制造业供应链企业间知识流动优化研究
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摘要
随着知识经济的崛起,知识的战略性价值日益凸显,在推动经济发展、提升组织核心竞争力中发挥了重要作用。识别知识、获取有效知识,并将知识转化为组织核心竞争力,成为目前研究的热点和难点问题。近年来,知识流动和共享作为组织获取知识的有效途径,受到学者们的普遍关注,尤其是以战略联盟、跨国公司等为代表的组织间知识流动问题的研究得以逐步深入。供应链作为一种新兴的组织合作模式,在物流、资金流和信息流方面,已有较为充分的研究,但有关知识流的研究还不深入,有待进一步探讨。本文以供应链企业之间的知识流动为研究对象,从供应链链条企业如何在合作过程中获得有效知识,促进知识在链条上的有效流动和共享的研究角度出发,构建供应链企业间知识流动影响要素指标体系,评价知识流动效果,并对知识流动路径进行优化研究,这对丰富知识流动理论和实践研究,提升供应链企业间的合作水平有重要的现实意义。
本文的研究工作主要围绕供应链企业间知识流动分析展开,具体包括以下几个方面：
(1)构建了供应链企业间知识流动影响要素指标体系,并首次运用结合分析法和模糊层次分析法对比确定指标权重
在对知识流动相关文献分析汇总的基础上,运用鱼刺图梳理出影响知识流动的96项指标要素,结合供应链企业间合作的特点,构建了供应链企业间知识流动影响要素指标体系,其中包含知识自身特性、企业间关系特性、知识流动主体特性和知识流动环境特性四个一级指标,以及知识可表达性、知识模糊性、关系质量、知识距离、文化差异、组织距离、物理距离、知识流动主体意愿和能力、知识流动渠道和信息技术支撑共11个二级指标。为探讨各指标的重要程度,分别运用结合分析法和模糊层次分析法计算出各指标的相对权重,证实了企业间关系特性是影响供应链企业间知识流动的最重要的指标,知识流动主体特性次之；二级指标中的关系质量对供应链企业间知识流动的影响比较突出。
(2)运用粗糙集—BP神经网络对制造业供应链企业间知识流动静态效果进行研究,并以烟草制造业进行实例验证
在对指标体系及权重确定的基础上,将粗糙集理论和BP神经网络方法结合,引入到知识流动的研究领域,实现了对制造业供应链企业间知识流动效果的静态评价。通过粗糙集理论对复杂的指标体系进行约简,得出能够代表原有指标体系的约简指标组合,减轻BP神经网络计算的负担,实现对制造业供应链企业间知识流动效果快速有效的模拟过程。运用模拟实现的最优网络对烟草制造业供应链的知识流动效果进行评价,判断出烟草制造业供应链知识流动效果一般,这一评价结果与《中国科技统计年鉴》中烟草制造业的新产品产出及专利数量产出数据显示排名基本一致,证实粗糙集—BP神经网络方法能够有效评估制造业供应链企业间知识流动的效果,同时表明烟草制造业供应链企业间知识流动水平尚需要大幅度提升,以满足烟草制造业的高、尖、精发展。
(3)运用基于时间的DEA方法对制造业供应链企业间知识流动动态效果进行研究,并对制造业不同行业进行了分析
静态评价实现了对不同链条整体知识流动效果的考核,但知识流动毕竟是个动态概念,其变化必然会影响整体知识流动效果,本文从时间维度研究知识流动,运用基于时间的DEA方法(数据包络方法)对制造业10个行业(食品制造业、饮料制造业、通用设备制造业、医药制造业、电气机械及器材制造业、金属制品业、交通运输设备制造业、通信设备、计算机制造业、文教体育用品制造业)的知识流动效果进行了动态评价。使用《中国科技统计年鉴》中关于制造业的R&D投入(研发投入)强度、行业新产品产值/工业生产总值、行业拥有的专利数量(2004年—2009年)以及专家打分数据评价了制造业10个行业知识流动效果,结果表明因为时间的累积效应,知识流动效果在初始年份较差,但随着时间的推移,知识流动效果渐佳。同时表明,通信设备、计算机制造业供应链企业间的知识流动效果最佳,而以金属制造业为代表的传统制造业供应链企业间知识流动效果较差,需要从知识需求度、知识合作度以及关系质量等方面进行提升。
(4)构建了烟草制造业供应链企业间知识流动路径优化模型,并从单向路径和循环路径两个方面进行了优化仿真研究
对供应链企业间知识流动评价的最终目的是对知识流动效果较差的行业进行有效地改进,从知识流动路径成本最低的角度,考核烟草制造业供应链企业间知识流动最短路径问题,分别运用Dijkstra算法(迪杰斯特拉)和Lingo计算出了单向的最短知识流动路径,运用改进的蚁群算法模拟了循环的最短知识流动路径,通过对计算结果的对比发现,业务紧密程度对烟草制造业供应链企业间知识流动路径选择有重要影响。
本文从供应链企业间合作的角度,分析构建了供应链企业间知识流动影响要素指标体系及各指标的相对权重；以时间维度划分静态和动态评价研究来评价知识流动的效果；从知识流动路径成本最低角度优化知识流动的路径,尽可能对研究内容进行定量研究,以期为不同行业供应链企业间知识流动的研究提供借鉴,为我国企业加强知识管理提供参考。
With the development of knowledge economy, the strategic value of knowledge has become increasingly prominent in promoting economic development and enhancing the core competitiveness of organizations. Identifying knowledge, accessing knowledge and absorbing knowledge to organization's core competitiveness, has become hot and difficult research pots. In recent years, knowledge flows and knowledge sharing attracts scholars to study as an effective way to get knowledge. Especially, the knowledge flow in strategic alliances, multinational corporations and other organizations can be gradually in-depth study. Supply chain as a new mode of cooperation has been more fully studied in logistics, capital flow and information flow, but knowledge flows would have to be further explored.
In this paper, knowledge flow between enterprises in supply chain would be studied, specifically including the following:
(1) Building an affecting index system of knowledge flow in supply chain, and using Conjoint Analysis and fuzzy-AHP to determine the index weights firstly.
On the basis of analysis and summary about the literature on knowledge flow,96 indicators are teased with fishbone diagram to combine with features of enterprise in supply chain. It builds the indicator system, which contains four-level indicators, such as knowledge characteristics, characteristics of inter-firm relations, characteristics of different firms themselves and environmental features; 11 secondary indicators, including the knowledge expressiveness, knowledge ambiguity, quality of relationship, knowledge distance, cultural differences, organizational distance, physical distance, knowledge willingness and ability of different firms, knowledge flow channels and IT support. And then, to explore the importance of various indicators, the paper applies Conjoint Analysis and fuzzy-AHP respectively to determine the relative index weights what has confirmed the characteristics of relationship between firms is the most important indicator of knowledge flow, and then characteristics of different firms themselves; quality of relationship of the secondary indicators seemed more prominent.
(2) Static effects of knowledge flow for manufacturing supply chain has been studied by rough set-BP neural network and validated with tobacco industry
In the index system and the weight determined based on the rough set theory and the combination of BP neural network method, introduced into the flow of knowledge research, the manufacturing supply chain to achieve a flow of knowledge between enterprises evaluate the static effects. By rough set theory about complex indicator system for simple, come to represent the original reduction target indicator system combination, thus reducing the burden of BP neural network, to achieve the manufacturing supply chain effects of inter-firm knowledge flows quickly and effectively simulation. The use of simulation to achieve the optimal supply chain network of the tobacco industry to evaluate the effect of knowledge flows, determine the tobacco industry supply chain, the general effect of knowledge flows, the results of this evaluation and《China Statistical Yearbook of Science and Technology》of new products in tobacco manufacturing output and output data show the number of patents are basically the same ranking, proved rough set-BP neural network can effectively assess the manufacturing supply chain effects of inter-firm flow of knowledge, also shows that the tobacco industry supply chain, the flow of knowledge between enterprises still need to substantially upgrade the level of to meet the tobacco industry's high, sharp, fine development.
(3) Dynamic effects of knowledge flow for manufacturing supply chain has been studied by DEA
Static evaluation of the chain to achieve the overall flow of knowledge on the effects of different assessment, but the flow of knowledge, after all, is a dynamic concept, the changes will inevitably affect the overall effect of knowledge flows, this flow of knowledge from the study of the time dimension, using the DEA method based on time (packet network method) for 9 manufacturing industries (food industry, beverage industry, general equipment manufacturing, pharmaceutical manufacturing, electrical machinery and equipment manufacturing, fabricated metal products, transportation equipment manufacturing, communication equipment, computer manufacturing, Educational and Sports Goods) the effect of the dynamic flow of knowledge evaluation.
