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Increasing complexity in construction projects evokes interest in application of innovative digital technologies in construction. Digital twins (DT), which bring these innovative technologies together, have strong interactions with lean construction (LC). To highlight the collaborative nature of DT and LC, the paper explores the interactions between LC and DT and assesses benefits, costs, opportunities and risks (BOCR) of DT in LC to analyze significant obstacles and enablers in DT adoption in LC.

BOCR approach comprehensively considers both the positive and the negative attributes of a problem. At the first step, BOCR criteria for DT are identified through literature review and expert opinions, at the second step dependencies among BOCR criteria for DT in LC are determined by neutrosophic analytic hierarchy process (AHP), through a questionnaire survey. Integrating BOCR into neutrosophic AHP enables achieving more meaningful preference scores.

Cost of skilled workforce is the most important factor and opportunity to reduce waste is the second most important factor in adoption of DT in LC. The results were analyzed to rank the BOCR of adoption of DT in LC.

This study, in a novel way, performs BOCR analysis through neutrosophic AHP to reflect experts' judgments more effectively by neutrosophic AHP's better handling of vagueness and uncertainty. The paper provides a model to better understand the significant factors that influence adoption of DT in LC.

The purpose of this paper is to investigate social and technical drivers of data science practices and develop a standard model for assisting organizations in their digital transformation by providing data science capability/maturity level assessment, deriving a gap analysis, and creating a comprehensive roadmap for improvement in a standardized way.

This paper systematically reviews and synthesizes the existing literature-related to data science and 183 practitioners' considerations by employing a survey-based research method. By blending the findings of this research with a well-established process capability maturity model standard, International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 330xx, and following a methodological maturity development framework, a theoretically grounded model, entitled as the data science capability maturity model (DSCMM) was developed.

It was found that organizations seek a capability/maturity model standard to evaluate and improve their current data science capabilities. To close this research gap, the DSCMM is developed. It consists of six capability maturity levels and twenty-seven processes categorized under five process areas: organization, strategy management, data analytics, data governance and technology management.

This paper validates the need for a process capability maturity model for the data science domain and develops the DSCMM by integrating literature findings and practitioners' considerations into a well-accepted process capability maturity model standard to continuously assess and improve the maturity of data science capabilities of organizations.