Creator vs. Brand Content Efficiency
In paid advertising, does a brand achieve higher conversion efciency by boosting creator-produced content compared to boosting brand-produced content, and to what extent is the diference driven by perceived trust and reduced ad skepticism?
Creator-produced content generally achieves higher conversion efciency than brand-produced content in paid advertising, with the advantage substantially driven by greater perceived trustworthiness and reduced persuasion knowledge activation, though these efects are contingent on maintaining perceived authenticity and are moderated by infuencer size, content-creator ft, audience brand attachment, and platform context.
Abstract
Meta-analytic evidence indicates that creator-produced content achieves higher conversion efciency than brand-produced content in paid advertising, with social media infuencers demonstrating a small but signifcant advantage over brand-only advertising (d=0.16, p=.004). Field studies corroborate this fnding, with infuencer marketing generating more than twice the sales of sponsored advertisements in direct comparisons, and directed consumer-generated content yielding superior conversion rates and return on ad spend. The advantage is substantially driven by trust mechanisms: infuencer trustworthiness signifcantly predicts purchase intentions (b=.41, p<.001), and user-generated content is trusted 3.2 times more than brand-created claims. Reduced ad skepticism operates through lower persuasion knowledge activation—user-generated content does not trigger defensive processing or negative afect, leading to higher purchase intention.
However, the creator content advantage is contingent on several boundary conditions. The efect is strongest for mega-infuencers (d=0.33 vs. celebrities) but reverses for nano-infuencers (d=-0.46), depends on high content-infuencer ft, and disappears or reverses for consumers with high brand attachment who perceive infuencer partnerships as norm violations. Platform context also matters: creator content advantages are robust on Instagram and YouTube but celebrity endorsements outperform infuencer content on traditional media. Sponsorship disclosure presents a double-bind, with platform-initiated disclosures reducing trustworthiness while self-generated disclosures can enhance authenticity perceptions. Thus, while creator-produced content generally achieves superior conversion efciency through trust and reduced skepticism mechanisms, efectiveness depends critically on maintaining perceived authenticity and appropriate matching of infuencer characteristics to brand and audience contexts.
Paper search
We performed a semantic search using the query "In paid advertising, does a brand achieve higher conversion efciency by boosting creator-produced content compared to boosting brand-produced content, and to what extent is the diference driven by perceived trust and reduced ad skepticism?"
We retrieved the 499 papers most relevant to the query.
Screening
We screened in sources based on their abstracts that met these criteria:
• Paid Digital Advertising Context: Does this study examine paid advertising or sponsored content promotion across digital platforms (rather than organic content only)?
• Content Type Comparison: Does this study compare both creator-generated content and brand-generated content in advertising contexts (rather than examining only one content type)?
• Conversion Outcomes: Does this study measure conversion-related outcomes such as click-through rates, purchase intent, actual purchases, or conversion rates?
• Trust-Related Variables: Does this study measure trust-related variables or ad skepticism as outcomes or mediating factors?
• Study Design Quality: Is this study an experimental study, observational study with comparison groups, systematic review, or meta-analysis (rather than a case study, opinion piece, or purely descriptive study without empirical data)?
• Adult Target Audience: Does this study involve adult consumers (18+ years) as the primary target audience?
• Beyond Brand Awareness Only: Does this study measure outcomes beyond exclusively brand awareness or recall (i.e., includes conversion or trust/skepticism measures as specifed in previous criteria)?
We considered all screening questions together and made a holistic judgement about whether to screen in each paper.
Data extraction
We asked a large language model to extract each data column below from each paper. We gave the model the extraction instructions shown below for each column.
Content Comparison
• Extract details about the content types compared in paid/boosted advertising contexts, including:
• Specifc operationalization of 'creator-produced' vs 'brand-produced' content
• Whether the study involved paid promotion, boosted posts, or sponsored content (not organic)
• Type of creators involved (infuencers, users, celebrities, etc.)