Using《China Statistical Yearbook of Science and Technology》on Manufacturing R & D investment (R & D) intensity, the industry output value of new products/industrial GDP, the industry has a growth rate of the number of patents (2004 to 2009) and expert evaluation of the manufacturing data scoring industry nine industry knowledge flow effect, the results show that the cumulative effect because of the time, knowledge flows in the initial year of poor results, but as time goes on, getting good results flow of knowledge. At the same time that communications equipment, computer manufacturing supply chain flow of knowledge between the best, and metal manufacturing industry, represented by the traditional manufacturing supply chain less effective flow of knowledge between enterprises, need to demand from the degree of knowledge, knowledge, and degree of cooperation and other aspects of relationship quality improved.
(4) path-optimization model has been built to optimize knowledge flow path inter-enterprise supply chain about tobacco industry, and one-way path and cycle path have been used to simulate.
Inter-enterprise supply chain flow of knowledge is the ultimate goal of the evaluation is less effective flow of knowledge to effectively improve the industry, knowledge flow path from the lowest cost point of view, assessment of the tobacco industry supply chain flow of knowledge between the shortest path problem, namely the use of Dijkstra's algorithm (Dijkstra) and Lingo calculated the shortest one-way flow of knowledge path, using improved ant colony algorithm to simulate the flow of knowledge the shortest cycle path, calculated by comparing the results found that the tightness of the tobacco business manufacturing supply chain, the flow of knowledge between enterprises have an important impact on path selection.
This cooperation between enterprises from supply chain perspective to build a supply chain flow of knowledge between the indicators and the indicators of factors affecting the relative weight; The time dimension into static and dynamic evaluation studies to assess the effect of the flow of knowledge; The lowest cost path from the perspective of knowledge flows to optimize the flow path of knowledge, as much as possible the content of the quantitative research study, with a view to the supply chain between enterprises in different sectors of the study provide a reference flow of knowledge, to promote knowledge management to strengthen our business to provide a reference.
本文的研究工作主要围绕供应链企业间知识流动分析展开,具体包括以下几个方面：
(1)构建了供应链企业间知识流动影响要素指标体系,并首次运用结合分析法和模糊层次分析法对比确定指标权重
在对知识流动相关文献分析汇总的基础上,运用鱼刺图梳理出影响知识流动的96项指标要素,结合供应链企业间合作的特点,构建了供应链企业间知识流动影响要素指标体系,其中包含知识自身特性、企业间关系特性、知识流动主体特性和知识流动环境特性四个一级指标,以及知识可表达性、知识模糊性、关系质量、知识距离、文化差异、组织距离、物理距离、知识流动主体意愿和能力、知识流动渠道和信息技术支撑共11个二级指标。为探讨各指标的重要程度,分别运用结合分析法和模糊层次分析法计算出各指标的相对权重,证实了企业间关系特性是影响供应链企业间知识流动的最重要的指标,知识流动主体特性次之；二级指标中的关系质量对供应链企业间知识流动的影响比较突出。
(2)运用粗糙集—BP神经网络对制造业供应链企业间知识流动静态效果进行研究,并以烟草制造业进行实例验证
在对指标体系及权重确定的基础上,将粗糙集理论和BP神经网络方法结合,引入到知识流动的研究领域,实现了对制造业供应链企业间知识流动效果的静态评价。通过粗糙集理论对复杂的指标体系进行约简,得出能够代表原有指标体系的约简指标组合,减轻BP神经网络计算的负担,实现对制造业供应链企业间知识流动效果快速有效的模拟过程。运用模拟实现的最优网络对烟草制造业供应链的知识流动效果进行评价,判断出烟草制造业供应链知识流动效果一般,这一评价结果与《中国科技统计年鉴》中烟草制造业的新产品产出及专利数量产出数据显示排名基本一致,证实粗糙集—BP神经网络方法能够有效评估制造业供应链企业间知识流动的效果,同时表明烟草制造业供应链企业间知识流动水平尚需要大幅度提升,以满足烟草制造业的高、尖、精发展。
(3)运用基于时间的DEA方法对制造业供应链企业间知识流动动态效果进行研究,并对制造业不同行业进行了分析
静态评价实现了对不同链条整体知识流动效果的考核,但知识流动毕竟是个动态概念,其变化必然会影响整体知识流动效果,本文从时间维度研究知识流动,运用基于时间的DEA方法(数据包络方法)对制造业10个行业(食品制造业、饮料制造业、通用设备制造业、医药制造业、电气机械及器材制造业、金属制品业、交通运输设备制造业、通信设备、计算机制造业、文教体育用品制造业)的知识流动效果进行了动态评价。使用《中国科技统计年鉴》中关于制造业的R&D投入(研发投入)强度、行业新产品产值/工业生产总值、行业拥有的专利数量(2004年—2009年)以及专家打分数据评价了制造业10个行业知识流动效果,结果表明因为时间的累积效应,知识流动效果在初始年份较差,但随着时间的推移,知识流动效果渐佳。同时表明,通信设备、计算机制造业供应链企业间的知识流动效果最佳,而以金属制造业为代表的传统制造业供应链企业间知识流动效果较差,需要从知识需求度、知识合作度以及关系质量等方面进行提升。
(4)构建了烟草制造业供应链企业间知识流动路径优化模型,并从单向路径和循环路径两个方面进行了优化仿真研究
对供应链企业间知识流动评价的最终目的是对知识流动效果较差的行业进行有效地改进,从知识流动路径成本最低的角度,考核烟草制造业供应链企业间知识流动最短路径问题,分别运用Dijkstra算法(迪杰斯特拉)和Lingo计算出了单向的最短知识流动路径,运用改进的蚁群算法模拟了循环的最短知识流动路径,通过对计算结果的对比发现,业务紧密程度对烟草制造业供应链企业间知识流动路径选择有重要影响。
本文从供应链企业间合作的角度,分析构建了供应链企业间知识流动影响要素指标体系及各指标的相对权重；以时间维度划分静态和动态评价研究来评价知识流动的效果；从知识流动路径成本最低角度优化知识流动的路径,尽可能对研究内容进行定量研究,以期为不同行业供应链企业间知识流动的研究提供借鉴,为我国企业加强知识管理提供参考。
With the development of knowledge economy, the strategic value of knowledge has become increasingly prominent in promoting economic development and enhancing the core competitiveness of organizations. Identifying knowledge, accessing knowledge and absorbing knowledge to organization's core competitiveness, has become hot and difficult research pots. In recent years, knowledge flows and knowledge sharing attracts scholars to study as an effective way to get knowledge. Especially, the knowledge flow in strategic alliances, multinational corporations and other organizations can be gradually in-depth study. Supply chain as a new mode of cooperation has been more fully studied in logistics, capital flow and information flow, but knowledge flows would have to be further explored.
In this paper, knowledge flow between enterprises in supply chain would be studied, specifically including the following:
(1) Building an affecting index system of knowledge flow in supply chain, and using Conjoint Analysis and fuzzy-AHP to determine the index weights firstly.
On the basis of analysis and summary about the literature on knowledge flow,96 indicators are teased with fishbone diagram to combine with features of enterprise in supply chain. It builds the indicator system, which contains four-level indicators, such as knowledge characteristics, characteristics of inter-firm relations, characteristics of different firms themselves and environmental features; 11 secondary indicators, including the knowledge expressiveness, knowledge ambiguity, quality of relationship, knowledge distance, cultural differences, organizational distance, physical distance, knowledge willingness and ability of different firms, knowledge flow channels and IT support. And then, to explore the importance of various indicators, the paper applies Conjoint Analysis and fuzzy-AHP respectively to determine the relative index weights what has confirmed the characteristics of relationship between firms is the most important indicator of knowledge flow, and then characteristics of different firms themselves; quality of relationship of the secondary indicators seemed more prominent.
(2) Static effects of knowledge flow for manufacturing supply chain has been studied by rough set-BP neural network and validated with tobacco industry
In the index system and the weight determined based on the rough set theory and the combination of BP neural network method, introduced into the flow of knowledge research, the manufacturing supply chain to achieve a flow of knowledge between enterprises evaluate the static effects. By rough set theory about complex indicator system for simple, come to represent the original reduction target indicator system combination, thus reducing the burden of BP neural network, to achieve the manufacturing supply chain effects of inter-firm knowledge flows quickly and effectively simulation. The use of simulation to achieve the optimal supply chain network of the tobacco industry to evaluate the effect of knowledge flows, determine the tobacco industry supply chain, the general effect of knowledge flows, the results of this evaluation and《China Statistical Yearbook of Science and Technology》of new products in tobacco manufacturing output and output data show the number of patents are basically the same ranking, proved rough set-BP neural network can effectively assess the manufacturing supply chain effects of inter-firm flow of knowledge, also shows that the tobacco industry supply chain, the flow of knowledge between enterprises still need to substantially upgrade the level of to meet the tobacco industry's high, sharp, fine development.
