Strategic drivers and business model innovation in cloud-based BIM

Building Information Modelling (BIM) is driving transformation in the built environment. While technical aspects have been widely explored, less is known about how cloud-based BIM (CBIM) supports business strategy and growth. This gap poses a challenge for organisations aiming to integrate digital innovation with competitive advantage. This study develops a CBIM business model framework, identifying strategic drivers for business expansion. Unlike prior BMC adaptations, the proposed framework integrates both microeconomic dimensions (e.g., pricing logic, competition, diversification) and macroeconomic dimensions (e.g., governance, sustainability, reskilling), explicitly linking firm-level strategies with broader institutional and digital infrastructure factors. A qualitative multi-case study was conducted, involving nine semi-structured interviews with CBIM experts across Germany, Finland, the Netherlands, and the UK. Participants represented both BIM consumers (e.g., construction companies and consultants) and BIM producers (e.g., software vendors and platform providers). Purposeful sampling ensured sectoral and role diversity. Data was coded and analysed thematically using Atlas. ti qualitative analysis software. The study identifies nine strategic drivers for CBIM business growth, including a redefined value proposition, customer awareness, cloud infrastructure, product and service pricing logic, distribution channels, human resource capabilities, strategic use of competition, diversification, governance and sustainability. The study proposes a CBIM-adapted Business Model Canvas framework, providing actionable insights for startups and SMEs, including guidance on integrating cloud pricing models and aligning internal capabilities with digital strategy such as guidance on integrating cloud pricing models and aligning internal capabilities with digital strategy, while also highlighting strategies that incumbents can adopt to maintain competitiveness in a rapidly digitising construction ecosystem.

1 Introduction

1.1 Background

Cloud-based Building Information Modelling (CBIM) refers to the application of BIM products and services through cloud computing. CBIM is increasingly shaping digital transformation in the construction industry. Unlike traditional desktop-based platforms, CBIM leverages cloud infrastructure to enhance scalability, flexibility, and interoperability (Moon, 2019; Sacks et al., 2022). Its application spans a wide range of construction-related activities, from early design and planning to project monitoring, collaboration, and decision-making (Zhao and Taib, 2022). As organisations adopt CBIM innovations, their role in enabling digital construction assets, enhancing coordination, and influencing business decisions has become more evident. However, despite these advances, a significant knowledge gap remains regarding how CBIM contributes to business growth strategies and long-term competitive advantage.

1.2 Digital transformation

The importance of addressing this gap lies in the broader context of digital transformation. Globally, digital disruption compels organisations to expand operations and develop new forms of competitiveness through technologies such as Big Data, Artificial Intelligence, the Internet of Things, smart factories, and cyber-physical systems (Kim, 2017). Within the Architecture, Engineering and Construction (AEC) sector, Industry 4.0 tools—including cloud computing, Augmented Reality, BIM, 3D printing, and robotics—are driving significant shifts in business practices (Newman et al., 2020; Akinradewo et al., 2024). BIM, understood as the integration of policies, processes, and technologies, has become central to digital construction, with a growing number of BIM-based services now hosted on cloud platforms. For instance, Sompolgrunk et al. (2024) develop models linking large BIM data sets with strategic decision-making in AEC organisations.

1.3 Research gap

Previous studies have provided valuable insights into CBIM’s technical, operational, and managerial dimensions. However, most have concentrated on technical implementation—such as interoperability, system integration, and collaborative workflows or on change management for adoption (Wang et al., 2023). While these studies enhance understanding of CBIM implementation, few have explicitly benchmarked business model innovation within digital construction ecosystems. Earlier BIM and SaaS-based business model research explored value delivery and collaboration structures but seldom examined how such mechanisms drive growth strategy (Hartmann et al., 2012; Mu and Antwi-Afari, 2024). More recent studies, such as focus on managerial and quality challenges, yet still overlook how cloud-enabled business models evolve competitively (Chang et al., 2022). This persistent gap motivates the present study.

1.4 Research aim and rationale

Therefore, this study aims to develop a CBIM Business Model Framework that identifies the strategic components necessary to enable incremental shifts in organisational growth. By benchmarking against previous BIM and SaaS business model studies, it introduces an adapted BMC that incorporates both microeconomic (e.g., pricing, competition, diversification) and macroeconomic (e.g., governance, sustainability, reskilling) layers, making it superior to the original BMC, which focused solely on firm-level mechanisms. This dual-level structure allows the CBIM-BMC to address both internal and external factors shaping digital business transformation. The adapted framework extends the original BMC by incorporating new strategic dimensions, such as digital reskilling, cybersecurity, and sustainability, that reflect the dynamic, data-driven, and multi-stakeholder realities of CBIM operations. Unlike the traditional BMC, which primarily explains value creation and delivery, the CBIM-BMC connects business strategy with digital transformation outcomes.

1.5 Paper structure

After this introductory section, Section 2 discusses the relevant literature. Section 3 presents the research methodology. Section 4 introduces the CBIM Business Model Framework and defines key components from the perspectives of BIM producers and consumers, exploring their significance for business growth strategy. Section 5 concludes the study, and Section 6 presents limitations and recommendations for future work.

2 Literature review

2.1 Cloud-based building information modelling

Building Information Modelling (BIM) integrates people, technology, and processes to enhance project outcomes through digital representations of AEC facilities (Ghaffarianhoseini et al., 2016). As BIM capabilities evolve, the demand for offsite integration, advanced visualization, and cloud-based collaboration has intensified (Chuang et al., 2011). Cloud-based BIM (CBIM) extends this by leveraging cloud computing to enable scalable, flexible, and interoperable digital workflows. It is defined y as an on-demand model for shared computing resources (Mell and Grance, 2011; Technion, 2020). Therefore, CBIM operates as both a product (the digital twin) and a process (continuous collaboration, management, and decision-making).

