重大科技项目合作网络结构理论与评价决策模型
|
摘要
为从系统科学整体论视角探索重大科技项目合作网络结构理论与评价决策模型,首先在界定重大科技项目合作网络系统相关定义与性质的基础上阐述了合作网络系统与结构界面系统的运行效率及影响因素,然后从项目投入产出、项目组织结构、项目管理过程三个方面对重大科技项目中可能存在的结构界面系统进行了A、B、C三级分类辨识,并在引申和发展传统序参量(MOP)的基础上构建了关键结构界面(系统)识别方法与结构界面系统运行机理模型,之后从层次性、协调度和交叉性三个维度对结构界面系统MOP状态评价问题进行类别划分,并提出了用于解决系统进化过程一次涌现问题(要素→结构界面系统)的SLSP、DLSP、UDDP、BDDP结构界面系统MOP状态的评价模型及分析方法,最后基于ANP的决策思想提出了用于解决系统进化过程中二次涌现问题(结构界面系统→合作网络系统)的合作网络系统整合优化决策模型。模拟验证与案例应用结果表明,重大科技项目合作网络结构理论与评价决策模型具有科学有效性和应用可行性。
Major scientific and technical (S&T) programs such as scientific research projects or technology development projects are fund to solve the important or significant difficult problems existing in scientific research or social production. In general, national S&T master plans that continued supported by state financial or to provide public technology products, e.g., 973 programs and 863 programs, are generally regarded as major S&T programs. From the related policy oriented direction and the S&T development trend, it can be concluded that a new cooperative mode among universities, institutes and enterprises is developed into a major implement form in major S&T programs. Thus, cooperative behavior management to major S&T programs is not only propitious to breakthrough major technology problems but also propitious to achieve state strategic goal. Aiming to solve the existing problems in major S&T program cooperation, it is significant to explore cooperative network structure theories and evaluation decision models.
The evaluation and optimization for cooperative network system, a key cooperative management problem in major S&T programs, are presented in a new viewpoint of recourse allocation interface, and the corresponding conceptions, properties, classifications and influence factors are defined or analyzed. In allusion to major S&T program characters (i.e., high innovation, high risk, high integration, high input and high influence), the structure interface and the cooperative network structure are respectively defined as the related recourse allocation relationship and relationship-set in program implement procedures; the structure interface system is composed of subjective elements, objective elements (recourse allocation problems) as well as their complex relationships, and cooperative network system is composed of structure interface systems (subsystem) with complex relationships. After that, several major S&T program properties such as relativity, holism, intention, and openness are proposed, and the cooperative network system is classified into horizontal, vertical, and hybridized system. Finally, the operation efficiencies of structure interface systems and cooperative network systems are analyzed to be influenced by element/ subsystem state and element/ subsystem structure sequence degree.
A structure interface system operation mechanism model is established originally to reflect system function emergence in allusion to subjective/ objective element hierarchies and subjective bounded rationalities. According to the related theory and knowledge, the structure interfaces existing probably in program implement procedures are classified and identified by A-B-C scale, and subjective/ objective elements (especially the latter) in each kind of structure interfaces are analyzed from the viewpoint of system function firstly. Moreover subjective/ objective element hierarchies are discussed by extending and developing the traditional concept and connotation of order parameter, resulting that the former means the properties or functions of top level (macro order parameter, MOP) are emerged from low level elements with interacting each other, and the latter means the higher control and the lower autonomy both in the hierarchy relationship among cooperators. Finally, a structure interface system operation mechanism model is established, and four kinds of MOP state evaluation problems in structure interface systems (i.e., single level & single platform (SLSP), double levels & single platform (DLSP), unary dependency & double platforms (UDDP), and binary dependencies & double platforms (BDDP)) are proposed. Specially, the presented operation mechanism model not only designs reference frame and the state level mechanisms, but also integrates prospect theory to describe uncertainty inference behaviors, as a result it is able to effectively reflect emergence from quantity to quality in system evolution.
A decision making method is proposed to identify key structure interface systems based on the thought of decision making trial and evaluation laboratory (DEMATEL). There are lots of structure interface systems in major S&T programs, and it is a quality assurance strategy theoretically to administrate all of structure interface systems, however the program recourses will be wasted from the viewpoint of ?20%-80%? management theorem in actually. Thus the macro order parameters of structure interface systems are proposed to reflect overall system functions, based on which a method for identifying key structure interface systems is established via a general argument in complex network that the importance of structure interface system is equivalent to its outstanding characteristic in connection with others.
The SLSP evaluation model and analysis method are established to solve such a MOP state evaluation problem that elements without hierarchies and outcome values with coordination. To solve above problem, several bounded rational hypotheses (i.e., risk preference is invariable, decision making cost exists, and group recognizing ability is bounded) are proposed in allusion to unreasonable arguments in current research firstly. After that, a SLSP coordination evaluation model is presented by data envelopment analysis with assurance region (DEA/AR), based on which a SLSP interactive analysis method for deriving prospect values is presented. The proposed evaluation model and the analysis method have several characteristics, i.e., the relationships are balanced well between the precision and completeness (PC) levels and acquisition costs for information, group subjective risk preferences are reflected resulting in high satisfactions, prospects from the efficient to the optimal are gradually derived, and the multiple styles of PC information are contained simultaneously. The DLSP evaluation model and analysis method are established to solve such a MOP state evaluation problem that elements with hierarchies and outcome values with coordination. To solve above problem, a DLSP coordination evaluation model (including theoretical model, computable model and revisable model) for deriving the optimal outcome value information and the optimal integration theorem for balancing both levels’risk preferences are established, besides a DLSP interactive decision mechanism is proposed to deriving prospect values. The proposed evaluation model and the analysis method have four characteristics, i.e., (1) knowledge authorities and power authorities are propitious to improve interactive decision making efficiencies, (2) the interactive mode for deriving decision information is propitious to achieve scientific and reasonable outcome values, (3) the DLSP coordination evaluation model can not only reflect two levels’satisfactions but also revise information in cognition set alternatively, (4) the DLSP interactive analysis method may guarantee the selected alternative is optimal for the reason that the bounded rationalities and hierarchies are both reflected in decision procedures.
The UDDP evaluation model and analysis method are established to solve such a MOP state evaluation problem that outcome values and probability weights without hybrid relationship and both with coordination. To solve above problem, the interactive anonymously vote method is presented to derive subjective decision information based on pair-wise comparison mode and three-point interval number, after that a decision making structure for two-level double platform coordination and its running mechanism are proposed and analyzed in allusion to distribution characteristics of two levels' knowledge on double platforms (if cooperators’relationship is hierarchical than the coordination weight should be set greater than 0.5, else equal to 0.5), and finally the UDDP evaluation model (including theoretical model, applicable model) and decision mechanism for deriving both levels’decision information effectively and ranking alternatives scientifically and satisfactorily are established by optimizing two-level satisfactory coordination degree. The proposed evaluation model and the analysis method have four characteristics, i.e., (1) the coordination weight is able to integrate two levels' knowledge on double platforms, (2) the two-level double platform decision making structure is propitious to derive outcome values with reference to the same point and probability weights reflecting subjective risk preferences, (3) the UDDP evaluation model guarantees the decision information be revised in allusion to cognitive errors, (4) the UDDP interactive analysis method is propitious to assure decision efficiency and decision effectiveness. The BDDP evaluation model and analysis method are established to solve such a MOP state evaluation problem that outcome values and probability weights with hybrid relationship and both with coordination. To solve above problem, a double platform mode for deriving decision information is established with regard to prospect theory, after that a decision making structure for two-level double platform learning and coordinating and its operation mechanism are proposed and analyzed aiming to overcome group thought (if cooperators’relationship is hierarchical than the coordination weight is set greater than 0.5, else equal to 0.5), and finally the BDDP evaluation model (including single platform learning and revising model and double platform systematic coordination model) and decision mechanism are established for deriving both levels’decision information effectively and ranking alternatives scientifically and satisfactorily. The proposed evaluation model and the analysis method have four characteristics, i.e., (1) prospect theory is employed to deal with alternative ranking so as to decision makers’hierarchies, tasks, knowledge and risks can be reflected, (2) the double platform mode and the double platform learning and coordinating structure are propitious to overcome group thought and systematically derive and integrate decision information in single or double levels, (3) the single platform learning and revising model is propitious to outstand learning objects and revising orientation, (4) the double platform systematic coordination model is able to balance two levels’distributing knowledge structure and derive basic prospect values efficiently.
An integrated optimizing decision model for the cooperative network system is established by means of analytic network process (ANP). In allusion to the complex characteristics of emergence in cooperative network system, an integrated optimizing thought for cooperative network system is presented to reflect gradually emergence procedures in system evolution. After that, a systematic analysis structure for cooperative network system, to reflect complex influence relationships among factors/subsystems, is established respectively from control level, network level and alternative level based on complex system theory. Finally, an integrated optimizing decision model is proposed to reflect the second emergence and evaluate system operation efficiency with the reference of ANP decision thought. The proposed integrated optimizing decision model has three characteristics, i.e., (1) the hypothesis that interfaces are irrelative in current research is relaxed, and the complex decision making problem that interfaces are correlative is able to be solved, (2) the restrict for single undertaker is broken down, and the decision making problem that several undertakers is suitable be solved, (3) the recourse allocation problem is systematically solved in a new viewpoint of cooperative interfaces, which may guarantee the major S&T programs be finished high-qualitatively.
In order to testify the proposed cooperative network structure theories and evaluation decision models in major S&T programs, the method for identifying key structure interface systems and the model for integrating and optimizing the cooperative network system are testified by numeric examples, and four kinds of models for evaluating interface system MOP states (i.e., SLSP, DLSP, UDDP, BDDP) are testified by simulated cases. In addition, the overall procedure for integrating and optimizing the cooperative network system is testified by an applicable case. All results show the proposed cooperative network structure theories and evaluation decision models are scientific, efficient, and well applicable.
