Creator vs. Brand Content Efficiency

Paper search

We performed a semantic search using the query "In paid advertising, does a brand achieve higher conversion efficiency by boosting creator-produced content compared to boosting brand-produced content, and to what extent is the difference 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 specified 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:

• Specific 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 (influencers, users, celebrities, etc.)

• How brand-produced content was defined

• Any other content types tested for comparison

Conversion Metrics

• Extract all conversion efficiency 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)

• Effect sizes and statistical significance of differences

• Specific numerical values, percentages, or ratios comparing the two content types

• Any secondary performance metrics (engagement, reach, impressions)

• Direction of effects (which type performed better)

Trust Mechanisms

• Extract data on trust, credibility, and ad skepticism as explanatory mechanisms for conversion differences, 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 differences)

• Identification, 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:

• Specific social media platform(s) or advertising channel(s)

• Type of paid promotion format (boosted posts, sponsored content, display ads, etc.)

• Platform-specific features that may influence 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, field 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 effects)

Product Context

• Extract details about the products, brands, or categories involved in the creator vs brand content comparison, including:

• Specific product categories or industries tested

• Brand characteristics (size, familiarity, luxury vs mass market)

• Product-creator fit or relevance considerations

• Any product/brand factors that moderated the effectiveness differences

Audience Factors

• Extract characteristics of the target audience that may moderate the effectiveness 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 effects differed

Characteristics of Included Studies

This systematic review synthesizes evidence from 40 sources examining the comparative effectiveness 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 field 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

The meta-analytic evidence provides the most robust estimate, with SMIs demonstrating a small but significant advantage over brand-only advertising (d=0.16). Notably, the meta-analysis found no significant difference between SMIs and celebrity endorsers overall (d=0.07, p=.303), though mega-influencers showed stronger effects than celebrities (d=0.33, p=.012) while nano-influencers showed weaker effects (d=-0.46, p=.024).

Field study evidence from Yosra Jarrar et al. reveals an interesting paradox: while influencer 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 efficiency and engagement metrics may not align, with creator content potentially driving more meaningful behavioral outcomes despite lower surface-level engagement.

The magnitude of effects varies considerably. At the high end, Chen Lou et al. report that influencer 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 effects 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

The pattern across secondary metrics reinforces the primary finding that creator content generally outperforms brand content, though the advantage varies by context and platform. Influencer-promoted ads on Instagram receive significantly 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 effects.

Trust and Skepticism as Explanatory Mechanisms

Evidence for Trust as a Mediator

The evidence consistently supports trust as a key mechanism explaining creator content advantages. Chen Lou et al. demonstrate that influencer trustworthiness positively affects followers' trust in branded posts, which subsequently influences purchase intentions with a substantial effect (b=.41, p<.001). Similarly, Schouten et al. establish that trustworthiness mediates the relationship between endorser type and advertising effectiveness, with influencers perceived as more trustworthy than traditional celebrities.

Influencers derive their trust advantage from multiple sources. Perceived similarity to followers positively influences trust, as does perceived authenticity and relatability. Abbas et al. quantify this effect, finding 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 Reduced Ad Skepticism

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 affect, leading to higher purchase intention compared to disclosed advertisements and brand posts. Kaoutar Sarhour et al. specifically 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 influencers. Kim et al. demonstrate that consumer inferences of manipulative intent serve as a mediator for the interaction effects between content sponsorship and content types.

The Disclosure Paradox

Multiple studies reveal a nuanced relationship between disclosure, trust, and conversion:

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 effects, 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 characteristics significantly moderate effectiveness. 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 effective. Cross-platform research by Guido Grunwald et al. reveals that celebrity ads are more effective in traditional media, while influencer ads show only slightly higher (non-significant) purchase intentions on social media.

Influencer and Creator Characteristics

The meta-analysis reveals a "sweet spot" for influencer effectiveness: mega-influencers outperform celebrities (d=0.33, p=.012), but nano-influencers are actually less persuasive than celebrities (d=-0.46, p=.024). This suggests that perceived credibility serves as a crucial moderator, with influencers needing sufficient following to establish credibility while maintaining authenticity.

M. Cheng et al. document a reputation-burning effect where posting sponsored videos costs influencers 0.19% of their reputation (measured as subscriber count), with this effect being stronger among influencers with larger audiences. Walsh et al. corroborate this on TikTok, finding that the negative effect of sponsorship on consumer engagement is observed only among popular creators with large followings.

Brand and Product Factors

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 influencer partnerships as a norm violation. This effect is mediated by perceptions of the relationship between brand and SMI, suggesting that highly attached consumers prefer direct brand communication.

Product involvement also significantly moderates effects. Diwanji et al. found that user-generated video reviews elicited significantly greater effects 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

Generational differences significantly influence responsiveness to creator content. Tarun Sharma et al. report that over 70% of consumers, particularly Millennials and Gen Z, make purchase decisions based on influencer recommendations. However, Generation Z's status as digital natives means they recognize the promotional nature of influencer 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 effective for reaching ad-averse consumers.

Synthesis

Reconciling Heterogeneous Findings

The evidence reveals apparent contradictions: while most studies favor creator content, several find null effects or brand advantages. These contradictions can be largely reconciled through careful attention to boundary conditions.

The Credibility-Authenticity Tradeoff: Studies finding creator advantages consistently emphasize authenticity and reduced persuasion knowledge activation as mechanisms. However, studies finding null or negative effects 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 effect is moderated by perceived credibility (b=0.22, p=.002). This suggests that the effectiveness of creator content follows an inverted-U pattern relative to audience size: too few followers undermines credibility (nano-influencers less effective than celebrities), while very large followings create higher expectations that are more easily violated by commercial content.

The Platform-Content Fit Principle: Platform differences 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 influencer content, likely because the medium itself connotes professional production values inconsistent with influencer authenticity.

The Disclosure Double-Bind: The disclosure literature reveals that transparency can either enhance or diminish creator content effectiveness depending on execution. Platform-initiated disclosures (such as Instagram's branded content tool) negatively affect 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 finding that high brand attachment consumers prefer brand posts over SMI posts identifies an important boundary condition. For consumers with established brand relationships, influencer partnerships may be perceived as a norm violation that dilutes the brand's identity. This suggests that creator content strategies are most effective 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 efficiency under the following conditions:

1. Platform native to UGC (Instagram, YouTube, TikTok)

2. Moderate influencer size (mega-influencers rather than nano- or micro-)

3. High product involvement contexts

4. New customer acquisition rather than existing customer engagement

5. Content-influencer fit is high

6. Disclosure is self-generated rather 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-influencers who lack credibility.

The trust mechanism explains 67% of the variance in consumer trust based on authenticity indicators, and trust in branded posts significantly 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 efficiency: it leverages source credibility while circumventing defensive processing that undermines brand-originated persuasion attempts.

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Appendix

Table 1.1