CBIM operates within multi-actor ecosystems, including software vendors, service companies, and BIM users (construction firms) (Warsame, 2006; Eriksson, 2007; Müller, 2019). Integrating insights from platform ecosystems, SaaS pricing, and coopetition theory highlights how these actors interact strategically to drive growth and competitiveness. Walterbusch, Martens and Teuteberg (2013) Demonstrate that SaaS pricing and governance models influence the creation of value in digital ecosystems. Likewise, Chun, (2019) highlights how usage-based pricing enhances financial scalability in cloud service environments. Also, Mazrekaj, Shabani and Sejdiu (2016) argue that value-based pricing links revenue directly to perceived digital performance.

Moreover, Industry 4.0 technologies, cloud computing, augmented reality, 3D printing, robotics, and AI, further enable digital transformation, while integrated CBIM workflows and advanced fabrication methods can reduce GHG emissions in construction (Newman et al., 2020; Akinradewo et al., 2024; Oladunni et al., 2025). Despite these advances, the economic and strategic dimensions of CBIM as a business model remain underexplored. This study aims to develop a CBIM-adapted Business Model Canvas (BMC) that identifies strategic growth drivers, offering actionable guidance for both SMEs/startups and incumbents in navigating digital transformation, while extending the traditional BMC to include microeconomic (firm-level) and macroeconomic (institutional and regulatory) considerations. The adapted framework is superior to the original BMC as it explicitly integrates cloud-service monetisation, platform logic, and construction-specific regulatory dynamics, enabling more precise strategy formulation in CBIM ecosystems.

2.2 Business model and business growth strategy

2.2.1 Conceptual relationship between business model (BM) and strategy

The alignment between business models and strategy has become increasingly dynamic in the era of digital transformation. Rather than functioning as independent constructs, the business model (BM) and business strategy operate as interdependent mechanisms for digital decision-making and organisational competitiveness. A BM explains how value is created, delivered, and captured within a network, while strategy defines the choices that position the firm within a competitive and policy-driven environment identify value disciplines, operational excellence, product leadership, and customer intimacy, as foundations for digital business model alignment (Wiersema and Treacy, 1993; Seddon and Lewis, 2003; Teece, 2010).

In conventional firms, this relationship is relatively stable, strategies guide business model design, and business models operationalise strategic intent. However, Cloud-based Building Information Modelling (CBIM) disrupts this linear alignment. Because CBIM integrates multiple stakeholders across cloud infrastructures, data pipelines, and lifecycle processes, strategic decisions increasingly depend on platform orchestration, data governance, and interoperability rather than on static market positioning. The CBIM environment, therefore, transforms BM–strategy alignment from a top-down directive to a multi-actor, adaptive configuration, where producers and consumers jointly shape value creation and capture.

This synthesis marks a paradigm shift in CBIM ecosystems, the business model becomes a living component of strategy, continuously refined through cloud-enabled feedback loops, API integrations, and user-driven innovations. Such alignment enables firms to transition from fixed competitive boundaries to dynamic digital ecosystems that support resilience, sustainability, and cross-sector collaboration. (Schön, 2012). emphasises reflective practice as key to continuously aligning business models with changing technological contexts.

Empirical evidence reinforces this transformation at both the micro and macro levels. Studies indicate that economic growth, digitalisation, and emissions trajectories are dynamically interlinked through technological infrastructures such as CBIM (Ansoff, 1957; Oladunni et al., 2024). As a result, CBIM adoption evolves into a strategic policy instrument—balancing productivity with decarbonisation and sustainability goals. This convergence connects firm-level digital strategy with national innovation and low-carbon development objectives, positioning CBIM as both an operational enabler and a macro-economic catalyst.

2.2.2 Growth strategy typologies for CBIM

Growth strategies in CBIM environments extend beyond traditional frameworks of market penetration, diversification, and product development (Ansoff, 1957). CBIM’s cloud-centric architecture introduces new strategic logics built around connectivity, co-creation, and platform scalability. Rather than competing through market dominance, organisations grow by expanding collaborative networks and enhancing digital interoperability across value chains. Similarly, Verhoef et al. (2021) identify three digital growth paths particularly relevant to CBIM platform development, platform-based market penetration, and co-creation platforms. These strategies emphasized ecosystem expansion and user participation as primary growth levers. Also, Grönroos and Voima (2013) and Bohnsack and Pinkse, (2017) noted that within CBIM, co-creation manifests through shared project data, federated models, and real-time collaboration environments that allow both internal and external stakeholders to contribute to value creation note that firms must balance sustaining and disruptive innovation when pursuing business model growth in digital ecosystems.

CBIM also supports digital diversification, where firms establish autonomous digital units or spin-offs to accelerate innovation and experimentation (Abd Rahim et al., 2022; Eggers and Park, 2017. This diversification fosters digital functional expertise and opens new business model pathways—such as Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), and Data-analytics-as-a-Service (DaaS), that expand revenue streams beyond traditional project delivery (Srinivasan and Venkatraman, 2018).

Finally, sustainability-driven growth integrates environmental, economic, and social considerations into the CBIM framework. As construction digitalisation contributes to global decarbonisation efforts, CBIM becomes a strategic medium for achieving sustainable growth Ebekozien et al., 2024; Martino, 2021) Embedding sustainability within CBIM growth strategies ensures that digital transformation simultaneously supports competitiveness, policy compliance, and the low-carbon transition.

