Applications and Implementation DECISION SUPPORT SYSTEMS: AN EMPIRICAL ASSESSMENT FOR DECISION MAKING

A laboratory experiment examined the effects of applying decision support system (DSS) technology to decision making in ill-structured problem environments under varying information conditions. Marketing executives participated in the experiment which investigated the effects of DSS availability, DSS training, and data availability on dependent variables that included: (1) the number of alternatives considered by a subject during decision making, (2) the period of time spent by a subject to complete the decision-making process, (3) the subject's perceived confidence in the decisions he or she had made, (4) the amount of data considered by a subject's during decision making, (5) the individual subject's decision processing, and (6) the subject's performance overall. Our results indicate that all three factors significantly affect the number of alternatives considered by subjects during the decision-making process. We therefore suggest that DSS training be coordinated with decision training in order to realize the potential of DSSs as described in the DSS literature.     

CATEGORY WIDTH COGNITIVE STYLE AS A FACTOR IN ACCOUNTANTS' PERCEPTIONS OF ACCOUNTING INFORMATION

This paper reports the findings of a laboratory experiment designed to investigate the relationship of category width (CW) cognitive style with accountants' perceptions of accounting information. Subjects drawn from large accounting firms in Sydney, Brisbane, and Melbourne, Australia, were classified into broad, medium, and narrow categories following the test devised by Pettigrew [19]. Subjects were requested to state their level of confidence in decisions they had made after receiving (1) conventional accounting information or (2) conventional accounting information and human resources accounting (HRA) information using a one-group pretest-posttest design. The results indicated a significant relationship between CW cognitive style and the accountants' confidence in their decisions. Furthermore, CW cognitive style moderated the accounting-information/decision-making relationship.     

A Bayesian mixture of experts approach to covariate misclassification

This article considers misclassification of categorical covariates in the context of regression analysis; if unaccounted for, such errors usually result in mis-estimation of model parameters. With the presence of additional covariates, we exploit the fact that explicitly modelling non-differential misclassification with respect to the response leads to a mixture regression representation. Under the framework of mixture of experts, we enable the reclassification probabilities to vary with other covariates, a situation commonly caused by misclassification that is differential on certain covariates and/or by dependence between the misclassified and additional covariates. Using Bayesian inference, the mixture approach combines learning from data with external information on the magnitude of errors when it is available. In addition to proving the theoretical identifiability of the mixture of experts approach, we study the amount of efficiency loss resulting from covariate misclassification and the usefulness of external information in mitigating such loss. The method is applied to adjust for misclassification on self-reported cocaine use in the Longitudinal Studies of HIV-Associated Lung Infections and Complications.     

Probabilistic Analysis of Dam Accidents Worldwide: Risk Assessment for Dams of Different Purposes in OECD and Non-OECD Countries with Focus on Time Trend Analysis

This study presents probabilistic analysis of dam accidents worldwide in the period 1911–2016. The accidents are classified by the dam purpose and by the country cluster, where they occurred, distinguishing between the countries of the Organization for Economic Cooperation and Development (OECD) and nonmember countries (non-OECD without China). A Bayesian hierarchical approach is used to model distributions of frequency and severity for accidents. This approach treats accident data as a multilevel system with subsets sharing specific characteristics. To model accident probabilities for a particular dam characteristic, this approach samples data from the entire data set, borrowing the strength across data set and enabling to model distributions even for subsets with scarce data. The modelled frequencies and severities are combined in frequency-consequence curves, showing that accidents for all dam purposes are more frequent in non-OECD (without China) and their maximum consequences are larger than in OECD countries. Multipurpose dams also have higher frequencies and maximum consequences than single-purpose dams. In addition, the developed methodology explicitly models time dependence to identify trends in accident frequencies over the analyzed period. Downward trends are found for almost all dam purposes confirming that technological development and implementation of safety measures are likely to have a positive impact on dam safety. The results of the analysis provide insights for dam risk management and decision-making processes by identifying key risk factors related to country groups and dam purposes as well as changes over time.     

纵向沟通中隐性信息获取的激励兼容机制研究

下属隐性信息的获取是确保上级决策者有效决策的一个重要基础,现有的研究未能解决隐性信息获取的效率与预算平衡的两难问题。本文通过确认决策者的作用,在分析模型中引入决策者成本,并借鉴政府征收“所得税”的思路,不仅解决了隐性信息获取的真实性,而且解决了原有方案未能同时兼顾的预算平衡、利润最大化以及向下属转移损失等问题。     

Toyota in the 21st century in Asian countries: Information exchange system and best mix

This article explains the general nature of the business style of Toyota management strategy, its specific performance, and its development in China. It is quite difficult to find specific revolutionary policies and unusual strategies for the success of the Toyota Motor Corporation.     

Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data

Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine‐tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross‐validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.     

The Dynamic Pivot Mechanism

We consider truthful implementation of the socially efficient allocation in an independent private‐value environment in which agents receive private information over time. We propose a suitable generalization of the pivot mechanism, based on the marginal contribution of each agent. In the dynamic pivot mechanism, the ex post incentive and ex post participation constraints are satisfied for all agents after all histories. In an environment with diverse preferences it is the unique mechanism satisfying ex post incentive, ex post participation, and efficient exit conditions. We develop the dynamic pivot mechanism in detail for a repeated auction of a single object in which each bidder learns over time her true valuation of the object. The dynamic pivot mechanism here is equivalent to a modified second price auction.     

企业体系结构的建模框架研究

经济全球化的趋势深刻影响着企业组织管理的各方面.在新的环境下,企业的业务处在不断变化的过程中,企业信息系统(Enterprise Information Systems,EIS)作为企业管理系统的重要组成部分,面临前所未有的挑战.EIS项目的开发方式存在的问题使得业务与技术的演进难以同步,阻碍了企业的变革.近年来,企业体系结构(En-terprise Architecture,EA)的研究在信息管理领域正得到越来越多的关注.EA作为业务流程和IT架构的组织逻辑,反映了标准化和集成化的公司运作模型,目标是得到业务的敏捷性和利润的增长.本文探讨EIS开发方法的问题,介绍EA的概念和代表框架,总结EA的一般特点,研究EA对EIS开发的重要意义.