Purpose – This paper aims to develop a multivariate model that will be applicable to the Nigeria construction industry. Design/methodology/approach – A self-administered questionnaire survey was used to source information on project scope factors and qualitative factors considered in the study. Principal component regression was used for data analysis and model development, using SPSS 16.0 for windows, while T-test was used for model testing and validation. Findings – The study found that delay in progress payment by owner, lateness in revising and approving design document by owner, delay in delivering the site to the contractor by the owner, change order by owner during construction, complexity of project design, poor site management and supervision by contractors, and rain effect on construction activities are qualitative/non-project scope factors with good predictive abilities. Research limitations/implications – Cost, gross floor area and number of floors were the only quantitative/project scope factors considered in the study. The developed models therefore do not account for any variation in duration which may arise from other project scope factors, such as location, procurement route and type of contract. Originality/value – The qualitative factors which emerged as predictors in the derived models increased the accuracy of the models. The models developed therefore serve as useful construction time prediction tools for both consultancy firms and contractor organizations in the Nigerian construction industry.
PurposeThis study aims to develop a model which incorporates the impact of both aleatory and epistemic uncertainties into construction duration predictions, in a manner that is consistent with the nature/quality of information available about various factors which bring about uncertainties.Design/methodology/approachData relating to 178 completed Tertiary Education Trust Fund (TETfund) building construction projects were obtained from construction firms via questionnaire survey. Using 90% of the data, the model was developed in the form of a hybrid-based algorithm implemented through a suitable user-friendly graphical user interface (GUI) using MATLAB programming language. Bayesian model averaging, Monte Carlo simulation and fuzzy logic were the statistical methods used for the algorithm development, prior to its GUI implementation in MATLAB. Using the remaining 10% data, the model's predictive accuracy was assessed via the independent samples t-test and the mean absolute percentage error (MAPE).FindingsThe developed model's predictions were found not statistically different from those of actual duration estimates in the 10% test data, with a MAPE of just 2%. This suggests that the model's ability to incorporate both aleatory and epistemic uncertainties improves accuracy of duration predictions made using it.Research limitations/implicationsThe model was developed using a particular type of building projects (TETfund building construction projects), and so its use is limited to projects with characteristics similar to those used for its development.Practical implicationsThe developed model's predictions are expected to serve as a useful basis for consultancy firms and contractor organisations to make more realistic schedules and benchmark measures of construction period, thereby facilitating effective planning and successful execution of construction projects.Originality/valueThe study presented a model which permits combined manipulation of aleatory and epistemic uncertainties, hence ensuring a more realistic incorporation of uncertainty into construction duration predictions.
Despite the roles which small and medium construction enterprises (SMCEs) play in the Nigeria construction industry and the economy at large, extant research revealed that their participation in public procurement is poor as large firms have dominated the public procurement market. Previous studies have identified barriers responsible for their low participation and also suggested improvement measures. However, improvement measures best suited to dismantle/mitigate each barrier is not known. This study thus, assessed measures for improving SMCEs’ participation in Nigeria public procurement. The study collected data from 100 SMCEs in Nigeria, via a questionnaire survey. Mean/relative importance index was used for ranking the measures and ANOVA for checking difference between the studied measures. The result of the study revealed that E-procurement is the best measure for improving SMCEs’ participation in public procurement as it is best in dismantling barriers related to “problematic procurement procedure”, “lack of awareness of public procurement” and “legal issues/corruption”. Training which is suitable for eliminating barriers related to “lack of skilled employees and partners”, emerged as the second best followed by Division of Contract into lots that is best suitable for tackling barriers related to “lack of resources” which is contrary to previous researchers’ belief that it is the best measure. The study concludes that E-procurement, training and division of contract into lots are all suitable measures for improving SMCEs’ participation in Nigeria public procurement in their other of priority. The study’s findings show that it is desirous that the measures are applied together as none of these measures is singularly sufficient in dismantling all barriers to SMCEs’ participation in public procurement.
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