Joowon Klusowski

Klusowski, J., Small, D.A., &, Goldenberg, J. Even number preference in quantity selection. Manuscript in preparation.

We find that people choose even numbers more frequently than odd numbers when selecting quantities to acquire or consume (e.g., choosing how many apples to buy for oneself at a store). Drawing on a mix of data sources—including the Kilts-NielsenIQ consumer panel data and online survey data—we find evidence of this tendency not only in shopping decisions but also in a variety of other quantity selection contexts. We propose that this phenomenon occurs because even numbers are more accessible and complete than odd numbers. This extends previous research findings related to round numbers (e.g., typically those ending in 0 or 5) and shows that disproportionate selection of certain numbers occurs at a more granular level. We discuss implications for understanding and influencing quantity selection.

Fulmer, Alexander G. & Klusowski, J. When and why consumers prefer high refunds to low prices. Manuscript in preparation.

Consumers often make purchase decisions under uncertainty without knowing whether they will be able to utilize a product or service or will have to forego it. In such cases, they frequently face tradeoffs between low-price, inflexible options and high-price, flexible ones (e.g., value vs. premium tickets). Across five pre-registered studies (N = 7,398), we show that consumers systematically prefer high-price options with higher proportional refunds—even when these options are strictly dominated. This preference arises across diverse domains, including hedonic and utilitarian purchases, and persists in incentive-compatible settings. We find that this counterintuitive preference stems from evaluability: refunds are comparable and interpretable relative to prices, whereas prices often lack clear benchmarks. Consistent with this framework, this phenomenon attenuates when clear price benchmarks are present that heighten the evaluability of prices. These findings have both practical and theoretical implications, offering marketers guidance on how pricing structure can be used to steer customers toward options with higher expected costs, and advancing our understanding of attribute evaluability and choice architecture.

Banker, M., Klusowski, J. & Zauberman, G. Positive Information is Generalized More than Negative Information When Controlling for Prior Beliefs. Revise and Resubmit at Management Science.

Information is often generalized to similar objects and situations. For example, having a positive (or negative) experience at a restaurant may lead one to predict having positive (or negative) experiences at similar restaurants. Previous research has shown divergent results on whether people are more likely to generalize positive or negative information without a consistent explanation, largely finding stronger negative generalizations if anything. In five preregistered studies and four supplemental studies (N = 8,645), we provide a unifying framework of when and why positive or negative generalizations are stronger. In many previous experimental contexts, people hold positive prior beliefs (e.g., most restaurants are good), which leads to stronger negative generalizations because (a) measurement in these paradigms allow greater shifts in beliefs in the negative direction and magnify negative generalizations and (b) negative information contrasts with their positive prior beliefs more than positive information. For similar reasons, when people hold negative prior beliefs (e.g., most restaurants are bad), positive generalizations are stronger than negative generalizations. To isolate the true effect independent of priors, we employ a novel paradigm to control for prior beliefs. Across several domains, we demonstrate that positive generalizations are stronger than negative generalizations, all else equal. Evidence suggests this effect is not simply driven by impression management concerns, but reflects a belief that positive information is more diagnostic of other objects than negative information.

Klusowski, J. Prediction horizon effects on forecasting. Revise and Resubmit at Organizational Behavior and Human Decision Processes.

People often make forecasts across different horizons. A manager may predict stock prices or employee performance for the next period only or for several periods ahead. Although such variation should not affect forecasts for the overlapping period (e.g., the next period), the present research shows that it does. Specifically, people judged past trends as more likely to continue in the near future when forecasting that period alone than when forecasting it as part of a longer sequence. These effects emerged consistently across different types of trends (e.g., increasing, binary, repeating) and domains (e.g., skill- vs. chance-oriented). These effects appear to arise because longer horizons elicit beliefs that imbalanced trends cannot persist indefinitely; accordingly, they attenuate when trends are more balanced. These results suggest that forecasting depends not only on the characteristics of past trends but also on features of the prediction task itself—offering insights for managers and policymakers seeking to shape forecasts.

Klusowski, J. & Lewis, J. (2025). Typical ranges as scale-specific benchmarks: When and why percentages amplify relative magnitudes and their differences, Management Science, 71(11), 9423-9436.

Business managers and policymakers must often communicate magnitudes. Yet, conveying large relative magnitudes without desensitizing people to further increases can be challenging due to diminishing sensitivity to large numbers. In this research, we propose that percentage expressions not only make large relative magnitudes (e.g., 500%) appear larger than equivalent non-percentage expressions but also make large increases in relative magnitudes (e.g., from 500% to 600%) appear larger. We posit an explanation: percentages typically have values between 0% and 100%, so when percentages and percentage-point differences reach 100% or more, they seem unusually large. This hypothesis is supported by data scraped from the New York Times articles and a series of online experiments employing both management-relevant scenarios and incentive-compatible decisions. Existing theories of magnitude perception either cannot predict all the results of these studies (e.g., numerosity and unitosity) or need further specification to do so (e.g., decision-by-sampling and range-frequency theory). We discuss implications for the theory of magnitude and difference perception and the practice of communicating large magnitudes and changes.

Milkman, K.L., . . . Klusowski, J., . . . Duckworth, A.L. (2021). Megastudies improve the impact of applied behavioural science. Nature, 600(7889), 478-483.

Policy-makers are increasingly turning to behavioural science for insights about how to improve citizens’ decisions and outcomes. Typically, different scientists test different intervention ideas in different samples using different outcomes over different time intervals. The lack of comparability of such individual investigations limits their potential to inform policy. Here, to address this limitation and accelerate the pace of discovery, we introduce the megastudy—a massive field experiment in which the effects of many different interventions are compared in the same population on the same objectively measured outcome for the same duration. In a megastudy targeting physical exercise among 61,293 members of an American fitness chain, 30 scientists from 15 different US universities worked in small independent teams to design a total of 54 different four-week digital programmes (or interventions) encouraging exercise. We show that 45% of these interventions significantly increased weekly gym visits by 9% to 27%; the top-performing intervention offered microrewards for returning to the gym after a missed workout. Only 8% of interventions induced behaviour change that was significant and measurable after the four-week intervention. Conditioning on the 45% of interventions that increased exercise during the intervention, we detected carry-over effects that were proportionally similar to those measured in previous research. Forecasts by impartial judges failed to predict which interventions would be most effective, underscoring the value of testing many ideas at once and, therefore, the potential for megastudies to improve the evidentiary value of behavioural science.

Klusowski, J., Small, D.A., & Simmons, J.P. (2021). Does choice cause an illusion of control? Psychological Science , 32(2), 159-172.

Previous research suggests that choice causes an illusion of control—that it makes people feel more likely to achieve preferable outcomes, even when they are selecting among options that are functionally identical (e.g., lottery tickets with an identical chance of winning). This research has been widely accepted as evidence that choice can have significant welfare effects, even when it confers no actual control. In this article, we report the results of 17 experiments (N = 10,825 online/laboratory participants) examining whether choice truly causes an illusion of control. We find that choice rarely makes people feel more likely to achieve preferable outcomes—unless it makes the preferable outcomes actually more likely—and when it does, it is not because choice causes an illusion, but because choice reflects some participants’ pre-existing (illusory) beliefs that the functionally identical options are not identical.

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