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).
