Customer satisfaction is the evaluative judgment a consumer forms after comparing the performance of a product or service against some standard of reference. It is at once the most surveyed construct in marketing practice and one of the most theoretically demanding: a satisfaction score is not a direct reading of an attitude, but the residual of a comparison process, and the standard against which performance is judged is itself shaped by the very experience being evaluated. This chapter treats satisfaction as two coupled objects. The first is a psychological construct: a post-purchase judgment built from affective and cognitive inputs and anchored to prior expectations. The second is a financial asset: a forward-looking signal that, when it moves, moves cash flows, equity returns, and the risk those returns carry. A serious account must connect the two, and the modern empirical literature, drawing heavily on the American Customer Satisfaction Index (ACSI), increasingly does.
The chapter proceeds from formation to consequence. We begin with how satisfaction is formed (i.e., the expectancy–disconfirmation paradigm and its affective and cognitive components) and how it is measured, including the modern move from survey scales to text-derived sentiment. We then formalize the disconfirmation model to make its identification problems explicit. From there, we turn to consequences: the satisfaction–profit chain that runs from cash flow growth and volatility to shareholder value; the asymmetric and sector-specific way equity markets price satisfaction; and the effect of satisfaction on the risk of returns rather than their level. We confront two findings that discipline naive optimism (i.e., the negative market-share–satisfaction relationship and the moderating role of financial leverage) and close with monopoly settings and the communication of uncertainty.
4.1 Formation: The Expectancy–Disconfirmation Paradigm
The dominant theory of how satisfaction arises is the expectancy–disconfirmation model. Its intuition is ordinary: a diner who expects a mediocre meal and receives a good one leaves delighted, while a diner who expects greatness and receives merely good leaves disappointed, even though the meals were identical. Satisfaction tracks not performance itself but performance relative to a pre-consumption expectation. The gap between the two is disconfirmation: positive when performance exceeds expectations, negative when it falls short, and zero when they coincide.
Let \(E\) denote a consumer’s pre-consumption expectation of performance along some attribute, \(P\) the perceived actual performance, and \(D = P - E\) the disconfirmation. The paradigm posits that satisfaction \(S\) rises in both the baseline expectation and the disconfirmation, \[S = \alpha + \beta_E\, E + \beta_D\, (P - E) + \varepsilon, \tag{4.1}\] with \(\beta_D > 0\) the central claim of the theory and \(\beta_E\) capturing the direct, assimilative pull of expectations on the final judgment. Equation Equation 4.1 makes the construct’s subtlety precise: because \(E\) enters both directly (through \(\beta_E\)) and as the benchmark inside the disconfirmation term, raising expectations has offsetting effects on satisfaction. It lifts the assimilative component while simultaneously raising the bar that performance must clear, a tension that any manager who has over-promised understands and that any estimator of Equation 4.1 must confront.
A field study of a novel restaurant experience by Oliver and Burke (1999) sharpens the dynamics behind this static equation. Pre-consumption expectations were manipulated with realistic reviews, the dining experience itself was recorded by protocol methods, and perceptions were captured immediately after. Four results recur in subsequent work. First, the manipulated expectation exerts an immediate effect that decays over the course of the experience, so the weight \(\beta_E\) is not constant but fades as direct evidence accumulates. Second, expectations shape perceived performance (i.e., forward assimilation) so \(E\) and \(P\) are not independent inputs but causally linked, which (as we note below) breaks the clean separation Equation 4.1 assumes. Third, expectations are revised retrospectively by what actually happened (i.e., backward assimilation), so the measured \(E\) depends on \(P\) when it is elicited after consumption. Fourth, the disconfirmation \(D\) remains the pivotal driver of \(S\), and the whole process operates in a dimension-specific way: disconfirmation on one attribute drives satisfaction with that attribute, not a diffuse global affect.
Disconfirmation. Satisfaction is the consumer’s response to the perceived discrepancy between prior expectations and the actual performance of a product as experienced after consumption.
4.1.1 Affect and Cognition
A consumer’s assessment of satisfaction draws on two channels that the disconfirmation equation collapses into a single \(S\). The cognitive channel is the deliberate, attribute-by-attribute comparison of performance against expectation (i.e., precisely the \(P - E\) computation of Equation 4.1). The affective channel is the emotional residue of consumption: the pleasure, delight, irritation, or regret experienced during use, which colors the judgment independently of any calculated discrepancy. The two are not substitutes. A technically flawless service encounter delivered coldly can produce low satisfaction despite zero cognitive disconfirmation, and a warm recovery from a service failure can produce high satisfaction despite a large negative one. Treating satisfaction as a purely cognitive ledger, therefore, mis-specifies its formation; the affective component is a separate input, and measurement instruments that capture only attribute ratings miss it.
4.2 Theoretical Foundations
Satisfaction is a comparison judgment, and the theories that ground it are theories of what gets compared, against what standard, and with what asymmetry. The organizing account is the expectancy-disconfirmation paradigm developed above: satisfaction tracks performance relative to a pre-consumption expectation, with the disconfirmation \(D = P - E\) as the pivotal driver (Oliver and Burke 1999). (Oliver’s original 1980 cognitive model of satisfaction established the paradigm; the field study cited here sharpens its dynamics.) Disconfirmation is itself a reference-dependence claim, which is why prospect theory is the second foundation (Kahneman and Tversky 1979; Tversky and Kahneman 1981). The expectation \(E\) is a reference point, performance is coded as a gain or a loss relative to it, and because losses loom larger than gains, negative disconfirmation depresses satisfaction more than equal positive disconfirmation raises it. This loss-averse asymmetry reappears downstream in how equity markets price satisfaction news, where dissatisfaction loads roughly 2.4 times as heavily as an equal satisfaction gain (Malshe, Colicev, and Mittal 2020).
Two motivational and informational theories refine the comparison. Regulatory focus(Higgins 1997) holds that a promotion-focused consumer evaluates an experience against ideals and the presence or absence of gains, while a prevention-focused consumer evaluates against oughts and the presence or absence of losses; the same objective performance therefore yields different satisfaction depending on which standard the consumer brings, and on whether the offering’s framing fits that orientation. The affect-as-information account (Schwarz and Clore’s work on feelings as information) grounds the affective channel introduced above: consumers read their momentary feelings during consumption as data about how satisfied they are, which is why a warm recovery can lift satisfaction despite zero cognitive disconfirmation, and a cold but flawless encounter can depress it. This is the theoretical reason the affective input cannot be reduced to the cognitive ledger of Equation 4.1. The asymmetry between the two valences is itself asymmetric across outcomes: Mittal, Ross, and Baldasare (1998) show that negative and positive information about a firm are weighted very differently depending on whether the judgment concerns the product or the relationship, so a single loss-aversion coefficient understates the structure.