• How brand-produced content was defned
• Any other content types tested for comparison
Conversion Metrics
• Extract all conversion efciency and performance metrics comparing creator-produced vs brand-produced content in paid advertising, including:
• Primary conversion measures (purchase rates, click-through rates, conversion rates, ROAS)
• Efect sizes and statistical signifcance of diferences
• Specifc numerical values, percentages, or ratios comparing the two content types
• Any secondary performance metrics (engagement, reach, impressions)
• Direction of efects (which type performed better)
Trust Mechanisms
• Extract data on trust, credibility, and ad skepticism as explanatory mechanisms for conversion diferences, including:
• Measures of perceived trust, trustworthiness, or credibility for each content type
• Ad skepticism, manipulative intent perceptions, or advertising recognition measures
• Statistical evidence of mediation (whether trust/skepticism explains the conversion diferences)
• Identifcation, similarity, or authenticity perceptions
• Any other psychological mechanisms explaining why one content type outperforms the other
Platform Context
• Extract details about where the paid advertising comparison took place, including:
• Specifc social media platform(s) or advertising channel(s)
• Type of paid promotion format (boosted posts, sponsored content, display ads, etc.)
• Platform-specifc features that may infuence results
• Cross-platform comparisons if conducted
Study Design
• Extract methodological details for assessing the quality and interpretability of the comparison, including:
• Study design type (experiment, feld study, survey, etc.)
• Sample size and characteristics
• How participants were recruited and assigned to conditions
• Control variables and potential confounds addressed
• Measurement timing (immediate vs delayed efects)
Product Context
• Extract details about the products, brands, or categories involved in the creator vs brand content comparison, including:
• Specifc product categories or industries tested
• Brand characteristics (size, familiarity, luxury vs mass market)
• Product-creator ft or relevance considerations
• Any product/brand factors that moderated the efectiveness diferences
Audience Factors
• Extract characteristics of the target audience that may moderate the efectiveness of creator vs brand content in paid advertising, including:
• Demographic characteristics (age, gender, income)
• Social media usage patterns and platform familiarity
• Prior brand relationships or purchase history
• Creator following or engagement behaviors
• Any audience segments where efects difered
Characteristics of Included Studies
This systematic review synthesizes evidence from 40 sources examining the comparative efectiveness of creator-produced versus brand-produced content in paid advertising contexts, with particular attention to trust mechanisms and ad skepticism as explanatory factors. See appendix 1.1
The included studies predominantly employed experimental designs (28 studies), with several feld studies and surveys providing real-world validation. Instagram emerged as the most frequently studied platform, followed by YouTube and Facebook. Sample sizes ranged from 131 to 13,766 participants, with the meta-analysis by Jiyoung Lee et al. providing the largest aggregated sample.
Effects on Conversion and Performance Metrics
Primary Conversion Findings
Study
Jiyoung Lee et al., 2024
V. Diwanji et al., 2022
Mira Mayrhofer et al., 2019
Eleni Ntousi et al., 2025
Qianhui Hou et al., 2023
Content
Creator (SMIs)
Creator (UGV)
Creator (UGC)
Creator (DCGC)
Creator (non-sponsored)
Yosra Jarrar et al., 2020 Mixed
SMIs more efective than brand-only advertising d=0.16, p=.004
Higher brand attitudes and purchase intentions for UGV vs. BGA in high-involvement conditions
User-generated content leads to higher purchase intention
Higher conversion rates and superior ROAS for DCGC
t(190)=2.79, p<.01 for attitudes
Signifcant negative indirect effect for brand content
Not specifed
Purchase intention: UGR=4.098, IR=4.217, SIR=3.685 p=0.014
Infuencer posts: 736 sales; Sponsored posts: 332 sales
Engagement higher for sponsored (129,891 vs. 63,056)
Tarun Sharma et al., 2025 Creator
Chen Lou et al., 2019 Creator
Shahan Abbas et al. (n.d.) Creator (UGC)
K. Bentley et al., 2024 Brand (for high BA)
M. Cheng et al., 2024 Organic
Dr. Gunjan Sharma et al., 2025 Mixed
Up to 3x higher engagement rates; ROI of $5.78 per dollar spent
Infuencer marketing yields 11x ROI of traditional advertising
38% higher engagement rates; 47% higher sales velocity for high-UGC products
High BA consumers: Lower WTP for SMI posts ($18.52 vs. $21.05)
Some campaigns achieving 11x returns
Trust afects brand awareness (b=.22, p<.001) and purchase intentions (b=.41, p<.001)
UGC trusted 3.2x more than brand claims
Signifcant diferences for purchase intentions
Sponsored videos cost 0.19% of reputation Larger efect for larger audiences
Social media: 5-7% conversion; E-commerce: 6-8% conversion ROAS: 4:1 to 5:1
The meta-analytic evidence provides the most robust estimate, with SMIs demonstrating a small but signifcant advantage over brand-only advertising (d=0.16). Notably, the meta-analysis found no signifcant diference between SMIs and celebrity endorsers overall (d=0.07, p=.303), though mega-infuencers showed stronger efects than celebrities (d=0.33, p=.012) while nano-infuencers showed weaker efects (d=-0.46, p=.024).
