Iron nitride films, including single phase films of α-FeN (expanded bcc Fe), γ′-Fe4N, ε-Fe3 − xN (0 ≤ x ≤ 1), and γ″-FeN, were sputtered onto AlN buffered glass substrates. It was found possible to control the phases in the films merely by changing the nitrogen partial pressure during deposition. The magnetization decreased with increased nitrogen concentration and dropped to zero when the N:Fe ratio was above 0.5. The experimental results, along with spin polarized band calculations, have been used to discuss and analyze the magnetic properties of iron nitrides. It has been demonstrated that in addition to influencing the lattice constant of the various iron nitrides, the nearest N atoms have a significant influence on the exchange splitting of the Fe atoms. Due to the hybridization of Fe-3d and N-2p states, the magnetic moment of Fe atoms decreases with an increase in the number of nearest neighbor nitrogen atoms.

The promises and challenges of a cognitive approach to strategic planning and subjective forecasting are examined. Strategic thinking is viewed as comprising three components: (1) knowledge base, (2) problem representation, and (3) inference processes linking the first two. Analysis of these components, it is argued, can be an important aid to understanding and guiding managerial problem solving. We review several techniques for modeling strategic thinking and planning, including network representations, production systems, causal maps, and analyses of argumentation. We consider the strengths and weaknesses of different cognitive analysis techniques and discuss how they might be implemented. Much more experience and refinement will be needed to produce well-specified procedures for cognitive analysis of planning. We conclude, however, that even at the current stage of development, significant benefits can accrue from a cognitive approach to strategic planning.

Under increasing Pressure to make better decisions in less time, managers often use the quickest and easiest decision-making method: going on ''gut feel.'' But recent decision research shows that intuition is much less reliable than most people believe. Managers need to use more sophisticated methods. This article describes a series of increasingly accurate (and demanding) decision-making approaches. It starts with purely intuitive choices, which are quickest and least accurate, and then examines heuristic short-cuts and rules-of-thumb. It then discusses more demanding and reliable methods, such as bootstrapping and value analysis. This article then examines the strengths and weaknesses of each approach in terms of speed, accuracy, and justifiability, with illustrative applications to managerial practice. Finally, the authors offer practical advice for managers on how the more sophisticated techniques can be incorporated into the organization.

ABSTRACTVarious models or lenses have been used to predict and understand strategic decisions in organizations. This article examines four classes: (1) the unitary rational; (2) the organizational; (3) the political; and (4) the contextual. They are conceptualized as stemming from different assumptions about goal congruency and co‐ordinative efficiency. the contextual view is especially highlighted, as it is a relatively new perspective, both organizationally and cognitively.A brief discussion is offered of disciplines and findings that either support or refute some of these models. Possible syntheses and reconciliations of the four views are explored, focusing on: (1) assumptional fit; (2) level of analysis; (3) cost of fashioning collective rationality; (4) information processing limits in organizational design; and (5) the role of adaptation lags and disequilibrium. the article concludes with a call for a meta‐theory that places the various perspectives in a larger framework.

This paper examines the multiple scenario approach as an important corporate innovation in strategic planning. Using a participant/observer perspective, I examine how scenario planning tries to meet certain methodological, organizational and psychological challenges facing today's senior managers. Three prime characteristics are identified as setting the scenario approach apart from more traditional planning tools: (1) the script or narrative approach, (2) uncertainty across rather than within models, and (3) the decomposition of a complex future into discrete states. After exploring the intellectual roots of scenario planning, I examine such organizational aspects as the need for diversity of views and the importance of simplicity and manageability. Both benefits and obstacles to using scenarios in organizations are identified. Cognitive biases are examined as well, especially the well-known biases of overconfidence and the conjunction fallacy. Two experiments test the impact of scenarios on people's subjective confidence ranges. Another two experiments test the internal coherence of subjects' beliefs. The psychological benefit of scenario planning appears to lie in the exploitation of one set of biases (e.g., conjunction fallacies) to counteract another (such as overconfidence).

We build on an emerging strategy literature that views the firm as a bundle of resources and capabilities, and examine conditions that contribute to the realization of sustainable economic rents. Because of (1) resource-market imperfections and (2) discretionary managerial decisions about resource development and deployment, we expect firms to differ (in and out of equilibrium) in the resources and capabilities they control. This asymmetry in turn can be a source of sustainable economic rent. The paper focuses on the linkages between the industry analysis framework, the resource-based view of the firm, behavioral decision biases and organizational implementation issues. It connects the concept of Strategic Industry Factors at the market level with the notion of Strategic Assets at the firm level. Organizational rent is shown to stem from imperfect and discretionary decisions to develop and deploy selected resources and capabilities, made by boundedly rational managers facing high uncertainty, complexity, and intrafirm conflict.

