A discriminant model for measuring competition intensity of construction market

A discriminant model for measuring competition intensity of construction market Purpose – “Competition intensity” is a factor in addressing competitiveness. The understanding on competition intensity is prerequisite to the formulation of industrial competition policies as well as firms’ competition strategies. In the construction context, whereas competition intensity can be measured using a number of traditional approaches (e.g. competitor number, concentration), the measurement is often criticized for poor efficiency. The purpose of this paper is to propose a new model for measuring competition intensity in light of the theory of discriminant analysis. Design/methodology/approach – The proposed model is composed of predictor variables concerned with market operation as well as criterion variables that classify markets into a few predefined groups based on the values of competition intensity. Empirical data of China's local construction markets were collected to verify the proposed model. Findings – The research findings indicate that the model can offset the drawbacks of traditional measures in the construction market. Research limitations/implications – It is recommended using the proposed model to predict the competition trend of construction market especially when data for the traditional approaches are poor or not readily available. Originality/value – The proposed model is a development of the literature in examining competition intensity. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Engineering, Construction and Architectural Management Emerald Publishing

A discriminant model for measuring competition intensity of construction market

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Publisher
Emerald Publishing
Copyright
Copyright © 2014 Emerald Group Publishing Limited. All rights reserved.
ISSN
0969-9988
DOI
10.1108/ECAM-05-2012-0053
Publisher site
See Article on Publisher Site

Abstract

Purpose – “Competition intensity” is a factor in addressing competitiveness. The understanding on competition intensity is prerequisite to the formulation of industrial competition policies as well as firms’ competition strategies. In the construction context, whereas competition intensity can be measured using a number of traditional approaches (e.g. competitor number, concentration), the measurement is often criticized for poor efficiency. The purpose of this paper is to propose a new model for measuring competition intensity in light of the theory of discriminant analysis. Design/methodology/approach – The proposed model is composed of predictor variables concerned with market operation as well as criterion variables that classify markets into a few predefined groups based on the values of competition intensity. Empirical data of China's local construction markets were collected to verify the proposed model. Findings – The research findings indicate that the model can offset the drawbacks of traditional measures in the construction market. Research limitations/implications – It is recommended using the proposed model to predict the competition trend of construction market especially when data for the traditional approaches are poor or not readily available. Originality/value – The proposed model is a development of the literature in examining competition intensity.

Journal

Engineering, Construction and Architectural ManagementEmerald Publishing

Published: Mar 11, 2014

Keywords: China; Market competitiveness; Construction competition; Concentration; Multivariate discriminant analysis

References

  • Competition, imitation and growth with step‐by‐step innovation
    Aghion, P.; Harris, P.H.; Vickers, J.
  • Decomposing Lorenz and concentration curves
    Bishop, J.A.; Chow, K.V.; Zeager, L.A.
  • Competitive intensity and technical efficiency in public sector firms: evidence from India
    Ramaswamy, K.; Renforth, W.
  • Modelling competition intensity in construction market
    Ye, K.H.
  • An overview of the Chinese construction market and construction management practice
    Zou, P.X.W.; Fang, D.; Wang, S.; Loosemore, M.

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