(3) Dynamic effects of knowledge flow for manufacturing supply chain has been studied by DEA
Static evaluation of the chain to achieve the overall flow of knowledge on the effects of different assessment, but the flow of knowledge, after all, is a dynamic concept, the changes will inevitably affect the overall effect of knowledge flows, this flow of knowledge from the study of the time dimension, using the DEA method based on time (packet network method) for 9 manufacturing industries (food industry, beverage industry, general equipment manufacturing, pharmaceutical manufacturing, electrical machinery and equipment manufacturing, fabricated metal products, transportation equipment manufacturing, communication equipment, computer manufacturing, Educational and Sports Goods) the effect of the dynamic flow of knowledge evaluation.
Using《China Statistical Yearbook of Science and Technology》on Manufacturing R & D investment (R & D) intensity, the industry output value of new products/industrial GDP, the industry has a growth rate of the number of patents (2004 to 2009) and expert evaluation of the manufacturing data scoring industry nine industry knowledge flow effect, the results show that the cumulative effect because of the time, knowledge flows in the initial year of poor results, but as time goes on, getting good results flow of knowledge. At the same time that communications equipment, computer manufacturing supply chain flow of knowledge between the best, and metal manufacturing industry, represented by the traditional manufacturing supply chain less effective flow of knowledge between enterprises, need to demand from the degree of knowledge, knowledge, and degree of cooperation and other aspects of relationship quality improved.
(4) path-optimization model has been built to optimize knowledge flow path inter-enterprise supply chain about tobacco industry, and one-way path and cycle path have been used to simulate.
Inter-enterprise supply chain flow of knowledge is the ultimate goal of the evaluation is less effective flow of knowledge to effectively improve the industry, knowledge flow path from the lowest cost point of view, assessment of the tobacco industry supply chain flow of knowledge between the shortest path problem, namely the use of Dijkstra's algorithm (Dijkstra) and Lingo calculated the shortest one-way flow of knowledge path, using improved ant colony algorithm to simulate the flow of knowledge the shortest cycle path, calculated by comparing the results found that the tightness of the tobacco business manufacturing supply chain, the flow of knowledge between enterprises have an important impact on path selection.
This cooperation between enterprises from supply chain perspective to build a supply chain flow of knowledge between the indicators and the indicators of factors affecting the relative weight; The time dimension into static and dynamic evaluation studies to assess the effect of the flow of knowledge; The lowest cost path from the perspective of knowledge flows to optimize the flow path of knowledge, as much as possible the content of the quantitative research study, with a view to the supply chain between enterprises in different sectors of the study provide a reference flow of knowledge, to promote knowledge management to strengthen our business to provide a reference.
引文
5 Ikujiro nonaka,the knowledge-creating company[J].Harvard business review.1991(11):94-104
6 http://baike.baidu.com/view/46944.htm
7 Hai Zhuge. A knowledge flow model for peer-to-peer team knowledge sharing and management [J]. Expert Systems with Applications,2002, (23):23-30.
8Max H., Boisot. Is your firm a creative destroyer? Competitive learning and knowledge flows in the technological strategies of firm [J]. Research Policy,1995, (24):489-506.
9 Hai Zhuge, Weiyu Guo. Virtual knowledge service market-for effective knowledge flow within knowledge grid[J]. The Journal of Systems and Software,2007,80,1833-1842
10顾新,李久平,王维成.知识流动、知识链与知识链管理[J].软科学.2006年,20卷,第2期,p10-p16.P10-16.
12Davenport TH. Laurence P. Working Knowledge:How Organizations Manage What They Know. Boston(Mass):Harvard Business School Press,1998
13Baptista R, Swanm P. Do firms in clusters innovate more? [J]. Research Policy,1998,27(5):525-540.
16http://baike.baidu.com/view/1753676.htm
17史成东,陈菊红,郭福利.粗糙集和BP神经网络在供应链绩效评价中的应用研究[J].软科学,2008,Vol.22,No.3,9-13
19张勤,马费成.国外知识管理研究范式——以共词分析为方法[J].管理科学学报,2007年12月65-74
20吴越,谷明玉.DEA分析方法研究综述[J].价值工程,2003,
21魏权龄.数据包络分析[J].科学出版社,2004.
22西安交通大学经济管理学院.应用DEA方法对经济系效率的评价[学术报告讲义].
23李强,供应链知识流动路径规划研究[M]昆明理工大学硕士毕业论文,2009
[1]Ikujiro nonaka, the knowledge-creating company [J].Harvard business review.1991 (11):94-104
[2]Syed Z. Shariq. How Does Knowledge Transform as it is Transferred? Speculations on the Possibility of a Cognitive Theory of Knowledgescapes [J]. Journal of Knowledge Management, 1999, Vol.03 (4):67-75.
[3]罗索.人类的知识[M].北京：商务印书馆,1983：10-11
[4]冯进路,冯丽婷,闫化海.知识整合的效率：市场、组织和企业家的作用[J].科学学与科学技术管理,2004(8),43-47.
[5][美]科塔达等.网络时代的管理：IBM和其他公司是如何成功的[M].北京：生活·读书·新知三联书店,2001,107.
[6]OECD. Economy based economy [R].1996.
[7]李凤云.基于新型企业观的知识流管理[J].中国质量,2004,2：6,p79-86
[8]李炳英.个体、群体、组织间知识转移影响因素的分析研究[J].情报科学,2007,Vol.25No.10,1458-1462.
[9]施翔,陈永高,孙文建.隐性知识在项目团队中的转移效率[J].工业技术经济,2008,Vol.27 No.12,84-86
[10]常荔,邹珊刚,李顺才.基于知识链的知识扩散的影响因素研究[J].科研管理,2001,Vol.22 No.5,122-127.
[11]原长弘,周林海.知识转移效率的研究现状[J].中国科技论坛,2011(3),113-120
[12]Petty R E, Cacioppo J T. Communication and persuasion:central and peripheral routes to attitude change [M]. New York:Springer-Verlag,1986.
[13]Sveiby K.E. A Knowledge-based Theory of the Firm to Guide Strategy Formulation [DB/OL].http://www.sveiby.com/articles/Knowledge get theory of firm. Htm,2000.11-25.
[14]Gilbert M, Cordep-Hayes M. Understanding the process of knowledge transfer to achieve successful technological innovation [J]. Technovation,1996,16(6):301-312
[15]李凤云.基于新型企业观的知识流管理[J].中国质量,2004,2：6,p79-86
[16]李顺才,邹珊刚.知识流动机理的三维分析模式[J].研究与发展管理,2003,Vol15,NO.2,39-43
[17]王日芬,黄加虎,王海丹.竞争情报中的知识流及转换机制研究[J].2007, Vol.26,No.3,415-421.
[18]Marjolein C. J. Canids and Bart Verspagen. Barriers to knowledge and regional convergence in an evolutionary modal [J]. Journal of evolutionary economies,2001, (11):307-329.
[19]杨志锋,邹珊刚.知识资源、知识存量和知识流量：概念、特征和测度[J].科研管理,2001,21(4)：105—111
[20]司训练,陈金贤.基于知识流的产业结构进化模型[J].当代经济科学,2003,Vol.25,No.26,36-40
[21]谢守美.知识生态系统知识流动的生态学分析[J].图书馆学研究,2009(5),p7-10
[22]陈有富.知识流动的控制[J].图书馆,2002,No4,18-20
[23]Verspagen B. Estimating international technology spillovers using technology flow matrices [J]. Review of World Economics,1997,133:226-324
[24]Craham Bannock, RE Baxter, and Evan Davis. The Penguin Dictionary of Economics[M].1992.
[25]Nieuwenhuijsen H. En and van Stel. Kennis-spillovers en economische groei[R].Research Report,20000,007/N, EIM On-dermek voor Beddrijf & Beleid.
[26]刘光容,娄策群.知识扩散机制研究[J].图书馆理论与实践,2007,(6),47-48
[27]Rogers EM. Diffusion of Innovations(3rd Edition). New York:Free Press,1983
[28]徐占忱,何明升.知识转移障碍纾解与集群企业学习能力构成研究[J].情报科学,2005,(5)：559-663
[29]Max H., Boisot. Is your firm a creative destroyer? Competitive learning and knowledge flows in the technological strategies of firm [J]. Research Policy,1995, (24):489-506.
