Construction Procurement: Modelling Bidders’ Learning in Recurrent Bidding
Construction remains a significant area of public expenditure. An understanding of the process of changes in construction pricing, and how the process can be manipulated through the release of bidding feedback information is vital, in order to best design clients’ procurement policies. This paper ai...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
UTS ePRESS
2015-11-01
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| Series: | Construction Economics and Building |
| Subjects: | |
| Online Access: | https://learning-analytics.info/journals/index.php/AJCEB/article/view/4653 |
| _version_ | 1818056609757134848 |
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| author | Bee Lan Oo Florence Yean Yng Ling Alexander Soo |
| author_facet | Bee Lan Oo Florence Yean Yng Ling Alexander Soo |
| author_sort | Bee Lan Oo |
| collection | DOAJ |
| description | Construction remains a significant area of public expenditure. An understanding of the process of changes in construction pricing, and how the process can be manipulated through the release of bidding feedback information is vital, in order to best design clients’ procurement policies. This paper aims to statistically model inexperienced individual bidders’ learning in recurrent bidding under partial and full information feedback conditions. Using an experimental dataset, the developed linear mixed model contains three predictor variables, namely: time factor, information feedback conditions, and bidding success rate in the preceding round. The results show nonlinearity and curvature in the bidders’ learning curves. They are generally less competitive in time periods after a winning bid with lower average bids submitted by those subjected to full information feedback condition. In addition, the model has captured the existence of heterogeneity across bidders with individual-specific parameter estimates that demonstrate the uniqueness of individual bidders’ learning curves in recurrent bidding. The findings advocate for adequate bidding feedback information in clients’ procurement design to facilitate learning among contractors, which may in turn lead to increased competitiveness in their bids. |
| first_indexed | 2024-12-10T12:31:34Z |
| format | Article |
| id | doaj.art-f0dcc78192c3490fbbe24365052c896b |
| institution | Directory Open Access Journal |
| issn | 2204-9029 |
| language | English |
| last_indexed | 2024-12-10T12:31:34Z |
| publishDate | 2015-11-01 |
| publisher | UTS ePRESS |
| record_format | Article |
| series | Construction Economics and Building |
| spelling | doaj.art-f0dcc78192c3490fbbe24365052c896b2022-12-22T01:48:48ZengUTS ePRESSConstruction Economics and Building2204-90292015-11-0115410.5130/AJCEB.v15i4.46532972Construction Procurement: Modelling Bidders’ Learning in Recurrent BiddingBee Lan Oo0Florence Yean Yng Ling1Alexander SooUNSWNational University of SingaporeConstruction remains a significant area of public expenditure. An understanding of the process of changes in construction pricing, and how the process can be manipulated through the release of bidding feedback information is vital, in order to best design clients’ procurement policies. This paper aims to statistically model inexperienced individual bidders’ learning in recurrent bidding under partial and full information feedback conditions. Using an experimental dataset, the developed linear mixed model contains three predictor variables, namely: time factor, information feedback conditions, and bidding success rate in the preceding round. The results show nonlinearity and curvature in the bidders’ learning curves. They are generally less competitive in time periods after a winning bid with lower average bids submitted by those subjected to full information feedback condition. In addition, the model has captured the existence of heterogeneity across bidders with individual-specific parameter estimates that demonstrate the uniqueness of individual bidders’ learning curves in recurrent bidding. The findings advocate for adequate bidding feedback information in clients’ procurement design to facilitate learning among contractors, which may in turn lead to increased competitiveness in their bids.https://learning-analytics.info/journals/index.php/AJCEB/article/view/4653Construction procurementbiddinginformation feedbacklearning. |
| spellingShingle | Bee Lan Oo Florence Yean Yng Ling Alexander Soo Construction Procurement: Modelling Bidders’ Learning in Recurrent Bidding Construction Economics and Building Construction procurement bidding information feedback learning. |
| title | Construction Procurement: Modelling Bidders’ Learning in Recurrent Bidding |
| title_full | Construction Procurement: Modelling Bidders’ Learning in Recurrent Bidding |
| title_fullStr | Construction Procurement: Modelling Bidders’ Learning in Recurrent Bidding |
| title_full_unstemmed | Construction Procurement: Modelling Bidders’ Learning in Recurrent Bidding |
| title_short | Construction Procurement: Modelling Bidders’ Learning in Recurrent Bidding |
| title_sort | construction procurement modelling bidders learning in recurrent bidding |
| topic | Construction procurement bidding information feedback learning. |
| url | https://learning-analytics.info/journals/index.php/AJCEB/article/view/4653 |
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