集群产业集成化过程的知识共享问题研究

集群产业集成过程是集成参与企业知识整合的过程.对集群产业集成过程中知识共享问题的研究,可较为深入的揭示集成过程中企业间协同的本质.本文通过设计知识信号机制并构建定量的优化模型,分析了集成化过程中知识共享的各影响因素间的作用关系.结果表明,在合理的知识信号机制和知识共享收益机制的指导下,集成参与企业会根据知识要素间的作用关系做出合理的知识共享决策.     

科技成果转化团队知识网络中知识共享的博弈模型

构建了科技成果转化团队知识网络中知识共享的演化博弈模型,分析了核心节点的收益分配策略和普通节点的知识共享策略选择决策,得到了模型的均衡解、复制动态和稳定性,讨论了知识共享的影响因素,并进行了数值实验.研究结果表明:1)核心节点选择"高收益分配策略"、普通节点选择"知识共享策略"的概率是一个震荡性螺旋提升过程;2)影响普通节点知识共享的正向因素有"普通节点用于知识共享的资金投入量"等;3)影响核心节点收益分配策略选择的正向因素有"普通节点出入度期望值"等.     

基于社会网络的核心-普通农户间知识转移的博弈分析

基于社会网络的核心外围结构,构建了核心农户与普通农户知识转移的博弈模型,分析了在分散式决策、集中式决策与收益共享契约的协调三种情况下的最优知识定价、知识转移水平和社会网络各参数对知识转移和协调机制的影响.研究表明:在核心外围结构下,由于信息不对称,集中式决策的所得利润高于分散式决策,而利用收益共享契约可以解决分散式决策存在的"双边效应"问题并能够提高社会网络各节点收益.     

创意团队隐性知识转移绩效影响因素的实证研究

文章采用问卷调研获取一手数据,运用PLS—SEM方法测度了各类因素对创意团队隐性知识转移绩效的影响。研究发现,PLS—SEM方法对观测数据拟合精度较高,在样本数据非正态分布、共线性等情况下仍然稳定。实证结果表明:团队情境、吸收能力、转移意愿、转化能力、媒介丰富度由大到小对创意团队隐性知识转移绩效产生正向影响,而隐性知识特性必须通过对知识源转化能力的显著负影响才能间接影响知识转移绩效。     

延迟效应影响下的产学研协同创新知识共享研究

在高校和企业组成的产学研协同创新知识共享体系中,考虑成员的知识共享策略均存在延迟效应时的影响,构建了基于双延迟效应的动态微分模型。运用极大值原理,得到了协同创新主体在集中和分散两种决策下的最优知识共享策略、最优收益和体系的知识水平。研究发现:延迟时间的存在对高校和企业的知识共享策略具有负面效应,且均低于发生即时效应的情形;集中式决策并不总是优于分散式决策,两个延迟时间在满足一定的条件时,选择集中式决策为最优,否则双方企业将追求自身利益最大化而选择分散式决策;分散决策下,利润分配比例达到一定条件时,企业才愿意为高校提供补贴。且高校的知识共享延迟时间增加时,企业将提高该补贴比例。补贴比例越大,高校的知识共享水平越高,但对企业的知识共享策略没有影响。     

考虑风险的产业技术联盟知识共享演化博弈研究

知识共享是促进产业技术联盟绩效增长的重要途径。考虑产业技术联盟成员风险偏好的差异性,引入成员风险因子与知识共享收益因素,构建基于风险因子和知识共享收益非线性关系的演化博弈支付矩阵,并求得博弈的均衡解。通过TD产业联盟的案例分析,仿真模拟风险因子、收益分配系数等因素对知识共享意愿的影响。研究结果表明:联盟成员的共享意愿对收益分配系数的变化非常敏感;风险因子和知识共享程度对联盟成员的共享意愿影响较大。     

IT外包知识共享行为的演化博弈分析

针对IT外包知识共享障碍的问题,在已有IT外包知识共享研究的基础上,依据演化博弈论建立了IT外包知识共享行为的演化博弈模型,通过求解复制动态方程与演化稳定策略,分析了影响IT外包知识共享行为的因素,并利用Matlab 7.6软件进行仿真。研究结果表明,知识可共享量、知识互补程度、信任、知识吸收转化能力、知识共享成本和风险以及激励机制等是影响IT外包知识共享行为的关键因素。最后提出相应的对策与建议。     

