Circular economy business model: an exploratory study on the key resources for construction organisations

Construction activities have a significant impact on the environment. The construction industry is among the most critical natural resources and energy consumers (Abdullahi et al., 2023). According to Pomponi and Moncaster (2017), construction accounts for about 40% of global energy use and 30% of greenhouse gas emissions. In addition, it generates a large volume of waste resources. For example, construction and demolition waste (C&DW) represents more than 35% of total waste resources generated in the European Union (European Commission, 2020). Thus, this contributes to environmental degradation and climate change. Construction often involves extracting raw materials, leading to resource depletion. It also disrupts natural habitats and contributes to air and water pollution. Hence, there is an urgent need to adopt more sustainable practices in the industry. Circular Economy (CE) offers a valuable solution to these problems. CE focuses on minimising waste, keeping materials in use, and regenerating natural systems (Otasowie et al., 2023a, b, c). In contrast to the traditional linear model of “take-make-dispose,” CE promotes the reuse, repair, recycling, reducing, refusing, remanufacturing, refurbishing, recovering, repurposing, and replacing of materials. Thus, it helps reduce the consumption of virgin resources (Adekunle et al., 2024). CE in construction supports practices such as designing for disassembly, using products with recycled content (PwRC), and extending the life of buildings. Akinade et al. (2017) highlight that CE principles can reduce construction waste and encourage material recovery. Furthermore, adopting CE in construction helps reduce carbon emissions, energy consumption, and environmental footprint. However, the Circular Economy Business Model (CEBM) is essential for adopting CE practices in various industries, including construction (Otasowie et al., 2024).

CEBM helps organisations shift from traditional linear models to more sustainable, circular systems. It provides a structured framework for integrating circular principles into core business activities (Lüdeke-Freund et al., 2019; Lewandowski, 2016). It supports the development of strategies that reduce resource consumption and promote recycling and reuse. In addition, CEBM aligns business operations with environmental and social sustainability goals, which makes it more suitable for long-term competitiveness (Bocken et al., 2014). According to Lewandowski (2016), the CEBM framework has nine fundamental components. These nine components include value proposition, client segment, channels, client relationship, key activities, key resources, key partnerships, cost structure, and revenue stream. Hence, the key resources in the CEBM framework are critical for value creation, delivery, and capture in organisations. Osterwalder and Pigneur (2010) posit that key resources include assets essential to business operations. These resources enable firms to shift from linear to circular practices. They support the reuse, recycling, and regeneration of materials, which are central principles of CE. Hence, key resources are foundational to the adoption of CEBM in construction organisations. They support the operational shift to circularity and drive competitive advantage in a sustainability-focused market. Furthermore, although the CEBM is holistic, this study focuses on the key resource components that stem from its foundational role in enabling other components.

Studies (Lüdeke-Freund et al., 2019; Lewandowski, 2016) have investigated the role of key resources in business models. However, studies focusing specifically on key resources for CEBM in construction organisations are still limited. Most existing research has been conducted in broader sectors like manufacturing and retail. Hence, it does not address the unique characteristics of the construction industry. Also, some recent studies have begun to examine CEBM in construction (Jayakodi et al., 2024; Otasowie et al., 2024; Zhang et al., 2025). While these studies provide valuable insights, they often focus on general strategies or external drivers and do not go into detail about organisational enablers like the CEBM constructs and their attributes. Thus, there is still limited understanding of the key resources construction firms require to adopt and implement CEBM effectively. This gap is significant because the construction industry faces increasing pressure to reduce waste, use resources efficiently, and respond to sustainability goals. Hence, construction organisations can no longer ignore CE, the role of CEBM, and key resources. CEBM adoption relies on the assets that organisations use to deliver circular value propositions. Key resources are a core element of the CEBM, which can be used to gain a competitive advantage. If ignored, the success of CEBM implementation in construction organisations would be affected. Hence, this study seeks to identify and validate the attributes that comprise the key resources construct in CEBM for construction organisations.

