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Decades of research suggest that information exchange in groups and organizations can reliably improve judgment accuracy in tasks such as financial forecasting, market research, and medical decision making. However, we show that improving the accuracy of numeric estimates does not necessarily improve the accuracy of decisions. For binary-choice judgments, also known as classification tasks—for example, yes/no or build/buy decisions—social influence is most likely to grow the majority vote share, regardless of the accuracy of that opinion. As a result, initially, inaccurate groups become increasingly inaccurate after information exchange, even as they signal stronger support. We term this dynamic the “crowd classification problem.” Using both a novel data set and a reanalysis of three previous data sets, we study this process in two types of information exchange: (1) when people share votes only, and (2) when people form and exchange numeric estimates prior to voting. Surprisingly, when people exchange numeric estimates prior to voting, the binary-choice vote can become less accurate, even as the average numeric estimate becomes more accurate. Our findings recommend against voting as a form of decision making when groups are optimizing for accuracy. For those cases where voting is required, we discuss strategies for managing communication to avoid the crowd classification problem. We close with a discussion of how our results contribute to a broader contingency theory of collective intelligence. This paper was accepted by Lamar Pierce, organizations.

In this paper, we reflect on research in decision analysis that has appeared in Management Science and its impact on decision analysis practice and applications. We consider professional applications of decision analysis in business and government settings as well as everyday conversational applications. This paper was accepted by David Simchi-Levi, Special Section of Management Science: 65th Anniversary.

In a contest in which heterogeneous solvers make effort to develop solutions, existing theories predict different outcomes about how solvers will change their effort levels as more participants compete for a prize. Specifically, one theory prescribes that when solvers are heterogeneous in their initial expertise, every solver will reduce effort with more participants due to a lower probability of winning the contest. In contrast, another theory prescribes that when solvers are heterogeneous in their costs of exerting effort, high-ability solvers raise their effort with more participants, while low-ability solvers reduce their effort; but it does not provide an explanation for such a prescription. Yet, a recent empirical study corroborates the prescription of the second theory. This paper presents a unifying model that encompasses both types of heterogeneity in solvers, and proves that the result prescribed by the second theory holds in the unifying model, suggesting that the first theory needs to be re-evaluated. Thus, we present the correct analysis of the first theory, and identify a second effect of increased competition on solvers' incentives: More solvers in a contest raise the expected best performance among other solvers, and hence solvers have positive incentives to exert higher effort to win the contest. Due to this positive effect that has been neglected in prior literature, we find that a free-entry open contest is more likely to be optimal to a contest organizer than what prior literature asserted.

A robust finding in the escalation literature, termed as the preference effect, is that involvement in the period 1 initial project assessment decision increases the tendency for decision makers to stick with a losing course of action during the period 2 project reassessment decision. The proposed solution is to bring in a new decision maker in period 2. Across multiple studies, we show that providing period 1 information in real options format increases the tendency for decision makers to view period 2 focal event information as both more negative and more important. Consequently, such decision makers exhibit less escalation in period 2, i.e., exhibit behavior opposite to the preference effect. This suggests that, in real option contexts, not only do we not need to bring in a new decision maker, but also (counterintuitively) it is beneficial to retain the same decision maker in situations where escalation is likely to occur. This paper was accepted by Yuval Rottenstreich, judgment and decision making.

This article outlines a network approach to the study of technological change. We propose that new inventions reshape networks of interlinked technologies by shifting inventors’ attention to or away from the knowledge on which those inventions build. Using this approach, we develop novel indexes of the extent to which a new invention consolidates or destabilizes existing technology streams. We apply these indexes in analyses of university research commercialization and find that, although federal research funding pushes campuses to create inventions that are more destabilizing, deeper commercial ties lead them to produce technologies that consolidate the status quo. By quantifying the effects that new technologies have on their predecessors, the indexes we propose allow patent-based studies of innovation to capture conceptually important phenomena that are not detectable with established measures. The measurement approach presented here offers empirical insights that support theoretical development in studies of innovation, entrepreneurship, technology strategy, science policy, and social network theory. This paper was accepted by Lee Fleming, entrepreneurship and innovation.

Policy makers and managers often turn to experts when in need of information, but we should expect experts to be systematically biased. This is because the decision to research a question implies a belief that research will be fruitful. If priors about the impact of one’s work are correct on average, then those who choose to research a question are optimistic about the quality of their work. The bias varies predictably with attributes of the question being studied. This fact has implications for a variety of mechanism design applications and yields predictions in accordance with a large literature in psychology. This paper was accepted by Teck-Hua Ho, behavioral economics.

