Using context-dependent DEA to analyze the efficiency of highly funded scientists in China

Abstract

Funding inputs and research outputs have always been two central issues in the science of science. In recent decades, research funding plays an increasingly important role in scientific research. Thus, it is progressively significant for management authorities to measure the research efficiency of highly funded scientists, which can be helpful for them to make effective policies. However, few researchers use quantitative analysis to study these issues. To promote the research in this field, we begin with collecting a dataset. This dataset contains research funding and other information from 345 highly funded scientists in Mainland China. Next, we use the dataset to measure the efficiency of highly funded scientists based on the data envelopment analysis. In this way, highly funded scientists are placed into several levels according to their research inputs and outputs. We also give their attractiveness and progress scores compared to other grades. The learning path for less efficient scientists is also provided. We find that highly funded scientists have relatively high efficiency in three kinds of projects, such as the Major Research Plan. Besides, the career length and career start year are demonstrated to have a limited impact on the highly funded scientists. These patterns are beneficial for the development of the scientific community and management authorities to make policies.

Guest editorial on “data-driven operations management”

“…There are three papers that focused on the data-driven healthcare operations management. In the first paper, "Nursing rescheduling problem with multiple rescheduling methods under uncertainty" [9], the authors developed a stochastic programming model and a distributionally robust model for the nurse rescheduling problem with uncertain demands. They conducted a study on three joint hospitals in Chengdu, Chongzhou, and Guanghan, Sichuan Province to illustrate the applicability and validity of the proposed model.…”

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Data-driven operations managementWe can roughly divide the accepted 15 papers into four groups according to their topics: data-driven supply chain management [1-4], data-driven process scheduling [5-8], data-driven healthcare operations management [9][10][11], and other data-driven operations management problems [12][13][14][15]. In the following, we formally introduce related works in detail.

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