Leonard J. Parsons

The Product Life Cycle and Time-Varying Advertising Elasticities

A model of sales response to advertising using advertising elasticities that change over the product life cycle. The model is applied to the case history of a household cleanser.

Productivity Versus Relative Efficiency in Marketing: Past and Future?

A variety of measures have been proposed to assess marketing performance Drucker [1974, p. 75] notes these measures must relate either to effectiveness, doing the right thing, or efficiency, doing things right. Together efficiency and effectiveness make up a broad conceptualization of productivity—perhaps too broad. The terms productivity, efficiency,and effectiveness are often confounded. This ambiguity is prevalent in the marketing literature; these terms are often used loosely without being precisely defined. A narrow view would equate productivity with efficiency, an output-to-input relation. Sevin [1965, p 9], for example, makes an analogy with the physical sciences and defines marketing productivity or efficiency as the ratio of sales or net profits (effect produced) to marketing costs (energy expended).

Chapter 9 Econometric and time-series market response models

Publisher Summary Marketing has seen a rapid expansion in the widespread use of quantitative methods. Correlation and regression analysis were among the first techniques used as marketing research emerged as a discipline after World War II. In the early 1970s regression analysis became econometrics. Simultaneous equation systems could be estimated almost as easily as single regression equations. While econometrics as a whole continues to flourish as new and more sophisticated estimation techniques and associated computer software have become available, simultaneous-equation systems have not become widely prevalent. The dynamic structure of marketing variables themselves is addressed in the chapter, followed by discussions of leads and lags among marketing variables and the assessment of the direction of causality. Dynamic properties of sales-response functions have been discussed in more detail. Marketing generalizations that have been uncovered as well as empirical evidence on the shape of the sales response function is reported.

ACCEPTANCE OF COMPUTER TECHNOLOGY: UNDERSTANDING THE USER AND THE ORGANIZATIONAL CHARACTERISTICS

A systematic analysis of acceptance of computer technology was conducted to identify variables that would provide insight to understanding technology acceptance. This led to a development of a comprehensive qualitative model that captures the individual and the organizational user characteristics that influence the acceptance of computer technology. This model suggests that designers must be mindful of the role that sociological and organizational variables play in technology acceptance. Such factors go beyond basic usability issues in the design process. Attention to these variables may increase the acceptance and therefore the diffusion of new computer technologies.

Understanding Acceptance of High Technology Products: 50 Years of Research

This survey of research on acceptance of technology over the past fifty years was conducted to identify and clarify those variables that influence technology acceptance, particularly those that are related to aspects of the technology itself. We surveyed the literature across many domains, selected articles related to this research area, and coded these articles based on the studied variables and products. The results of this survey are an organizational schema for all of the variables as well as specific guidance on the generalized effects of relevant variables such as perceived usefulness, perceived compatibility, and perceived privacy. For each critical variable, we discuss the implications to guide designers of high-technology products.

Improving Marketing Decisions

Response models can be used to improve organizational effectiveness through a process involving three steps: (1) intervention, (2) implementation, and (3) improvement (Schultz and Henry 1981). Intervention takes place when management recognizes a need for change in the way decisions are made and activity to meet that need is initiated. One example would be the development of a marketing decision model to aid in planning and forecasting. If the resulting model actually changes the way decisions are made, we can think of the model as having been implemented. If, in addition, the model improves the decision process, we would call this implemented model successful. Successful implementation is the goal of marketing science.

Forecasting market response

The basic premise of marketing is that a company can take actions that affect its own sales. These actions include offering a product to a market; pricing; distribution (or place); and promotion through advertising or sales force effort. Taken together, this set of marketing decision variables constitutes the ‘marketing mix.’ Market response models show the relationship between such controllable decision variables as well as noncontrollable variables reflecting competitive actions and environmental factors, such as interest rates, and performance measures including unit sales and market share. Thus, market response models provide a basis for forecasting. On this view, forecasting follows planning because plans for marketing actions drive sales. Correlation studies of the impact of marketing decision variables on sales were first completed in the 1960s. Since that time-and particularly since 197O-econometric and time series analysis (ETS) has been applied to a wide variety of situations so that, today, market response models are an important tool of academic research and practical application; see, for example, Gold (1992) and Parsons et al. (1994). Nevertheless, questions remain about how to

Adaptive Pricing by a Retailer

A retailer in an environment in which a competitor is the price leader must determine how to adapt his prices to those of this competitor. The sequential decision problem of the manager is formulated as a Markov process with rewards.

Response Models in Marketing

Econometric and time series analysis (ETS) in marketing is the process of building models of marketing systems that delineate relations between organizations and markets through flows of communication and exchange. These models, called market response models, are useful for understanding the behavior of markets and for predicting the impact of marketing actions. The purpose of this book is to explain how ETS models are created and used.

Parameter Estimation and Model Testing

When the causal ordering, functional form, and lag structure are believed known, all that needs to be done is to estimate the model. Nonetheless, any assumptions underlying a model should be tested. In what follows, our discussion is primarily on a conceptual level. Technical details can be found in most leading econometric texts; Judge et al. (1985) is especially helpful in providing guidance for applied work.1