A further complication is that satisfaction depends on who is held responsible for the outcome, not only on the outcome. Botti and McGill (2011) show that a consumer who chose an option and got a poor result is less satisfied than one who received the same result from someone else’s choice, because personal causality converts a bad draw into a bad decision. This matters directly for the design of assortments, recommendation systems, and self-service: shifting the locus of choice to the customer shifts the locus of blame with it.
Two further theories bound the construct. Equity (fairness) theory (Adams’s work on inequity, extended to consumption by Oliver and Swan) holds that satisfaction depends not only on one’s own outcome-to-input ratio but on its comparison to the seller’s and to other customers’, so a consumer who feels fairly treated can be satisfied with a modest outcome and one who feels exploited dissatisfied with a good one; this is the channel through which perceived price fairness and service-recovery justice enter satisfaction, and it links to the fairness constraint on pricing in Chapter 20. Finally, adaptation-level dynamics explain why satisfaction is unstable over time: the reference point itself migrates toward recent experience, so a sustained improvement is re-encoded as the new normal and ceases to delight, the hedonic-adaptation logic behind the decay of the expectation effect documented in Oliver and Burke (1999) and behind the difficulty of maintaining satisfaction through incremental gains. Together, these theories explain why the construct is the residual of a comparison rather than a direct attitude reading, and why its measurement and its financial consequences (the subjects of the remaining sections) inherit the reference-dependence and asymmetry built into its formation.
4.3 Measurement
Historically, satisfaction has been measured by survey: a respondent rates overall satisfaction, expectations, and performance on Likert or semantic-differential scales, and these ratings are aggregated—at the national level by indices such as the ACSI, whose structure, purpose, and estimated cross-industry findings are set out in Fornell et al. (1996), and which anchors most of the marketing–finance evidence discussed below. Survey measurement is well understood, but it is slow, sparse, and reactive: it samples a fraction of customers at fixed intervals and asks them to introspect, which invites recall and social-desirability bias.
The frontier alternative reads satisfaction directly from what customers write. Sentiment analysis treats a corpus of customer-generated text—reviews, support transcripts, social posts—as a high-frequency, census-scale signal of satisfaction, classifying the polarity (and increasingly the intensity and target) of expressed affect (Kumar and Zymbler 2019; Wei et al. 2020). The appeal is operational as much as scientific: a sentiment pipeline observes the affective channel of Chapter 4 in near real time, covers customers who would never return a survey, and scales to firms and markets that no survey panel reaches.
The two measurement regimes trade off along several axes, summarized in Table 4.1. Neither dominates: survey scales remain the validated anchor against which text measures are benchmarked, while sentiment supplies coverage and timeliness that surveys structurally cannot.
Table 4.1: Survey versus text-based measurement of satisfaction
Property
Survey scales (e.g., ACSI)
Text sentiment
Sampling
Panel, intermittent
Census of writers, continuous
Construct captured
Cognitive + elicited affect
Expressed affect, in context
Latency
Weeks to quarters
Near real time
Principal bias
Recall, social desirability
Self-selection of who writes
Validation status
Field standard
Benchmarked to surveys
A worked illustration makes the text measure concrete. A minimal lexicon sentiment score classifies each document by the net count of positive and negative tokens it contains, normalized by length—a transparent baseline that more elaborate supervised classifiers refine but do not conceptually replace.
Code
set.seed(42)# Minimal, dependency-free lexicon sentiment score in [-1, 1].positive<-c("great", "excellent", "love", "happy", "satisfied","recommend", "good", "fast", "reliable", "delighted")negative<-c("bad", "terrible", "hate", "slow", "broken","disappointed", "refund", "rude", "late", "useless")sentiment_score<-function(texts){clean<-gsub("[^a-z ]", " ", tolower(texts))tokens<-strsplit(trimws(clean), "\\s+")vapply(tokens, function(w){p<-sum(w%in%positive); n<-sum(w%in%negative)if(p+n==0)0else(p-n)/(p+n)}, numeric(1))}reviews<-c("Great service and a reliable product, I would recommend it.","Terrible experience, the item was broken and the staff were rude.","It arrived; nothing remarkable to report about the purchase.")data.frame(review =reviews, score =sentiment_score(reviews))#> review score#> 1 Great service and a reliable product, I would recommend it. 1#> 2 Terrible experience, the item was broken and the staff were rude. -1#> 3 It arrived; nothing remarkable to report about the purchase. 0
4.4 Estimation and Identification
The static disconfirmation model Equation 4.1 looks like an ordinary regression of satisfaction on expectations and a performance gap, estimable by ordinary least squares (OLS). The dynamic field evidence shows why that estimator is fragile. Three assumptions must hold for the OLS coefficients to carry their structural interpretation, and the Oliver and Burke (1999) findings attack each in turn.
The first assumption is exogenous expectations: \(E\) must be uncorrelated with the error \(\varepsilon\). Backward assimilation violates this directly. When \(E\) is elicited after consumption, the measured expectation is contaminated by the realized performance, so \(\mathrm{Cov}(E, \varepsilon) \neq 0\) and \(\hat
\beta_E\) is biased. Eliciting \(E\) strictly before consumption—as the manipulation in the field study did—is the design that restores exogeneity; a post-hoc expectation measure cannot.
The second assumption is separability of\(E\) and \(P\). Forward assimilation violates it: because expectations shape perceived performance, \(P = \gamma E +
u\) with \(\gamma > 0\), the regressors \(E\) and \(D = P - E\) are mechanically correlated, inflating standard errors and making the separate identification of the assimilative path (\(\beta_E\)) from the contrast path (\(\beta_D\)) delicate. Experimental manipulation of \(E\) independent of \(P\) is again the clean route to separation; in purely observational data the two effects are only weakly distinguishable.
