A Multiple Influence Model of Leadership

Abstract

This report describes efforts to test a model of leadership effectiveness which centers on 'macro variables' and 'discretionary leadership' (DL). Marco variables were represented by the complexity of the environment, context and structure of a unit. Results of propositional tests were: (1) greater complexity in the structure of the unit was associated with more discretionary leadership; (2) structural complexity was directly related ti employee maintenance (employee maintenance included several measures of satisfaction and attachment to the system) and environmental complexity was marginally related to unit performance (unit performance included machine error rates in messages sent); (3) discretionary leadership was related to both performance and employee maintainance and associations were clearer than for more traditional measures of leader behavior; (4) generally, as complexity in macro variables increased, more discretionary leadership was needed to achieve higher performance and employee maintainance; (5) selected characteristics of the group being supervised did not alter the relationship between leadership and criteria; and (6) the expertise of the unit did not make a difference in the effectiveness of discretionary leadership.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1981
Accession Number
ADA128679

Entities

People

  • H. J. Martin
  • J. G. Hunt
  • R. N. Osborn

Organizations

  • Southern Illinois University Carbondale

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Human Systems

DTIC Thesaurus Topics

  • Behavioral Sciences
  • Business Administration
  • Civilian Personnel
  • Descriptive Analytics
  • Employment
  • Factor Analysis
  • Human Behavior
  • Information Science
  • Leadership
  • Management Personnel
  • Materials
  • Organizational Structure
  • Personnel Management
  • Plastic Explosives
  • Psychology
  • Regression Analysis
  • Surveys

Readers

  • Instructional Design and Training Evaluation.
  • Military Leadership and Professional Education.
  • Neural Network Machine Learning.