Major scientific and technical (S&T) programs such as scientific research projects or technology development projects are fund to solve the important or significant difficult problems existing in scientific research or social production. In general, national S&T master plans that continued supported by state financial or to provide public technology products, e.g., 973 programs and 863 programs, are generally regarded as major S&T programs. From the related policy oriented direction and the S&T development trend, it can be concluded that a new cooperative mode among universities, institutes and enterprises is developed into a major implement form in major S&T programs. Thus, cooperative behavior management to major S&T programs is not only propitious to breakthrough major technology problems but also propitious to achieve state strategic goal. Aiming to solve the existing problems in major S&T program cooperation, it is significant to explore cooperative network structure theories and evaluation decision models.
The evaluation and optimization for cooperative network system, a key cooperative management problem in major S&T programs, are presented in a new viewpoint of recourse allocation interface, and the corresponding conceptions, properties, classifications and influence factors are defined or analyzed. In allusion to major S&T program characters (i.e., high innovation, high risk, high integration, high input and high influence), the structure interface and the cooperative network structure are respectively defined as the related recourse allocation relationship and relationship-set in program implement procedures; the structure interface system is composed of subjective elements, objective elements (recourse allocation problems) as well as their complex relationships, and cooperative network system is composed of structure interface systems (subsystem) with complex relationships. After that, several major S&T program properties such as relativity, holism, intention, and openness are proposed, and the cooperative network system is classified into horizontal, vertical, and hybridized system. Finally, the operation efficiencies of structure interface systems and cooperative network systems are analyzed to be influenced by element/ subsystem state and element/ subsystem structure sequence degree.
A structure interface system operation mechanism model is established originally to reflect system function emergence in allusion to subjective/ objective element hierarchies and subjective bounded rationalities. According to the related theory and knowledge, the structure interfaces existing probably in program implement procedures are classified and identified by A-B-C scale, and subjective/ objective elements (especially the latter) in each kind of structure interfaces are analyzed from the viewpoint of system function firstly. Moreover subjective/ objective element hierarchies are discussed by extending and developing the traditional concept and connotation of order parameter, resulting that the former means the properties or functions of top level (macro order parameter, MOP) are emerged from low level elements with interacting each other, and the latter means the higher control and the lower autonomy both in the hierarchy relationship among cooperators. Finally, a structure interface system operation mechanism model is established, and four kinds of MOP state evaluation problems in structure interface systems (i.e., single level & single platform (SLSP), double levels & single platform (DLSP), unary dependency & double platforms (UDDP), and binary dependencies & double platforms (BDDP)) are proposed. Specially, the presented operation mechanism model not only designs reference frame and the state level mechanisms, but also integrates prospect theory to describe uncertainty inference behaviors, as a result it is able to effectively reflect emergence from quantity to quality in system evolution.
A decision making method is proposed to identify key structure interface systems based on the thought of decision making trial and evaluation laboratory (DEMATEL). There are lots of structure interface systems in major S&T programs, and it is a quality assurance strategy theoretically to administrate all of structure interface systems, however the program recourses will be wasted from the viewpoint of ?20%-80%? management theorem in actually. Thus the macro order parameters of structure interface systems are proposed to reflect overall system functions, based on which a method for identifying key structure interface systems is established via a general argument in complex network that the importance of structure interface system is equivalent to its outstanding characteristic in connection with others.
The SLSP evaluation model and analysis method are established to solve such a MOP state evaluation problem that elements without hierarchies and outcome values with coordination. To solve above problem, several bounded rational hypotheses (i.e., risk preference is invariable, decision making cost exists, and group recognizing ability is bounded) are proposed in allusion to unreasonable arguments in current research firstly. After that, a SLSP coordination evaluation model is presented by data envelopment analysis with assurance region (DEA/AR), based on which a SLSP interactive analysis method for deriving prospect values is presented. The proposed evaluation model and the analysis method have several characteristics, i.e., the relationships are balanced well between the precision and completeness (PC) levels and acquisition costs for information, group subjective risk preferences are reflected resulting in high satisfactions, prospects from the efficient to the optimal are gradually derived, and the multiple styles of PC information are contained simultaneously. The DLSP evaluation model and analysis method are established to solve such a MOP state evaluation problem that elements with hierarchies and outcome values with coordination. To solve above problem, a DLSP coordination evaluation model (including theoretical model, computable model and revisable model) for deriving the optimal outcome value information and the optimal integration theorem for balancing both levels’risk preferences are established, besides a DLSP interactive decision mechanism is proposed to deriving prospect values. The proposed evaluation model and the analysis method have four characteristics, i.e., (1) knowledge authorities and power authorities are propitious to improve interactive decision making efficiencies, (2) the interactive mode for deriving decision information is propitious to achieve scientific and reasonable outcome values, (3) the DLSP coordination evaluation model can not only reflect two levels’satisfactions but also revise information in cognition set alternatively, (4) the DLSP interactive analysis method may guarantee the selected alternative is optimal for the reason that the bounded rationalities and hierarchies are both reflected in decision procedures.
The UDDP evaluation model and analysis method are established to solve such a MOP state evaluation problem that outcome values and probability weights without hybrid relationship and both with coordination. To solve above problem, the interactive anonymously vote method is presented to derive subjective decision information based on pair-wise comparison mode and three-point interval number, after that a decision making structure for two-level double platform coordination and its running mechanism are proposed and analyzed in allusion to distribution characteristics of two levels' knowledge on double platforms (if cooperators’relationship is hierarchical than the coordination weight should be set greater than 0.5, else equal to 0.5), and finally the UDDP evaluation model (including theoretical model, applicable model) and decision mechanism for deriving both levels’decision information effectively and ranking alternatives scientifically and satisfactorily are established by optimizing two-level satisfactory coordination degree. The proposed evaluation model and the analysis method have four characteristics, i.e., (1) the coordination weight is able to integrate two levels' knowledge on double platforms, (2) the two-level double platform decision making structure is propitious to derive outcome values with reference to the same point and probability weights reflecting subjective risk preferences, (3) the UDDP evaluation model guarantees the decision information be revised in allusion to cognitive errors, (4) the UDDP interactive analysis method is propitious to assure decision efficiency and decision effectiveness. The BDDP evaluation model and analysis method are established to solve such a MOP state evaluation problem that outcome values and probability weights with hybrid relationship and both with coordination. To solve above problem, a double platform mode for deriving decision information is established with regard to prospect theory, after that a decision making structure for two-level double platform learning and coordinating and its operation mechanism are proposed and analyzed aiming to overcome group thought (if cooperators’relationship is hierarchical than the coordination weight is set greater than 0.5, else equal to 0.5), and finally the BDDP evaluation model (including single platform learning and revising model and double platform systematic coordination model) and decision mechanism are established for deriving both levels’decision information effectively and ranking alternatives scientifically and satisfactorily. The proposed evaluation model and the analysis method have four characteristics, i.e., (1) prospect theory is employed to deal with alternative ranking so as to decision makers’hierarchies, tasks, knowledge and risks can be reflected, (2) the double platform mode and the double platform learning and coordinating structure are propitious to overcome group thought and systematically derive and integrate decision information in single or double levels, (3) the single platform learning and revising model is propitious to outstand learning objects and revising orientation, (4) the double platform systematic coordination model is able to balance two levels’distributing knowledge structure and derive basic prospect values efficiently.
An integrated optimizing decision model for the cooperative network system is established by means of analytic network process (ANP). In allusion to the complex characteristics of emergence in cooperative network system, an integrated optimizing thought for cooperative network system is presented to reflect gradually emergence procedures in system evolution. After that, a systematic analysis structure for cooperative network system, to reflect complex influence relationships among factors/subsystems, is established respectively from control level, network level and alternative level based on complex system theory. Finally, an integrated optimizing decision model is proposed to reflect the second emergence and evaluate system operation efficiency with the reference of ANP decision thought. The proposed integrated optimizing decision model has three characteristics, i.e., (1) the hypothesis that interfaces are irrelative in current research is relaxed, and the complex decision making problem that interfaces are correlative is able to be solved, (2) the restrict for single undertaker is broken down, and the decision making problem that several undertakers is suitable be solved, (3) the recourse allocation problem is systematically solved in a new viewpoint of cooperative interfaces, which may guarantee the major S&T programs be finished high-qualitatively.
In order to testify the proposed cooperative network structure theories and evaluation decision models in major S&T programs, the method for identifying key structure interface systems and the model for integrating and optimizing the cooperative network system are testified by numeric examples, and four kinds of models for evaluating interface system MOP states (i.e., SLSP, DLSP, UDDP, BDDP) are testified by simulated cases. In addition, the overall procedure for integrating and optimizing the cooperative network system is testified by an applicable case. All results show the proposed cooperative network structure theories and evaluation decision models are scientific, efficient, and well applicable.
引文
[1]陈省平,李子和,刘涛.科技项目管理[M].广州:中山大学出版社, 2007.
[2]陈宝敏.科斯定理的重新解释——兼论中国新制度经济学研究的误区[J].中国人民大学学报, 2002(2): 68-72.
[3]李刚,牛冲槐,张敏,等.科技型人才聚集效应与组织冲突消减的研究[J].管理学报, 2006, 3(3): 302-308.
[4]吴贵生,王毅.产学研合作中粘滞知识的成因与转移机制研究[J].科研管理, 2001, 22(6): 114-121.
[5] Park S H, Russo M V. When Competition Eclipses Cooperation: An Event History Analysis of Joint Venture Failure[J]. Management Science, 1996, 42(6): 875-890.
[6] Khanna T, Gulati R, Nohria N. The Dynamics of Learning Alliances: Competition, Cooperation, and Relative Scope[J]. Strategic Management Journal, 1998, 19(3): 193-210.
[7]兰劲松,薛天祥.重大科技项目的概念、特征与组织[J].研究与发展管理, 1999, 11(5): 52-55.
[8]张智文,申金升.国家重大科技项目组织管理的若干思考[J].中国软科学, 1998, 12: 50-53.
[9]倪健.基于重大科技项目的管理创新研究[J].中国科技论坛, 2006(5): 36-37, 55.
[10]王化兰,王长峰.基于现代项目管理理论的重大科技项目管理模式研究[J].科学学与科学技术管理, 2004(2): 74-77.
[11]陈省平,郑湘峙,罗轶,等.重大科技项目组织实施模式的探讨与实践——以国家863计划?深海抗风浪网箱的研制?项目为例[J].科技管理研究, 2005, 25(4): 93-94.