The third assumption is stable coefficients. The decay of the expectation effect over the consumption episode means \(\beta_E\) is time-varying, so a single cross-sectional regression estimates a horizon-specific average rather than a structural constant. The practical implication is that satisfaction must be modeled dimension by dimension and stage by stage; a pooled global regression averages over heterogeneity the theory says is real. Figure 4.1 collects these paths: the solid arrows are the structural relationships of Equation 4.1, and the dashed arrows are the assimilation effects that defeat the OLS estimator.
flowchart LR
E["Pre-consumption<br/>expectation E"] -->|beta_E| S["Satisfaction S"]
E --> D["Disconfirmation<br/>D = P - E"]
P["Perceived<br/>performance P"] --> D
D -->|beta_D| S
E -.->|forward<br/>assimilation| P
P -.->|backward<br/>assimilation| E
Figure 4.1: The expectancy-disconfirmation process. Solid arrows are the structural paths of equation Equation 4.1; dashed arrows are the assimilation effects that break the identifying assumptions of an OLS estimator.
4.5 Consequences: The Satisfaction–Value Chain
Why does a post-purchase judgment matter to a firm’s owners? The answer is that satisfaction is a leading indicator of cash flow. Satisfied customers buy again, defect less, cost less to serve, and complain less, so a movement in satisfaction today forecasts the level, growth, and volatility of future cash flows—and the present value of those flows is what equity is worth. The chain runs satisfaction \(\to\) cash flow characteristics \(\to\) shareholder value (Figure 4.2), and each link has been estimated.
Gruca and Rego (2005) make the cash-flow link explicit. Combining longitudinal ACSI data with COMPUSTAT financials and estimating the relationship in a hierarchical Bayesian framework—appropriate because firms are sampled repeatedly and the satisfaction effect is allowed to vary across them—they find that customer satisfaction both raises the growth of future cash flows and lowers their variability. Because shareholder value rises with expected cash flow and falls with its risk, satisfaction contributes to value through both channels at once. The result is robust across firm and industry characteristics and survives multi-measure, multi-method re-estimation, which matters because a single ACSI series invites concern about measurement idiosyncrasy.
Not every satisfaction metric carries this predictive content, however. Firms collect many customer-feedback measures, and Morgan and Rego (2006) adjudicate among six of them—top-2-box satisfaction, average satisfaction, repurchase-intention loyalty, net promoters, repurchase likelihood, and the proportion of customers complaining—against COMPUSTAT and CRSP performance over 1994–2000. The ranking is instructive and somewhat deflating for popular practice. Average satisfaction scores are the most predictive of future business performance, with top-2-box scores close behind; repurchase likelihood and complaint proportion carry moderate, performance-dimension-specific value; and recommendation-based metrics—net promoters and the average number of recommendations—show little to no predictive value. The metric a firm chooses to optimize is therefore not innocuous: an obsession with recommendation scores can mistake a poorly predictive proxy for the asset itself. Keiningham et al. (2007) reach the same verdict from a different direction, tracking net promoter against firm revenue growth over time and finding the relationship far weaker and less stable than the metric.s promoters claim.
flowchart LR
SAT["Customer<br/>satisfaction"] --> RET["Retention &<br/>repurchase"]
SAT --> COST["Lower<br/>cost-to-serve"]
RET --> CF["Cash-flow<br/>growth"]
RET --> VOL["Lower cash-flow<br/>volatility"]
COST --> CF
CF --> SV["Shareholder<br/>value"]
VOL --> SV
Figure 4.2: The satisfaction-to-shareholder-value chain. Satisfaction is priced because it forecasts the level, growth, and volatility of future cash flows.
4.6 How Markets Price Satisfaction
If satisfaction forecasts cash flows, an efficient equity market should impound it into prices. Whether it fully does—and where—has been the subject of a long empirical argument that turns on a subtle point: a satisfaction level that is already public should be priced, so the testable object is whether unexpected changes in satisfaction earn abnormal returns.
An early and influential reading found that a portfolio formed on ACSI strength earned positive abnormal returns at the overall market level (Fornell et al. 2006), suggesting satisfaction was a broadly mispriced intangible. Subsequent work narrowed the claim. Re-examining the evidence, Jacobson and Mizik (2009b) locate the abnormal returns specifically in the Internet and computer sector rather than across the whole market, and a companion analysis confirms this sector-bounded reading as the current academic consensus (Jacobson and Mizik 2009a). The lesson is methodological as much as substantive: an apparent market-wide anomaly dissolved into a sector-specific effect once the portfolios were reconstructed, a reminder that asset-pricing tests of marketing constructs are sensitive to portfolio formation and risk adjustment.
The pricing of satisfaction is also asymmetric, and the asymmetry has a behavioral signature consistent with prospect theory. Malshe, Colicev, and Mittal (2020) estimate that a one-unit increase in customer satisfaction raises abnormal returns by about \(0.56\) percentage points, while a one-unit decrease lowers them by about \(1.34\) percentage points—dissatisfaction is priced roughly \(2.4\) times as heavily as an equal-sized satisfaction gain, the loss-aversion pattern in which losses loom larger than gains. The mechanism is informative: short interest, a measure of short-seller activity, mediates the effect of customer (dis)satisfaction on abnormal returns, so sophisticated traders act on satisfaction news and transmit it into prices. The policy implication for managers is stark—defending against dissatisfaction protects value more than an equivalent investment in raising already-satisfied customers does.
4.6.1 Satisfaction and the Risk of Returns
Most of the discussion above concerns the level of returns. Satisfaction also affects their risk, which matters independently because a risk reduction lowers the discount rate applied to future cash flows and thus raises value even if expected returns are unchanged. Tuli and Bharadwaj (2009) decompose this effect: positive changes in customer satisfaction produce negative changes in systematic risk (both overall market-movement exposure and downside exposure specifically) and in idiosyncratic risk (the firm-specific volatility of returns). A more satisfied customer base therefore makes a firm’s equity less volatile and less exposed to market downturns—precisely the cash-flow-stability channel of Gruca and Rego (2005), now visible in the second moment of returns rather than the first. The marketing–finance machinery used to estimate these abnormal-return and risk effects—market models, event windows, and the screening of confounds—is developed in Chapter 24.
4.7 Two Disciplining Findings
A reader could leave the preceding sections believing that satisfaction is an unambiguous good to be maximized without limit. Two findings discipline that view, and Table 4.2 contrasts each with the naive expectation it overturns.
