Grey evaluation empirical study based on center-point triangular whitenization weight function of Jiangsu Province industrial technology innovation strategy alliance

Purpose – This paper aims to investigate the performance of Jiangsu Province industrial technology innovation strategy alliance. Design/methodology/approach – Through a preliminary investigation of 30 Jiangsu industrial technology innovation strategic alliances, this paper analyzed the status and extracted 18 alliances to conduct an in-depth investigation. By grey evaluation method based on center-point triangular whitenization weight function, the paper classified and analyzed alliances. Findings – The results show that university or research institutions-oriented alliance perform better, but the government/enterprise-oriented alliance perform diverse, and majority is rated “general”. Originality/value – The paper succeeds in clustering analysis to Jiangsu Province industrial technology innovation strategy alliance with insufficient data. And according to the result of clustering, it analyzes the causes, which provide value information for the sustainable development of Jiangsu Province industrial techno...

[1]  Li Rui The Analysis of the Stability of the MNCs’ Strategic Technology Alliance Based on Game Theory , 2005 .

[2]  Joshua Zhexue Huang,et al.  Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values , 1998, Data Mining and Knowledge Discovery.

[3]  Chen Jia An Analysis on the Governance Mode of Strategic Alliances for Industrial Technology Innovation , 2011 .

[4]  T. Das,et al.  Managing risks in strategic alliances , 1999 .

[5]  Keith D. Brouthers,et al.  International alliance commitment and performance of small and medium-size enterprises: The mediating role of process control , 2008 .

[6]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[7]  Scott Gallagher,et al.  Explaining Alliance Partner Selection: Fit, Trust and Strategic Expediency , 2007 .

[8]  Bing-Sheng Teng,et al.  Strategic risk behaviour and its temporalities: between risk propensity and decision context , 2001 .

[9]  T. Das,et al.  Instabilities of Strategic Alliances: An Internal Tensions Perspective , 2000 .

[10]  Wen-Bao Lin,et al.  Factors affecting the correlation between interactive mechanism of strategic alliance and technological knowledge transfer performance , 2007 .

[11]  Sudipto Guha,et al.  CURE: an efficient clustering algorithm for large databases , 1998, SIGMOD '98.

[12]  Joseph W. Leonard,et al.  Alliance Advantage: The Art of Creating Value through Partnering , 1998 .

[13]  Vipin Kumar,et al.  Chameleon: Hierarchical Clustering Using Dynamic Modeling , 1999, Computer.

[14]  Aidong Zhang,et al.  WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases , 1998, VLDB.

[15]  Andrew C. Inkpen,et al.  KNOWLEDGE, BARGAINING POWER, AND THE INSTABILITY OF INTERNATIONAL JOINT VENTURES , 1997 .

[16]  Jeffrey H. Dyer,et al.  How To Make Strategic Alliances Work , 2001 .

[17]  Dimitrios Gunopulos,et al.  Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.

[18]  Pamela R. Haunschild,et al.  Friends or Strangers? Firm-Specific Uncertainty, Market Uncertainty, and Network Partner Selection , 2004, Organ. Sci..

[19]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[20]  Alexi Delgado,et al.  Environmental conflict analysis using an integrated grey clustering and entropy-weight method: A case study of a mining project in Peru , 2016, Environ. Model. Softw..

[21]  R. Gulati,et al.  The dynamics of learning alliances: competition, cooperation, and relative scope , 1998 .

[22]  Zhang Chidong,et al.  Types of Industrial Technology Innovation Strategy Alliance and the Government's Support , 2011 .

[23]  Sudipto Guha,et al.  CURE: an efficient clustering algorithm for large databases , 1998, SIGMOD '98.

[24]  Richard N. Osborn,et al.  Contextual leadership, transformational leadership and the performance of international innovation seeking alliances , 2009 .

[25]  Jiong Yang,et al.  STING+: an approach to active spatial data mining , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[26]  Hu Long-ying,et al.  Modeling the efficiency of knowledge transfer in the industrial technology innovation alliance , 2010 .

[27]  George Karypis,et al.  C HAMELEON : A Hierarchical Clustering Algorithm Using Dynamic Modeling , 1999 .

[28]  Arvind Parkhe Strategic Alliance Structuring: A Game Theoretic and Transaction Cost Examination of Interfirm Cooperation , 1993 .

[29]  S. Lauritzen The EM algorithm for graphical association models with missing data , 1995 .

[30]  José María Carazo,et al.  Smoothly distributed fuzzy c-means: a new self-organizing map , 2001, Pattern Recognit..

[31]  Liu Si-feng,et al.  New grey evaluation method based on reformative triangular whitenization weight function , 2011 .

[32]  Domenico Talia,et al.  P-AutoClass: Scalable Parallel Clustering for Mining Large Data Sets , 2003, IEEE Trans. Knowl. Data Eng..