Collectively, these typologies indicate that CBIM transforms growth strategy from a firm-centred expansion model to an ecosystem-enabled transformation model. Martino (2021) stresses the role of digital channels and blockchain in strengthening customer relationships. Here, success depends on orchestrating multi-stakeholder collaboration, sustaining platform innovation, and aligning digital evolution with sustainability imperatives.

2.3 Theoretical framework: the business model canvas

The Business Model Canvas (BMC) by Osterwalder and Pigneur (2011) remains a foundational framework for mapping how firms create, deliver, and capture value through nine core components: value proposition, customer segments, channels, customer relationships, revenue streams, key resources, key partners, and cost structure, as described in Table 1.

Table 1

Table 1. The Business Model Canvas building components and their description (Osterwalder and Pigneur, 2012).

Sectoral adaptations of the BMC—such as the Triple-Layered BMC (Joyce and Paquin, 2016), construction-oriented reconfigurations (Das et al., 2020), and low-carbon versions (Brown, 2018) demonstrate the framework’s flexibility. However, none integrate cloud-service economics with the regulatory complexity of construction. The CBIM-BMC bridges this by embedding platform economics (tiered pricing, coopetition) within institutional and procurement contexts that define AEC digitalisation.

Macro-level studies of emission drivers and governance readiness further underscore how policy, infrastructure, and skills shape the sustainability outcomes of digital transformation (Ebekozien et al., 2025; Oladunni and Olanrewaju, 2022). Similarly, government-driven initiatives, such as Germany’s BIM mandate for all federally funded projects by 2025 Mitera-Kiełbasa and Zima (2024), and national digital infrastructure policies Alaraj (2025) demonstrate the institutional pressures guiding CBIM scalability. Broader economic shocks also influence reskilling and institutional adaptation in construction (Ebekozien and Samsurijan, 2024). As an implication, the study validates the importance of considering macroeconomic variables in business model frameworks, especially for sectors like construction that straddle formal and informal economies.

The microeconomic block, therefore, explains firm-level strategy, while the macroeconomic block situates these dynamics within the wider institutional, infrastructural, and environmental context. Together, they form a dual-level analytical instrument enhancing the BMC’s explanatory and predictive power for CBIM firms.

To maintain conceptual clarity, strategic drivers such as competition intensity, coopetition, and diversification are treated as market-level influences rather than formal BMC components. Within this framing, the CBIM-BMC extends business model innovation theory Zott and Amit (2010), demonstrating how cloud-based BIM firms reconfigure value creation and governance across Industry 4.0 ecosystems as described in Figure 1. Therefore, by integrating these new blocks, the adapted BMC provides a dual-level analytical instrument. The microeconomic block addresses endogenous firm-level economic logics, while the macroeconomic block situates these within broader institutional and infrastructural landscapes. This holistic refinement enhances the explanatory and predictive capabilities of the BMC for CBIM business models in a digitally evolving built environment.

Figure 1

Figure 1. The Adapted Cloud-BIM Business Model Canvas (CBIM-BMC) showing microeconomic and macroeconomic extensions to the traditional BMC.

3 Methodology

This study employed a qualitative, descriptive, and exploratory research design. Such an approach is well-suited to investigating the complex and underexplored phenomenon of how cloud-based Building Information Modelling (CBIM) business models evolve and operate, where the aim is to generate rich, context-specific insights rather than statistical generalisation (Eisenhardt, 1989). Given the limited prior empirical work on CBIM business models, an exploratory strategy allows flexibility to probe participants’ experiences, while a descriptive lens enables systematic mapping of how business model elements interrelate in practice. This approach is therefore most appropriate for answering the research questions, which centre on identifying drivers of growth strategies and explaining mechanisms of CBIM business model change.

The interview guide was developed from an extensive review of literature on CBIM, digital innovation, and business model theory. Core themes were structured around the nine constructs of the Osterwalder Business Model Canvas, ensuring theoretical grounding, while additional prompts reflected contemporary issues such as sustainability, digital reskilling, and cybersecurity. Semi-structured questions were semi-tailored to reflect participants’ roles and organisational contexts, allowing both comparability across cases and depth of probing. (A list of sample interview questions and interview protocol is included in Appendix A).

Empirical data collection proceeded in two stages. First, exploratory pilot interviews were conducted with four business managers working in CBIM-related roles across the United Kingdom, the Netherlands, and Finland. Insights from these unstructured discussions informed refinement of the main interview guide. Second, semi-structured interviews were carried out with nine senior professionals, five from BIM-producing firms (Producer Managers, PMs) and four from construction firms (Consumer Managers, CMs). Participants were purposefully selected to meet explicit criteria: (i) seniority in decision-making roles, (ii) direct expertise in BIM implementation or CBIM service delivery, and (iii) strategic involvement in business operations.

A multiple-case study design was adopted to identify similarities and differences across companies and to trace how Business Model Canvas components shape growth strategies (Miles and Huberman, 1994; Eisenhardt and Graebner, 2007). All interviews were transcribed and analysed in Atlas. ti 23 using a three-stage coding process (Charmaz and Belgrave, 2018). First-order codes captured informant-centric terms and phrases (e.g., “Data-sharing reluctance,” “Integration of tools and systems”). These were clustered into second-order themes (e.g., “Workforce adaptability,” “Operational efficiency”), which were then synthesised into aggregate dimensions representing nine strategic drivers of CBIM business model evolution as seen in Table 2. The study employed the principle of theoretical data saturation to determine the final sample size. Data collection continued until a point where no new major themes or concepts emerged from successive interviews. Saturation was assessed iteratively, confirming that the ninth interview yielded largely redundant information and did not significantly contribute novel insights beyond those already gathered from the preceding participants.

