The Influence of the Number of Solvers on the Contest Performance: Quantile Regression Analysis Based on the Data from Designing and Coding Innovation Contests

Abstract

Abstract: The influence of the number of solvers on the contest performance is a long-lasting question in innovation contests research. However, the findings about this effect in the existing literature are not consistent. Some are positive, while others are negative. In order to reconcile those contradictory insights, this research sorts out and sums up the existing researches, defines the measurement of innovation performance in terms of the average quality of solutions or the quality of the best solution, and proposes the incentive effect and the parallel path effect that underline the influence of the number of solvers on the contest performance. Based on these reasoning, this research collects behavioral data of 6,771 innovation contests for designing and coding problems from two well-known online platforms, builds a quantile regression model, and empirically proves the existence of the two effects, namely, the number of solvers negatively influences the average quality of solutions, and positively influences the quality of the best solution. Besides this, compared with the ordinary least square regression, results from quantile regression provide more subtle and comprehensive insights about these effects. At last, academic contribution, managerial implication, and future research direction are presented.

Key words: innovation contests, the number of solvers, contest performance, quantile regression

Hu Feng, Zhao Hong, Liu Chao. The Influence of the Number of Solvers on the Contest Performance: Quantile Regression Analysis Based on the Data from Designing and Coding Innovation Contests[J]. Management Review, 2020, 32(11): 102-111,259.

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