企业家·生命·周期及其价值提升研究

互企业家生命周期是指企业家所面临的一个从起步到成长、从成长到成熟、从成熟到衰退的过程。本文从分析企业家生命周期的内涵出发,探讨了影响企业家生命周期的因素,企业家生命周期与企业家价值之间的关系,得出可以通过优化企业家生命周期来提升企业家价值;最后提出了一些优化企业家生命周期的具体途径。     

EDI技术在物流配送中心的应用研究

本文对EDI技术在物流配送中心的应用进行了深入研究,设计了EDI在物流配送供应链系统中的应用框架,对我国物流配送企业的发展有一定的指导作用。     

国有企业“债转股”政策:功能缺陷与制度依赖——项基于国有企业“债转股”政策跟踪调查的研究

伴随着我国经济体制改革向纵深发展,国有企业负债比率过高的问题更加突出显示出来。为了解决这个问题,我国实施了“债转股”政策。本文讨论四个命题:(1)国有企业是负债比率过高还是收益率过低;(2)“债转股”政策究竟是使国有企业“财务解困”还是“体制解困”;(3)“债转股”政策究竟是转移金融风险还是化解金融风险;(4)资产管理公司为国有企业和国有商业银行解困,将来谁为资产管理公司解困。本文认为“债转股”政策设计存在功能缺陷与制度依赖。国有企业“债转股”政策的成功依赖于国有企业(商业银行)现代企业制度的建立与完善。     

企业并购活动中目标企业选择的层次分析评估研究

企业并购是企业进行资本扩张、促进企业间存量资源合理统流动的重要途径。对企业并购中的目标企企的选择、价值评估进行研究，构建我国企业并前过程中目标企业选择的评价指标体系，运用AHP法对目标企业选择进行定量化评估，将有利于企业通过并购后，实现资源的最优配置．保证企业经营机制协调、有效地运行。     

基于改进技术接受模型的ERP系统实施方案研究

为了解决ERP系统实施过程中的诸多管理问题,引入了技术接受模型,分析了其心理学基础并做了改进。在此基础上提出了系统实施的原型方案,为企业顺利实施ERP系统提供新的思路和方法。     

基于Agent的物流配送决策支持系统研究

物流配送是一个涉及多种影响因素的综合性复杂问题。本文探讨了基于Agent(智能体技术)的物流配送决策支持系统设计，在该系统中的智能体模型中引入学习机制，初步建立了一个具有专家系统特点、可以不断进化的物流配送决策支持系统。     

工程项目的价值管理问题探讨——价值管理在设计-施工模式中的应用

本文介绍了建筑工程项目中应用价值管理的方法,包括实施的组织、价值体系、工程价值链和研究方法,在设计-施工模式中具体的应用。首先介绍价值管理的理论,然后分析在设计-施工模式中价值管理应用的优势,以及在这种模式中如何应用价值管理。     

浅析人力资本的保值与增值

本文从熵的角度解释了人力资本的贬值问题，认为熵与人力资本有着紧密的联系，并从人力资本的生产过程和使用过程两方面提出了人力资本保值与增值的对策。     

Probability theory in fuzzy sample spaces

This paper tries to develop a neat and comprehensive probability theory for sample spaces where the events are fuzzy subsets of The investigations are focussed on the discussion how to equip those sample spaces with suitable -algebras and metrics. In the end we can point out a unified concept of random elements in the sample spaces under consideration which is linked with compatible metrics to express random errors. The result is supported by presenting a strong law of large numbers, a central limit theorem and a Glivenko-Cantelli theorem for these kinds of random elements, formulated simultaneously w.r.t. the selected metrics. As a by-product the line of reasoning, which is followed within the paper, enables us to generalize as well as to bring together already known results and concepts from literature.Acknowledgement. The author would like to thank the participants of the 23rd Linz Seminar on Fuzzy Set Theory for the intensive discussion of the paper. Especially he is indebted to Professors Diamond and Höhle whose remarks have helped to get deeper insights into the subject. Additionally, the author is grateful to one anonymous referee for careful reading and valuable proposals which have led to an improvement of the first draft.This paper was presented at the 23rd Linz Seminar on Fuzzy Set Theory, Linz, Austria, February 5–9, 2002.     

Robustness issues in multilevel regression analysis

A multilevel problem concerns a population with a hierarchical structure. A sample from such a population can be described as a multistage sample. First, a sample of higher level units is drawn (e.g. schools or organizations), and next a sample of the sub‐units from the available units (e.g. pupils in schools or employees in organizations). In such samples, the individual observations are in general not completely independent. Multilevel analysis software accounts for this dependence and in recent years these programs have been widely accepted. Two problems that occur in the practice of multilevel modeling will be discussed. The first problem is the choice of the sample sizes at the different levels. What are sufficient sample sizes for accurate estimation? The second problem is the normality assumption of the level‐2 error distribution. When one wants to conduct tests of significance, the errors need to be normally distributed. What happens when this is not the case? In this paper, simulation studies are used to answer both questions. With respect to the first question, the results show that a small sample size at level two (meaning a sample of 50 or less) leads to biased estimates of the second‐level standard errors. The answer to the second question is that only the standard errors for the random effects at the second level are highly inaccurate if the distributional assumptions concerning the level‐2 errors are not fulfilled. Robust standard errors turn out to be more reliable than the asymptotic standard errors based on maximum likelihood.