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Revisiting status quo bias: Replication of Samuelson and Zeckhauser (1988)
Abstract
Status quo bias refers to people’s general preference to stick to, or continue with, a previously chosen option. In two pre-registered experiments with U.S. participants recruited on the Amazon Mechanical Turk (n1 = 311, n2 = 316), we attempted to replicate four decision scenarios (Question 1, 2, 4, and 6) from Samuelson and Zeckhauser (1988), the seminal article that provided the first experimental demonstration of the status quo bias. We found strong empirical support for the status quo bias in three decision scenarios out of the four, including budget allocation (Scenario 1/Question 1 in the original article), investment portfolios (Scenario 3/Question 2), and college job offers (Scenario 4/Question 4). However, we failed to find substantial support for the status quo bias in the wagon color choice scenario (Scenario 2/Question 6). We discuss the implications of our results and possible explanations using multiple accounts put forward in the status quo bias literature.
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Research on action and inaction in judgment and decision making now spans over 35 years, with ever-growing interest. Accumulating evidence suggests that action and inaction are perceived and evaluated differently, affecting a wide array of psychological factors from emotions to morality. These asymmetries have been shown to have real impact on choice behavior in both personal and interpersonal contexts, with implications for individuals and society. We review impactful action-inaction related phenomena, with a summary and comparison of key findings and insights, reinterpreting these effects and mapping links between effects using norm theory's (Kahneman & Miller, 1986) concept of normality. Together, these aim to contribute towards an integrated understanding of the human psyche regarding action and inaction.
Loss aversion, the principle that losses impact decision making more than equivalent gains, is a fundamental idea in consumer behavior and decision making, though its existence has recently been called into question. Across five unique samples (Ntotal = 17,720), we tested predictions about what moderates loss aversion, which were derived from a preference construction account. Across studies, more domain knowledge, experience, and education were associated with lower loss aversion, though people of all knowledge, experience, and education levels were loss averse. Among car buyers, those who knew more about a particular car attribute (e.g., fuel economy) were less loss averse for that attribute but not other attributes (e.g., comfort), consistent with the idea that people with less attribute knowledge are more likely to construct preferences, thereby increasing loss aversion. Additionally, older consumers were more loss averse across different loss aversion measures and studies. We discuss implications for several accounts of loss aversion, including alternative accounts rooted in status quo bias, emotion, or feelings of ownership. In addition to discovering key loss aversion moderators, we cast doubt on recent claims that loss aversion is a fallacy or is fully explained by status quo bias, risk aversion, or the educated laboratory samples often used to study loss aversion.
The importance of replication is becoming increasingly appreciated, however, considerably less consensus exists about how to evaluate the design and results of replications. We make concrete recommendations on how to evaluate replications with more nuance than what is typically done currently in the literature. We highlight six study characteristics that are crucial for evaluating replications: replication method similarity, replication differences, investigator independence, method/data transparency, analytic result reproducibility, and auxiliary hypotheses’ plausibility evidence. We also recommend a more nuanced approach to statistically interpret replication results at the individual-study and meta-analytic levels, and propose clearer language to communicate replication results.
Modeling human cognition is challenging because there are infinitely many mechanisms that can generate any given observation. Some researchers address this by constraining the hypothesis space through assumptions about what the human mind can and cannot do, while others constrain it through principles of rationality and adaptation. Recent work in economics, psychology, neuroscience, and linguistics has begun to integrate both approaches by augmenting rational models with cognitive constraints, incorporating rational principles into cognitive architectures, and applying optimality principles to understanding neural representations. We identify the rational use of limited resources as a unifying principle underlying these diverse approaches, expressing it in a new cognitive modeling paradigm called resource-rational analysis . The integration of rational principles with realistic cognitive constraints makes resource-rational analysis a promising framework for reverse-engineering cognitive mechanisms and representations. It has already shed new light on the debate about human rationality and can be leveraged to revisit classic questions of cognitive psychology within a principled computational framework. We demonstrate that resource-rational models can reconcile the mind's most impressive cognitive skills with people's ostensive irrationality. Resource-rational analysis also provides a new way to connect psychological theory more deeply with artificial intelligence, economics, neuroscience, and linguistics.