[12]蒋玉涛,招富刚,朱星华.基于全生命周期的重大科技专项管理模式构想[J].中国科技论坛, 2008(10): 14-17.
[13]王芳,赵兰香.重大科技项目模块化创新管理方法研究——对美国国防采办管理方法的探析[J].科研管理, 2009, 30(1): 1-7.
[14]陈强,鲍悦华,程妤.重大科技项目的过程管理及协同机制研究[M].北京:化学工业出版社, 2009.
[15]胡宝民,王婷,李子彪.重大科技专项的特征研究[J].中国科技论坛, 2007(9): 81-85.
[16]李春好,杜元伟.我国科技合作项目管理机制的缺陷分析与改进对策[J].管理学报, 2010, 7(2): 1-7.
[17]郭斌.企业界面管理实证研究[J].科研管理, 1999, 20(5): 73-79.
[18]聂柯渐.界面管理系统化研究——管理科学研究的新方向[J].市场论坛, 2006(3): 44-45.
[19]刘兰剑,党兴华.合作技术创新界面管理研究及其新进展[J].科研管理, 2007, 28(3): 1-7.
[20] Al-Mutairi M S, Hipel K W, Kamel M S. Trust and cooperation from a fuzzy perspective[J]. Mathematics and Computers in Simulation, 2008, 76(5-6): 430-446.
[21] Casari M, Luini L. Cooperation under alternative punishment institutions: An experiment[J]. Journal of Economic Behavior & Organization, 2009, 71(2): 273-282.
[22] Cassar A. Coordination and cooperation in local, random and small world networks: Experimental evidence[J]. Games and Economic Behavior, 2007, 58(2): 209-230.
[23] De Cremer D, Tyler T R. The Effects of Trust in Authority and Procedural Fairness on Cooperation[J]. Journal of Applied Psychology, 2007, 92(3): 639-649.
[24] Gao J, Lee J D, Zhang Y. A dynamic model of interaction between reliance on automation and cooperation in multi-operator multi-automation situations[J]. International Journal of Industrial Ergonomics, 2006, 36(5): 511-526.
[25] Grimm V, Mengel F. Cooperation in viscous populations--Experimental evidence[J]. Games and Economic Behavior, 2009, 66(1): 202-220.
[26] Kirchkamp O, Nagel R. Naive learning and cooperation in network experiments[J]. Games and Economic Behavior, 2007, 58(2): 269-292.
[27] Kocher M G, Cherry T, Kroll S, et al. Conditional cooperation on three continents[J]. Economics Letters, 2008, 101(3): 175-178.
[28] Li J, Wang S, Cheng T C E. Competition and cooperation in a single-retailer two-supplier supply chain with supply disruption[J]. International Journal of Production Economics, 2010, 124(1): 137-150.
[29] Silipo D B. Incentives and forms of cooperation in research and development[J]. Research in Economics, 2008, 62(2): 101-119.
[30] Wei G, Yu L, Wang S, et al. Study on Incentive Factors of Team Cooperation based on Synergy Effect[J]. Systems Engineering - Theory & Practice, 2007, 27(1): 1-9.
[31]李新春,顾宝炎.中外企业合作的战略联盟特征与技术学习[J].管理科学学报, 1998, 1(4): 44-50.
[32]吴海平,宣国良.结网合作策略选择的能力依赖模型及其启示[J].管理科学学报, 2007, 10(1): 29-38.
[33]易余胤,肖条军,盛昭瀚.合作研发中机会主义行为的演化博弈分析[J].管理科学学报, 2005, 8(4): 80-87.
[34]钟德强,仲伟俊,梅姝娥,等.合作竞争下的供应商数量优化问题研究[J].管理科学学报, 2003, 6(3): 57-65.
[35]金锐,韩文秀.多人多准则非常和合作型对策研究[J].管理科学学报, 1999, 2(1): 32-36.
[36] Axelrod R, Dion D. The Further Evolution of Cooperation[J]. Science, 1998, 242(4884): 1385-1390.
[37] Singh K, Mitchell W. Growth Dynamics: The Bidirectional Relationship Between Interfirm Collaboration and Business Sales in Entrant and Incumbent Alliances[J]. Strategic Management Journal, 2005, 26(6): 497-521.
[38] Nielsen R P. Cooperative Strategy[J]. Strategic Management Journal, 1988, 9(5): 475-492.
[39] Chan P S, Heide D. Strategic alliances in technology: key competitive weapon[J]. SAM Advanced Management Journal, 1993, 28(4): 9-19.
[40]王泽华,万映红.虚拟企业网上合作模式探讨[J].中国软科学, 2001(4): 79-83.
[41]王鹏.海峡两岸高科技产业竞争力比较及分工合作模式[J].国际经贸探索, 2005, 21(2): 26-29.
[42]侯晓丽,贾若祥,刘毅.企业合作模式及其对区域经济发展的影响——以江苏省通州市企业为例[J].地理研究, 2005(4): 641-651.
[43]首藤信彦.超越国际技术联合[J].世界经济评论, 1993, 8: 25-27.
[44]桑建平,李焱焱,叶冰,等.产学研合作模式分类及其选择思路[J].科技进步与对策, 2004(10): 98-99.
[45]雷亮,陈国荣,邓菊丽,等.关于产学研合作模式的探讨[J].重庆科技学院学报(社会科学版), 2008(9): 195-196.
[46]宝胜.论知识创新的?智力互补型?合作模式[J].科技管理研究, 2008(9): 231-237.
[47]赵希男,丁勇,靖可,等.企业间技术嵌入式合作模式研究[J].科学学研究, 2007, 25(2): 364-368.
[48]赵晶媛.探索企业新型产学研合作模式[J].科技管理研究, 2008(9): 14-16.
[49]李廉水.论产学研合作创新的组织方式[J].科研管理, 1998, 19(1): 30-34.
[50]刘贵伟,吴立贤.产学研合作创新的模式及评价[J].辽宁教育学院学报, 2001, 18(5): 21-23.
[51]陈建军.长三角区域经济合作模式的选择[J].南通大学学报(社会科学版), 2005(2): 42-46.
[52]周静珍,万玉刚,高静.我国产学研合作创新的模式研究[J].科技进步与对策, 2005(3): 70-72.
[53]吕磊,马军,陈林峰,等.基于政府视角的国际科技合作模式研究[J].科研管理, 2008(s1): 80-84, 414.
[54]陈红喜.基于三螺旋理论的政产学研合作模式与机制研究[J].科技进步与对策, 2009(24): 6-8.
[55]官建成,靳平安.企业经济学中的界面管理研究[J].经济理论与经济管理, 1995(6): 67-69.
[56] Etzkowitz H, Leydesdorff L. The dynamics of innovation: from National Systems and "Mode 2" to a Triple Helix of university-industry-government relations[J]. Research Policy , 2000, 29(2): 109-123.
[57]刁兆峰,余东方.论现代企业中的界面管理[J].科技进步与对策, 2001(05): 85-86.
[58] Patrice H. A Tentative Definition of the Interface of Innovation:the Model of the Three Millstones[D]. 2000.
[59]章琰.大学技术转移中的界面及其移动分析[J].科学学研究, 2003(s1): 25-29.
[60]吴涛,海峰,李必强.界面和管理界面分析[J].管理科学, 2003(1): 6-10.
[61]郭斌,陈劲,许庆瑞.企业创新过程中的界面管理[J].数量经济技术经济研究, 1997(07): 38-41.
[62] Araujo L, Dubois A, Gadde L. Managing Interfaces with Suppliers[J]. Industrial Marketing Management, 1999, 28(5): 497-506.
[63]徐磊,赵刚,叶德平.如何建立有效的界面——关于技术创新界面管理的探讨[J].科研管理, 2002, 23(3): 79-83.
[64]李立新,曹霞.大型建设项目设计界面的有效管理[J].建筑经济, 2004(10): 71-74.
[65] Peng M W, Shenkar O. The meltdown of trust A process model of strategic alliance dissolution[Z]. 1997.
[66] Calantone R, Droge C, Vickery S. Investigating the manufactur-ing-marketing interface in new product development:does context affect the strength of relationships[J]. Journal of Operation Management, 2002, 20: 273-287.
[67]陈震红,董俊武.战略联盟伙伴的冲突管理[J].科学学与科学技术管理, 2004(3): 106-109.
[68]叶德平,赵刚.高技术企业与风险投资公司界面管理分析[J].科技创业月刊, 2004(7): 28-30.
[69] Brockhoff K, Hauschildt J. Schnittstellen management Koordinaion ohne Hierarchie[J]. Zeitsehrift Fuchrung and Organization, 1993(62): 396-403.
[70]赵玉林.高技术产业化的界面管理原理[J].武汉理工大学学报, 2004(3): 100-102.
[71]徐和平,孙林岩,慕继丰.产品创新网络中的信任与信任机制探讨[J].管理工程学报, 2004(2): 55-59.
[72] Barney J B, Hansen M H. Trust worthiness as a source of competitive advantage[J]. Strategic Management Journal, 1994, 15: 175-190.
[73]李垣,范诵,赵永彬.不同企业文化模式对技术创新的影响分析[J].预测, 2005(4): 26-30.
[74]欧光军,欧阳明德.面向产品创新的界面管理集成[J].科技进步与对策, 2005(5): 54-56.
[75]李宝山.集成管理——高科技时代的管理创新[M].北京:中国人民出版社, 1998.
[76]赵玉林,单元媛,谭弟庆.企业界面管理的组织结构[J].武汉工业大学学报, 2000, 22(4): 100-103.
[77]尤建新,朱岩梅.设计-制造链的界面管理及效果评价[J].上海管理科学, 2007(1): 37-39.
[78]龚艳萍.动态环境下企业R&D-Marketing界面管理柔性化研究[J].科研管理, 2007, 28(3): 92-97.
[79]王芳,吴祈宗.基于不完全权重信息下模糊多属性决策的供应链合作伙伴选择[J].数学的实践与认识, 2008, 38(24): 18-24.
[80]吴广谋.系统原理与方法[M].南京:东南大学出版社, 2005.
