Understanding the Participation of Critical Task Specialists in Strategic Decision Making*

This paper explores the role of critical task specialists in strategic decision making and presents a theoretical model relating critical task specialist participation in decision making to the organization's overall strategy and the nature of the decision. This exploratory study examines scope and intensity of physician participation in hospital decision making. Intensity of critical task specialist participation is explained by content of the decision and by the organization's strategy, while scope of participation is explained by decision content. The findings suggest the need for more complex models of participation than are normally used in decision-making research. The findings also suggest that executives, in managing strategic decision-making processes, should pay attention to questions of both scope and intensity of participation. The results suggest that critical task specialists play a different role in the decision process, depending on specific decision content and organization strategy.     

A Microeconomic Model of the Focused Factory-Vertical Integration Strategic Decision Problem

The design of a manufacturing strategy incorporates the decision of whether to focus or vertically integrate, or adopt a policy somewhere between the two extremes. Unfortunately, the literature contains few models that aid a manager in this decision process. In this paper we design a model to evaluate and compare various strategic alternatives along the focused factory-vertical integration continuum. By defining a point along this continuum as the percent of components manufactured internally that are needed to make one finished item (where 0 indicates complete focus and one manufacturing step, and 1 indicates full integration and 100 percent internal manufacturing) we are able to delineate the effects of the alternative decision strategies on flexibility and the firm's cash flow. The capital asset pricing model is invoked to assess the impact on the firm's risk and value.     

PAHAP: A Pairwise Aggregated Hierarchical Analysis of Ratio-Scale Preferences

In this paper, we present a Pairwise Aggregated Hierarchical Analysis of Ratio-Scale Preferences (PAHAP), a new method for solving discrete alternative multicriteria decision problems. Following the Analytic Hierarchy Process (AHP), PAHAP uses pairwise preference judgments to assess the relative attractiveness of the alternatives. By first aggregating the pairwise judgment ratios of the alternatives across all criteria, and then synthesizing based on these aggregate measures, PAHAP determines overall ratio scale priorities and rankings of the alternatives which are not subject to rank reversal, provided that certain weak consistency requirements are satisfied. Hence, PAHAP can serve as a useful alternative to the original AHP if rank reversal is undesirable, for instance when the system is open and criterion scarcity does not affect the relative attractiveness of the alternatives. Moreover, the single matrix of pairwise aggregated ratings constructed in PAHAP provides useful insights into the decision maker's preference structure. PAHAP requires the same preference information as the original AHP (or, altematively, the same information as the Referenced AHP, if the criteria are compared based on average (total) value of the alternatives). As it is easier to implement and interpret than previously proposed variants of the conventional AHP which prevent rank reversal, PAHAP also appears attractive from a practitioner's viewpoint.     

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

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

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

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

On flexible product-mix decision problems under randomness and fuzziness

This paper considers several models of product-mix decision problems and production planning problems under uncertain conditions, and shows that these are extensional and versatile models for resolving previous product-mix problems. These proposed models include randomness derived from statistical analysis based on historical data, ambiguity of decision maker's intuition and the quality of received information, and flexibility in accomplishing the original plan. Furthermore, given that the upper limit values of some constraints have flexibility, and given a decision maker's level of satisfaction, we propose a flexible product mix of problems using the theory of constraints (TOC), and develop an efficient solution method. We then provide a numerical example that compares our models with some previous basic models. Efficiency of flexibility is obtained when our proposed models are applied to several conditions, such as measurable changes from the expected value of future returns.     

The Effects of Task Information and Outcome Feedback on Individuals' Insight into Their Decision Models

When making business decisions, people generally receive some form of guidance. Often, this guidance might be in the form of instructions about which inputs to the decision are most important. Alternatively, it might be outcome feedback concerning the appropriateness of their decisions. When people receive guidance in making difficult judgments, it is important that they do not confuse this guidance with insight into their own decision models. This study examined whether people confuse their actual decision model with task information and outcome feedback. Subjects predicted the likelihood that various hypothetical companies would experience financial distress and then reported the decision models they believed they had used. Their reported models were compared with their actual models as estimated by a regression of the subjects' predictions on the inputs to their decisions. In a 2times2 factorial design, some subjects were provided with task information regarding the relative importance of each input to their decisions while others were not. Some subjects were provided with outcome feedback regarding the quality of their decisions while others were not. The subjects tended to confuse the task information and outcome feedback with their actual decision models. Implications for the results are discussed.     

