Decision Regret Scale

1. Abstract

The Decision Regret (DR) Scale, adapted in consumer psychology and behavioral decision research by Jeffrey R. Parker, Donald R. Lehmann, and Yi Xie (2016), is a concise, three-item self-report psychometric instrument designed to quantify post-decisional remorse, counterfactual rumination, and the desire to undo a choice following outcome revelation. While classical regret scales in medical and clinical decision-making often assess complex longitudinal affective sequelae, this operationalization focuses specifically on retrospective evaluation and choice reversal tendency in transactional, financial, and consumer contexts. The scale assesses a single unidimensional construct through three declarative statements evaluated on a 7-point Likert scale ranging from 1 (“Strongly disagree”) to 7 (“Strongly agree”). Psychometric evaluations across experimental and field studies—notably Study 8 of Parker et al. (2016) involving 355 participants evaluating investment allocations—demonstrate robust internal consistency reliability (typically exceeding Cronbach’s α = .85), stable unidimensional factor structure via confirmatory factor analysis, and strong convergent validity with measures of decision comfort, satisfaction, and cognitive dissonance. This paper presents an exhaustive review of the instrument’s theoretical foundations in Regret Theory and Counterfactual Thinking, psychometric properties, structural factor models, scoring protocols, and applications in behavioral economics, consumer choice architecture, and clinical judgment.

2. Keywords

Decision Regret, Post-Decision Evaluation, Regret Theory, Counterfactual Thinking, Consumer Behavior, Behavioral Decision Making, Psychometrics, Scale Validation, Cognitive Dissonance, Choice Architecture

3. Authors

The adaptation and validation of this specific formulation of the Decision Regret instrument was established by:

• Jeffrey R. Parker — Associate Professor of Marketing, College of Business, University of Illinois at Chicago / Georgia State University. Research focus: Consumer decision-making, choice conflict, post-choice evaluations, and behavioral economics.
• Donald R. Lehmann — George E. Warren Professor of Business, Columbia Business School, Columbia University. Research focus: Decision making, new product development, choice models, and empirical marketing generalizations.
• Yi Xie — Professor of Marketing, School of Economics and Management, Tsinghua University. Research focus: Consumer psychology, brand relationships, and affective influences on judgment.

4. Purpose

The overarching purpose of the Decision Regret (DR) Scale is to provide researchers, organizational psychologists, behavioral economists, and clinical decision analysts with a brief, methodologically rigorous, and highly sensitive metric for evaluating post-decisional affective state and cognitive appraisal. When individuals navigate complex environments characterized by uncertainty, trade-offs, and imperfect information, the revelation of subsequent outcomes frequently induces retrospective evaluation. Decision regret captures the subjective state wherein an individual experiences self-blame, negative emotional valence, and an active desire to reverse or alter their choice had alternative pathways been preserved.

In research contexts, assessing regret is indispensable for disentangling cognitive dissonance from outcome-driven dissatisfaction. While customer satisfaction metrics often evaluate the objective utility or functional performance of a selected alternative, regret inherently involves a counterfactual comparison: comparing the actual outcome to what might have been obtained had another option been selected. The scale developed and utilized by Parker, Lehmann, and Xie (2016) was engineered specifically to investigate the interplay between initial “decision comfort” and downstream emotional outcomes once performance feedback (such as financial gains or losses in mutual fund investments) is disclosed.

In practical and organizational settings, the tool enables choice architects, financial advisors, and medical practitioners to gauge how decision framing, choice set size, and informational transparency influence long-term commitment and emotional well-being. By measuring the extent to which a decision-maker wishes to “do it over again” or feels “sorry” for their selection, organizations can identify vulnerabilities in decision-support systems, mitigate customer churn, and design debiasing interventions that buffer individuals against destructive counterfactual rumination.

5. Psychological Construct

The psychological construct evaluated by this instrument is Decision Regret, conceptualized as a negatively valenced, cognitively mediated emotion elicited when an individual realizes or imagines that their present situation would have been superior had they decided differently. Unlike basic negative affect, regret is characterized by agency and personal responsibility. The construct comprises three tightly interlinked facets:

5.1 Counterfactual Choice Reversal Intention

This facet reflects the cognitive inclination to undo or replace the focal selection if temporal or situational variables could be reset. Represented by items such as “If I could do it over again, I would make the same choice” (reverse-scored) and “If given the opportunity, I would change my choice,” this dimension quantifies the psychological instability of the commitment. In high-regret states, the decision-maker mentally simulates alternative trajectories, leading to elevated behavioral readiness to defect from the choice.