GOOD DECISION MAKING REQUIRES MORE THAN KNOWLEDGE OF FACTS, CONCEPTS, AND RELATIONSHIPS. IT ALSO REQUIRES METAKNOWLEDGE - an understanding of the limits of our knowledge. Unfortunately, we tend to have a deeply rooted overconfidence in our beliefs and judgments. Because metaknowledge is not recognized or rewarded in practice, nor instilled during formal education, overconfidence has remained a hidden flaw in managerial decision making. This paper examines the costs, causes, and remedies for overconfidence. It also acknowledges that, although overconfidence distorts decision making, it can serve a purpose during decision implementation.

Recent research has documented various violations of procedural invariance, ranging from preference reversals to discrepancies among theoretically equivalent utility encoding methods. This paper explores multiple causes for such discrepancies, focusing on the effects and possible interactions of (1) random noise, (2) insufficient adjustment due to anchoring, and (3) refraining. The case of certainty-equivalence (CE) versus probability-equivalence (PE) judgments is used to model and study the above effects. First, a random noise model is developed. Second, anchoring and insufficient adjustment are formalized, including the notion of variable anchors. Third, our earlier PE-reframing hypothesis (Hershey and Schoemaker, 1985) is examined for the mixed as well as the gain and loss domains. Finally, the effects are combined to yield aggregate predictions for different domains and response sequences. The second part of the paper reports an experiment aimed at testing the component and composite predictions. Some evidence is found for each main effect, although none of the three alone can explain the results. Our data suggest that multiple effects are operative, specifically PE reframing and random noise. We briefly discuss the challenges this poses for both utility encoding and psychological theories concerning violations of procedural invariance.

TO GENERATE A FIRM'S STRATEGIC VISION, MANAGERS MUST UNDERSTAND A HIGHLY COMPLEX SET OF INTERACTING FACTORS, INCLUDING THE INDUSTRY'S HISTORY and structure, the company's and its competition's core capabilities, and the various strategic segments in which the firm competes. They must also take into account the unpredictable future. What if there is a technological breakthrough? What if new markets open up? The author gives a methodology for organizing all of this information, centering on a core capabilities matrix. He shows step-by-step how to envision the possible futures and your company's place in them. When you finish, you'll know which of your core capabilities are most important and how to leverage them for maximum advantage.

AbstractThis paper offers a step‐by‐step analysis of a heuristic approach to scenario planning, taking a managerial perspective. The scenario method is contrasted in general with more traditional planning techniques, which tend to perform less well when faced with high uncertainty and complexity. An actual case involving a manufacturing company is used to illustrate the main steps of the proposed heuristic. Its essence is to identify relevant trends and uncertainties, and blend them into scenarios that are internally consistent. In addition, the scenarios should bound the range of plausible uncertainties and challenge managerial thinking. Links to decision making are examined next, including administrative policies as well as integrative techniques. At the strategic level, a key‐success‐factor matrix is proposed for integrating scenarios, competitor analysis and strategic vision. At the operational level, Monte Carlo simulation is suggested and illustrated as one useful technique for combining scenario thinking with formal project evaluation (after appropriate translations). The paper concludes with a general discussion of scenario planning, to place it in a broader perspective.

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This paper examines the strengths and weaknesses of one of science's most pervasive and flexible metaprinciples: Optimality is used to explain utility maximization in economics, least effort principles in physics, entropy in chemistry, and survival of the fittest in biology. Fermat's principle of least time involves both teleological and causal considerations, two distinct modes of explanation resting on poorly understood psychological primitives. The rationality heuristic in economics provides an example from social science of the potential biases arising from the extreme flexibility of optimally considerations, including selective search for confirming evidence, ex post rationalization, and the confusion of prediction with explanation. Commentators are asked to reflect on the extent to which optimality is (1) an organizing principle of nature, (2) a set of relatively unconnected techniques of science, (3) a normative principle for rational choice and social organization, (4) a metaphysical way of looking at the world, or (5) something else still.

This paper examines experimentally the nature of people's risk-attitudes across different payoff domains and response modes. Only simple gambles are examined, entailing just two monetary outcomes. The main issue of interest is to what extent risk-preferences in one domain or response mode predict anything beyond chance about risk-preferences in another domain or mode. Three domains are examined: gain, mixed and loss. The three response modes used are certainty equivalence CE, probability equivalence PE and outcome equivalence OE judgments. In general, weak associations were found among ordinal risk-preferences within-subjects across domains, especially with respect to losses. To make the parametric responses i.e., the CE, PE and OE judgments comparable across domains and subjects, linear as well as utility-based risk-measures were examined. In the gain and loss domains, the linear risk-premia measures exhibited higher CE-PE correlations within domain than the utility-based measures. Using a multitrait-multimethod comparison, the highest correlations were found within domains across response modes. The main findings are 1 strong risk-aversion for gain and mixed gambles, 2 risk-seeking for symmetric loss gambles, although less pronounced, 3 low correlation of risk-preferences within subjects across domains, 4 high convergent validity of response methods within domain, and 5 increased risk-aversion for loss but not gain gambles when using real payoffs.