The circular economy (CE) is an economic model that focuses on reducing waste and keeping resources in use for as long as possible (Akinade et al., 2017). The goal is to create a closed-loop system where materials are constantly reused, and waste is minimised. The CE is based on key principles such as designing out waste and pollution, keeping materials and products in use, and regenerating natural systems (Geissdoerfer et al., 2017). These principles not only help the environment but also support economic innovation. Hence, businesses can benefit from new service models, such as leasing and product-as-a-service, allowing long-term use and better resource management (Kirchherr et al., 2018). However, collaboration among different stakeholders is needed for CE to work well. It also requires changes in how people think about product ownership, value, and responsibility (Lieder and Rashid, 2016). While the concept is gaining attention globally, applying it to specific industries, like construction, can bring major environmental and economic benefits.

The construction industry is one of the largest consumers of raw materials and producers of waste. Thus, it has a significant role in supporting CE. CE in construction aims to reduce the use of new resources and prevent waste through strategies like material reuse, recycling, and designing buildings for future disassembly and adaptability (Pomponi and Moncaster, 2017). Circular practices in construction include designing buildings using modular components, selecting recyclable materials, and using digital tools like Building Information Modelling (BIM) to track resources (Akbarnezhad and Xiao, 2017). Other strategies include adaptive reuse of old buildings to extend their life and reduce the demand for new materials (Lanz and Pendlebury, 2022). This saves resources and reduces the environmental impact of demolition and new construction. However, despite these benefits, challenges such as a lack of a Circular Economy Business Model (CEBM) still slow the adoption of CE in construction (Otasowie et al., 2024).

The CEBM represents a shift from the traditional linear business model to regenerative by design. In construction, this model aims to reduce waste, extend the life of materials and assets, and promote sustainable practices throughout the project lifecycle (Zhang et al., 2025). The CEBM can be understood using nine constructs commonly associated with business model frameworks. These are: value proposition, client segment, channels, client relationship, key activities, key resources, key partnerships, cost structure, and revenue stream (Osterwalder and Pigneur, 2010). Each component plays a unique role in enabling circularity in construction. However, recent related construction studies (Struck et al., 2025) treat these components unevenly, with many focusing on value creation but not on how value is enabled by key resources.

According to Lewandowski (2016), the value proposition in a CEBM refers to the offer of a product, product-related service or a pure service to construction clients. While this provides a starting point, it lacks clarity on how such propositions can be practically delivered in construction settings. Similarly, Kirchherr et al. (2018) highlight client collaboration as important; however, the study does not explain how organisations can manage the internal resources needed to maintain long-term relationships. The key resource component is particularly important in construction, where resource use is material and labour-intensive. However, most existing studies only describe resources without critically analysing their role in enabling CEBM adoption in construction organisations. For instance, Pieroni et al. (2019) list digital tools as enablers but do not explain how these tools influence CEBM. In addition, some studies treat CEBM components as stand-alone elements rather than as interrelated parts of a system. This limits the understanding of how one component, such as key resources, supports CEBM adoption (Lüdeke-Freund et al., 2019). Hence, this study addresses these gaps by focusing on the key resource component and its attributes in construction organisations. It investigates how these resources influence the CEBM and support the delivery of circular value. Also, this study contributes to the growing body of literature that calls for a more integrated understanding of CEBMs in the construction industry.

Key resources support how organisations create, deliver, and capture value within CEBMs. According to Osterwalder and Pigneur (2010), they are central to the functioning of any business model. However, while traditional models focus on resources that support linear value delivery, CEBMs require resources that enable closed-loop systems and long-term environmental sustainability (Bocken et al., 2016; Geissdoerfer et al., 2017). Teece (2010) argues that key resources must align with an organisation’s value proposition, client relationships, channels and other components of the CEBM. This idea is also reflected in Chesbrough (2007), who notes that resources must support innovation and sustainability. In CE, this means focusing on financial or physical assets and regenerative, reusable, and digital resources. While traditional resources support scale and efficiency, circular models depend more on flexibility, regeneration, and material recovery (Lewandowski, 2016).