We experimentally explore the effect of performance information and production uncertainties on (i) subjective entitlements derived from the production process and (ii) bargaining over the jointly produced surplus. We hypothesize that performance information and details of the production process affect entitlements, which in turn influence bargaining behavior. We find that, without performance information, subjective entitlements are mostly mutually consistent, and bargaining mainly ends with an equal split. In stark contrast, negotiators derive strong, mutually inconsistent, subjective entitlements when there is performance information. These subjective entitlements affect opening proposals, concessions, and bargaining duration and lead to asymmetric agreements. Moreover, given performance information, endogenous variations in entitlements influence bargaining, suggesting an independent role of subjective entitlements. Production uncertainties influence bargaining, especially when performance information is present, but do not substantially mitigate the effect of entitlements. Theoretical bargaining models allowing for reference points or fairness principles can partly account for the empirical results. Yet, important aspects are left unexplained and our results suggest ways for extending these models. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.2012 . This paper was accepted by Uri Gneezy, behavioral economics.

This study develops optimal transfer pricing schemes that manage software reuse in incremental software development, namely, a development regime wherein users begin utilizing parts of the system that are released to them even before the system is entirely completed. In this setting, conflicts can arise between developers and users from divergent interests concerning the release of functionalities in the project. The release of functionalities is influenced by reuse, i.e., the effort spent by the development team to write code that can be reused within the same project or in future projects. For example, the development team may choose to spend extra effort to make certain portions of the system reusable because doing so could reduce the effort needed to develop the entire system. However, the additional effort spent on reuse could delay the release of certain critical functionality, making such a strategy suboptimal for the users. Thus, optimal reuse decisions for developers and users could be different. In addition, from the firm's perspective, reuse decisions must not only balance the objectives of developers and users for the current project, but reuse effort may be spent to benefit future projects. Our study also highlights the fact that reuse may not always be beneficial for the firm. To this end, we consider different instances of the user–developer conflict and provide transfer pricing schemes that operate under information asymmetry and achieve two key properties: firm-level optimality and truth revelation. This paper was accepted by Sandra Slaughter, information systems.

We study the hiring and retention of heterogeneous workers who learn over time. We show that the problem can be analyzed as an infinite-armed bandit with switching costs, and we apply results from Bergemann and Välimäki [Bergemann D, Välimäki J (2001) Stationary multi-choice bandit problems. J. Econom. Dynam. Control 25(10):1585–1594] to characterize the optimal hiring and retention policy. For problems with Gaussian data, we develop approximations that allow the efficient implementation of the optimal policy and the evaluation of its performance. Our numerical examples demonstrate that the value of active monitoring and screening of employees can be substantial. This paper was accepted by Yossi Aviv, operations management.

This paper deals with a basic issue: How does one approach the problem of designing the “right” objective for a given resource allocation problem? The notion of what is right can be fairly nebulous; we consider two issues that we see as key: efficiency and fairness. We approach the problem of designing objectives that account for the natural tension between efficiency and fairness in the context of a framework that captures a number of resource allocation problems of interest to managers. More precisely, we consider a rich family of objectives that have been well studied in the literature for their fairness properties. We deal with the problem of selecting the appropriate objective from this family. We characterize the trade-off achieved between efficiency and fairness as one selects different objectives and develop several concrete managerial prescriptions for the selection problem based on this trade-off. Finally, we demonstrate the value of our framework in a case study that considers air traffic management. This paper was accepted by Yossi Aviv, operations management.

Starting from the premise that individuals within an organization are fallible, this paper advances the study of relationships between the organization's decision-making structure and its performance. We offer a general treatment that allows one to analyze the full range of organizational architectures between extreme centralized and decentralized forms (often referred to as hierarchies and polyarchies). Our approach furthermore allows designers to examine the change in the overall reliability of the organizational structure as the number of actors within the organization changes. We provide general proofs that show how decision-making structures can be constructed so they maximize reliability for a given number of agents. Our model can be used directly for a qualitative assessment of decision-making structures. It is thereby useful for assessment of the many complicated hybrid structures that we see in actual decision-making organizations, such as banks, purchasing departments, and military intelligence. An application from a bank illustrates how our framework can be used in practice.