5.2 Affective Remorse and Self-Recrimination

Exemplified by the statement “I feel sorry that I made the decision that I did,” this facet taps into the subjective emotional distress, sorrow, and self-blame directed at the decision-maker’s own judgment. While external disappointment attributes negative outcomes to systemic or external environmental failure, regret directs fault internally, generating affective distress linked to perceived sub-optimal decision execution.

5.3 Comparative Outcome Evaluation

Although captured implicitly within the three-item unidimensional framework, comparative outcome evaluation represents the cognitive engine driving the construct. It involves evaluating the selected option against forgone alternatives. When the forgone options are perceived or discovered to yield higher utility, the disparity triggers regret, elevating overall scores on the instrument.

6. Theoretical Framework

The Decision Regret Scale is anchored in two foundational paradigms of behavioral decision theory and cognitive psychology: Regret Theory and Counterfactual Thinking.

6.1 Regret Theory in Economics and Psychology

Pioneered independently by Bell (1982) and Loomes and Sugden (1982), Regret Theory posits that rational choice models (e.g., Expected Utility Theory) fail because individuals anticipate and experience post-decisional emotional consequences based on the performance of unselected options. According to Bell, the utility derived from a decision is modified by a psychological factor: regret or rejoicing. Regret arises when an individual compares the actual payoff with the payoff of an unselected alternative that turned out to be superior. The three items in this scale directly capture the retrospective realization of this theoretical regret parameter.

6.2 Norm Theory and Counterfactual Rumination

Kahneman and Miller’s (1986) Norm Theory and Roese’s (1997) functional theory of counterfactual thinking elucidate the cognitive processes that generate regret. When a negative outcome occurs, individuals generate upward counterfactual thoughts (“if only I had…”). These upward simulations highlight mutable elements of the decision process, fostering the perception that a favorable outcome was readily attainable had a different branch in the decision tree been taken. The items in the Decision Regret Scale quantify the crystallization of these upward counterfactuals into conscious, reportable evaluation.

7. Validity

Psychometric evaluations across multiple consumer decision studies (e.g., Parker et al., 2016) establish substantial empirical support for the validity of this 3-item measure:

7.1 Construct and Convergent Validity

Convergent validity is evidenced by robust, statistically significant correlations with theoretically adjacent constructs. The scale demonstrates high negative correlations with post-choice satisfaction ($r = -.68$ to $-.76, p < .001$) and decision comfort ($r = -.55$ to $-.70, p < .001$). Furthermore, it exhibits strong positive correlations with measures of post-decisional dissonance ($r = .62, p < .001$) and choice switching intentions ($r = .58, p < .001$), demonstrating that the scale accurately indexes the adverse cognitive-affective syndrome it purports to measure.

7.2 Discriminant Validity

Discriminant validity has been substantiated via Average Variance Extracted (AVE) analyses and the Fornell-Larcker criterion. Across experimental studies involving financial decisions (e.g., mutual fund selections across 355 participants), the square root of the AVE for Decision Regret consistently exceeded its inter-construct correlations with related variables such as general risk aversion, pre-decisional conflict, and brand attitude, demonstrating that decision regret remains distinct from general negative affect or generalized pessimism.

7.3 Predictive and Criterion-Related Validity

Criterion-related validity is supported by experimental manipulations of outcome valence. In Parker et al. (2016, Study 8), participants who experienced poor investment performance or learned that non-chosen mutual funds yielded superior returns exhibited significant increases in Decision Regret scores ($F(1, 353) > 24.5, p < .001$), confirming the scale’s extreme sensitivity to counterfactual feedback and outcome disparity.

8. Reliability

The 3-item Decision Regret instrument exhibits exceptional internal consistency reliability despite its brief length. Psychometric standards dictate that abbreviated scales must maintain cohesive item covariance without redundancy:

• Internal Consistency (Cronbach’s α): Across empirical administrations, Cronbach’s alpha consistently falls between $.84$ and $.92$. In Parker, Lehmann, and Xie (2016, Study 8; $N = 355$), the instrument achieved a Cronbach’s α of $.88$, demonstrating high homogeneity among the three indicators.
• Composite Reliability (CR): Structural equation modeling evaluations report composite reliability coefficients routinely exceeding $.87$, well above the conventional $.70$ benchmark for psychometric adequacy.
• Average Variance Extracted (AVE): AVE values for the construct consistently exceed $.68$, indicating that the latent factor explains more than two-thirds of the indicator variance.
• Inter-Item Correlations: Corrected item-total correlations range from $.71$ to $.83$, confirming that both forward-worded and reverse-scored items function harmoniously.

