Prediction of organizational effectiveness in construction companies

Irem Dikmen, M. Talat Birgonul, Semiha Kiziltas

Research output: Contribution to journalReview articlepeer-review


Investigation of literature on organizational effectiveness (OE) reveals that the researchers have been in consensus for the difficulty of defining, modeling, and measuring OE, which is important for attaining high performance. Major focuses of this paper are, therefore, to construct a conceptual framework to model OE, to derive major determinants of OE from this framework, and to measure OE by constructing prediction models based on artificial neural network (ANN) and multiple regression (MR) techniques. Based on the proposed framework that investigates OE from the perspectives of organization and its subsystems, business, and macroenvironments, the most significant variables that determine OE have been collected and used as inputs for the two prediction models, which have been constructed by using the information associated with 116 Turkish construction companies obtained from a designed survey. According to the prediction results and comparative study, ANN slightly outperformed the MR model in terms of errors, correlations between desired versus actual outputs, and relations between input-output parameters. The ANN model is proposed for use as a tool to assess company effectiveness and to guide decision makers about the major determinants of OE to increase firm performance.

Original languageEnglish (US)
Pages (from-to)252-261
Number of pages10
JournalJournal of Construction Engineering and Management
Issue number2
StatePublished - Feb 2005


  • Construction industry
  • Management methods
  • Models
  • Neural networks
  • Organizations
  • Turkey

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Industrial relations
  • Strategy and Management


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