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Akbari, S, Khanzadi, M and Gholamian, M R (2018) Building a rough sets-based prediction model for classifying large-scale construction projects based on sustainable success index. Engineering, Construction and Architectural Management, 25(04), 534–58.

Bordass, B and Leaman, A (1997) Future buildings and their services. Building Research & Information, 25(04), 190–5.

Carter, K and Fortune, C (2007) Sustainable development policy perceptions and practice in the UK social housing sector. Construction Management and Economics, 25(04), 399–408.

Chan, P W and Dainty, A R J (2007) Resolving the UK construction skills crisis: a critical perspective on the research and policy agenda. Construction Management and Economics, 25(04), 375–86.

Chen, J J (1997) The impact of Chinese economic reforms upon the construction industry. Building Research & Information, 25(04), 239–45.

Friedman, A and Cammalleri, V (1997) Selection criteria for energy-efficient windows. Building Research & Information, 25(04), 234–8.

Gooding, D and Thomas, T (1997) Soilcrete blocks. Building Research & Information, 25(04), 202–9.

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Harris, D J and Dudek, S J M (1997) Heat losses from suspended timber floors. Building Research & Information, 25(04), 226–33.

Ismail, Z B (2018) Improving contractor social networking on IBS infrastructure maintenance projects: a review. Engineering, Construction and Architectural Management, 25(04), 479–96.

Jha, K N and Misra, S (2007) Ranking and classification of construction coordination activities in Indian projects. Construction Management and Economics, 25(04), 409–21.

Kelly, J (2007) Making client values explicit in value management workshops. Construction Management and Economics, 25(04), 435–42.

Kindangen, J I (1997) Window and roof configurations for comfort ventilation. Building Research & Information, 25(04), 218–25.

Lam, K-C, Ning, X and Ng, T (2007) The application of the ant colony optimization algorithm to the construction site layout planning problem. Construction Management and Economics, 25(04), 359–74.

Li, C Q (1997) Deterioration of concrete building structures. Building Research & Information, 25(04), 196–201.

Li, J, Yu, M and Wang, H (2018) A taxonomy of performance shaping factors for shield tunnel construction. Engineering, Construction and Architectural Management, 25(04), 574–96.

Lingard, H C, Yip, B, Rowlinson, S and Kvan, T (2007) The experience of burnout among future construction professionals: a cross-national study. Construction Management and Economics, 25(04), 345–57.

Mahjoubpour, B, Nasirzadeh, F, Mohammad Hosein Zadeh Golabchi, M, Ramezani Khajehghiasi, M and Mir, M (2018) Modeling of workers’ learning behavior in construction projects using agent-based approach. Engineering, Construction and Architectural Management, 25(04), 559–73.

Olsen, J, Lee, Z-H and Pong, W (2007) Optimal selection of concrete anchors for connection cost reduction. Construction Management and Economics, 25(04), 423–34.

Oo, B-L, Drew, D S and Lo, H P (2007) Applying a random coefficients logistic model to contractors' decision to bid. Construction Management and Economics, 25(04), 387–98.

  • Type: Journal Article
  • Keywords: Decision making; bidding; logistic modelling
  • ISBN/ISSN: 0144-6193
  • URL: http://www.informaworld.com/openurl?genre=article&issn=0144%2d6193&issue=4&spage=387&volume=25
  • Abstract:
    Contractors' decision to bid is dependent on many individual characteristics, including some that are unobservable by their competitors. There is natural heterogeneity across contractors in terms of their (i) intrinsic bid/no-bid preferences; and (ii) responses to decision to bid factors. This heterogeneity can be accounted for by applying a random coefficients approach to multiple bid/no-bid responses through logistic modelling. The bid/no-bid data were collected from managers of large and medium-sized contractors in Hong Kong via a designed bidding experiment. Two random coefficients logistic models are developed. Model 1 considers only two groups of decision to bid factors, namely market environment factors (i.e. number of bidders, market conditions) and project-specific factors (i.e. type and size of project). Model 2 extends Model 1 by adding two subject factors (i.e. years of experience, firm size) to study the effect of these individual factors on decision to bid. The results show that there is significant unobserved heterogeneity across contractors and that ignoring its effect results in a downward bias in the parameter estimates of the decision to bid factors. In using this approach contractors can better account for unobserved characteristics of their competitors when formulating their competitive strategies in deciding to bid.

Shahsavand, P, Marefat, A and Parchamijalal, M (2018) Causes of delays in construction industry and comparative delay analysis techniques with SCL protocol. Engineering, Construction and Architectural Management, 25(04), 497–533.