Despite the crucial importance of the 'bid/no bid' decision in the construction industry, it has been given little attention by researchers. This paper describes the development and testing of a novel bid/no bid model using the artificial neural network (ANN) technique. A back-propagation network consisting of an input buffer with 18 input nodes, two hidden layers and one output node was developed. This model is based on the findings of a formal questionnaire through which key factors that affect the 'bid/no bid' decision were identified and ranked according to their importance to contractors operating in Syria. Data on 157 real-life bidding situations in Syria were used in training. The model was tested on another 20 new projects. The model wrongly predicted the actual bid/no bid decision only in two projects (10%) of the test sample. This demonstrates a high accuracy of the proposed model and the viability of neural network as a powerful tool for modelling the bid/no bid decision-making process. The model offers a simple and easy-to-use tool to help contractors consider the most influential bidding variables and to improve the consistency of the bid/no bid decision-making process. Although the model is based on data from the Syrian construction industry, the methodology would suggest a much broader geographical applicability of the ANN technique on bid/no bid decisions.ANN, ANN bidding model, 'bid/no bid' criteria, construction, Syria,

Governments are increasingly entering partnerships with the private sector through the public–private partnership (PPP) model for the development of public projects. Value for money analysis is used to assess the viability of these ventures. This research aims to investigate the contribution of the PPP critical success factors to value for money viability analysis. Relevant data were collected through a questionnaire to establish the PPP critical success factors and value for money success criteria. Data were collected from 92 participants. The data obtained were analyzed using mean score, t-test, and regression analysis. The research found that government guarantees, macroeconomic conditions, shared authority between the public and private sectors, social support, and transparent procurement process contributed positively to value for money viability analysis. The results imply that practitioners should consider these key indicators for improving the value for money viability of PPP projects.

Purpose There has been a mounting interest in building information modelling (BIM) in the construction industry sector worldwide due to its perceived benefits. However, reliance on information technology is associated with risks. The purpose of this paper is to offer a better understanding of the emerging contractual and legal risks, which might influence the successful adoption of BIM, in order to facilitate the successful implementation of BIM in the construction industry. Design/methodology/approach The risks used in the study were documented from the literature, and primary data were collected by a questionnaire survey. The analysis of the results was driven by univariate and inferential statistics (Analysis of Variance) to identify the emerging contractual and legal risks. Findings The findings showed that there were little significant differences in the mean rating of the occurrence of contractual and legal risks between the respondents. The study confirmed that emerging risks are likely to be related to BIM documentations, intellectual rights and liability, missing data and misplaced assumptions among project stakeholders. The results showed that BIM success depends on close collaboration, at the outset of the project, with the client, designers, contractors and consultants. Practical implications The findings suggest that contract documents and contract agreements may need to be created in accordance with the identified risks, so that the questions of contractual and legal responsibilities are appropriately defined and allocated among the participants. Originality/value Important legal and contractual risks have been identified in the application of BIM. It renders a new understanding of the risks that might influence the successful adoption of BIM.

Purpose There is lack of literature on the evaluation of PPP projects performance based on critical success factors (CSFs). Thus, the purpose of this paper is to investigate and establish which of the CSFs are good predictor of PPP projects performance in terms of success criteria. Design/methodology/approach A questionnaire survey was developed based on PPP performance indicators and CSFs identified through a rigorous literature review. It was administrated among experts in PPP from the UK and the UAE. The respondents were selected purely on their work experience in PPP projects. The sites for collecting data were selected based on the similarity of the procurement methods between the two countries. The data were initially analysed using descriptive statics to identify the association between CSFs and PPP performance indicators. Multiple regression analysis was used to examine which of the CSFs were significant predictor of PPP projects performance. Findings The results demonstrated that “project technical feasibility, social support and local financial market assessment” contribute significantly to time performance. Detailed cost/benefits assessment contributed significantly to the cost, time and quality performance. Appropriate risk allocation and multi-benefit objectives of all stakeholders were found to be significant predictors of the service performance. CSFs “social support and detailed cost/benefits assessment” contribute positively to profit and variation performances. CSFs “profit and transparent procurement” are negatively associated with the variation performance. Cost and quality were the least performance criteria that could be predicted by the factors associated with this study. Practical implications The findings are expected to benefit the upper management of local governments and stakeholders to make informed decisions by understanding the link between the CSFs and the generic performance success measures at the onset of the of PPP project. Originality/value This study expands the existing literature by using the CSFs to predict the performance success of PPP projects.

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