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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.
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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.
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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.