[30]Davenport TH. Laurence P. Working Knowledge:How Organizations Manage What They Know. Boston(Mass):Harvard Business School Press,1998
[31]Bartol KM. Encouraging knowledge sharing:the role of organizational reward systems [J]. Journal of Leadership & Organiz Studies,2002,9(1):64-76
[32]Teece, D. Technology transfer by multinational firms:the resource cost of transferring technological know-how [J].The Economic Journal,1977, (87):242-261
[33]Joseph. L. Radaracco, Jr. Alliances Speed Knowledge Transfer. Planning Review,1991;19(2),10-16
[34]Bernard L. Simonin. Ambiguity and the Process of Knowledge Transfer in Strategic Alliances [J]. Strategic Management Journal,1999:20(7)
[35]Szulanski G. The processes of knowledge transfer:a diachronic analysis stickiness [J].Organazational Behavior and Human Decision Processes,2000,82(l):9-27.
[36]龚毅,谢恩.中外企业战略联盟知识转移效率的实证分析[J].科学学研究,2005,Vol.23,No.4,500-505
[37]卢兵,岳亮,廖貅武.联盟中知识转移效果的研究[J].科学学与科学技术管理,2006(8),84-88
[38]卢兵,岳亮,廖貅武.联盟中知识转移效率的分析[J].系统工程,2006,Vol.24,No.6,46-51
[39]林莉.知识联盟中知识转移的障碍因素及应对策略分析[J].科技导报,2004(4),29-32
[40]卢兵,岳亮,廖貅武.企业联盟中知识转移的影响因素分析——一个分析模型[J].预测,2006,Vol.25,No.2,31-36
[41]张睿,于渤.技术联盟知识转移影响因素实证研究[J].科学学研究,2008, Vol.26No.5,1024-1030.
[42]Barbara Weber, Christiana Weber. Corporate venture capital as a means of radical innovation: Relational fit, social capital and knowledge transfer [J]. Engineering Technology Management. 2007,(24):11-35
[43]Shu-Haien Liao, Ta-Chien Hu. Knowledge transfer and competitive advantage on environmental uncertainty:an empirical study of the Taiwan semiconductor industry [J]. Technovation.2007(27):402-411
[44]焦俊,李垣.联盟中显性知识转移和企业内部创新[J].预测,2007,Vol.26,No.5,31-35
[45]吴翠花,王三义,刘新梅,万威.联盟网络社会资本对知识转移影响路径研究[J].科学学研究,2008,Vol.26,No.5,1031-1036
[46]王荣朴,宣国良.跨国公司知识反哺的经济分析[J].东华大学学报(自然科学版),2006,Vol.32 No.3,38-42
[47]Gupta, Anil K. and Vijay Govindarajan. Knowledge Flows and the Structure of Control within Multinational Corporations [J]. The Academy of Management Review,1991 (16), 768-792
[48]Anil K Gupta, Vijay Govindarajan. Knowledge flows within multinational corporations [J]. Strategic Management Journal,2000(21),473-496.
[49]Kogut, Bruce and Udo Zander. Knowledge ofthe Firm, Combinative Capabilities, and the Replication of Technology [J]. Organization Science,1992,3
[50]Kogut and Zander. Knowledge of the Firm and the Evolutionary Theory of the Multinational Corporation[J], Journal of International Business,1993 (24),25-45
[51]Mohan S. N. Venkatraman. Determinants of transnational new product development capability:testing the influence of transferring and deploying tacit overview knowledge [J]. Strategic management Journal,2001(22)
[52]陈菲琼.我国企业与跨国公司知识联盟的知识转移层次研究[J].科研管理,2001,Vol.22,No.2,66-73.
[53]Ram Mudambi. Knowledge management in multinational firms [J]. Journal of International Management,2002(8),1-9
[54]X Martin, R Salomon. Knowledge transfer capacity and its implications for the theory of the multinational corporation [J]. Journal of international business studies,2003(34),
[55]Fossn J Pedersent. The MNC as a knowledge structure:the roles of knowledge sources and organizational instruments in MNC knowledge management [J]. Aanish Research Unit for Industrial Dynamics,2003(5):1-33.
[56]刘芳,欧阳令南.跨国公司知识转移过程、影响因素与对策研究[J].科学学与科学技术管理,2005(10),40-43
[57]刘帮成,王重鸣.影响跨国知识转移效能的因素研究：以在华进行跨国创业企业为例[J].科研管理,2007,Vol.28,No.6,1-11
[58]王清晓,杨忠.跨国公司内部网络结点之间知识转移的影响因素分析——一个概念模型[J].科研管理,2006,Vol.27,No.2,102-108.
[59]王清晓.子公司创新倾向对跨国公司内部知识转移影响的实证研究[J].科学学与科学技术管理,2008(1),88-92.
[60]王岚.跨国合资企业中知识转移影响因素研究[J].情报杂志,2009(2),125-128
[61]Cohen W M, Florida R,Randazzese L, et ai. Industry and the academy:uneasyparters in the cause of technological advance [M]. Washington:Brooking Institute Press,1998.
[62]Cohen W. M. and Levinthal, D.A. Innovation and learning:the two faces of R&D. The economic Journal,1989(99),569-596.
[63]Cohen W. M. and Levinthal, D.A. Absorptive capacity:a new perspective on learning and innovation. Administrative Science Quarterly,1990(35),128-152.
[64]Cockburn, I. and Henderson, R. Absorptive capacity, coauthoring behavior, and the organization of research in drug discovery. Journal of Industrial Economics, XLVI,1998(2), 157-182
[65]Ajay Agrawal. University-to-industry knowledge transfer:literature review and unanswered questions [J]. International Journal of Management Reviews,2003(4):185-302
[66]周晶晶,周国华.企业一大学合作中的知识转移机制研究——以某转制院所实施项目管理模式为例[J].中国软科学,2006(3),139-144.
[67]叶飞,周蓉,张红.产学研合作过程中知识转移绩效的关键影响因素研究[J].工业技术经济,2009,Vol.28,No.6,116-120
[68]徐国东,叶金福,邹艳.企业一大学合作中的知识转移影响因素分析[J].情报杂志,2008(2),87-89.
[69]张力,聂鸣.产学合作中影响知识转移的因素：全景式解释模型[J].情报杂志,2009,Vol.28,No.7,87-90.
[70]Jaffe A, Trajtenberg M, Henderson R. Geographic location of knowledge spillovers as evidenced by patent citations [J]. Quarterly Journal of Economics,1993,108(3),577-598.
[71]Davenport TH. Laurence P. Working Knowledge:How Organizations Manage What They Know. Boston(Mass):Harvard Business School Press,1998
[72]Baptista R, Swanm P. Do firms in clusters innovate more? [J]. Research Policy,1998, 27(5):525-540.
[73]Feldman M P. The new economics of innovation, spillovers and agglomeration:a review of empirical studies [J]. Economics of innovation and new technology,1999,8:5-25.
[74]吴先华,郭际,胡汉辉.产业集群中非正式交流影响知识流动的实证研究[J].科学学研究,2008,Vol.26 No.5,1014-1023.
[75]Brown J S, Duguid P. Organizational learning and communities of practice:toward a unified view of working, learning, and innovation. Organization Science,1991,2(1):40-57.
[76]I Maignan, O. C. Ferrell, G. T. Hult. Corporate citizenship:Cultural antecedents and business benefits [J] Journal of the Academy of Marketing Science,1999
[77]Frohlich M. T. and Westbrook R. Arcs of integration:An international study of supply chain strategies, Journal of operations management,2001,19(2):185-200
[78]Batenburg R, Rutten R. Managing innovation in regional supply networks:A dutch case of "Knowledge industry clustering" [J]. Supply chain management,2003,8(3):263-270.
[79]Ke W L, Wei K K. Factors affecting trading partners'knowledge sharing:Using the lens of transaction cost economics and socio-political the ories [J]. Electronic commerce research and applications,2007,6(3):297-308
[80]Sivakumar K, Subroto Roy. Knowledge redundancy in supply chains:a framework [J]. Supply chain management,2004,9(3):241-249.
[81]Adrian Done. Frohlich M.2005. The impact on performance of knowledge in the supply chain:an empirical study. Working paper, London Business School
[82]Roper S, Crone M. Knowledge complementarity and coordination in the local supply chain: some empirical evidence [J]. British Journal of Management,2003,14(4):339-355.
