DECISION SUPPORT SYSTEMS FOR ILL-STRUCTURED PROBLEMS: AN EMPIRICAL STUDY

Decision support systems (DSSs) are more complex than most other traditional decision-aid systems. For what types of problems are they more effective, and what design characteristics make them more effective? The laboratory experiment reported here examined the effect of three design characteristics of these systems in the context of decision makers faced with ill-structured problems. The characteristics were presence or absence of decision-aid heuristics, degree of interaction between the user and the system, and whether or not the system was computerized. The dependent variables were (1) quality of user performance, (2) user productivity of ideas, (3) user confidence in the quality of his/her performance, (4) user satisfaction with the decision aid or support system, (5) changes in user attitude toward the problem addressed, and (6) changes in user attitude toward computers. Use of heuristics and increased interaction had positive effects on decision quality, user productivity, and attitude toward computers; they had negative effects on user confidence, satisfaction, and attitude toward the problem addressed. Whether or not the system was computerized did not have a significant effect on any dependent variable. The findings concerning negative effects, in particular, suggest the need for research on the design of heuristics for addressing ill-structured problems—heuristics that will deliver the positive but not the negative effects observed in this study. The findings also suggest the need for research on how to benefit from computers in the context of solving ill-structured problems.     

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.     

New Product Development Decision‐Making Effectiveness: Comparing Individuals,Face‐To‐Face Teams,and Virtual Teams*

A total of 411 subjects participated in two decision‐making experiments in order to examine the effectiveness of new product development project continuation decisions. Using escalation of commitment theory, in Study 1, individual versus face‐to‐face team decision‐making effectiveness was compared. Study 2, an extension of Study 1, compared the new product development decision‐making effectiveness of individuals, face‐to‐face teams, and virtual teams. A virtual team is a geographically and temporally dispersed and electronically communicating work group. In Study 2, the virtual teams communicated asynchronously via groupware technology. Our findings suggest that teams make more effective decisions than individuals, and virtual teams make the most effective decisions.     

Overcoming Learning Aversion in Evaluating and Managing Uncertain Risks

Decision biases can distort cost‐benefit evaluations of uncertain risks, leading to risk management policy decisions with predictably high retrospective regret. We argue that well‐documented decision biases encourage learning aversion, or predictably suboptimal learning and premature decision making in the face of high uncertainty about the costs, risks, and benefits of proposed changes. Biases such as narrow framing, overconfidence, confirmation bias, optimism bias, ambiguity aversion, and hyperbolic discounting of the immediate costs and delayed benefits of learning, contribute to deficient individual and group learning, avoidance of information seeking, underestimation of the value of further information, and hence needlessly inaccurate risk‐cost‐benefit estimates and suboptimal risk management decisions. In practice, such biases can create predictable regret in selection of potential risk‐reducing regulations. Low‐regret learning strategies based on computational reinforcement learning models can potentially overcome some of these suboptimal decision processes by replacing aversion to uncertain probabilities with actions calculated to balance exploration (deliberate experimentation and uncertainty reduction) and exploitation (taking actions to maximize the sum of expected immediate reward, expected discounted future reward, and value of information). We discuss the proposed framework for understanding and overcoming learning aversion and for implementing low‐regret learning strategies using regulation of air pollutants with uncertain health effects as an example.     

A Decisional Framework of Offshoring: Integrating Insights from 25 Years of Research to Provide Direction for Future*

Mirroring the growing trend for firms to support their operations by locating activities abroad, research on the practice of offshoring has increased considerably in recent years. However, despite the mounting research, understanding of the key factors influencing decision‐making for offshoring remains surprisingly limited due to fragmentation. In this study, we synthesize and integrate insights from different research domains in order to develop a comprehensive decisional framework for key offshoring decisions. The integrative decisional framework is based on a systematic review of offshoring research published in the most influential management and business journals in the past 25 years. In addition to providing a snapshot of the state of research on decision‐making for offshoring, this study aims to stimulate future research by identifying promising research opportunities. In particular, we propose that future research should use alternative theories to incorporate overlooked aspects of decision‐making, integrate different theories to account for the interdependencies between decisions, and adopt a portfolio perspective that considers each decision as part of an overall offshoring strategy.     

Physician perspectives on the structure and function of group practice HMOs

This article explores physicians' perspectives regarding how their HMOs function and their satisfaction with and loyalty to HMOs. Three HMOs were studied: a mature (28-year-old) staff model, a 16-year-old staff model, and a 13-year-old group model with both HMO and fee-for-service patients. While these HMOs were found to vary somewhat in terms of emphasis on patient care versus costs, methods used to control costs and degrees of centralization of decision making, they all received high overall satisfaction and loyalty scores. The staff model HMO with a more decentralized decision making structure received the highest satisfaction/loyalty score from its physicians. The degree to which physicians perceive the HMO to be effective and supportive and the use of educational programs and peer review to influence resource use were also found to be significantly related to physician satisfaction and loyalty.     