A vital circular resource is virtualisation. This is the replacement of physical products with digital services. Lewandowski (2016) posits that this reduces material use and lowers emissions. For example, digital design platforms or virtual training could reduce the need for paper and physical materials. Geissdoerfer et al. (2018) corroborate this, noting that virtualisation can lower environmental impact across construction value chains. However, some authors argue that virtualisation alone is insufficient unless supported by renewable infrastructure (Bocken et al., 2016). Similarly, another essential resource type is regenerative resources, such as renewable energy systems or restoration-focused assets. Elia et al. (2017) highlight that this can help rebuild natural capital, aligning with the restorative aims of CE. Compared to merely reducing harm, regeneration actively improves the environment. However, some organisations may struggle to adopt such systems due to high costs or knowledge gaps, suggesting a need for capacity building and policy incentives.

Furthermore, recovering resources obtained from clients, users, or third parties and reintroducing them into the production cycle can be crucial. Kirchherr et al. (2017) posit that such resources support closed-loop systems by keeping materials in circulation. This is particularly relevant in construction, where materials like reclaimed bricks, salvaged timber, or recycled steel can replace virgin resources (Pomponi and Moncaster, 2017). Nevertheless, suppliers play a significant role in CEBM’s resource strategy. Construction organisations may have to prioritise suppliers offering durable, energy-efficient, or recyclable materials (Nußholz, 2017). These materials reduce environmental impact while also improving project outcomes and client satisfaction. Hence, while several studies have described key resources, none have considered them as a component in the CEBM for construction organisations. This study addresses this gap.

This study adopted an exploratory sequential mixed-methods approach to examine the key resources construct in the CEBM for construction organisations. Combining these methods offers a more robust understanding than using either approach alone. In addition, this integration provides deeper insights into complex issues. Hence, it results in a more complete and accurate analysis (Mangan et al., 2004; Takona, 2024). Given the limited empirical research on this subject matter, the qualitative phase allowed the study to explore and gain deep insights from stakeholders on the key resource attributes in CEBM. The findings from this phase informed the development of a structured questionnaire. This sequence enabled the study to identify and validate the attributes, which align well with the principles of the exploratory sequential design.

In the qualitative phase, purposive sampling was used to select participants with relevant expertise in CE. The recruitment started with email invitations sent to construction professionals. Each email included a short description of the study’s purpose. Those who responded with interest received a more detailed explanation. Also, participants were asked to submit their curricula vitae to confirm their qualifications. This step ensured that only those who met the required criteria were selected. A total of 30 professionals were invited. However, 15 responded, and 13 experts agreed to take part. According to Patton (2022) and Guest et al. (2013), this sample size is enough to reach data saturation. This was evident as some experts mentioned the same attributes already identified from the interview with a different respondent. Furthermore, all participants held senior roles or chief executive officer (CEO) positions in construction organisations. Thus, they lead the change to CE in their respective organisations. The demographic details of the experts are presented in Table 1. Furthermore, Els and Delarey (2006) defined reliability as the degree to which a process consistently gives similar results under the same conditions. However, this level of reliability is difficult in interviews, as different experts may give different views based on their background. Thus, the study focused on ensuring credibility, consistency, and relevance in experts’ responses. Experts were selected based on predetermined criteria and their vast experience. These criteria include the experts needed to understand CE principles and their application in construction. They were required to hold senior roles in construction organisations and have academic qualifications in construction, such as a Bachelor’s, Master’s, or PhD degree. They also needed practical experience with public and private construction clients and a strong theoretical background in CE. In addition, participants had to hold managerial or supervisory roles in CE-related construction projects, be members of a professional body, and be willing to participate fully in the study. Their anonymity was also maintained to reduce bias and improve validity.