[83]张慧涛.供应链企业间知识转移中的知识破损及对策研究[J].科技与管理,2008,Vol.10,No.6,46-48.
[84]邢文凤,严建援.供应链知识纵向转移特征研究[J].科技进步与对策,2009,Vol.26,No.6,145-148.
[85]朱庆.供应链企业间的知识共享及其知识交易研究[D].硕士学位论文,重庆大学,2006
[86]吴冰,刘仲英,赵林度.供应链协同的知识转移研究[J].情报杂志,2008(1),15-20
[87]翁莉,仲伟俊,鲁芳.供应链知识共享的决策行为及影响因素研究[J].管理学报,2009,Vol.6,No.12,1648-1652
[88]Fairchild A. M. Knowledge management metrics via a balanced scorecard methodology [A]. Proceesings of the 35th Hawaii international conference on system science USA,2002.
[89]Kun Chang Lee, Sang Jae Lee, Won Kang. KMPI:measuring knowledge management performance [J]. Information & Management,2005(42):469-482
[90]朱启红,张钢.基于人工神经网络的企业知识管理评价模型[J].科学学与科学技术管理,2003(8)：32—34.
[91]王君,樊治平.组织知识管理绩效的一种综合评价方法[J].管理工程学报,2004,18(2)：44—48
[92]蒋翠清,叶春森,杨善林.组织知识管理绩效评估研究[J].科学学研究,2007,25(2)：296—300.
[93]丁勇,粱昌勇,朱俊红.一种供应链中的知识管理绩效评价方法研究[J].运筹与管理,2006,15(4)：49—154.
[94]王道平,许有志,王锐兵.供应链知识共享绩效的模糊综合评价方法[J].软科学,2008,22(12)：14—17.
[95]陆杉,黄福华,赵中平.供应链知识协同管理绩效评价研究[J].科技管理研究,2010,193-195
[96]MAURA SOEKIJAD, ERIK ANDRIESSEN. Conditions for Knowledge Sharing in Competitive Alliances [J]. European Management Journal,2003, Vol.21, No.5, pp.578-587
[97]Jeffrey L. Cummings, Bing-Sheng Teng. Transferring R&D knowledge:the key factors affecting knowledge transfer success [J]. J. Eng. Technol. Manage.2003, Vol.20,39-68
[98]Michael Polanyi. Personal Knowledge, toward a Post-critical Philosophy [M]. Chicago:The University of Chicago Press,1958, and 1962(corrected edition)
[99]丁秀好,黄瑞华.合作创新中媒介丰度对知识模糊性和知识转移绩效关系的调节作用研究[J].研究与发展管理,2008,Vol.20 No.5,9-13
[100]Zander U, Kogut B. Knowledge and the speed of the transfer and imitation of organizational capabilities:An empirical test[J].Organization Science,1995,6(1):76-92.
[101]Sabel, C.F. Studied trust:Building new forms of cooperation in a volatile economy [J]. Human Relations,1993,46:1133-1170.
[102]Dodgson, M. Learning, trust, and technological collaboration [J]. Human Relations,1993, 46(1):11-95
[103]郭韧,丰吉闯.知识溢出的测度研究[J].科技管理研究,2008(7),393-395
[104]Dixon, N. M. Common Knowledge:How Companies Thrive by Sharing What They Know? Harvard Business School Press, Boston, MA.,2000
[105]Tushman M L. Special boundary roles in the innovation process [J].Administrative Science Quarterly,1977,22(4):587-605.
[106]卢兵,岳亮,廖貅武.企业联盟中知识转移的影响因素分析——一个分析模型[J].预测,2006,25(2)：31-36
[107]Davenport TH. Laurence P. Working Knowledge:How Organizations Manage What They Know. Boston(Mass):Harvard Business School Press,1998
[08]Baptista R, Swanm P. Do firms in clusters innovate more? [J]. Research Policy,1998, 27(5):525-540.
[109]徐青,马庆国等.ERP知识转移影响因素的实证对比研究——与Dong-Gil研究的比较[J].科学学研究,2007,Vol.25,No.3,498-504
[110]尹洁,葛世伦,李锋,吴洁,施琴芬.ERP关键用户向实施顾问知识转移影响因素研究[J].情报杂志,2009,Vol.28,No.11,139-144
[111]Zander U, Kogut B. Knowledge and the speed of the transfer and imitation of organizational capabilities:An empirical test[J].Organization Science,1995,6(1):76-92
[112]Ghoshal,S. and C.A.Barflet. The multinational corporation as an inter- Organizational network [J]. Academy of management review,1988,15:603-625
[113]张兵,王文平.知识流动的小世界——基于关系强度的观点[J].科学学研究,2009,Vol.27 No.9,1312-1321
[114]Purser, R.E., Pasmore, W.A., Tenkasi, R.V. The influence of deliberations on learning in new product development teams [J]. Journal of Engineering and Technology Management,1992, 9 (1),1-28.
[115]Ekaterina V. Karniouchina, William L. Moore, Bo van der Rhee, Rohit Verma. Issues in the use of ratings-based versus choice-based conjoint analysis in operations management research [J]. European Journal of Operational Research,2009,197,340-348
[116]陈衍泰,陈国宏,李美娟.综合评价方法分类及研究进展[J].管理科学学报2004年4月69-80
[117]田志友,吴瑞明,王浣尘.基于过程熵的供应商多元加工能力评价[J].管理工程学报,2006年第1期36-41
[118]陈衍泰,陈国宏,李美娟.综合评价方法分类及研究进展[J].管理科学学报2004年4月69-80
[119]杜世海,丁慧平,姜文生.供应链竞争优势的租金贡献分析与评价研究[J].管理科学学报,2006年6月24-31
[120]陈衍泰,陈国宏,李美娟.综合评价方法分类及研究进展[J].管理科学学报2004年4月69-80
[121]孙寿亮.供应链环境下的供应商评价模型应用研究[J].管理现代化,2007年第一期62-65
[122]毕功兵,梁棵,杨锋.两阶段生产系统的DEA效率评价模型[J]中国管理科学2007年4月92-97
[123]陈久,康世瀛.基于粗糙集理论的供应链协同效果评价[J].统计与决策,2007年第22期170-173
[124]Ikujiro Nonaka. A Dynamic Theory of Organizational Knowlesge Creation [J]. Organization Science,1994, Vol.5, No.1,14-37.
[125]Szulanski, G. The Process of Knowledge Transfer:A diachronic analysis of stickness [J]. Organizational Behavior and Human Decision Processes,2000,82(1):9-27.
[126]Gilbert M, Cordep-Hayes M. Understanding the process of knowledge transfer to achieve successful technological innovation [J]. Technovation,1996,16(6):301-312
[127]Cummings J L., Teng B S.. Transfering R&D knowledge:The key factors affecting knowledge transfer success [J]. Journal of Engineering and Technology Management,2003, 20:39-68.
[128]Vito Albino, Claudio Garavelli A, Giovanni Schiuma. Knowledge transfer and inter-firm relationships in industrial districts:The role of the leader firm[J]. Technovation,1999(19):53-63.
[129]徐建锁,王正欧,李淑伟.基于知识链的管理[J].天津大学学报(社会科学版),2003,Vol.5,No.2,133-136.
[130]张悟移.供应链企业知识链管理方法研究[D].华中科技大学博士论文
[131]全力,顾新.知识链组织之间冲突的三维动因模型[J].科学学与科学技术管理,2008(12),92-96.
[132]王道平,李贺.基于知识流的敏捷供应链知识服务模式研究[J].软科学,2010,Vol.24,No.3,1-9
[133]高智勇.基于中国供应链中知识型核心企业的动态知识评价模型研究[D].昆明理工大学硕士毕业论文,2008
[134]张文修,仇国芳.基于粗糙集的不确定决策[M].第1版.北京：清华大学出版社,2005.
[135]王吉培,张志伟.基于粗糙集神经网络的商业银行信贷风险研究[J].
[136]何勇,冯雷,吴春霞.基于粗糙集与神经网络的农机化发展水平评估方法[J].农业机械学报,2004,Vol.35,No.2,100-103
[137]朱军勇,贺红燕,张春生.粗糙集—神经网络在构建企业供应链合作伙伴选择中的应用[J].河南理工大学学报(社会科学版),2007,Vol.8,No.1,33-36
[138]史成东,陈菊红,郭福利.粗糙集和BP神经网络在供应链绩效评价中的应用研究[J].软科学,2008,Vol.22,No.3,9-13
[139]罗彬,邵培基,罗尽尧,刘独玉,夏国恩.基于粗糙集理论—神经网络—蜂群算法集成的客户流失研究[J].管理学报,2011,Vol.8,No.2,265-272
[140]魏权龄.数据包罗分析[M].第二版.科学出版社,2005：14
[141]Necmi K.Avkiran,Terry Rowlands. How to better identify the ture managerial performance: State of the art using DEA [J]. The International Journal of Management Science,36 (2008) 317-324
[142]李国勇.智能控制及其MATLAB实现[M].第1版.北京：电子工业出版社,2005.
