Social

WOM Economics

Reviews

Academic
Effort: low
Volume: medium

Also filed underE-Commerce

Myth:Star rating is the only review metric that matters.

Evidence:Meta-analysis of 51 studies (339 volume and 271 valence elasticities): eWOM volume elasticity is 0.236 and valence elasticity 0.417 - with stronger effects on independent review sites and for durable, privately consumed, low-trialability products.

The Nuance

Valence hits harder than volume, but volume is easier to move; platform trustworthiness moderates both.

The Receipt

A Meta-Analysis of Electronic Word-of-Mouth Elasticity

Ya You, Gautham G. Vadakkepatt & Amit M. Joshi, Journal of Marketing · 2015 · Academic

Impact7.9/10
Consensus8.4/10
Evidence88/100

Channels: social · reviews

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WOM Economics

Word of Mouth

Myth:Word of mouth is a soft bonus next to paid media.

Evidence:Vector-autoregression modelling of a social network's growth shows WOM referrals have substantially longer carryover effects and substantially higher response elasticities than traditional marketing actions - and a referral's value can be monetized from the downstream ad revenue a new member generates.

Impact8/10
Consensus8/10

WOM Economics

Influencers

Myth:Seed your campaign with the most influential people and it spreads.

Evidence:A 1.4-million-user randomized experiment on Facebook shows influence and susceptibility are separate, measurable traits - jointly targeting influential AND susceptible users outperforms chasing 'influentials' alone, and influence is demographic- and context-specific rather than a general property of people.

Impact7.7/10
Consensus8/10

Distribution mechanics

Link in comments on LinkedIn

Myth:"Link in comments" is a busted hack — LinkedIn stopped penalising outbound links.

Evidence:Outbound links in the post body still suppress reach. Algorithm InSights (1.8M posts) puts the body-link penalty near 50% of organic reach, and controlled A/B tests show first-comment placement recovering most of it: GrowthRocks measured 2.9x reach for link-in-comment in round one and 1.8x in round two — with more clicks, not fewer.

Impact9/10
Consensus7/10

Engagement signals

Dwell time beats reactions

Myth:Likes are the signal that drives distribution.

Evidence:LinkedIn's own ranking model uses a dwell-time prediction weighted independently of — and more heavily than — likes, with no fixed second threshold. Content that gets read all the way through outranks content that gets tapped and skipped.

Impact9/10
Consensus8/10

Engagement signals

Comment weighting vs likes

Myth:A comment is worth 15x a like — every creator says so.

Evidence:The 15x multiplier has no traceable primary source. AuthoredUp's analysis of 621,833 posts puts a comment at roughly 2x a like, a save at roughly 2x a meaningful comment, and threaded replies at up to 2.4x more reach. Comments still beat likes — just not by an order of magnitude.

Impact8/10
Consensus8/10

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