Randall L. Schultz

Top Management Decision Sharing and Adherence to Plans

Abstract This article describes a study of the impact of top management decision sharing on the tendency of firms to adhere to their strategic plans, and the effects of strategic mission and environmental hostility on this decision sharing-adherence to plans relationship. As a secondary focus, the research also examined the impact of environmental hostility on the relationship between adherence to plans and firm financial performance. For the pur- poses of this study, adherence to plans was defined as an organizational outcome reflected in whether firms characteristically persist with predetermined and intended business plans or whether they regularly and extensively adjust their choice of business strategies and tactics in unplanned ways. Data were collected using questionnaires which were sent to the senior executives (presidents and/or CEOs) of 109 independent, nondiversified manufacturing firms. Approximately 20 industry segments are represented in the sample. Moderated regression analysis and subgroup analysis were used to analyze the data. Tests for monotonicity were conducted to clarify the regression analysis findings. Results indicate that firms with a participative or shared approach to top management decision making are no more likely to adhere to their plans than are firms which do not practice decision sharing. However, top management decision sharing was found to be positively related to adherence to plans among firms with “harvest” strategies and among firms operating in benign environments. Conversely, top management decision sharing was found to be negatively related to adherence to plans among firms with “build” strategies and among firms operating in hostile environments. Additional analyses revealed that adherence to plans is more positively related to financial performance among firms in hostile environments than among firms in more benign environments. The implications of these results and the limitations of the research are discussed.

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.

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

Determining Functional Form and Lag Structure

The techniques of parameter estimation and model testing set forth in chapter 3 can be used only when the market response model is fully specified. In a number of cases, however, full model specification is not possible without some preliminary data searches, in particular the search for a functional form and the specification of lag structures. In chapter 2, we referred to such cases as level 1 prior knowledge. In other words, we start from a situation where the causal ordering of a model is known but not its functional form or lag structure.

Sales and Market Share Response Models

The relation between sales response and marketing variables is the core of the theory and practice of marketing management. In making decisions, marketing managers must have some ideas about how their actions will influence sales and profits. Usually these ideas concerning the link between apparent causes (marketing decision variables, the actions of competitors, and certain environmental factors) and measurable market responses (sales or market share) are based on experience—a “feel” for the implications of a firm’s marketing decisions. Such casual interpretations of market response may be expedient, serving managers as guides to marketing planning, but they are severely limited in their ability to provide managers with more objective evidence on how to improve the quality of their decisions. Sales response models are formal ways of describing the complex relation between a firm and its market. They are designed to overcome as much uncertainty as possible regarding the nature of sales response and, in addition, to provide the behavioral mechanism in a decision model that allows management to explore optimal policies.

An Application of ETS Modeling

The previous chapters have developed a combined econometric-time series analytic approach to market response modeling. Since the integration of these techniques is fairly new, few published market response models have been built using ETS. Instead, we typically find applications of either econometric or time series techniques in market response models, as evidenced by the articles on sales response and competition surveyed in chapter 6.

ETS in Marketing Science and Practice

The main use of ETS in marketing, as we have seen, is to establish relations among variables—response functions—that serve as generalizations of marketing behavior. These relations, in turn, provide the mechanism for making decision models useful tools for marketing planning and forecasting. Most of this book has been concerned with how this can be done. In this chapter, we look at three questions that bear on the impact of this work: (1) How is knowledge generated? (2) How are models implemented? and (3) How can decisions be supported with ETS?

Design of Response Models

The design of response models involves variables, relations among variables, functional forms, and data. Variables represent the building blocks of a response study. An analysis of price elasticity, for example, would require at least two variables, price and unit sales. Relations are the connections among variables. To answer a question about the magnitude of price elasticity, it would be necessary to examine the special relation of price to unit sales. Functional forms refer to the nature of a relation. One form of a relation between price and sales could be linear; a form like this would give both mathematical and substantive meaning to the relation. Finally, data are the actual realizations of variables. Taken together, these four elements provide the materials for building a response model.

Determining Causal Ordering

Determining the direction of causality may be straightforward when only two variables are involved, but real-world marketing systems are often so complex that the causal chains cannot be easily established a priori. For example, in competitive markets, causal relations may exist in many directions among the following variables: product sales, industry sales, market share, profits, marketing efforts, competitive marketing efforts, and environmental conditions. Although in this case we would have a good idea of the elements in the information set (level 0), it would be difficult to posit one structural marketing model from prior insight alone.