Intelligence gathering for decision making

Empirical studies of decision making seldom consider the intelligence gathering activities required for decision making. In an attempt to fill this void, this study set out to identify and assess some of the key steps in gathering intelligence, considering the difficulty of the decision and available resources. The study found performance gapping and premising to be crucial activities and explored how each is carried out. A variety of premising and gapping tactics were uncovered, with some having better success than others. These tactics were found to influence the search approach selected to uncover alternatives and the success of the resulting decision. The best results were noted when search efforts are guided by needs documented with a quantitative performance gap; and when formal search or negotiation is used to identify alternatives. These findings hold for decisions that have high and low difficulty and for those with high and low resource support. The implications of these findings for decision makers and decision making are discussed.     

Evaluating the performance capability of a multi-organization system

A model for evaluating the performance capability of a multi-organization system is introduced. The model is based on hierarchical-modular decomposition of the components which determine system performance up to the level where they can be measured quantitatively, or at least qualitatively, in an objective or subjective manner. Performance measures are obtained by incorporating these components into multi-attribute evaluation functions. The model provides decision makers with a tool for quantitative assessment of performance, identification of bottlenecks, a framework for ‘what-if’ simulation and a mean for conflict resolution during planning. Application of the model to the Israeli Water Resources Development System provides an illustrative example.     

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.     

AN EMPIRICAL ASSESSMENT OF COMPUTER-ASSISTED DECISION ANALYSIS*

Decision analysis tools often are used in semistructured and ill-structured situations. While some researchers have argued that computerized decision analysis programs may improve decision quality in such situations, research support for this assertion is weak. In this laboratory study, business students used a computerized decision-analysis program with short strategic-management cases to prepare decision reports. Independent raters' perceptions of aided and unaided decision performance were contrasted, attitudes of students toward the program were assessed, individual differences were correlated with attitudes, and the program's impact on students' perceptions of their decision processes and performance was examined. Student attitudes toward the computerized aid were favorable, and individual differences in reactions generally were as predicted. However, the program did not improve the independent ratings of students' decision reports and recommendations. These findings suggest that computerized decision aids should be adopted cautiously. If such aids result in positive user affect and heightened decision confidence without corresponding improvements in decision quality, they may be dysfunctional. Rigorous research methodologies which incorporate independent evaluations of analyses and decisions are recommended for use in future research on computerized decision-analysis programs.     

Complexity Factors and Intuition-based Methods for Facility Network Design

Logistics managers frequently utilize decision support systems (DSS) to make facility network design decisions. Many DSS do not provide optimization capabilities, but instead rely on scenario evaluation as a means for developing solutions. We experimentally assessed the performances of decision makers, including experienced managers, who used four variants of a scenario evaluation-based DSS to solve realistically sized network design problems of varying complexities. Complexity factors included DSS attributes, problem size, network types, and demand dispersion patterns. Decision makers' performances were assessed relative to optimal solutions. Overall, the decision makers generated relatively high-quality solutions using the DSS variants. The type of design problem solved did not significantly impact problem-solving performance. However, performance degraded and variability in solution quality escalated as problem size was increased. The availability of incremental solution cost improvement cues in the DSS significantly improved solution quality and reduced performance variability. Iconic graphic enhancements to the DSS did not consistently affect performance. However, significant interactions existed among the effects of DSS graphics capabilities, DSS information cues, and problem attributes.     

Automating Knowledge Acquisition and Refinement for Decision Support: A Connectionist Inductive Inference Model*

An important application of expert systems technology is to provide support for nonstructured decision making. Usually, nonstructured decision making is characterized by heavy reliance on heuristic knowledge, which is very difficult to articulate or document, and therefore traditional knowledge acquisition approaches are not very successful. The quality and effectiveness of an expert system supporting unstructured decision making is affected when traditional knowledge acquisition approaches are used. To alleviate this problem a model is proposed that combines inductive inference and neural network computing, and an example is presented that illustrates the potential of this model in unstructured decision support.     

When do Public Managers Imitate Each Other? Mimetic Decision Making in Contracting Decisions of Danish Municipalities

Using organizational new institutional theory, this paper explores a core mechanism underlying contracting decisions in public organizations. A central proposition of this branch of institutional theory is that uncertainty leads to organizational isomorphism. The present study investigates this proposition by asking: When does perceived uncertainty lead public managers to imitative behavior in contracting out decisions? Contrary to most previous studies, we apply an individual level approach and relate different types of perceived uncertainty of decision makers to mimetic decision making. We define mimetic decision making as when decision makers deliberately obtain information about other organizations in order to possibly imitate them. In a survey of Danish municipal managers facing important and complex contracting decisions, we test our hypotheses about three types of perceived uncertainty and mimetic decision making. The results show that technological uncertainty is strongly related to mimetic decision making among public managers. However, we do not find significant results for either volume uncertainty or performance uncertainty. The paper illustrates how uncertainty, through mimetic decision making, is connected to organizational isomorphism. It further highlights that future studies should pay attention to the multidimensionality of uncertainty and its consequences.     