The study used semi-structured interviews because they allowed flexibility and gave room for unexpected insights (Kallio et al., 2016). The interview question was on the key resources that construction organisations can adopt to create circular value propositions. Each interview lasted between 30 and 45 min and was conducted via Zoom. All interviews were audio-recorded with the participant’s consent. The key resource attributes identified from the qualitative phase were then used to design the quantitative survey instrument. Before distribution, the survey was piloted with five experts to check for clarity and content relevance (Hirshfield and Fowler, 2020). Given the classifications of construction organisations (grades 1–9) in South Africa, a stratified random sampling technique was adopted to ensure fair representation. The strata were determined based on the classifications as defined by the Construction Industry Development Board (CIDB) of South Africa. However, only grades 7 to 9 were considered for this study due to the significant years of experience of the organisations. The total population (5036) was divided into the relevant strata, and the sample size of 357 (based on Yamane’s formula) was proportionally allocated across the strata based on their population ratios. Respondents were then randomly selected within each stratum. However, only 208 valid responses were returned from the 357 questionnaires distributed to the construction organisations, which is considered suitable for the data analysis method employed in this study (Bagozzi and Yi, 2012)

The recordings from the interview were transcribed verbatim using Microsoft Word. This process ensured the accuracy of the data (Braun and Clarke, 2022). After transcription, the data were imported into Atlas.Ti software, which helped with content analysis. The analysis followed a straightforward process. First, the transcripts were read several times to familiarise with the content. Then, phrases related to key resources were highlighted. The phrases were then compared across participants to identify common views and differences. The findings were reviewed and refined to reflect the raw data accurately. For the quantitative phase, basic demographic information was analysed using percentages. The study used the Relative Importance Index (RII), the Kruskal–Wallis test, and the Confirmatory Factor Analysis (CFA) to assess the key resources construct. The RII was applied to rank the key resources based on their perceived importance. Meanwhile, the Kruskal–Wallis test was used to identify differences in perceptions based on professional roles (Pallant, 2020; Otasowie et al., 2023a, b, c). In addition, CFA was carried out to validate the constructs identified in the qualitative phase. This analysis was performed using EQation Software (EQS) version 6.4. The model’s fit was evaluated using multiple indices. These include the Root Mean Square Error of Approximation (RMSEA), Satorra-Bentler scaled chi-square, Standardised Root Mean Square Residual (SRMR), Goodness-of-Fit Index (GFI), the RMSEA with 90% or 95% confidence intervals, and the Bentler Comparative Fit Index (CFI). Lastly, the Cronbach’s alpha value for the key resources construct was 0.862. This indicates that the reliability test for the construct variables for this study is above the 0.7 threshold. The result demonstrates that the questionnaire’s consistency was acceptable for the study’s aim. A research design framework is presented in Figure 1.

Key resources are essential components of a CEBM. They support the creation, delivery, and capture of circular value. It includes circular enablers that support sustainability and regeneration. The findings of this study reveal that energy from renewable sources is a key resource attribute in CEBM for construction organisations (P1, P2, P4, P5, P6, P10, P11). P1 says, “From my perspective, renewable energy is critical as a key resource in the circular economy business model, especially in the construction sector. It helps reduce the carbon footprint of construction processes and aligns with global sustainability goals. For example, using solar panels or wind energy during site operations where possible will significantly reduce reliance on fossil fuels.” Other experts corroborated this sentiment. These findings imply that energy from renewable sources is a key resource attribute for construction organisations in the CEBM. It supports the shift from non-renewable energy, which often contributes to environmental degradation. According to Geissdoerfer et al. (2017), renewable energy helps reduce the carbon footprint of construction activities, which is essential in promoting sustainable development. The findings show that using solar panels and wind turbines on construction sites lowers dependence on fossil fuels. This aligns with Chen et al. (2024). Thus, integrating renewable energy will enhance the regenerative aspect of CEBM. It will ensure the long-term availability of clean energy sources, making operations more resilient and efficient.

