Exploring data envelopment analysis for measuring collaborated innovation efficiency of small and medium-sized enterprises in Korea

Abstract In this research, we focus on the collaboration efficiency that arises from the research and development (RD however, only a few focus on the efficiency. Thus, this study investigates the effect of collaboration and collaboration type in terms of innovation efficiency for SMEs. First, we evaluate and compare the innovation efficiency of collaboration by applying data envelopment analysis-based global Malmquist productivity analysis. Then, we characterized the collaboration typology through cluster analysis and analyzed the features of the configured collaboration types. Finally, we analyze innovation efficiency to evaluate the performance of the collaboration types. More specifically, we examine the stepwise effect of collaboration for the phases of innovation, and we confirmed that collaboration had different effects on R&D and commercialization. Throughout this research, we found that DEA can be applied to innovation management area in the sense of developing collaborated innovation strategies and collaboration modes, in fact we confirm that the innovation efficiency resulting from DEA may generate suitable managerial strategies for collaboration. Also, four collaboration types identified in this study also showed considerably different innovation characteristics and efficiency. In addition, the results imply that differentiated collaboration strategy is required considering the technological intensity.

[1]  Jong de Jpj,et al.  Open innovation in SMEs : trends, motives and management challenges , 2009 .

[2]  Shouyang Wang,et al.  Measuring efficiencies of multi-period and multi-division systems associated with DEA: An application to OECD countries' national innovation systems , 2016, Expert Syst. Appl..

[3]  Hugo Hollanders,et al.  Measuring innovation efficiency , 2007 .

[4]  J. Hair.  Multivariate data analysis , 1972 .

[5]  Shiu-Wan Hung,et al.  Factors affecting the choice of technology acquisition mode: An empirical analysis of the electronic firms of Japan, Korea and Taiwan , 2008 .

[6]  Mingting Kou,et al.  Staged efficiency and its determinants of regional innovation systems: a two-step analytical procedure , 2014 .

[7]  V. Parida,et al.  Inbound Open Innovation Activities in High‐Tech SMEs: The Impact on Innovation Performance , 2012 .

[8]  Raynold A. Svenson,et al.  Measuring R&D Productivity , 1988 .

[9]  Ulrich Kaiser,et al.  Balancing Internal and External Knowledge Acquisition: The Gains and Pains from R&D Outsourcing , 2010 .

[10]  Girish N. Punj,et al.  Cluster Analysis in Marketing Research: Review and Suggestions for Application , 1983 .

[11]  S. Malmquist.  Index numbers and indifference surfaces , 1953 .

[12]  Jiancheng Guan,et al.  Measuring the innovation production process: A cross-region empirical study of China’s high-tech innovations , 2010 .

[13]  Marcelo Nogueira Cortimiglia,et al.  The effect of innovation activities on innovation outputs in the Brazilian industry: Market-orientation vs. technology-acquisition strategies , 2016 .

[14]  Z. Griliches.  Patent Statistics as Economic Indicators: a Survey , 1990 .

[15]  Miin-Shen Yang,et al.  A new clustering approach using data envelopment analysis , 2009, Eur. J. Oper. Res..

[16]  Thomas Hatzichronoglou.  Revision of the High-Technology Sector and Product Classification , 1997 .

[17]  S. Brunswicker,et al.  Open Innovation in Small and Medium‐Sized Enterprises (SMEs): External Knowledge Sourcing Strategies and Internal Organizational Facilitators , 2015 .

[18]  Sungjoon Lee,et al.  Open innovation in SMEs—An intermediated network model , 2010 .

[19]  Jiann-Chyuan Wang,et al.  External technology acquisition and firm performance: A longitudinal study , 2008 .

[20]  Reinhilde Veugelers,et al.  Innovation strategies, process and product innovations and growth: Firm-level evidence from Brazil , 2012 .

[21]  R. Narula.  R&D Collaboration by SMEs: new opportunities and limitations in the face of globalisation , 2004 .

