Path to “Stardom” in Globally Distributed Hybrid Teams: An Examination of a Knowledge‐Centered Perspective using Social Network Analysis

Although distributed teams have been researched extensively in information systems and decision science disciplines, a review of the literature suggests that the dominant focus has been on understanding the factors affecting performance at the team level. There has however been an increasing recognition that specific individuals within such teams are often critical to the team's performance. Consequently, existing knowledge about such teams may be enhanced by examining the factors that affect the performance of individual team members. This study attempts to address this need by identifying individuals who emerge as “stars” in globally distributed teams involved in knowledge work such as information systems development (ISD). Specifically, the study takes a knowledge‐centered view in explaining which factors lead to “stardom” in such teams. Further, it adopts a social network approach consistent with the core principles of structural/relational analysis in developing and empirically validating the research model. Data from U.S.–Scandinavia self‐managed “hybrid” teams engaged in systems development were used to deductively test the proposed model. The overall study has several implications for group decision making: (i) the study focuses on stars within distributed teams, who play an important role in shaping group decision making, and emerge as a result of a negotiated/consensual decision making within egalitarian teams; (ii) an examination of emergent stars from the team members’ point of view reflects the collective acceptance and support dimension decision‐making contexts identified in prior literature; (iii) finally, the study suggests that the social network analysis technique using relational data can be a tool for a democratic decision‐making technique within groups.     

Multiple criteria decision making for maritime investment policy in the developing countries

This model for multiple criteria decision making enables one to select an optimal investment policy for developing the marine industry in developing countries. The model is composed of: the mathematical model of national economy (LP model) for optimizing an investment scheme under economic and technological constraints; the model of I-O analysis for estimating the change of industrial structures induced by the investment; and the decision making model to which the results of the preceding models are fed back so as to enable the policy planner to select a feasible policy based on his own multiple criteria such as the value of GDP growth rate, international payments, unemployment rate, etc. Due consideration is paid in this study to the role of the marine industries, e.g. shipping and ports, which can be said to be the pivot of economic development in a developing country in connection with its foreign trade policy. A case study is carried out to verify this hypothesis.     

Voices and values: Linking values with participation in OR/MS in public policy making

It is now widely accepted that public policy development requires both an appreciation of public values and an ability to involve insights from local people. Operational research (OR) has made some contributions to public policy development, and there has been a call to use problem-structuring methods (PSM) in this environment. This growing need for greater use of OR/management science (MS) in policy making is due to its ability to work with insights that are sometimes hard to pin down. This paper presents some research about values and local people's voices in public policy making, which the authors believe present a challenge to OR/MS and to the use of PSM. The paper will describe a framework for understanding values and exploring insights into including local voices in policy making using PSMs. Key to the framework is in the emphasis on differences, rather than similarities, in value priorities. A case study in which local people as well as decisions makers and politicians were engaged in a process to decide the future of a local hospital will be described.     

An Empirical Investigation of Information Search Strategies with Implications for Decision Support System Design

Understanding how people search through and combine information before making decisions is an important concern in the study of decision making and in the design of decision support systems (DSS). The purpose of this study is to examine DSS search strategies in relation to the body of empirical research on information load. Prior research is examined in relation to basic empirically-testable hypotheses and compared to the results of this study as a way of validating the procedures used here. The transfer of information load empirical research to a DSS scenario is confirmed statistically. Then, results of an analysis of information search patterns under DSS conditions are described. Finally, implications for DSS design are discussed in terms of possible search support mechanisms for specific search strategies.     

A Public Transportation Decision‐Making Model within a Metropolitan Area

Understanding the decision‐making factors associated with public transportation is essential in strategic development of public transportation to improve acceptance and utilization of mass transit systems. This research analyzes factors affecting attitudes toward public transportation and the choice of transportation mode by investigating the public transportation decision‐making process of working professionals using a survey methodology. The objectives of this research are to model the transportation decision‐making process of public transportation users in a metropolitan area and to determine key factors that affect the public transportation choices made by potential public transportation users. This study contributes to the literature by developing and testing an integrated theoretical framework for modeling an individual's public transportation decision‐making process using four independent variables: Perceived Public Transportation Security, Knowledge, Price, and Convenience. We develop the proposed theoretical framework based upon the extant literature and tested it using partial least squares structural equation modeling (PLS‐SEM). Based on the Theory of Reasoned Action, the Theory of Planned Behavior, and utility theory, we develop the factors and refine associated items using confirmatory factor analysis.     

