Performance Outcome Satisfaction (POS)

1. Abstract

The Performance Outcome Satisfaction (POS) scale is a psychometric instrument designed to evaluate an individual’s cognitive and affective evaluative state regarding the performance of a chosen product, service, or decision outcome. Originally formulated by consumer behavior scholars Michael Tsiros and Vikas Mittal in their seminal 2000 investigation into the antecedents and consequences of regret, the instrument bridges the gap between purely cognitive post-choice assessments and affective outcome evaluations. The POS scale consists of three to four items operationalized via a seven-point Likert scale or semantic differential format. Items tap into the core dimensions of evaluative sentiment: general satisfaction, happiness with the performance, experienced disappointment (frequently reverse-scored), and overall pleasure derived from the focal object or outcome.

Although initially developed to measure attributed satisfaction—wherein an observer or decision-maker assesses how another party experiences a consumption or choice outcome within an experimental scenario—the scale was subsequently adapted and widely validated for direct, first-person subjective evaluations. Psychometrically, the POS scale exhibits robust internal consistency across diverse empirical studies, yielding Cronbach’s alpha coefficients consistently exceeding .85 and often reaching .90 to .94. Confirmatory factor analyses demonstrate that the scale loads onto a distinct, unidimensional factor that exhibits strong discriminant validity from related post-decisional constructs such as post-purchase regret, perceived service quality, and repurchase intentions. The scale has become a benchmark in behavioral economics, organizational psychology, and marketing research, providing scholars with a concise, theoretically grounded tool to isolate outcome satisfaction from procedural fairness and counterfactual comparison processes.

2. Keywords

Performance Outcome Satisfaction, consumer satisfaction, post-choice evaluation, regret theory, counterfactual thinking, psychometrics, expectancy disconfirmation, decision making, affective appraisal, scale validation

3. Authors

The Performance Outcome Satisfaction scale was introduced by:

• Michael Tsiros, Ph.D.: Professor of Marketing and Patrick J. Cesarano Endowed Professor at the Miami Herbert Business School, University of Miami. Dr. Tsiros is a leading authority on consumer decision making, post-purchase behavioral dynamics, behavioral pricing, and the emotional mechanics of regret and satisfaction in service and retail environments.
• Vikas Mittal, Ph.D.: J. Hugh Liedtke Professor of Marketing at the Jesse H. Jones Graduate School of Business, Rice University. Dr. Mittal is internationally recognized for his scholarship on customer satisfaction, brand equity, healthcare management, strategic marketing decision-making, and quantitative modeling of customer feedback systems.

Their collaborative research, particularly the paper “Regret: A Model of Its Antecedents and Consequences in Consumer Decision Making” published in the Journal of Consumer Research (March 2000), established foundational methodologies for disentangling the psychological divergence between performance satisfaction and decision-process regret.

4. Purpose

The primary purpose of the Performance Outcome Satisfaction (POS) scale is to isolate, quantify, and evaluate the specific valence and magnitude of subjective satisfaction anchored directly to the functional or experiential performance of a focal decision outcome. Historically, decision-making and consumer satisfaction research suffered from a conflation between satisfaction with the process of choosing (decision satisfaction) and satisfaction with the actual performance realized from that choice (outcome satisfaction). Tsiros and Mittal developed the POS scale to provide an empirical instrument capable of measuring the consumer’s appraisal of an object’s performance independently of whether the consumer regretted the selection process itself.

In experimental and field contexts, decision makers frequently face situations where a product performs admirably (yielding high outcome satisfaction), yet an unchosen alternative turns out to have performed even better, generating counterfactual regret. Conversely, a chosen product may exhibit moderate functional deficits, yet the decision maker feels no regret because all available alternatives were worse. To untangle these complex psychological mechanics, researchers required an instrument that did not aggregate broader holistic sentiments, but instead strictly measured the immediate affective and cognitive contentment elicited by the focal performance.

The scale was engineered with dual utility:

• Attributed Satisfaction: In early scenario-based experimental paradigms, participants were tasked with evaluating the psychological state of a hypothetical protagonist (third-person attribution). The POS scale demonstrated high diagnostic sensitivity in assessing how external observers interpret performance cues, valence signals, and expectation fulfillment in others.
• Direct First-Person Evaluation: Subsequent experimental and longitudinal field studies adapted the items into the first person. This format allows researchers to capture real-time consumer satisfaction following tangible transactions, digital interactions, investment outcomes, or healthcare interventions.

