Information and Task Entry Decisions: An Empirical Study on Open Innovation Contests
COM2 Level 4
Executive Classroom, COM2-04-02
ABSTRACT:
Open innovation has become widely adopted as a 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) preference in solutions, and firms' (seekers') evaluation standards. This study examines how solvers leverage available information to form beliefs regarding these three uncertain aspects and then make task entry decisions. Using a unique dataset, we find that information influences task entering decisions differently when tasks proceeded to different stages. For example, when observing a higher ratio of positive solution reviews included in the focal task versus other tasks during the early task stage, solvers are more likely to enter. However, solvers are less likely to enter tasks when observing a higher ratio of positive solution reviews during the late task stage. We discuss the managerial implications of this study at the end.
BIODATA:
Jiahui Mo joined NTU in 2014 as an Assistant Professor of Information Technology & Operations Management (ITOM). She received her Ph.D. in Management Science (specialized in Information Systems) from the Jindal School of Management at the University of Texas at Dallas in 2014. Her research focus is to investigate various economic issues arising from the emergence of new IT-enabled business platforms, such as crowdsourcing platform. Her primary research interests are in the field of crowdsourcing and open innovation, online forum community, social media and social network, and business analytics.