1992•Organization ScienceRequires access

An Organizational Learning Model of Convergence and Reorientation

Theresa K. Lant, Stephen J. Mezias

Open publisher page 529 citations

Abstract

A critical challenge facing organizations is the dilemma of maintaining the capabilities of both efficiency and flexibility. Recent evolutionary perspectives have suggested that patterns of organizational stability and change can be characterized as punctuated equilibria (Tushman and Romanelli 1985). This paper argues that a learning model of organizational change can account for a pattern of punctuated equilibria and uses a learning framework to model the tension between organizational stability and change. A simulation methodology is used to create a population of organizations whose activities are governed by a process of experiential learning. A set of propositions is examined that predict how patterns of organizational change are affected by environmental conditions, levels of ambiguity, organizational size, search rules, and organizational performance. Implications of this learning model of convergence and reorientation for theory and research are discussed.

About this research paper

What this paper is about

A critical challenge facing organizations is the dilemma of maintaining the capabilities of both efficiency and flexibility. Recent evolutionary perspectives have suggested that patterns of organizational stability and change can be characterized as punctuated equilibria (Tushman and Romanelli 1985). This paper argues that a learning model of organizational change can account for a pattern of punctuated equilibria and uses a learning framework to model the tension between organizational stability and change. A simulation methodology is used to create a population of organizations whose activities are governed by a process of experiential learning. A set of propositions is examined that predict how patterns of organizational change are affected by environmental conditions, levels of ambiguity, organizational size, search rules, and organizational performance. Implications of this learning model of convergence and reorientation for theory and research are discussed.

Why it matters

OpenAlex reports 529 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

A critical challenge facing organizations is the dilemma of maintaining the capabilities of both efficiency and flexibility. Recent evolutionary perspectives have suggested that patterns of organizational stability and change can be characterized as punctuated equilibria (Tushman and Romanelli 1985). This paper argues that a learning model of organizational change can account for a pattern of punctuated equilibria and uses a learning framework to model the tension between organizational stability and change. A simulation methodology is used to create a population of organizations whose activities are governed by a process of experiential learning. A set of propositions is examined that predict how patterns of organizational change are affected by environmental conditions, levels of ambiguity, organizational size, search rules, and organizational performance. Implications of this learning model of convergence and reorientation for theory and research are discussed.

Key concepts: Organizational learning, Ambiguity, Punctuated equilibrium, Experiential learning, Organization development, Organizational studies, Knowledge management, Flexibility (engineering)

Related papers

Back to paper searchBrowse research topicsOriginal source
An Organizational Learning Model of Convergence and Reorientation — Research Paper | ScholarLens