基于不同网络层次的集群企业知识共享演化分析

基于不同网络层次嵌入视角,考虑集群内网络和超集群网络嵌入的不同情况,综合运用Hotelling博弈和演化博弈,建立集群企业与集群外部企业间知识共享决策与演化的动态模型,探讨在无政府补贴和有政府补贴条件下高技术与低技术集群企业间的博弈决策。研究发现,高技术集群企业会挤占低技术集群企业与集群外企业的知识共享,知识共享成本是影响集群内外企业知识共享的关键因素,政府可以引导集群内企业更多地与集群外企业进行知识共享。最后运用数值仿真对研究结论进行验证。     

知识获取视角下用户持续参与众包创新的动态控制策略

众包创新作为企业充分利用外部群体智慧以实现创新的新型开放式创新模式,其本质是企业突破组织界限,获取与整合外部网络知识以提升创新绩效。由于创新任务复杂多样、用户参与自由自愿等特征,导致在众包创新实践中用户持续参与意愿不足、创新效果不好等现象。为此,基于关系营销和知识共享相关理论,界定面向众包创新的知识承诺概念,认为知识获取视角下用户持续参与众包创新的本质是众包参与双方的知识承诺建立,进而分析众包创新中知识承诺的关键影响因素,构建用户持续参与众包创新的动态优化决策模型,通过求解和算例分析探讨最优决策结果,给出引导用户持续参与众包创新的最优控制准则。     

基于降低失效停放区比率的共享单车系统稳定性研究

共享单车失效停放区使得用户无法取车或还车,影响共享单车系统运营稳定性。本文基于排队论与马尔科夫链构建了共享单车系统稳定状态平均场方程组并求解稳态解,发现停放区交互作用使得失效停放区对相邻停放区产生需求溢出效应,增加系统失效停放区比率,影响系统运营稳定性。本文以降低系统失效停放区比率目标,引入(M,S)交互策略,调节失效停放区对相邻停放区的交互作用程度。进一步,以算例分析比较相同类型与不同类型停放区交互作用、(M,S)交互策略下共享单车系统的失效停放区比率。结果表明(M,S)交互策略能够有效降低失效停放区比率,提高系统运营稳定性。本研究为共享单车企业运营稳定性决策提供了有价值的参考。     

多粒度语言信息的交互式多属性群决策方法及应用

针对目前多阶段交互式决策的研究多是基于点值的缺陷,以及大多数研究忽视了从多个属性对方案进行评价等不足,本文探讨了一种基于多粒度语言信息的交互式多属性群决策方法。首先基于不确定语言变量提出了多粒度语言信息的转换函数,并在给出若干假设的前提下描述了交互式多属性群决策的过程,然后提出了交互影响因子来度量决策者的“话语权”,通过稳定性指标探讨交互终止条件;接着在不确定语言变量的加权算术平均(ULWA)算子和诱导多阶段交互加权算子(I-UOWA和I-UOWGA)的基础上对偏好信息进行集结,再提出优势可能度法对方案排序。最后给出了一个物流供应商选择的案例研究。     

IMGD: an interactive method for multiobjective group decision aid

Multiobjective methods for group decision situations that are proposed in the literature do not generally model power and influence. On the other hand, papers dealing with influence and power in group decision support system (GDSS) are looking for the effects of GDSS on the distribution of power among the group members. This paper proposes an interactive method for group decision aid in multiobjective context integrating the concept of power and influence within the multiperson–multicriteria aspect. The method is designed to be used by a committee to solve a multiple criteria allocation problem. The method is tested on a resource allocation problem in the Municipality of Tunis.     

On the convergence of reference point methods in multiobjective programming

The purpose of this paper is to present some results about the convergence of interactive reference point methods in multiobjective programming. In particular, we describe how dual information may guide the decision maker in his choice of the successive reference points.In the literature different convergence models have been proposed. The analyst may induce convergence by selecting appropriate rules of the communication. Or he may rely on the learning process of the decision maker to induce some kind of ‘psychological’ convergence. In neither case are the activities of the decision maker precisely described. Consequently, the quality of the final decision cannot be established, and the question of convergence remains an unsolved issue.We describe different ways in which the decision maker may select his successive reference points, and we discuss the convergence of the resulting reference point procedures. Also, we comment on the relevance of these different assumptions about the decision maker's behavior. The procedures are illustrated by a small numerical example.     

