Empirical Approach to Identify Operational Critical Success Factors for BIM Projects

Badrinath, Amarnath Chegu; Hsieh, Shang‐Hsien

doi:10.1061/(asce)co.1943-7862.0001607

Cited by 27 publications

(16 citation statements)

References 22 publications

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“…Prior research has assessed BIM adoption across different markets, using a model of diffusion area, macro-maturity components, macro-diffusion dynamics, and so forth, and validated this model by applying it to assess BIM adoption amongst 21 countries (Kassem and Succar, 2017). This work has determined the BIM project objectives, critical success factors, and operative critical success factors for effective implementation of BIM (Chegu, Badrinath, and Hsieh, 2019). It has identified the success factors for adoption of BIM in a company, selection of projects within the company to implement BIM, and selection of BIM services and software (Won et al, 2013).…”

Section: Limits Of Existing Researchmentioning

confidence: 99%

Codification Challenges for Data Science in Construction

Soman

Whyte

2020

J. Constr. Eng. Manage.

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New forms of data science, including machine learning and data analytics, are enabled by machinereadable information but are not widely deployed in construction. A qualitative study of information flow in three projects using Building Information Modelling (BIM) in the late design and construction phase is used to identify the challenges of codification which limit the application of data science. Despite substantial efforts to codify information with 'Common Data Environment (CDE)' platforms to structure and transfer digital information within and between teams, participants work across multiple media in both structured and unstructured ways.Challenges of codification identified in this paper relate to software usage (interoperability, translation, modelling, and file-based sharing), information sharing (unstructured information, document control, workarounds, process change, and multiple CDEs), and construction process information (loss of constraints and low level of detail). This paper contributes to the current understanding of data science in construction by articulating the codification challenges and their implications for data quality dimensions, such as accuracy, completeness, accessibility, consistency, timeliness, and provenance. It concludes with practical implications for developing and using machine-readable information and directions for research to extract insight from data and support future automation.

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Section: Limits Of Existing Researchmentioning

confidence: 99%

Codification Challenges for Data Science in Construction

Soman

Whyte

2020

J. Constr. Eng. Manage.

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“…The implementation of key areas identified through CSFs can help to facilitate the successful implementation of BIM (Antwi-Afari et al , 2018). Various studies regarding CSFs of BIM implementation for specific countries are found in the literature for the Czech Republic (Nyvlt, 2018), South Korea (Kim et al , 2016; Won et al , 2013), Nigeria (Amuda-Yusuf, 2018), Singapore (Liao and Teo, 2017), Taiwan (Badrinath and Hsieh, 2019; Tsai et al , 2014), Turkey (Ozorhon and Karahan, 2017) and the United Kingdom (Dakhil et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

Critical success factors for BIM implementation: a Malaysian case study

Sinoh

Othman

Ibrahim

2020

ECAM

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PurposeThis paper aimed to identify critical success factors (CSFs) of building information modeling (BIM) implementation among architectural, engineering and construction (AEC) firms in Malaysia using quantitative statistical methods.Design/methodology/approachA questionnaire survey was conducted on the population of all AEC firms in Malaysia to rank the relative importance of 15 success factors (SFs) for BIM implementation in the firm on a 5-point Likert scale. The sampling frame consisted of all members registered with the Construction Industry Development Board (CIDB) from which 184 responses were received. The distinction between SFs and CSFs was achieved by a normalized mean cutoff value. Principal component analysis (PCA) was used to determine the relative groupings of the CSFs and structural equation modeling (SEM) were used to examine the underlying relationships between the CSF groupings.FindingsResults of this study validated 11 previously identified CSFs for successful implementation of BIM among Malaysian AEC firms. Non-technical factors such as management, leadership and coordination were found to have higher relevance compared to technical factors such as software and hardware. Ranking analysis of the CSFs found internal coordination factors to have more importance compared to external coordination factors. PCA revealed 4 principal components (PCs) from the 11 CSFs. Together, these four PCs explain 74.3% of the variance in the data. Also, six hypothesized relationships between these groupings were supported using PLS-SEM.Research limitations/implicationsThis study found an interaction between internal and external coordination factors for the implementation of BIM. Future research could explore the dynamics of both intra- and inter-firm coordination, which may create an environment suitable for BIM implementation.Practical implicationsThis study provides Malaysian AEC firms with a general strategy on how to approach the implementation of BIM within the firm. In addition, government initiatives should focus on increasing BIM competency of graduates entering the workforce. This can be achieved through the development of a national BIM curriculum. Apart from that, the implementation of BIM requires a radical shift in the traditional building delivery process, not just simply acquiring new software and hardware.Originality/valueThis study revealed that the interactions between different firms at the industry level can influence successful implementation of BIM at the firm level. CSFs for BIM implementation at the firm level have been extensively studied. However, few studies exist which explore the inter-firm collaborations at the industry level that facilitate successful BIM implementation.

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“…In Malaysia, Enegbuma, Aliagha, and Ali [6] reported that the lack of comprehensive BIM related legal standards slowed BIM diffusion across the construction sector. Similarly, recent studies conducted in China, Taiwan, and Australia stressed the importance of a collaborative approach by stakeholders to have an open standard and guideline for BIM implementation in the construction industry [56][57][58]. In the perspective of the impact of technological innovations in construction projects, [59] reported the advancement of the UK construction sectors towards devising BIM standards for a successful BIM adoption and diffusion in building construction projects.…”

Section: Component IImentioning

confidence: 99%

Key BIM Adoption Drivers to Improve Performance of Infrastructure Projects in the Ethiopian Construction Sector: A Structural Equation Modeling Approach

Belay

Goedert

Woldesenbet

et al. 2021

Advances in Civil Engineering

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The aim of this paper is to explore the critical BIM adoption drivers across the Ethiopian public infrastructure construction sector. In this regard, a comprehensive systematic literature review was employed to identify potential BIM implementation attributes in developing countries and validated through a pilot test. Then, quantitative data was collected from experts working in various organizations using a structured questionnaire survey. A structural equation model was then developed based on five key BIM adoption constructs and 14 adoption drivers. Based on the path analysis, Application, Environment, and Project related factors positively affect BIM adoption in infrastructure projects, whereas Organization and Information Management are insignificant and negatively affect BIM adoption in the Ethiopian construction industry. The study highlighted key BIM adoption attributes that are helpful to enhance the overall project management performance in infrastructure projects. The proposed action plan is beneficial to various professionals, government, and stakeholders in an effort to improve the current level of BIM uptake in the horn of Africa. More so, the findings of this paper can be used to facilitate and promote BIM adoption in public infrastructure construction projects across the Ethiopian construction market.

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Empirical Approach to Identify Operational Critical Success Factors for BIM Projects

Cited by 27 publications

References 22 publications

Codification Challenges for Data Science in Construction

Codification Challenges for Data Science in Construction

Critical success factors for BIM implementation: a Malaysian case study

Key BIM Adoption Drivers to Improve Performance of Infrastructure Projects in the Ethiopian Construction Sector: A Structural Equation Modeling Approach

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