Is the Precautionary Principle Really Incoherent?

The Precautionary Principle has been an increasingly important principle in international treaties since the 1980s. Through varying formulations, it states that when an activity can lead to a catastrophe for human health or the environment, measures should be taken to prevent it even if the cause‐and‐effect relationship is not fully established scientifically. The Precautionary Principle has been critically discussed from many sides. This article concentrates on a theoretical argument by Peterson (2006) according to which the Precautionary Principle is incoherent with other desiderata of rational decision making, and thus cannot be used as a decision rule that selects an action among several ones. I claim here that Peterson's argument fails to establish the incoherence of the Precautionary Principle, by attacking three of its premises. I argue (i) that Peterson's treatment of uncertainties lacks generality, (ii) that his Archimedian condition is problematic for incommensurability reasons, and (iii) that his explication of the Precautionary Principle is not adequate. This leads me to conjecture that the Precautionary Principle can be envisaged as a coherent decision rule, again.     

Scenario Discovery with Multiple Criteria: An Evaluation of the Robust Decision‐Making Framework for Climate Change Adaptation

There is increasing concern over deep uncertainty in the risk analysis field as probabilistic models of uncertainty cannot always be confidently determined or agreed upon for many of our most pressing contemporary risk challenges. This is particularly true in the climate change adaptation field, and has prompted the development of a number of frameworks aiming to characterize system vulnerabilities and identify robust alternatives. One such methodology is robust decision making (RDM), which uses simulation models to assess how strategies perform over many plausible conditions and then identifies and characterizes those where the strategy fails in a process termed scenario discovery. While many of the problems to which RDM has been applied are characterized by multiple objectives, research to date has provided little insight into how treatment of multiple criteria impacts the failure scenarios identified. In this research, we compare different methods for incorporating multiple objectives into the scenario discovery process to evaluate how they impact the resulting failure scenarios. We use the Lake Tana basin in Ethiopia as a case study, where climatic and environmental uncertainties could impact multiple planned water infrastructure projects, and find that failure scenarios may vary depending on the method used to aggregate multiple criteria. Common methods used to convert multiple attributes into a single utility score can obscure connections between failure scenarios and system performance, limiting the information provided to support decision making. Applying scenario discovery over each performance metric separately provides more nuanced information regarding the relative sensitivity of the objectives to different uncertain parameters, leading to clearer insights on measures that could be taken to improve system robustness and areas where additional research might prove useful.     

Health care decision-support systems needed now

The technology exists now for developing a "Health Care Web" that would greatly improve health care decision making and research. But leaders must first be willing to cooperate to build the system.     

How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy

Decision making is a complex task that involves a multitude of perspectives, constraints, and variables. Multiple Criteria Decision Analysis (MCDA) is a process that has been used for several decades to support decision making. It includes a series of steps that systematically help Decision Maker(s) (DM(s)) and stakeholders in structuring a decision making problem, identifying their preferences, and building a decision recommendation consistent with those preferences. Over the last decades, many studies have demonstrated the conduct of the MCDA process and how to select an MCDA method. Until now, there has not been a review of these studies, nor a proposal of a unified and comprehensive high-level representation of the MCDA process characteristics (i.e., features), which is the goal of this paper. We introduce a review of the research that defines how to conduct the MCDA process, compares MCDA methods, and presents Decision Support Systems (DSSs) to recommend a relevant MCDA method or a subset of methods. We then synthesize this research into a taxonomy of characteristics of the MCDA process, grouped into three main phases, (i) problem formulation, (ii) construction of the decision recommendation, and (iii) qualitative features and technical support. Each of these phases includes a subset of the 10 characteristics that helps the analyst implementing the MCDA process, while also being aware of the implication of these choices at each step. By showing how decision making can be split into manageable and justifiable steps, we reduce the risk of overwhelming the analyst, as well as the DMs/stakeholders during the MCDA process. A questioning strategy is also proposed to demonstrate how to apply the taxonomy to map MCDA methods and select the most relevant one(s) using real case studies. Additionally, we show how the DSSs for MCDA method recommendation can be grouped into three main clusters. This proposal can enhance a traceable and categorizable development of such systems.     

