Executive information system (EIS) is a computer-based information system that supports decision makings ana management activities for senior executives. Data warehouse is a database that receives data copies from legacy systems and external data sources. Data warehouse is typically optimized for decision supports and can be an attractive solution for EIS implementation. This paper proposes an architecture for implementing EIS by the use of data warehouse. The architecture consists of ten implementation layers. Interrelationships among these layers are investigated for an effective EIS implementation. An EIS prototype for a real-fife enterprise is implemented to demonstrate the usefulness of the proposed architecture.

This study examines whether expenditures in information technology (IT) are associated with increases in the Tobin's q ratios a measure of organizational performance. It uses two groups of sample, Korean and American firms that disclose IT expenditures. For the all-firms group of each country, the association between IT expenditures and Tobin's q ratios is positive and statistically significant. But the association varies among industries. For Korean firms, IT expenditures appear to increase Tobin's q ratios for the machine and equipment manufacturing industry group (SIC3-2). IT expenditure ratio of this group as a percent of total sales is highest among the industry groups. For all service industry groups(SIC4&5), the estimated coefficient of IT expenditures is positive but statistically insignificant. For American firms IT expenditures in most of the manufacturing industry groups appear to increase only a little, if at all, for the Tobin's q ratios. But IT expenditures appear to have a greater impact on Tobin's q ratios for all service industries (SIC4-7). For three service industries tested (transportation and telecommunication- SIC4, financial- SIC6, consulting and other service industry- SIC7), the estimated coefficient of IT expenditures is positive and statistically significant. The evidence from both Korean and American firms suggests that IT expenditures in service industries provide a greater impact on an organizational performance than ones in manufacturing industry. To test whether service industries use a competitive strategy utilizing IT as a core competence, the samples ore divided into two groups, service and manufacturing industry. For Korean firms, both IT and R&D expenditures in manufacturing industry are associated with increases in Tobin's q ratios. But for service industry, the estimated coefficient of only IT expenditure is positive. For American firms, the estimated coefficients of both IT and advertising and R&D (ARD) expenditures in manufacturing industry are positive but the coefficient of only ARD is statistically significant. For service industry, the estimated coefficient of only IT expenditure is positive and statistically significant. The evidence may suggest that manufacturing industry uses both R&D and IT strategies to increase a competitive advantage but uses R&D strategy as a core competence. However, service industry uses IT strategy as a core competence to increase a competitive advatage.

Matching individual career desire and career path is desirable for individual motivation and organizational effectiveness. Some studies reported that major career orientations of MIS personnel are technical and managerial orientation. Furthermore, dual ladder system is based on the same notion above. However, subsequent research conclusions of this issues have been contradictory. Mnwhile, past studies on the work value of MIS personnel are mainly concentrated on the differences between MIS personnel and other occupations. To date, the study on the differences in the values of job factors between major career orientations is rare. The purpose of this study is to find out major career orientation of Korean MIS personnel and the differences of values of job factors between major orientations. The research result is two conclusions. First, major career orientations of Korean MIS personnel are technical and managerial orientation. Second, both group do not have different values about job property related factors, but managerial orientation group gives high values to the some organization related factors such as company policy, responsibility, authority, salary level and opportunity of promotion.

The utility of learning techniques in investment analysis has been demonstrated in many areas, ranging from forecasting individual stocks to entire market indexes. To date, however, the application of artificial intelligence to financial forecasting has focused largely on short predictive horizons. Usually the forecast window is a single period ahead; if the input data involve daily observations, the forecast is for one day ahead; if monthly observations, then a month ahead; and so on. Thus far little work has been conducted on the efficacy of long-term prediction involving multiperiod forecasting. This paper examines the impact of alternative procedures for extended prediction using knowledge discovery techniques. One dimension in the study involves temporal granularity: a single jump from the present period to the end of the forecast window versus a web of short-term forecasts involving a sequence of single-period predictions. Another parameter relates to the numerosity of input variables: a technical approach involving only lagged observations of the target variable versus a fundamental approach involving multiple variables. The dual possibilities along each of the granularity and numerosity dimensions entail a total of 4 models. These models are first evaluated using neural networks, then compared against a multi-input jump model using case based reasoning. The computational models are examined in the context of forecasting the S&P 500 index.

