Framework for Organizational Change

Abstract

Having an integrated model of change management can simplify the inherent complexity of change by recognizing the relation between critical elements. Because of dynamic nature of variables and their interaction, developing a static model is not useful. This study tries to generate a new method for constructing a dynamic organizational change model in four steps with combining qualitative methods (content analysis and Delphi technique) and artificial neural networks (Fuzzy theory and genetic algorithm). In addition, by governing restrictions of various conditions, a road map is provided for managers to leading change. After recognizing and screening variables by qualitative methods, due to the complexity and scale of the problem the initial model is built with fuzzy theory and genetic algorithms are used for more accuracy. For governing restrictions and finding acceptable amount of variables to have a successful change program, genetic algorithm is used.

Keywords

• change management
• integrated organizational change model
• genetic algorithm (ga), fuzzy inference system (fis)