International audience; Since Osborn's Applied Imagination book in 1953 (Osborn, A. F. 1953. Applied Imagination: Principles and Procedures of Creative Thinking. Charles Scribner's Sons, New York), the effectiveness of brainstorming has been widely debated. While some researchers and practitioners consider it the standard idea generation and problem-solving method in organizations, part of the social science literature has argued in favor of nominal groups, i.e., the same number of individuals generating solutions in isolation. In this paper, we revisit this debate, and we explore the implications that the underlying problem structure and the team diversity have on the quality of the best solution as obtained by the different group configurations. We build on the normative search literature of new product development, and we show that no group configuration dominates. Therefore, nominal groups perform better in specialized problems, even when the factors that affect the solution quality exhibit complex interactions (problem complexity). In cross-functional problems, the brainstorming group exploits the competence diversity of its participants to attain better solutions. However, their advantage vanishes for extremely complex problems.

Objectives have long been considered a basis for sound decision making. This research examines the ability of decision makers to generate self-relevant objectives for consequential decisions. In three empirical studies, participants consistently omitted nearly half of the objectives that they later identified as personally relevant. More surprisingly, omitted objectives were perceived to be almost as important as those generated by participants on their own. These empirical results were replicated in a real-world case study of strategic decision making at a high-tech firm. Overall, our research suggests that decision makers are considerably deficient in utilizing personal knowledge and values to form objectives for the decisions they face.

This introductory essay connects the various contributions included in the special issue on strategic dynamics and contrasts them with the static analyses that predominate in the strategy field. In addition to highlighting a variety of methodological approaches, the contributions shed substantive light on strategic dynamics at the value system and industry and firm levels. Taken together, they also suggest some broad directions for further work aimed at making dynamics a more important part of the future of the field of strategy.

This article is an introduction to the focused issue on entrepreneurship. It provides motivation for greater scholarly investigation of the phenomenon of entrepreneurship, explains the evolution of the focused issue, offers an overview of the seven papers in the issue, and offers the editor’s thoughts on the relationship of the papers in the focused issue to research on entrepreneurship in general.

This paper presents an introductory survey for this special issue of Management Science on electronic markets. We acquaint the reader with some fundamental concepts in the study of electronic market mechanisms, while simultaneously presenting a survey and summary of the essential literature in this area. Along the way, we position each of the papers presented in this special issue within the existing literature, demonstrating the deep impact of these 14 articles on an already broad body of knowledge.

Management Science, V. 51, No. 4, pp 655-661. http://dx.doi.org/10.1287/mnsc.1040.0290 Peer review of research proposals and articles is an essential element in research and development processes worldwide. Here we consider a problem that, to the best of our knowledge, has not been addressed until now: how to assign subsets of proposals to reviewers where the reviewers supply their evaluations through ordinal ranking. The solution approach we propose for this assignment problem maximizes the number of proposal pairs that will be evaluated by one or more reviewers. This new approach should facilitate meaningful aggregatin of partial rankings of subsets of proposals by multiple reviewers into a consensus ranking. We offer two ways to implement the approach: an integer programming set-covering model and a heuristic procedure. The effectiveness and efficiency of the two models are tested through an extensive simulation experiment.

The paper that I authored and that was published in Management Science in 1969 (Bass 1969) has become widely known as the “Bass Model” (see Morrison and Raju 2004). The model of the diffusion of new products and technologies developed in the paper is one of the most widely applied models in management science. It was especially gratifying for me to learn that INFORMS members have voted the “Bass Model” paper as one of the Top 10 Most Influential Papers published in the 50-year history of Management Science in connection with the 50th anniversary of the journal. In this commentary on the paper I shall discuss some background and history of the development of the paper, the reasons why the model has been influential, some important extensions of the model, some examples of applications, and some examples of the frontiers of research involving the Bass Model. In the current period, in which there is much discussion about the marketing of applications of management science methods and practice, I hope that this commentary will be useful in providing insights about some of the properties of models that will be applied.

Modern hierarchical organizations, like corporations, must motivate agents to work hard. Given their pyramid structure, it is not surprising that one commonly used motivator is the promotion tournament. In such tournaments, agents compete to advance to positions at higher organizational levels. Though these tournaments are common, little research has empirically looked at the interface of organizational structure and tournament design. This paper aims to take a step in filling this void by comparing the performance of various tournament designs using controlled laboratory techniques.

This year marks the 50th anniversary of Management Science. We take this opportunity to trace the history of the Marketing Department in Management Science, outline the role that the Marketing Department has played in supporting management science research in Marketing, its impact on the field, and its plans for the future.

