Production efficiency in Indian agriculture: An assessment of the post green revolution years

In this paper we use the nonparametric method of Data Envelopment Analysis (DEA) to obtain Pareto-Koopmans measures of technical efficiency of individual states over the years 1970–71 through 2000–01 in a multi-output, multi-input model of agricultural production. We disaggregate overall efficiency into two distinct components representing output and input efficiencies and identify the contribution of individual outputs and inputs to the measured level of overall efficiency. Because introduction of modern inputs has been a major component of the process of modernization of Indian agriculture, we examine to what extent different states succeeded in utilizing the modern inputs compared to the traditional inputs. Variations in the DEA efficiency scores across states and over years is explained in terms of differences in various institutional and demographic factors in a second stage regression analysis.     

Task partitioning in new product development teams: A knowledge and learning perspective

R&D alliances and outsourcing elements of the new product development process are now commonplace practices among many firms. However, little previous work has examined how these organizational choices influence project knowledge and learning. Based on a comparison of three new product development projects in the software industry, this paper examines how task partitioning in the project influences learning and knowledge development within the firm. The paper suggests that internal development projects encourage synthetic learning and development of architectural and tacit knowledge; in contrast, outsourcing and joint ventures encourage analytic learning and development of component and explicit knowledge.     

Using the End‐User Computing Satisfaction (EUCS) Instrument to Measure Satisfaction with a Web Site

The purpose of this study is to revise and revalidate the End‐User Computing Satisfaction (EUCS) instrument to measure satisfaction with a Web site from a usability perspective. This study is especially important given the increased significance of the Web and the uniqueness of the Web as a computing environment. A total of 176 students participated in a lab simulation that involved a usability evaluation of the Lands' End Web site ( http://www.landsend.com ). Students were asked to complete a set of tasks, record their answers, and then complete the EUCS instrument. Confirmatory factor analysis and invariance analyses were conducted to test the reliability, validity, and generalizability of the revised EUCS. The results show that the EUCS is a valid and robust instrument in the Web environment but that one of the subfactors, timeliness, will need further refinement in the future. Usability practitioners can use the EUCS to measure end‐user satisfaction with a Web site and use the feedback for improving Web‐site design. We describe a case study of an actual usability application that utilized the revised EUCS effectively to support the design of building supply Web sites involving two types of end users, homeowners and contractors. We also propose a typology that researchers can use as a starting point to judge when it is necessary to revalidate an instrument like the EUCS. Finally, we discuss the limitations of our study and present avenues for future research.     

企业经营管理预警：主成分分析在logistic回归方法中的应用

logistic回归分析是度量企业信用风险的一种主流方法,它的假设比较符合经济现实和金融数据分布的特点。但是考虑到现阶段我国上市公司的信用数据具有的高维性和高相关性等特点对logistic分析产生的负面影响,本文在logistic分析中引入了能够有效降维和消除logistic方程共线性等问题的主成分分析,并对我国沪深两市上市公司的经营失败进行了实证研究,结果表明结合主成分分析法的logistic回归分析在模型解释和预测准确率等力面均优于简单的logistic分析。     

A robust ranking method extending ELECTRE III to hierarchy of interacting criteria,imprecise weights and stochastic analysis

A great majority of methods designed for Multiple Criteria Decision Aiding (MCDA) assume that all assessment criteria are considered at the same level, however, decision problems encountered in practice often impose a hierarchical structure of criteria. The hierarchy helps to decompose complex decision problems into smaller and manageable subtasks, and thus, it is very attractive for computational efficiency and explanatory purposes. To handle the hierarchy of criteria in MCDA, a methodology called Multiple Criteria Hierarchy Process (MCHP), has been recently proposed. MCHP permits to consider preference relations with respect to a subset of criteria at any level of the hierarchy. Here, we propose to apply MCHP to the ELECTRE III ranking method adapted to handle three types of interaction effects between criteria: mutual-weakening, mutual-strengthening and antagonistic effect. We also involve in MCHP an imprecise elicitation of criteria weights, generalizing a technique called the SRF method. In order to explore the plurality of rankings obtained by the ELECTRE III method for possible sets of criteria weights, we apply the Stochastic Multiobjective Acceptability Analysis (SMAA) that permits to draw robust conclusions in terms of rankings and preference relations at each level of the hierarchy of criteria. The novelty of the whole methodology consists of a joint consideration of hierarchical assessments of alternatives performances on interacting criteria, imprecise criteria weights, and robust analysis of ranking recommendations resulting from ELECTRE III. An example regarding the multiple criteria ranking of some European universities will show how to apply the proposed methodology on a decision problem.     

