Improving Computer Training Effectiveness for Decision Technologies: Behavior Modeling and Retention Enhancement*

Managers and analysts increasingly need to master the hands‐on use of computer‐based decision technologies including spreadsheet models. Effective training can prevent the lack of skill from impeding potential effectiveness gains from decision technologies. Among the wide variety of software training approaches in use today, recent research indicates that techniques based on behavior modeling, which consists of computer skill demonstration and hands‐on practice, are among the most effective for achieving positive training outcomes. The present research examines whether the established behavior‐modeling approach to software training can be improved by adding a retention enhancement intervention as a substitute for, or complement to, hands‐on practice. One hundred and eleven trainees were randomly assigned to one of three versions of a training program for spreadsheets: retention enhancement only, practice only, and retention enhancement plus practice. Results obtained while controlling for total training time indicate that a combination of retention enhancement and practice led to significantly better cognitive learning than practice alone. The initial difference in cognitive achievement was still evident one week after training. Implications for future computer training research and practice are discussed.     

The Role of Organizational and Information Technology Antecedents in Reengineering Initiation Behavior*

Business Process Reengineering (BPR) has been a major catalyst of the pervasive organizational change we have witnessed over the past decade. Although one can speculate on the reasons for the popularity of this phenomenon, it is important that we carefully examine its underlying antecedents for initiation, implementation, and ultimately success, if we are to add value to practitioners of this concept. This study empirically examines the importance of facets of the organizational structure, IT knowledge resources and infrastructure, and the IS function in the initiation of BPR. Data from 313 corporations were gathered using a carefully validated survey, and initiators were compared with noninitiators. The results strongly suggest that client-server architectures, the strategic integration of IS, and cross departmental interaction are among the more important factors facilitating initiation and can be important inputs in a BPR decision-making process. The study attempts to build a contingent theory for BPR, and the more sustainable notion of fundamental process change.     

基于渠道管理的消费者信息、中间商与企业经济关系分析

在营销渠道的管理中,企业通常会通过设计一系列契约条件对中间商进行监督和激励,整个过程很少考虑来自市场第三方——消费者的信息反馈。本文分析了消费者信息在三方经济关系中的作用并通过企业、消费者和中间商的定量模型,分析消费者信息对企业激励与监督营销渠道中间商行为的影响以及提高营销渠道效率的影响因素和条件。结果表明,当消费者偏好和企业偏好一致时,消费者信息反馈有助于提高营销渠道管理绩效与消费者利益效用。     

A Simulation of Partial Information Use in Decision Making: Implications for DSS Design

Information matrices are often the output produced by a decision support system. These matrices are a common method for expressing a decision situation under different decision-making scenarios. The decisions involved in designing a decision support system to generate the information matrix are important and involve several cost and benefit components. A designer needs guidance in making effective design decisions in this context. Such guidance can be provided by considering the relationships among specific design decisions, costs, and benefits. The general objective of this study is to provide a comprehensive framework for this purpose. This study is the first to develop and present a comprehensive cost-benefit framework for evaluating design decisions for a variety of scenarios. The specific objective of this research is to provide guidance regarding the number of available information dimensions to incorporate in a computer-based decision aid. Simulation experiments are conducted with a completely specified model based on the cost-benefit framework (including needed assumptions) to evaluate how many information dimensions to include for a specific information matrix size to achieve a balance between information use costs and decision quality. Based upon extensive simulation analyses for a hypothetical decision maker, the practical guideline found for designers is to include only the top half of the relevant information dimensions in any specific decision support system. Over a large number of repeating choice decisions, the savings in cognitive effort and information gathering costs clearly offset relatively minor losses in decision quality.     

The Dark Side of Information and Market Efficiency in E‐Markets*

Price dispersion reflects the differences in prices for identical products. While in physical markets such dispersion is prevalent due to high search costs, many researchers argue that search costs and price dispersion will be much lower in electronic markets (e‐markets). Empirical evidence does not support this contention, and researchers have studied search costs, market factors, and service‐quality factors to explain this dispersion. Previous research has largely assumed that more information is better. By ignoring the dark side of information, we argue that only a partial understanding of price dispersion is possible. In this article, information overload and equivocality are studied as two dark attributes of information that lead sellers to different pricing decisions in e‐markets. Hypotheses relating these attributes to price dispersion are supported through analysis of 161 product markets. This work opens up new avenues in the study of e‐markets and discusses the implications of these findings for research and practice on consumer and seller decisions.     

Using Real‐Time Decision Tools to Improve Distributed Decision‐Making Capabilities in High‐Magnitude Crisis Situations*

Multi‐organizational collaborative decision making in high‐magnitude crisis situations requires real‐time information sharing and dynamic modeling for effective response. Information technology (IT) based decision support tools can play a key role in facilitating such effective response. We explore one promising class of decision support tools based on machine learning, known as support vector machines (SVM), which have the capability to dynamically model and analyze decision processes. To examine this capability, we use a case study with a design science approach to evaluate improved decision‐making effectiveness of an SVM algorithm in an agent‐based simulation experimental environment. Testing and evaluation of real‐time decision support tools in simulated environments provides an opportunity to assess their value under various dynamic conditions. Decision making in high‐magnitude crisis situations involves multiple different patterns of behavior, requiring the development, application, and evaluation of different models. Therefore, we employ a multistage linear support vector machine (MLSVM) algorithm that permits partitioning decision maker response into behavioral subsets, which can then individually model and examine their diverse patterns of response behavior. The results of our case study indicate that our MLSVM is clearly superior to both single stage SVMs and traditional approaches such as linear and quadratic discriminant analysis for understanding and predicting behavior. We conclude that machine learning algorithms show promise for quickly assessing response strategy behavior and for providing the capability to share information with decision makers in multi‐organizational collaborative environments, thus supporting more effective decision making in such contexts.