Research

Existing rivalry research finds that people try harder and perform better when competing against their rivals. However, are there conditions under which rivalry can harm performance? We integrate rivalry theory with regulatory fit theory to propose two moderators of rivalry: individual skill and situational risk for status change. We test our predictions using data from software programming contests involving more than 4.6 million competitive encounters across 16,846 software developers (“coders”) to examine the causal effects of rivalry and the conditions under which it may backfire. We find that, on average, coders who are randomly assigned to compete against a field of competitors with whom they share a rivalrous history exhibit higher performance, above and beyond other established drivers of performance in competition. Importantly, however, this positive effect of rivalry is moderated by (1) coders’ skill level, such that rivalry is more beneficial for more skilled coders and is harmful for less skilled coders, and (2) coders’ risk of experiencing a status change, such that coders who face a possible status loss exhibit decreased performance when competing against rivals. Thus, we extend research on rivalry by revealing the conditions under which it can harm performance, which is vital to understanding its role in organizations.

Crowdsourcing has evolved as an organizational approach to distributed problem solving and innovation. As contests are embedded in online communities and evaluation rights are assigned to the crowd, community members face a tension: They find themselves exposed to both competitive motives to win the contest prize and collaborative participation motives in the community. The competitive motive suggests they may evaluate rivals strategically according to their self-interest, the collaborative motive suggests they may evaluate their peers truthfully according to mutual interest. Using field data from Threadless on 38 million peer evaluations of more than 150,000 submissions across 75,000 individuals over 10 years and two natural experiments to rule out alternative explanations, we answer the question of how community members resolve this tension. We show that as their skill level increases, they become increasingly competitive and shift from using self-promotion to sabotaging their closest competitors. However, we also find signs of collaborative behavior when high-skilled members show leniency toward those community members who do not directly threaten their chance of winning. We explain how the individual-level use of strategic evaluations translates into important organizational-level outcomes by affecting the community structure through individuals’ long-term participation. Although low-skill targets of sabotage are less likely to participate in future contests, high-skill targets are more likely. This suggests a feedback loop between competitive evaluation behavior and future participation. These findings have important implications for the literature on crowdsourcing design, and the evolution and sustainability of crowdsourcing communities.

Many platforms host user-generated content (UGC) and content developed by professionals side by side. However, thus far, their impact on platform ecosystems has been mostly studied in isolation. In this paper, we use data from a network of 122 local news outlets hosted by an online news platform to study the spillover effects from UGC developed by citizen journalists to the content developed by professional journalists. We use the removal of a status index associated with citizen journalists as an exogenous shock to their supply of UGC to identify these spillover effects. We find that experienced citizen journalists reduce their production of content when this status index is removed. We then find that inexperienced professional journalists increase their output in response to this behavior. However, as a result of these changes, we find a reduction in the overall content hosted by the platform, especially in the case of local news and in more isolated regions. We further show that this is likely to have detrimental effects for the platform. In particular, there is a decline in overall viewership, and the platform may need to hire and pay salaries to more professional journalists to produce enough articles to close the gap left by the departing citizen journalists. Our work contributes to the literature on UGC and online platforms and to the literature on local news.

With the help of more than 700 reviewers, we assess the reproducibility of nearly 500 articles published in the journal Management Science before and after the introduction of a new Data and Code Disclosure policy in 2019. When considering only articles for which data accessibility and hardware and software requirements were not an obstacle for reviewers, the results of more than 95% of articles under the new disclosure policy could be fully or largely computationally reproduced. However, for 29% of articles, at least part of the data set was not accessible to the reviewer. Considering all articles in our sample reduces the share of reproduced articles to 68%. These figures represent a significant increase compared with the period before the introduction of the disclosure policy, where only 12% of articles voluntarily provided replication materials, of which 55% could be (largely) reproduced. Substantial heterogeneity in reproducibility rates across different fields is mainly driven by differences in data set accessibility. Other reasons for unsuccessful reproduction attempts include missing code, unresolvable code errors, weak or missing documentation, and software and hardware requirements and code complexity. Our findings highlight the importance of journal code and data disclosure policies and suggest potential avenues for enhancing their effectiveness.