In dynamic work environments, individuals are expected to simultaneously refine existing knowledge and explore novel approaches – a resource-intensive challenge known as ambidexterity. Drawing on the conservation of resources theory, we examine how task autonomy and transformational leadership, as key resources at work, shape individual exploration, exploitation, and ambidexterity. Using a vignette experiment with 331 MBA students from Slovenia, Austria, and Kosovo, we find that exploration is higher under conditions of high task autonomy and transformational leadership. In contrast, exploitation is stronger when task autonomy is restricted, while leadership has no significant effect. Ambidexterity emerges only when considering both factors jointly in the sense of an interaction: Transformational leadership buffers the decline in ambidexterity typically associated with low autonomy. These findings suggest that autonomy and leadership influence exploration and exploitation in partly opposing ways, impeding the straightforward enhancement of ambidexterity. Our study reveals which resource configurations unlock individual ambidexterity.

Human choices are often both multi-dimensional and interactive. For example, a person deciding which of two immigrants is more worthy of admission to a country might weigh their education, and the weight placed on education may depend on other factors, such as their age, country of origin and employment history. We develop a response-adaptive experimental design that summarizes the range of effects of one attribute as a function of all other attributes. Our approach changes several aspects of the experimental design based on the ex ante choice to study the heterogeneous effects of one focal attribute (i.e., education). We update treatment assignment probabilities over the course of the experiment to search for the attribute vector at which the focal attribute has the most positive and most negative effects. By summarizing the full range of effects that exist, our approach complements existing approaches to conjoint experiments that typically aggregate over heterogeneity by marginalizing. We illustrate through two online experiments and provide customizable code infrastructure via a Docker container that other researchers can use to deploy adaptive randomization in online conjoint experiments.

I study the optimal design of monetary incentives in experiments where incentives are a treatment variable. I propose a novel framework called the Budget Minimization problem in which a researcher chooses the level of incentives that allows her to detect a predicted treatment effect while minimizing her expected budget. The Budget Minimization problem builds upon the power analysis and structural modeling. It extends the standard optimal design approach by explicitly incorporating the budget as a part of the objective function. I prove theoretically that the problem has an interior solution under fairly mild conditions. To showcase the practical applications of the Budget Minimization problem, I provide examples of its implementation in several well-known experiments. I also offer a practical guide to assist researchers in utilizing the proposed framework. The Budget Minimization problem contributes to the experimental economists’ toolkit for an optimal design, however, it also challenges some conventional design recommendations.

Existing approaches to conducting inference about the Local Average Treatment Effect or LATE require assumptions that are considered tenuous in many applied settings. In particular, Instrumental Variable techniques require monotonicity and the exclusion restriction while principal score methods rest on some form of the principal ignorability assumption. This paper provides new results showing that an estimator within the class of principal score methods allows conservative inference about the LATE without invoking such assumptions. I term this estimator the Compliance Probability Weighting estimator and show that, under very mild assumptions, it provides an asymptotically conservative estimator for the LATE. I apply this estimator to a recent survey experiment and provide evidence of a stronger effect for the subset of compliers than the original authors had uncovered.

In experimental social science, precise treatment effect estimation is of utmost importance, and researchers can make design choices to increase precision. Specifically, block-randomized and pre-post designs are promoted as effective means to increase precision. However, implementing these designs requires pre-treatment covariates, and collecting this information may decrease sample sizes, which in and of itself harms precision. Therefore, despite the literature’s recommendation to use block-randomized and pre-post designs, it remains unclear when to expect these designs to increase precision in applied settings. We use real-world data to demonstrate a counterintuitive result: precision gains from block-randomized or pre-post designs can withstand significant sample loss that may arise during implementation. Our findings underscore the importance of incorporating researchers’ practical concerns into existing experimental design advice.

When it comes to experiments with multiple-round decisions under risk, the current payoff mechanisms are incentive compatible with either outcome weighting theories or probability weighting theories, but not both. In this paper, I introduce a new payoff mechanism, the Accumulative Best Choice (“ABC”) mechanism that is incentive compatible for all rational risk preferences. I also identify three necessary and sufficient conditions for a payoff mechanism to be incentive compatible for all models of decision under risk with complete and transitive preferences. I show that ABC is the unique incentive compatible mechanism for rational risk preferences in a multiple-task setting. In addition, I test empirical validity of the ABC mechanism in the lab. The results from both a choice pattern experiment and a preference (structural) estimation experiment show that individual choices under the ABC mechanism are statistically not different from those observed with the one-round task experimental design. The ABC mechanism supports unbiased elicitation of both outcome and probability transformations as well as testing alternative decision models that do or do not include the independence axiom.