Drivers for implementation of building information modeling (BIM) within the Nigerian construction industry

Purpose: The Nigerian construction industry, like in most emerging economies has been slow with technological advances such as building information modeling (BIM). More so, the application of BIM among Nigerian practitioners is rather limited to architects’ usage for schematic design and presentation of drawings. The purpose of this study is to enhance BIM’s uptake, this study explores the drivers for the implementation of BIM within the Nigerian construction industry. Design/methodology/approach: An interpretivist epistemological design was adapted to extensively manually review and search the literature on BIM implementation drivers. Based on 14 drivers for BIM implementation drivers identified, a survey questionnaire was used to collect data from the Nigerian construction practitioners. The data obtained is then subjected to descriptive, inferential statistics and multivariate techniques such as factor analysis. Findings: The study, through factor analysis, categorized the 14 drivers to BIM implementation into four principal factors. The factors are construction related-drivers; process digitalization and economic-related drivers; sustainability and efficiency-related drivers; and visualization and productivity-related drivers. Practical implications: To effectively adopt BIM in the construction industry, it is necessary to identify the BIM implementing drivers, which can act as catalysts of change and, thus leading to sustained adoption of BIM. Therefore, the identified drivers and categorization of principal factors could provide managerial implications for better execution and adoption of BIM, as well as the creation of the required change for the BIM implementation. Originality/value: The research provides insights into the implementation drivers of BIM in lesser studies in a developing country such as Nigeria. The study further contributes to this research sphere by using factor analysis to customize and contextualize the drivers that were previously identified.