Field study evidence from Yosra Jarrar et al. reveals an interesting paradox: while infuencer marketing generated more than twice the sales (736 vs. 332), sponsored advertisements achieved substantially higher engagement metrics (129,891 vs. 63,056). This suggests that conversion efciency and engagement metrics may not align, with creator content potentially driving more meaningful behavioral outcomes despite lower surface-level engagement.
The magnitude of efects varies considerably. At the high end, Chen Lou et al. report that infuencer marketing yields 11 times the ROI of traditional advertising, while Tarun Sharma et al. document engagement rates up to three times higher for creator content. However, these efects appear context-dependent, as K. Bentley et al. found that consumers with high brand attachment actually respond more favorably to brand-originated posts than SMI posts.
Secondary Performance Metrics
Study
Emma Truvé et al., 2019
Kirsten Cowan et al., 2018
A. Schouten et al., 2019
Guido Grunwald et al., 2025
Chen Lou et al., 2019a
Metric
Purchase intention
Brand attitudes
Purchase intention
Purchase intentions
Consumer engagement
Creator Content
Higher for infuencer posts
Higher with infuencer + UGC
Higher for infuencer endorsements
Slightly higher on social media (not signifcant)
Signifcantly higher liking and commenting
Brand Content
Lower for company-sponsored posts
Higher with celebrity + MGC
Lower for celebrity endorsements
Higher for celebrity ads on TV
Lower engagement; interests in online stores still positive
The pattern across secondary metrics reinforces the primary fnding that creator content generally outperforms brand content, though the advantage varies by context and platform. Infuencer-promoted ads on Instagram receive signifcantly higher engagement in terms of consumer liking and commenting compared to brand-promoted ads. However, consumers also demonstrate positive interest in online stores through brand-promoted content, suggesting complementary rather than purely substitutive efects.
Trust and Skepticism as Explanatory Mechanisms
Evidence for Trust as a Mediator
Study
Chen Lou et al., 2019
Trust/Credibility Measure
Trust in branded posts
A. Schouten et al., 2019 Trustworthiness
Matthew A. Hawkins et al., 2024 Infuencer trust
D. Balaban et al., 2021 SMI trustworthiness
M. Kolářová et al., 2018 Trustworthiness, expertise
Shahan Abbas et al. (n.d.)
Authenticity indicators
Direction of Efect
Infuencers' trustworthiness positively infuences trust
Infuencers more trusted than celebrities
Mediation Evidence
Trust signifcantly afects brand awareness (b=.22) and purchase intentions (b=.41)
Trustworthiness mediates endorser type → advertising efectiveness
Congruent image increases trust Infuencer trust mediates ft → purchase intention
Paid partnership disclosure positively afects trustworthiness
Indirect efect via CPK and trustworthiness is signifcant
Improve micro-celebrity efects Mediate efect on purchase intention and brand trust
67% increase in consumer trust
UGC reviews trusted 3.2x more than brand claims
The evidence consistently supports trust as a key mechanism explaining creator content advantages. Chen Lou et al. demonstrate that infuencer trustworthiness positively afects followers' trust in branded posts, which subsequently infuences purchase intentions with a substantial efect (b=.41, p<.001). Similarly, Schouten et al. establish that trustworthiness mediates the relationship between endorser type and advertising efectiveness, with infuencers perceived as more trustworthy than traditional celebrities.
