flowchart LR WOM["Word of mouth<br/>(goal-directed)"] WOM --> IM["Impression<br/>management"] WOM --> ER["Emotion<br/>regulation"] WOM --> IA["Information<br/>acquisition"] WOM --> SB["Social<br/>bonding"] WOM --> P["Persuasion"] IM --> IMc["entertaining, useful,<br/>status-, identity-relevant"] ER --> ERc["emotional, high-arousal,<br/>valence-managed"] IA --> IAc["risky / complex /<br/>uncertain decisions"] SB --> SBc["common ground,<br/>shared emotion"] P --> Pc["polarized valence,<br/>arousing"]
Almost everything a consumer learns about a product arrives through someone else. A friend’s offhand recommendation, a one-star review, a forwarded video, a retweeted complaint—these consumer-to-consumer transmissions move demand more reliably than most paid messages, and they do so at a cost the firm does not pay. This chapter is about how such transmission works: what gets passed along, why people pass it, how far and how fast it travels, and how much of the resulting spread a manager can actually engineer rather than merely observe.
Two questions organize the field, and they are easy to conflate. The first is psychological: given that a person is talking, what makes them choose one piece of content over another to share? This is the literature on word of mouth (WOM) and its drivers—social currency, emotion, usefulness, narrative. The second is structural: given that content is shared, what shape does the resulting diffusion take through the social network, and is that shape predictable? This is the literature on cascades and structural virality. A serious treatment needs both. Content drivers explain the probability that any individual relays a message; network structure determines whether those individual relays compound into an epidemic or fizzle. We develop the content account first, formalize the diffusion account second, and close on the measurement and identification problems that make virality genuinely hard to study—chief among them the fact that what looks “viral” is often neither viral nor caused by the content at all.
By the end the reader should be able to define virality precisely, distinguish it from popularity, write down a model of cascade size and state what limits its predictability, and reason about seeding and measurement with the right identification caveats in hand.
27.1 Defining the Construct
Word of mouth predates the internet by the entire history of language, and its canonical definition is correspondingly old. We adopt Westbrook (1987)’s formulation as the field’s landmark statement.
Word of mouth is “informal communications directed at other consumers about the ownership, usage, or characteristics of particular goods and services or their sellers” (Westbrook 1987).
Three features of this definition do real work. The communication is informal (it is not the firm’s paid voice), it is consumer-to-consumer (the firm is at best an indirect party), and it is about market objects (products, services, sellers). The migration of this activity online produces electronic word of mouth (e-WOM, sometimes “word of mouse”): the same construct, now mediated by platforms that record, timestamp, and graph every utterance. Both classical WOM and e-WOM exploit the network effect to reach audiences the firm could not address directly (Vilpponen, Winter, and Sundqvist 2006), but e-WOM does so with lower marginal cost, faster diffusion, and sharper targeting (Bampo et al. 2008; Feroz Khan and Vong 2014).
Virality is the construct that e-WOM makes salient, and the literature uses the word in two incompatible ways that must be kept apart. One usage is processual: virality is the act or trajectory of successive sharing—“a process whereby an ad is successively shared by viewers.” The other is dispositional: virality is a property of content, namely its latent potential to be spread. We adopt the dispositional reading throughout, because it is the one that supports prediction and design: we want to know what makes a given piece of content likely to diffuse, holding the network fixed.
WOM, virality, and sharing as near-synonyms
We treat WOM, virality, and sharing interchangeably, following the strong construct overlap documented by Camarero and San José (2011) and Phelps et al. (2004). The distinctions that matter for this chapter are not lexical but mechanical: the content drivers of a single relay versus the network dynamics of aggregate diffusion. We are explicit about which we mean at each point.
Formally, fix a population of potential relayers indexed by \(i\) and a piece of content \(c\). Let \(S_{ic} \in \{0,1\}\) indicate that \(i\) relays \(c\). The dispositional virality of \(c\) is the share probability marginalized over the population, where \(\mathbf{z}_c\) are content/context features (the levers a marketer can pull) and \(\mathbf{w}_i\) are person- and tie-level moderators. The content literature in this chapter is, in effect, a catalog of the partial derivatives \(\partial v / \partial \mathbf{z}_c\): which features of content raise the per-person relay probability. The structural literature (Section 27.5) asks what happens when these individual relays are composed through the edges of a social graph.
Why this matters commercially is blunt: sharing is necessary but not sufficient for value. Intentions overstate behavior, and behavior overstates profit. In one telecommunications referral program, 81% of customers said they would refer others, only 33% actually did, and of those referred only 8% became profitable customers (Kumar, Petersen, and P.Leone 2007). The funnel from stated willingness to share, to actual sharing, to monetized outcome leaks at every stage—a recurring caution when reading self-reported sharing studies.
