Construction projects are prone to a number of risks due to their complexity, dynamic nature, capital intensive nature and involvement of many stakeholders. These risks if left unmanaged will negatively influence the completion cost and other primary objectives of construction projects. Numerous studies have been conducted globally to determine the potential risks that negatively impacts construction projects; however, the risks aren’t alike across all the regions and the potential degree of impact may changes with time. This study assessed the impact of risk factors on completion cost of construction projects in Nigeria. Data was collected using structured questionnaires administered to 192 construction practitioners using convenience sampling technique. Descriptive statistics (mean and standard deviation) were used to analyse the data. The study found ‘inadequate cost estimate’ (MS = 4.39), ‘risk incurred due to bribery and corruption’ (4.30), ‘increase in prices of materials’ (4.25), ‘increase in cost of labour’’ (4.11), ‘poor cash flow management’ (4.04) ‘mistakes/errors in design’ (4.04) and ‘mistakes during construction’ to be the topmost risk factors that impact on project completion cost. The study concludes that ‘economic’, ‘financial’ and ‘contract administration and project management’ related factors group are those with high impact on project completion cost.
Conflict is inherent in construction projects due to uniqueness, complexity and involvement of several stakeholders. Occurrence of conflicts affects projects outcomes if not properly managed. This study assessed the influence of factors responsible for conflicts on time, cost and quality performance of construction projects. A quantitative approach which entails questionnaire survey was adopted. Data were collected through questionnaire administered to 125 construction professionals, out of which. 83 were properly filled and returned. The data collected was analysed using both descriptive and inferential statistics. Influence of the conflict factors on project performance parameters was ranked using mean and standard deviation. Kruskal-Wallis Test was used to determine the statistical significance of the variation in influence of the conflict factors on time, cost and quality performance of construction projects. Results indicated that the influence of all the 21 assessed conflict factors ranges from ‘moderate’ to ‘high’. 15, 13 and 13 factors have high influence on time, cost and quality performance respectively. Findings of the study also indicated that the variation in the influence of conflict factors on time, cost and quality performance of construction project was statistically not significant (p value obtained is 0.286 for P≤ 0.05). The study concludes that 13 conflict factors have high influence across time, cost and quality performance of construction projects. Furthermore, the influence of conflict factors on time, cost and quality performance of construction projects is similar. The study recommends that construction stakeholders should strive to minimise the occurrence of factors which give rise to conflict in any given project so as to achieve optimal project performance.
Purpose Building developments are often capital intensive, have a long payback period and many associated risks and uncertainties. This makes investments in building projects to be a big challenge. This study aims to develop a computerized simulation-based binomial model (CSBBM) for building investment appraisal with a view to improving the economic sustainability of proposed building projects. Design/methodology/approach Mathematical equations and algorithms were developed based on the binomial method (BM) of real options analysis and then implemented on a computer system. A hybrid algorithm that integrates Monte Carlo simulation (MCS) and BM was also developed. A real-life project was used to test the model. Sensitivity analysis was also conducted to explore the influence of input variables on development option value (DOV). Findings The test result shows that the model developed provides a better estimate of the value of an investment when compared with traditional net present value technique, which underestimate the value. Moreover, inflation rate (i) and rental value (Ri) are the most sensitive variables for DOV. An increase in i and Ri by just 5% causes a corresponding increase in DOV by 202% and 132%, respectively. While the least sensitive variable is the discount rate (r), as an increase in r by 5% causes a corresponding decrease in DOV by just 9%. The CSBBM is capable of determining the optimal time of development of buildings with an accuracy of 80.77%. Practical implications The hybrid model produces higher DOV than that of only the BM because MCS considers randomness in uncontrollable variables. Thus, building investment decision-makers should always use MCS to complement the BM in an investment analysis. Originality/value There is limited evidence on the use of this kind of hybrid model for determining DOV in practice.
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