From Credibility to Clickability: The New Economics of Influencer Signals

 

Figure 1: TikTok Shop (Available: https://newsroom.tiktok.com/introducing-tiktok-shop?lang=en)

 

Have you ever clicked into a TikTok skincare routine “just for a look” — only to end up seconds later checking out the exact products used in the video in your cart? On TikTok Shop, a product no longer waits quietly on a shelf to be discovered; it actively arrives with a face, a voice, and a carefully curated performance of excitement.  UK beauty creator Cariad Ryan shows the economic scale of this shift: TikTok reports that she sold £90,000 worth of products in four hours during a LIVE shopping event, while her 12-hour LIVE Creator Showcase later generated $153k and sold 6.7k items through TikTok shop (TikTok, 2023). In such an environment, consumers are not judging cosmetics in isolation-  they interpret them through Cariad’s demonstrations, energy, and the visibility the platform gives her content.

Here is where it gets questionable– in markets shaped by asymmetric information, are consumers really responding to product quality itself, or to the signals surrounding it, especially when repeated visibility begins to resemble proof that the crowd has already decided?

Figure 2: TikTok LIVE shopping session with Ryan (Available: https://www.linkedin.com/posts/tiktokshop_12-hours-26-brands-record-breaking-results-activity-7260622128519999490-cL66/)

 

Trust to distortion: the changing nature of influencer signals

One reason this happens is that consumers cannot directly observe the quality of many online products (especially experience goods such as cosmetics and clothing) before buying them. This creates a classic case of asymmetric information, where sellers know more about products than buyers do. To deal with this uncertainty, markets rely on signals — observable cues that help consumers infer hidden quality.

Influencer recommendations initially appear to serve this role. When a creator frequently features certain products to their audience, followers are likely to take it as a credible signal, assuming the influencer has personally tested them. This trust does not emerge by accident. Influencers have invested time in building parasocial relationships via past content posting congruent to their followers' interests and lifestyles (Casaló et al., 2020; Dhanesh & Duthler, 2019). In that sense, influencers are seen as information intermediaries, reducing uncertainty and making hidden quality easier to interpret.

However, this signalling mechanism begins to break down under platform markets. Algorithmic curation significantly lowers the cost of producing and spreading signals, weakening the core condition of signalling theory: that credible signals must be costly to fake (Spence,1973). Even Nano- and micro-influencers can gain visibility through algorithmic amplification rather than long-term reputation, making credibility look easier to acquire.

This problem deepens once affiliate marketing is added. Creators do not simply recommend products; they can feature items in videos and LIVE streams while earning commission on each sale. What appears to be spontaneous enthusiasm could be more compelling than a banner advertisement, but end up being financially loaded.

Figure 3: Embedded links/hints for affiliate marketing (Source: TikTok)

The ambiguity matters because influencer marketing has not only changed who advertises, but also the cost structure of signalling itself. For instance, traditional celebrities, such as athletes and film stars, who built prestige through prolonged professional performance, faced significant reputational and financial costs from endorsing poor-quality products (Djafarova & Rushworth, 2017; Schouten et al., 2019). Modern influencers benefit from low entry barriers, allowing credibility to be reproduced more easily without underlying quality, as represented in the graph below. This creates an incentive for some creators to prioritise short-term commercial gains. 

Figure 4: A Signalling Framework Comparing Influencers and Traditional Celebrities

 

Virality becomes quality: How to fix it?

 

When signals are unreliable, the market is pushed towards a pooling equilibrium, where high- and low-quality products become difficult to distinguish. Consumers are increasingly forced to interpret signals based on immediate engagement and appreciation of content — factors that are even easier to manipulate. As a result, genuinely high-quality products struggle to differentiate themselves, while lower-quality alternatives that mimic credibility more cheaply can compete and potentially dominate.

 

In the face of broken signals, why don’t consumers undertake independent research to identify the ‘right’ products? One explanation lies in informational cascades — a behavioural phenomenon where individuals follow the actions of predecessors rather than relying on their own judgment when they fail to tell good from bad (Lee, 1993). This behavioural tendency reinforces the effects of failed signalling. On social media, likes and views become the new currency of credibility, where high engagement is often mistaken for product reliability. In platform markets, repeated visibility generates the appearance of collective approval, even when the underlying signal is commercially motivated — making low-quality products appear trustworthy.