Infuencers derive their trust advantage from multiple sources. Perceived similarity to followers positively infuences trust, as does perceived authenticity and relatability. Abbas et al. quantify this efect, fnding that authenticity indicators in user-generated content increase consumer trust by 67%, with user-generated performance reviews trusted 3.2 times more than brand-created claims.
Evidence for ReducedAd Skepticism
Study Skepticism Measure Efect on Creator Content Efect on Brand Content
Mikyoung Kim et al., 2018 Inferences of manipulative intent Mediates sponsorship × content type interaction
Guolan Yang et al., 2024 Inferences of manipulative intent More prominent for sponsored comparative posts
Higher for sponsored promotional content
Lower perceived authenticity
Mira Mayrhofer et al., 2019
K. Majid et al., 2019
Persuasion knowledge activation
Advertising skepticism
Kaoutar Sarhour et al., 2025 PKA activation
Rishi Dwesar et al., 2025
Skepticism toward advertising
Lower for UGC; no negative affect triggered
Consumer-disseminated content may bypass skepticism
Lower for UGC (perceived as authentic)
Reduced when reviews precede ads
Higher for disclosed ads and brand posts
High skeptics show greater online purchase intentions
Higher for macro-infuencer content
Reduced when combined with online reviews
The Persuasion Knowledge Model provides a theoretical framework explaining why creator content often outperforms brand content. Mayrhofer et al. found that user-generated content does not trigger persuasion knowledge and subsequent negative afect, leading to higher purchase intention compared to disclosed advertisements and brand posts. Kaoutar Sarhour et al. specifcally examined Generation Z and found that UGC is perceived as more authentic due to its lack of overt commercial intent, which reduces Persuasion Knowledge Activation and fosters higher trust.
Inferences of manipulative intent emerge as a particularly potent mechanism. Guolan Yang et al. found that such inferences are more prominent than counterarguing in explaining negative consumer responses to sponsored content, suggesting that consumers considerably value the genuineness behind product promotion from infuencers. Kim et al. demonstrate that consumer inferences of manipulative intent serve as a mediator for the interaction efects between content sponsorship and content types.
The Disclosure Paradox
Multiple studies reveal a nuanced relationship between disclosure, trust, and conversion:
Study
Disclosure Type
D. Balaban et al., 2021 Paid partnership tool
S. Kim et al., 2019
Parker J. Woodroof et al., 2020
Zeynep Karagür et al., 2021
Sponsorship disclosure
Ambiguous vs. clear disclosure
Platform-initiated vs. self-generated
Efect on Trust
Positive (increases trustworthiness)
Increases suspicion about ulterior motives
Clear disclosure increases transparency perceptions
Platform-initiated reduces trustworthiness
Serena Iacobucci et al., 2020 Instagram branded content tool Higher ad recognition
Efect on Purchase Intention
Positive outcomes
Decreases when review is positive
Transparency afects product effcacy perceptions
Transparency can increase engagement (transparency bonus)
Negatively afects brand attitude and eWOM
The evidence reveals a disclosure paradox. While clear sponsorship disclosure can enhance transparency perceptions and trustworthiness in some contexts, it can also trigger persuasion knowledge activation and reduce favorable outcomes in others. Zeynep Karagür et al. identify both efects, noting that platform-initiated disclosures negatively relate to trustworthiness while also generating a "transparency bonus" where consumers appreciate honest disclosure.
Moderating Factors
Platform and Context Effects
Platform
Creator Advantage
Instagram Generally yes
YouTube
TikTok
TV
Yes (with caveats)
Context-dependent
Moderated by popularity
Brand/celebrity favored
Key Findings
Higher engagement for infuencer-promoted ads; native ads resemble user posts
UGV outperforms BGA in high-involvement conditions; sponsored videos cost 0.19% reputation
Blogs more efective than Facebook for expertise-driven campaigns; hedonic content more efective on Facebook
Popular creators experience negative sponsorship efects; less popular creators do not
Celebrity ads more efective than infuencer ads on traditional media
Platform characteristics signifcantly moderate efectiveness. Christian Hughes et al. found that blogs represent high-involvement, low-distraction environments where source expertise matters more, while Facebook represents a low-involvement, high-distraction environment where hedonic content is more efective. Cross-platform research by Guido Grunwald et al. reveals that celebrity ads are more efective in traditional media, while infuencer ads show only slightly higher (non-signifcant) purchase intentions on social media.
