2. Emerging Number of Solvers Firm's capability of exploiting external knowledge is a critical component of innovative performance in R&D (Cohen and Levinthal1990) Our coordination effort - Capability of exploiting external knowledge Firm's internal search strategy within a technological trajectory can largely influence the innovative performance (Katila and Ahuja 2002) Our emerging number of solvers - search
3. Emerging Number of Solvers Define: Vp = potential performance = emerging number of solvers ≠ V = real performance Our No. Solvers is a emerging number, a natural selection result. No. Solvers in Terwiesch and Xu’s model and previous study is a fixed number. Solvers are enlisted.
4. Emerging Number of Solvers The empirical study done by Laursen and Salter (2005), has proved that searching widely and deeply is curvilinearly (inverted U shape) related to open innovation performance.
5. Emerging Number of Solvers Searching widely – Emerging number of solvers (Vp) Searching deeply – Coordination effort (proper) Attention allocation is critical to seekers (Simon 1947, Ocasio 1997) It’s critical for solvers to have proper number of solvers, not too many, not too less. A good prediction function for emerging number of solvers is very important. A high R square prediction function is needed. We need to emphasize the importance of precise, not just the validity of hypotheses.
6. Prediction Manual Since our contribution is not just those hypotheses, but also the high R squared prediction model, I think we should include a part to demonstrate how to help seekers (managers) to predict accurately.
7. Robust of Using Accomp Nearly all marketplaces display No. solvers and No. joiners. Accomplishment ratio=No. solvers/No. joiners Seeker can only change exogenous project variables: award, duration, description length. Seeker couldn’t change the endogenous project variables including complexity, uncertainty and etc. Accomp is irrelevant with any exogenous variables, thus Accomp is an endogenous variable. (all Pearson correlations are lower than 0.1) It’s valid to use accomplishment ratio to predict No.solvers, although it’s derived by No. Solvers.
8. Interesting Accomp It’s very interesting that accomplishment ratio is irrelevant with any exogenous variables. An increase of award may increase higher incentive to spur solvers to submit. An increase of award will attract more solvers, thus may cause deeper probabilistic discounting and give solvers less incentive to submit. The irrelevance between award and Accomp means: (1) the two effects above mitigate ;(2) the existence of probability discounting ;(3) the probabilistic discounting function = reverse of award incentive function; (any thought of this one? If you agree, we can get the probabilistic function in a special way)
9. Interesting Joining Ratio Similarly, Joining ratio is irrelevant to award. This is very interesting to me. Intuitively it should be increased when award increases.
10. Maturity Snil and Hitt use maturity in their auction prediction model. Maturity = distance from start time to a fixed history point. I have tested maturity and found that: (1) it’s highly and negatively correlated with joining ratio. This suggests that solvers have learned that probability of winning is low, and they are joining less contests in average. I may explain that solvers are having the trend to allocate attention to less contest.(2) negative impact to number of solvers. This suggests … ? Hard to say.
11. Discounting and Procrastination Duration length is a problematic issue which has not been noticed by any previous study yet. Longer duration will:(1) attract more solvers, thus can bring more diversified ideas. (2) introduce deeper probability discounting. (3) make early submitters perceive less incentive. (4) make high cost project show more serious procrastination. Only the first one have positive impact to performance, the other three have negative impact.
12. U Shape Simulation U shape = Delayed Reward discounting theory + Dynamic probabilistic discounting theory + Procrastination theory + Browsing behavior Procrastination theory is usually used to predict individual behaviors. When predict group behaviors, it could be treated as a constant*complexity + error. (Akerlof 1999) The quasi-hyperbolic discounting function has been proved to be the best model for nearly all cases. (Green and Myerson 2004, ) Browsing behavior can be simulated by position model which is used in area of information search area. (Crashwell and etc 2008)However this one couldn’t be generalized to all contest marketplaces since users browsing behavior could be very complicated.
13. Delayed Reward Discounting During the first 24 hours of any contest project in a category, say naming, the procrastination behavior is similar, and probabilistic discounting is very weak, thus we can get the discounting ratio purely due to delayed rewards with the number of submissions received within the first 24 hours. The discounting ratio for different types of projects (different category, or different ACCOMP) may be different. This discounting ratio can give implications of how to set durations for different types of projects.
14. U shape Data All U shape data and graphs are ready, but it’s hard to demonstrate it with excel or access, so I am building a website to show it. Probably the site will be finished by tomorrow.
15. Choice Uncertainty Choice Uncertainty = No.Watch/No.Joining Sonsino and etc (2002) have done an experiment and shows that choice uncertainty is negatively related to complexity. Using choice uncertainty as dependent variables, it’s associated with several other variables such as ACCOMP, award and etc. Some are consistent with Sonsino’s study.