[81]欧阳莹之.复杂系统理论基础[M].上海:上海科技教育出版社, 2002.
[82]许国志.系统科学[M].上海:上海科技教育出版社, 2000.
[83]范冬萍.复杂系统的因果观和方法论——一种复杂整体论[J].哲学研究, 2008(2): 90-97.
[84]董旭,李云鹏.整合神经网络的人工生命:一种脑回路研究方式[J].复杂系统与复杂性科学, 2007, 4(4): 64-70.
[85]张伟.团队复杂系统分析[J].复杂系统与复杂性科学, 2005, 2(2): 77-86.
[86]鲜于波,梅琳.经济复杂性系统主体学习算法理论综述[J].复杂系统与复杂性科学, 2007, 4(2): 77-92.
[87]樊耘,余宝琦,张旭.组织文化激励性与公平性特征的实证研究[J].西安交通大学学报:社会科学版, 2009, 29(6): 14-19.
[88]曹科岩,龙君伟.组织文化、知识分享与组织创新的关系研究[J].科学学研究, 2009, 27(12): 1869-1876.
[89] Holsapple C W, Lee-Post A. Behavior-based analysis of knowledge dissemination channels in operations management[J]. Omega, 2010, 38(3-4): 167-178.
[90] Tang D, Zhu R, Tang J, et al. Product design knowledge management based on design structure matrix[J]. Advanced Engineering Informatics, 2010, 24(2): 159-166.
[91] Vaccaro A, Parente R, Veloso F M. Knowledge Management Tools, Inter-organizational Relationships, Innovation and Firm Performance[J]. Technological Forecasting and Social Change, 2010, In Press.
[92]包兴,季建华.考虑管理者风险态度的大型服务运作系统能力应急管理[J].系统管理学报, 2009(5): 555-561.
[93]郭新燕,原毅军,王勤.基于成员风险态度变化的合作研发动态激励机制研究[J].科学学与科学技术管理, 2008, 29(6): 169-171.
[94]余海,黄波.对投资者风险态度处理方法的探讨[J].经济研究导刊, 2009(28): 56-60.
[95]戚安邦.项目管理学[M].北京:科学出版社, 2007.
[96]周三多,陈传明,鲁明泓.管理学:原理与方法[M].上海:复旦大学出版社, 2009.
[97] Sun Q, Li R, Zhang P. Stable and optimal adaptive fuzzy control of complex systems using fuzzy dynamic model[J]. Fuzzy Sets and Systems, 2003, 133(1): 1-17.
[98] Mitchell M. Complex systems: Network thinking[J]. Artificial Intelligence, 2006, 170(18): 1194-1212.
[99] Amaral L A N, Ottino J M. Complex systems and networks: challenges and opportunities for chemical and biological engineers[J]. Chemical Engineering Science, 2004, 59(8-9): 1653-1666.
[100]郑广祥.浅议可持续发展的协同作用与序参量[J].系统辩证学学报, 2003, 11(2): 68-70.
[101] Neils W K, Van Harlingen D J. Search for complex order parameters in grain boundary junctions[J]. Physica B: Condensed Matter, 2000, 284-288(Part 1): 587-588.
[102] Haken H. Synergeties[M]. Berlin: Springer, 1978.
[103]董春雨.层次论:意义及其问题—析王志康《突变和进化》中的层次论思想[J].自然辩证法研究, 1998, 14(12): 12-15.
[104]董春雨,姜璐.层次性:系统思想与方法的精髓[J].系统辩证学学报, 2001, 9(1): 1-4.
[105]罗吉贵,刘曾荣.从同步到涌现[J].复杂系统与复杂性科学, 2005, 2(1): 29-32.
[106] Antiqueira L, Oliveira Jr O N, Costa L D F, et al. A complex network approach to text summarization[J]. Information Sciences, 2009, 179(5): 584-599.
[107] Sun W, Chen Z, L Y, et al. An intriguing hybrid synchronization phenomenon of two coupled complex networks[J]. , In Press, Accepted Manuscript.
[108]鲜于波,梅琳.间接网络效应下的产品扩散——基于复杂网络和计算经济学的研究[J].管理科学学报, 2009, 12(1): 70-81.
[109] Lin C, Tzeng G. A value-created system of science (technology) park by using DEMATEL[J]. Expert Systems with Applications, 2009, 36(6): 9683-9697.
[110] Sengupta A. Quantum non-locality and complex holism[J]. Nonlinear Analysis: Theory, Methods & Applications, 2008, 69(3): 1000-1010.
[111]刘带,李锐锋.论中医理论中的系统整体性思想[J].系统科学学报, 2009, 17(3): 90-93.
[112]董春雨.试论时间方向的层次性[J].自然辩证法研究, 2008, 24(5): 6-10.
[113]顾文涛,王以华,吴金希.复杂系统层次的内涵及相互关系原理研究[J].系统科学学报, 2008, 16(2): 34-39.
[114] Saaty T L. The Analytic Network Process [M]. Pittsburgh: RWS Publications, 1996.
[115] Saaty T L. Time dependent decision-making; dynamic priorities in the AHP/ANP : Generalizing from points to functions and from real to complex variables[J]. Mathematical and Computer Modelling, 2007, 46(7-8): 860-891.
[116] Kahneman D, Tversky A. Prospect Theory: An Analysis of Decision under Risk[J]. Econometrica, 1979, 47(2): 263-291.
[117] Tamura H. Behavioral models for complex decision analysis[J]. European Journal of Operational Research, 2005, 166(3): 655-665.
[118] Tamura H. Behavioral models of decision making under risk and/or uncertainty with application to public sectors[J]. Annual Reviews in Control, 2008, 32(1): 99-106.
[119] Bonner B L, Baumann M R, Dalal R S. The effects of member expertise on group decision-making and performance[J]. Organizational Behavior and Human Decision Processes, 2002, 88(2): 719-736.
[120] Goncalo J A, Duguid M M. Hidden consequences of the group-serving bias: Causal attributions and the quality of group decision making[J]. Organizational Behavior and Human Decision Processes, 2008, 107(2): 219-233.
[121] Moon H, Conlon D E, Humphrey S E, et al. Group decision process and incrementalism in organizational decision making[J]. Organizational Behavior and Human Decision Processes, 2003, 92(1-2): 67-79.
[122] Song F. Intergroup trust and reciprocity in strategic interactions: Effects of group decision-making mechanisms[J]. Organizational Behavior and Human Decision Processes, 2009, 108(1): 164-173.
[123] Yeh C, Chang Y. Modeling subjective evaluation for fuzzy group multicriteria decision making[J]. European Journal of Operational Research, 2009, 194(2): 464-473.
[124] Wang Y M. On fuzzy multiattribute decision-making models and methods with incomplete preference information[J]. Fuzzy Sets and Systems, 2005, 151(2): 285-301.
[125]罗党,周玲,罗迪新.灰色风险型多属性群决策方法[J].系统工程与电子技术, 2008, 30(9): 1674-1678.
[126]徐泽水,孙在东.一类不确定型多属性决策问题的排序方法[J].管理科学学报, 2002, 5(3): 35-39.
[127]于义彬,王本德,柳澎,等.具有不确定信息的风险型多目标决策理论及应用[J].中国管理科学, 2003, 11(6): 9-13.
[128] Li D F. An approach to fuzzy multiattribute decision making under uncertainty[J]. Information Sciences, 2005, 169(1-2): 97-112.
[129] Tversky A, Kahneman D. Advances in Prospect Theory: Cumulative Representation of Uncertainty[J]. Journal of Risk and Uncertainty, 1992, 5(4): 297-323.
[130] Mustajoki J, H?m?l?inen R P, Sinkko K. Interactive computer support in decision conferencing: Two cases on off-site nuclear emergency management[J]. Decision Support Systems,2007, 42(4): 2247-2260.
[131] Salo A A. Interactive decision aiding for group decision support[J]. European Journal of Operational Research, 1995, 84(1): 134-149.
[132] Tsoukiàs A. From decision theory to decision aiding methodology[J]. European Journal of Operational Research, 2008, 187(1): 138-161.
[133] Ryu C, Kim Y J, Chaudhury A, et al. Knowledge acquisition via three learning processes in enterprise information portals: Learning-by-investment, learning-bydoing, and learning-from-others[J]. MIS Quarterly, 2005, 29(2): 245-278.
[134] Kim J K, Choi S H. A utility range-based interactive group support system for multiattribute decision making[J]. Computers & Operations Research, 2001, 28(5): 485-503.
[135] Samaddar S, Kadiyala S S. An analysis of interorganizational resource sharing decisions in collaborative knowledge creation[J]. European Journal of Operational Research, 2006, 170(1): 192-210.
[136]王众托.知识系统工程:知识管理的新学科[J].大连理工大学学报, 2000, 40(s1): 115-122.
[137]段志蓉,邱海鹰,朱玉杰.企业的风险态度对国际化决策的影响[J].清华大学学报(哲学社会科学版), 2008, 23(2): 149-160.
[138] Simon H A. Administrative Behavior-A Study of Decision Making Processes in Administrative Organization[M]. New York : Macmillan Publishing Co, lnc, 1971.
[139]李春好,孙永河. ANP内部循环依存递阶系统的方案排序新方法[J].管理科学学报, 2008, 11(6): 25-34.
[140]马剑虹.决策渐进调整过程的个案研究[J].应用心理学, 2001, 7(3): 7-12.
[141]魏权龄.数据包络分析[M].北京:科学出版社, 2004.
[142] Sarrico C S, Dyson R G. Restricting virtual weights in data envelopment analysis[J]. European Journal of Operational Research, 2004, 159(1): 17-34.
[143]李春好.对W—B乘子置信域约束构造方法的改进及模型[J].中国管理科学, 2003, 11(3): 51-55.
[144]郭菊娥,白云涛,席酉民,等.权威类型、决策程序对高管决策过程影响研究[J].管理科学学报, 2008, 11(6): 1-10.
[145] Al-Nowaihi A, Dhami S. A simple derivation of Prelec's probability weighting function[J]. Journal of Mathematical Psychology, 2006, 50(6): 521-524.
[146] Armantier O, Treich N. Star-shaped probability weighting functions and overbidding in first-price auctions[J]. Economics Letters, 2009, 104(2): 83-85.