We conducted preregistered replications of 28 classic and contemporary published findings, with protocols that were peer reviewed in advance, to examine variation in effect magnitudes across samples and settings. Each protocol was administered to approximately half of 125 samples that comprised 15,305 participants from 36 countries and territories. Using the conventional criterion of statistical significance (p< .05), we found that 15 (54%) of the replications provided evidence of a statistically significant effect in the same direction as the original finding. With a strict significance criterion (p< .0001), 14 (50%) of the replications still provided such evidence, a reflection of the extremely high-powered design. Seven (25%) of the replications yielded effect sizes larger than the original ones, and 21 (75%) yielded effect sizes smaller than the original ones. The median comparable Cohen’s ds were 0.60 for the original findings and 0.15 for the replications. The effect sizes were small (< 0.20) in 16 of the replications (57%), and 9 effects (32%) were in the direction opposite the direction of the original effect. Across settings, the Q statistic indicated significant heterogeneity in 11 (39%) of the replication effects, and most of those were among the findings with the largest overall effect sizes; only 1 effect that was near zero in the aggregate showed significant heterogeneity according to this measure. Only 1 effect had a tau value greater than .20, an indication of moderate heterogeneity. Eight others had tau values near or slightly above .10, an indication of slight heterogeneity. Moderation tests indicated that very little heterogeneity was attributable to the order in which the tasks were performed or whether the tasks were administered in lab versus online. Exploratory comparisons revealed little heterogeneity between Western, educated, industrialized, rich, and democratic (WEIRD) cultures and less WEIRD cultures (i.e., cultures with relatively high and low WEIRDness scores, respectively). Cumulatively, variability in the observed effect sizes was attributable more to the effect being studied than to the sample or setting in which it was studied
Societies invest in scientific studies to better understand the world, and attempt to harness such improved understanding to address pressing societal problems. Published research, however, can only be useful for theory or application if it is credible. In science, a credible finding is one that has repeatedly survived risky falsification attempts. However, state-of-the-art meta-analytic approaches cannot determine the credibility of an effect because they do not account for the extent to which each included study has survived such attempted falsification. To overcome this problem, the following paper outlines a unified framework to estimate the credibility of published research by examining four fundamental falsifiability-related dimensions: (1) method/data transparency, (2) analytic reproducibility, (3) analytic robustness, and (4) effect replicability. A standardized workflow is proposed to quantify the degree to which a finding has survived scrutiny along these four credibility facets. The framework is demonstrated by applying it to published replications in the psychology literature. A web platform implementation of the framework is outlined, and we conclude by encouraging the community of researchers to contribute to the development and crowdsourcing of the platform. Vanpaemel, W. (in press). A unified framework to quantify the credibility of scientific findings. Advances in Methods and Practices in Psychological Science, in press. Available online ahead of print at https://psyarxiv.com/uwmr8.
Several independent lines of research bear on the question of why individuals avoid decisions by postponing them, failing to act, or accepting the status quo. This review relates findings across several different disciplines and uncovers 4 decision avoidance effects that offer insight into this common but troubling behavior: choice deferral, status quo bias, omission bias, and inaction inertia. These findings are related by common antecedents and consequences in a rational-emotional model of the factors that predispose humans to do nothing. Prominent components of the model include cost-benefit calculations, anticipated regret, and selection difficulty. Other factors affecting decision avoidance through these key components, such as anticipatory negative emotions, decision strategies, counterfactual thinking, and preference uncertainty, are also discussed.
Societies invest in scientific studies to better understand the world, and attempt to harness such improved understanding to address pressing societal problems. Published research, however, can only be useful for theory or application if it is credible. In science, a credible finding is one that has repeatedly survived risky falsification attempts. However, state-of-the-art meta-analytic approaches cannot determine the credibility of an eect because they do not account for the extent to which each included study has survived such attempted falsification. To overcome this problem, the following paper outlines a unified framework to estimate the credibility of published research by examining four fundamental falsifiability-related dimensions: (1) method/data transparency, (2) analytic reproducibility, (3) analytic robustness, and (4) eect replicability. A standardized workflow is proposed to quantify the degree to which a finding has survived scrutiny along these four credibility facets. The framework is demonstrated by applying it to published replications in the psychology literature. A web platform implementation of the framework is outlined, and we conclude by encouraging the community of researchers to contribute to the development and crowdsourcing of the platform.
Research in commitment–trust perspective overlooks the effect of status quo bias on consumer brand loyalty. This study aims to integrate the bias including consumers’ deliberate inertia and cognitive lock-in with consumers’ trust and commitment in the perspective. We empirically analyze a research model and hypothetical relationships using structural equation modeling with survey data from smartphone consumers. The results show that the inertia meaningfully and positively enhances consumers’ brand loyalty, and the lock-in significantly predicts consumers’ deliberate inertia and commitment. The findings significantly advance extant knowledge with the positive effects of deliberate inertia and cognitive lock-in on consumers’ brand loyalty.