, which are increasingly central in digital persuasion (Lou & Yuan, 2.
The sample's cultural and geographic specificity further narrows the study's external validity, limiting generalizability beyond its demographic scope (Bryman, 2.
In treating all product types as equal, the study also overlooks how different categories .
edonic vs.
utilitarian, high vs.
might alter how influencer traits are received.
Finally, the cross-sectional design, though efficient, captures only a temporal snapshot, impeding causal inference and the ability to track credibility dynamics over time.
These limitations chart a path for more longitudinal, nuanced, and context-sensitive future inquiries into influencer ACKNOWLEDGEMENTS The authors extend sincere appreciation to all parties who contributed to the completion of this study, especially the survey respondents for their valuable participation, and to academic advisors and colleagues for their insightful feedback and guidance.
Gratitude is also given to peers in digital marketing and communication studies for enriching discussions, and to the institutional research committee for their essential support and resources throughout the research process.
REFERENCES