27.2 Theoretical Foundations
The two literatures this chapter joins, content drivers and network structure, each rest on established theory, and naming the governing accounts shows why the empirical regularities are not coincidental. The structural side descends from social contagion and diffusion of innovations: new products and ideas spread through a population by person-to-person communication over time, the process that the formal model of Bass (1969) partitions into innovators (external influence) and imitators (internal, word-of-mouth influence). Diffusion theory supplies the contagion hypothesis that the cascade literature (Section 27.5) then tests and qualifies, and it is the reason an S-curve is so often, and so often wrongly, read as evidence of transmission. Network structure enters through the strength of weak ties (Granovetter 1973): novel information flows disproportionately across bridging ties that connect otherwise separate clusters, which is why tie structure governs whether individual relays compound into reach and why the weak-tie thesis organizes the seeding and tie-strength results below.
On the content side, three social-psychological theories account for why a person relays. Social influence and social proof hold that descriptive and injunctive norms shift behavior, often more than people anticipate (Cialdini, Influence, a monograph cited here by name; for the empirical norm result see Goldstein, Cialdini, and Griskevicius (2008)): people share, adopt, and review in part because others do, which is why consensus cues and visible popularity move transmission. The emotion/arousal account of sharing holds that the operative driver is not the valence of content but its physiological arousal: high-arousal states, in both positive and negative valence, potentiate sharing, while low-arousal states suppress it (Berger and Milkman 2012), a claim formalized in Equation 27.2. And social identity holds that consumption and communication are identity-expressive: people relay content that signals who they are and that affirms valued group memberships, the mechanism underneath the impression-management and social-bonding functions developed next (Tajfel and Turner’s social identity theory, an edited-volume statement cited by name). Together these theories predict both the probability that any individual relays content and the shape of the diffusion that results.
27.6 Network Position, Ties, and Seeding
If structure governs whether relays compound, then where in the network a message starts and which ties it crosses should matter. This section covers the position/seeding literature and the surprisingly subtle role of tie strength.
27.6.1 Seeding strategy
The seeding question is whether a firm should inject content at well-connected hubs or at ordinary nodes. Analytical models and simulations had suggested that targeting hubs does not pay—hubs are no more influential over any given peer than ordinary nodes. Hinz et al. (2011) overturn this in small-scale field experiments and real campaigns: seeding to well-connected people yields up to eight times more adoptions, not because hubs are more persuasive per contact but because they are more active and exploit their greater reach. The lesson is that seeding returns come from reach and activity, not from per-tie influence—a distinction that matters because it cannot be recovered from peer-level persuasion data alone. Seeding can also have competitive effects: a well-placed campaign raises focal-brand WOM while suppressing conversation about rival products in the same category (Chae et al. 2017).
27.6.2 Network management and identification
Studying how a firm’s own networking activity drives online success raises an acute endogeneity problem: firms that succeed may simply network more, so position and success are jointly determined. Ansari et al. (2018) model the co-evolution of an actor’s ego-network and success with stochastic actor-based models, characterizing each actor by ego-network density and degree, betweenness, and closeness centrality. To identify the effect of networking on success they combine time fixed effects with a control-function approach instrumented by the firm’s activity on an independent US social platform—activity correlated with the firm’s European networking effort but plausibly excludable from European-market success. The design is a template for network studies generally: position is endogenous, and credible identification needs an instrument that shifts position without directly shifting the outcome.
27.6.3 Tie strength and the strength of weak ties
Granovetter’s classic thesis is that weak ties—acquaintances rather than close friends—are disproportionately valuable for information diffusion, because they bridge otherwise disconnected clusters and so carry novel information. The modern e-WOM literature both confirms and qualifies this.
On the confirmation side, Peng et al. (2018) show that sharing propensity rises with network overlap between sender and receiver—common followees, common followers, and common mutual followers—using a dyadic hazard model on Twitter and Digg data. High overlap signals shared interests, so the receiver infers the content suits her own audience and relays it; popular content, conversely, is shared less, because many others already have. Tie strength also conditions persuasion: Lee and Kronrod (2020) find that consensus language—expressions implying widespread agreement—is more persuasive coming from weak ties than strong ones, because a weak tie evokes a broader, more diverse agreeing group and thus signals higher validity, providing social proof.
