基于案例学习的知识管理系统

简单了介绍知识管理的特点以及知识管理系统具有的功能,说明将基于案例的推理技术(CBR)和其它机器学习技术结合起来应用于知识管理系统的必要性和有效性,并在此基础上提出一种基于案例学习的知识管理系统的实现框架。     

基于工程项目知识管理的文化建设

建立以知识为导向的文化是成功实施知识管理的关键因素之一。文章阐述了知识管理与项目文化的关系,提出了建立以信任和学习型文化为基础的知识导向型项目文化,使知识管理在工程项目组织中能得到成功实施。     

知识联盟"学习赛跑"的影响因素分析

通过分析影响知识联盟知识转移的各种因素和联盟动态演进的过程,重新定义了知识联盟成功的含义,并根据“学习赛跑”的理论模型讨论了知识联盟演进过程中各类影响因素的作用,给出了研究知识联盟动态演进过程的因素分析模型,为研究知识联盟提供了新的思路和方法。     

Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks

Corporate bankruptcy prediction has attracted significant research attention from business academics, regulators and financial economists over the past five decades. However, much of this literature has relied on quite simplistic classifiers such as logistic regression and linear discriminant analysis (LDA). Based on a large sample of US corporate bankruptcies, we examine the predictive performance of 16 classifiers, ranging from the most restrictive classifiers (such as logit, probit and linear discriminant analysis) to more advanced techniques such as neural networks, support vector machines (SVMs) and “new age” statistical learning models including generalised boosting, AdaBoost and random forests. Consistent with the findings of Jones et al. ( 2015 ), we show that quite simple classifiers such as logit and LDA perform reasonably well in bankruptcy prediction. However, we recommend the use of “new age” classifiers in corporate bankruptcy modelling because: (1) they predict significantly better than all other classifiers on both the cross‐sectional and longitudinal test samples; (2) the models may have considerable practical appeal because they are relatively easy to estimate and implement (for instance, they require minimal researcher intervention for data preparation, variable selection and model architecture specification); and (3) while the underlying model structures can be very complex, we demonstrate that “new age” classifiers have a reasonably good level of interpretability through such metrics as relative variable importances (RVIs).     

Dimensions of strategic intervention for risk reduction and mitigation: a case study of the MV Sewol incident

Organizations in disaster management system should learn from previous experience and strategically use their lesson for the refinement of a system’s competencies for risk management. However, the MV Sewol incident revealed the absence of the organizational learning in the Korean disaster management system. With mixed methods of content analysis, in-depth interview, and social network analysis, this study identified key failure factors in response to the incident and categorized them by managerial, structural, and institutional domains. While the Korean government took bold steps to rebuild its risk management system, those efforts were biased to structural reforms and lacked fundamental changes in human and informational resources management. Based on the findings, this study suggests the balanced efforts for system refinement for effective risk management.     

Learning,large deviations and rare events

We examine the role of generalized stochastic gradient constant gain (SGCG) learning in generating large deviations of an endogenous variable from its rational expectations value. We show analytically that these large deviations can occur with a frequency associated with a fat-tailed distribution even though the model is driven by thin-tailed exogenous stochastic processes. We characterize these large deviations, driven by sequences of consistently low or consistently high shocks and then apply our model to the canonical asset pricing framework. We demonstrate that the tails of the stationary distribution of the price–dividend ratio will follow a power law.     

个体主动性人格对学习能力影响的实证研究

探讨了员工个体的主动性人格特质对其学习能力的影响及其作用机制,以及员工的工作卷入和反馈寻求在这一关系中的中介作用。运用企业236个员工数据进行分析,得出以下结论:企业员工的主动性人格显著促进其学习能力;员工的主动性人格显著强化其工作卷入和反馈寻求;员工对内的工作卷入促进了其学习能力,且在主动性人格和学习能力的关系中起部分中介的作用;员工对外的反馈寻求促进其学习能力,且在主动性人格和学习能力的关系中起部分中介作用。研究结论为组织个人学习能力的提升提供了一个重要的前因变量,并对员工工作卷入与反馈寻求的中介作用机制进行了探讨,丰富了个人学习研究,并提出了相关的实践建议。     

Belief consistency and trade consistency

Interpersonal consistency can be described in epistemic terms as a property of beliefs, or in economic terms as the impossibility of certain trades. The existence of a common prior from which all agentsʼ beliefs are derived is of the first kind. The non-existence of an agreeable bet, that is, a contingent zero-sum trade which is always favorable to all agents, is of the second kind. It is well established that these two notions of consistency are equivalent for finite type spaces but not for countable ones. We present three equivalences of epistemic consistency and economic consistency conditions for countable type spaces, defining in this way three levels of consistency of type spaces: weak consistency, consistency, and strong consistency. These three levels coincide in the finite case. We fully analyze the level of consistency of type spaces based on the knowledge structure of Rubinsteinʼs email game. The new notion of belief consistency introduced here helps to justify the requirement of boundedness of payoff functions in countable type spaces by showing that in a large class of spaces there exists an agreeable unbounded bet even when a common prior exists.     

Learning about the real-world in MICE education: The case of exhibition learning from Thailand

Abstract

The use of experiential learning in tourism and hospitality education is well-documented in literature. Experiential learning studies in this field may include, for example, internship experiences, field trip perceptions, conferences, and social events. However, there is still insufficient literature to understand students’ learning and their real-world experience in MICE education, especially in the exhibition sector. This study, therefore, addresses this gap by reporting the experiential learning of graduate students in an event course with the objectives to investigate student perceptions on academic learning experiences and the development of work-related skills by carrying out the exhibition project. Students are challenged to perform a complicated task as a real exhibition organizer, and to deal with other stakeholders of the exhibition industry (e.g., exhibition venue, exhibitors, contractors, and visitors). The experiential learning method is discussed through the Plan-Do-Check-Act (PDCA) process. The results indicate that students not only gained in-depth learning about the exhibition industry, but also developed important work skills (e.g., teamwork, planning, and coordinating skills). Moreover, classroom learning, industry visits, and real-world experience are found to be the important factors contributing to exhibition learning. The current study contributes to the limited exhibition learning literature and provides event educators new insights into the teaching and learning of exhibition-based projects in regard to how students plan, learn and carry out the exhibition event through the case of Thailand. Other similar courses may apply the learning processes and results of this study to develop effective experiential learning in MICE education.     

A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.