Two selected topics in resources management and crowd participation

Abstract

The imperative need for reducing inequality and fostering innovation is highlighted in the Sustainable Development Goals set by the United Nations. Unfortunately, unfair relief allocation in pandemics and regional disasters may worsen inequality, revealing the necessity for concerted efforts to address this issue. On the other hand, innovation is helpful for firms’ sustainable development, especially in recovering from a pandemic. One effective approach to foster innovation is through an innovation contest platform that allows firms to leverage human crowds to solve their innovation-related problems. Setting a certain number of prizes can attract crowd participation. However, there is an ongoing debate that granting the award only to the winner may attract more crowd participation, which requires further investigation. Due to these concerns, we conduct two studies on the management of resources and crowd participation. The first study focuses on allocating reliefs in the post-disaster stage. The distribution of reliefs has been widely studied in recent years to improve the effectiveness of humanitarian logistics. However, regional inequality is still a significant issue that remains unsolved. In relief allocation, we should consider not only the total degree of demand satisfaction, but also the equality of distributing the reliefs among the affected population. Therefore, we develop a model for the vehicle routing problem that allocates the reliefs to each affected area several times in multiple periods over the planning horizon while minimizing the transportation cost, shortage cost, and inequality among the affected areas for fairness consideration. To solve this problem, we use a branch-and-price algorithm. Our results provide managerial insights in resources management concerning costs, equity, and efficiency in humanitarian rescue. The second study focuses on the award-dividing policy of an innovation contest platform to attract crowd participation. In the past decade, platforms implemented the multiple prizes (MP) policy, i.e., the award is equally shared among the best participants. Recently, some platforms have adopted the winner-takes-all (WTA) policy, i.e., the award is only given to the best participant. Therefore, there is a need to investigate and compare the impacts of the WTA and MP policies on the number of submissions to an innovation contest platform. To the best of our knowledge, there is no study on this issue in the literature. We develop a behavioral model based on contestants’ subjective value for the MP policy where the award is equally shared among the best participants, which includes the WTA policy when = 1, and use order statistics and expectations to analyze the choice of participants under different circumstances to maximize the number of submissions. Then, we analyze the model and derive valuable analytical results under some reasonable assumptions. Furthermore, to empirically testify our results, we conduct a quasi-experiment based on the gathered data using the difference-in-differences analysis. We also consider the moderating impact of two kinds of information provided by the platform, i.e., current information provided by the number of tags, and historical data provided by the number of contests the sponsor has launched and the sponsor's feedback ratio. Our results provide valuable theoretical and practical insights for sponsors seeking to attract participants in an innovation contest.