Applications and Implementation DECISION SUPPORT SYSTEMS: AN EMPIRICAL ASSESSMENT FOR DECISION MAKING

A laboratory experiment examined the effects of applying decision support system (DSS) technology to decision making in ill-structured problem environments under varying information conditions. Marketing executives participated in the experiment which investigated the effects of DSS availability, DSS training, and data availability on dependent variables that included: (1) the number of alternatives considered by a subject during decision making, (2) the period of time spent by a subject to complete the decision-making process, (3) the subject's perceived confidence in the decisions he or she had made, (4) the amount of data considered by a subject's during decision making, (5) the individual subject's decision processing, and (6) the subject's performance overall. Our results indicate that all three factors significantly affect the number of alternatives considered by subjects during the decision-making process. We therefore suggest that DSS training be coordinated with decision training in order to realize the potential of DSSs as described in the DSS literature.     

Use and Abuse of Weights in Multiple Objective Decision Support Models*

This paper examines difficulties with the use of weights to solve multiple objective decision support models: misunderstanding of the meaning of weights, issues of commensurability and, most important, the likely inability of weights alone to isolate the decision maker's most-preferred point. The constraint method is shown to be an attractive alternative.     

Evaluation of the green supply chain management practices: a fuzzy ANP approach

Environmentally responsible manufacturing, green supply chain management (GSCM), and related principles have become important strategies for companies to achieve profit and gain market share by lowering their environmental impacts and increasing their efficiency. As environment has become a key strategic consideration in supply chains, this study examines the components and elements of GSCM and suggests a novel GSCM evaluation framework. It also provides a real-case study of Ford Otosan, one of the pioneering companies about environmental subjects in Turkey, to illustrate the industrial application of our theoretical assessment model. The identified components are integrated into a strategic assessment and evaluation tool using analytical network process (ANP). The dynamic characteristics and complexity of the GSCM analysis environment make the ANP technique a suitable tool for this study. Moreover, to cope with ambiguity and vagueness of the decision maker's evaluations, the fuzzy extension of the ANP method is preferred.     

HIGH-LEVEL DECISIONS: A LESSON FROM THE IRAN HOSTAGE RESCUE OPERATION

We use the analytic hierarchy process to analyze the role of subjective factors in decision making as illustrated in the Iran rescue operation. Essentially, we show that a decision maker and that decision maker's advisors may differ in their estimates on whether an action should or should not be taken depending on what intangible factors the leader holds as personally important, factors that the others would not necessarily include in their thinking-a serious point for which we provide a particularly effective methodology.     

ASSESSMENT OF MULTIATTRIBUTED MEASURABLE VALUE AND UTILITY FUNCTIONS VIA MATHEMATICAL PROGRAMMING*

A major restriction on the use of decision analysis in practice is the frequent difficulty of determining a decision maker's multiattribute utility function. The assessment process can be complex and tedious and generally involves: (1) identifying relevant independence conditions, (2) assessing conditional utility functions, (3) assessing scaling constants, and (4) checking for consistency. Some of the assessment and modeling complexities encountered include an assessor's inability to respond in a quantitatively meaningful and consistent way to hypothetical gambles and an analyst's problem in selecting an appropriate functional form that accurately characterizes the conditional utility assessments. A simplified procedure that mitigates these difficulties is proposed. This procedure facilitates the determination of scaling constants by obtaining (via mathematical programming) a multiattributed measurable value function which is converted to a multiattributed utility function. The methodology can be developed advantageously to produce an interactive software package for use as an assessment aid.     

A case-based distance method for screening in multiple-criteria decision aid

Screening is a process of multiple-criteria decision aid (MCDA) in which a large set of alternatives is reduced to a smaller set that most likely contains the best choice. We propose screening using a distance model calibrated on the basis of the decision-maker's own judgement. Viewing MCDA as preference aggregation based on consequence data, we define consequence and preference expressions (values and weights) and describe how they are aggregated. Then we define screening and explain some of its properties. Using an appropriate definition of distance, our case-based distance method screens a set of alternatives using criterion weights and a distance threshold obtained by quadratic optimization using the decision-maker's selection of alternatives from a test set. This case-based method can elicit the decision maker's preferences more expeditiously and accurately than direct inquiry. An application in water supply planning is used to demonstrate the procedure.     

Decision Processes for Developing Strategic Applications of Information Systems: A Contingency Approach*

Strategic applications of information systems (IS) are considered by IS researchers to be determined by contextual factors such as environmental uncertainty, and influenced by attributes of the processes preceding them, such as planning and top management support. For better management of the process leading to these applications, it is essential to understand the relationship between process attributes and contextual factors. Utilizing a contingency approach, this article takes a step toward such an understanding. Based on successful strategic IS applications from 81 large companies, it examines the relationship between the context of these applications and the decision-making process leading to them. The results indicate that the external environment is related to whether a rational or political decision-making process is used. The IS function seems to influence the use of the decision process implied by IS researchers, one in which the top management champions the strategic application, while the IS managers contribute by conducting in-depth analysis. However, the organization structure was not related to any decision process attribute. The implications of these findings for future research and practice are also discussed.     