The sharpest recent test qualifies the weak-tie thesis itself. Rajkumar et al. (2022) exploit large-scale randomized experiments on LinkedIn’s “People You May Know” algorithm—rare causal variation in tie formation—to estimate how tie strength affects job mobility. They find that weak ties do increase job transmission but with diminishing returns: the relationship is an inverted U, not monotone. Moderately weak ties, not the weakest possible, maximize mobility, and the effect is industry-dependent—weak ties dominate in digital industries, strong ties in less digital ones. The headline revision is that “the strength of weak ties” is nonlinear and context-contingent, not a universal law.
Table 27.2 contrasts the three studies, which agree that tie structure matters but disagree on the direction once the outcome and mechanism change.
| Study | Setting / identification | Tie variable | Effect on outcome |
|---|---|---|---|
| Peng et al. (2018) | Twitter, Digg; dyadic hazard | Network overlap | Overlap raises sharing; popularity lowers it |
| Lee and Kronrod (2020) | Experiments | Tie strength × consensus language | Weak ties more persuasive with consensus cues |
| Rajkumar et al. (2022) | LinkedIn field experiments (causal) | Tie strength | Inverted-U; moderately weak ties maximize job mobility |
27.7 Accessibility and the Mere-Exposure Channel
A driver that operates orthogonally to emotion is accessibility: content and brands that are easier to retrieve from memory are talked about more, however mundane they are. The theoretical basis is spreading activation in semantic memory: priming activates related constructs, and a primed brand becomes more likely to enter the consideration set (Berger and Fitzsimons 2008). Berger and Fitzsimons (2008) show that more frequent exposure to perceptually or conceptually related cues raises product accessibility and, through it, evaluation and choice—conceptual priming with real consumer consequences. Accessibility is why publicly visible and heavily advertised products sustain ongoing WOM (Onishi and Manchanda 2012), and why familiar content is selected more often for retransmission (Kim 2015). It also supplies an alternative, non-emotional reason content travels, which any well-specified sharing model must include as a control lest the emotion coefficients absorb a pure-accessibility effect.
27.8 Moderators: Audience and Channel
Which WOM function dominates—and therefore which content travels—depends on two moderators introduced above and worth consolidating. The audience is characterized by tie strength, audience size, and the status of the tie; the channel by whether communication is written or oral, whether the communicator is identifiable, and whether the audience is salient during composition. The recurring result is that asynchronous, identifiable, audience-salient channels amplify self-presentational motives and so favor interesting, status-relevant content, whereas synchronous channels favor accessible, mundane content that fills conversational time. Demographics moderate too: the 18–24 cohort responds less to viral content because saturation raises their activation threshold, making their emotions harder to move (Libert 2014). And conversation has its own dynamics—a wide gap between two opposing opinions stimulates future discussion, whereas fluctuation around a single opinion does not, with informational content strengthening the opposition-to-discussion link (Nagle and Riedl 2014).
27.9 Cultural and Dispositional Antecedents
Sharing is not culturally invariant. Two constructs from the cross-cultural WOM literature illustrate how disposition shapes transmission.
Locus of control—the generalized expectancy about whether one’s outcomes are internally or externally caused (Lam and Mizerski 2005)—predicts whom people talk to. Internals (who believe they control their lives) tend to be more educated (Lachman and Leff 1989), higher-income (Hoffman, Novak, and Schlosser 2003), and more senior and male (P. B. Smith, Dugan, and Trompenaars 1997), and they engage in WOM with out-groups; externals, oriented toward affiliation and avoidance, engage more with in-groups (Lam and Mizerski 2005).
Horizontal/vertical individualism–collectivism shapes what communicative role people adopt. Singelis et al. (1995) cross individualism–collectivism with a horizontal (equality-emphasizing) versus vertical (hierarchy-emphasizing) dimension, yielding four cultural patterns. Applied to WOM, Choi and Kim (2019) find that vertical orientations—where standing is signaled by display—channel into both opinion leadership and opinion seeking, while horizontal orientations feed opinion leadership through a norm of being socially appropriate. The broader regularity is that collectivist cultures do more opinion seeking and individualist cultures more information giving (Fong and Burton 2008).
27.10 Linguistic Style
Because e-WOM is text, how something is said is measurable and consequential, and a sizable literature maps linguistic features onto WOM outcomes. The features that have been shown to matter include the abstraction level of language (Schellekens, Verlegh, and Smidts 2010), the explanatory framing a storyteller uses—which feeds back to shape the storyteller’s own attitudes (Moore 2012)—figurative language (Kronrod and Danziger 2010), markers of modesty versus boastfulness (Packard, Gershoff, and Wooten 2016), “dispreferred” markers that soften disagreement (Hamilton, Vohs, and McGill 2014), the personal pronouns a communicator selects (Packard and Wooten 2013), and linguistic mimicry between parties (Moore and McFerran 2017). The practical upshot is that the style of a message is a manipulable lever distinct from its content, and—because style is extractable from text at scale—an empirically tractable one.
