USING CATEGORICAL DEA TO ASSESS THE EFFECT OF SUBSIDY POLICIES AND TECHNOLOGICAL LEARNING ON R&D EFFICIENCY OF IT INDUSTRY
研究开发了一个两阶段方法，利用学习经验曲线衡量128家IT企业2008-2015年的技术学习效应，并将政府补贴强度作为分类变量纳入DEA模型，评估2015年研发效率，发现专利不足和技术学习缺乏是低效主因。
Government subsidies are an important policy tool that can help firms develop technological learning, and this technological learning effect plays a key role in firms’ research and development (R&D) efficiency. Thus, this study develops a two-stage approach to illustrate the effect of subsidy policies and technological learning on R&D efficiency in the information technology (IT) industry. The technological learning effect in 128 firms in the IT industry from 2008 to 2015 was measured using the learning experience curve. Subsequently, government R&D subsidy intensity was considered as a categorical variable, and this estimated result was treated as an intangible input into a data envelopment analysis (DEA) structure to evaluate R&D efficiency in 2015. This study makes three major contributions. First, the developed approach incorporates the effect of subsidy policies and technological learning into the DEA structure. Second, the empirical results demonstrate the appropriateness of incorporating subsidy policies and technological learning into evaluations of R&D efficiency. Finally, our results identify the key sources of inefficiency as a shortfall in the number of patents and a lack of technological learning. Based on these key findings, some improved strategies were recommended to decision makers.