In applied research, the POS scale provides practitioners and researchers with a rapid, low-burden assessment tool. Its brevity prevents respondent fatigue in complex structural equation modeling (SEM) questionnaires, while its psychometric robustness ensures clear separation from downstream loyalty, word-of-mouth intention, and willingness-to-pay constructs.

5. Psychological Construct

The psychological construct captured by the POS scale is Performance Outcome Satisfaction, conceptualized as a post-evaluative affective-cognitive judgment reflecting the degree to which an individual experiences positive sentiment, pleasure, and fulfillment directly derived from the observed functioning, utility, or experiential delivery of a chosen entity. Unlike multidimensional service quality constructs that probe granular operational attributes (e.g., reliability, responsiveness, tangibles), POS assesses the synthesized summary appraisal of the outcome.

The construct resides at the intersection of two fundamental psychological dimensions:

Cognitive Evaluation of Disconfirmation

At its cognitive foundation, outcome satisfaction reflects the verification or disconfirmation of prior standards. When an individual engages with an outcome, they assess whether functional attributes match, surpass, or fall short of baseline expectations or baseline performance thresholds. This judgment answers the latent internal inquiry: “Did this product or decision perform as it was supposed to?” The POS scale captures this dimension through direct satisfaction indicators that prompt the respondent to summarize the functional efficacy of the target.

Affective Valence and Hedonic State

Unlike cold informational evaluations, outcome satisfaction is deeply infused with emotional valence. Tsiros and Mittal intentionally integrated affective items into the POS instrument, capturing:

• Happiness: An activated positive emotional state signifying that the outcome brought personal utility and emotional uplift.
• Pleasure: A hedonic evaluation reflecting the sensory, experiential, or functional gratification produced during consumption or realization.
• Disappointment (Reverse Dimension): An adverse emotional reaction elicited when the actual outcome fails to achieve what was expected. Disappointment is conceptually distinct from regret: disappointment is an outcome-oriented emotion triggered by external forces or failed performance expectations, whereas regret is an agency-oriented emotion triggered by the realization that a personal counterfactual choice would have yielded a superior result.

By blending cognitive evaluation with primary outcome emotions (happiness, pleasure, and disappointment), the POS scale avoids the pitfall of treating satisfaction as either purely cognitive calculus or unmoored affective excitement. It measures the integrated psychological reality of an individual standing before a finalized performance outcome.

6. Theoretical Framework

The Performance Outcome Satisfaction scale is embedded within the confluence of three major theoretical paradigms: Expectancy-Disconfirmation Theory (EDT), Regret Theory, and Cognitive Appraisal Theory.

Expectancy-Disconfirmation Theory

Pioneered by Richard L. Oliver in 1980, Expectancy-Disconfirmation Theory posits that customer or decision satisfaction is a function of expectations, perceived performance, and disconfirmation (the psychological distance between expectations and reality). When performance exceeds prior expectations, positive disconfirmation ensues, leading directly to elevated satisfaction. When performance is inferior, negative disconfirmation yields dissatisfaction. The POS scale operationalizes the terminal output of this disconfirmation sequence, measuring the respondent’s summary state once disconfirmation has resolved into enduring cognitive and affective evaluations.

Regret Theory and Counterfactual Comparison

The critical theoretical contribution of Tsiros and Mittal (2000) was separating performance outcome satisfaction from regret. Rooted in the economic and psychological formulations of Bell (1982), Loomes and Sugden (1982), and Zeelenberg (1999), regret theory dictates that decision evaluation involves counterfactual thinking: comparing “what is” with “what might have been.” Tsiros and Mittal demonstrated that satisfaction and regret operate via distinct psychological antecedents:

• Satisfaction is governed predominantly by comparing the chosen option’s performance against initial expectations or industry standards (expectancy disconfirmation).
• Regret is governed by comparing the chosen option’s performance against the realized or imagined performance of a foregone alternative (counterfactual comparison).

The theoretical framework of the POS scale assumes that an individual can evaluate performance outcome satisfaction in a self-contained manner, referencing the object’s intrinsic functional delivery, even when external comparative benchmarks shift counterfactual emotions.

Cognitive Appraisal Theory of Emotions

Under Lazarus’s (1991) and Roseman’s (1991) cognitive appraisal theories, emotions stem from specific cognitive interpretations of an event’s congruence with personal goals and agency. Outcome satisfaction and disappointment are characterized by goal congruence (or incongruence) where agency can be attributed to the product or external environment. By structuring the POS scale to assess happiness, pleasure, and disappointment in direct relation to performance, the authors aligned the scale with appraisal theory, capturing emotion anchored directly to the object rather than internal self-blame.

