Assessment of Delay Factors Affecting Public Sector Building Projects
Edward-Alikor, Chimnumezi Daniel; Ben Amade PhD; Gregory Chimere Enyinna PhD
Abstract
This research aims at assessing the delay factors affecting public sector building projects in Rivers State, Nigeria. Additionally, the study sought to identify and evaluate the construction delay factors affecting public sector projects, analyse the extent to which the construction delay factors affect public sector projects and establish a structural relationship that will priotize the construction delay factors affecting public sector projects. The study was guided by the fuzzy set theory. The study used an exploratory and descriptive research design methods of investigation, and a purposive/convenient sampling technique was used to select a sample size of 278 from a population of 997 practitioners using the Krejcie and Morgan’s method of sample size determination from the practitioners in Rivers State. The study employed a well-structured standardised questionnaire designed in Likert five-point scales, in addition to personal observations to elicit responses from construction firms and projects situated in Rivers State, Nigeria. The questionnaires underwent pre-testing and validation to ensure their reliability. The study utilised various statistical tools via descriptive statistics to present the collected data. Additionally, Partial Least Square Structural Equation Modeling (PLS-SEM) was employed to analyse the major issues in the study. The statistical analysis was conducted using IBM SPSS Statistics version 26.0 and LISREL. The study's results show that out of the twenty-six (26) construction project delay factors classified into ten (10) sub-groups of delay factors which were identified via literature search. The client-related induced delay factors (CLR) had the greatest impact on construction project delay with path coefficient ?-values of 0.349, while material related induced delay factors (MAR) had the least impact on construction project delay with path coefficient ?-values of 0.120. The R2 value of the model is
Keywords
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