Increasing levels of urbanisation and the rapid growth of modern cities require that particular attention be paid to ensuring the safety and protection of living conditions for their inhabitants. In this context, natural and human-induced disasters pose a major threat to the safety and normal operational procedures of buildings and infrastructures. In consequence, disaster management and built assets operations demand modern tools to be effectively prepared in order to better respond to such critical events. This study explores the potential of artificial intelligence in these operational fields by developing a deep learning model that is able to provide a rapid assessment of an asset’s structural condition in the case of a seismic excitation. The proposed simulation model makes an accurate prediction of the damage status of individual elements in a built asset, thus leading to operational improvements across all disaster management phases. In addition, the above development integrates the deep learning algorithm into building information modelling and then uploads the graphical information to a web dashboard. By following the framework proposed, an integrative model is designed that provides a visual and user-friendly interface that allows different stakeholders to navigate and comprehend essential information on the effects of a disaster; thus enabling quicker decision making and strengthening operational resilience in critical events.

Off-site manufacturing is emerging as an advantageous production model in the construction industry. In recent years, many tier 1 construction companies are including off-site production as part of their portfolio. Likewise, this change of model is attracting new entrants to the sector. The shift from the traditional on-site construction model to off-site manufacturing is unleashing positive impacts on projects in terms of cost, time efficiency, sustainability, and improved quality. Nonetheless, this phenomenon has yet to be analysed from the perspective of how this change in production processes influences the inclusion of services in company business models. This study explores whether and how industrialisation arises as an enabler for servitization in the construction sector. By means of an in-depth case study, our research identifies different product-service system (PSS) typologies associated with industrial construction and reveals their potential to increase additional services. Furthermore, the research sheds light on how industrial construction provides an opportunity to integrate offers and features that work towards reducing the environmental impact of construction projects and the operation and maintenance of built assets.

Purpose: As industries increasingly integrate Digital Twins (DTs) into their operations and service offerings, it becomes crucial to understand the specific technological and organizational capabilities associated with DTs and their role in effective Digital Service Innovation (DSI). This paper aims to contribute to the literature on DSI-enabling factors by examining the role of DT capabilities in driving DSI within organizations, highlighting the areas of interconnection. Design/methodology/approach: This study adopts a multiple case study approach, examining two organizations with different engagements with DTs: a consultancy engineering firm offering DT solutions, and a technology company specializing in DT services for different sectors. These diverse perspectives aim to provide a comprehensive understanding of the role of DTs in DSI in different organizational contexts. Findings: The study identifies distinct capabilities needed to develop and exploit DTs, as well as new capabilities acquired by organizations through DTs deployment. It reveals that several technical and non-technical DT capabilities overlap with established DSI capabilities, illustrating a logical pathway from DT capabilities to DSI. These insights are synthesized into a theoretical framework which illustrates how DT capabilities support and enable DSI. Practical implications: This research delineates critical DT capabilities and highlights which of these contribute to the effective deployment of DSI. These findings provide actionable strategies for organizations aiming to develop and strengthen DT capabilities to maximize their potential impact on DSI. Originality/value: This paper expands beyond the prevalent technical and engineering emphasis in DT literature by adopting a holistic capabilities-based approach. It provides an in-depth examination of how DT capabilities can drive DSI, delivering valuable insights for both academia and industry practice.

This exploratory study investigates how digital service innovation (DSI) can support environmental performance in industrial ecosystems. Drawing on a multiple-case study design comprising two cases, an advanced manufacturer and a knowledge-intensive business service (KIBS) provider, we analyse how digital, organizational, and institutional factors configure to enable outcomes aimed at sustainability. We draw on socio-technical systems theory and institutional theory, which together specify the internal readiness conditions and the external regulatory, funding, and normative pressures that determine whether digital services yield environmental gains.,The findings reveal two distinct but complementary pathways, a production-embedded model, where environmental performance emerges as a by-product of efficiency and compliance, and a service-intermediary model, where environmental performance is actively pursued through policy translation and knowledge brokerage.,These results advance understanding of DSI by showing that environmental performance depends not on individual enablers, but on their alignment within specific organizational and institutional contexts. The study contributes to research on digital servitization and sustainability, while offering guidance for managers and policymakers on how to design, govern, and scale digital services that generate measurable environmental value. Comparing two organizations that share a region, a regulatory regime, and a funding environment isolates what separates them, namely whether an actor in the revenue model purchases the environmental outcome. Where none does, environmental gains remain a by-product. Where public funding and compliance requirements constitute such a purchaser, they become the product. We offer this as a proposition for further testing rather than as a general theory.