As part of the 50th anniversary of Management Science, the journal is publishing articles that reflect on the past, present, and future of the various subfields the journal represents. In this article, we consider decision analysis research as it has appeared in Management Science. After reviewing the foundations of decision analysis and the history of the journal’s decision analysis department, we review a number of key developments in decision analysis research that have appeared in Management Science and offer some comments on the current state of the field.

We reflect on the evolution of the strategy field as seen through the window of Management Science. Reflecting the diverse disciplinary roots of strategy research, we identify a broad-ranging body of work that varies with respect to the assumptions made regarding individual rationality and the level of analysis at which the research is carried out. We argue that recent developments begin to delineate a potentially unifying conceptual framework for treating the field's defining questions—the conceptual apparatus of evolutionary economics. We conclude by laying out important challenges for evolutionary economics if it is to serve as a foundation for both the positive and the normative research agendas of the strategy field.

The development of the information systems (IS) literature in Management Science during the past 50 years reflects the inception, growth, and maturation of several different research streams. The five research streams we identify incorporate different definitions of the managerial problems that relate to IS, the alternate theoretical perspectives and different methodological paradigms to study them, and the levels of the organization at which their primary results impact managerial practice. The decision support and design science research stream studies the application of computers in decision support, control, and managerial decision making. The value of information research stream reflects relationships established based on economic analysis of information as a commodity in the management of the firm. The human-computer systems design research stream emphasizes the cognitive basis for effective systems design. The IS organization and strategy research stream focuses the level of analysis on the locus of value of the IS investment instead of on the perceptions of a system or its user. The economics of information systems and technology research stream emphasizes the application of theoretical perspectives and methods from analytical and empirical economics to managerial problems involving IS and information technologies (IT). Based on a discussion of these streams, we evaluate the IS literature's core contributions to theoretical and managerial knowledge, and make some predictions about the road that lies ahead for IS researchers.

Previous work on the solution to analytical electronic customer relationship management (eCRM) problems has used either data-mining (DM) or optimization methods, but has not combined the two approaches. By leveraging the strengths of both approaches, the eCRM problems of customer analysis, customer interactions, and the optimization of performance metrics (such as the lifetime value of a customer on the Web) can be better analyzed. In particular, many eCRM problems have been traditionally addressed using DM methods. There are opportunities for optimization to improve these methods, and this paper describes these opportunities. Further, an online appendix (mansci.pubs.informs.org/ecompanion.html) describes how DM methods can help optimization-based approaches. More generally, this paper argues that the reformulation of eCRM problems within this new framework of analysis can result in more powerful analytical approaches.

This begins a two-part commentary on management science and e-business, the theme of this two-part special issue. After explaining the topical clusters that give organization to both parts, we pose two key questions concerning the impact of the emerging digital economy on management science research: What fundamentally new research questions arise, and what kind of research enables progress on them. We sketch the papers appearing in this part from the perspective of both these questions, and offer summary comments on the first question based on the papers in both parts. The principal conclusion is that the digital economy is giving birth to new research questions in three main ways (not all independent): by enabling and popularizing several types of technology-mediated interactions, by spawning large-scale digital data sources, and by creating recurring operational decisions that need to be automated.

Selecting the right technologies to incorporate in new products is a particularly challenging aspect of new product definition and development. While newer advanced technologies may offer improved performance, they also make the product development process more risky and challenging. In this paper, we focus on the problem of technology selection and commitment under uncertainty, a major challenge to firms in turbulent environments. We argue that the "pizza-bin" approach of rejecting prospective technologies outright may not serve firms well when the pressure to differentiate products is enormous. After motivating the challenges and decisions facing firms using a real-life application from Dell Computer Corporation, we formulate a mathematical model of a firm that must define its products in the presence of technology uncertainty. Specifically, the firm faces two options: (i) a proven technology that is known to be viable and (ii) a prospective technology that offers superior price to performance results but whose viability is not a fully certain outcome. To minimize the impact of technology uncertainty, we consider two approaches to design flexibility, termed parallel path and sufficient design, which allow the firm to concurrently develop its products while the technology is being validated. Our analysis helps understand appropriateness of the different flexible design approaches. We illustrate our model with the Dell portable computer example and note the managerial implications of our analysis.

Column generation is a well-known and widely practiced technique for solving linear programs with too many variables or constraints to include in the initial formulation explicitly. Instead, the required column information is generated at each iteration of the simplex algorithm. This paper shows that, even if the number of variables is low enough for explicit inclusion in the model with the available technology, it may still be more efficient to resort to column generation for some class of problems.