Influencer and Creator Characteristics
Creator Type
Mega-infuencers
Micro-celebrities
Nano-infuencers
Popular creators (large following)
Less popular creators
Relative Efectiveness
More persuasive than celebrities (d=0.33)
More efective than traditional celebrities
Less persuasive than celebrities (d=-0.46)
Stronger negative sponsorship efects
No negative sponsorship efect
Key Mechanism
Balance of reach and credibility
Higher trustworthiness and expertise mediate efects
May lack sufcient credibility
Higher expectations lead to greater norm violation perceptions
Lower baseline expectations
The meta-analysis reveals a "sweet spot" for infuencer efectiveness: mega-infuencers outperform celebrities (d=0.33, p=.012), but nano-infuencers are actually less persuasive than celebrities (d=-0.46, p=.024). This suggests that perceived credibility serves as a crucial moderator, with infuencers needing sufcient following to establish credibility while maintaining authenticity.
M. Cheng et al. document a reputation-burning efect where posting sponsored videos costs infuencers 0.19% of their reputation (measured as subscriber count), with this efect being stronger among infuencers with larger audiences. Walsh et al. corroborate this on TikTok, fnding that the negative efect of sponsorship on consumer engagement is observed only among popular creators with large followings.
Brand and Product Factors
Factor
Brand familiarity
Brand attachment
Brand size (small vs. large)
Product involvement
Content-infuencer ft
Efect on Creator vs. Brand Comparison
Micro-celebrities more efective with familiar brands
High BA consumers prefer brand posts over SMI posts
Small brands beneft more from popular creator sponsorship
UGC more efective for high-involvement products
High ft mitigates reputation-burning
Supporting Evidence
Combination with no disclosure most efective
Norm violation mediates negative SMI efects
Authenticity perceptions enhanced for small brands
Greater elaboration leads to stronger efects
Congruence enhances trust
Brand attachment emerges as a critical moderator that can reverse the typical creator advantage. K. Bentley et al. found that consumers with high brand attachment respond less favorably to SMI posts, perceiving infuencer partnerships as a norm violation. This efect is mediated by perceptions of the relationship between brand and SMI, suggesting that highly attached consumers prefer direct brand communication.
Product involvement also signifcantly moderates efects. Diwanji et al. found that user-generated video reviews elicited signifcantly greater efects on brand attitudes and purchase intentions compared to brand-generated ads, but only when product involvement was high. This aligns with elaboration likelihood model predictions that high-involvement contexts favor content perceived as more credible and authentic.
Audience Characteristics
Audience Factor
Generation (Gen Z, Millennials)
Advertising skepticism
Instagram usage frequency
Platform familiarity
Moderating Efect
Stronger creator preference
Skeptics favor consumer-disseminated content
Better recognition of ads
Afects persuasion knowledge activation
Evidence
Digital natives more infuenced by infuencers; recognize promotional nature of content
Greater online purchase intentions when exposed to consumer promotions
Positive relationship with ability to recognize sponsored posts
Long-term followers (2+ years) show diferent response patterns
Generational diferences signifcantly infuence responsiveness to creator content. Tarun Sharma et al. report that over 70% of consumers, particularly Millennials and Gen Z, make purchase decisions based on infuencer recommendations. However, Generation Z's status as digital natives means they recognize the promotional nature of infuencer content, leading to increased skepticism when content appears overtly commercial.
Advertising skepticism operates in counterintuitive ways. K. Majid et al. found that those most skeptical of advertising actually had greater intentions to purchase online when exposed to consumer-disseminated promotions, suggesting that creator content may be particularly efective for reaching ad-averse consumers.
Synthesis
Reconciling Heterogeneous Findings
The evidence reveals apparent contradictions: while most studies favor creator content, several fnd null efects or brand advantages. These contradictions can be largely reconciled through careful attention to boundary conditions.