The Development Manager's Advisory System: A Knowledge-Based DSS Tool for Project Assessment*

This research investigates whether the knowledge-based decision support system (KBDSS) paradigm provides the necessary supporting structure and developmental framework for product development evaluation. To address the research questions posed in this study, it is necessary to develop and implement KBDSS's at specific decision points along the product development cycle. This paper describes the design, development, and implementation of a KBDSS to support a product development manager's decision concerning full-scale development of a new product. From the systems design perspective, this paper addresses the integration and innovative use of a variety of techniques for knowledge acquisition, modeling, and processing. The approach utilized obtains the benefits of normative modeling as well as the flexibility and developmental advantages of knowledge-based systems. Since its implementation, the system has been successfully used by a development manager to support his recommendation for an ongoing project. His complete satisfaction with this system served as the impetus for the design and development of a multi-expert system which was implemented at the strategic level.     

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.     

Feedback in DSS as a Source of Control: Experiments with the Timing of Feedback

Well-designed feedback can improve decision making but to date there has been no comprehensive study of feedback in decision support systems (DSS) that could guide developers in its design. This work examines the opportunities and means to enhance cognitive control in decision making by providing appropriate feedback with DSS. It concentrates on the timing of feedback, which has been shown to affect the use of feedback and also demonstrates the potential advantages of using information technology over and above manual decision environments. Two experiments test the effects of controlling the timing of cognitive feedback on the user's cognitive control. The first tests the effect of timing one source of cognitive feedback, and the second tests the effect of timing two sources of feedback. The findings suggest that the design of feedback should become a routine part of the development of DSS.     

Designing Referral Network Structures and Decision Rules to Streamline Provision of Urgent Health and Human Services*

This study uses fully factorial computer simulation to identify referral network attributes and referral decision rules that streamline the routing of people to urgent, limited services. As an example of a scenario, the model represents vaccine delivery in a city of 100,000 people during the first 30 days of a pandemic. By modeling patterns of communication among health care providers and daily routing of overflow clients to affiliated organizations, the simulations determine cumulative effects of referral network designs and decision rules on citywide delivery of available vaccines. Referral networks generally improve delivery rates when compared with random local search by clients. Increasing the health care organizations’ tendencies to form referral partnerships from zero to about four partners per organization sharply increases vaccine delivery under most conditions, but further increases in partnering yield little or no gain in system performance. When making referrals, probabilistic selection among partner organizations that have any capacity to deliver vaccines is more effective than selection of the highest‐capacity partner, except when tendencies to form partnerships are very low. Implications for designing health and human service referral networks and helping practitioners optimize their use of the networks are discussed. Suggestions for using simulations to model comparable systems are provided.     

Organizational Computing as a Facilitator of Operational and Managerial Decision Making: An Exploratory Study of Managers' Perceptions*

Much of the current knowledge pertaining to information technology (IT) and decision making is based on decades old technologies that revolved around a central computing function and application-specific systems. The purpose of this research is to examine the IT decision-making relationship within the emerging organizational computing (OC) environment permeated by spontaneous utilization of both application-and nonapplication-specific computing and communication technologies. Specifically, this study seeks to explore managers' perceptions of the emerging OC environment as a facilitator of their decision-making activities. To achieve a higher level of clarity than previous works, a two-dimensional research framework is developed with the IT dimension consisting of computing and communication, and the decision-making dimension differentiated between operational and managerial decisions. A survey instrument was constructed that measured the computing and communication dimensions of information technology use and their perceived effects upon operational and managerial decisions. The major findings of the study confirmed that managers recognize the value of general, nonapplication-specific information technologies in decision making, and that this recognition is highly associated with how intensively these information technologies are used. Additionally, it was found that the two dimensions of IT differ in their relationships to decision making, and that IT usage relates to managerial decisions differently than operational decisions. These study findings have significant implication for practice and research, especially in the context of information resource management in which the primary purpose of the IS function is the delivery of general information service to users rather than the development of specific IS applications.     