This article develops a model of a project as a payoff function that depends on the state of the world and the choice of a sequence of actions. A causal mapping, which may be incompletely known by the project team, represents the impact of possible actions on the states of the world. An underlying probability space represents available information about the state of the world. Interactions among actions and states of the world determine the complexity of the payoff function. Activities are endogenous, in that they are the result of a policy that maximizes the expected project payoff. A key concept is the adequacy of the available information about states of the world and action effects. We express uncertainty, ambiguity, and complexity in terms of information adequacy. We identify three fundamental project management strategies: instructionism, learning, and selectionism. We show that classic project management methods emphasize adequate information and instructionism, and demonstrate how modern methods fit into the three fundamental strategies. The appropriate strategy is contingent on the type of uncertainty present and the complexity of the project payoff function. Our model establishes a rigorous language that allows the project manager to judge the adequacy of the available project information at the outset, choose an appropriate combination of strategies, and set a supporting project infrastructure—that is, systems for planning, coordination and incentives, and monitoring.

In view of the distressingly low rate of success in new product introduction, it is important to identify predictive guidelines early in the new product development process so that better choices can be made and unnecessary costs avoided. In this paper, we propose a framework for early analysis based on the success potential embodied in the product-idea itself and the circumstances of its emergence. Based on two studies reporting actual introductions, we identified several determinants (such as how the ideas originated, their specific configurations, and the level of technology required for their implementation) that significantly distinguish successful from unsuccessful new products in the marketplace. We suggest that these factors, together with already known factors of success/failure, may aid in the estimation of the potential of a concept early in its development.

Not many authors have attempted to classify projects according to any specific scheme, and those who have tried rarely offered extensive empirical evidence. From a theoretical perspective, a traditional distinction between radical and incremental innovation has often been used in the literature of innovation, and has created the basis for many classical contingency studies. Similar concepts, however, did not become standard in the literature of projects, and it seems that theory development in project management is still in its early years. As a result, most project management literature still assumes that all projects are fundamentally similar and that “one size fits all.” The purpose of this exploratory research is to show how different types of projects are managed in different ways, and to explore the domain of traditional contingency theory in the more modern world of projects. This two-step research is using a combination of qualitative and quantitative methods and two data sets to suggest a conceptual, two-dimensional construct model for the classification of technical projects and for the investigation of project contingencies. Within this framework, projects are classified into four levels of technological uncertainty, and into three levels of system complexity, according to a hierarchy of systems and subsystems. The study provides two types of implications. For project leadership it shows why and how management should adapt a more project-specific style. For theory development, it offers a collection of insights that seem relevant to the world of projects as temporary organizations, but are, at times, different from classical structural contingency theory paradigms in enduring organizations. While still exploratory in nature, this study attempts to suggest new inroads to the future study of modern project domains.

Researchers examining loosely coupled systems, knowledge management, and complementary practices in organizations have proposed, informally, that the complexity of a successful business strategy can deter imitation of the strategy. This paper explores this proposition rigorously. A simple model is developed that parametrizes the two aspects of strategic complexity: the number of elements in a strategy and the interactions among those elements. The model excludes conventional resource-based and game-theoretic barriers to imitation altogether. The model is used to show that complexity makes the search for an optimal strategy intractable in the technical sense of the word provided by the theory of NP-completeness. Consequently, would-be copycats must rely on search heuristics or on learning, not on algorithmic “solutions,” to match the performance of superior firms. However, complexity also undermines heuristics and learning. In the face of complexity, firms that follow simple hill-climbing heuristics are quickly snared on low “local peaks,” and firms that try to learn and mimic a high performer's entire strategy suffer large penalties from small errors. The model helps to explain why some winning strategies remain unmatched even though they are open to public scrutiny; why certain bundles of organizational practices diffuse slowly even though they lead to superior performance; and why some strategies yield superior returns even after many of their critical ingredients are adopted by competitors. The analysis also suggests roles for management science and managerial choice in a world of complex strategies.

In this introductory note, we describe the motivation for this special issue on complex systems. We begin by noting the potential management opportunities offered by recent advances in complexity science. After defining the nature of complex systems and the many ways they are expressed in organizations and markets, we briefly describe the main tools and concepts of complexity theory. We close with a brief review of the articles in this issue and their relevance to the interests and concerns of managers.