Furthermore, P3, P7, P8, P9, P12, and P13 mentioned circular sourcing and substituting resources with better-performing materials as key resource attributes in CEBM for construction organisations. P7 says, “In my experience working in the construction industry, I believe if we are serious about circular economy, circular sourcing should be an increasingly important part of how we think about materials and supply chains. It is not just about reducing waste anymore; it is about designing systems that allow materials to re-enter the process after use..” Other experts echoed the same sentiment. Circular sourcing involves obtaining renewable or PwRC rather than using virgin resources. This approach reduces environmental degradation and helps manage resource scarcity. According to Ghisellini et al. (2016), circular sourcing enhances material efficiency and promotes using components with a longer lifecycle. Also, substituting resources with better-performing materials involves replacing conventional materials with more durable, energy-efficient, and environmentally friendly alternatives. P13 added, “substituting traditional resources with better-performing alternatives can be a game-changer. These could be materials that are lighter, stronger, or more durable.” Thus, circular sourcing and material substitution contribute to building resilience in the construction industry. Di Summa et al. (2023) and Al-Majali et al. (2023) opine that they will help construction firms reduce waste, optimise costs, and align with sustainable development goals. Hence, construction organisations that integrate these attributes into their CEBM will likely thrive in a resource-constrained future.

In addition, P4, P6, P7, P9, and P12 suggested direct visualisation of materials (digitalisation) and operating in efficient buildings as key resource attributes in CEBM for construction organisations. P4 says, “I believe that direct visualisation of materials through digitalisation is becoming an essential resource for driving circular practices. With digital tools like BIM, we can see what materials go into a building in real-time, their lifecycle, and even where they can be reused. This level of transparency will support better decision-making, especially when planning for material recovery or reuse at the end of a building’s life.” The findings suggest that direct visualisation of materials through digitalisation is an important key resource for CEBM in construction organisations. It will allow for better tracking, monitoring, and managing construction materials throughout their lifecycle. Thus, this will enhance material efficiency and reduce waste. Digital tools such as Building Information Modelling (BIM) allow visualising and simulating material used before construction begins (Akbarnezhad and Xiao, 2017). Also, operating in efficient buildings is another key resource in the CEBM. P9 added that “operating in efficient buildings could be significant. We cannot aim to adopt circular economy in the industry when the buildings we operate from are not energy efficient.” Energy-efficient buildings will reduce resource consumption and lower operational costs (Harputlugil and de Wilde, 2021). It promotes environmental sustainability. Thus, these resources can make it easier to adopt CEBM while enhancing value delivery to different client segments.

Lastly, P5, P7, P8, and P13 identified sustainable construction sites and land restoration as key resource attributes in CEBM for construction organisations. P8 says, “In my opinion, land restoration is essential, especially when construction activities disturb ecosystems or degrade land quality. We can reverse any environmental damage and return the area to a natural state after construction.” P13 added, “We need to invest in sustainable construction site practices. Sustainable construction sites will help minimise the environmental impact during construction activities.” The findings imply that sustainable construction sites and land restoration are key resource attributes in the CEBM for construction organisations. These attributes ensure that construction activities are carried out with minimal environmental impact. Pomponi and Moncaster (2017) posit that these attributes help preserve the ecological value of construction sites throughout the project lifecycle. Sustainable construction sites can mean efficient land use and reduced waste generation during construction. According to Mavi et al. (2021), this will promote energy and water efficiency during the construction phase. For instance, temporary facilities on-site could be designed for reuse in other projects. Also, land restoration could be carried out after the project is completed. This process involves repairing and improving land that was disturbed during construction, returning it to its original or better condition (Dai et al., 2022). Thus, these key resources support long-term environmental and economic value.

