Using categorical dea to assess the effect of subsidy policies and technological learning on R&D efficiency of it industry

Li Ting Yeh, Dong Shang Chang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)311-330
Number of pages20
JournalTechnological and Economic Development of Economy
Volume26
Issue number2
DOIs
StatePublished - 3 Feb 2020

Keywords

  • Data envelopment analysis
  • Government subsidies
  • Information technology industry
  • Learning experience curve
  • R&D efficiency
  • Technological learning

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