Table 2

Table 2. Coding framework for CBIM strategic drivers.

A researcher independently coded a subset of transcripts to establish an initial coding dictionary. Inter-coder reliability was assessed through iterative comparison and discussion until >85% agreement was achieved, after which the lead author coded the remaining data using the agreed framework. Constant comparison between cases enabled both intra-case and inter-case analysis, strengthening the validity of emerging patterns.

To enhance credibility and confirmability, preliminary findings and the coding structure were shared with three participants for member checking, and peer debriefing sessions were held with three external qualitative researchers. An audit trail of coding decisions and memos was maintained in Atlas. ti to ensure transparency. Although the small sample limits statistical generalisation, the inclusion of both Producer and Consumer cases from different European contexts provides analytical generalisation to CBIM markets facing similar cloud-based transformations. All participants provided informed consent, and interviews were conducted in accordance with ethical research guidelines. Identifying details were anonymised and organisational names replaced with codes to protect sensitive information.

4 Results and discussion

The result of the interview was analysed using the AtlasTi quantitative software. This study followed Bond et al. (2012) by communicating the outcome of the study to participants for approval. Following Osterwalder and Pigneur (2012), Brown, (2018), Schön (2012) and Das et al. (2020), this study synthesizes the components of the BMC into a conceptual framework in Figure 2. Managers from the CBIM producing organisation (Software vendors and Database providers) are referred to as “PM”, while managers from the consumer organisations are “CM”. The discussion below links participants’ responses directly to strategic growth implications, while situating findings in existing literature.

Figure 2

Figure 2. Research methodology.

4.1 Value proposition: Product and service; customer needs

The value proposition is the central element through which CBIM firms create and deliver unique value, and is essential for strategic growth (Bohnsack and Pinkse, 2017). As discussed in Section 2.2.1, aligning business models with strategy requires adaptable value propositions, as seen in PMs’ shift from static desktop products to dynamic cloud-enabled services. For example, PM2 noted:

“Historically, we have been a desktop license company; now, our products and services are everything that concerns cloud, BIM, and databases.”

This is consistent with previous research emphasizing the importance of adjusting value propositions in industries facing technological disruptions (Ansoff, 1957). This transformation allows for platform-based delivery, real-time data workflows, and API integration, expanding the firm’s market reach and entry into new markets (Ansoff, 1957).

Similarly, a CM 1 described how CBIM has altered their operational coordination: “Traditionally we coordinate every process on the construction site, which resulted in data silos. Now, with BIM workflows within the BIM methodology, and our current business model, is to handle all silos with the different information and linked via APIs.” This reflects a move from fragmented, manual coordination to digitally integrated project delivery, reinforcing the concept of operational value embedded in the CBIM model.

Furthermore, despite offering different digital products and services, both organizations target similar client segments, including public infrastructure agencies and private real estate developers. This dual-service model resembles a multi-sided platform where CBIM providers must deliver value to two distinct user groups: consumers (e.g., contractors, clients) and producers (e.g., software developers, data integrators). These findings align with the strategic principles of the Ansoff Matrix, focusing on market penetration and product development as key growth strategies (Ansoff, 1957). By redefining their value propositions, both firms have enhanced their product offerings, such as real-time collaboration tools, cloud-connected models, and subscription services, positioning them for expansion in existing and new markets as depicted in Table 3.

Table 3

Table 3. Comparison of PM vs. CM perspectives on value propositions.

As PM4 noted:“Clients want data they can use in real-time, not after the project is over. That’s where our CBIM tools come in—automating that value delivery while reducing rework.”

This operational integration improves customer satisfaction and enables firms to capture additional value throughout the construction lifecycle. Overall, these findings suggest that redefining the value proposition in CBIM businesses drives growth by enhancing service offerings, increasing customer engagement, and scaling multi-sided platforms.

4.2 Revenue model: Pricing logic; channels; customer relationship

The revenue model of a business outlines how value is monetized, services are delivered to customers, cash is generated, and the relationships needed to sustain the value exchange. In the context of digital platforms, especially in cloud-based Building Information Modelling (CBIM), the revenue model is closely linked to platform logic. Kim and Yoo (2019) characterize such businesses as platform-based, where value is co-created through interactions between various user groups, primarily service providers and consumers. This connection directly ties empirical findings back to theoretical literature.

4.2.1 Pricing logic

For CBIM vendors, the cloud serves as the digital infrastructure supporting service models like Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), which influence pricing mechanisms and channel strategies.

PM 3 emphasized the impact of digital transition on pricing logic, stating: “Due to new software versions in between with product enhancement and increment, there came a major shift through subscription models since we now use platform technology for the built environment.”

This viewpoint reflects an industry-wide trend towards subscription-based pricing models, replacing traditional one-time license fees with recurring charges. These subscriptions can vary in duration, from monthly to biannual or multi-year contracts, providing greater flexibility for both vendors and clients. This aligns with Walterbusch, Martens and Teuteberg (2013), who argued that SaaS pricing models are often structured as complete packages with fixed rates, while Chun (2019) highlighted usage-based pricing as common in PaaS offerings.