A Decision-Theoretic Valuation of Information in Sealed-Bid Auctions for Items of Known Value

This paper considers the question of how much time and effort should be spent in preparing a bid for a single item of known value sold at a first-price sealed-bid auction. A decision-theoretic approach to this bid decision summarizes the decision maker's knowledge of the competitive environment through his or her subjective probability distribution of the highest competing bid. Research activities such as collecting and analyzing bid histories are efforts to obtain additional information that reduces the uncertainty in the highest competing bid. The decision-theoretic concepts of expected value of perfect and imperfect information are used to place an economic value on such research activities. The results presented allow the decision maker to quantify the expected value of imperfect information when the uncertainty is normally distributed. The results show that additional research is most valuable prior to auctions the bidder expects to win.     

META-DESIGN CONSIDERATIONS IN DEVELOPING MODEL MANAGEMENT SYSTEMS*

This paper examines cognitive considerations in developing model management systems (MMSs). First, two approaches to MMS design are reviewed briefly: one based on database theory and one based on knowledge-representation techniques. Then three major cognitive issues—human limitations, information storage and retrieval, and problem-solving strategies—and their implications for MMS design are discussed. Evidence indicates that automatic modeling, which generates more complicated models by integrating existing models automatically, is a critical function of model management systems. In order to discuss issues pertinent to automatic modeling, a graph-based framework for integrating models is introduced. The framework captures some aspects of the processes by which human beings develop models as route selections on a network of all possible alternatives. Based on this framework, three issues are investigated: (1) What are proper criteria for evaluating a model formulated by an MMS? (2) If more than one criterion is chosen for evaluation, how can evaluations on each of the criteria be combined to get an overall evaluation of the model? (3) When should a model be evaluated? Finally, examples are presented to illustrate various modeling strategies.     

RANK GENERATION,PRESERVATION, AND REVERSAL IN THE ANALYTIC HIERARCHY DECISION PROCESS

Decision making has the objective of finding the best alternative or set of alternatives by considering a number of goals, objectives, criteria, competitors, and other important factors. The analytic hierarchy process is a decision aid used to assist a decision maker in sorting out the complexity of a decision problem and making use of his or her judgments. A decision maker must be assured that the arithmetic operations of any such decision process are the right ones—that they surface the correct ranking and values of the alternatives and preserve or alter ranks appropriately when new alternatives are added or deleted. In this paper it will be shown that with absolute measurement, rank always is preserved, with relative measurement, rank changes with nspect to scveral criteria only because of the structural dependence (involving both numbers and measurements) of criteria on alternatives. A discussion of the effect on rank of replicas and near replicas of the alternatives also is given.     

DEVIL'S ADVOCACY AND DIALECTICAL INQUIRY EFFECTS ON PREDICTION PERFORMANCE: TASK INVOLVEMENT AS A MEDIATING VARIABLE

Critics of previous laboratory experiments comparing devil's advocacy (DA) to dialectical inquiry (DI) have suggested that these experiments produced misleading results because (1) they used subjects who had low levels of task involvement and (2) the DI treatment used was confusing to subjects and required further explanation to be useful. The present study examines the effects of four inquiry methods—expert (E), DA, DI, and DI with explanatory statement (DI+)—on subjects' performance at a financial prediction task. Results show that DA, DI, and DI + were superior to E when the state of the world differed significantly from assumptions underlying the expert's plan. For subjects with high task involvement, DI and DI + were more effective than E and DA. The results support some of the criticisms of previous laboratory research and suggest that future research on these decision aids should include task involvement as a factor.     

COMPUTER-BASED SUPPORT OF ORGANIZATIONAL DECISION MAKING*

The central issue of this research is the extent to which computer facilities can be used to support organizational decision-making processes beyond mere performance of information retrieval. This depends upon the extent to which computers can be made to emulate human perceptual and judgmental processes. We present a framework for understanding these cognitive processes and examine how it applies to organizational decisions. Moreover, the framework furnishes a basis for the design of a generalized, intelligent problem processor. This processor is general in the sense of its ability to support a decision maker's activities, regardless of the decision maker's application area (e.g., urban planning, water-quality planning, etc.). It is intelligent in the sense of its ability to comprehend English-like queries and subsequently formulate models, interface appropriate data with those models, and execute the models to produce some facts or expectations about the problem under consideration.