4.7.1 The Market-Share–Satisfaction Tension
Practitioners often assume that market share and customer satisfaction move together—that a firm winning the market must be pleasing its customers. The data say otherwise. Re-examining the relationship over an extended horizon in a representative sample of U.S. consumer markets, Rego, Morgan, and Fornell (2013) find a consistently significant negative relationship: satisfaction is a weak predictor of future market share, while market share is a strong negative predictor of future satisfaction. The mechanism is preference heterogeneity. A brand that serves a large share of the market necessarily serves a more diverse set of customers, whose heterogeneous preferences cannot all be satisfied by a single offering, so average satisfaction falls as share rises. The negative relationship is mediated by this heterogeneity, and it attenuates under two conditions—when satisfaction is benchmarked against the firm’s nearest competitor rather than an industry average, and when brand switching costs are low. The strategic resolution is a diversified brand portfolio: marketing a broader range of brands lets a firm cover heterogeneous segments without forcing one offering to satisfy them all, easing the inherent trade-off between share and satisfaction.
4.7.2 Financial Leverage as a Moderator
The satisfaction–value link is also conditioned by the firm’s capital structure. Malshe and Agarwal (2015) show that financial leverage shapes marketing outcomes and firm value through two distinct effects. First, leverage lowers customer satisfaction, an effect mediated by advertising intensity—debt service crowds out the marketing investment that sustains satisfaction. Second, leverage moderates the satisfaction–firm-value relationship itself. The damage is not uniform: service firms and firms in competitive industries suffer more from financial leverage, because their value depends more heavily on the customer relationships that leverage erodes. The implication is that satisfaction cannot be managed in isolation from the balance sheet; a leveraged firm extracts less value from a given level of satisfaction and finds it harder to sustain that level in the first place.
Table 4.2: Findings that discipline a naive “maximize satisfaction” view
The marketing–finance evidence above is drawn almost entirely from competitive markets, where satisfaction plausibly works through retention and repurchase—a customer who can leave but chooses to stay. This raises a sharp test: does satisfaction matter where customers cannot leave? Regulated utilities supply the natural experiment, both because they are monopolies and because their stringent reporting yields unusually precise operating and accounting data (Bhattacharya, Morgan, and Rego 2020).
Studying U.S. public utility firms, Bhattacharya, Morgan, and Rego (2021) find that customer satisfaction has no detectable effect on future revenues—unsurprising, since captive customers cannot redirect their demand—yet a positive effect on future profitability. The reconciliation is a cost channel. Higher satisfaction substantially reduces operating costs, specifically the costs of distribution, customer service, and general administration. Two post-hoc mechanisms support this. First, satisfaction lowers the cost of direct customer interactions and improves employee engagement by relieving the burden of managing dissatisfied customers. Second, a satisfied customer base is associated with greater trust and cooperation, which lubricates the firm’s regulated operations. The monopoly case is therefore not a null result but a clarifying one: it strips away the revenue channel and reveals a cost channel that operates even when demand is fixed, completing the account of why satisfaction is valuable.
4.9 Communicating Uncertainty: A Lever on Satisfaction
The chapter has treated satisfaction as a consequence of performance relative to expectations. The expectancy–disconfirmation logic of Equation 4.1 implies a managerial corollary: because satisfaction depends on the expectation a firm sets, the way a firm communicates can move satisfaction without changing performance at all. Time predictions under uncertainty—a delivery window, a wait time, a service estimate—are a clean setting because the firm chooses how to frame an inherently uncertain quantity.
Hu, Gaertig, and Dietvorst (2024) show that customers prefer range estimates (for example, “40–50 minutes”) to point estimates (“45 minutes”) for time predictions in uncertain contexts, and that communicating uncertainty through ranges enhances satisfaction with the estimate. The preference is robust across domains, time durations, and outcome distributions, and it holds even when the point estimate is conservative—that is, when the single number is set to the cautious upper-bound value a firm might otherwise use to manage expectations. In the terms of Equation 4.1, a range sets an expectation \(E\) that the realized performance is more likely to confirm, reducing the chance of a negative disconfirmation \(D < 0\) and thereby protecting satisfaction.1 The result is a concrete instance of the chapter’s central theme: satisfaction is managed not only by improving \(P\) but by shaping \(E\), and the framing of uncertainty is one of the cheapest available levers on the gap between them.
4.10 Connections
Satisfaction does not stand alone among marketing’s intangible assets. Its relationship to brand equity is close: perceived quality is an input to satisfaction and a component of brand value, and the attribute-level dynamics of satisfaction—how warranty-resolvable versus irresolvable attributes decay at different rates and carry different weight over time (Slotegraaf and Inman 2004)—are developed alongside the quality-signaling material in Chapter 11. The asset-pricing and risk results invoked above rest on the event-study and risk-decomposition machinery formalized in Chapter 24.
4.11 Key Takeaways
Satisfaction is the response to disconfirmation, the gap between pre-consumption expectations and perceived performance (Equation 4.1); it draws on both cognitive and affective channels and is best modeled dimension by dimension (Oliver and Burke 1999).
Expectations are not exogenous: forward and backward assimilation entangle \(E\) and \(P\), so identifying the disconfirmation model requires expectations elicited before consumption, ideally under manipulation.
Measurement is migrating from survey indices to text sentiment (Kumar and Zymbler 2019; Wei et al. 2020); the two are complements, with sentiment supplying coverage and timeliness and surveys supplying validation.
Satisfaction forecasts cash-flow growth and stability and hence shareholder value (Gruca and Rego 2005); average satisfaction is the most predictive metric, and recommendation-based metrics the least (Morgan and Rego 2006).
Two findings discipline naive maximization: market share predicts lower future satisfaction via preference heterogeneity (Rego, Morgan, and Fornell 2013), and financial leverage both depresses satisfaction and weakens its value effect, especially for service and competitive firms (Malshe and Agarwal 2015).