医生的信息-情感交互模式对移动问诊服务满意度的影响——基于“激励-保健”理论的分析

移动问诊服务日益成熟,但患者满意度仍有待提高。现有研究对医患交互过程关注不足,且忽略患者不满意评价的影响因素。为此,本文引入"激励-保健"理论视角,研究医生的信息-情感交互模式对移动问诊服务满意及不满的影响机制。本文筛选国内某领先移动问诊平台中的300次医患沟通记录,采用"先定性后定量"的混合研究方法对数据进行分析。其中,定性研究旨在识别医生交互模式的细分维度,定量研究则用于提出并验证不同交互模式与患者满意及不满的关系。研究发现,医生的信息交互模式是移动问诊服务满意度的保健因素,而情感交互模式是激励因素。结论丰富了移动问诊服务患者满意度的研究框架,对改善医生行为和医疗服务APP设计提供了指导。     

Decisional Conflict and User Acceptance of Multicriteria Decision-Making Aids*

Despite the development of increasingly sophisticated and refined multicriteria decision-making (MCDM) methods, an examination of the experimental evidence indicates that users most often prefer relatively unsophisticated methods. In this paper, we synthesize theories and empirical findings from the psychology of judgment and choice to provide a new theoretical explanation for such user preferences. Our argument centers on the assertion that the MCDM method preferred by decision makers is a function of the degree to which the method tends to introduce decisional conflict. The model we develop relates response mode, decision strategy, and the salience of decisional conflict to user preferences among decision aids. We then show that the model is consistent with empirical results in MCDM studies. Next, the role of decisional conflict in problem formulation aids is briefly discussed. Finally, we outline future research needed to thoroughly test the theoretical mechanisms we have proposed.     

基于心理阈值的多属性决策问题目标调整研究

将决策者在决策指标上存在心理阈值的现状应用到多属性决策问题,提出先对决策目标调整再进行决策的方法。首先,对效益型、成本型、中间型指标构建相应的指标满意度函数和算法,针对不同类型的指标选择相应的算法,计算决策者在每个备选方案存在心理阈值属性上的满意度;其次,根据决策者满意度与心理阈值,调整决策目标组建新的决策信息表;再次,提出基于决策者满意度的属性赋权与信息集结算法模型,获得最优决策方案;最后,通过一个实际生活中的决策者购房案例,表明该方法的科学合理性。     

Designing Graphs that Promote Both Risk Understanding and Behavior Change

Graphs show promise for improving communications about different types of risks, including health risks, financial risks, and climate risks. However, graph designs that are effective at meeting one important risk communication goal (promoting risk‐avoidant behaviors) can at the same time compromise another key goal (improving risk understanding). We developed and tested simple bar graphs aimed at accomplishing these two goals simultaneously. We manipulated two design features in graphs, namely, whether graphs depicted the number of people affected by a risk and those at risk of harm (“foreground+background”) versus only those affected (“foreground‐only”), and the presence versus absence of simple numerical labels above bars. Foreground‐only displays were associated with larger risk perceptions and risk‐avoidant behavior (i.e., willingness to take a drug for heart attack prevention) than foreground+background displays, regardless of the presence of labels. Foreground‐only graphs also hindered risk understanding when labels were not present. However, the presence of labels significantly improved understanding, eliminating the detrimental effect of foreground‐only displays. Labels also led to more positive user evaluations of the graphs, but did not affect risk‐avoidant behavior. Using process modeling we identified mediators (risk perceptions, understanding, user evaluations) that explained the effect of display type on risk‐avoidant behavior. Our findings contribute new evidence to the graph design literature: unlike what was previously feared, we demonstrate that it is possible to design foreground‐only graphs that promote intentions for behavior change without a detrimental effect on risk understanding. Implications for the design of graphical risk communications and decision support are discussed.     

Seeing Is Believing: The Transitory Influence of Reputation Information on E‐Commerce Trust and Decision Making

This research examines the transitory influence of reputation information on consumer decision making regarding an e‐vendor. Using social judgment theory to explain how reputation information's effect on perceptions may be fleeting, we specifically examined how user trusting beliefs related to an e‐vendor change after a simple exposure to the Web site. A total of 369 college students participated in an experiment that found that reputation information was initially strongly related to trusting beliefs regarding the e‐vendor, but a brief nonpurchase‐related exposure to the e‐vendor's Web site—that is, direct experience—reduced reputation's effects significantly. This research provides insights into why reputation information may be more important in certain circumstances than in others and enhances our understanding of how consumer decision making is affected by different purchasing contexts. This research also has implications on the design and use of trust‐building technologies.     

