Construction Management and Economics

Siebelink, Sander; Voordijk, Hans J. T.; Endedijk, Maaike D.; Adriaanse, Arjen M.

doi: 10.1080/01446193.2024.2409456pmid: N/A

Abstract The construction industry has experienced digital transformation with the widespread adoption of Building Information Modelling (BIM). However, the implementation of BIM has not been uniform across construction organizations, leading to variations in their maturity levels in using BIM. Collaborative projects involving multiple organizations face challenges due to these variations. This study aims to investigate how these variations influence the overall project maturity. Contrary to the belief that project maturity is merely the average of partner maturities, this research reveals that project managers employ strategies to influence project maturity. These strategies involve adjusting ambitions, strategically limiting certain parties’ involvement, and fostering maturity development through structure and education. Consequently, project partners can achieve a project maturity level that surpasses their individual capabilities.

Nyqvist, Roope; Peltokorpi, Antti; Lavikka, Rita; Ainamo, Antti

doi: 10.1080/01446193.2024.2416033pmid: N/A

Abstract This study aims to deepen the understanding of digital transformation in the construction industry. While studies on digital transformation exist at the industry level, studies at the company level, looking at how companies can proactively manage change, are largely missing. This study addresses this gap by exploring how three companies in the construction industry are navigating and leveraging digital transformation for international growth. Employing a multiple case study methodology and a systemic framework, the research provides insights into the interplay of technological innovation, business development, and ecosystem evolution through company cases. The research synthesizes and details three key barriers to managing digital transformation in the construction sector: (1) fragmentation, (2) insufficient demand from clients, and (3) limited influence of a single actor in the ecosystem, and three key drivers: (1) shift to data-driven industry, (2) targeted technological solutions, and (3) standardization and process modification. This study pairs the barriers and drivers with relevant managerial actions, and dimensional positioning to provide new valuable insights on digital transformation in the construction industry and beyond. The research provides insights both for construction industry companies and researchers seeking to manage digital transformation.

Duman, Dilek U.; Green, Stuart D.

doi: 10.1080/01446193.2024.2418023pmid: N/A

Abstract Emerging international markets are frequently characterized by high levels of economic and political uncertainty. Such inherent uncertainties render established ideas of competitive positioning problematic. An alternative perspective is developed which focuses on the day-to-day enactment of strategy through the medium of narrative. Narrative interviews with senior managers from Turkish international contracting firms provide insights into the contested storylines of strategy making. The plot structures mobilized are seen to comprise strategy making “in flight”. Senior managers are further held continuously to test emergent narratives against different audiences for the purposes of enhancing their persuasiveness. The aim is to instil an overall sense of coherence and direction among those with whom they interact. The temporal narratives mobilize epic plot structures from the past for the purposes of ascribing Turkish contractors with heroic identities in their quest for a desirable future. Yet the shared narratives also serve as a means of identity work through which the narrators strive to position themselves within their networks as acknowledged strategy makers. The espoused plot structures are further seen to coalesce to form a narrative infrastructure with direct implications for the future enactment of strategy. The research focus on the essential temporality of strategy narratives has important implications beyond the specific context of enquiry.

Wanigarathna, Nadeeshani; Xie, Ying; Henjewele, Christian; Morga, Mariantonietta; Jones, Keith

doi: 10.1080/01446193.2024.2419413pmid: N/A

Abstract Restoring residential buildings following earthquake damage requires a significant level of resources. Being able to predict these resource requirements in advance and accurately improves the effectiveness of disaster preparedness and subsequent recovery activities. This research explored how the latest ML algorithms could be used for antecedent earthquake loss modelling. A cost database for repairing residential buildings damaged by the Emilia Romagna earthquake in Italy was analysed using six state-of-the-art ML models to explore their ability to predict repair cost rates(cost per floor area) for a domestic building damaged by earthquakes. A Gradient Boost Regression model outperformed five other models in predicting earthquake damage repair cost rate. The performance of this model was significantly accurate and covers about 76% of the cases. A further SHAP analysis revealed that operational level, damage level and non-housing area of the buildings as top 3 important features when predicting the resultant damage repair cost rate. Overall this research advanced antecedent earthquake loss modelling approaches to increase the accuracy of estimates by incorporating more variables than the widely used damage level based simple methodology.