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A single conservative point estimate also lowers the chance of negative disconfirmation, which is why firms pad their quotes. The Hu, Gaertig, and Dietvorst (2024) result is that a range achieves this and is preferred to the padded point estimate, so it dominates the conventional conservatism heuristic rather than merely replicating it.↩︎
Source Code
# Customer Satisfaction {#sec-satisfaction}*Customer satisfaction* is the evaluative judgment a consumer forms after comparing the performance of a product or service against some standard of reference. It is at once the most surveyed construct in marketing practice and one of the most theoretically demanding: a satisfaction score is not a direct reading of an attitude, but the residual of a comparison process, and the standard against which performance is judged is itself shaped by the very experience being evaluated. This chapter treats satisfaction as two coupled objects. The first is a **psychological** construct: a post-purchase judgment built from affective and cognitive inputs and anchored to prior expectations. The second is a **financial** asset: a forward-looking signal that, when it moves, moves cash flows, equity returns, and the risk those returns carry. A serious account must connect the two, and the modern empirical literature, drawing heavily on the American Customer Satisfaction Index (ACSI), increasingly does.The chapter proceeds from formation to consequence. We begin with how satisfaction is *formed* (i.e., the expectancy–disconfirmation paradigm and its affective and cognitive components) and how it is *measured*, including the modern move from survey scales to text-derived sentiment. We then formalize the disconfirmation model to make its identification problems explicit. From there, we turn to consequences: the satisfaction–profit chain that runs from cash flow growth and volatility to shareholder value; the asymmetric and sector-specific way equity markets price satisfaction; and the effect of satisfaction on the *risk* of returns rather than their level. We confront two findings that discipline naive optimism (i.e., the negative market-share–satisfaction relationship and the moderating role of financial leverage) and close with monopoly settings and the communication of uncertainty.## Formation: The Expectancy–Disconfirmation ParadigmThe dominant theory of how satisfaction arises is the **expectancy–disconfirmation** model. Its intuition is ordinary: a diner who expects a mediocre meal and receives a good one leaves delighted, while a diner who expects greatness and receives merely good leaves disappointed, even though the meals were identical. Satisfaction tracks not performance itself but performance *relative to* a pre-consumption expectation. The gap between the two is **disconfirmation**: positive when performance exceeds expectations, negative when it falls short, and zero when they coincide.Let $E$ denote a consumer's pre-consumption expectation of performance along some attribute, $P$ the perceived actual performance, and $D = P - E$ the disconfirmation. The paradigm posits that satisfaction $S$ rises in both the baseline expectation and the disconfirmation,$$S = \alpha + \beta_E\, E + \beta_D\, (P - E) + \varepsilon,$$ {#eq-03-disconfirmation}with $\beta_D > 0$ the central claim of the theory and $\beta_E$ capturing the direct, assimilative pull of expectations on the final judgment. Equation @eq-03-disconfirmation makes the construct's subtlety precise: because $E$ enters both directly (through $\beta_E$) and as the benchmark inside the disconfirmation term, raising expectations has *offsetting* effects on satisfaction. It lifts the assimilative component while simultaneously raising the bar that performance must clear, a tension that any manager who has over-promised understands and that any estimator of @eq-03-disconfirmation must confront.A field study of a novel restaurant experience by @oliver1999expectation sharpens the dynamics behind this static equation. Pre-consumption expectations were manipulated with realistic reviews, the dining experience itself was recorded by protocol methods, and perceptions were captured immediately after. Four results recur in subsequent work. First, the manipulated expectation exerts an **immediate effect that decays** over the course of the experience, so the weight $\beta_E$ is not constant but fades as direct evidence accumulates. Second, expectations shape perceived performance (i.e., **forward assimilation**) so $E$ and $P$ are not independent inputs but causally linked, which (as we note below) breaks the clean separation @eq-03-disconfirmation assumes. Third, expectations are revised *retrospectively* by what actually happened (i.e., **backward assimilation**), so the measured $E$ depends on $P$ when it is elicited after consumption. Fourth, the disconfirmation $D$ remains the pivotal driver of $S$, and the whole process operates in a **dimension-specific** way: disconfirmation on one attribute drives satisfaction with that attribute, not a diffuse global affect.> **Disconfirmation.** Satisfaction is the consumer's response to the perceived discrepancy between prior expectations and the actual performance of a product as experienced after consumption.### Affect and CognitionA consumer's assessment of satisfaction draws on two channels that the disconfirmation equation collapses into a single $S$. The **cognitive** channel is the deliberate, attribute-by-attribute comparison of performance against expectation (i.e., precisely the $P - E$ computation of @eq-03-disconfirmation). The **affective** channel is the emotional residue of consumption: the pleasure, delight, irritation, or regret experienced during use, which colors the judgment independently of any calculated discrepancy. The two are not substitutes. A technically flawless service encounter delivered coldly can produce low satisfaction despite zero cognitive disconfirmation, and a warm recovery from a service failure can produce high satisfaction despite a large negative one. Treating satisfaction as a purely cognitive ledger, therefore, mis-specifies its formation; the affective component is a separate input, and measurement instruments that capture only attribute ratings miss it.## Theoretical Foundations {#sec-ths-foundations}Satisfaction is a comparison judgment, and the theories that ground it are theories of what gets compared, against what standard, and with what asymmetry. The organizing account is the **expectancy-disconfirmation** paradigm developed above: satisfaction tracks performance relative to a pre-consumption expectation, with the disconfirmation $D = P - E$ as the pivotal driver [@oliver1999expectation]. (Oliver's original 1980 cognitive model of satisfaction established the paradigm; the field study cited here sharpens its dynamics.) Disconfirmation is itself a **reference-dependence** claim, which is why **prospect theory** is the second foundation [@kahneman1979prospect; @tversky1981]. The expectation $E$ is a reference point, performance is coded as a gain or a loss relative to it, and because losses loom larger than gains, *negative* disconfirmation depresses satisfaction more than equal *positive* disconfirmation raises it. This loss-averse asymmetry reappears downstream in how equity markets price satisfaction news, where dissatisfaction loads roughly 2.4 times as heavily as an equal satisfaction gain [@malshe2020].Two motivational and informational theories refine the comparison. **Regulatory focus** [@higgins1997regulatory] holds that a promotion-focused consumer evaluates an experience against ideals and the presence or absence of *gains*, while a prevention-focused consumer evaluates against oughts and the presence or absence of *losses*; the same objective performance therefore yields different satisfaction depending on which standard