From the responses, 3% of respondents have a doctoral degree as their highest qualification, while 24% have master’s degrees. Furthermore, 28% of the respondents have honours’ degrees, whereas 24% have bachelor’s degrees. In addition, 19% of the respondents have post-matric certificates, while 2% have matric certification as their highest qualification. Also, a total of 1.9% of respondents have less than one year of experience in the construction industry, 6.7% of respondents have between one and two years of experience, 10.6% of respondents have between three and five years of experience, 17.3% of respondents have between six and ten years of experience, 23.1% of respondents have between 11 and 15 years of experience, 12.5% of respondents have between 16 and 20 years of experience, 4.8% of respondents have between 21 and 25 years of experience, and 23.1% of respondents have more than 25 years of experience in the construction industry. Hence, the research includes both entry-level professionals and experienced construction experts. Similarly, 19.2% of respondents are engineers, 17.3% are construction managers, 39.7% are architects, 19.2% are quantity surveyors, and 3.9% are project managers. Thus, the research covers a diverse range of professionals within the construction industry.

The descriptive statistics in Table 2 show that key resource attributes play a significant role in the CEBM for construction organisations. The findings from the study reveal that sustainable construction sites had the highest Relative Importance Index (RII) of 0.84 and a p-value of 0.010. However, there is a significant difference in the professionals' views based on their roles in the construction industry. Also, operating in energy-efficient buildings and energy from renewable sources both have high RII values of 0.83. These attributes also had p-values of 0.009 and 0.043, respectively. Furthermore, the direct visualisation of materials through digitalisation had a high RII of 0.83, and its p-value (0.056) was just above the threshold for statistical significance. In addition, substituting resources with better-performing materials and circular sourcing, each had an RII of 0.82 and a p-value of 0.014. Land restoration ranked seventh, with an RII of 0.80 and a p-value of 0.006. Thus, there is a significant difference in the professionals' views based on their roles in the construction industry.

The construct was initially made up of seven measurement variables. All seven variables showed acceptable values before running the CFA. According to Byrne (2013), the residual covariance distribution should be symmetrical and centred around zero. Hence, a latent construct that meets these conditions is suitable for CFA (Boomsma, 2000). In addition, Gao et al. (2008) noted that such features help manage multicollinearity and high correlations, which could otherwise reduce the model’s accuracy. The analysis of the residual covariance for the model showed that the residual matrix meets the necessary standards. Thus, this suggests that the model shows evidence of convergence. Bentler (2005) opines that a good residual matrix should have values between −1.00 and + 1.00, preferably close to zero. The current model meets this criterion. Also, the average unstandardised off-diagonal residual was found to be 0.0255. Since Byrne (2013) stated that values above 2.58 are too high, this result indicates that the measurement model fits the data well. However, the results from the goodness-of-fit test will provide additional confirmation of the model’s appropriateness. Thus, these results will help validate the fit indices and further support the model’s strength.

The estimated parameters of a model are important because they help determine whether the proposed model should be accepted or rejected (Lei and Wu, 2007). Also, the fitness of a model for a given dataset is assessed by evaluating parameters such as reliability, validity, and statistical significance (Lei and Wu, 2007; Hair et al., 2013). In addition, Hair et al. (2013) recommend using multiple criteria to evaluate model fit, including incremental and absolute fit indices and the chi-square test. In this study, the results of the model fit indices are presented in Table 3. The findings show that the GFI and the CFI have values of 0.943 and 0.948, respectively. According to Iacobucci (2010), a GFI or CFI value of 0.95 or higher indicates a strong model fit, though values above 0.90 are still considered acceptable. Hence, the GFI and CFI values in this study suggest that the model has a good fit. Furthermore, the SRMR and the RMSEA values are 0.047 and 0.027, respectively. According to model evaluation standards, values of 0.05 or lower for SRMR or RMSEA indicate a good model fit, while values up to 0.08 are still acceptable. Thus, these results confirm that the model satisfies the good fit criteria. In addition, the model analysis produced a Satorra-Bentler Chi-square (S-Bχ²) value of 49.866 with 14 degrees of freedom and a p-value of 0.000. However, Zhong and Yuan (2011) point out that the chi-square test is highly sensitive to sample size and the assumption of data normality. As a result, it may sometimes give misleading conclusions. Hence, Kline (2005) suggests using a normed chi-square instead. This is calculated by dividing the chi-square value by the degrees of freedom. In this case, the normed chi-square value is 3.562. According to Byrne (2013), a normed chi-square value between 3.00 and 5.00 reflects a good model fit. Thus, the findings confirm that the proposed model is appropriate and meets accepted standards for model fit.