[147] Mattos F, Garcia P, Pennings J M E. Probability weighting and loss aversion in futures hedging[J]. Journal of Financial Markets, 2008, 11(4): 433-452.
[148] Prelec D. The Probability Weighting Function[J]. Econometrica, 1998, 66(3): 497-528.
[149] Abdellaoui M. Parameter-free elicitation of utility and probability weighting functions[J]. Management science, 2000, 46(11): 1497-1512.
[150] Abdellaoui M. Choice-based elicitation and decomposition of decision weights for gains and losses under uncertainty[J]. Management science, 2005, 51(9): 1384-1399.
[151] Bleichrodt H, Pinto J L. A Parameter-Free Elicitation of the Probability Weighting Function in Medical Decision Analysis[J]. Management science, 2000, 46(11): 1485-1496.
[152] Booij A S, Van De Kuilen G. A parameter-free analysis of the utility of money for the general population under prospect theory[J]. Journal of Economic Psychology, 2009, 30(4): 651-666.
[153]卓越.群决策支持系统中的一致性分析技术[J].控制与决策, 1999, 14(6): 636-641.
[154]陈建中,徐玖平.群决策的交互式TOPSIS方法及其应用[J].系统工程学报, 2008, 23(3): 276-281.
[155] Lai Y. Hierarchical optimization: a satisfactory solution[J]. Fuzzy Sets and Systems, 1996, 77(3): 321-335.
[156] Shih H S, Lai Y J, Lee E S. Fuzzy approach for multi-level mathematical programming problems[J]. Computers and Operational Research, 1996, 23(1): 73-91.
[157] Sakawa M, Nishizaki I. Interactive fuzzy programming for decentralized two-level linear programming problems[J]. Fuzzy Sets and Systems, 2002, 125(3): 301-315.
[158] Sakawa M, Nishizaki I, Uemura Y. Interactive fuzzy programming for multi-level linear programming problems with fuzzy parameters[J]. Fuzzy Sets and Systems, 2000(109): 3-19.
[159] Tiryaki F. Interactive compensatory fuzzy programming for decentralized multi-level linear programming (DMLLP) problems[J]. Fuzzy Sets and Systems, 2006, 157(23): 3072-3090.
[160] Ahlatcioglu M, Tiryaki F. Interactive fuzzy programming for decentralized two-level linear fractional programming (DTLLFP) problems[J]. Omega, 2007, 35(4): 432-450.
[161] Cannon-Bowers J A, Salas E. Reflections on shared cognition[J]. Journal of Organizational Behavior, 2001, 22(2): 195-202.
[162]黄文艳,马剑虹.组织中的心理契约[J].人类工效学, 2006, 12(1): 69-71.
[163]马剑虹,施建锋.风险偏爱特征的实验研究[J].应用心理学, 2002, 8(3): 28-34.
[164]徐寒易,马剑虹.共享心智模型:分布、层次与准确性初探[J].心理科学进展, 2008, 16(6): 933-940.
[165]马剑虹.组织决策的影响力分布[J].心理学报, 1997, 29(1): 82-90.
[166] Werners B M. Aggregation models in mathematical programming[M]. Berlin: Springer, 1988.
[167] Xu Z, Chen J. An interactive method for fuzzy multiple attribute group decision making[J]. Information Sciences, 2007, 177(1): 248-263.
[168] Chuu S. Group decision-making model using fuzzy multiple attributes analysis for the evaluation of advanced manufacturing technology[J]. Fuzzy Sets and Systems, 2009, 160(5): 586-602.
[169]廖貅武,唐焕文.基于不完全信息的一种群决策方法[J].大连理工大学学报, 2002, 42(1): 122-126.
[170] K?ksalan M, ?zpeynirci S B. An interactive sorting method for additive utility functions[J]. Computers & Operations Research, 2009, 36(9): 2565-2572.
[171] Mishra S, Ghosh A. Interactive fuzzy programming approach to Bi-level quadratic fractional programming problems[J]. Annals of Operations Research, 2006, 143(1): 251-263.
[172] Mishra S. Weighting method for bi-level linear fractional programming problems[J]. European Journal of Operational Research, 2007, 183(1): 296-302.
[173] Gurevich G, Kliger D, Levy O. Decision-making under uncertainty - A field study of cumulative prospect theory[J]. Journal of Banking & Finance, 2009, 33(7): 1221-1229.
[174] Nwogugu M. A further critique of cumulative prospect theory and related approaches[J]. Applied Mathematics and Computation, 2006, 179(2): 451-465.
[175] Trepel C, Fox C R, Poldrack R A. Prospect theory on the brain? Toward a cognitive neuroscience of decision under risk[J]. Cognitive Brain Research, 2005, 23(1): 34-50.
[176]朱建军,宋传平,刘思峰,等.一类三端点区间数判断矩阵的一致性及权重研究[J].系统工程学报, 2008, 23(1): 22-27.
[177]李春好,杜元伟,刘成明,等.基于基元前景交叉判断的前景价值模型[J].管理科学学报, 2010, 13(2): 12-23.
[178]毕鹏程,席酉民.群体决策过程中的群体思维研究[J].管理科学学报, 2002, 5(1): 25-34.
[179] Van Ginkel W P, Van Knippenberg D. Knowledge about the distribution of information and group decision making: When and why does it work?[J]. Organizational Behavior and HumanDecision Processes, 2009, 108(2): 218-229.
[180]蒋御柱,张朋柱,张兴学.群体研讨支持系统中的智能可视化研究[J].管理科学学报, 2009, 12(3): 1-11.
[181] Keeney R L. Measurement Scales for Quantifying Attributes[J]. Behavioral Science, 1981, 26(1): 29-36.
[182] Tegarden D P, Sheetz S D. Group cognitive mapping: a methodology and system for capturing and evaluating managerial and organizational cognition[J]. Omega, 2003, 31(2): 113-125.
[183] Irani Z, Sharif A M, Love P E D. Mapping knowledge management and organizational learning in support of organizational memory[J]. International Journal of Production Economics, 2009, 122(1): 200-215.
[184] Ahmad R, Ali N A. The use of cognitive mapping technique in management research: theory and practice[J]. Management Research News, 2003, 26(7): 1-16.
[185] De Campos L M, Fernández-Luna J M, Huete J F. Using context information in structured document retrieval: an approach based on influence diagrams[J]. Information Processing & Management, 2004, 40(5): 829-847.
[186] Derosa D M, Smith C L, Hantula D A. The medium matters: Mining the long-promised merit of group interaction in creative idea generation tasks in a meta-analysis of the electronic group brainstorming literature[J]. Computers in Human Behavior, 2007, 23(3): 1549-1581.
[187] Michinov N, Primois C. Improving productivity and creativity in online groups through social comparison process: New evidence for asynchronous electronic brainstorming[J]. Computers in Human Behavior, 2005, 21(1): 11-28.
[188] Roy M C, Gauvin S, Limayem M. Electronic group brainstorming: the role of feedback on productivity[J]. Small Group Research, 1996, 27(2): 215-247.
[189] Valacich J S, Schwenk C. Devil's Advocacy and Dialectical Inquiry Effects on Face-to-Face and Computer-Mediated Group Decision Making[J]. Organizational Behavior and Human Decision Processes, 1995, 63(2): 158-173.
[190] Schulz-Hardt S, Jochims M, Frey D. Productive conflict in group decision making: genuine and contrived dissent as strategies to counteract biased information seeking[J]. Organizational Behavior and Human Decision Processes, 2002, 88(2): 563-586.
[191] Hua Z, Jiang W, Liang L. Adjusting inconsistency through learning in group decision-making, and its application to China's MBA recruiting interview[J]. Socio-Economic Planning Sciences, 2007, 41(3): 195-207.
[192]朱建军,刘思峰.群决策中模糊偏好信息转化的若干性质研究[J].控制与决策, 2008, 21(1): 56-59.
[193] Da?deviren M, Yüksel ?. A fuzzy analytic network process (ANP) model for measurement of the sectoral competititon level (SCL)[J]. Expert Systems with Applications, 2010, 37(2): 1005-1014.
[194] Lee H, Kim C, Cho H, et al. An ANP-based technology network for identification of core technologies: A case of telecommunication technologies[J]. Expert Systems with Applications, 2009, 36(1): 894-908.
[195] Yüksel ?, Dagdeviren M. Using the analytic network process (ANP) in a SWOT analysis - A case study for a textile firm[J]. 2007, 177(16): 3364-3382.
[196]吴宪华,张列平.动态联盟伙伴选择的决策方法及其战略评估模型的建立[J].系统工程, 1998, 16(6): 38-43.
[197]邹仲海,宋国防.基于FANP方法的供应链配送中心评价选择[J].工业工程, 2009, 12(4): 86-90.
[198] Wu W. Choosing knowledge management strategies by using a combined ANP and DEMATEL approach[J]. Expert Systems with Applications, 2008, 35(3): 828-835.
[199] Meade L M, Presley A. R&D project selection using the analytic network process[J]. IEEE Transactions on Engineering Management, 2002, 49(1): 59-64.
[200] Poonikom K, Christopher O, Chansa-Ngavej C. An application of the analytic network process (ANP) for university selection decisions[J]. Science Asia, 2004, 30(4): 317-326.
[201] Sarkis J. Qualititative models for performance measure systems-alternate considerations[J]. International Journal of Production Economics, 2003, 86(1): 81-90.
[2]陈宝敏.科斯定理的重新解释——兼论中国新制度经济学研究的误区[J].中国人民大学学报, 2002(2): 68-72.
[3]李刚,牛冲槐,张敏,等.科技型人才聚集效应与组织冲突消减的研究[J].管理学报, 2006, 3(3): 302-308.
[4]吴贵生,王毅.产学研合作中粘滞知识的成因与转移机制研究[J].科研管理, 2001, 22(6): 114-121.
[5] Park S H, Russo M V. When Competition Eclipses Cooperation: An Event History Analysis of Joint Venture Failure[J]. Management Science, 1996, 42(6): 875-890.
[6] Khanna T, Gulati R, Nohria N. The Dynamics of Learning Alliances: Competition, Cooperation, and Relative Scope[J]. Strategic Management Journal, 1998, 19(3): 193-210.