This paper is concerned with knowledge representation schemes best suited for human resource management (HRM) problem domains including human resource planing, selection, placement, compensations, performance evaluation, training and labor-management relations. In order to suggest the scheme we consider two research gods. First, we evaluate and prioritize. The knowledge representation techniques of frames rules, semantic nets and predicate logic that hove been recommended to managerial domains. The combined Analytic Hierarchy Process technique is employed to combine individual judgments effectively between two different expert groups. As a result if we are to select a single knowledge representation technique, a frame representation is best for most HRM domains and to combine frames with others is another choice. Second as a strategy for knowledge representation schemes we show some examples for each damn in terms of labeled semantic nets and two types of rules derived from the semantic nets. We propose nine knowledge components as ontologies. The labeled semantic nets con provide some benefits compared with conventional one. More clearly definea node rode information maces it easy to find the ac information. In the rule sets, the variables are the node of the semantic nets. The consistency of rules is validated by the relationship of the knowledge components.

This paper presents an application of an artificial neural net to the implementation of decision class analysis (DCA), together with the generation of a decision model influence diagram. The diagram is well-known as a good tool for knowledge representation of complex decision problems. Generating influence diagram model is known to in practice require much time and effort, and the resulting model can be generally applicable to only a specific decision problem. In order to reduce the burden of modeling decision problems, the concept of DCA is introduced. DCA treats a set of decision problems having some degree of similarityz as a single unit. We propose a method utilizing a feedforward neural net with supervised learning rule to develop DCA based on influence diagram, which method consists of two phases: Phase l is to search for relevant chance and value nodes of an individual influence diagram from given decision and specific situations and Phase II elicits arcs among the nodes in the diagram. We also examine the results of neural net simulation with an example of a class of decision problems.

본 논문의 목적은 고객의 특정 감성을 유발할 수 있는 전자 상거래 시스템 사용자 인터페이스의 설계 방안 모색에 있다. 이를 위해 가상 은행 시스템의 사용자 인터페이스에 대해 모두 네 단계에 걸친 실증 연구를 수행하였다. 제1단계 실증 연구는 가상 은행과 관련된 제반 감성 요소를 충실하게 측정할 수 있는 감성 어휘들을 추출하는데 목적이 있었다. 제2단계의 실증 연구는 사용자 관점에서 중요하게 지각되는 시각적인 디자인 요소 파악이 주 목적이었다. 제3단계 실증 연구에서는 감성 요소와 디자인 요소 간의 상관 관계를 파악하였고, 최종적으로 제4단계의 실증 연구에서는 실험용 가상 은행 인터페이스를 개발하여 감성 요소와 디자인 요소 간의 상관 관계를 검증하였다. 본 연구 결과 사용자 인터페이스 내의 특정 시각 디자인 요소의 조작을 통해서 신뢰감과 같은 고객의 감성 유발에 영향을 미칠 수 있다는 사실이 입증되었다. 본 논문은 끝으로 이러한 연구 결과의 일반적인 전자 상거래 시스템 고객 인터페이스 구축 과정에의 적용 가능성을 타진한다.

본고는 장치산업형 부품 제조업체에서 주문관리와 제조정보를 SCM(Supply Chain Management) 관점에서 통합하기 위한 모형을 제시한다. 공정 특성을 고려하여 이 모형은 계획 계층과 실행 계측을 분리하고, 두 계층의 효율적 연계를 위한 가상 프로세스를 도입하며, 운영의 유연성을 최대화하는 계획과 실행의 분리 원칙을 적용하였다. 또한 이 모형이 장치산업형 제조업체의 생산성 혁신에 기여할 수 있음을 사례연구로 제시하였다.