Also, the results presented in Table 3 provide important details about the model’s performance. Specifically, the table includes the correlation coefficients, standard errors, and z-test statistics. These elements help to understand the strength and accuracy of the model. According to Bentler (2005), both the coefficient of determination (R²) and the z-values play a key role in assessing the significance of model parameters. The findings show that all standardised correlation coefficients are below 1.00, confirming no multicollinearity among the measurement variables. In addition, all z-statistics are above 1.96, supporting the relationships' statistical significance. According to Hair et al. (2014), a z-value greater than 1.96 in a two-tailed test at a 5% confidence level indicates a significant result. Thus, the measurement variables used in this model are appropriate and meaningful. Also, the R² values were examined to assess the model’s predictive accuracy. Kline (2010) opines that an R² value closer to 1.00 represents stronger prediction power. In this case, the R² values for all measurement variables were above 0.5. Thus, this shows that the model explains more than half of the variance in the indicator variables. Hence, the attributes used in the analysis account for a significant portion of the total variation. This implies that the model is both reliable and valid for measuring the key resource attributes in the CEBM. The results confirm that the measurement variables strongly predict and define the inner model structure.

Furthermore, the reliability and consistency of the key resources attribute were tested further using factor loadings, Cronbach’s alpha, and the Rho coefficient. These tests helped assess the dataset’s validity, reliability, and consistency (Hair et al., 2014). These measures are important to ensure that the findings are valid and reliable. The reliability coefficient is expected to range between 0 and 1.00, with values closer to 1.00 showing stronger reliability (Kline, 2005). Accordingly, the results in Table 3 show that Cronbach’s alpha and Rho coefficient values were 0.862 and 0.861, respectively. These values are high. Thus, they confirm that the indicator variables used to measure key resources are reliable and internally consistent. Furthermore, the factor loadings were examined to test the strength of the relationship between the measurement variables and the construct. The results showed that the coefficients were above 0.70 or close to 0.70 for most attributes. However, attributes KR5 and KR6 were slightly lower but still acceptable. Kline (2005) posits that a factor loading of 0.70 or more is required to confirm convergent validity. Also, when factor loadings are more than 0.5, there is a strong link between a measurement variable and the construct. Similarly, the Average Variance Extracted (AVE) was also used to assess construct validity. The AVE for each construct was above 0.5, the minimum threshold. Hence, these results confirm that the measurement variables have strong convergent validity. In summary, the key resource attributes in this study were measured using reliable and valid indicators, confirming that the findings based on these indicators are reliable.