[7]兰劲松,薛天祥.重大科技项目的概念、特征与组织[J].研究与发展管理, 1999, 11(5): 52-55.
[8]张智文,申金升.国家重大科技项目组织管理的若干思考[J].中国软科学, 1998, 12: 50-53.
[9]倪健.基于重大科技项目的管理创新研究[J].中国科技论坛, 2006(5): 36-37, 55.
[10]王化兰,王长峰.基于现代项目管理理论的重大科技项目管理模式研究[J].科学学与科学技术管理, 2004(2): 74-77.
[11]陈省平,郑湘峙,罗轶,等.重大科技项目组织实施模式的探讨与实践——以国家863计划?深海抗风浪网箱的研制?项目为例[J].科技管理研究, 2005, 25(4): 93-94.
[12]蒋玉涛,招富刚,朱星华.基于全生命周期的重大科技专项管理模式构想[J].中国科技论坛, 2008(10): 14-17.
[13]王芳,赵兰香.重大科技项目模块化创新管理方法研究——对美国国防采办管理方法的探析[J].科研管理, 2009, 30(1): 1-7.
[14]陈强,鲍悦华,程妤.重大科技项目的过程管理及协同机制研究[M].北京:化学工业出版社, 2009.
[15]胡宝民,王婷,李子彪.重大科技专项的特征研究[J].中国科技论坛, 2007(9): 81-85.
[16]李春好,杜元伟.我国科技合作项目管理机制的缺陷分析与改进对策[J].管理学报, 2010, 7(2): 1-7.
[17]郭斌.企业界面管理实证研究[J].科研管理, 1999, 20(5): 73-79.
[18]聂柯渐.界面管理系统化研究——管理科学研究的新方向[J].市场论坛, 2006(3): 44-45.
[19]刘兰剑,党兴华.合作技术创新界面管理研究及其新进展[J].科研管理, 2007, 28(3): 1-7.
[20] Al-Mutairi M S, Hipel K W, Kamel M S. Trust and cooperation from a fuzzy perspective[J]. Mathematics and Computers in Simulation, 2008, 76(5-6): 430-446.
[21] Casari M, Luini L. Cooperation under alternative punishment institutions: An experiment[J]. Journal of Economic Behavior & Organization, 2009, 71(2): 273-282.
[22] Cassar A. Coordination and cooperation in local, random and small world networks: Experimental evidence[J]. Games and Economic Behavior, 2007, 58(2): 209-230.
[23] De Cremer D, Tyler T R. The Effects of Trust in Authority and Procedural Fairness on Cooperation[J]. Journal of Applied Psychology, 2007, 92(3): 639-649.
[24] Gao J, Lee J D, Zhang Y. A dynamic model of interaction between reliance on automation and cooperation in multi-operator multi-automation situations[J]. International Journal of Industrial Ergonomics, 2006, 36(5): 511-526.
[25] Grimm V, Mengel F. Cooperation in viscous populations--Experimental evidence[J]. Games and Economic Behavior, 2009, 66(1): 202-220.
[26] Kirchkamp O, Nagel R. Naive learning and cooperation in network experiments[J]. Games and Economic Behavior, 2007, 58(2): 269-292.
[27] Kocher M G, Cherry T, Kroll S, et al. Conditional cooperation on three continents[J]. Economics Letters, 2008, 101(3): 175-178.
[28] Li J, Wang S, Cheng T C E. Competition and cooperation in a single-retailer two-supplier supply chain with supply disruption[J]. International Journal of Production Economics, 2010, 124(1): 137-150.
[29] Silipo D B. Incentives and forms of cooperation in research and development[J]. Research in Economics, 2008, 62(2): 101-119.
[30] Wei G, Yu L, Wang S, et al. Study on Incentive Factors of Team Cooperation based on Synergy Effect[J]. Systems Engineering - Theory & Practice, 2007, 27(1): 1-9.
[31]李新春,顾宝炎.中外企业合作的战略联盟特征与技术学习[J].管理科学学报, 1998, 1(4): 44-50.
[32]吴海平,宣国良.结网合作策略选择的能力依赖模型及其启示[J].管理科学学报, 2007, 10(1): 29-38.
[33]易余胤,肖条军,盛昭瀚.合作研发中机会主义行为的演化博弈分析[J].管理科学学报, 2005, 8(4): 80-87.
[34]钟德强,仲伟俊,梅姝娥,等.合作竞争下的供应商数量优化问题研究[J].管理科学学报, 2003, 6(3): 57-65.
[35]金锐,韩文秀.多人多准则非常和合作型对策研究[J].管理科学学报, 1999, 2(1): 32-36.
[36] Axelrod R, Dion D. The Further Evolution of Cooperation[J]. Science, 1998, 242(4884): 1385-1390.
[37] Singh K, Mitchell W. Growth Dynamics: The Bidirectional Relationship Between Interfirm Collaboration and Business Sales in Entrant and Incumbent Alliances[J]. Strategic Management Journal, 2005, 26(6): 497-521.
[38] Nielsen R P. Cooperative Strategy[J]. Strategic Management Journal, 1988, 9(5): 475-492.
[39] Chan P S, Heide D. Strategic alliances in technology: key competitive weapon[J]. SAM Advanced Management Journal, 1993, 28(4): 9-19.
[40]王泽华,万映红.虚拟企业网上合作模式探讨[J].中国软科学, 2001(4): 79-83.
[41]王鹏.海峡两岸高科技产业竞争力比较及分工合作模式[J].国际经贸探索, 2005, 21(2): 26-29.
[42]侯晓丽,贾若祥,刘毅.企业合作模式及其对区域经济发展的影响——以江苏省通州市企业为例[J].地理研究, 2005(4): 641-651.
[43]首藤信彦.超越国际技术联合[J].世界经济评论, 1993, 8: 25-27.
[44]桑建平,李焱焱,叶冰,等.产学研合作模式分类及其选择思路[J].科技进步与对策, 2004(10): 98-99.
[45]雷亮,陈国荣,邓菊丽,等.关于产学研合作模式的探讨[J].重庆科技学院学报(社会科学版), 2008(9): 195-196.
[46]宝胜.论知识创新的?智力互补型?合作模式[J].科技管理研究, 2008(9): 231-237.
[47]赵希男,丁勇,靖可,等.企业间技术嵌入式合作模式研究[J].科学学研究, 2007, 25(2): 364-368.
[48]赵晶媛.探索企业新型产学研合作模式[J].科技管理研究, 2008(9): 14-16.
[49]李廉水.论产学研合作创新的组织方式[J].科研管理, 1998, 19(1): 30-34.
[50]刘贵伟,吴立贤.产学研合作创新的模式及评价[J].辽宁教育学院学报, 2001, 18(5): 21-23.
[51]陈建军.长三角区域经济合作模式的选择[J].南通大学学报(社会科学版), 2005(2): 42-46.
[52]周静珍,万玉刚,高静.我国产学研合作创新的模式研究[J].科技进步与对策, 2005(3): 70-72.
[53]吕磊,马军,陈林峰,等.基于政府视角的国际科技合作模式研究[J].科研管理, 2008(s1): 80-84, 414.
[54]陈红喜.基于三螺旋理论的政产学研合作模式与机制研究[J].科技进步与对策, 2009(24): 6-8.
[55]官建成,靳平安.企业经济学中的界面管理研究[J].经济理论与经济管理, 1995(6): 67-69.
[56] Etzkowitz H, Leydesdorff L. The dynamics of innovation: from National Systems and "Mode 2" to a Triple Helix of university-industry-government relations[J]. Research Policy , 2000, 29(2): 109-123.
[57]刁兆峰,余东方.论现代企业中的界面管理[J].科技进步与对策, 2001(05): 85-86.
[58] Patrice H. A Tentative Definition of the Interface of Innovation:the Model of the Three Millstones[D]. 2000.
[59]章琰.大学技术转移中的界面及其移动分析[J].科学学研究, 2003(s1): 25-29.
[60]吴涛,海峰,李必强.界面和管理界面分析[J].管理科学, 2003(1): 6-10.
[61]郭斌,陈劲,许庆瑞.企业创新过程中的界面管理[J].数量经济技术经济研究, 1997(07): 38-41.
[62] Araujo L, Dubois A, Gadde L. Managing Interfaces with Suppliers[J]. Industrial Marketing Management, 1999, 28(5): 497-506.
[63]徐磊,赵刚,叶德平.如何建立有效的界面——关于技术创新界面管理的探讨[J].科研管理, 2002, 23(3): 79-83.
[64]李立新,曹霞.大型建设项目设计界面的有效管理[J].建筑经济, 2004(10): 71-74.
[65] Peng M W, Shenkar O. The meltdown of trust A process model of strategic alliance dissolution[Z]. 1997.
[66] Calantone R, Droge C, Vickery S. Investigating the manufactur-ing-marketing interface in new product development:does context affect the strength of relationships[J]. Journal of Operation Management, 2002, 20: 273-287.
[67]陈震红,董俊武.战略联盟伙伴的冲突管理[J].科学学与科学技术管理, 2004(3): 106-109.
[68]叶德平,赵刚.高技术企业与风险投资公司界面管理分析[J].科技创业月刊, 2004(7): 28-30.
[69] Brockhoff K, Hauschildt J. Schnittstellen management Koordinaion ohne Hierarchie[J]. Zeitsehrift Fuchrung and Organization, 1993(62): 396-403.
[70]赵玉林.高技术产业化的界面管理原理[J].武汉理工大学学报, 2004(3): 100-102.
[71]徐和平,孙林岩,慕继丰.产品创新网络中的信任与信任机制探讨[J].管理工程学报, 2004(2): 55-59.
[72] Barney J B, Hansen M H. Trust worthiness as a source of competitive advantage[J]. Strategic Management Journal, 1994, 15: 175-190.
[73]李垣,范诵,赵永彬.不同企业文化模式对技术创新的影响分析[J].预测, 2005(4): 26-30.
[74]欧光军,欧阳明德.面向产品创新的界面管理集成[J].科技进步与对策, 2005(5): 54-56.
[75]李宝山.集成管理——高科技时代的管理创新[M].北京:中国人民出版社, 1998.