This paper discusses important friction forces that future theories of strategy must incorporate. Some of these are technological and environmental; but the most important ones-it is argued-are psychological. The view is developed that strategy, at its core, concerns the development and testing of heuristics for high stake decisions in environments too unstable and complex to be optimized. The paper especially highlights the behavioral dimension, in the belief that strategies should incorporate both the rational and suboptimal aspects of human behavior. The rational approach is in many ways the easier, as there may be only one way to be right. Yet, the great variety in which people and companies can err gives strategy its creative and real-world challenge. The tension between the rational and behavioral components is what the field of strategy should seek to exploit.

This paper examines a probabilistic dominance measure to assess the extent to which a given multi-attribute alternative might be preferred over another. It determines analytically the probability of choosing the first alternative (of a given pair) when using an additive utility function with uniformly random weights. Two uniform random weight models are examined. In one, the weights (i.e., scalar coefficients) of the additive utility function are each uniformly distributed on [0,1] prior to being rescaled to sum to one. In the other, the weight vector w̃ = (w̃i, …, w̃n) is presumed to be uniformly distributed on the simplex defined by Σwi = 1 and wi≥0 for all i. Differences between these two types of uniform random weight models are discussed. Since the second (or vector) model implies maximum ignorance (in the entropy sence), only its distributional aspects are analyzed in detail. The middle of the paper explores analytically the probability that a given vector x is preferred to a given vector y, when using an additive utility model with uniformly random vector weights. Closed form solutions for this probability are derived up to four dimensions using analytic geometry. Algorithmic solutions are offered for n>4, using existing computer codes. The analytic results are demonstrated numerically with four dimensions, using data from a college admission's experiment. Various applications of this particular measure for probabilistic dominance are examined. The concluding sections assess to what extent our measure satisfies weak stochastic transitivity and other important characteristics.

Certainty equivalence CE and probability equivalence PE methods are the two most frequently used procedures for constructing von Neumann-Morgenstern utility functions. In this paper, we compare these methods experimentally, using a two-stage within-subject design. By asking subjects first for a CE judgment and later for a related PE judgment or vice versa, a consistency test is devised which any deterministic expectation model, including those allowing probability transformations, should meet. Using four related experiments, this consistency test is applied separately to gain and loss questions, and to the two sequences of linked equivalence judgments, namely CE - PE and PE - CE . The empirical results reveal serious inconsistencies between the CE and PE responses for each of the four experiments. The extent of discrepancy depends strongly on the subject's initial risk attitude and whether the gain or loss domain is examined. To explain the complex pattern of results, the second part of the paper explores several plausible hypotheses. The first of these concerns the role of random error, in either the responses or the utility function itself. It is shown that both can lead to bias, although not of a type that could explain our results. Thereafter shifts in reference points are examined. A particular reframing of the PE response mode is postulated in which a pure gamble is psychologically translated into a mixed one, leading to increased risk aversion. This hypothesis, which is also supported by other evidence, offers a complete and simple explanation of the results. Finally, several other behavioral hypotheses are examined, after developing a weighted average model to simulate them. They concern anchoring effects, differences in salience between the probability and outcome dimensions, strategic misrepresentation, regret or rejoice influences, and endowment effects. Although each hypothesis predicts some type of bias, none of these five could singly explain the particular pattern of bias observed. In general, the study demonstrates 1 that serious discrepancies exist between the CE and PE methods of utility measurement, 2 that the particular results are incompatible with traditional deterministic choice models, 3 how random response errors, through propagation, can induce systematic biases in the utility function, 4 that reframing of the PE mode offers a simple reference shift explanation of the complex findings, and 5 how various heuristics and biases can be operationalized and simulated to assess their effects on utility measurement. As such, this study represents a further step toward a systematic investigation of response mode biases in utility measurement.

Utility functions are an important component of normative decision analysis, in that they characterize the nature of people's risk-taking attitudes. In this paper we examine various factors that make it difficult to speak of the utility function for a given person. Similarly we show that it is questionable to pool risk-propensity data across studies for descriptive purposes that differ in the elicitation methods employed. The following five sources of bias or indeterminacy are hypothesized and demonstrated. First, certainty equivalence methods generally yield greater risk-seeking than probability equivalence methods. Second, the probability and outcome levels used in reference lotteries induce systematic bias. Third, combining gain and loss domains yields different utility measures than separate examinations of the two domains. Fourth, whether a risk is assumed or transferred away exerts a significant influence on people's preferences in ways counter to expected utility theory. Finally, context or framing differences strongly affect choice in a nonnormative manner. The above five factors are first discussed as essential choices to be made by the decision scientist in constructing Von Neumann-Morgenstern utility functions. Next, each is examined separately in view of existing literature, and demonstrated via experiments. The emerging picture is that basic preferences under uncertainty exhibit serious incompatibilities with traditional expected utility theory. An important implication of this paper is to commence development of a systematic theory of utility encoding which incorporates the many information processing effects that influence people's expressed risk preferences.