27.12 Contractuality: Engineering Authentic WOM
Firms that try to manufacture WOM through incentives confront a credibility trade-off captured by contractuality, defined by Lisjak, Bonezzi, and Rucker (2021) as “the extent to which a perk is perceived to be conditional on specific behaviors and contingencies dictated by a company.” Consumers prefer perks with less salient contractuality, and a prior negative relationship with the firm leads them to read a high- contractuality perk as a manipulative quid pro quo with ulterior motives. Low-contractuality perks foster authentic WOM but trade off against effectiveness and efficiency—the firm gives up the control that an explicit share-for-reward contract would provide. The construct formalizes the intuition that the most valuable WOM is the kind the firm can least directly buy.
27.13 Empirical Regularities and Open Problems
A handful of well-replicated facts and one persistent confound deserve listing as the chapter’s empirical bottom line. WOM dispersion predicts higher future ratings, but its impact declines over the product life cycle, so it should be measured early (Godes and Mayzlin 2009)—WOM is both a precursor and a consequence of behavior, which complicates causal reading. Contagion is detectable even in consumer packaged goods (Du and Kamakura 2011), a category once thought too low-involvement for social transmission. Authorship matters: articles by business academics, psychologists, and economists are shared more than those by physicists and biochemists, and—net of content—men rate scientific summaries as more comprehensible, interesting, and useful and share them more (Milkman and Berger 2014). And identifying influential users in a network is itself a modeling problem with no off-the-shelf solution (Trusov, Bodapati, and Bucklin 2010).
The persistent confound is structural virality itself. Many sharing experiments—including the influential arousal studies—ask respondents how likely they would be to share, an intention that (i) carries little social risk in the lab, inflating the estimate, and (ii) abstracts away the network through which real diffusion runs. Two pieces of content with identical per-person share probability \(v(c)\) can diffuse to radically different sizes depending on where they seed and how the branching process resolves (Equation 27.4, 1). The frontier of the field is precisely the integration we have kept separate for expository clarity: joining the content model of \(v(c)\) to the structural model of how \(v(c)\) propagates, so that designable content features and irreducible network stochasticity are estimated in one frame rather than two.
27.14 Key Takeaways
- Word of mouth is goal-directed. The five functions—impression management, emotion regulation, information acquisition, social bonding, persuasion—predict what content travels, not merely that sharing happens (Berger 2014).
- Arousal, not valence, drives sharing. High-activation states in both valences raise transmission; incidental-arousal manipulations identify the effect by moving arousal without moving content (Berger and Schwartz 2011; Berger and Milkman 2012).
- Viral \(\neq\) popular. Broadcasts and cascades both reach many people; only cascades are structurally viral, and genuine cascades are extreme-tail rare (Goel, Watts, and Goldstein 2012; Goel et al. 2015).
- Predictability is capped. Cascade success decomposes into skill and luck, and the irreducible luck component imposes a theoretical \(R^2\) ceiling
- An S-curve is not contagion. Heterogeneity and marketing effort generate the same shape; survivorship bias compounds the inference problem (Chatterjee and Eliashberg 1990; Bulte and Lilien 2001).
- Seeding pays through reach, not per-tie influence, and tie-strength effects are nonlinear and context-dependent (Hinz et al. 2011; Rajkumar et al. 2022).
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27.4.1 Social currency
The dominant driver of posting behavior is image-related utility rather than intrinsic enjoyment of the content: Toubia and Stephen (2013), in a field experiment that randomized whether contributions were attributed to their authors, find that image-related utility drives posting more than intrinsic utility. People share to accrue social currency—to look interesting, smart, or in-the-know. A consequence worth flagging is that “useful” and “interesting” are not interchangeable currencies: useful information signals helpfulness, but the signal is valued less than the signal of being entertaining (Berger 2014).
Social currency interacts with the channel, and the interaction is identifying. Interesting content is shared more in written (asynchronous) settings but not in oral (synchronous) ones. The mechanism is time to self-edit: asynchronous communication lets people polish what they say, which amplifies self-enhancement motives, whereas synchronous conversation must be filled in real time, pushing people toward mundane, accessible topics merely to avoid silence. This is a clean example of a moderator (channel) switching a driver (interestingness) on and off.