Artificial Neural Network for Measuring Organizational Effectiveness

Abstract

An artificial neural network based methodology is applied for predicting the level of organizational effectiveness in a construction firm. The methodology uses the competing value approach to identify 14 variables. These are conceptualized from four general categories of organizational characteristics relevant for examining effectiveness: structural context; person-oriented processes; strategic means and ends; and organizational flexibility, rules, and regulations. In this study, effectiveness is operationalized as the level of performance in construction projects accomplished by the firm in the past 10 years. Cross-sectional data has been collected from firms operating in institutional and commercial construction. A multilayer back-propagation neural network based on the statistical analysis of training data has been developed and trained. Findings show that by applying a combination of the statistical analysis and artificial neural network to a realistic data set, high prediction accuracy is possible.