[22]  L. R. Christensen,et al.  THE ECONOMIC THEORY OF INDEX NUMBERS AND THE MEASUREMENT OF INPUT, OUTPUT, AND PRODUCTIVITY , 1982 .

[23]  David J. Ketchen,et al.  THE APPLICATION OF CLUSTER ANALYSIS IN STRATEGIC MANAGEMENT RESEARCH: AN ANALYSIS AND CRITIQUE , 1996 .

[24]  Jiann-Chyuan Wang,et al.  External technology sourcing and innovation performance in LMT sectors: An analysis based on the Taiwanese Technological Innovation Survey , 2009 .

[25]  Richard C.M. Yam,et al.  Does country-level R&D efficiency benefit from the collaboration network structure? , 2016 .

[26]  Luca Berchicci,et al.  Towards an open R&D system: Internal R&D investment, external knowledge acquisition and innovative performance , 2013 .

[27]  Yongyoon Suh,et al.  Effects of SME collaboration on R&D in the service sector in open innovation , 2012 .

[28]  Ning Ma,et al.  A study of the relationship between competitiveness and technological innovation capability based on DEA models , 2006, Eur. J. Oper. Res..

[29]  Yorgos Goletsis,et al.  A multilevel and multistage efficiency evaluation of innovation systems: A multiobjective DEA approach , 2016, Expert Syst. Appl..

[30]  M. Nieto,et al.  Beyond formal R&D: Taking advantage of other sources of innovation in low- and medium-technology industries , 2009 .

[31]  Jiancheng Guan,et al.  Modeling the relative efficiency of national innovation systems , 2012 .

[32]  Ali Emrouznejad,et al.  Influential DMUs and outlier detection in data envelopment analysis with an application to health care , 2014, Ann. Oper. Res..

[33]  B. Clarysse,et al.  Heterogeneous Firm‐Level Effects of Knowledge Exchanges on Product Innovation: Differences between Dynamic and Lagging Product Innovators* , 2010 .

[34]  Lowell W. Steele.  Evaluating the Technical Operation , 1988 .

[35]  Francisco J. Arcelus,et al.  On the efficiency of national innovation systems , 2003 .

[36]  Akira Goto,et al.  Patent Statistics as an Innovation Indicator , 2010 .

[37]  Manfredi Bruccoleri,et al.  The Effect Of Inbound, Outbound And Coupled Innovation On Performance , 2012 .

[38]  A. Salter,et al.  Open for innovation: the role of openness in explaining innovation performance among U.K. manufacturing firms , 2006 .

[39]  Ali Emrouznejad,et al.  Some clarifications on the DEA clustering approach , 2011, Eur. J. Oper. Res..

[40]  Kuen-Hung Tsai,et al.  Collaborative networks and product innovation performance: Toward a contingency perspective , 2009 .

[41]  Dirk Czarnitzki,et al.  The Relationship between R&D Collaboration, Subsidies and R&D Performance: Empirical Evidence from Finland and Germany , 2007 .

[42]  Daniel A. Levinthal,et al.  ABSORPTIVE CAPACITY: A NEW PERSPECTIVE ON LEARNING AND INNOVATION , 1990 .

[43]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[44]  Martin Heidenreich.  Innovation patterns and location of European low- and medium-technology industries , 2009 .

[45]  A. Hashimoto,et al.  Measuring the change in R&D efficiency of the Japanese pharmaceutical industry , 2008 .

[46]  Junseok Hwang,et al.  External knowledge search, innovative performance and productivity in the Korean ICT sector , 2010 .

[47]  Dingxi Qiu,et al.  A comparative study of the K-means algorithm and the normal mixture model for clustering: Bivariate homoscedastic case , 2010 .

[48]  E. Wang,et al.  Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach , 2007 .

[49]  Claudio Cruz-Cázares,et al.  You can’t manage right what you can’t measure well: Technological innovation efficiency☆ , 2013 .

[50]  Jiancheng Guan,et al.  Measuring the Efficiency of China's Regional Innovation Systems: Application of Network Data Envelopment Analysis (DEA) , 2012 .