Different Models of Decision-Making in Business

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In modern business, decisions have been classified in many ways. A common classification distinguishes between decisions related to objectives and those that pertain to more specific goals. The latter can be judged based on efficiency; the former depends on normative values. Strategic decisions regarding major directions of an organization are contrasted with tactical decisions that carry out the strategies. Reciprocal decisions, in which one individual interacts with another and causes a reaction (such as competition), are distinguished from controlling decisions, in which one person’s actions control the actions of another without interaction. Sequential decisions involving multiple actions and reactions, like those in the marketplace, are differentiated from single decisions. The topic was selected for analysis because it reflects the needs of current business and will help to understand the model and approaches in decision-making. The problem is that many researchers talk about decision-making and its importance but omit models and approaches in decision-making. The paper aims to evaluate and analyze different models of decision-making and their advantages for business.

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Theoretical and Empirical Evidence Of Literature

The main similarity between current approaches in decision-making is that they see it as a complex process that involves a spectrum from the programmed or highly rigid, routine, repetitious, specific type at one extreme to the non-programmed, less definite, unknown, uncertain, loosely constructed type at the other. The former can be handled by habit, clerical routines, standard operating procedures, and mathematical calculations, whereas the latter requires creativity, intuition, judgment, and persuasive problem-solving techniques (Bazerman 1995; Salas and Klein, 2001). At the other extreme, decisions may be made under conditions of complete uncertainty. The decision-maker brings nothing to the actual choice by way of experience or marketing information that helps in selecting among choices. This situation is an unrealistic one and is closely approximated in marketing by a new product with no marketing-research information available. Choices here might be made on a random basis, such as by a flip of a coin. As a rule, the actual situation lies between these extremes (Russo and Shoemaker 1990). Often, through evidence gained from experimentation or statistical inference, management can assess both the probabilities and the consequences of outcomes and reduce ignorance (Harrison 1999). The theory-based approach underlines that decisions are usually made under conditions of considerable risk, and an executive can’t be sure that any specific decision will turn out to be the best one. All the decision-maker can hope to do is obtain information to restrict the elements of indecision in a situation. Managers are forced to accept risks; they must gamble. Marketing executives are placed in the uncomfortable situation of having to choose the best course of action by evaluating relevant factors based on imperfect information at a specific point in time. They know that the decision may eventually prove to be a poor one (Fitzgerald, 2002; see table 1).

In the book, The Managerial Decision Making Process, E.F. Harrison (1999) states that decision theory refers to an organized body of concepts that are useful in the analysis of marketing situations. It is concerned with the formal and logical analysis of alternatives and their consequences in uncertain situations. Decision theory usually assumes that decisions are made in response to a single motivating interest, such as maximization of profits to assure a firm’s survival and growth. When several groups are involved in a decision, it is assumed that they agree on the objectives of the firm. The actual choice is difficult because the consequences of an act do not depend on conditions that can be predicted or estimated with complete accuracy. In indecision theory, these uncertain situations are dealt with as “states of nature.” The states of nature include the multiplicity of factors that determine the outcomes of any particular marketing strategy. Customer behavior and response, a competitor’s action, and the impact of socioeconomic forces, all fit this prescription. They are complex factors beyond the control of the marketing decision-maker, though having a marked impact on the results of decisions. For instance, a favorable economic climate, though uncontrollable by the decision-maker, may nevertheless affect the payoff of any particular strategy. Similarly, competitors’ actions, which cannot be controlled by an individual may easily affect the market impact of any choice. While decision-makers “control” the acts, nature “controls” the events.

In contrast to other researchers, Lee et al (1999) state that in determining which alternative to choose, it is desirable to try to assess future states of nature. Complete assessment can never be attained, since it is difficult, if not impossible, to identify all these states. Thus, in most marketing decisions, optimization is not usually possible, and less than optimal results accrue. Marketing decision-makers should think in terms of strategies they can select, events that might occur, the probability of occurrence, and the resulting payoff of the intersection of each strategy and event. To do so, they can apply conditional reasoning. Decision-makers can assume that they have selected a given strategy and that a given state of nature exists. Conditional on these factors, they can estimate the payoff likely to occur. Payoffs can be measured in terms of net dollars, which include allowances for various types of intangibles such as dealer goodwill, salesman’s morale, and product image. Such payoffs should be estimated on some adjusted net basis by deducting the relevant costs from the estimated gross payoffs for particular acts and states.