PM 2 noted the diversity of pricing models adopted to reflect customer and project variability: “We are in a value-based market, with a range of pricing models like user model, total project model, and framework model.”

Such models reflect the value-based pricing approach described by Mazrekaj, et al. (2016), where pricing is not merely cost-driven but is tied to perceived value and strategic objectives. Further, for firms seeking growth, a consumption-based or pay-per-use model offers scalability and cost transparency: “The pay-per-use pricing running on the consumption-based model helps us match pricing with actual client usage, which they appreciate.”

In contrast, CM 3 drew attention to the incompatibility of traditional pricing models with emerging digital approaches: “A digital business model like SaaS can’t thrive in an existing brain-per-hour business model.”

This underscores the conflict between legacy models based on hourly billing and the risk-managed service pricing structure of cloud ecosystems. CM 4 added: “Risk appetite and the risk management in a new business model like SaaS is higher compared to the traditional business model.”

These viewpoints emphasise the necessity for strategic realignment in revenue modelling as firms shift from analogue to digital economies as shown in Table 4.

Table 4

Table 4. PM versus CM perspectives on pricing logic Matrix.

These findings indicate that CBIM firms’ pricing strategies are increasingly aligned with platform economics: subscription and usage-based models foster network effects, reduce switching costs, and enhance multi-sided platform adoption.

4.2.2 Channels and customer relationship

Channels and customer relationships play a crucial role in the revenue model, impacting customer acquisition, retention, and lifetime value. PM 1 described their channel strategy as twofold:

“The first is customer relationship that is already established through company acquisition and customer retention in a large base. The second channel is a new acquisition, which often happens when customers inform themselves because topics like BIM, common data environment (CDE), digital twin are in vogue.”

This illustrates a hybrid approach that combines organic customer discovery with a strong foundation of legacy relationships, creating a multi-channel environment for engagement and growth.

Customer relationships, as echoed by both PMs and CMs, are considered a strategic growth lever:

“We have a fantastic relationship with our customers because we put our customers first. When customers trust us and invest in us, we ensure they have a positive experience.”

This customer-centric approach fosters long-term engagement through value assurance, responsive service, and integration into broader service agreements:

“We exceed their expected value in return, and with a very good user experience, we link them into a long-term relationship… we help customers when needed, and also help them with a completely new experience, especially as related to digitalization of the business model.”

This customer-focused model aligns with recent research by Martino (2021), which highlights how blockchain-enabled BIM platforms have transformed distribution channels and customer relationship strategies in digital business models. By leveraging technology, strategic pricing, and customer interaction logic, construction businesses can enhance revenue generation and customer engagement in the digital age. Digital platforms have the potential to revolutionize distribution and engagement strategies in the construction industry.

4.3 Cost model: core-assets and capabilities; core activities

In traditional business models, the cost structure outlines the significant operational and strategic costs an organization must bear to deliver its value proposition. In the context of cloud-based Building Information Modelling (CBIM), the cost model is not just a financial component but also a strategic foundation reflecting the organization’s investment in digital infrastructure, talent, and processes. Our study finds that in CBIM, the cost model needs to be viewed as an integrated perspective of core assets, core capabilities, and core activities that support competitive advantage and growth. This connection links empirical findings to theoretical literature as depicted in Figure 3.

Figure 3

Figure 3. Cost Enabler Versus Cost Constraints for Scalable CBIM delivery.

4.3.1 Core assets and capabilities

Core assets include tangible infrastructure and intangible capabilities necessary for providing CBIM services, such as digital infrastructure, hardware, intellectual capital, and software tools. CM 5 explained: “Our cloud product needs to be hosted somewhere, right? So, we use the Amazon platform to host our technology.”

Similarly, PM 4 highlighted the role of fundamental digital tools and communication infrastructure:

“We use physical resources, have good laptops and cell phones to communicate with the client, and of course, the right software.”

These digital assets represent strategic investments and align with the concept of dynamic digital capabilities (Verhoef et al., 2021). Beyond infrastructure, human capital and technical knowledge emerged as key intangible resources.

CM 3 emphasized: “If you want to implement BIM technologies or BIM methodologies, then you need technical know-how.”

PM 2 added that the success of digital transformation depends on a layered human resource approach:

“We need people who have a wide overview of the company and the processes, but also depth in technology. Our senior consultants advise junior BIM consultants on how to do certain things.”

These observations support earlier scholarship on core competencies—rare, valuable, and hard-to-imitate resources that drive sustained competitive advantage (Amit and Schoemaker, 1993; Barney, 2016). Similarly, CBIM ecosystem’s cost model can be categorized into cost enablers and cost constraints. Cost enablers are Cloud infrastructure, software licenses, APIs, technical workforce, AI analytics while the Cost constraints are High initial investment in digital infrastructure, specialized talent acquisition, integration complexity. These assets and capabilities form the backbone of the digital value chain, shaping strategic advantage across key BMC components, including key resources, partners, and revenue streams. This resonates with the study that emerging AI techniques, such as deep stable learning for fault diagnostics, illustrate how advanced analytics can further strengthen CBIM platforms by improving reliability and decision-making (Wu et al., 2025).

4.3.2 Core activities

Complementing the capabilities are the core activities, the routines, systems, and workflows through which firms utilize their assets to deliver value. In CBIM environments, these include digital process modeling, information coordination, customer support, and solution deployment.

CM 2 highlighted the importance of aligning internal and external activities:

“There are internal and external processes, and the strategy we have devised to keep them is to ensure that people are being appreciated.”

PM 3 echoed this sentiment, connecting employee culture and retention to core operations:

“The digital team wants to ensure that they are somewhere they are appreciated and where there is a culture.”

