Academic journal article Public Administration Quarterly

Measuring Influence Distribution in a Public Organization: A Test of the Control Graph Technique

Academic journal article Public Administration Quarterly

Measuring Influence Distribution in a Public Organization: A Test of the Control Graph Technique

Article excerpt

ABSTRACT

This article presents a statistical test of Tannenbaum's control graph technique, a tool designed to measure the distribution of influence within organizations. Data for the study were obtained through two surveys conducted approximately one year apart in a public agency. Analysis of survey results showed that the organization's influence distribution pattern was highly atypical when compared to almost all previously published control graph research. ANOVA and MANOVA were used to determine the degree of consensus among agency members regarding the atypical influence distribution pattern.

The analysis showed a high level of agreement within the agency regarding the relative influence of organizational actors. Differences in the perceived influence of actors were shown to be statistically significant. Influence ratings were shown not to be affected by respondents' length of service and only marginally by their organizational level. Thus, contrary to criticisms of the control graph technique, the author concludes that the tool provides a reasonably reliable means of measuring power distribution within organizations.

INTRODUCTION

Given a broad definition of the term, power differentials are a basic feature of organizational life.1 Power represents an organizational resource, which affects outcomes in all kinds of decisions ranging from who gets the most attractive office to the definition of organizational goals and mission. Power differentials also establish the bounds for interaction between individuals and sub-groups, serving to shape social relationships and thereby an organization's culture.

Despite its importance to organizational processes, however, who actually holds power in an organization may be difficult to discover. Modern managerial training includes a heavy dose of human relations approaches with their emphasis on methods of eliciting willing compliance and cooperation from employees and this may discourage overt applications of power as well as discussions of them. Additionally, the truly powerful often have little need to demonstrate their power. As long as organization members recognize and accept their dominance, compliance is more or less automatic. It may not even be necessary for those who call the shots to express their wishes openly since subordinates may act in anticipation of perceived preferences of power-holders based on past experience. Thus, displays of power may be unrecognizable as such to an observer--or even to some participants!--making life difficult for those who study power and its consequences in organizations.

Identifying how power is distributed in organizations is especially critical for those who hope to change them. A basic tenet of organization development, for example, holds that the change agent should enter the client system at or near the top in order to assure support and legitimacy for his or her efforts. However, the organization chart may be a poor indicator of who actually controls or influences what goes on in a particular organization. Nominal leaders may in reality be relatively powerless to direct and control the behavior of their formal subordinates. Time spent developing planned change programs with top management may be wasted if subordinates can effectively neuter initiatives from on high.

Numerous means of assessing organizational power structures have been suggested (Harrison, 1987:97-98) but the focus here is on a single observational method--the control graph technique (Tannenbaum, 1968). This article describes the influence distribution pattern in a public agency as defined by the control graph and uses analysis of variance to test the extent to which the pattern reflects the shared perceptions of the organization's members. The purpose is twofold. The major goal is to evaluate the reliability of the pattern in response to specific criticisms of the control graph technique. A secondary purpose lies in exploring the implications of the analysis for managing and changing public agencies. …

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