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Is a large award truly attractive to solvers? The impact of award size on crowd size in innovation contests
Author(s) -
Liu Zhongzhi,
Hatton M. Ryan,
Kull Thomas,
Dooley Kevin,
Oke Adegoke
Publication year - 2021
Publication title -
journal of operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.649
H-Index - 191
eISSN - 1873-1317
pISSN - 0272-6963
DOI - 10.1002/joom.1132
Subject(s) - contest , crowdsourcing , context (archaeology) , crowds , set (abstract data type) , task (project management) , marketing , expectancy theory , business , economics , computer science , management , political science , geography , archaeology , world wide web , law , programming language , computer security
Firms increasingly use human crowds to solve their innovation related needs. Innovation sourcing managers who sponsor innovation contests seek to maximize crowd size to increase the chance of creative outcomes. One strategy they use is to set an award size. Although many studies have examined the influence of award size on crowd size in innovation contests, the empirical findings are inconsistent and suggest further empirical study is needed. Drawing from expectancy‐value theory, we propose that award size and crowd size are related to one another in the form of an inverted U‐shape, namely that moderate awards maximize crowd size. Additional propositions cover contingencies around the number of awards and task difficulty of an innovation contest. Based on data representing 5342 programming innovation contests, our empirical analyses confirm the inverted U‐shaped relationship and indicate that the number of awards and task difficulty steepen the inverted U‐shaped relationship. These results add nuanced, theoretical understanding of how innovation contests work. The findings indicate that innovation sourcing managers should set award sizes at moderate levels, and that they should experiment with slightly larger or smaller awards to see if it impacts crowd size in their context.