Many of the concepts and procedures of product quality control can be applied to the problem of producing better quality information outputs. From this perspective, information outputs can be viewed as information products, and many information systems can be modeled as information manufacturing systems. The use of information products is becoming increasingly prevalent both within and across organizational boundaries. This paper presents a set of ideas, concepts, models, and procedures appropriate to information manufacturing systems that can be used to determine the quality of information products delivered, or transferred, to information customers. These systems produce information products on a regular or as-requested basis. The model systematically tracks relevant attributes of the information product such as timeliness, accuracy and cost. This is facilitated through an information manufacturing analysis matrix that relates data units and various system components. Measures of these attributes can then be used to analyze potential improvements to the information manufacturing system under consideration. An illustrative example is given to demonstrate the various features of the information manufacturing system and show how it can be used to analyze and improve the system. Following that is an actual application, which, although not as involved as the illustrative example, does demonstrate the applicability of the model and its associated concepts and procedures.

The evaluation of alternatives during organizational decision making was investigated to uncover evaluation tactics used by decision makers and how these tactics and complexity influenced success. Evaluation tactics that relied upon subjective, judgmental, bargaining, and analytical inferences were uncovered from 317 strategic decisions. The complexity of these decisions was measured by the numbers of alternatives considered, number of criteria used, and perceived difficulty of the evaluation task to identify conditions under which the evaluation tactics were successful. The managerial implications of evaluating alternatives with the tactics that used judgmental, bargaining, analytical, and subjective inferences under different levels of complexity are discussed.

Expanding product variety and high customer service provision are both major challenges for manufacturers to compete in the global market. In addition to many ongoing programs, such as lead-time reduction, redesigning products and processes so as to delay the point of product differentiation is becoming an emerging means to address these challenges. Such a strategy calls for redesigning products and processes so that the stages of the production process in which a common process is used are prolonged. This product/process redesign will defer the point of differentiation (i.e., defer the stage after which the products assume their unique identities). In this paper, we develop a simple model that captures the costs and benefits associated with this redesign strategy. We apply this simple model to analyze some special cases that are motivated by real examples. These special cases enable us to formalize three different product/process redesign approaches (standardization, modular design, and process restructuring) for delaying product differentiation that some companies are beginning to pursue. Finally, we analyze some special theoretical cases that enable us to characterize the optimal point of product differentiation and derive managerial insights.

This paper illustrates a methodology to evaluate how dissimilar military systems support the accomplishment of the United States' counterproliferation objectives. The key questions in evaluating counterproliferation systems are identified. By using decision analysis, an influence diagram model is developed that represents military activities in the counterproliferation process. A value model is developed that enables systems to be evaluated against common criteria. An analysis of intelligence, defensive, and offensive counterproliferation systems suggests that intelligence system improvements may provide the greatest potential to meet the United States' counterproliferation objectives. Sensitivity analysis is conducted to determine which factors in the model are most important. To demonstrate the model, nine systems from the Air Force Vulcan's Forge 1995 wargame are evaluated. This paper illustrates the value of decision analysis, and influence diagrams in particular, by involving decision makers and subject matter experts in structuring complex problems for analysis.

In collaboration with industry partners, a normative model of the product concept decision process was developed, supported with tools and techniques, and codified as a decision support process for product development teams. This process (Concept Engineering) was then introduced into a number of product development teams in different companies. A comparative analysis of actual product concept development activities, with and without the use of Concept Engineering, was conducted. All of the observed teams viewed time to market as a critical measure of their success. However, the development processes differed significantly depending on whether relatively more emphasis was placed on time or market considerations. Key variables associated with the product concept development decision process and time-to-market dynamics were identified and a theory of the concept development process was developed using the inductive system diagram technique, a research methodology developed in the course of this work. We believe this work contributes to the operations management literature in three ways. First, it introduces a very detailed, structured decision process for product concept development, enhancing the literature on Quality Function Deployment (QFD). Second, it presents a theory of product concept development that can improve understanding of success and failure in product concept development. Third, this work develops new methodology (Inductive Systems Diagrams) for field work in operations management. This methodology marries the grounded theory methods familiar to sociologists with causal-loop modeling familiar to systems dynamicists, yielding a rigorous tool for systematically collecting, organizing, and distilling large amounts of field-based data.