[143]刘辉霞.企业的知识流动机理及优化途径分析[J].岳阳职业技术学院学报,2004,Vol.19,No.4,58-61
[144]付相君,彭颖红.社会网络分析促进组织结构及其知识流优化[J].计算机集成制造系统,2007,Vol.13,No.11,2069-2177
[145]白瑛,蔡建峰,骞永博.基才运作优化向可持续竞争优势演化的知识流动路径研究[J].科学学与科学技术管理,2007,80-83
[146]李岚,姜伟强.蚁群遗传优化算法在物流配送路径选择中的应用[J].哈尔滨商业大学 学报(自然科学版),2009,Vol.25,No.6,707-710
[147]吴云志,乐毅,王超,张友华.蚁群算法在物流路径优化中的应用及仿真[J].合肥工业大学学报(自然科学版),2009,Vol.32,No.2,211-214
[148]Yan Qiao, Wiyi Zhang. Study of optimizing the knowledge acquisition route based on core-corporation in supply chain [J].2008 IEEE/INFORS International Conferrence on Service Operations and Logistics, and Informatics,2072-2077
6 http://baike.baidu.com/view/46944.htm
7 Hai Zhuge. A knowledge flow model for peer-to-peer team knowledge sharing and management [J]. Expert Systems with Applications,2002, (23):23-30.
8Max H., Boisot. Is your firm a creative destroyer? Competitive learning and knowledge flows in the technological strategies of firm [J]. Research Policy,1995, (24):489-506.
9 Hai Zhuge, Weiyu Guo. Virtual knowledge service market-for effective knowledge flow within knowledge grid[J]. The Journal of Systems and Software,2007,80,1833-1842
10顾新,李久平,王维成.知识流动、知识链与知识链管理[J].软科学.2006年,20卷,第2期,p10-p16.P10-16.
12Davenport TH. Laurence P. Working Knowledge:How Organizations Manage What They Know. Boston(Mass):Harvard Business School Press,1998
13Baptista R, Swanm P. Do firms in clusters innovate more? [J]. Research Policy,1998,27(5):525-540.
16http://baike.baidu.com/view/1753676.htm
17史成东,陈菊红,郭福利.粗糙集和BP神经网络在供应链绩效评价中的应用研究[J].软科学,2008,Vol.22,No.3,9-13
19张勤,马费成.国外知识管理研究范式——以共词分析为方法[J].管理科学学报,2007年12月65-74
20吴越,谷明玉.DEA分析方法研究综述[J].价值工程,2003,
21魏权龄.数据包络分析[J].科学出版社,2004.
22西安交通大学经济管理学院.应用DEA方法对经济系效率的评价[学术报告讲义].
23李强,供应链知识流动路径规划研究[M]昆明理工大学硕士毕业论文,2009
[1]Ikujiro nonaka, the knowledge-creating company [J].Harvard business review.1991 (11):94-104
[2]Syed Z. Shariq. How Does Knowledge Transform as it is Transferred? Speculations on the Possibility of a Cognitive Theory of Knowledgescapes [J]. Journal of Knowledge Management, 1999, Vol.03 (4):67-75.
[3]罗索.人类的知识[M].北京：商务印书馆,1983：10-11
[4]冯进路,冯丽婷,闫化海.知识整合的效率：市场、组织和企业家的作用[J].科学学与科学技术管理,2004(8),43-47.
[5][美]科塔达等.网络时代的管理：IBM和其他公司是如何成功的[M].北京：生活·读书·新知三联书店,2001,107.
[6]OECD. Economy based economy [R].1996.
[7]李凤云.基于新型企业观的知识流管理[J].中国质量,2004,2：6,p79-86
[8]李炳英.个体、群体、组织间知识转移影响因素的分析研究[J].情报科学,2007,Vol.25No.10,1458-1462.
[9]施翔,陈永高,孙文建.隐性知识在项目团队中的转移效率[J].工业技术经济,2008,Vol.27 No.12,84-86
[10]常荔,邹珊刚,李顺才.基于知识链的知识扩散的影响因素研究[J].科研管理,2001,Vol.22 No.5,122-127.
[11]原长弘,周林海.知识转移效率的研究现状[J].中国科技论坛,2011(3),113-120
[12]Petty R E, Cacioppo J T. Communication and persuasion:central and peripheral routes to attitude change [M]. New York:Springer-Verlag,1986.
[13]Sveiby K.E. A Knowledge-based Theory of the Firm to Guide Strategy Formulation [DB/OL].http://www.sveiby.com/articles/Knowledge get theory of firm. Htm,2000.11-25.
[14]Gilbert M, Cordep-Hayes M. Understanding the process of knowledge transfer to achieve successful technological innovation [J]. Technovation,1996,16(6):301-312
[15]李凤云.基于新型企业观的知识流管理[J].中国质量,2004,2：6,p79-86
[16]李顺才,邹珊刚.知识流动机理的三维分析模式[J].研究与发展管理,2003,Vol15,NO.2,39-43
[17]王日芬,黄加虎,王海丹.竞争情报中的知识流及转换机制研究[J].2007, Vol.26,No.3,415-421.
[18]Marjolein C. J. Canids and Bart Verspagen. Barriers to knowledge and regional convergence in an evolutionary modal [J]. Journal of evolutionary economies,2001, (11):307-329.
[19]杨志锋,邹珊刚.知识资源、知识存量和知识流量：概念、特征和测度[J].科研管理,2001,21(4)：105—111
[20]司训练,陈金贤.基于知识流的产业结构进化模型[J].当代经济科学,2003,Vol.25,No.26,36-40
[21]谢守美.知识生态系统知识流动的生态学分析[J].图书馆学研究,2009(5),p7-10
[22]陈有富.知识流动的控制[J].图书馆,2002,No4,18-20
[23]Verspagen B. Estimating international technology spillovers using technology flow matrices [J]. Review of World Economics,1997,133:226-324
[24]Craham Bannock, RE Baxter, and Evan Davis. The Penguin Dictionary of Economics[M].1992.
[25]Nieuwenhuijsen H. En and van Stel. Kennis-spillovers en economische groei[R].Research Report,20000,007/N, EIM On-dermek voor Beddrijf & Beleid.
[26]刘光容,娄策群.知识扩散机制研究[J].图书馆理论与实践,2007,(6),47-48
[27]Rogers EM. Diffusion of Innovations(3rd Edition). New York:Free Press,1983
[28]徐占忱,何明升.知识转移障碍纾解与集群企业学习能力构成研究[J].情报科学,2005,(5)：559-663
[29]Max H., Boisot. Is your firm a creative destroyer? Competitive learning and knowledge flows in the technological strategies of firm [J]. Research Policy,1995, (24):489-506.
[30]Davenport TH. Laurence P. Working Knowledge:How Organizations Manage What They Know. Boston(Mass):Harvard Business School Press,1998
[31]Bartol KM. Encouraging knowledge sharing:the role of organizational reward systems [J]. Journal of Leadership & Organiz Studies,2002,9(1):64-76
[32]Teece, D. Technology transfer by multinational firms:the resource cost of transferring technological know-how [J].The Economic Journal,1977, (87):242-261
[33]Joseph. L. Radaracco, Jr. Alliances Speed Knowledge Transfer. Planning Review,1991;19(2),10-16
[34]Bernard L. Simonin. Ambiguity and the Process of Knowledge Transfer in Strategic Alliances [J]. Strategic Management Journal,1999:20(7)
[35]Szulanski G. The processes of knowledge transfer:a diachronic analysis stickiness [J].Organazational Behavior and Human Decision Processes,2000,82(l):9-27.
[36]龚毅,谢恩.中外企业战略联盟知识转移效率的实证分析[J].科学学研究,2005,Vol.23,No.4,500-505
[37]卢兵,岳亮,廖貅武.联盟中知识转移效果的研究[J].科学学与科学技术管理,2006(8),84-88
[38]卢兵,岳亮,廖貅武.联盟中知识转移效率的分析[J].系统工程,2006,Vol.24,No.6,46-51
[39]林莉.知识联盟中知识转移的障碍因素及应对策略分析[J].科技导报,2004(4),29-32
[40]卢兵,岳亮,廖貅武.企业联盟中知识转移的影响因素分析——一个分析模型[J].预测,2006,Vol.25,No.2,31-36
[41]张睿,于渤.技术联盟知识转移影响因素实证研究[J].科学学研究,2008, Vol.26No.5,1024-1030.
