Winner Determination of Open Innovation Contests in Online Markets

Online innovation contests have been used by more and more firms for idea seeking and problem solving. Most studies of contests take the perspective of innovation seekers, and little is known about solvers’ strategies and responses. However, contest performance also relies on understanding solver responses. This paper provides insights to these questions. Specifically, we show that past experience of a solver is a good predictor of his future winning probability and that winners are more likely to be those who submit early or later during the submission period as opposed to those submit in the middle. We also find that “strategic waiting” (to submit solutions) is associated with higher winning probability. Furthermore, we show that different contests appear to attract solvers with different expertise, which invalids the common assumption of fixed solver expertise distribution across projects in previous literature. This finding has strategic implications to the design of contest parameters

Open Innovation: An Empirical Study of Online Contests

Online contests for open innovation – seekers posting innovation projects to which solvers submit solutions – have been developed into a new online commerce model. This study is one of the first to lift the veil of online contests. We identify that real world online contests are very different from what is assumed by previous studies. A real world online contest has uncertain number of solvers due to dynamic participation process. Feedback can encourage solvers to contribute more than the equilibrium effort. With a given award, if the seeker\u27s feedback effort is high enough, the emerging number of solvers is a proxy measure of contest performance. By examining large-scale data from an online contest marketplace, we find that a contest with higher award, longer duration, shorter description, lower time cost, and higher popularity will attract more solvers. Specifically simple and ideation based projects are the most efficient in capturing solvers

EFFICIENT APPROXIMATION FOR LARGE-SCALE KERNEL CLUSTERING ANALYSIS

Kernel k-means is useful for performing clustering on nonlinearly separable data. The kernel k-means is hard to scale to large data due to the quadratic complexity. In this paper, we propose an approach which utilizes the low-dimensional feature approximation of the Gaussian kernel function to capitalize a fast linear k-means solver to perform the nonlinear kernel k-means. This approach takes advantage of the efficiency of the linear solver and the nonlinear partitioning ability of the kernel clustering. The experimental results show that the proposed approach is much more efficient than a normal kernel k- means solver and achieves similar clustering performance

An Investigation on The Broadband Customers\u27 Satisfaction in Hsinchu Area

With the rapid growth of internet there are about fifty thousands of users get access to the internet in Taiwan area. Given the consensus of internet service providers (ISP’s) that soon will reach seventy-five thousands within two years. This would imply that the users of traditional dial up modems will be facing serious insufficient bandwidth problems. To make the problem worse, the internet applications are moving toward multimedia which always eats up bandwidth faster than expected. Moreover, to join WTO forces Taiwan to relin quish its telecom market to international players. These new players sure will jump into the broadband market. This research is to investigate the household consumers’ satisfaction in Hsinchu area on the broadband networking. The findings pointed out that important demographic variables affecting satisfaction are gender, age, education, and vocation. Additionally, dimensions of customers’ satisfaction do have a negative correlation with customers’ satisfaction. This is to say that when the higher the degree of a concern the lower the customer satisfaction. In general, expected service levels are always higher than that really experienced