the consumer brings, and on whether the offering's framing fits that orientation. The affect-as-information account (Schwarz and Clore's work on feelings as information) grounds the **affective channel** introduced above: consumers read their momentary feelings during consumption as data about how satisfied they are, which is why a warm recovery can lift satisfaction despite zero cognitive disconfirmation, and a cold but flawless encounter can depress it. This is the theoretical reason the affective input cannot be reduced to the cognitive ledger of @eq-03-disconfirmation. The asymmetry between the two valences is itself asymmetric across outcomes: @ross1998asymmetriimpact show that negative and positive information about a firm are weighted very differently depending on whether the judgment concerns the product or the relationship, so a single loss-aversion coefficient understates the structure.A further complication is that satisfaction depends on *who is held responsible* for the outcome, not only on the outcome. @botti2011locuschoice show that a consumer who chose an option and got a poor result is less satisfied than one who received the same result from someone else's choice, because personal causality converts a bad draw into a bad decision. This matters directly for the design of assortments, recommendation systems, and self-service: shifting the locus of choice to the customer shifts the locus of blame with it.Two further theories bound the construct. **Equity (fairness) theory** (Adams's work on inequity, extended to consumption by Oliver and Swan) holds that satisfaction depends not only on one's own outcome-to-input ratio but on its comparison to the seller's and to other customers', so a consumer who feels fairly treated can be satisfied with a modest outcome and one who feels exploited dissatisfied with a good one; this is the channel through which perceived price fairness and service-recovery justice enter satisfaction, and it links to the fairness constraint on pricing in @sec-pricing. Finally, **adaptation-level** dynamics explain why satisfaction is unstable over time: the reference point itself migrates toward recent experience, so a sustained improvement is re-encoded as the new normal and ceases to delight, the hedonic-adaptation logic behind the decay of the expectation effect documented in @oliver1999expectation and behind the difficulty of maintaining satisfaction through incremental gains. Together, these theories explain why the construct is the residual of a comparison rather than a direct attitude reading, and why its measurement and its financial consequences (the subjects of the remaining sections) inherit the reference-dependence and asymmetry built into its formation.## MeasurementHistorically, satisfaction has been measured by survey: a respondent rates overall satisfaction, expectations, and performance on Likert or semantic-differential scales, and these ratings are aggregated—at the national level by indices such as the ACSI, whose structure, purpose, and estimated cross-industry findings are set out in @johnson1996americancustomer, and which anchors most of the marketing–finance evidence discussed below. Survey measurement is well understood, but it is slow, sparse, and reactive: it samples a fraction of customers at fixed intervals and asks them to introspect, which invites recall and social-desirability bias.The frontier alternative reads satisfaction directly from what customers write. **Sentiment analysis** treats a corpus of customer-generated text—reviews, support transcripts, social posts—as a high-frequency, census-scale signal of satisfaction, classifying the polarity (and increasingly the intensity and target) of expressed affect [@kumar2019; @wei2020]. The appeal is operational as much as scientific: a sentiment pipeline observes the affective channel of @sec-satisfaction in near real time, covers customers who would never return a survey, and scales to firms and markets that no survey panel reaches.The two measurement regimes trade off along several axes, summarized in @tbl-measurement. Neither dominates: survey scales remain the validated anchor against which text measures are benchmarked, while sentiment supplies coverage and timeliness that surveys structurally cannot.| Property | Survey scales (e.g., ACSI) | Text sentiment ||------------------------|------------------------|------------------------|| Sampling | Panel, intermittent | Census of writers, continuous || Construct captured | Cognitive + elicited affect | Expressed affect, in context || Latency | Weeks to quarters | Near real time || Principal bias | Recall, social desirability | Self-selection of who writes || Validation status | Field standard | Benchmarked to surveys |: Survey versus text-based measurement of satisfaction {#tbl-measurement}A worked illustration makes the text measure concrete. A minimal lexicon sentiment score classifies each document by the net count of positive and negative tokens it contains, normalized by length—a transparent baseline that more elaborate supervised classifiers refine but do not conceptually replace.```{r sentiment-baseline, message=FALSE, warning=FALSE}set.seed(42)# Minimal, dependency-free lexicon sentiment score in [-1, 1].positive <-c("great", "excellent", "love", "happy", "satisfied","recommend", "good", "fast", "reliable", "delighted")negative <-c("bad", "terrible", "hate", "slow", "broken","disappointed", "refund", "rude", "late", "useless")sentiment_score <-function(texts) { clean <-gsub("[^a-z ]", " ", tolower(texts)) tokens <-strsplit(trimws(clean), "\\s+")vapply(tokens, function(w) { p <-sum(w %in% positive); n <-sum(w %in% negative)if (p + n ==0) 0else (p - n) / (p + n) }, numeric(1))}reviews <-c("Great service and a reliable product, I would recommend it.","Terrible experience, the item was broken and the staff were rude.","It arrived; nothing remarkable to report about the purchase.")data.frame(review = reviews, score =sentiment_score(reviews))```## Estimation and IdentificationThe static disconfirmation model @eq-03-disconfirmation looks like an ordinary regression of satisfaction on expectations and a performance gap, estimable by ordinary least squares (OLS). The dynamic field evidence shows why that estimator is fragile. Three assumptions must hold for the OLS coefficients to carry their structural interpretation, and the @oliver1999expectation findings attack each in turn.The first assumption is **exogenous expectations**: $E$ must be uncorrelated with the error $\varepsilon$. Backward assimilation violates this directly. When $E$ is elicited after consumption, the measured expectation is contaminated by the realized performance, so $\mathrm{Cov}(E, \varepsilon) \neq 0$ and $\hat\beta_E$ is biased. Eliciting $E$ strictly before consumption—as the manipulation in the field study did—is the design that restores exogeneity; a post-hoc expectation measure cannot.The second assumption is **separability of** $E$ and $P$. Forward assimilation violates it: because expectations shape perceived performance, $P = \gamma E +u$ with $\gamma > 0$, the regressors $E$ and $D = P - E$ are mechanically correlated, inflating standard errors and making the separate identification of the assimilative path ($\beta_E$) from the contrast path ($\beta_D$) delicate. Experimental manipulation of $E$ independent of $P$ is again the clean route to separation; in purely observational data the two effects are only weakly distinguishable.The third assumption is **stable coefficients**. The decay of the expectation effect over the consumption episode means $\beta_E$ is time-varying, so a single cross-sectional regression estimates a horizon-specific average rather than a structural constant. The practical implication is that satisfaction must be modeled dimension by dimension and stage by stage; a pooled global regression averages over heterogeneity the theory says is real. @fig-disconfirmation collects these paths: the solid arrows are the structural relationships of @eq-03-disconfirmation, and the dashed arrows are the assimilation effects that defeat the OLS estimator.