Innovation Tournaments with Multiple Contributors

Abstract

T his study examines innovation tournaments in which an organizer seeks solutions to an innovation-related problem from a number of agents. Agents exert effort to improve their solutions but face uncertainty about their solution performance. The organizer is interested in obtaining multiple solutions-agents whose solutions contribute to the organizer's utility are called contributors. Motivated by mixed policies observed in practice, where some tournaments are open and others restrict entry, we study when it is optimal for the organizer to conduct an open tournament or to restrict entry. Our analysis shows that whether an open tournament is optimal is tied to: (1) the variance of uncertainty as compared to the impact of effort; (2) the number of contributors, and (3) the skewness of the uncertainty distribution. Our results help explain mixed policies about restricting entry observed in practice as well as recent empirical and experimental findings.

The Role of Feedback in Dynamic Crowdsourcing Contests: A Structural Empirical Analysis

“…Crowdsourcing contests have been examined as platforms for sourcing innovation Xu 2008, Terwiesch andUlrich 2009). Existing literature on the design of crowdsourcing contests has looked at the impact of award structure (Moldovanu and Sela 2001, Yang et al 2009, Liu et al 2014; optimal competition size (Taylor 1995, Fullerton and McAfee 1999, Che and Gale 2003, Terwiesch and Xu 2008, Boudreau et al 2011, Körpeoglu and Cho 2015, Ales et al 2016a, Boudreau et al 2016; problem specification (Boudreau et al 2011, Erat andKrishnan 2012); and the disclosure of intermediate solutions Lakhani 2015, Wooten andUlrich 2016b) on the outcome (both the quality and quantity of the crowdsourced solutions) of crowdsourcing contests. Most of the previous research studies crowdsourcing contests as static problems; however, in reality, a large portion of the crowdsourcing contests are dynamic in nature.…”

Parallel Innovation Contests

“…Terwiesch and Xu (2008) also pioneer this literature by proposing a modeling framework and by showing that a free-entry open-innovation contest is optimal. By generalizing Terwiesch and Xu (2008), Boudreau et al (2011) show empirically and Ales et al (2017b) show analytically that free entry is optimal only when the solver's output uncertainty is sufficiently large. 5 Similarly, building on the modeling framework of Terwiesch and Xu (2008), Nittala and Krishnan (2016) study the design of innovation contests within firms, Ales et al (2017c) study the optimal set of awards in a contest, Mihm and Schlapp (2019) analyze whether and how to give feedback to solvers, Hu and Wang (2017) examine whether to run a single-stage or a sequential contest in the presence of multiple attributes, and Korpeoglu et al (2018) study the the optimal duration and award scheme.…”

Resource Allocation Among Competing Innovators

“…Most previous studies have focused on the design of the prize scheme (Terwiesch and Xu, 2008), information disclosure policy (Zhang and Zhou, 2015;Bimpikis et al, 2019), or other contest rules to induce agents to actively participate and exert effort. Typical objectives of the contest designer (principal) include maximizing total effort (Moldovanu and Sela, 2001), the highest or average performance outcomes (Hu and Wang, 2017;Ales et al, 2017;Körpeo glu and Cho, 2018) and K-best outputs (Ales et al, 2014). In a contest, competition occurs during the process of innovation, and agents receive monetary transfers after exerting innovative effort.…”