These insights underscore the link between organisational culture, employee engagement, and effective resource application. The CBIM BMC’s core activities reflect talent retention strategies linked to digital delivery, Internal capability development, and process transformation, as well as cross-functional collaboration between digital and operational teams. These activities are strategically configured to maximise the impact of costly and rare resources and to sustain innovation in a rapidly evolving construction ecosystem.

4.4 Microeconomics: potential competition; diversification

In the digital construction industry, especially within cloud-based Building Information Modelling (CBIM) ecosystems, traditional firm-level strategies are no longer enough for sustained growth and competitiveness. This study connects empirical findings to theoretical literature by identifying microeconomic forces, such as competition and diversification, as crucial additions to the CBIM Business Model Canvas (BMC). It acknowledges that organizational decisions are influenced not only by internal configurations but also by firm-level behaviors in dynamic markets.

4.4.1 Potential competition

The integration of digital tools with traditional project delivery methods shapes the competitive landscape in CBIM-enabled environments. With technological capabilities becoming more accessible in the AEC sector, firms face increasing horizontal competition from established players and digitally native entrants.

As PM 3 observed: “We, of course, do have competitors; there are also competitors that are quite similar to us in terms of doing design and other services, while others are young enterprises.”

This observation highlights the market dynamics resulting from the widespread use of digital tools and cloud platforms. However, the expert also mentioned the challenges of strategic collaboration in a competitive environment: “It is also important to work well with competition, but that is hard because there is a lack of trust in the construction industry with each other. It is a partnership in a strategic way.”

This tension underscores the importance of viewing competition not just as a threat but as a strategic asset, especially in situations where collaborative innovation or coopetition (cooperative competition) is necessary for significant digital transformation. The microeconomic aspect of competition in the CBIM-adapted BMC encompasses market saturation, customer behavior, strategic partnerships, and shared platforms, as well as competitive positioning based on speed, scale, or service quality. By including these components, business leaders have a practical tool to assess competitive dynamics in real-time and adjust their value propositions, partnerships, or cost structures accordingly.

4.4.2 Diversification as a digital growth strategy

Our study also highlights diversification as a key response to market saturation, price erosion, and business model obsolescence. Particularly for firms shifting from legacy service models to SaaS-based offerings, diversification enables adaptability. CM 4 explained:

“Our competitors are using the same tools, so the price customers are charged reduces. On a profit level, we do not benefit anymore from being more efficient.” To address this, firms are innovating beyond their core service lines:

“We build our solutions to augment our services, to automate it ourselves with our domain knowledge, and potentially bring those digital solutions to the market in business models, as SaaS solutions or others.”

This mirrors a dual-structure diversification model, where new business models are incubated independently of the legacy structure:

“We put a separate company or innovation hub in a place where we can position new business models and bring new business models to the market and learn how that is being done.”

Such strategic diversification allows firms to Experiment with lean innovation units outside of core structures; Transition gradually from labor-intensive to digitally automated business lines, and Retain competitive positioning despite price compression in core services.

In the CBIM BMC, the microeconomic block on diversification captures New digital service lines (e.g., SaaS products from internal innovations); Spin-off ventures and innovation labs, and Multi-business unit strategies for hedging against digital disruption.

4.4.3 Core activities (traditional BMC), micro and macroeconomics (CBIM BMC): a distinction

Core activities in the traditional BMC refer to primary operations required to deliver value, the microeconomic block introduced here goes deeper by examining strategic market-facing decisions like competition, pricing, and diversification. An example is where C4 indicated that:

“We propose a new business model where we create digital solutions for clients to access our domain knowledge. So, in the end, our clients are using our created digital solutions to access our domain knowledge”.

These are not operational actions but economic configuration choices that determine how value is monetised, defended, or scaled in digital ecosystems. For instance, adopting a SaaS-based pricing model or launching a spin-off innovation unit are not routine activities, but strategic market adaptations tied to economic logic as shown in Table 5.

Table 5

Table 5. Distinction between core activities (traditional BMC) and microeconomics (CBIM BMC).

4.5 Macroeconomics: governance; sustainability; reskilling

Expanding the conceptual scope of the CBIM Business Model Canvas (BMC), this study suggests incorporating macroeconomic elements to account for the broader institutional and environmental factors influencing the sustainability of digital business models. Unlike microeconomic aspects that focus on individual firm behavior, macroeconomic influences such as governance frameworks, sustainability requirements, and workforce upskilling impact how organizations position themselves within national and global transformation initiatives. These factors are particularly crucial in digitally-driven sectors like cloud-based Building Information Modelling (BIM), where market access and innovation adoption are shaped by regulations, data policies, and societal acceptance. This integration bridges empirical findings with theoretical literature.

4.5.1 Governance and regulatory environment

The design of business models in digital construction relies heavily on institutional structures like cybersecurity protocols, data governance, and privacy regulations. Public policies such as GDPR enforcement, cybersecurity legislation, and investments in digital infrastructure directly shape the configuration of key resources, partnerships, and customer relationships within business models (Smith, 2007; Zott and Amit, 2010).

CM 4 emphasised: “Data security is more important for the (digital) business model. There should be awareness of cybersecurity attacks because of our vulnerability in a digital environment.”

Similarly, PM 4 noted the regional legal expectations: “This security is not only about cybersecurity, it is also about certainty. This also means we fulfil GDPR requirements here in Europe locally.”