The Credibility-Authenticity Tradeof:Studies fnding creator advantages consistently emphasize authenticity and reduced persuasion knowledge activation as mechanisms. However, studies fnding null or negative efects often involve contexts where sponsorship is salient or where audience expectations are violated. The key insight is that creator content advantages depend on maintaining perceived authenticity—when commercial motives become obvious, the advantage diminishes or reverses.
The meta-analysis by Jiyoung Lee et al. provides quantitative resolution: SMIs outperform brand-only advertising (d=0.16), but the efect is moderated by perceived credibility (b=0.22, p=.002). This suggests that the efectiveness of creator content follows an inverted-U pattern relative to audience size: too few followers undermines credibility (nano-infuencers less efective than celebrities), while very large followings create higher expectations that are more easily violated by commercial content.
The Platform-Content Fit Principle:Platform diferences explain substantial heterogeneity. On Instagram and YouTube, where user-generated content is native to the platform experience, creator content shows consistent advantages. On Facebook, where the environment is characterized by low involvement and high distraction, hedonic content matters more than source type. On traditional media like TV, celebrity endorsements outperform infuencer content, likely because the medium itself connotes professional production values inconsistent with infuencer authenticity.
The Disclosure Double-Bind:The disclosure literature reveals that transparency can either enhance or diminish creator content efectiveness depending on execution. Platform-initiated disclosures (such as Instagram's branded content tool) negatively afect trustworthiness, while self-generated disclosures can enhance perceived authenticity through what Zeynep Karagür et al. term the "transparency bonus". This suggests that the form of disclosure matters as much as its presence—disclosures that appear externally imposed signal regulatory compliance rather than authentic transparency.
The Brand Attachment Boundary:K. Bentley et al.'s fnding that high brand attachment consumers prefer brand posts over SMI posts identifes an important boundary condition. For consumers with established brand relationships, infuencer partnerships may be perceived as a norm violation that dilutes the brand's identity. This suggests that creator content strategies are most efective for acquisition rather than retention, or for brands without highly attached consumer bases.
Contextual Recommendations
Based on the synthesized evidence, creator content outperforms brand content for conversion efciency under the following conditions:
1. Platform native to UGC(Instagram, YouTube, TikTok)
2. Moderate infuencer size(mega-infuencers rather than nano- or micro-)
3. High product involvementcontexts
4. New customer acquisitionrather than existing customer engagement
5. Content-infuencer ftis high
6. Disclosure is self-generatedrather than platform-imposed
7. Brand is smaller or less familiar
Conversely, brand content may be preferred when targeting consumers with high brand attachment, using traditional media channels, or when working with nano-infuencers who lack credibility.
The trust mechanism explains 67% of the variance in consumer trust based on authenticity indicators, and trust in branded posts signifcantly predicts purchase intentions (b=.41). Reduced ad skepticism operates primarily through avoiding persuasion knowledge activation, which UGC achieves by lacking overt commercial intent. Together, these mechanisms explain why creator content achieves superior conversion efciency: it leverages source credibility while circumventing defensive processing that undermines brand-originated persuasion attempts.
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Zeynep Karagür, Jan-Michael Becker, Kristina Klein, and Alexander Edeling. “How, Why, and When Disclosure Type Matters for Infuencer Marketing.” International Journal of Research in Marketing, 2021.