Too often the relationship between clients and external consultants is perceived as one of protagonist versus antagonist. Stories on dramatic, failed consultancies abound, as do related anecdotal quips. A contributing factor to many “apparently” failed consultancies is a poor appreciation by both the client and consultant of the client's true goals for the project and how to assess progress toward these goals. This paper presents and analyses a measurement model for assessing client success when engaging an external consultant. Three main areas of assessment are identified: (1) the consultant's recommendations, (2) client learning, and (3) consultant performance. Engagement success is empirically measured along these dimensions through a series of case studies and a subsequent survey of clients and consultants involved in 85 computer-based information system selection projects. Validation of the model constructs suggests the existence of six distinct and individually important dimensions of engagement success. Both clients and consultants are encouraged to attend to these dimensions in pre-engagement proposal and selection processes, and post-engagement evaluation of outcomes.

Rapid advances in information technology have brought decision makers the mixed blessing of an increasingly vast amount of easily available data. Designers of decision support systems (DSS) have focused on incorporating the latest technology with little attention to whether these new systems are compatible with the psychology of decision makers. Our premise is that DSS should be designed to take advantage of the distinctive competencies of decision makers while using technology to compensate for their inherent weaknesses. In this study we apply this approach to a forecasting task. We find that to arrive at a forecast decision makers often search their experience for a situation similar to the one at hand and then make small adjustments to this previous situation. Our theoretical model of the performance of this intuitively appealing strategy shows that it performs reasonably well in highly predictable environments, but performs quite poorly in less predictable environments. Results from an experiment confirm these predictions and show that providing decision makers with a simple linear model in combination with a computerized database of historical cases improves performance significantly. We conclude by discussing how these results can be used to help improve forecasting in applied contexts, such as promotion forecasting in the retail grocery industry.

Researchers in artificial intelligence and decision analysis share a concern with the construction of formal models of human knowledge and expertise. Historically, however, their approaches to these problems have diverged. Members of these two communities have recently discovered common ground: a family of graphical models of decision theory known as influence diagrams or as belief networks. These models are equally attractive to theoreticians, decision modelers, and designers of knowledge-based systems. From a theoretical perspective, they combine graph theory, probability theory and decision theory. From an implementation perspective, they lead to powerful automated systems. Although many practicing decision analysts have already adopted influence diagrams as modeling and structuring tools, they may remain unaware of the theoretical work that has emerged from the artificial intelligence community. This paper surveys the first decade or so of this work.

Most decision support systems (DSS) contain stored data, data analysis procedures, and decision models. However, many DSS have grown to the point that the average end user is presented with a bewildering array of information resources that are difficult to manage in an effective manner. As a result users often gravitate to a few familiar models and are unaware of the data resources available to them and how these resources relate to the various models. For example, they may think that a model requires data that is unavailable, when in fact that data has recently been added to the data base or could be calculated from another model. Or they may believe that all of the data needed to execute a set of models is available and find out well into the analysis that it is not. Existing tools for DSS design do not provide an effective and comprehensive foundation for modeling all the components of a DSS, or for addressing all the important DSS analysis and design issues. In this paper we show how a new graph-theoretic structure, called a metagraph, can be used as a unifying basis for addressing many important questions in DSS development and use.

Choices that require multiple stakeholders to balance conflicting objectives are among today's most controversial decisions. Although many techniques exist for helping decision makers to select among projects, little attention has been given to processes for identifying improved alternatives based on clearly articulated stakeholder values. In this paper we describe a general process to inform controversial social decisions by first structuring stakeholder objectives and then using this information to create policy alternatives. We also report the results of a workshop in Sabah, Malaysia which used the proposed approach as the basis for multiple stakeholder negotiations.

In this research we extend theoretical development about decision making in organizations in which many kinds of errors cannot be tolerated. Catastrophic consequences can be associated with faulty decision making in reliability-seeking organizations, a situation which does not occur in most organizations studied in the past. Observations are drawn from two nuclear-powered aircraft carriers. We find decision processes which appear to change often in these organizations. Important decisions can be made by a number of men even at the lowest levels of the organization. Task-related factors such as technical complexity, high interdependence, and catastrophic consequences associated with rare events and more cognitive factors such as accountability and salience affect decision processes. A model is presented that accounts for dynamic change in decision processes in these organizations.

In analyzing bidding, modeling matters. This paper is a critical analysis of the models available to aid competitive bidding decision making—bidding strategy and auction design—in real transactions. After an introductory overview, this paper describes the contexts in which auctions arise, reviews the “mainstream” theory of single, isolated auctions and discusses the important work involved in enrichment of this theory. In doing so, it indicates results that have been obtained and the sort of changes in analytical approach that are needed to tackle other critical enrichments. The paper summarizes briefly what is known about the direct use of models by bidders and auction designers. A general theme of this paper is that enriched models are needed to bring bidding theory closer to direct applicability in decision making.