A Comparison of Arbitration Procedures for Risk‐Averse Disputants*

We propose an arbitration model framework that generalizes many previous quantitative models of final offer arbitration, conventional arbitration, and some proposed alternatives to them. Our model allows the two disputants to be risk averse and assumes that the issue(s) in dispute can be summarized by a single quantifiable value. We compare the performance of the different arbitration procedures by analyzing the gap between the disputants' equilibrium offers and the width of the contract zone that these offers imply. Our results suggest that final offer arbitration should give results superior to those of conventional arbitration.     

High value manufacturing: Capability,appropriation, and governance

Manufacturing competitiveness is on many policy agendas, born out of a concern for firms in high-cost economies finding themselves outcompeted by low-cost rivals. Government policy makers and manufacturing firm strategists have put their faith in what we label as high value manufacturing (HVM). We see HVM as an incipient phenomenon currently in a situation of prescience, as something that is still “in-the-making,” with manufacturing firms trying to find ways to be able to step away from having to compete on price. This paper consults relevant strategy theories with the purpose to pinpoint the issues and problems that need to be accommodated for bringing HVM into being and for creating the effects that are anticipated. We found that HVM must be seen as a distributed activity, thus realizing complex functionality for a system-of-use, while being subjected to path constitution. For HVM to function, the firms involved need to find solutions to the capability problem, the appropriation problem, and the governance problem. We suggest that further research needs to involve itself in problem-solving activity to assist in bringing HVM about while simultaneously further developing strategy theory geared toward firms that are involved in a distributed activity like HVM.     

额外资源按需分配方法研究

文献Wei Quanling等^[1]的最后一段提出了这样一类额外资源分配问题：假设有一些额外的投入资源将要分配给全部或者部分决策单元,如果我们希望分配结果对于整个系统（该系统由全部决策单元组成）来说是最有益的,那么应当如何分配这些额外的投入资源?他们同时还指出系统中哪些决策单元能够获得额外的投入资源,这不仅取决于决策单元的效率,还需要考虑规模报酬。针对此类问题,本文提出一种额外资源按需分配方法：首先,综合决策单元的技术效率与其投影点的规模弹性构建发展曲线,并由决策单元的发展曲线获知其投入变动与产出变动的对应关系;然后,把额外资源分成若干等份,依次分配每一个等份的额外资源,每次分配使系统的总产出增加实现最大,直至分配完全部的额外资源,或者分配某一份额外资源不再带来任何的产出增加,分配就会停止;最后,把决策单元在每一次分配过程中获得的额外资源进行累加,得到这个决策单元最终的分配结果。全文的最后分别在"一个投入一个产出"和"两个投入一个产出"情况下给出两个具体的分配算例。分配结果表明,这种按需分配方法不会缩减任意一个决策单元当前占有的投入资源数量,并且还可以有效地在辨识出某种额外资源在规模上和结构上存在的冗余,从而有效地避免不必要的分配。     

FEPA-金融时间序列自适应组合预测模型

本文报告一种金融时间序列预测的信号分析、信息融合与智能计算组合模型,简称FEPA,由针对金融时间序列（FTS）信号分析的经验模态分解（EMD）、用于数据降维的主成分分析（PCA）和用于非线性建模的人工神经网络（ANN）三部分组成。该模型首先应用滑动窗口截取原始金融时间序列最近期数据集,应用EMD分解算法把数据集分解成不同尺度的本征模态函数（IMF）,然后通过主成分分析将分解后的数据降维,提取最有信息量的特征;然后将这些特征输入到神经网络进行组合预测。本文提出的组合预测模型FEPA是基于分解-提优-合成的信息融合思想,有效提高了预测可靠性。其创新点在于：1）首次给出了EMD算法的结构化表达,提供了今后融合更多信息的算法接口;2）通过多步长预测输出深入研究EMD分解的有效信息结构;3）通过切换到更细时间框架来处理EMD的端点效应,并探索了两级时间框架下的预测效果;4）给出了金融时间序列组合预测模型的一般性架构,具有可升级性和可扩展性。并且通过滑动窗口EMD使得实证更能切近实际。通过在沪深300股指和澳大利亚股指上的实证,结果表明FEPA预测模型在沪深300股指日线和15分钟线上的预测命中率高达78%和82%,在澳大利亚股指日线上也达到了74%的命中率,经比较,明显高于文献中常见的5种模型。