Modelling a research portfolio using AHP — a group decision process

The problem of choosing a reseatch portfolio has been in the literature for many years. Suggested solutions have ranged from simple scoring models to complex mathematical resource allocation models, but the acceptance rate has been low. In this paper we present an application of a model based on th Analytic Hierarchy Process to a group decision situation. The results are encouraging, showing the positive nature of a model as a focal point in the process. They show that the interactive nature of the methodology encourages the development of subjective estimates and their subsequent discussion. An attempt to counter the problems of biasing was also made. The methodology presented is seen to be easy to use and adaptable, and becomes an integrating mechanism for the group.     

The utility efficient set and its interactive reduction

We assume a decision situation under risk with incomplete information on preferences modelled as a vector utility function. We consider an additive aggregation of its components and partial information on the scaling constants. We develop the concept of utility efficiency to identify efficient strategies in discrete problems when the information about the scaling constants of the decision maker is in the form of a polyhedral cone. A characterization of the utility efficient set provides a practical way to compute such efficient strategies. We then discuss an interactive method based on the assessment of the scaling constants via an interactive paired comparison with its convergence. The method is complemented by a procedure to reduce the utility efficient set to aid in the process of reaching a final strategy.     

Designing Firm Integrating Processes from the Knowledge-Based View—Graduate Student Best Paper Award, CASOS 2002 Conference

The knowledge-based view provides a powerful perspective for analyzing and understanding the firm. However, some of its assumptions have proven difficult to operationalize, making hypothesis testing difficult. One strategically important assumption is that firm competitive advantage depends on effective integrating processes. This paper develops a more precise specification of effective integrating processes. Then, two integrating processes are modeled using an agent-based simulation approach to test a set of factors believed to influence knowledge transfer and application. Results support several hypotheses generated by the knowledge-based view including: (1) greater levels of tacit knowledge transfer enhance the performance benefits gained from integrating processes, and (2) integrating processes with greater task uncertainty benefit from more formal information communication. The results also suggest that the design and management of firm integrating processes is a complex undertaking that requires a clear understanding of (1) the primary performance measure(s) used to evaluate the process, (2) the nature of the process tasks (e.g., routine vs. idiosyncratic), and (3) the knowledge required to perform those tasks (e.g., explicit vs. tacit).     

An interactive approach for Bayesian network learning using domain/expert knowledge

Using domain/expert knowledge when learning Bayesian networks from data has been considered a promising idea since the very beginning of the field. However, in most of the previously proposed approaches, human experts do not play an active role in the learning process. Once their knowledge is elicited, they do not participate any more. The interactive approach for integrating domain/expert knowledge we propose in this work aims to be more efficient and effective. In contrast to previous approaches, our method performs an active interaction with the expert in order to guide the search based learning process. This method relies on identifying the edges of the graph structure which are more unreliable considering the information present in the learning data. Another contribution of our approach is the integration of domain/expert knowledge at different stages of the learning process of a Bayesian network: while learning the skeleton and when directing the edges of the directed acyclic graph structure.     

Choice behavior in interactive multiple-criteria decision making

Choice behavior in an interactive multiple-criteria decision making environment is examined experimentally. A free search discrete visual interactive reference direction approach was used on a microcomputer by management students to solve two realistic and relevant multiple-criteria decision problems. The results revealed persistent patterns of intransitive choice behavior, and an unexpectedly rapid degree of convergence of the reference direction approach on a preferred solution. The results can be explained using Tversky' [20] additive utility difference model and Kahneman-Tversky's [5] prospect theory. The implications of the results for the design of interactive multiple-criteria decision procedures are discussed.     

An interactive decision-making approach based on intelligent systems to redraw district boundaries in regional planning

Environmental planning decisions have become a major challenge and are receiving increasing social attention. To contend with the absence of a decision theory in this domain and to solve conflicts in group decision-making, an interactive decision approach has been defined and supported by a computer-based system to automate the different steps of the modelling process. In this paper, the emphasis is on the advent of intelligent systems and the improvement of management support. This support enables individual decision-makers to augment their knowledge about a problem domain which results in a gain in productivity. The different steps of the approach are described and illustrated through a worked example which is a specific decision problem and deals with redrawing the district boundaries in regional planning.     

Relating value-focused thinking and interactive planning

Value-focused thinking, using the dialogue decision process (DDP), and interactive planning appear to be two totally unrelated processes for making decisions. As this paper shows, new results on the interpretation of utility functions and new ways of thinking about downstream decisions allows us to reinterpret interactive planning as an ideal-focused decision process which is theoretically equivalent to DDP's value-focused decision process. But Ackoff's ideal-focused decision process may be more natural for certain organizational decision settings.