In order to measures the extent to which information technology provides competitive advantages, the construct “Competitive Advantage Provided by an Information Application” (CAPITA) was operationalized. A field survey gathered data from 185 top information systems executives regarding information technology applications which had been developed to gain competitive advantage. A confirmatory analysis revealed that CAPITA may be conceptualized in terms of nine dimensions which satisfy key measurement criteria including unidimensionality and convergent validity, discriminant validity, predictive validity, and reliability. The nine dimensions from the basis of a preliminary multidimensional measure or index of competitive advantage which has practical uses for competitive assessment. These include justifying and evaluating applications and acting as dependent variables in empirical competitive advantage research. Extensions entail formulating alternative measures of CAPITA to clarify the theoretical foundations of the construct, validating the latent-structure model on another data set, use of multiple informants for data collection, and exploring complex factor structures for the construct.

A central task of management is decision making and a crucial aspect of decision making is having good alternatives from which to choose. This paper investigates whether computer-based procedures for idea generation can help individuals to develop solution alternatives more creatively. Our conceptual framework considers creative processes, environments, outputs, and individuals. In particular, generating alternatives is viewed as a process of “making connections” —internal connections among problem elements and external connections between a problem and its environment. We have developed a computer program (GENI) which incorporates a variety of techniques to assist in making these different types of connections. The program is described and an experimental test with several managerial problems is presented. The results show that use of the program leads to the development of significantly more creative alternatives than does a control treatment. There is an amplification effect in that the performance of the more creative individuals is improved the most. Thus the findings suggest that computerized idea generation leads to more creative alternatives resulting in potentially better managerial decisions.

This research investigated the idea development stage of a strategic decision making process. The tactics that decision makers apply to identify ideas were uncovered from a systematic study of 168 decision cases. These tactics and contextual factors describing the decision situation were analyzed to determine how each influences success measured by decision merit, development time, initial adoption, and sustained adoption. A “synthesized template” which integrated useful practices and procedures from several sources had the most success, but this tactic was seldom used. A “cyclical search” in which repeated searches were carried out to learn about opportunities was quite successful when used under conditions of low importance, low urgency, and good staff support. The success of the design tactic, which calls for innovation, improved when urgency was present and multiple alternatives were sought. The idea tactic which imposed a fully developed solution was widely used and seldom successful.

To help meet competitive realities operations managers need to know more about the strategic aspects of manufacturing flexibility. This paper takes steps toward meeting that need by critically reviewing the literature and establishing a research agenda for the area. A conceptual model, which places flexibility within a broad context, helps to identify certain assumptions of theoretical studies which need to be challenged. The model also provides a basis for identifying specific flexibility dimensions. The manner in which these dimensions may limit the effectiveness of a manufacturing process, and the problems in operationalizing them are discussed. Focusing next on the neglected area of applied work, concepts are presented for analyzing whether desired amounts of flexibility are being achieved and whether the potential for flexibility built into a manufacturing process is being tapped. Once more, a procedure is outlined for altering a plant's types and amounts of flexibility over time. The research agenda, which grows out of the appraisal of theoretical and applied work, indicates the value in studying generic flexibility strategies, the flexibility dimensions, methods of delivery, ways of evaluating and changing a process's flexibility, and above all measurement problems. The conclusions indicate principles for strategic research, some of which have relevance for the development of mathematical models.

We discuss problem restructuring involving evolution of the problem representation in group decision and negotiation support systems. We develop the evolutionary systems design (ESD) approach to restructuring involving a heuristic controls/goals/values referral process and other domain-independent methodological knowledge, such as constraint relaxation, contingency planning, coalition formation and flexible goal target. These ideas are applied to multiple problem restructuring in a scenario motivated by labor-management negotiations and buyout in the airline industry, in particular, a composite of negotiations at Eastern Airlines, TWA and UAL.

This paper reviews several of the current controversies in the relative value of judgemental and statistical forecasting methods. Where expert, informed judgemental forecasts are being used, a critical analysis of the evidence suggests that their quality is higher than many researchers have previously asserted, and circumstances favourable to this are identified. The issue of the interaction of judgemental and statistical methods is, however, identified as a more worthwhile line of inquiry, and research in this area is reviewed, differentiating approaches aimed at synthesising both of these inputs.