Descriptive Summary for Key Findings

The decision-making process consists of that complex of activities by which an executive seeks to overcome obstacles to the attainment of his goals by adjusting activities through his decisions. It indicates the main decision activities and their flow in choosing a course of action. Like all graphic models, it is a simplification that leaves much to be desired in terms of the dynamics of a real decision situation. It does present a useful summary of the major activities in the making of marketing decisions and what is involved in them. The major decision activities are (1) appraisal of the marketing-decision situation; (2) determination of alternatives; (3) evaluation of alternatives; and (4) making the decision (Fitzgerald, 2002).

The first phase, appraisal of the decision situation, is concerned with determining problematic situations and specifying the problems to be solved. This stage relies heavily on the assessment of the information concerning both company goals and actual situations, as well as the discrepancies between them. Company goals may be conceived in such terms as profitability, market share, sales volume, reputation, rate of return on investments, and industry position. By gathering information about performance and assessing it against management standards, executives may spot symptoms of problems and discern the problems themselves. To help management recognize marketing problems, such standards as product-line goals, sales quotas, territorial quotas, productivity ratios, and advertising objectives are established. But the executive must be sure to use them properly to distinguish symptoms from problems (Bazerman 1995). For example, decreasing profits or sales volume, increasing sales costs, and costs of distribution are not problems — they may be symptomatic of problems. Effective decision-making is based on the operational definition of problems. The feedback of information regularly provides the marketing manager with information for assessing and reevaluating market position. Phase two of the decision process is concerned with the determination of alternatives. In attempting to make a decision and solve problems, marketing managers, either explicitly or implicitly, must specify available alternative solutions. The solutions perceived will be limited by management’s insight, creativity, and experience. Although no manager will perceive all the choices available, this is not a severe handicap. Companies can consider the most practicable solutions as they are detected by experienced management. resources available to the firm such as wholesalers, advertising agencies, and transportation agencies. Through matching, a set of feasible courses will be determined and the most practicable ones will be established. For instance, a perceived alternative may involve an extensive expenditure on product development. It may not be a practicable solution for the company at a point in time, owing to current heavy financial commitments. It would be wasteful to attempt to evaluate this alternative since it is not within the decision set of the executive (Lee et al 1999).

Phase three is concerned with the evaluation of various alternatives. Here value theory and a knowledge of applicable decision criteria are useful. Given the feasible set of solutions, marketing executives must determine the “relative value” of each choice. This cannot be determined precisely, yet the task must somehow be accomplished. If a decision-maker chooses a course without explicitly evaluating the relative effects of each, he has in essence imputed a value and indicated that his choice is the “best” one. It seems desirable, therefore, to evaluate the alternative courses of action on some more formal basis, according to criteria that the decision-maker specifies. This leads to a more objective selection of courses of action and permits more scientific decision-making (Harrison 1999).

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The fourth and final phase of the decision process concerns the actual choice or decision. Here the executive assesses the evaluated options, applies his executive judgment, and chooses a course of action. Once a choice is made based on particular criteria, it might not prove to be the best decision after the fact, owing to unforeseen or unexpected circumstances. Whether a decision is a good one or a poor one depends on future situations and evaluations. All a decision-maker can do is reach the best possible decision, given his incomplete information at a point in time. Making marketing decisions is a subjective process. The decision-maker must exercise judgment in specifying problems and their dimensions, in associating probabilities and payoffs with various courses, and in assessing outcomes. The process is one of narrowing the field of possible actions until the choice of the best one is achieved. Various tools and techniques that formalize these processes are useful, but insight and creative abilities are still among the greatest decision-making assets of executives. Whether a marketing decision proves to be effective depends not merely on whether the process used in arriving at a choice is a logical one. It also depends on the implementation of the actual decision. Management action after a decision is made can have a great effect on the outcome (Flin et al 1996).

