Avner Strulov-Shlain

People and organizations often set goals to self-motivate and achieve better outcomes in challenging tasks. But goals—and their effectiveness—might depend on what people expect to happen. Do goals reflect expectations, or do goals set expectations? How do goals and expectations affect performance? These distinctions are important for motivation and intervention design. We run an online real-effort task to answer these two questions by introducing exogenous variation in goals and expectations. First, we find that goals mostly reflect existing expectations rather than set expectations. Second, practicing an easier version of a task leads to higher expectations and higher performance. Third, eliciting a goal also leads to higher average performance, and changing the goal level has local effects that pull people toward the goal. However, revising goals has little effect on average performance. Our findings imply that increasing expectations is an important motivating tool that increases goals and performance. Having any goal helps performance; however, people cannot fool themselves into expecting and doing more simply by choosing a different goal. This paper was accepted by John Beshears, behavioral economics and decision analysis. Funding: Financial support from the University of California San Diego Academic Senate and the Willard Graham Research Fund at Chicago Booth is gratefully acknowledged. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2025.00480 .

User interface designs that maximize data collection consent rates may lead to biased data samples; we provide empirical evidence and propose a better alternative.

Companies often deploy some form of "choice architecture" when collecting consumer data, designed to nudge consumers towards sharing more private information. This study examines when an emphasis on maximizing the volume of data shared when deploying choice architecture can alter the composition of the collected data, hence creating a trade-off between the quantity and representativeness of data collected. To this end, we ran a large-scale choice experiment to elicit consumers' incentive-compatible valuation for their private Facebook data while randomizing the choice frames they encountered. Within participants, we elicited WTA using a multiple-price list, followed by a free-text entry. Across participants, we randomized the choice default and the price anchor. The default varied between opt-in, opt-out, and active choice. Price anchor was the range of prices in the multiple price list, which was either $0--$50 (low) or $50--$100 (high).