An Empirical Study of Task Entry Decisions on Open Innovation Contests

Open innovation has become widely adopted as an innovation search mechanism for firms seeking to save costs and create innovation variety. Members (solvers) face different uncertainty when making decisions regarding whether or not to enter a task, such as task competition intensity, firm (seeker) taste in solutions, and firms’ (seekers’) requirement standards. This study examines how problem solvers leverage available information to mitigate uncertainty and then make task entry decisions. Through an empirical study, we find that all the information has impact on task entering decisions. While the number of solvers in a task would influence task entry decisions positively, the effects from other information sources vary at different task stages. This research makes contributions to both open innovation literature and signaling theory