In making decisions, marketing managers regularly employ what scholars describe as models. They rely on analogies, constructs, verbal descriptions, idealizations, and graphic representations of systems and situations. Procurement models, pricing models, forecasting models, physical distribution models, and models of organizations are highly useful management devices. Models provide new methods and perspectives for solving marketing problems and making predictions. Used in marketing research, designing experiments, and measuring the effectiveness of various aspects of the marketing mix, they are significant tools for providing and analyzing information. A marketing model is a representation that can be used to study a marketing process or institution. It is the perception or diagraming of some complex or system. Building a model involves translating perceived marketing relationships; into a construct, into symbols, and perhaps into mathematical terms. As a result, models are developed to represent wholesaling and retailing systems, advertising, pricing, branding, and distribution situations. , Where the representations consist of a set of mathematical statements about some aspect of marketing, we have a mathematical model. Models may be classified according to whether they are replicas or representations. Replicas, such as scale models of layouts of a warehouse or retail store, look like the “phenomena” they portray. Representative models depict real marketing situations but do not look like the situation they portray. Graphs and equations are examples. Representative models may be subdivided into verbal models, graphs, flow charts, analogs, and mathematical models (Stephen et al 2001).

Verbal models merely describe some pattern or impression. Graphs and flow charts are widely used in making marketing decisions. The latter is particularly useful in computer applications. Analogs are models that behave like the real situation, although they do not look like what they represent. Two lines appearing on an oscilloscope, depicting the interaction of price and quantity demanded or dollars spent on promotion and sales, are examples. Mathematical models use symbols to represent the phenomena they portray and are powerful tools of analysis. We can array marketing models as follows: replica, verbal, chart and graph, analog, and mathematical. As we go down the array, the degree of abstraction increases, the resemblance to the real phenomena that the models represent decreases, and the model’s capacity for manipulation, degree of flexibility, and problem-solving power increase. Thus, marketing models run the gamut from loose verbal models to precise mathematical models, from physical to abstract models. Some marketing models are referred to as dynamic and others as static. Stochastic models (probability models), in which some of the variables are random factors, are differentiated from deterministic models. In addition, there are linear and nonlinear models, macro and micro models, system and goal models (Salas and Klein 2001).

Theories developed from marketing models can be accepted or rejected based on how well they work — how well they solve problems. Models, on the other hand, are right or wrong only on logical grounds. If they are internally consistent, they are good models — even if they can’t be used to solve problems. Therefore, correlation, exponential smoothing, and linear programming are good models and can even result in potential theories. The major reason for using marketing models is that they aid in making predictions. Models have produced the most successful predicting systems in science. Marketing executives employ various types of forecasting and inventory models to help deal with the future. Marketing models can furnish a frame of reference for the logical consideration and solution of problems. They play a descriptive role, indicating lines of inquiry to be followed and types of information to be gathered. By presenting a simple representation of a more complex situation, they invite comprehension. Because they are explicative, marketing models attempt to explain relationships and reactions. In addition to quantifying information, they attempt to determine both the dependency and the significance of various variables. Models often generate possible solutions to problems and permit testing of policies. For instance, in simulation, which refers to experimentation on a model, the best choices of policy are often discovered without actually conducting the physical experiment. Frequently, models furnish solutions that could not be reached in other ways. To be effective, marketing models should be plausible, solvable, and based on realistic assumptions, and should have the necessary data inputs (Russo and Shoemaker 1990). The linear-programming model, for example, is very useful because in many marketing instances it meets these criteria. Increasing numbers of models are needed to handle complex situations and furnish more adequate and pertinent information for decision-making.

