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ЭЛЕКТРОННАЯ БИБЛИОТЕКА ПО ТЕМЕ МАГИСТЕРСКОЙ ДИССЕРТАЦИИ:Экономико-математическое моделирование маркетинговых решений в управлении производственным процессомСтатья по теме: DEVELOPING MARKETING MODELSScott M.Smith and William R.Swinyard Introduction to marketing models,1999 Marketing models, like all management science models, are developed through either inductive or deductive logic which leads to generalization about market behavior. From these generalizations, sets of premises or theories are developed. These lead to sets of relationships which constitute marketing models. This process is depicted graphically in Figure 1-2. 
1. Induction moves from the particular to the general--using particular observations to build a general model. Deduction moves from the general to the particular--using inference to establish the particulars from a general model. PROCESS OF MARKETING MODEL BUILDING Model Development Objectives Since models are intended to represent reality, a fundamental issue is the convergence between the model and the reality it is designed to represent. We might hope that a model would confidently represent reality on all significant issues. Model builders should measure the quality of their models against the criteria of validity and utility. Validity refers to the accuracy of the model in describing and predicting reality. A sales forecasting model which does not forecast sales with reasonable accuracy is probably worse than no sales forecasting model at all. Yet, a major obstacle to the adoption of early marketing models was not caused by their being incomplete, but because they were too complete. Their developers, in trying achieve validity, were led to include so many variables (with correspondingly difficult data collection problems) that the basic structure of the model was buried, input data costs were escalated, and confidence in them was lost. The models may have been reasonably valid, but they had little utility because they slowed down the decision making and increased its cost. The completeness and validity required in a model depends on the accuracy required in the results. Model users should not expect a model to make their decisions for them. The output from a model should typically be taken as one additional piece of information to help the managers make their own decisions. Given this perspective, models can be excused from not representing reality perfectly and, in fact, will probably benefit from it if they are simple enough for the managers to understand and deal with. Clearly, though, models used to help make hundred-million-dollar decisions should be more complete than those used to make hundred-dollar decisions. Too, the model to be used depends on the model's purpose. A simplified model does not preclude its user from considering other factors not included in it. We measure the value of a model on the basis of its efficiency in helping us arrive at a decision. If we arrive at better decisions more easily without the model, then the model is inefficient. In fact, models should be used only if they can help us arrive at results faster, with less expense, or with more validity. Building Blocks for Models The building blocks for models are concepts, constructs, variables, operational definitions, and propositions. Let us take a brief look at each of these. Concepts and Constructs. A concept is an abstraction formed by generalization about particulars. "Mass", "strength", and "love" are all concepts, as well as "advertising effectiveness", "consumer attitude", and "price elasticity." Constructs are also concepts, but they are the conscious inventions of researchers to be used for a special research purpose. When we refer to "consumer attitude" as a construct, we are suggesting not only that it exists as a concept, but that it can be observed and measured, and is related to other constructs. Variables. Model builders loosely call the constructs they study "variables." Variables are constructs that can be measured and quantified. A variable takes on different values (a variable varies). Treated as a variable, "consumer attitudes" suggests a some form of measurement which has produced data that represents consumer attitudes. Cause and Effect. Relationships between variables usually involve cause and effect. For example, if we turn up the heat under a pan of water, the water will boil. We conclude that the heat caused the water to boil. Or if we increase our advertising expenditures we might see our sales increase. We can conclude that the advertising caused the sales to increase. Or can we? A model builder would argue that we can not. To establish a cause-effect relationship, three conditions must be met: 1. Concommitant variation is necessary. If variable X has an effect upon variable Y, movement of the two variables must be associated with each other. Increasing X should increase (or decrease, or otherwise change) Y. 2. Proper time order of effects. If we want to believe that variable X has a causal effect on variable Y, then X should precede Y in time. If an increase in advertising expenditures is causing a sales increase, then the advertising increase should precede the sales increase. (Have you ever noticed ... the more firefighters, the larger the fire?) 3. Absence of competing explanations. To be convinced that X is causing Y, we must be convinced that other variables are not responsible for the change in Y. If these are not controlled, we may ultimately discover that X is be causing Z, and Z is causing Y, or that both X and Y are caused by Z, etc. For example, our increase in advertising might have coincided with a price increase in our competitor's product and it was this price change that increased our sales. Using the above three points to establish that the advertising increase caused the sales increase, we might argue that movement of those two variables is associated, and even that they have the proper time order of events. But we might be hard pressed to establish that no other variables are accountable for the sales increase. Those variables must be controlled, or at least monitored, before we could comfortably draw such a conclusion. Operational Definitions. We can talk about "consumer attitudes" as if we know what it means, but the term makes no sense at all until we define it in a specific, measurable way. An operational definition assigns meaning to a variable by specifying how it is to be measured. It is a set of instructions about how we are going to treat a variable. For example, the variable "height" could be operationally defined in a number of different ways: --as measured, in inches, with a precision ruler, with the person wearing shoes, --as above, but without shoes, --as measured by an altimeter or barometer, --as measured by the number of "hands", --etc. As another example, suppose we were interested in "purchase intentions" for Brand X window cleaner. We might operationally define the variable as the answer to the following question: Please indicate your intention to purchase Brand X window cleaner the next time you purchase a window cleaning product: P = PI = Ai * Bi Propositions. A proposition is a statement of the relationships between variables. Propositions require an explicit statement of the relationship between variables, including both the variables influencing the relationship and the form of the relationship. It is not enough to simply state that the concept "sales" is a function of the concept "advertising." More appropriately, any intervening variables must be specified, along with the relevant ranges for the effect, including saturation and threshold effects, and the symbolic form of the relationship. A proposition is quite close, you can see, to a model. It is produced by linking propositions together in a way that gives us a meaningful explanation for a system or process. EVALUATING MODELS As we discussed above, the modeling process is helpful to managers because it sensitizes them to variables that are important in explaining a process. Modeling forces both managers and researchers to scrutinize and select appropriate variables, and to consider the relationships between them. A checklist to serve as a guide in evaluating this model-building process can be helpful. Some important questions to be asked (and answered) are: [ ] Are concepts and propositions specified in the model? [ ] Are the concepts relevant to solving the problem at hand? [ ] Are the principle components of the concept clearly defined? [ ] Is there concensus as to which concepts are relevant in explaining the problem? [ ] Are the concepts properly defined and labeled? [ ] Is the concept specific enough to be operationalized reliably and with validity? [ ] Are assumptions made in the model clear? [ ] Are the limitations of the model stated? [ ] Does the model predict? [ ] Does the model explain? [ ] Are normative guidelines given for model use? [ ] Can the model be readily quantified? [ ] Are the outcomes of the model supported by common sense? If the model does not meet the relevant criteria, it should probably be revised. Concept definitions may be made more precise, variables may be added or deleted, operational definitions may be tested for validity, mathematical forms may be revised, and assumptions may be strengthened or weakened. Главная страницаАвтореферат < Электронная библиотека < Каталог ссылок < Отчет о поиске < Индивидуальное задание |
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