 

So, what can be done to combat this? We have already established that virality is not a reliable approximation for quality, so platforms should offer alternatives to the unreliable metrics of likes and followers to help prevent informational cascades.

 

TikTok, for example, displays product reviews when influencers promote items, offering a quick signal (e.g. star ratings) of actual quality. While reviews in the UK are subject to oversight by the  Competition and Markets Authority (CMA), which actively investigates misleading reviews, making this signal harder to manipulate than engagements (CMA, 2026). This alone is not sufficient. The CMA should mandate platforms to detect, remove manipulated reviews and penalise coordinated attempts for artificially inflated ratings. Such enforcement helps ensure consumers make informed decisions on reliable signals during their purchases.

 

However, promotions that are not disclosed by influencers could fly under the radar, which is why policies should also focus on educating consumers of these hidden endorsements. There have already been some attempts at this in the UK, with the CMA’s “online rip-off tips off” campaign aimed to raise awareness of misleading online tactics, encouraging consumers to be aware of what they see online (Merrifield, 2022). This could be expanded to the influencer space, to help viewers better recognise when recommendations are commercially motivated rather than genuinely independent.

 

Beyond the Hype, Beyond the Signal

 

Ultimately, an influencer you trust can still function as a useful signal if they have built genuine credibility over time. Influencer signalling may never be perfect, but the key issue is that modern marketing has fundamentally altered its economics. In platform markets, it is much easier to imitate credibility through monetised visibility and engagement. The real danger is not simply that bad products are promoted, but that virality can begin to substitute for quality in the eyes of the consumer. Regulated reviews and clear disclosure rules can help separate the signals, but a deeper fix is always awareness. Next time, be sure to look beyond that influencer’s post before adding that lipstick to your cart.

Bibliography:

Casaló, L.V., Flavián, C. and Ibáñez-Sánchez, S. (2020) ‘Influencers on Instagram: Antecedents and consequences of opinion leadership’, Journal of Business Research, 117, pp. 510–519. doi:10.1016/j.jbusres.2018.07.005. 

Dhanesh, G.S. and Duthler, G. (2019) ‘Relationship management through social media influencers: Effects of followers’ awareness of paid endorsement’, Public Relations Review, 45(3), p. 101765. doi:10.1016/j.pubrev.2019.03.002. 

Djafarova, E. and Rushworth, C. (2017) ‘Exploring the credibility of online celebrities’ Instagram profiles in influencing the purchase decisions of young female users’, Computers in Human Behavior, 68, pp. 1–7. doi:10.1016/j.chb.2016.11.009.

Fake and misleading reviews: 5 businesses under CMA investigation (2026) GOV.UK. Available at: https://www.gov.uk/government/news/fake-and-misleading-reviews-5-businesses-under-cma-investigation (Accessed: 16 April 2026). 

Lee, I.H. (1993) ‘On the convergence of informational cascades’, Journal of Economic Theory, 61(2), pp. 395–411. doi:10.1006/jeth.1993.1074.

Merrifield, N. (2022) ‘sneaky’ online retailer tricks exposed in CMA campaign. Available at: https://www.campaignlive.co.uk/article/sneaky-online-retailer-tricks-exposed-cma-campaign/1739846 (Accessed: 18 April 2026). 

Schouten, A.P., Janssen, L. and Verspaget, M. (2019) ‘Celebrity vs. Influencer endorsements in advertising: The role of identification, credibility, and product-endorser fit’, International Journal of Advertising, 39(2), pp. 258–281. doi:10.1080/02650487.2019.1634898. 

Spence, M. (1973) ‘Job market signalling’, The Quarterly Journal of Economics, 87(3), p. 355. doi:10.2307/1882010. 

TikTok (2023). Investing for our 150m strong community in Europe. Available at: https://newsroom.tiktok.com/investing-for-our-150-m-strong-community-in-europe?lang=en-150 (Accessed: 6 April 2026).