An Analysis of Strategic Determinants,Learning and Decision‐Making in Sino‐British Joint Ventures

This paper examines how partners’ strategic objectives, inputs and competencies affect the learning they achieve in joint ventures and their ability to influence decision‐making in such ventures. Quantitative and qualitative research in 20 Sino‐British joint ventures permits an investigation of these factors for both British and Chinese joint venture partners. Findings indicate that the transfer by British partners of technology and managerial expertise to joint ventures enhances their ability to achieve their strategic objectives by influencing decisions in such ventures. The learning achieved by British partners depends on their commitment of strategic inputs to joint ventures and is therefore associated with their influence. The case of Chinese partners is different, because they look to learn from the knowledge and resource inputs provided by the British partners. They therefore rely more on legal ownership rights to retain an influence over joint venture decision‐making. The achievement of Chinese partners’ strategic objectives depends more critically on the quality of the partnership itself.     

Managerial Hubris,Trade Associations and Regulatory Knowledge in Micro‐Firms

To avoid breaking the law for regulatory non‐compliance, it is essential that micro‐firm owner‐managers are aware of deficiencies in their knowledge, so that they can seek improvement and avoid over‐confidence (i.e. hubris) in their knowledge levels. Using newly collected survey data from micro‐firms in the English accommodation sector and multivariate techniques, the authors explore the possibility of hubris by making a novel distinction between the Perceived‐Knowledge and Actual‐Knowledge of regulation held by micro‐firm owner‐managers. Both Perceived‐Knowledge (from self‐assessment) and Actual‐Knowledge (from a simple test) over four core areas of regulation are found to be different, generally poor and suggestive of hubris. The relationship between these knowledge levels is further explored by considering the role of trade association membership (since they support members) and attitude (since it effects learning). Attitude is found to be positively associated with both forms of knowledge, while trade association memberships are also found to be associated with enhanced Perceived‐Knowledge, but not Actual‐Knowledge. In light of the results, the authors suggest several priority areas for improving Actual‐Knowledge and self‐assessment skills, and areas for future research.     

Media Effects on Group Collaboration: An Empirical Examination in an Ethical Decision‐Making Context

Even though it is widely acknowledged that collaboration underlies much of the decision‐making efforts in contemporary organizations, and that organizational groups are increasingly making decisions that have ethical implications, few studies have examined group ethical decision‐making processes and outcomes. In addition, while there is increasing evidence that groups often collaborate/communicate using different mediating technologies, few studies have examined the effect of the characteristics of the media in group ethical decision‐making contexts. Finally, there is a clear paucity of studies that have investigated group decision making pertaining to information technology (IT)‐related ethical dilemmas, an area of rising importance for information systems (IS) and decision science researchers. This article seeks to address the gaps described above through an experimental study where groups collaborating either in a face‐to‐face context or in a computer‐mediated context (using NetMeeting or Wiki) were required to make a decision with respect to a scenario with an IT‐related ethical dilemma. Results indicate that media characteristics (e.g., anonymity, immediacy of feedback, parallelism) do not have an effect on whether groups make ethical (or unethical) decisions. However, several media characteristics were found to play a significant role on downstream variables, such as the quality of a follow‐up task (i.e., creation of a decision justification document), and overall process satisfaction of the group members.     

THE IMPACT OF ALTERNATE DECISION TECHNIQUES ON USER BEHAVIOR

The quantitative disciplines have focused their efforts primarily upon more complete and efficient methods for analyzing decision problems. Almost totally neglected in these efforts is the interplay between a decision technique and its ultimate user. This interface is of crucial importance to the continued success of the decision sciences in organizations. Data are presented to support the contention that decision techniques and their resultant impact on job content interact with psychological variables to cause variation in user satisfaction and performance. These results are similar to the effects hypothesized to occur at blue collar levels in organizations.     

Learning and Open Source Software License Choice

Licensing is the defining characteristic of open source software (OSS) and often has tremendous impact on the success of OSS projects. However, OSS licenses are very different from those for proprietary software, and our understanding of the choice of OSS licenses is very limited. In this study, we explore this important decision from a learning perspective. We build collaboration networks and trace paths through which potential learning and knowledge flow across projects using a dataset derived from SourceForge. We identify that both experiential learning and vicarious learning have significant influence on OSS license choice. We provide reasons why experiential learning and vicarious learning affect decision‐making regarding OSS license choice, and explore important contingencies under which the two modes of learning are more effective. We find that leadership roles on prior projects and similarities between projects significantly moderate these two modes of learning, respectively. More importantly, we argue and empirically illustrate that experiential learning is more effective than vicarious learning in influencing OSS license choice. Our research sheds new light on our understanding of license choice for OSS projects and provides practical guidelines for future OSS development.     