These observations underscore the critical role of institutional compliance and trust-building in driving business model scalability and customer confidence. The macroeconomic governance block of the CBIM BMC encompasses regulatory compliance (e.g., GDPR, data sovereignty), trust-building mechanisms, national digital infrastructure maturity, and cybersecurity preparedness—essential elements for firms aiming for long-term success in increasingly digitized construction markets. Table 6 depicts this.

Table 6

Table 6. Policy and institutional implications.

4.5.2 Sustainability and environmental strategy

Sustainability emerged as a significant macroeconomic driver of innovation and future business orientation. Interviewees described sustainability not as a peripheral value but as central to business model transformation. CM 4 stated:

“We also want to go more digital. The plan is to combine digitalisation and sustainability. We are looking futuristic by being a big digital giant and also helping our clients in having their assets to be carbon-neutral, zero, and resilient.”

In describing the future trajectory of the business model, CM 3 added:

“In the future, our business model will change to construction-as-a-service. We sell the building alongside data for the client.”

This aligns with the concept of sustainable business models that embed environmental goals into value propositions and revenue strategies (Nidumolu et al., 2009; Stubbs and Cocklin, 2008). The CBIM-adapted BMC’s sustainability block reflects.

Additionally, from a technological standpoint, PM 4 emphasized the role of artificial intelligence (AI) in driving sustainable outcomes: “The key value is thinking of moving forward, and this was always within the topic of machine learning. I think that is going to be the next big disruption.”

This reflects research positioning AI and machine learning as critical enablers of SDG attainment through optimization and automation (Vinuesa et al., 2020). In CBIM-enabled construction, sustainability is not only a compliance goal but also a strategic differentiator. Finally, both PM and CM respondents highlighted emerging client demands for low-carbon workflows and compliance with green procurement mandates. This reinforces the findings of Stubbs and Cocklin, (2008) and Oladunni, Olanrewaju and Lee (2024), positioning CBIM as a strategic enabler of sustainable growth within national decarbonisation frameworks.

4.5.3 Reskilling and workforce development

The implications of digital reskilling and upskilling are crucial for organizational growth and national competitiveness, as highlighted in expert opinions. To successfully transition to CBIM platforms, companies need to address the skills gap by investing in continuous training, digital literacy, and change management. Research suggests that effective workforce digital adoption requires structured frameworks for competency development, such as formal training programs, e-learning platforms, and collaborative knowledge networks (Ebekozien et al., 2025; Akinradewo et al., 2024; Ebekozien & Samsurijan, 2024).

Macroeconomic readiness for digital adoption is influenced by education policy, government-supported workforce development incentives, and talent availability, which directly impact the Key Resources and Key Activities blocks of the CBIM BMC. The macroeconomic reskilling dimension includes Government-supported training initiatives (e.g., digital literacy subsidies, upskilling programs), Organizational learning systems (continuous training, internal knowledge sharing and Industry-academic partnerships for digital capacity building.

These measures enable the human infrastructure necessary to implement and sustain new digital business models while enhancing innovation capability. The integration of structured reskilling ensures firms remain competitive in a rapidly evolving CBIM ecosystem, fostering workforce adaptability and long-term organizational resilience. Figure 4 depicts the CBIM BMC framework, and Figure 5 shows the macro-level enablers in CBIM Business Model Implementation. Table 7 depicts the CBIM business model as a conceptual framework while Table 8 summarizes the Key impacts of CBIM Business Model Canvas block based on the five thematic areas.

Figure 4

Figure 4. Cbim BMC conceptual framework.

Figure 5

Figure 5. Macro-level enablers in CBIM business model implementation.

Table 7

Table 7. CBIM business model canvas.

Table 8

Table 8. Key impacts of CBIM Business Model Canvas block based on the five thematic areas.

5 Conclusion and significance of the study

5.1 Theoretical contribution

This study makes a substantial theoretical contribution by extending the Business Model Canvas (BMC) to the context of Cloud-Based Building Information Modelling (CBIM) within the Architecture, Engineering, and Construction (AEC) industry. It conceptualises a dual-level framework that integrates both Microeconomic and Macroeconomic dimensions, allowing researchers to examine not only firm-level innovation dynamics but also the institutional and systemic conditions that shape digital transformation.

The introduction of these two additional layers enhances the analytical scope of the BMC. The Microeconomic layer facilitates the study of market behaviours, pricing logic, demand elasticity, and coopetition strategies, capturing how firms respond to competitive pressures in digital construction ecosystems. The Macroeconomic layer, in contrast, embeds broader governance, sustainability, and reskilling considerations, offering a theoretical bridge between organizational strategy and policy infrastructure.

Furthermore, the study reframes the Value Proposition in CBIM contexts as dynamic and platform-based, emphasizing adaptive services, API integration, and data-driven customer engagement. The Cost and Revenue Models are also reconceptualised as strategic growth enablers, linking digital infrastructure, human capital, and trust-building mechanisms to organizational performance. Collectively, these refinements advance the theoretical positioning of the BMC as a living framework, one that evolves alongside technological innovation, institutional change, and market fluidity in the AEC sector.

5.2 Managerial implications

From a managerial perspective, the CBIM-adapted BMC provides a practical decision-making tool for digital transformation in construction enterprises. Managers can employ the framework to identify critical success factors and align them with relevant business model components, ensuring coherence between operational capabilities and strategic ambitions (as seen in Figure 6).

Figure 6

Figure 6. Operationalisation of CBIM-BMC by firm size.