PERCEIVED INFORMATION STRUCTURE: IMPLICATIONS FOR DECISION SUPPORT SYSTEM DESIGN

Top-level decision making in business organizations is characterized by high degrees of uncertainty, incomplete information, and conflicting objectives. To support top-level decision making effectively, decision support systems (DSSs) have been proposed. Information supplied by a DSS must be selective in that not all possible information sets may be feasibly or economically represented in the data base. This study suggests that discovery of perceptual complexity (dimensionality) of information items, and the subsequent categorization of decision makers having the same perceptions of those information items, is a first step in the ultimate design of an effective DSS. Through the use of multidimensional scaling in a field setting, this study shows the feasibility of creating relatively homogeneous groups of decision makers according to the content and number of dimensions associated with various information items. Further results of the research suggest that information can be tailored to classes of users, which has cost-benefit implications as well as the potential to improve the quality of the resultant decisions.     

Escalation: The Role of Problem Recognition and Cognitive Bias

Escalation of commitment to a failing course of action is an enduring problem that remains central to the study of managerial behavior. Prior research suggests that escalation behavior results when decision makers choose to ignore negative feedback concerning the viability of a previously chosen course of action. Previous work has also suggested that certain cognitive biases might promote escalation behavior, but there has been little attempt to explore how biases other than framing affect escalation. In this article, we explore the extent to which decision makers actually perceive negative feedback as indicative of a problem and how this influences their decision to escalate. Although problem recognition and cognitive biases have been intensively studied individually, little is known about their effect on escalation behavior. In this research, we construct and test an escalation decision model that incorporates both problem recognition and two cognitive biases: selective perception and illusion of control. Our results revealed a significant inverse relationship between problem recognition and escalation. Furthermore, selective perception and illusion of control were found to significantly affect both problem recognition and escalation. The implications of these findings for research and practice are discussed. To improve problem recognition and reduce the incidence of escalation, practicing managers should implement modern project management practices that can help to identify and highlight potential problems while guarding against these two key cognitive biases that promote the behavior.     

基于犹豫直觉模糊语言集均值-标准差偏好距离的多属性决策研究

在犹豫直觉模糊语言集和语言尺度函数的基础上定义了均值-标准差偏好的Hamming距离，并提出了基于犹豫直觉模糊语言集距离TOPSIS和TODIM的多属性决策方法，进一步利用这两种方法对实例问题建筑商的招标方案进行排序，并讨论了偏好参数对排序结果的灵敏度分析。为验证上述方法的有效性，与已有方法对同一实例的排序结果进行了比较分析。     

Framing Effects in Narrative and Non‐Narrative Risk Messages

Narrative messages are increasingly popular in health and risk campaigns, yet gain/loss framing effects have never been tested with such messages. Three experiments examined framing in narrative messages. Experiment 1 found that only the character's decision, not framing, influenced judgments about characters in a narrative derived from a prospect theory context. Experiment 2 found that a framing effect that occurred when presented in a decision format did not occur when the same situation was presented as a narrative. Using a different story/decision context, Experiment 3 found no significant difference in preference for surgery over radiation therapy in a narrative presentation compared to a non‐narrative presentation. The results suggest that health and risk campaigns cannot assume that framing effects will be the same in narrative messages and non‐narrative messages. Potential reasons for these differences and suggestions for future research are discussed.     

PERFORMANCE DIFFERENCES IN THE USE OF GRAPHIC AND TABULAR DISPLAYS OF MULTIVARIATE DATA

It has been suggested that graphically displayed multivariate data help decision makers better understand information they are called on to analyze. This study compares judgments made from one recently suggested multivariate display technique with judgments made from traditional tabular displas of financial figures. Significant differences in task performance are found to be related both to differences in the stimulus sets and to individual differences among the subjects. Our results suggest that the relative contribution of graphic displays to decision making may vary considerably from situation to situation.     

The bureaucratic side of computers: Memory,evocation and management information

Management information systems fail to support management because they are not flexible enough to keep pace with the unstructured nature of decision making processes. Observations made in four welfare management information systems suggest that computerization improves evocation—the means by which decision makers pool data from the collective memory, only to the extent that choices and the relevancy of data are predetermined. Facing rigid procedures and irresponsiveness to irregular data needs, managers of all ranks duplicate the computerized memory with manual records and files so as to retain access to their memory under their terms.     

