基于集对分析的企业技术依赖预警及其演化趋势测度

doi:10.12005/orms.2015.0073

摘要/Abstract

摘要： 国际经济交流与区域经济协同的深化加速了企业的外部技术获取。为避免外部技术的过度依赖,尤其是与技术内化不匹配的依赖,将模糊集与集对分析进行改进并运用到技术依赖预警的研究中。借助集对分析对我国各省份规模上工业企业技术依赖同一度计算,构建各省技术依赖预警集。利用模糊隶属度联系数化改进提取技术依赖联系数并对其进行演化测度,参照集对势表剖析和诠释技术依赖预警的风险排列与演化趋势。研究结果表明,模糊隶属度补数的改进及其联系数化在对成组数据进行联系数提取时简便有效,偏联系数对演化趋势的测度准确详尽;经技术依赖预警与演化趋势测度,发现现阶段外部技术对我国自主研发的挤出效应不明显,但由于企业外部技术创造性依赖能力不足导致的外部技术内化风险凸显。

关键词: 同一度, 联系数, 技术依赖, 预警, 演化趋势

Abstract: International economic exchange and regional economic collaboration promote the external technology acquisition among enterprises. In order to avoid the excessive dependence on external technology, especially the dependence which is uncoordinated with technological internalization, we improve the fuzzy-pair analysis and use this method to study technology dependence. This study firstly evaluates identical degree of technological dependence and constructs the early warning set of Chinese industrial enterprises above designated size by region using set pair analysis. On this basis, we extract connection number by improving fuzzy membership connection number digitizing and evaluate evolution tendency. Finally, we order these technology-dependent risks according to set pair situation. The results show that the fuzzy membership complement improvement and its combination with contact number can extract contact number of grouped data easily and effectively. Partial connection number can measure evolutionary trends accurately and minutely. The crowding-out risk of external technology to independent research and development is not significant. But technology-dependent risks clearly exist in the stage of technological internalization due to lack of creative dependence capacity of external technology in China.

Key words: identical degree, connection number, technology dependence, early warning, evolution tendency

中图分类号:

F204

李柏洲, 李新. 基于集对分析的企业技术依赖预警及其演化趋势测度[J]. 运筹与管理, 2015, 24(2): 262-271.

LI Bai-zhou, LI Xin. Technology Dependence Early Warning and Evolution Tendency Evaluation Based on Set Pair Analysis[J]. Operations Research and Management Science, 2015, 24(2): 262-271.

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