[42]Barbara Weber, Christiana Weber. Corporate venture capital as a means of radical innovation: Relational fit, social capital and knowledge transfer [J]. Engineering Technology Management. 2007,(24):11-35
[43]Shu-Haien Liao, Ta-Chien Hu. Knowledge transfer and competitive advantage on environmental uncertainty:an empirical study of the Taiwan semiconductor industry [J]. Technovation.2007(27):402-411
[44]焦俊,李垣.联盟中显性知识转移和企业内部创新[J].预测,2007,Vol.26,No.5,31-35
[45]吴翠花,王三义,刘新梅,万威.联盟网络社会资本对知识转移影响路径研究[J].科学学研究,2008,Vol.26,No.5,1031-1036
[46]王荣朴,宣国良.跨国公司知识反哺的经济分析[J].东华大学学报(自然科学版),2006,Vol.32 No.3,38-42
[47]Gupta, Anil K. and Vijay Govindarajan. Knowledge Flows and the Structure of Control within Multinational Corporations [J]. The Academy of Management Review,1991 (16), 768-792
[48]Anil K Gupta, Vijay Govindarajan. Knowledge flows within multinational corporations [J]. Strategic Management Journal,2000(21),473-496.
[49]Kogut, Bruce and Udo Zander. Knowledge ofthe Firm, Combinative Capabilities, and the Replication of Technology [J]. Organization Science,1992,3
[50]Kogut and Zander. Knowledge of the Firm and the Evolutionary Theory of the Multinational Corporation[J], Journal of International Business,1993 (24),25-45
[51]Mohan S. N. Venkatraman. Determinants of transnational new product development capability:testing the influence of transferring and deploying tacit overview knowledge [J]. Strategic management Journal,2001(22)
[52]陈菲琼.我国企业与跨国公司知识联盟的知识转移层次研究[J].科研管理,2001,Vol.22,No.2,66-73.
[53]Ram Mudambi. Knowledge management in multinational firms [J]. Journal of International Management,2002(8),1-9
[54]X Martin, R Salomon. Knowledge transfer capacity and its implications for the theory of the multinational corporation [J]. Journal of international business studies,2003(34),
[55]Fossn J Pedersent. The MNC as a knowledge structure:the roles of knowledge sources and organizational instruments in MNC knowledge management [J]. Aanish Research Unit for Industrial Dynamics,2003(5):1-33.
[56]刘芳,欧阳令南.跨国公司知识转移过程、影响因素与对策研究[J].科学学与科学技术管理,2005(10),40-43
[57]刘帮成,王重鸣.影响跨国知识转移效能的因素研究：以在华进行跨国创业企业为例[J].科研管理,2007,Vol.28,No.6,1-11
[58]王清晓,杨忠.跨国公司内部网络结点之间知识转移的影响因素分析——一个概念模型[J].科研管理,2006,Vol.27,No.2,102-108.
[59]王清晓.子公司创新倾向对跨国公司内部知识转移影响的实证研究[J].科学学与科学技术管理,2008(1),88-92.
[60]王岚.跨国合资企业中知识转移影响因素研究[J].情报杂志,2009(2),125-128
[61]Cohen W M, Florida R,Randazzese L, et ai. Industry and the academy:uneasyparters in the cause of technological advance [M]. Washington:Brooking Institute Press,1998.
[62]Cohen W. M. and Levinthal, D.A. Innovation and learning:the two faces of R&D. The economic Journal,1989(99),569-596.
[63]Cohen W. M. and Levinthal, D.A. Absorptive capacity:a new perspective on learning and innovation. Administrative Science Quarterly,1990(35),128-152.
[64]Cockburn, I. and Henderson, R. Absorptive capacity, coauthoring behavior, and the organization of research in drug discovery. Journal of Industrial Economics, XLVI,1998(2), 157-182
[65]Ajay Agrawal. University-to-industry knowledge transfer:literature review and unanswered questions [J]. International Journal of Management Reviews,2003(4):185-302
[66]周晶晶,周国华.企业一大学合作中的知识转移机制研究——以某转制院所实施项目管理模式为例[J].中国软科学,2006(3),139-144.
[67]叶飞,周蓉,张红.产学研合作过程中知识转移绩效的关键影响因素研究[J].工业技术经济,2009,Vol.28,No.6,116-120
[68]徐国东,叶金福,邹艳.企业一大学合作中的知识转移影响因素分析[J].情报杂志,2008(2),87-89.
[69]张力,聂鸣.产学合作中影响知识转移的因素：全景式解释模型[J].情报杂志,2009,Vol.28,No.7,87-90.
[70]Jaffe A, Trajtenberg M, Henderson R. Geographic location of knowledge spillovers as evidenced by patent citations [J]. Quarterly Journal of Economics,1993,108(3),577-598.
[71]Davenport TH. Laurence P. Working Knowledge:How Organizations Manage What They Know. Boston(Mass):Harvard Business School Press,1998
[72]Baptista R, Swanm P. Do firms in clusters innovate more? [J]. Research Policy,1998, 27(5):525-540.
[73]Feldman M P. The new economics of innovation, spillovers and agglomeration:a review of empirical studies [J]. Economics of innovation and new technology,1999,8:5-25.
[74]吴先华,郭际,胡汉辉.产业集群中非正式交流影响知识流动的实证研究[J].科学学研究,2008,Vol.26 No.5,1014-1023.
[75]Brown J S, Duguid P. Organizational learning and communities of practice:toward a unified view of working, learning, and innovation. Organization Science,1991,2(1):40-57.
[76]I Maignan, O. C. Ferrell, G. T. Hult. Corporate citizenship:Cultural antecedents and business benefits [J] Journal of the Academy of Marketing Science,1999
[77]Frohlich M. T. and Westbrook R. Arcs of integration:An international study of supply chain strategies, Journal of operations management,2001,19(2):185-200
[78]Batenburg R, Rutten R. Managing innovation in regional supply networks:A dutch case of "Knowledge industry clustering" [J]. Supply chain management,2003,8(3):263-270.
[79]Ke W L, Wei K K. Factors affecting trading partners'knowledge sharing:Using the lens of transaction cost economics and socio-political the ories [J]. Electronic commerce research and applications,2007,6(3):297-308
[80]Sivakumar K, Subroto Roy. Knowledge redundancy in supply chains:a framework [J]. Supply chain management,2004,9(3):241-249.
[81]Adrian Done. Frohlich M.2005. The impact on performance of knowledge in the supply chain:an empirical study. Working paper, London Business School
[82]Roper S, Crone M. Knowledge complementarity and coordination in the local supply chain: some empirical evidence [J]. British Journal of Management,2003,14(4):339-355.
[83]张慧涛.供应链企业间知识转移中的知识破损及对策研究[J].科技与管理,2008,Vol.10,No.6,46-48.
[84]邢文凤,严建援.供应链知识纵向转移特征研究[J].科技进步与对策,2009,Vol.26,No.6,145-148.
[85]朱庆.供应链企业间的知识共享及其知识交易研究[D].硕士学位论文,重庆大学,2006
[86]吴冰,刘仲英,赵林度.供应链协同的知识转移研究[J].情报杂志,2008(1),15-20
[87]翁莉,仲伟俊,鲁芳.供应链知识共享的决策行为及影响因素研究[J].管理学报,2009,Vol.6,No.12,1648-1652
[88]Fairchild A. M. Knowledge management metrics via a balanced scorecard methodology [A]. Proceesings of the 35th Hawaii international conference on system science USA,2002.
[89]Kun Chang Lee, Sang Jae Lee, Won Kang. KMPI:measuring knowledge management performance [J]. Information & Management,2005(42):469-482
[90]朱启红,张钢.基于人工神经网络的企业知识管理评价模型[J].科学学与科学技术管理,2003(8)：32—34.
[91]王君,樊治平.组织知识管理绩效的一种综合评价方法[J].管理工程学报,2004,18(2)：44—48
[92]蒋翠清,叶春森,杨善林.组织知识管理绩效评估研究[J].科学学研究,2007,25(2)：296—300.
[93]丁勇,粱昌勇,朱俊红.一种供应链中的知识管理绩效评价方法研究[J].运筹与管理,2006,15(4)：49—154.