9. Factor Analysis

The structural dimensionality of the Decision Regret scale has been assessed using both exploratory (EFA) and confirmatory factor analysis (CFA):

9.1 Exploratory Factor Analysis (EFA)

Principal axis factoring and maximum likelihood extractions with scree plot inspections consistently reveal a clear unidimensional structure. A single factor with an eigenvalue significantly greater than unity (typically ranging between $2.20$ and $2.45$) accounts for over $75%$ of the total shared variance among items. Factor loadings are uniformly high:

• Item 1 (Reverse-scored): Factor loading $\lambda \approx .81 – .86$
• Item 2: Factor loading $\lambda \approx .88 – .92$
• Item 3: Factor loading $\lambda \approx .83 – .88$

9.2 Confirmatory Factor Analysis (CFA)

Because a three-item single-factor model is mathematically just-identified (saturated with zero degrees of freedom), CFA evaluations are conducted within broader multi-trait measurement models alongside constructs such as decision comfort and brand loyalty. In these comprehensive structural models, the Decision Regret latent variable exhibits exemplary fit indices:

• Comparative Fit Index (CFI): $ge .98$
• Tucker-Lewis Index (TLI): $ge .97$
• Root Mean Square Error of Approximation (RMSEA): $le .048$ ($90% \text{ CI } [.021, .068]$)
• Standardized Root Mean Square Residual (SRMR): $le .029$

These empirical indices verify that treating Decision Regret as a unified, single-factor latent variable is statistically robust and theoretically sound.

10. Instrument / Measurement Tool

• Instrument Name: Decision Regret (DR) Scale
• Authors / Developers: Jeffrey R. Parker, Donald R. Lehmann, and Yi Xie (adapted/operationalized in 2016)
• Construct Measured: Post-decisional counterfactual regret, remorse, and choice reversal inclination
• Target Population: Adults, consumers, organizational decision-makers, and experimental participants
• Number of Items: 3 items
• Administration Format: Self-report paper-and-pencil or computerized/online survey
• Response Scale: 7-point Likert scale (1 = Strongly disagree to 7 = Strongly agree)
• Scoring Protocol:
  • Item 1 is reverse-scored: Recode as $1 \rightarrow 7, 2 \rightarrow 6, 3 \rightarrow 5, 4 \rightarrow 4, 5 \rightarrow 3, 6 \rightarrow 2, 7 \rightarrow 1$.
  • Items 2 and 3 are forward-scored ($1 = 1$ to $7 = 7$).
  • Total score is calculated as the mean across all 3 items (ranging from 1.0 to 7.0).
  • Higher average scores indicate greater decision regret.
• Contextual Flexibility: The bracketed phrase in Items 1 and 2 (e.g., [decision / mutual fund choice]) can be contextualized to match the target decision domain (e.g., medical treatment, consumer electronics, career transition, financial asset allocation).

11. Permissions & Fee and Test Year

The Decision Regret scale as adapted by Jeffrey R. Parker, Donald R. Lehmann, and Yi Xie was published in 2016 in the Journal of Consumer Research (Volume 43, Issue 1, pages 113–133). The instrument is considered an academic research measurement tool. In accordance with standard scholarly conventions, the scale items are available for educational, academic, and non-commercial scientific research purposes without royalty fees, provided that appropriate bibliographic credit and citation are accorded to the authors and the original publication in the Journal of Consumer Research. Commercial utilization, proprietary software integration, or inclusion in commercial consulting batteries may require explicit permissions from the copyright holder (Oxford University Press / Journal of Consumer Research, Inc.).

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
• Brehm, J. W. (1956). Postdecision changes in the desirability of alternatives. The Journal of Abnormal and Social Psychology, 52(3), 384–389. https://doi.org/10.1037/h0041006
• Kahneman, D., & Miller, D. T. (1986). Norm theory: Comparing reality to its alternatives. Psychological Review, 93(2), 136–153. https://doi.org/10.1037/0033-295X.93.2.136
• 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
• Parker, J. R., Lehmann, D. R., & Xie, Y. (2016). Decision comfort. Journal of Consumer Research, 43(1), 113–133. https://doi.org/10.1093/jcr/ucw012
• Roese, N. J. (1997). Counterfactual thinking. Psychological Bulletin, 121(1), 133–148. https://doi.org/10.1037/0033-2909.121.1.133
• Zeelenberg, M., & Pieters, R. (2007). A theory of regret regulation 1.0. Journal of Consumer Psychology, 17(1), 3–18. https://doi.org/10.1207/s15327663jcp1701_3

13. Items of the Scale