Toggle Main Menu Toggle Search

Open Access padlockePrints

Digital transformation through Building Information Modelling: Spanning the macro-micro divide

Lookup NU author(s): Professor Mohamad Kassem



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Benefits of Building Information Modelling (BIM) have encouraged its adoption within organisations (i.e., micro level) and prompted many policy makers to establish market-wide BIM initiatives (i.e. macro level) to encourage its adoption. The adoption of a systemic innovation such as BIM within a complex socio-technical environment such as the construction sector requires a thorough understanding of its adoption dynamics at the micro-level and their interactions with the macro level. Studies addressing this theme are lacking which is a significant gap given the growing evidence of decoupling issues between the two levels and the need to support macro level initiatives with both theoretical foundation and empirical evidence.This research proposes a novel analytical investigation of micro BIM adoption and proposes an approach to link micro BIM adoption dynamics to the institutional initiative at the macro level. To analyse the dynamics of micro BIM adoption, the adoption of BIM by 177 organisations was studied. Two methods are used; the fuzzy decision making trial and evaluation laboratory (F-DEMATEL), and systems thinking.The theoretical and empirical results include a classification of BIM adoption factors in cause-and-effect factors; the identification of causal loop diagrams (CLD) representing the causation chains ending with the decision to adopt BIM by organisations; and an approach that links the micro BIM adoption to the macro BIM initiatives. The proposed approach serves as the basis for designing a coordinated collaborative effort for effective BIM-focussed digital transformation programmes.

Publication metadata

Author(s): Mohamad K, Ahmed AL

Publication type: Article

Publication status: Published

Journal: Technological Forecasting and Social Change

Year: 2022

Volume: 184

Print publication date: 01/11/2022

Online publication date: 14/09/2022

Acceptance date: 28/08/2022

Date deposited: 07/10/2022

ISSN (print): 0040-1625

ISSN (electronic): 1873-5509

Publisher: Elsevier Inc.


DOI: 10.1016/j.techfore.2022.122006

ePrints DOI: 10.57711/z99p-jp03


Altmetrics provided by Altmetric