User Perceptions of Decision Support Effectiveness: Two Production Planning Experiments*

Decision support systems continue to be very popular in business, despite mixed research evidence as to their effectiveness. We hypothesize that what-if analysis, a prominent feature of most decision support systems, creates an “illusion of control” causing users to overestimate its effectiveness. Two experiments involving a production planning task are reported which examine decision makers' perceptions of the effectiveness of what-if analysis relative to the alternatives of unaided decision making, and quantitative decision rules. Experiment 1 found that almost all subjects believed what-if analysis was superior to unaided decision making, although using what-if analysis had no significant effect on performance. Experiment 2 found that decision makers were indifferent between what-if analysis and a quantitative decision rule which, if used, would have led to significant cost savings. Thus, what-if analysis did create an illusion of control: decision makers perceived performance differences where none existed, and did not detect large differences when they were present. In both experiments, decision makers exhibited difficulty realizing that their positive beliefs about what-if analysis were exaggerated. Such misjudgments could lead people to continue using what-if analysis even when it is not beneficial and to avoid potentially superior decision support technologies.     

Treating and Drinking Well Water in the Presence of Health Risks from Arsenic Contamination: Results from a U.S. Hot Spot

The Safe Drinking Water Act of 1974 regulates water quality in public drinking water supply systems but does not pertain to private domestic wells, often found in rural areas throughout the country. The recent decision to tighten the drinking water standard for arsenic from 50 parts per billion (ppb) to 10 ppb may therefore affect some households in rural communities, but may not directly reduce health risks for those on private wells. The article reports results from a survey conducted in a U.S. arsenic hot spot, the rural area of Churchill County, Nevada. This area has elevated levels of arsenic in groundwater. We find that a significant proportion of households on private wells are consuming drinking water with arsenic levels that pose a health risk. The decision to treat tap water for those on private wells in this area is modeled, and the predicted probability of treatment is used to help explain drinking water consumption. This probability represents behaviors relating to the household's perception of risk.     

高层管理团队内部社会资本、团队冲突和决策效果的关系——研究综述与理论分析框架

反映在结构因素、关系因素和认知因素三个维度上的高管团队内部社会资本为决策信息的交换和共享提供了机会、意愿和能力,有利于团队内部形成建设性的冲突模式,促进决策效果的提高.本文以团队冲突为中介变量,建立了高管团队内部社会资本与决策效果的关系的理论框架,提出了研究命题,并对企业高管团队内部社会资本的培育提出建议.     

The 1991 Cholera Epidemic in Peru: Not a Case of Precaution Gone Awry

The precautionary principle calls on decisionmakers to take preventive action in light of evidence indicating that there is a potential for harm to public health and the environment, even though the nature and magnitude of harm are not fully understood scientifically. Critics of the precautionary principle frequently argue that unbridled application of the principle leads to unintended damage to health and ecosystems (risk tradeoffs) and that precautious decision making leaves us vulnerable to "false-positive" risks that divert resources away from "real risks." The 1991 cholera epidemic in Peru is often cited as an example of these pitfalls of the precautionary principle. It has been mistakenly argued that application of the precautionary principle caused decisionmakers to stop chlorinating the water supply due to the risks of disinfection byproducts (DBPs), resulting in the epidemic. Through analyses of investigations conducted in the cities of Iquitos and Trujillo, Peru, literature review, and interviews with leading Peruvian infectious disease researchers, we determined that the epidemic was caused by a much more complex set of circumstances, including poor sanitation conditions, poor separation of water and waste streams, and inadequate water treatment and distribution systems. The evidence indicates that no decision was made to stop chlorinating on the basis of DBP concerns and that concerns raised about DBPs masked more important factors limiting expansion of chlorination. In fact, outside of Peru's capital Lima, chlorination of drinking water supplies at the time of the epidemic was limited at best. We conclude that the Peruvian cholera epidemic was not caused by a failure of precaution but rather by an inadequate public health infrastructure unable to control a known risk: that of microbial contamination of water supplies.     

Quantitative methods in a developing country: Managers' perceptions and desires

The role of quantitative methods in business decision making has been a subject of much discussion in the literature. Most of this discussion has emanated from developed countries. In this paper, the current practice in a developing country is examined, as well as the desires and perceptions of management. It is evident that the current practice lags that of the developed countries. Moreover, while the practice in companies with international association is not markedly different from local companies, the management of these companies desire a situation closer to that found in the developed countries. It is suggested that adequately trained personnel are not currently available and that a career orientated masters programme in quantitative methods is necessary in developing countries.