```{mermaid}%%| label: fig-disconfirmation%%| fig-cap: "The expectancy-disconfirmation process. Solid arrows are the structural paths of equation @eq-03-disconfirmation; dashed arrows are the assimilation effects that break the identifying assumptions of an OLS estimator."flowchart LR E["Pre-consumption<br/>expectation E"] -->|beta_E| S["Satisfaction S"] E --> D["Disconfirmation<br/>D = P - E"] P["Perceived<br/>performance P"] --> D D -->|beta_D| S E -.->|forward<br/>assimilation| P P -.->|backward<br/>assimilation| E```## Consequences: The Satisfaction–Value ChainWhy does a post-purchase judgment matter to a firm's owners? The answer is that satisfaction is a *leading indicator of cash flow*. Satisfied customers buy again, defect less, cost less to serve, and complain less, so a movement in satisfaction today forecasts the level, growth, and volatility of future cash flows—and the present value of those flows is what equity is worth. The chain runs satisfaction $\to$ cash flow characteristics $\to$ shareholder value (@fig-value-chain), and each link has been estimated.@gruca2005customer make the cash-flow link explicit. Combining longitudinal ACSI data with COMPUSTAT financials and estimating the relationship in a hierarchical Bayesian framework—appropriate because firms are sampled repeatedly and the satisfaction effect is allowed to vary across them—they find that customer satisfaction both **raises the growth** of future cash flows and **lowers their variability**. Because shareholder value rises with expected cash flow and falls with its risk, satisfaction contributes to value through both channels at once. The result is robust across firm and industry characteristics and survives multi-measure, multi-method re-estimation, which matters because a single ACSI series invites concern about measurement idiosyncrasy.Not every satisfaction metric carries this predictive content, however. Firms collect many customer-feedback measures, and @morgan2006value adjudicate among six of them—top-2-box satisfaction, average satisfaction, repurchase-intention loyalty, net promoters, repurchase likelihood, and the proportion of customers complaining—against COMPUSTAT and CRSP performance over 1994–2000. The ranking is instructive and somewhat deflating for popular practice. **Average satisfaction scores are the most predictive** of future business performance, with top-2-box scores close behind; repurchase likelihood and complaint proportion carry moderate, performance-dimension-specific value; and recommendation-based metrics—net promoters and the average number of recommendations—show **little to no** predictive value. The metric a firm chooses to optimize is therefore not innocuous: an obsession with recommendation scores can mistake a poorly predictive proxy for the asset itself. @andreassen2007longitudiexaminati reach the same verdict from a different direction, tracking net promoter against firm revenue growth over time and finding the relationship far weaker and less stable than the metric.s promoters claim.```{mermaid}%%| label: fig-value-chain%%| fig-cap: "The satisfaction-to-shareholder-value chain. Satisfaction is priced because it forecasts the level, growth, and volatility of future cash flows."flowchart LR SAT["Customer<br/>satisfaction"] --> RET["Retention &<br/>repurchase"] SAT --> COST["Lower<br/>cost-to-serve"] RET --> CF["Cash-flow<br/>growth"] RET --> VOL["Lower cash-flow<br/>volatility"] COST --> CF CF --> SV["Shareholder<br/>value"] VOL --> SV```## How Markets Price SatisfactionIf satisfaction forecasts cash flows, an efficient equity market should impound it into prices. Whether it fully does—and where—has been the subject of a long empirical argument that turns on a subtle point: a satisfaction *level* that is already public should be priced, so the testable object is whether *unexpected changes* in satisfaction earn abnormal returns.An early and influential reading found that a portfolio formed on ACSI strength earned positive abnormal returns at the **overall market** level [@fornell2006], suggesting satisfaction was a broadly mispriced intangible. Subsequent work narrowed the claim. Re-examining the evidence, @jacobson2009 locate the abnormal returns specifically in the **Internet and computer sector** rather than across the whole market, and a companion analysis confirms this sector-bounded reading as the current academic consensus [@jacobson2009a]. The lesson is methodological as much as substantive: an apparent market-wide anomaly dissolved into a sector-specific effect once the portfolios were reconstructed, a reminder that asset-pricing tests of marketing constructs are sensitive to portfolio formation and risk adjustment.The pricing of satisfaction is also **asymmetric**, and the asymmetry has a behavioral signature consistent with prospect theory. @malshe2020 estimate that a one-unit increase in customer satisfaction raises abnormal returns by about $0.56$ percentage points, while a one-unit *decrease* lowers them by about $1.34$ percentage points—dissatisfaction is priced roughly $2.4$ times as heavily as an equal-sized satisfaction gain, the loss-aversion pattern in which losses loom larger than gains. The mechanism is informative: **short interest**, a measure of short-seller activity, mediates the effect of customer (dis)satisfaction on abnormal returns, so sophisticated traders act on satisfaction news and transmit it into prices. The policy implication for managers is stark—defending against dissatisfaction protects value more than an equivalent investment in raising already-satisfied customers does.### Satisfaction and the Risk of ReturnsMost of the discussion above concerns the *level* of returns. Satisfaction also affects their *risk*, which matters independently because a risk reduction lowers the discount rate applied to future cash flows and thus raises value even if expected returns are unchanged. @tuli2009 decompose this effect: positive changes in customer satisfaction produce negative changes in **systematic risk** (both overall market-movement exposure and downside exposure specifically) and in **idiosyncratic risk** (the firm-specific volatility of returns). A more satisfied customer base therefore makes a firm's equity less volatile and less exposed to market downturns—precisely the cash-flow-stability channel of @gruca2005customer, now visible in the second moment of returns rather than the first. The marketing–finance machinery used to estimate these abnormal-return and risk effects—market models, event windows, and the screening of confounds—is developed in @sec-marketing-finance.## Two Disciplining FindingsA reader could leave the preceding sections believing that satisfaction is an unambiguous good to be maximized without limit. Two findings discipline that view, and @tbl-disciplining contrasts each with the naive expectation it overturns.### The Market-Share–Satisfaction TensionPractitioners often assume that market share and customer satisfaction move together—that a firm winning the market must be pleasing its customers. The data say otherwise. Re-examining the relationship over an extended horizon in a representative sample of U.S. consumer markets, @rego2013reexamining find a **consistently significant negative** relationship: satisfaction is a *weak* predictor of future market share, while market share is a *strong negative* predictor of future satisfaction. The mechanism is **preference heterogeneity**. A brand that serves a large share of the market necessarily serves a more diverse set of customers, whose heterogeneous preferences cannot all be satisfied by a single offering, so average satisfaction falls as share rises. The negative relationship is mediated by this heterogeneity, and it attenuates under two conditions—when satisfaction is benchmarked against the firm's *nearest competitor* rather than an industry average, and when brand **switching costs are low**. The strategic resolution is a *diversified brand portfolio*: marketing a broader range of brands lets a firm cover heterogeneous segments without forcing one offering to satisfy them all, easing the inherent trade-off between share and satisfaction.