Field Evidence on Individual Behavior & Performance in Rank-Order Tournaments

Abstract

Economic analysis of rank-order tournaments has shown that intensi ied competition leads to declining performance. Empirical research demonstrates that individuals in tournament-type contests perform less well on average in the presence of larger number of competitors in total and superstars. Particularly in ield settings, studies often lack direct evidence about the underlying mechanisms, such as the amount of effort, that might account for these results. Here we exploit a novel dataset on algorithmic programming contests that contains data on individual effort, risk taking, and cognitive errors that may underlie tournament performance outcomes. We ind that competitors on average react negatively to an increase in the total number of competitors, and react more negatively to an increase in the number of superstars than non-superstars. We also ind that the most negative reactions come from a particular subgroup of competitors: those that are highly skilled, but whose abilities put them near to the top of the ability distribution. For these competitors, we ind no evidence that the decline in performance outcomes stems from reduced effort or increased risk taking. Instead, errors in logic lead to a decline in performance, which suggests a cognitive explanation for the negative response to increased competition. We also ind that a small group of competitors, who are at the very top of the ability distribution (non-superstars), react positively to increased competition from superstars. For them, we ind some evidence of increased effort and no increase in errors of logic, consistent with both economic and psychological explanations.

Crowdsourcing Contests: Understanding the Effect of Environment and Organization Specific Factors on Sustained Participation

“…Previous studies extensively investigated individuals' behavior in crowdsourcing platforms including micro-task platforms [22], idea-innovation contests [23], and problem-solving contests [24]. The extant literature has focused on four general categories that may affect individuals' behavior and their performance in crowdsourcing contests.…”

“…These four general categories are: (1) task/contest-specific factors (reward, task type, task complexity, and contest duration for task, etc.) [25]- [32]; (2) individualspecific factors (extrinsic motivations, intrinsic motivations, individuals' strategy, and individuals' experience) [25], [28], [29], [33], [34]; (3) environment-specific factors (competitors' rating, number of competitors, number of super-star competitors, number of non-super-star competitors, collaboration, and communication) [21], [24], [35], [36]; and (4) organization-specific factors (brandstrength and marketplace maturity) [37]. The effect of these factors have been investigated on different elements including individuals' motivation to join a platform, individuals' sustained participation, and individuals' high quality solutions and their performance [38].…”

“…This variable shows the number of teams competing with each other in a certain competition. The literature shows that the number of competitors negatively affects individuals participation behavior [24]. We controlled for "previous team performance" which indicates the rank that the individual's team received in the previous competition.…”

“…Individuals typically do not work in isolation in crowdsourcing platforms. Some crowdsourcing platforms are designed as contests where the individuals compete with each other and the best submission will be awarded [20]. Some crowdsourcing platforms provide an environment in which individuals can collaborate with other members [21].…”

“…Therefore, it is important to pay particular attention to the effect of environmentspecific factors on individuals' behavior in crowdsourcing platforms. Previous studies have examined some environment-specific factors such as competitors' rating, number of competitors, number of super-star competitors, number of non-super-star competitors, collaboration, and communication on individuals' participation behavior in crowdsourcing platform [21], [24], [35], [36]. But, none of these studies have examined how the structural capital (the number of times team members have teamed up) affect individuals' sustained participation.…”