The findings reveal that competition in CBIM environments is both dynamic and cooperative, requiring firms to engage in coopetition through shared digital platforms and data ecosystems. Managers are therefore encouraged to view competitors as potential collaborators in scaling digital services and achieving interoperability. Likewise, the shift from traditional “billable-hour” models to SaaS-oriented or pay-per-use pricing strategies calls for new monetization approaches centered on data value, subscription flexibility, and trust-based relationships.

Another central implication concerns reskilling and organizational learning. The study highlights digital talent as a strategic resource that directly influences the Key Resources and Key Activities of the CBIM-BMC. Managers should institutionalize continuous digital upskilling through structured training programs, AI-assisted learning platforms, and collaborative knowledge networks. Furthermore, to maintain agility and avoid legacy entanglement, organizations should operationalize innovation through dedicated digital units—such as internal CBIM task forces, innovation labs, or digital-twin teams—that can pilot new business logics under controlled risk conditions.

5.3 Policy implications

At the policy level, the findings underscore the need for coordinated institutional frameworks that support the digitalization of the construction sector. The Macroeconomic layer of the CBIM-BMC illustrates how national policies, infrastructure investment, and regulatory certainty collectively determine the pace and scale of CBIM adoption.

Governance mechanisms such as GDPR compliance, data sovereignty laws, and cybersecurity standards enhance institutional trust and are essential for scaling cross-border digital collaboration. Similarly, public investment in digital infrastructure, including broadband connectivity, open BIM standards, and interoperability platforms, creates an enabling environment for innovation diffusion. In addition, environmental and sustainability policies play a pivotal role. Integrating sustainability into digital business models not only aligns with global climate goals but also supports long-term competitiveness.

Finally, reskilling initiatives and workforce policies are vital for national readiness. Governments should prioritize digital education, industry–academic partnerships, and capacity-building programs to ensure that human capital development keeps pace with technological evolution. By aligning policy instruments with industrial innovation, regulators can accelerate the transition towards a sustainable, data-driven construction economy.

5.4 Integrative reflection

By bridging internal business logic and external policy frameworks, the revised CBIM-BMC acts as a comprehensive guide for theory, practice, and policy. It helps:

• Researchers contextualize business model innovation in digital ecosystems.

• Managers operationalize CBIM strategies for agile, sustainable growth.

• Policymakers align governance, sustainability, and workforce programs with digital transformation goals.

In doing so, this study repositions the Business Model Canvas as a living framework—one that evolves alongside technology, institutional policy, and market dynamics in the global construction industry.

This visual framework contrasts the startup pathway (niche offerings, agile iteration, SaaS/Pay-per-use revenue, microeconomic levers) with the incumbent pathway (portfolio integration, innovation hubs, cost-model optimization, macroeconomic levers), both converging toward an Operationalized CBIM-BMC. It highlights the distinct yet complementary strategies that different organization types can employ to capture value in cloud-based BIM ecosystems across global contexts. This study offers a significant contribution to the understanding and advancement of digital business models within the Architecture, Engineering, and Construction (AEC) industry by adapting and extending the widely used Business Model Canvas (BMC). Specifically, the study contextualizes the BMC for cloud-based Building Information Modelling (CBIM) enterprises, proposing a revised framework that reflects the operational complexities and strategic demands of the digital construction environment.

6 Limitation and further research

This study has several limitations that should be acknowledged.

6.1 Sample and regional scope

The research draws on insights from senior professionals across five large organisations in Northern and Western Europe. While the participants offered globally informed perspectives, this regional concentration may limit generalizability. Therefore, Future studies should include diverse contexts—especially in Asia, North America, and Sub-Saharan Africa, to capture regional variations in digital infrastructure maturity, institutional frameworks, and BIM adoption levels.

6.2 Methodological constraints

The study employed a qualitative, exploratory approach, which provided in-depth insights but limited empirical validation of the proposed CBIM-BMC framework. Hence, future research should employ mixed-method validation, integrating large-scale surveys, case studies, and structural equation modelling (SEM) to test relationships among constructs such as interoperability, cybersecurity readiness, and business performance.

6.3 Temporal dynamics

Digital transformation in construction is continuous and adaptive; hence, cross-sectional data may not capture long-term business model evolution. In that case, Longitudinal studies are recommended to track how CBIM value propositions, revenue models, and cost structures evolve under shifting technological and regulatory pressures.

6.4 Stakeholder diversity

Current findings reflect the perspectives of organizational professionals but may underrepresent other key ecosystem actors. We recommend that Future research could also incorporate a more diverse stakeholder base, including clients, regulatory bodies, public-sector decision-makers, and technology vendors in emerging markets.

6.5 Cross-regional comparative research

Broader cross-regional comparison remains essential to uncover how institutional, cultural, and economic factors shape CBIM business model innovation. Therefore, Collaborative, multi-country studies and policy benchmarking will be critical for establishing globally, especially around such as Asia, North America, and Sub-Saharan Africa applicable CBIM frameworks.

In summary, while this research establishes a strong foundation for the CBIM-BMC, its future refinement requires broader geographic coverage, mixed-method validation, and longitudinal, stakeholder-inclusive investigations. Such efforts will strengthen theoretical generalizability, empirical robustness, and the global relevance of CBIM-driven business innovation.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Ethics statement

Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

TO: Writing – original draft, Writing – review and editing, Conceptualization, Formal Analysis, Methodology, Project administration, Resources, Validation, Visualization. MH: Writing – review and editing. BW: Writing – review and editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This research project is funded under the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860555.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The handling editor SA declared a past collaboration with the author(s).

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fbuil.2025.1655868/full#supplementary-material

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