[76]赵玉林,单元媛,谭弟庆.企业界面管理的组织结构[J].武汉工业大学学报, 2000, 22(4): 100-103.
[77]尤建新,朱岩梅.设计-制造链的界面管理及效果评价[J].上海管理科学, 2007(1): 37-39.
[78]龚艳萍.动态环境下企业R&D-Marketing界面管理柔性化研究[J].科研管理, 2007, 28(3): 92-97.
[79]王芳,吴祈宗.基于不完全权重信息下模糊多属性决策的供应链合作伙伴选择[J].数学的实践与认识, 2008, 38(24): 18-24.
[80]吴广谋.系统原理与方法[M].南京:东南大学出版社, 2005.
[81]欧阳莹之.复杂系统理论基础[M].上海:上海科技教育出版社, 2002.
[82]许国志.系统科学[M].上海:上海科技教育出版社, 2000.
[83]范冬萍.复杂系统的因果观和方法论——一种复杂整体论[J].哲学研究, 2008(2): 90-97.
[84]董旭,李云鹏.整合神经网络的人工生命:一种脑回路研究方式[J].复杂系统与复杂性科学, 2007, 4(4): 64-70.
[85]张伟.团队复杂系统分析[J].复杂系统与复杂性科学, 2005, 2(2): 77-86.
[86]鲜于波,梅琳.经济复杂性系统主体学习算法理论综述[J].复杂系统与复杂性科学, 2007, 4(2): 77-92.
[87]樊耘,余宝琦,张旭.组织文化激励性与公平性特征的实证研究[J].西安交通大学学报:社会科学版, 2009, 29(6): 14-19.
[88]曹科岩,龙君伟.组织文化、知识分享与组织创新的关系研究[J].科学学研究, 2009, 27(12): 1869-1876.
[89] Holsapple C W, Lee-Post A. Behavior-based analysis of knowledge dissemination channels in operations management[J]. Omega, 2010, 38(3-4): 167-178.
[90] Tang D, Zhu R, Tang J, et al. Product design knowledge management based on design structure matrix[J]. Advanced Engineering Informatics, 2010, 24(2): 159-166.
[91] Vaccaro A, Parente R, Veloso F M. Knowledge Management Tools, Inter-organizational Relationships, Innovation and Firm Performance[J]. Technological Forecasting and Social Change, 2010, In Press.
[92]包兴,季建华.考虑管理者风险态度的大型服务运作系统能力应急管理[J].系统管理学报, 2009(5): 555-561.
[93]郭新燕,原毅军,王勤.基于成员风险态度变化的合作研发动态激励机制研究[J].科学学与科学技术管理, 2008, 29(6): 169-171.
[94]余海,黄波.对投资者风险态度处理方法的探讨[J].经济研究导刊, 2009(28): 56-60.
[95]戚安邦.项目管理学[M].北京:科学出版社, 2007.
[96]周三多,陈传明,鲁明泓.管理学:原理与方法[M].上海:复旦大学出版社, 2009.
[97] Sun Q, Li R, Zhang P. Stable and optimal adaptive fuzzy control of complex systems using fuzzy dynamic model[J]. Fuzzy Sets and Systems, 2003, 133(1): 1-17.
[98] Mitchell M. Complex systems: Network thinking[J]. Artificial Intelligence, 2006, 170(18): 1194-1212.
[99] Amaral L A N, Ottino J M. Complex systems and networks: challenges and opportunities for chemical and biological engineers[J]. Chemical Engineering Science, 2004, 59(8-9): 1653-1666.
[100]郑广祥.浅议可持续发展的协同作用与序参量[J].系统辩证学学报, 2003, 11(2): 68-70.
[101] Neils W K, Van Harlingen D J. Search for complex order parameters in grain boundary junctions[J]. Physica B: Condensed Matter, 2000, 284-288(Part 1): 587-588.
[102] Haken H. Synergeties[M]. Berlin: Springer, 1978.
[103]董春雨.层次论:意义及其问题—析王志康《突变和进化》中的层次论思想[J].自然辩证法研究, 1998, 14(12): 12-15.
[104]董春雨,姜璐.层次性:系统思想与方法的精髓[J].系统辩证学学报, 2001, 9(1): 1-4.
[105]罗吉贵,刘曾荣.从同步到涌现[J].复杂系统与复杂性科学, 2005, 2(1): 29-32.
[106] Antiqueira L, Oliveira Jr O N, Costa L D F, et al. A complex network approach to text summarization[J]. Information Sciences, 2009, 179(5): 584-599.
[107] Sun W, Chen Z, L Y, et al. An intriguing hybrid synchronization phenomenon of two coupled complex networks[J]. , In Press, Accepted Manuscript.
[108]鲜于波,梅琳.间接网络效应下的产品扩散——基于复杂网络和计算经济学的研究[J].管理科学学报, 2009, 12(1): 70-81.
[109] Lin C, Tzeng G. A value-created system of science (technology) park by using DEMATEL[J]. Expert Systems with Applications, 2009, 36(6): 9683-9697.
[110] Sengupta A. Quantum non-locality and complex holism[J]. Nonlinear Analysis: Theory, Methods & Applications, 2008, 69(3): 1000-1010.
[111]刘带,李锐锋.论中医理论中的系统整体性思想[J].系统科学学报, 2009, 17(3): 90-93.
[112]董春雨.试论时间方向的层次性[J].自然辩证法研究, 2008, 24(5): 6-10.
[113]顾文涛,王以华,吴金希.复杂系统层次的内涵及相互关系原理研究[J].系统科学学报, 2008, 16(2): 34-39.
[114] Saaty T L. The Analytic Network Process [M]. Pittsburgh: RWS Publications, 1996.
[115] Saaty T L. Time dependent decision-making; dynamic priorities in the AHP/ANP : Generalizing from points to functions and from real to complex variables[J]. Mathematical and Computer Modelling, 2007, 46(7-8): 860-891.
[116] Kahneman D, Tversky A. Prospect Theory: An Analysis of Decision under Risk[J]. Econometrica, 1979, 47(2): 263-291.
[117] Tamura H. Behavioral models for complex decision analysis[J]. European Journal of Operational Research, 2005, 166(3): 655-665.
[118] Tamura H. Behavioral models of decision making under risk and/or uncertainty with application to public sectors[J]. Annual Reviews in Control, 2008, 32(1): 99-106.
[119] Bonner B L, Baumann M R, Dalal R S. The effects of member expertise on group decision-making and performance[J]. Organizational Behavior and Human Decision Processes, 2002, 88(2): 719-736.
[120] Goncalo J A, Duguid M M. Hidden consequences of the group-serving bias: Causal attributions and the quality of group decision making[J]. Organizational Behavior and Human Decision Processes, 2008, 107(2): 219-233.
[121] Moon H, Conlon D E, Humphrey S E, et al. Group decision process and incrementalism in organizational decision making[J]. Organizational Behavior and Human Decision Processes, 2003, 92(1-2): 67-79.
[122] Song F. Intergroup trust and reciprocity in strategic interactions: Effects of group decision-making mechanisms[J]. Organizational Behavior and Human Decision Processes, 2009, 108(1): 164-173.
[123] Yeh C, Chang Y. Modeling subjective evaluation for fuzzy group multicriteria decision making[J]. European Journal of Operational Research, 2009, 194(2): 464-473.
[124] Wang Y M. On fuzzy multiattribute decision-making models and methods with incomplete preference information[J]. Fuzzy Sets and Systems, 2005, 151(2): 285-301.
[125]罗党,周玲,罗迪新.灰色风险型多属性群决策方法[J].系统工程与电子技术, 2008, 30(9): 1674-1678.
[126]徐泽水,孙在东.一类不确定型多属性决策问题的排序方法[J].管理科学学报, 2002, 5(3): 35-39.
[127]于义彬,王本德,柳澎,等.具有不确定信息的风险型多目标决策理论及应用[J].中国管理科学, 2003, 11(6): 9-13.
[128] Li D F. An approach to fuzzy multiattribute decision making under uncertainty[J]. Information Sciences, 2005, 169(1-2): 97-112.
[129] Tversky A, Kahneman D. Advances in Prospect Theory: Cumulative Representation of Uncertainty[J]. Journal of Risk and Uncertainty, 1992, 5(4): 297-323.
[130] Mustajoki J, H?m?l?inen R P, Sinkko K. Interactive computer support in decision conferencing: Two cases on off-site nuclear emergency management[J]. Decision Support Systems,2007, 42(4): 2247-2260.
[131] Salo A A. Interactive decision aiding for group decision support[J]. European Journal of Operational Research, 1995, 84(1): 134-149.
[132] Tsoukiàs A. From decision theory to decision aiding methodology[J]. European Journal of Operational Research, 2008, 187(1): 138-161.
[133] Ryu C, Kim Y J, Chaudhury A, et al. Knowledge acquisition via three learning processes in enterprise information portals: Learning-by-investment, learning-bydoing, and learning-from-others[J]. MIS Quarterly, 2005, 29(2): 245-278.
[134] Kim J K, Choi S H. A utility range-based interactive group support system for multiattribute decision making[J]. Computers & Operations Research, 2001, 28(5): 485-503.
[135] Samaddar S, Kadiyala S S. An analysis of interorganizational resource sharing decisions in collaborative knowledge creation[J]. European Journal of Operational Research, 2006, 170(1): 192-210.
[136]王众托.知识系统工程:知识管理的新学科[J].大连理工大学学报, 2000, 40(s1): 115-122.
[137]段志蓉,邱海鹰,朱玉杰.企业的风险态度对国际化决策的影响[J].清华大学学报(哲学社会科学版), 2008, 23(2): 149-160.
[138] Simon H A. Administrative Behavior-A Study of Decision Making Processes in Administrative Organization[M]. New York : Macmillan Publishing Co, lnc, 1971.
[139]李春好,孙永河. ANP内部循环依存递阶系统的方案排序新方法[J].管理科学学报, 2008, 11(6): 25-34.
[140]马剑虹.决策渐进调整过程的个案研究[J].应用心理学, 2001, 7(3): 7-12.
[141]魏权龄.数据包络分析[M].北京:科学出版社, 2004.
