Loss Aversion and Attribute Framing in Consumer Durables: A Registered Report

Abstract

How do consumers weigh product attributes when faced with risky choices? Standard decision theory predicts attribute independence, but loss aversion suggests that framing an attribute as a potential loss should amplify its impact. We preregistered a multi-study investigation (N = 1,245 across four studies) examining whether loss-framed attributes receive disproportionate weight in consumer decisions. Studies 1-2 used within-subject designs with real products; Studies 3-4 employed incentive-compatible choice experiments. Loss framing increased attribute weight by 31% on average (95% CI [24, 38]), an effect mediated by perceived risk. This holds even after controlling for attribute salience and numeric magnitude. Implications for marketing and policy-level disclosure are discussed.

Introduction

Decision-making in consumer contexts involves evaluating multiple product attributes under conditions of uncertainty. Classical expected utility theory assumes that the decision weight attached to an attribute depends solely on its objective probability and magnitude, independent of how that attribute is linguistically framed. However, prospect theory and related frameworks propose that the same attribute, when described as a loss rather than a foregone gain, should receive enhanced decision weight due to the heightened emotional salience of potential losses.

Despite decades of research on loss aversion in lottery and investment contexts, its role in consumer durables—products with substantial real-world consequences for household utility—remains under-explored. We designed a pre-registered multi-study investigation to test whether loss framing systematically alters attribute weights in consumer choice. Our approach combines observational studies with incentive-compatible experiments and employs computational modelling to isolate the effect of framing from confounding factors such as attribute salience or numeric properties.

Method

Participants

Four studies recruited independent samples totalling N = 1,245 participants (Mean age = 36.4, SD = 11.2; 58% female). Studies 1-2 recruited from Prolific Academic (N = 312 and N = 287, respectively), while Studies 3-4 used local university samples (N = 283 and N = 363). All participants were native English speakers and reported regular consumer decision-making responsibilities. The study was approved by the Research Ethics Board and pre-registered on the Open Science Framework (https://osf.io/m7xpk/) before data collection began. The analysis plan specified primary hypotheses, exclusion criteria, and planned covariates.

Procedure

Studies 1-2 employed a within-subject design comparing product choices across loss-framed and gain-framed conditions. Participants evaluated four product categories (washing machines, laptops, refrigerators, air purifiers). For each product, two attributes were independently manipulated: one was presented as a loss frame ("Risk of water damage: 5%") while the alternative comparison condition used neutral framing ("Water damage resistance: 95%"). Order of presentation was counterbalanced, and participants made stated choices with accompanying confidence ratings. Studies 3-4 employed incentive-compatible approaches: participants made choices among lotteries paired with real payoffs (£0.50 per trial, up to £15 total earnings). A computational choice model (logistic regression with attribute-level coefficients) estimated decision weights for each attribute under each frame condition.

Results

Loss framing significantly increased attribute weight across all four studies. In Studies 1-2, attributes presented in loss-frame conditions received an average relative weight increase of 31% (95% CI [24, 38], t(598) = 9.24, p < 0.001, d = 0.75). This effect persisted in Studies 3-4 (incentive-compatible: M = 27%, 95% CI [18, 36], t(646) = 6.18, p < 0.001, d = 0.48). Meta-analytic integration across the four studies (random-effects model) yielded a pooled effect size of g = 0.61, 95% CI [0.51, 0.71]. Importantly, the magnitude of the effect was independent of the numeric value of the attribute (r = 0.08, p = 0.31), ruling out a confound with numeric magnitude processing. Mediation analysis revealed that perceived risk explained 58% of the loss-framing effect (indirect effect = 0.18, SE = 0.03, 95% CI [0.13, 0.24]), while attribute salience contributed an additional 12% (indirect effect = 0.04, SE = 0.01, 95% CI [0.02, 0.06]). The residual direct effect remained significant (c' = 0.09, SE = 0.02, p < 0.001).

Individual differences in trait loss aversion (measured via the standard gamble task) moderated the effect: participants scoring high on loss aversion showed a 39% framing effect, while those scoring low showed an 18% effect (interaction term: β = 0.21, SE = 0.08, t(1243) = 2.64, p = 0.008). There was no significant interaction between framing condition and product category (F(3,1242) = 1.31, p = 0.27), indicating the effect generalizes across domains.

Discussion

Our pre-registered findings provide robust evidence that loss framing systematically amplifies attribute weights in consumer decision-making, with an effect size consistent across incentive-compatible and stated-preference methods. The effect is not reducible to salience or numeric factors, and operates partially through increased perception of risk. Notably, individual differences in trait loss aversion reliably predict heterogeneity in susceptibility to this framing effect, suggesting that dual-process or nested-dissociation explanations may be less plausible than an account grounded in risk perception.

From a policy perspective, these results suggest that product disclosures emphasizing potential losses (e.g., "Risk of water damage") may inadvertently distort consumer decisions relative to objectively equivalent information phrased as benefits or absence of harm. However, loss frames may be appropriate in contexts where risk communication is the genuine regulatory goal, as in health and safety contexts. Future research should examine the boundary conditions of this effect—for example, whether repeated exposure or statistical literacy attenuates the framing effect—and test interventions designed to reduce loss-framing bias in high-stakes consumer decisions.

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