### Financial Leverage as a ModeratorThe satisfaction–value link is also conditioned by the firm's capital structure. @Malshe_2015 show that financial leverage shapes marketing outcomes and firm value through two distinct effects. First, leverage **lowers customer satisfaction**, an effect mediated by advertising intensity—debt service crowds out the marketing investment that sustains satisfaction. Second, leverage **moderates** the satisfaction–firm-value relationship itself. The damage is not uniform: **service firms and firms in competitive industries suffer more** from financial leverage, because their value depends more heavily on the customer relationships that leverage erodes. The implication is that satisfaction cannot be managed in isolation from the balance sheet; a leveraged firm extracts less value from a given level of satisfaction and finds it harder to sustain that level in the first place.| Finding | Naive expectation | Empirical reality | Mechanism ||------------------|------------------|------------------|------------------|| Market share vs. satisfaction | Positively related | Negative; share predicts *lower* future satisfaction | Preference heterogeneity [@rego2013reexamining]|| Pricing of satisfaction news | Symmetric | Dissatisfaction priced \~2.4x as heavily | Loss aversion; short-interest channel [@malshe2020]|| Financial leverage | Neutral to value link | Lowers satisfaction and weakens its value effect | Advertising crowd-out; relationship erosion [@Malshe_2015]|: Findings that discipline a naive "maximize satisfaction" view {#tbl-disciplining}## Satisfaction in Monopoly: The Cost ChannelThe marketing–finance evidence above is drawn almost entirely from competitive markets, where satisfaction plausibly works through retention and repurchase—a customer who can leave but chooses to stay. This raises a sharp test: does satisfaction matter where customers *cannot* leave? Regulated utilities supply the natural experiment, both because they are monopolies and because their stringent reporting yields unusually precise operating and accounting data [@bhattacharya2020].Studying U.S. public utility firms, @bhattacharya2021customer find that customer satisfaction has **no detectable effect on future revenues**—unsurprising, since captive customers cannot redirect their demand—yet a **positive effect on future profitability**. The reconciliation is a **cost channel**. Higher satisfaction substantially reduces operating costs, specifically the costs of distribution, customer service, and general administration. Two post-hoc mechanisms support this. First, satisfaction lowers the cost of direct customer interactions and improves employee engagement by relieving the burden of managing dissatisfied customers. Second, a satisfied customer base is associated with greater trust and cooperation, which lubricates the firm's regulated operations. The monopoly case is therefore not a null result but a clarifying one: it strips away the revenue channel and reveals a cost channel that operates even when demand is fixed, completing the account of *why* satisfaction is valuable.## Communicating Uncertainty: A Lever on SatisfactionThe chapter has treated satisfaction as a consequence of performance relative to expectations. The expectancy–disconfirmation logic of @eq-03-disconfirmation implies a managerial corollary: because satisfaction depends on the *expectation* a firm sets, the *way* a firm communicates can move satisfaction without changing performance at all. Time predictions under uncertainty—a delivery window, a wait time, a service estimate—are a clean setting because the firm chooses how to frame an inherently uncertain quantity.@hu2024should show that customers prefer **range estimates** (for example, "40–50 minutes") to **point estimates** ("45 minutes") for time predictions in uncertain contexts, and that communicating uncertainty through ranges *enhances* satisfaction with the estimate. The preference is robust across domains, time durations, and outcome distributions, and it holds even when the point estimate is conservative—that is, when the single number is set to the cautious upper-bound value a firm might otherwise use to manage expectations. In the terms of @eq-03-disconfirmation, a range sets an expectation $E$ that the realized performance is more likely to confirm, reducing the chance of a negative disconfirmation $D < 0$ and thereby protecting satisfaction.[^03-satisfaction-1] The result is a concrete instance of the chapter's central theme: satisfaction is managed not only by improving $P$ but by shaping $E$, and the framing of uncertainty is one of the cheapest available levers on the gap between them.[^03-satisfaction-1]: A single conservative point estimate also lowers the chance of negative disconfirmation, which is why firms pad their quotes. The @hu2024should result is that a range achieves this *and* is preferred to the padded point estimate, so it dominates the conventional conservatism heuristic rather than merely replicating it.## ConnectionsSatisfaction does not stand alone among marketing's intangible assets. Its relationship to **brand equity** is close: perceived quality is an input to satisfaction and a component of brand value, and the attribute-level dynamics of satisfaction—how warranty-resolvable versus irresolvable attributes decay at different rates and carry different weight over time [@slotegraaf2004longitudinal]—are developed alongside the quality-signaling material in @sec-branding. The asset-pricing and risk results invoked above rest on the event-study and risk-decomposition machinery formalized in @sec-marketing-finance.## Key Takeaways- Satisfaction is the response to disconfirmation, the gap between pre-consumption expectations and perceived performance (@eq-03-disconfirmation); it draws on both cognitive and affective channels and is best modeled dimension by dimension [@oliver1999expectation].- Expectations are not exogenous: forward and backward assimilation entangle $E$ and $P$, so identifying the disconfirmation model requires expectations elicited *before* consumption, ideally under manipulation.- Measurement is migrating from survey indices to text sentiment [@kumar2019; @wei2020]; the two are complements, with sentiment supplying coverage and timeliness and surveys supplying validation.- Satisfaction forecasts cash-flow growth and stability and hence shareholder value [@gruca2005customer]; average satisfaction is the most predictive metric, and recommendation-based metrics the least [@morgan2006value].- Markets price satisfaction asymmetrically—dissatisfaction loads far more heavily, through a short-interest channel [@malshe2020]—and the level effect is concentrated in the Internet/computer sector [@jacobson2009; @jacobson2009a; @fornell2006]; satisfaction also lowers systematic and idiosyncratic risk [@tuli2009].- Two findings discipline naive maximization: market share predicts *lower* future satisfaction via preference heterogeneity [@rego2013reexamining], and financial leverage both depresses satisfaction and weakens its value effect, especially for service and competitive firms [@Malshe_2015].- In monopoly utilities, satisfaction raises profitability through a cost channel rather than revenue [@bhattacharya2021customer; @bhattacharya2020], and framing uncertainty as a range rather than a point raises satisfaction by reshaping expectations [@hu2024should].