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Neuropeptides represent the largest single class of signal compounds and are involved in regulation of development, growth, reproduction, metabolism and behavior of insects. Over the last few years there has been a tremendous increase in our knowledge of neuropeptide signaling due to genome sequencing, peptidomics, gene micro arrays, receptor characterization and targeted gene interference combined with physiological and behavior analysis. In this chapter we review the current knowledge of structure and distribution of insect neuropeptides and their receptors, as well as their diverse functions. We also discuss peptide biosynthesis, processing and expression, as well as classification of insect neuropeptides. Special attention is paid to the role insect neuropeptides play as potential targets for pest management and as a basis for development of insect control agents employing the rational/structural design approaches.

Conjoint analysis is a survey-based method managers often use to obtain consumer input to guide their new-product decisions. The commercial popularity of the method suggests that conjoint results improve the quality of those decisions. We discuss the basic elements of conjoint analysis, describe conditions under which the method should work well, and identify difficulties with forecasting marketplace behavior. We introduce one forecasting principle that establishes the forecast accuracy of new-product performance in the marketplace. However, practical complexities make it very difficult for researchers to obtain incontrovertible evidence about the external validity of conjoint results. Since published studies typically rely on holdout tasks to compare the predictive validities of alternative conjoint procedures, we describe the characteristics of such tasks, and discuss the linkages to conjoint data and marketplace choices. We then introduce five other principles that can guide conjoint studies to enhance forecast accuracy.

In this chapter we first discuss the specification and estimation of models with observed and unobserved variables based on LISREL. LISREL models are vehicles for bringing latent constructs and observational variables together. They encompass a wide variety of other models, such as simultaneous equation models (see also Section 16.4), seemingly unrelated regression models (see Section 16.3) and errorsin-variables models.

In the classic approach to model building, one generally considers three major steps: 1. specification; 2. parameterization; 3. validation.

Marketing models can be classified on many dimensions. In this chapter, we present several classifications. In model building, the following dimensions are of particular relevance: 1. intended use; 2. level of demand; 3. amount of behavioral detail; and it is also useful to distinguish: 4. time series versus causal econometric models; and 5. models for single products versus models for multiple products.

The development and use of models is justified if the (expected) benefits exceed the (expected) costs. For a firm that has adopted a model, this implies that profit with the model should be greater than without it. Of course this is not easily operationalized. After model adoption, we can quantify the benefits and make a comparison with the costs incurred. Before implementation of a model, both benefits and costs are based on potentially highly uncertain estimates.

In this chapter, models will be classified according to three distinguishable purposes or intended uses, namely: 1. descriptive models whose intention it is to describe decision processes;1 2. predictive models aiming at forecasting or prediction of future events; 3. normative models on the basis of which recommended or optimal courses of action can be determined.

Before going into details of how to build marketing models, it might be important to have an idea of the potential benefits that could possibly be derived from such models.

In Section 4.2, we proposed three classes of demand functions: product category/ product class sales, brand sales, and brand market share models.l Such a classification is useful because model specification — both in terms of variables and mathematical form — can have features that are distinct for each of these three categories. In addition, we can distinguish models of individual- and aggregate demand, which we discuss in Section 9.1. Individual demand refers to the individual- or household level. Aggregate demand can be measured at levels such as store-, chain- and market demand. Further distinctions can be made between segments, regions, etc. In this chapter we concentrate on aggregate demand models for the three classes of demand functions, mentioned above. In Section 9.2, we discuss product class sales models.

In Chapter 1, we defined a model as a representation of the most important elements of a perceived real world system. In this chapter, we consider a number of ways of representing these elements, the differentiating dimension being the ‘degree of explicitness’. As such, we distinguish implicit models, verbal models, formalized models, and numerically specified models. The different methods of representing systems will be illustrated with an example.

In this chapter we discuss specification issues that transcend the intended use of a model (Chapter 8), the specific level of demand (Chapter 9), the amount of behavioral detail (Chapter 10) and the specific models discussed in Chapters 11–13.

In this chapter we concentrate on developments that may influence the nature of model building and the use of models for marketing decisions in the future. In Section 21.1 we give examples of recent changes in the practice of model building and in the nature of academic research.