Waiting for News in the Market for Lemons

We study a dynamic setting in which stochastic information (news) about the value of a privately informed seller's asset is gradually revealed to a market of buyers. We construct an equilibrium that involves periods of no trade or market failure. The no‐trade period ends in one of two ways: either enough good news arrives, restoring confidence and markets reopen, or bad news arrives, making buyers more pessimistic and forcing capitulation that is, a partial sell‐off of low‐value assets. Conditions under which the equilibrium is unique are provided. We analyze welfare and efficiency as they depend on the quality of the news. Higher quality news can lead to more inefficient outcomes. Our model encompasses settings with or without a standard static adverse selection problem—in a dynamic setting with sufficiently informative news, reservation values arise endogenously from the option to sell in the future and the two environments have the same equilibrium structure.     

The Effects of Information Overload on Software Project Risk Assessment*

Managers are responsible for providing effective information technology governance of the software development process. Ineffective governance leads to serious resource misallocations and negative consequences concerning Sarbanes‐Oxley compliance. In order for managers to make informed decisions about software development projects, they often need more information than is available through normal information channels, that is, they need an in‐depth review of the at‐risk project. Such in‐depth reviews, however, are costly. Hence, accurate identification of at‐risk projects for in‐depth review is critical to management's ability to govern. This research considers how two factors, information load and time pressure, affect the quality of the project‐selection process. We examine quality by observing the decision strategies involved and then relating these strategies to subsequent decision making. An experiment was conducted with experienced information systems auditors using a combination of policy‐capturing and computerized process‐tracing techniques. The participants in our study cope with information overload by accelerating their decision‐making process and adopting noncompensatory decision processes. Contrary to prior research, our process‐tracing analysis suggests that participants rarely filter information, thus implying that decision makers are unable to process all the information. Coping by resorting to noncompensatory strategies did not decrease decision quality unless combined with accelerated information processing. Participants also increase their weight on the software project risk factors that they repeatedly access and that they view for longer periods of time. The theoretical contributions and practical implications regarding what actions managers can take to reduce the negative impact of information overload are discussed.     

Invisible Whispering: Restructuring Collaborative Decision Making with Instant Messaging

Organizational decision making is dominated by teams. When an important decision is required, a team is often formed to make it or to advise the individual decision maker, because a team has more resources, knowledge, and political insight than any one individual working alone. As teams have become geographically distributed, collaboration technology has come to play an important role in such collective decision making efforts. Instant messaging (IM) is an increasingly prevalent workplace collaboration technology that enables near‐synchronous text exchanges on a variety of devices. We examined the use of IM during face‐to‐face, telephone, and computer‐mediated team meetings, a practice we call “invisible whispering.” We introduce Goffman's characterization of social interaction as dramatic performance, differentiable into “front stage” and “backstage” exchanges, to analyze how invisible whispering alters the socio‐spatial and temporal boundaries of team decision making. Using IM, workers were able to influence front stage decision making through backstage conversations, often participating in multiple backstage conversations simultaneously. This type of interaction would be either physically impossible or socially constrained without the use of IM. We examine how invisible whispering changes the processes of collaborative decision making and how these new processes may affect the efficiency and effectiveness of collaborative decision making, as well as participation, satisfaction, relationships among team members, and individual attention.     

Collective Intuition: Implications for Improved Decision Making and Organizational Learning

This article establishes the foundation for research on collective intuition through a study of decision making and organizational learning processes in police senior management teams. We conceptualize collective intuition as independently formed judgement based on domain‐specific knowledge, experience and cognitive ability, shared and interpreted collectively. We contribute to intuition research, which has tended to focus its attention at the individual level, by studying intuition collectively in team settings. From a dual‐process perspective, we investigate how expert intuition and deliberation affect decision making and learning at various levels of the organization. Furthermore, we contribute to organizational learning research by offering an empirically derived elaboration of the foundational 4I framework, identifying additional ‘feed‐forward’ and ‘feedback’ loop processes, and thereby providing a more complete account of this organizational learning model. Bridging a variety of relevant but previously unconnected literatures via our focal concept of collective intuition, our research provides a foundation for future studies of this vitally important but under‐researched organizational phenomenon. We offer theoretical and practical implications whereby expert intuitions can be developed and leveraged collectively as valuable sources of organizational knowledge and learning, and contribute to improved decision making in organizations.