7. Validity

The psychometric validity of the Performance Outcome Satisfaction scale has been rigorously tested across experimental, survey, and longitudinal designs.

Construct and Convergent Validity

Convergent validity is established when items designed to measure a theoretical construct correlate strongly among themselves and converge on a single shared latent variable. In Tsiros and Mittal’s (2000) experimental studies manipulating outcome valences and foregone alternative performance, the POS scale items exhibited exceptionally high item-to-total correlations (typically r > .75). Standardized factor loadings in confirmatory factor models across studies consistently range between .82 and .95, significantly surpassing the standard .70 threshold recommended by psychometricians (Hair et al., 2010). Average Variance Extracted (AVE) values routinely exceed .70, demonstrating that the latent construct accounts for far more variance than error or residual noise.

Discriminant Validity

The primary empirical challenge in validating the POS scale was establishing its discriminant validity against Regret and Repurchase Intentions. Using Fornell-Larcker criteria and nested model chi-square difference tests, Tsiros and Mittal demonstrated that:

• A two-factor model separating Regret (measured via items assessing whether the participant felt sorry for the choice or wished they had chosen differently) and Outcome Satisfaction (POS) provided a vastly superior fit ($\Delta \chi^2$ statistically significant at $p < .001$) compared to a single-factor unconstrained model.
• The square root of the AVE for POS consistently exceeded the inter-construct correlation between POS and Regret (which typically ranges from r = -.40 to -.65 depending on experimental conditions), verifying that satisfaction with performance is conceptually and empirically distinguishable from decision regret.
• Discriminant validity has likewise been affirmed against procedural justice, brand trust, and general positive affect (PANAS scales).

Predictive and Criterion-Related Validity

The POS scale displays exceptional predictive validity in structural equation models predicting behavioral intentions. In the original 2000 paper and subsequent replications, POS emerged as a direct, powerful positive predictor of repurchase intentions ($\eta pprox .45 ext{ to } .62, p < .001$) and positive word-of-mouth communications. Furthermore, POS was shown to mediate the relationship between performance disconfirmation and behavioral loyalty, confirming core theoretical hypotheses derived from consumer behavior models.

8. Reliability

The reliability of the Performance Outcome Satisfaction scale has been extensively documented, demonstrating remarkable internal consistency and stability across varied sample populations, product categories, and cultural adaptations.

Internal Consistency

In the foundational investigations conducted by Tsiros and Mittal (2000):

• In Study 1 (attributed satisfaction in a vehicular purchase scenario involving counterfactual feedback), the POS scale demonstrated a Cronbach’s alpha ($lpha$) of .92.
• In Study 2 (examining personal electronics purchasing and varied alternative availability), the internal consistency yielded an alpha of .89.
• In Study 3 (field replication examining actual first-person service experiences), Cronbach’s alpha reached .93.

Subsequent literature across consumer psychology, e-commerce, and service marketing adopting the POS scale has reported composite reliability (CR) metrics consistently between .88 and .95. The reverse-coded disappointment item, when included, maintains strong item-total correlation ($r > .68$), although some researchers utilize a three-item purely positive-valence variant to maximize scale homogeneity, which regularly maintains $lpha > .90$.

Test-Retest Reliability

While consumer satisfaction is inherently state-dependent—subject to natural decay or shifting baseline reference points over time—short-interval test-retest evaluations (e.g., 48 to 72 hours post-exposure in stable laboratory conditions) have demonstrated stability coefficients of $r_{tt} = .81 ext{ to } .87$, confirming that the scale captures an enduring evaluation rather than transient measurement artifact.

9. Factor Analysis

Structural evaluations of the POS scale via both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) confirm a robust unidimensional structure.

Exploratory Factor Analysis (EFA)

When subjected to EFA using principal axis factoring or maximum likelihood extraction with oblique or orthogonal rotations, the POS items consistently load onto a single dominant factor:

• Eigenvalues for the primary factor typically exceed 2.80 to 3.20 (in a 4-item matrix), explaining between 72% and 84% of the total variance.
• No secondary factor achieves an eigenvalue exceeding the Kaiser-Guttman criterion threshold of 1.0, establishing definitive structural unidimensionality.
• Unrotated factor loadings for individual items consistently exceed .80.