只有输出的广义样本区间DEA模型

在应用多个绩效指标综合评价决策单元有效性时，决策者常常把这些决策单元与另外预先指定的标准（样本单元）进行比较。由于客观事物的复杂性和不确定性导致样本单元和决策单元的指标信息有时必须用区间数的形式给出。针对区间数指标信息的综合评价问题，本文通过分解的方法讨论样本单元和决策单元指标信息为区间数时用广义DEA模型评价决策单元有效性的方法，并相应地构建了只有输出的广义区间DEA模型。同时，对模型的含义、求解以及性质等进行了分析。之后，探讨了该方法在决策单元有效性分类和排序中的应用。最后，通过实例表明该方法的可行性和有效性。     

Participation in Strategic Decision Making: The Role of Organizational Predisposition and Issue Interpretation*

Each time managers are faced with a strategic decision they decide how to decide. Specifically, they make choices about who has necessary information and, therefore, who needs to participate in the decision. Such responses to strategic issues are believed to be affected by the way in which decision makers interpret issues. However, organizations develop habitual responses to issues and may be predisposed because of their attention to rules and routines, or because of past performance, to respond to strategic issues in certain ways regardless of how issues are interpreted. We examined the direct and indirect effects of predisposition (rule orientation and past financial performance) and interpretation of strategic issues on the participation of internal stakeholder groups in strategic decision making. Executives in 52 organizations indicated that rule orientation and performance are directly linked to participation in strategic decision making, and that interpretation and rule orientation are directly linked to each other. Implications for managers include the notion that any effort to improve decision-making effectiveness by shaping how organizational members frame and interpret issues will be constrained by the organization's existing routines as well as its past performance.     

Using Electronic Medical Records in Admission Decisions: A Cost Effectiveness Analysis

In recent years, the healthcare sector has invested heavily in medical information systems to improve decision making while reducing medical costs and integrating medical data from multiple sources. However, the overall contribution of this technology to the medical field remains controversial, especially in high‐stress environments such as the emergency department. This article evaluates the differential effects of accessing an electronic medical record (EMR) system on the decision to admit, based on diagnosis of acute myocardial infarction (AMI), which is one of the main reasons people go to the emergency department. The admission decision with or without accessing the EMR system is modeled as a decision tree and a Markovian process. A cost‐effectiveness analysis compares the added value of information (retrieved from the EMR system) against the cost of providing this information. This model is then tested on a simulation of patients presenting symptoms of AMI. The results show that use of the EMR led to greater cost‐effectiveness in cases of suspected AMI. The findings of this study may assist physicians by demonstrating a probable contribution of EMR to improved medical outcomes and may inform policy makers in the healthcare sector regarding the advisability of investing in such systems in an emergency department.     

基于情感关系的委员会决策的交互过程研究

在委员会决策过程中 ,决策者常常受到情感因素的影响而表现出非理性行为 .人们的非理性行为又往往导致决策的效能低下 ,浪费不必要的时间 ,甚至使得决策毫无结果 .通过分析 ,得出在奇数个决策成员 ,具有稳定的情感关系 ,采取多数规则的条件下能使委员会决策顺利进行的结论     

Practical implications of the resource-based view

Managers use many decision making tools when developing a firm’s strategic direction. Ideally, such tools yield a good solution for an acceptable amount of application effort. This paper presents the results of an experimental research project that compares the effectiveness of a theory-based strategic decision making tool, theVRIO-framework, with two alternative decision making heuristics for predicting the stock-market performance of different companies. First, we assess how the predictions of theVRIO-framework contrast with decisions based on “gut feeling” using the forecasts derived from a recognition-based decision making tool, theRecognition Heuristic. Secondly, theVRIO-framework’spredictive power is compared to predictions derived fromAnalyst Ratings. Our results suggest that the predictions of theVRIO-framework are superior to those of theRecognition Heuristic and theAnalyst Ratings, supporting the practical usefulness of resource-based theory. We conclude that resource analysis is important to strategic decision making and discuss the implications of our findings for future research and management practice.     

Examining the Antecedents and Consequences of CIO Strategic Decision‐Making Authority: An Empirical Study*

Despite the strategic importance of information technology (IT) to contemporary firms, chief information officers (CIO) often still have varying degrees of strategic decision‐making authority. In this study, we apply the theory of managerial discretion to define CIO strategic decision‐making authority and argue that the CIO's level of strategic decision‐making authority directly influences IT's contribution to organization performance. We also draw on the power and politics perspective in the strategic decision‐making literature to identify the direct antecedents to the CIO's strategic decision‐making authority. A theoretical model is presented and empirically tested using survey data collected from a cross‐industry sample of 174 matched pairs of CIOs and top business executives through structural equation modeling. The results suggest that organizational climate, organizational support for IT, the CIO's structural power, the CIO's level of strategic effectiveness, and a strong partnership between the CIO and top management team directly influence the CIO's level of strategic decision‐making authority within the organization. The results also suggest that the CIO's strategic decision‐making authority in the organization directly influences the contribution of IT to firm performance and that effective CIOs have a greater influence on IT's contribution when provided with strategic decision‐making authority.