Mathematical models have certain inherent advantages over other types of models in marketing. The translation of a model from verbal to mathematical form often clarifies existing relationships and interactions. It is a rigorous and demanding task, which often results in conceptual clarity and operational definitions. Mathematical models tend to be more logical, whereas verbal constructs lean heavily on intuition and persuasion. Analyses that are not feasible through verbal models may be advanced through mathematical models that lend themselves to manipulation. Although the notion may seem somewhat incongruous at first, mathematical models may promote greater ease of communication (Perrin et al 2001). Within the marketing and related subject-matter areas, cross-communication is often difficult because of the terminology used by specialized personnel and disciplines. Through mathematical models, disciplines may be reduced to a common language that may reveal relationships and the pertinence of research findings among disciplines, and uncover models hitherto not emphasized (Russo and Shoemaker 1990).

The utilization of formal analysis, which results in crude estimates of the probabilities and payoffs for different acts and states of nature, is an effective tool for decision-making. It gives management some idea of their expectations. Overall, it should result in better decisions than haphazard, intuitive approaches achieve. Sometimes, even though gross errors are made in the probability estimates, the results of the analysis may be relatively insensitive to errors. Sensitivity analysis can be done to determine the influence of estimated probabilities and so guide decision-makers in using these techniques. This formal analysis will help furnish better information for the decision centers within the firm. Decision constraints may be organizational, environmental, or personal. Organizational constraints depend on the scale and type of company operations. External or environmental constraints stem from such factors as the socioeconomic, technological, political, psychological, and physical aspects of the company’s environment.

List of References

Bazerman M.H. 1995, Judgment in Managerial Decision Making (3rd Edition), Wiley.

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Fitzgerald, S. G. 2002, Decision making, oxford: capstone publishing. Pp.7-19.

Flin, R., Stewart, K. Slaven, G. 1996, Emergency Decision Making the Offshore Oil and Gas Industry. Human Factors, 38, p. 262.

Harrison E.F. 1999, The Managerial Decision Making Process, 5th Edition, Houghton Mifflin, Boston.

Lee D., Newman P., and Price R. 1999, Decision Making in Organizations, Financial Times/Pitman Publishing.

Perrin, B. M., Barnett, B. J., Walrath, L., Grossman. J. D. 2001, Information Order and Outcome Framing: An Assessment of Judgment Bias in a Naturalistic Decision-Making Context. Human Factors, 43, p. 227.

Russo, J. E. and Shoemaker. P. J. H. 1990. Decision traps: the ten barriers to brilliant decision making and how to overcome them, New York: Fireside. pp. 173-210.

Salas, E., Klein, G. A. 2001. Linking Expertise and Naturalistic Decision Making. Lawrence Erlbaum; 1 edition.

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Stephen.R., Rolf.B., Ian.S., and Mary.C. 2005. Management (4th ed ) Lawrence Erlbaum.


Research topic: Decision-making models
Themes Literature
Theory-based approach
Decision-making as a part of planning
Bazerman M.H. 1995, Judgment in Managerial Decision Making(3rd Edition), Wiley. Flin, R., Stewart, K. Slaven, G. 1996, Emergency Decision Making the Offshore Oil and Gas Industry. Human Factors, 38, p. 262.
Decision-making and structural changeswithin organizations Lee D., Newman P., and Price R. 1999, Decision Making in Organizations, Financial Times/Pitman Publishing. Perrin, B. M., Barnett, B. J., Walrath, L., Grossman. J. D. 2001, Information Order and Outcome Framing: An Assessment of Judgment Bias in a Naturalistic Decision-Making Context. Human Factors, 43, p. 227.
Decision theory as “state of nature” pros and cons of approach Harrison E.F. 1999, The Managerial Decision Making Process, 5th Edition , Houghton Mifflin, Boston.
Stephen.R., Rolf.B., Ian.S., and Mary.C. 2005. Management(4th ed) Lawrence Erlbaum.
Decision-making theory and the main steps of decision-making
Modelsof decision-making
Verbal models
Marketing models
Mathematical models
Fitzgerald, S. G. 2002, Decision making, oxford: capstone publishing. Pp.7-19.
Russo, J. E. and Shoemaker. P. J. H. 1990. Decision traps: the ten barriers to brilliant decision making and how to overcome them, New York: Fireside. pp. 173-210.
Salas, E., Klein, G. A. 2001. Linking Expertise and Naturalistic Decision Making. Lawrence Erlbaum; 1 edition.

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