Exploiting evidence that sporting results affect fans' mood, we analyze whether National Football League game outcomes can affect the contributions of Wikipedia editors who identify as fans of a specific team. We find that the day after a team loses, their fans decrease their contributions towards football-related pages (relative to after a win). Relative decreases are bigger if losses are unexpected, or if losing margins are big. In contrast, unexpected wins do not cause more contributions relative to wins that were not unexpected. Neither do big wins result in more contributions relative to small wins. Additionally, contributions to non-football-related pages are not affected by NFL game results. Our findings add to the literatures on (i) the determinants of individual contributions to peer production communities, (ii) how community dynamics affect user contributions, (iii) the importance of emotions, (iv) the effect of offline events on online behavior, and (v) the applicability of behavioral economics concepts to the HCI literature.

We conduct a study of hiring bias on a simulation platform where we ask Amazon MTurk participants to make hiring decisions for a mathematically intensive task. Our findings suggest hiring biases against Black workers and less attractive workers, and preferences towards Asian workers, female workers and more attractive workers. We also show that certain UI designs, including provision of candidates' information at the individual level and reducing the number of choices, can significantly reduce discrimination. However, provision of candidate's information at the subgroup level can increase discrimination. The results have practical implications for designing better online freelance marketplaces.

We conduct a study of hiring bias on a simulation platform where we ask Amazon MTurk participants to make hiring decisions for a mathematically intensive task. Our findings suggest hiring biases against Black workers and less attractive workers, and preferences towards Asian workers, female workers and more attractive workers. We also show that certain UI designs, including provision of candidates' information at the individual level and reducing the number of choices, can significantly reduce discrimination. However, provision of candidate's information at the subgroup level can increase discrimination. The results have practical implications for designing better online freelance marketplaces.

Leaderboards are a workhorse within the gamification literature. While the effect of a leaderboard has been well studied, there is much less evidence how one's peer group affects the treatment effect of a leaderboard. Through a pre-registered field experiment involving more than 1000 users on an online movie recommender system, we expose users to leaderboards, but different sets of users are exposed to different peer groups. Contrary to what a standard behavioral model would predict, we find that a user's contribution increases when their peer's scores are more dispersed. We also find that decreasing average peer contributions motivates a user to contribute more. Moreover, these effects are themselves mediated by group size. This suggests that existing theories of motivation and demotivation with regards to leaderboards may need revision, and also illustrates the potential of using personalized leaderboards to increase contributions.