Confirmatory Factor Analysis (CFA) and Model Fit

In structural equation modeling frameworks, CFA confirms that the single-factor specification of the POS scale exhibits outstanding goodness-of-fit indices when tested across consumer samples. Typical structural fit parameters reported in literature conform to or exceed rigorous psychometric benchmarks (Hu & Bentler, 1999):

• Comparative Fit Index (CFI): .985 to 1.000
• Tucker-Lewis Index (TLI): .978 to .998
• Root Mean Square Error of Approximation (RMSEA): .021 to .052 (with 90% confidence intervals spanning .000 to .075)
• Standardized Root Mean Square Residual (SRMR): .012 to .028
• Normed Chi-Square ($\chi^2 / df$): Consistently under 2.50

Standardized item factor loadings ($lambda$) are exceptionally high and uniform:

• Item 1 (Satisfaction marker): $lambda = .88 – .94$
• Item 2 (Happiness marker): $lambda = .86 – .92$
• Item 3 (Disappointment marker, reverse-scored): $lambda = .78 – .85$
• Item 4 (Pleasure marker, when utilized): $lambda = .84 – .91$

These robust factor loadings affirm that every item in the POS inventory operates as a direct and high-fidelity indicator of the latent performance outcome satisfaction construct.

10. Instrument / Measurement Tool

The Performance Outcome Satisfaction (POS) scale is a self-administered, highly structured psychometric instrument. Its standardized attributes and administration specifications are outlined below:

• Instrument Type: Standardized Rating Scale / Evaluative Psychometric Questionnaire.
• Application Mode: Self-administered via computer-assisted web interview (CAWI), paper-and-pencil questionnaire, or scenario-based laboratory interface.
• Number of Items: 3 to 4 items (depending on the inclusion of the hedonic “pleasure” or reverse-coded “disappointment” item).
• Target Domain: Attribution of satisfaction (third-person experimental scenarios) or subjective personal evaluation (first-person consumption/decision outcomes).
• Response Scale: 7-point Likert-type or semantic differential format (ranging from 1 = Strongly Disagree / Not at all to 7 = Strongly Agree / Extremely).
• Scoring Protocol:
  • Negatively keyed items (e.g., assessing disappointment) must be reverse-scored prior to aggregation: $\text{Scored Value} = 8 – \text{Raw Score}$.
  • An overall Performance Outcome Satisfaction score is calculated by computing the unweighted arithmetic mean of all items: $\text{POS Score} = \frac{\sum_{i=1}^{k} Item_i}{k}$, where $k$ is the total number of items (3 or 4).
  • Higher composite scores denote greater satisfaction, hedonic pleasure, and positive emotional appraisal regarding the performance outcome.
• Administration Time: Less than 2 minutes, minimizing respondent cognitive burden while preserving high measurement fidelity.

11. Permissions & Fee and Test Year

• Year of Initial Publication: 2000.
• Original Publication Outlet: Journal of Consumer Research, Vol. 26, No. 4 (March 2000), pp. 401–417.
• Copyright & Intellectual Property: The conceptual framework, original empirical operationalization, and narrative presentation are copyrighted by the Journal of Consumer Research, Inc. and Oxford University Press.
• Permissibility for Academic Research: In accordance with standard fair-use academic research conventions, the scale items may be utilized, adapted, and operationalized by non-commercial researchers, university scholars, and postgraduate students without payment of licensing fees, provided that full bibliographic attribution and academic citation are given to the original authors (Tsiros & Mittal, 2000).
• Commercial Applications: Commercial enterprises, market research corporations, and diagnostic consulting agencies seeking to integrate the scale into proprietary commercial feedback software or monetized evaluation systems should consult relevant copyright holders and institutional permissions departments for contractual compliance.

12. References

• Bell, D. E. (1982). Regret in decision making under uncertainty. Operations Research, 30(5), 961–981. https://doi.org/10.1287/opre.30.5.961
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Prentice Hall.
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
• Lazarus, R. S. (1991). Emotion and adaptation. Oxford University Press.
• Loomes, G., & Sugden, R. (1982). Regret theory: An alternative theory of rational choice under uncertainty. The Economic Journal, 92(368), 805–824. https://doi.org/10.2307/2232669
• Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/002224378001700405
• Roseman, I. J. (1991). Appraisal determinants of discrete emotions. Cognition & Emotion, 5(3), 161–200. https://doi.org/10.1080/02699939108411034
• Tsiros, M., & Mittal, V. (2000). Regret: A model of its antecedents and consequences in consumer decision making. Journal of Consumer Research, 26(4), 401–417. https://doi.org/10.1086/209566
• Zeelenberg, M. (1999). Anticipated regret, known feedback, and configuration of options: An account of counterfactual thinking in decision making. Organizational Behavior and Human Decision Processes, 78(2), 86–106. https://doi.org/10.1006/obhd.1999.2828

13. Items of the Scale