[142] Sarrico C S, Dyson R G. Restricting virtual weights in data envelopment analysis[J]. European Journal of Operational Research, 2004, 159(1): 17-34.
[143]李春好.对W—B乘子置信域约束构造方法的改进及模型[J].中国管理科学, 2003, 11(3): 51-55.
[144]郭菊娥,白云涛,席酉民,等.权威类型、决策程序对高管决策过程影响研究[J].管理科学学报, 2008, 11(6): 1-10.
[145] Al-Nowaihi A, Dhami S. A simple derivation of Prelec's probability weighting function[J]. Journal of Mathematical Psychology, 2006, 50(6): 521-524.
[146] Armantier O, Treich N. Star-shaped probability weighting functions and overbidding in first-price auctions[J]. Economics Letters, 2009, 104(2): 83-85.
[147] Mattos F, Garcia P, Pennings J M E. Probability weighting and loss aversion in futures hedging[J]. Journal of Financial Markets, 2008, 11(4): 433-452.
[148] Prelec D. The Probability Weighting Function[J]. Econometrica, 1998, 66(3): 497-528.
[149] Abdellaoui M. Parameter-free elicitation of utility and probability weighting functions[J]. Management science, 2000, 46(11): 1497-1512.
[150] Abdellaoui M. Choice-based elicitation and decomposition of decision weights for gains and losses under uncertainty[J]. Management science, 2005, 51(9): 1384-1399.
[151] Bleichrodt H, Pinto J L. A Parameter-Free Elicitation of the Probability Weighting Function in Medical Decision Analysis[J]. Management science, 2000, 46(11): 1485-1496.
[152] Booij A S, Van De Kuilen G. A parameter-free analysis of the utility of money for the general population under prospect theory[J]. Journal of Economic Psychology, 2009, 30(4): 651-666.
[153]卓越.群决策支持系统中的一致性分析技术[J].控制与决策, 1999, 14(6): 636-641.
[154]陈建中,徐玖平.群决策的交互式TOPSIS方法及其应用[J].系统工程学报, 2008, 23(3): 276-281.
[155] Lai Y. Hierarchical optimization: a satisfactory solution[J]. Fuzzy Sets and Systems, 1996, 77(3): 321-335.
[156] Shih H S, Lai Y J, Lee E S. Fuzzy approach for multi-level mathematical programming problems[J]. Computers and Operational Research, 1996, 23(1): 73-91.
[157] Sakawa M, Nishizaki I. Interactive fuzzy programming for decentralized two-level linear programming problems[J]. Fuzzy Sets and Systems, 2002, 125(3): 301-315.
[158] Sakawa M, Nishizaki I, Uemura Y. Interactive fuzzy programming for multi-level linear programming problems with fuzzy parameters[J]. Fuzzy Sets and Systems, 2000(109): 3-19.
[159] Tiryaki F. Interactive compensatory fuzzy programming for decentralized multi-level linear programming (DMLLP) problems[J]. Fuzzy Sets and Systems, 2006, 157(23): 3072-3090.
[160] Ahlatcioglu M, Tiryaki F. Interactive fuzzy programming for decentralized two-level linear fractional programming (DTLLFP) problems[J]. Omega, 2007, 35(4): 432-450.
[161] Cannon-Bowers J A, Salas E. Reflections on shared cognition[J]. Journal of Organizational Behavior, 2001, 22(2): 195-202.
[162]黄文艳,马剑虹.组织中的心理契约[J].人类工效学, 2006, 12(1): 69-71.
[163]马剑虹,施建锋.风险偏爱特征的实验研究[J].应用心理学, 2002, 8(3): 28-34.
[164]徐寒易,马剑虹.共享心智模型:分布、层次与准确性初探[J].心理科学进展, 2008, 16(6): 933-940.
[165]马剑虹.组织决策的影响力分布[J].心理学报, 1997, 29(1): 82-90.
[166] Werners B M. Aggregation models in mathematical programming[M]. Berlin: Springer, 1988.
[167] Xu Z, Chen J. An interactive method for fuzzy multiple attribute group decision making[J]. Information Sciences, 2007, 177(1): 248-263.
[168] Chuu S. Group decision-making model using fuzzy multiple attributes analysis for the evaluation of advanced manufacturing technology[J]. Fuzzy Sets and Systems, 2009, 160(5): 586-602.
[169]廖貅武,唐焕文.基于不完全信息的一种群决策方法[J].大连理工大学学报, 2002, 42(1): 122-126.
[170] K?ksalan M, ?zpeynirci S B. An interactive sorting method for additive utility functions[J]. Computers & Operations Research, 2009, 36(9): 2565-2572.
[171] Mishra S, Ghosh A. Interactive fuzzy programming approach to Bi-level quadratic fractional programming problems[J]. Annals of Operations Research, 2006, 143(1): 251-263.
[172] Mishra S. Weighting method for bi-level linear fractional programming problems[J]. European Journal of Operational Research, 2007, 183(1): 296-302.
[173] Gurevich G, Kliger D, Levy O. Decision-making under uncertainty - A field study of cumulative prospect theory[J]. Journal of Banking & Finance, 2009, 33(7): 1221-1229.
[174] Nwogugu M. A further critique of cumulative prospect theory and related approaches[J]. Applied Mathematics and Computation, 2006, 179(2): 451-465.
[175] Trepel C, Fox C R, Poldrack R A. Prospect theory on the brain? Toward a cognitive neuroscience of decision under risk[J]. Cognitive Brain Research, 2005, 23(1): 34-50.
[176]朱建军,宋传平,刘思峰,等.一类三端点区间数判断矩阵的一致性及权重研究[J].系统工程学报, 2008, 23(1): 22-27.
[177]李春好,杜元伟,刘成明,等.基于基元前景交叉判断的前景价值模型[J].管理科学学报, 2010, 13(2): 12-23.
[178]毕鹏程,席酉民.群体决策过程中的群体思维研究[J].管理科学学报, 2002, 5(1): 25-34.
[179] Van Ginkel W P, Van Knippenberg D. Knowledge about the distribution of information and group decision making: When and why does it work?[J]. Organizational Behavior and HumanDecision Processes, 2009, 108(2): 218-229.
[180]蒋御柱,张朋柱,张兴学.群体研讨支持系统中的智能可视化研究[J].管理科学学报, 2009, 12(3): 1-11.
[181] Keeney R L. Measurement Scales for Quantifying Attributes[J]. Behavioral Science, 1981, 26(1): 29-36.
[182] Tegarden D P, Sheetz S D. Group cognitive mapping: a methodology and system for capturing and evaluating managerial and organizational cognition[J]. Omega, 2003, 31(2): 113-125.
[183] Irani Z, Sharif A M, Love P E D. Mapping knowledge management and organizational learning in support of organizational memory[J]. International Journal of Production Economics, 2009, 122(1): 200-215.
[184] Ahmad R, Ali N A. The use of cognitive mapping technique in management research: theory and practice[J]. Management Research News, 2003, 26(7): 1-16.
[185] De Campos L M, Fernández-Luna J M, Huete J F. Using context information in structured document retrieval: an approach based on influence diagrams[J]. Information Processing & Management, 2004, 40(5): 829-847.
[186] Derosa D M, Smith C L, Hantula D A. The medium matters: Mining the long-promised merit of group interaction in creative idea generation tasks in a meta-analysis of the electronic group brainstorming literature[J]. Computers in Human Behavior, 2007, 23(3): 1549-1581.
[187] Michinov N, Primois C. Improving productivity and creativity in online groups through social comparison process: New evidence for asynchronous electronic brainstorming[J]. Computers in Human Behavior, 2005, 21(1): 11-28.
[188] Roy M C, Gauvin S, Limayem M. Electronic group brainstorming: the role of feedback on productivity[J]. Small Group Research, 1996, 27(2): 215-247.
[189] Valacich J S, Schwenk C. Devil's Advocacy and Dialectical Inquiry Effects on Face-to-Face and Computer-Mediated Group Decision Making[J]. Organizational Behavior and Human Decision Processes, 1995, 63(2): 158-173.
[190] Schulz-Hardt S, Jochims M, Frey D. Productive conflict in group decision making: genuine and contrived dissent as strategies to counteract biased information seeking[J]. Organizational Behavior and Human Decision Processes, 2002, 88(2): 563-586.
[191] Hua Z, Jiang W, Liang L. Adjusting inconsistency through learning in group decision-making, and its application to China's MBA recruiting interview[J]. Socio-Economic Planning Sciences, 2007, 41(3): 195-207.
[192]朱建军,刘思峰.群决策中模糊偏好信息转化的若干性质研究[J].控制与决策, 2008, 21(1): 56-59.
[193] Da?deviren M, Yüksel ?. A fuzzy analytic network process (ANP) model for measurement of the sectoral competititon level (SCL)[J]. Expert Systems with Applications, 2010, 37(2): 1005-1014.
[194] Lee H, Kim C, Cho H, et al. An ANP-based technology network for identification of core technologies: A case of telecommunication technologies[J]. Expert Systems with Applications, 2009, 36(1): 894-908.
[195] Yüksel ?, Dagdeviren M. Using the analytic network process (ANP) in a SWOT analysis - A case study for a textile firm[J]. 2007, 177(16): 3364-3382.
[196]吴宪华,张列平.动态联盟伙伴选择的决策方法及其战略评估模型的建立[J].系统工程, 1998, 16(6): 38-43.
[197]邹仲海,宋国防.基于FANP方法的供应链配送中心评价选择[J].工业工程, 2009, 12(4): 86-90.
[198] Wu W. Choosing knowledge management strategies by using a combined ANP and DEMATEL approach[J]. Expert Systems with Applications, 2008, 35(3): 828-835.
[199] Meade L M, Presley A. R&D project selection using the analytic network process[J]. IEEE Transactions on Engineering Management, 2002, 49(1): 59-64.
[200] Poonikom K, Christopher O, Chansa-Ngavej C. An application of the analytic network process (ANP) for university selection decisions[J]. Science Asia, 2004, 30(4): 317-326.
[201] Sarkis J. Qualititative models for performance measure systems-alternate considerations[J]. International Journal of Production Economics, 2003, 86(1): 81-90.