Appendix
Table 1.1
Study
Guolan Yang et al., 2024 No
Emma Truvé et al., 2019 No
Charunayan Kamath et al., 2024 No
Özge Gözegir et al., 2018 No
Mikyoung Kim et al., 2018 No
Matthew A. Hawkins et al., 2024 No
Experiment Not specifed
Sponsored vs. non-comparative infuencer posts N=325
Experiment Instagram Infuencer posts vs. company-sponsored posts N=208
Experiment In-app advertising AI-generated vs. human-created ads and memes N=300
Experiment YouTube
Experiment Not specifed
Experiment Not specifed
Self-produced vs. brand-associated videos N=241
Experience-centric vs. promotional sponsored content Not mentioned
Sponsored vs. unsponsored SMI posts N=198
D. Balaban et al., 2021 Yes
Mira Mayrhofer et al., 2019 Yes
Kirsten Cowan et al., 2018 Yes
Dr. Gunjan Sharma et al., 2025 Yes
Benjamin K. Johnson et al., 2019 No
Parker J. Woodroof et al., 2020 No
Rishi Dwesar et al., 2025 No
Jiyoung Lee et al., 2024 Yes
K. Majid et al., 2019 No
K. Bentley et al., 2024 Yes
M. Kolářová et al., 2018 No
Qianhui Hou et al., 2023 Yes
Guido Grunwald et al., 2025 Yes
Eleni Ntousi et al., 2025 No
Tarun Sharma et al., 2025 Yes
F. Martínez-López et al., 2020 No
Chen Lou et al., 2019 Yes
Christian Hughes et al., 2019 Yes
Experiment Instagram Diferent disclosure types for sponsored content N=248
Experiment Facebook
User-generated vs. brand posts vs. disclosed ads N=293
Experiment Not specifed UGC vs. MGC with infuencers vs. celebrities N=138
Mixed methods Multiple Infuencer marketing vs. e-commerce channels N=200
Experiment Instagram Native ads vs. user-generated posts vs. traditional ads N=482
Experiment Not specifed Diferent disclosure types in infuencer posts N=321
Experiment Not specifed Online reviews combined with display advertising N=317, N=123
Meta-analysis Not specifed SMIs vs. brand-only advertising vs. celebrities N=13,766
Experiment Not specifed
Consumer-disseminated vs. frm-disseminated promotions
Not mentioned
Experiment Instagram SMI posts vs. brand posts N=302
Experiment Instagram Traditional celebrity vs. micro-celebrity infuencers Not mentioned
Experiment Not specifed
User-generated vs. infuencer vs. sponsored infuencer reviews N=289
Survey Social media vs. TV Infuencer vs. celebrity endorsements N=430
Field experiment Not specifed
DCGC vs. brand-created ads Not mentioned
Secondary analysis Instagram, TikTok, YouTube Infuencer-generated vs. traditional media campaigns 10,000+ marketers
Not mentioned Not specifed
Perceived brand control in infuencer posts Not mentioned
Survey Facebook, YouTube, Instagram Infuencer-generated branded content N=538
Field study + Experiment Blogs, Facebook
Sponsored blogging campaigns N=1,830 posts; N=395
Chen Lou et al., 2019a No
A. Schouten et al., 2019 Yes
S. Kim et al., 2019 Yes
Yosra Jarrar et al., 2020 Yes
Shahan Abbas et al. (n.d.) No
Susanna S. Lee et al., 2020 No
Zeynep Karagür et al., 2021 Yes
E. Cheng et al., 2017 No
M. Cheng et al., 2022 No
I. Mir et al., 2024 No
Kaoutar Sarhour et al., 2025 No
M. Cheng et al., 2024 No
V. Diwanji et al., 2022 Yes
Darlene Walsh et al., 2024 No
Serena Iacobucci et al., 2020 No
E. Kim et al., 2024 No
Text analysis Instagram Infuencer-generated vs. brand-reposted ads
Not mentioned
Experiment Instagram, YouTube Infuencer vs. celebrity endorsements N=131, N=446
Mixed methods E-commerce Sponsored vs. organic consumer reviews N=561
Field study Instagram, Facebook Infuencer marketing vs. sponsored posts N=1,136
Mixed methods Not specifed
Not mentioned Instagram
User-generated content vs. brand-created content
Stratifed sample
Diferent disclosure types in infuencer posts Not mentioned
Field study + Experiments Instagram Platform-initiated vs. self-generated disclosures N=3,593 posts; N=464
Qualitative Not specifed
Brand-generated vs. consumer-generated advertising
Not mentioned
Field study YouTube Sponsored vs. organic infuencer videos Beauty infuencers
Survey Not specifed Infuencer-generated branded content N=300
Survey Not specifed UGC vs. macro-infuencer marketing N=340
Field study YouTube
Experiment YouTube
Sponsored vs. organic infuencer videos Beauty infuencers
User-generated vlogs vs. brand-generated ads N=194
Experiment TikTok Sponsored vs. non-sponsored UGC Not mentioned
Experiment Instagram Disclosed vs. non-disclosed sponsored content N=231
Experiment Not specifed Human-like vs. anime-like virtual infuencers Not mentioned