ARTIFICIAL NEURAL NETWORK FOR MEASURING ORGANIZATIONAL EFFECTIVENESS

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 have been collected from firms operating in institutional and commercial construction. A multiyear backpropagation 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.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/08873801
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Sinha, S K
  • McKim, R A
• Publication Date: 2000-1

Language

• English

Media Info

• Features: Appendices; Figures; References; Tables;
• Pagination: p. 9-14
• Serial:
  • Journal of Computing in Civil Engineering
  • Volume: 14
  • Issue Number: 1
  • Publisher: American Society of Civil Engineers
  • ISSN: 0887-3801

Subject/Index Terms

• TRT Terms: Backpropagation; Construction; Construction management; Correlation analysis; Data collection; Machine learning; Mathematical models; Neural networks; Organizations; Regression analysis
• Subject Areas: Administration and Management; Construction; Data and Information Technology; Highways; I10: Economics and Administration; I50: Construction and Supervision of Construction;

Filing Info

• Accession Number: 00781773
• Record Type: Publication
• Contract Numbers: NSC 86-2221-E-009-070
• Files: TRIS
• Created Date: Jan 22 2000 12:00AM