[94]王道平,许有志,王锐兵.供应链知识共享绩效的模糊综合评价方法[J].软科学,2008,22(12)：14—17.
[95]陆杉,黄福华,赵中平.供应链知识协同管理绩效评价研究[J].科技管理研究,2010,193-195
[96]MAURA SOEKIJAD, ERIK ANDRIESSEN. Conditions for Knowledge Sharing in Competitive Alliances [J]. European Management Journal,2003, Vol.21, No.5, pp.578-587
[97]Jeffrey L. Cummings, Bing-Sheng Teng. Transferring R&D knowledge:the key factors affecting knowledge transfer success [J]. J. Eng. Technol. Manage.2003, Vol.20,39-68
[98]Michael Polanyi. Personal Knowledge, toward a Post-critical Philosophy [M]. Chicago:The University of Chicago Press,1958, and 1962(corrected edition)
[99]丁秀好,黄瑞华.合作创新中媒介丰度对知识模糊性和知识转移绩效关系的调节作用研究[J].研究与发展管理,2008,Vol.20 No.5,9-13
[100]Zander U, Kogut B. Knowledge and the speed of the transfer and imitation of organizational capabilities:An empirical test[J].Organization Science,1995,6(1):76-92.
[101]Sabel, C.F. Studied trust:Building new forms of cooperation in a volatile economy [J]. Human Relations,1993,46:1133-1170.
[102]Dodgson, M. Learning, trust, and technological collaboration [J]. Human Relations,1993, 46(1):11-95
[103]郭韧,丰吉闯.知识溢出的测度研究[J].科技管理研究,2008(7),393-395
[104]Dixon, N. M. Common Knowledge:How Companies Thrive by Sharing What They Know? Harvard Business School Press, Boston, MA.,2000
[105]Tushman M L. Special boundary roles in the innovation process [J].Administrative Science Quarterly,1977,22(4):587-605.
[106]卢兵,岳亮,廖貅武.企业联盟中知识转移的影响因素分析——一个分析模型[J].预测,2006,25(2)：31-36
[107]Davenport TH. Laurence P. Working Knowledge:How Organizations Manage What They Know. Boston(Mass):Harvard Business School Press,1998
[08]Baptista R, Swanm P. Do firms in clusters innovate more? [J]. Research Policy,1998, 27(5):525-540.
[109]徐青,马庆国等.ERP知识转移影响因素的实证对比研究——与Dong-Gil研究的比较[J].科学学研究,2007,Vol.25,No.3,498-504
[110]尹洁,葛世伦,李锋,吴洁,施琴芬.ERP关键用户向实施顾问知识转移影响因素研究[J].情报杂志,2009,Vol.28,No.11,139-144
[111]Zander U, Kogut B. Knowledge and the speed of the transfer and imitation of organizational capabilities:An empirical test[J].Organization Science,1995,6(1):76-92
[112]Ghoshal,S. and C.A.Barflet. The multinational corporation as an inter- Organizational network [J]. Academy of management review,1988,15:603-625
[113]张兵,王文平.知识流动的小世界——基于关系强度的观点[J].科学学研究,2009,Vol.27 No.9,1312-1321
[114]Purser, R.E., Pasmore, W.A., Tenkasi, R.V. The influence of deliberations on learning in new product development teams [J]. Journal of Engineering and Technology Management,1992, 9 (1),1-28.
[115]Ekaterina V. Karniouchina, William L. Moore, Bo van der Rhee, Rohit Verma. Issues in the use of ratings-based versus choice-based conjoint analysis in operations management research [J]. European Journal of Operational Research,2009,197,340-348
[116]陈衍泰,陈国宏,李美娟.综合评价方法分类及研究进展[J].管理科学学报2004年4月69-80
[117]田志友,吴瑞明,王浣尘.基于过程熵的供应商多元加工能力评价[J].管理工程学报,2006年第1期36-41
[118]陈衍泰,陈国宏,李美娟.综合评价方法分类及研究进展[J].管理科学学报2004年4月69-80
[119]杜世海,丁慧平,姜文生.供应链竞争优势的租金贡献分析与评价研究[J].管理科学学报,2006年6月24-31
[120]陈衍泰,陈国宏,李美娟.综合评价方法分类及研究进展[J].管理科学学报2004年4月69-80
[121]孙寿亮.供应链环境下的供应商评价模型应用研究[J].管理现代化,2007年第一期62-65
[122]毕功兵,梁棵,杨锋.两阶段生产系统的DEA效率评价模型[J]中国管理科学2007年4月92-97
[123]陈久,康世瀛.基于粗糙集理论的供应链协同效果评价[J].统计与决策,2007年第22期170-173
[124]Ikujiro Nonaka. A Dynamic Theory of Organizational Knowlesge Creation [J]. Organization Science,1994, Vol.5, No.1,14-37.
[125]Szulanski, G. The Process of Knowledge Transfer:A diachronic analysis of stickness [J]. Organizational Behavior and Human Decision Processes,2000,82(1):9-27.
[126]Gilbert M, Cordep-Hayes M. Understanding the process of knowledge transfer to achieve successful technological innovation [J]. Technovation,1996,16(6):301-312
[127]Cummings J L., Teng B S.. Transfering R&D knowledge:The key factors affecting knowledge transfer success [J]. Journal of Engineering and Technology Management,2003, 20:39-68.
[128]Vito Albino, Claudio Garavelli A, Giovanni Schiuma. Knowledge transfer and inter-firm relationships in industrial districts:The role of the leader firm[J]. Technovation,1999(19):53-63.
[129]徐建锁,王正欧,李淑伟.基于知识链的管理[J].天津大学学报(社会科学版),2003,Vol.5,No.2,133-136.
[130]张悟移.供应链企业知识链管理方法研究[D].华中科技大学博士论文
[131]全力,顾新.知识链组织之间冲突的三维动因模型[J].科学学与科学技术管理,2008(12),92-96.
[132]王道平,李贺.基于知识流的敏捷供应链知识服务模式研究[J].软科学,2010,Vol.24,No.3,1-9
[133]高智勇.基于中国供应链中知识型核心企业的动态知识评价模型研究[D].昆明理工大学硕士毕业论文,2008
[134]张文修,仇国芳.基于粗糙集的不确定决策[M].第1版.北京：清华大学出版社,2005.
[135]王吉培,张志伟.基于粗糙集神经网络的商业银行信贷风险研究[J].
[136]何勇,冯雷,吴春霞.基于粗糙集与神经网络的农机化发展水平评估方法[J].农业机械学报,2004,Vol.35,No.2,100-103
[137]朱军勇,贺红燕,张春生.粗糙集—神经网络在构建企业供应链合作伙伴选择中的应用[J].河南理工大学学报(社会科学版),2007,Vol.8,No.1,33-36
[138]史成东,陈菊红,郭福利.粗糙集和BP神经网络在供应链绩效评价中的应用研究[J].软科学,2008,Vol.22,No.3,9-13
[139]罗彬,邵培基,罗尽尧,刘独玉,夏国恩.基于粗糙集理论—神经网络—蜂群算法集成的客户流失研究[J].管理学报,2011,Vol.8,No.2,265-272
[140]魏权龄.数据包罗分析[M].第二版.科学出版社,2005：14
[141]Necmi K.Avkiran,Terry Rowlands. How to better identify the ture managerial performance: State of the art using DEA [J]. The International Journal of Management Science,36 (2008) 317-324
[142]李国勇.智能控制及其MATLAB实现[M].第1版.北京：电子工业出版社,2005.
[143]刘辉霞.企业的知识流动机理及优化途径分析[J].岳阳职业技术学院学报,2004,Vol.19,No.4,58-61
[144]付相君,彭颖红.社会网络分析促进组织结构及其知识流优化[J].计算机集成制造系统,2007,Vol.13,No.11,2069-2177
[145]白瑛,蔡建峰,骞永博.基才运作优化向可持续竞争优势演化的知识流动路径研究[J].科学学与科学技术管理,2007,80-83
[146]李岚,姜伟强.蚁群遗传优化算法在物流配送路径选择中的应用[J].哈尔滨商业大学 学报(自然科学版),2009,Vol.25,No.6,707-710
[147]吴云志,乐毅,王超,张友华.蚁群算法在物流路径优化中的应用及仿真[J].合肥工业大学学报(自然科学版),2009,Vol.32,No.2,211-214
[148]Yan Qiao, Wiyi Zhang. Study of optimizing the knowledge acquisition route based on core-corporation in supply chain [J].2008 IEEE/INFORS International Conferrence on Service Operations and Logistics, and Informatics,2072-2077