Key resource is one of the major components of the CEBM canvas. It shows the assets organisations use to create, deliver, and capture value in business models (Lewandowski, 2016). Hence, understanding which resources professionals consider most effective can guide better decision-making. The descriptive analysis shows that sustainable construction sites ranked highest among the key resources identified and evaluated. This corroborates Mavi et al. (2021) on construction organisations prioritising sustainability in site operations to support circular practices. One key aspect of sustainable construction sites is the integration of eco-friendly technologies and practices. According to Meena et al. (2022), construction organisations can adopt green building standards and eco-efficient technologies within their sites. These measures will reduce the environmental footprint and contribute to resource conservation and efficiency (Tukker et al., 2018). For instance, using renewable energy and water recycling systems in construction aligns with CE principles by minimising adverse environmental effects. Operating in energy-efficient buildings also ranked high. This is consistent with the literature (Harputlugil and de Wilde, 2021; Li et al., 2022). Thus, investing in such buildings can improve energy use and reduce the environmental impact. Furthermore, energy from renewable sources was another highly rated key resource. This aligns with Chen et al. (2024), who suggest that construction firms could consider integrating renewable energy systems, such as solar or wind power, into their projects. In addition, using direct visualisation of materials through digitalisation ranked high. As a result, digital tools like BIM can be used to improve material tracking and enhance reuse opportunities (Akbarnezhad and Xiao, 2017). Substituting materials with better-performing alternatives was identified as effective. This corroborates the position of existing studies (di Summa et al., 2023; Al-Majali et al., 2023) that choosing durable, recyclable materials with a lower carbon footprint would add sustainable value to the industry. However, land restoration ranked lowest. This indicates that construction professionals do not view it as a practical or impactful key resource. Thus, firms may not prioritise this activity unless policy or environmental pressures demand it. Nevertheless, it is important to note that although several studies have considered the various attributes of key resources in different contexts, none have examined them as key resources for the CEBM. This shows a gap in the literature. Hence, construction organisations that aim to implement CEBM may need to consider these key resources carefully. Doing so will help them build a strong foundation for creating, delivering, and capturing circular value.

Furthermore, the results show that the key resource attribute with the highest causality was operating in energy-efficient buildings, which was closely followed by energy from renewable sources. Direct visualisation of materials through digitalisation and substituting resources with better-performing materials were the least causative key resource attributes. This implies that construction organisations should prioritise using buildings that reduce energy consumption and support sustainability goals, as suggested by Harputlugil and de Wilde (2021). Also, construction organisations should prioritise the adoption of solar, wind, or other renewable energy options (Chen et al., 2024) that can significantly reduce the environmental impact of construction activities when adopting CEBM. However, direct visualisation of materials through digitalisation and substituting resources with better-performing materials, which were the least causative key resource attributes, might suggest that while digital tools and material substitution have potential, they are not yet widely adopted. Hence, there is a need for more investment in technologies that enable material tracking and performance evaluation, as suggested by Hepburn et al. (2021).

Finally, the assessment revealed strong outcomes, suggesting that key resources are central to CEBM; having a clear strategy around them will help construction firms boost their uptake. In addition, construction organisations can use these insights to stay competitive, meet sustainability targets, and actively support the shift towards CE. A framework is presented in Figure 2.

The findings have clear practical and theoretical implications for key resources as an attribute of the CEBM in construction organisations. First, the results show that all seven measurement variables met the required standards before conducting the CFA. This indicates that all the variables initially obtained from the interview phase were valid for defining key resources in the CEBM. Also, it confirms the appropriateness of the attributes for further analysis. The interfactor relationship results also show that the key resource attributes were significantly correlated with the latent variables. Thus, these relationships provide evidence that the attributes contribute meaningfully to understanding how key resources support CEBM. Furthermore, when evaluating the variation explained by the construct, all values were statistically significant at the 5% level. This confirms the strong influence of key resources on the adoption of CEBM in construction organisations. Similarly, the findings suggest that multicollinearity and high correlations were effectively addressed. These findings imply that construction organisations can rely on these key resources to create, deliver, and capture value across different client segments. Hence, organisations aiming to adopt CEBM can use these insights to develop and prioritise the most effective key resources for CEBM. This will enhance their capacity to operate sustainably and meet the expectations of CE. Furthermore, the findings help fill a research gap, as existing studies have not empirically examined key resources within the construction CEBM context.

This study investigates the features that define key resources in construction organisations moving toward a CEBM. The study is significant because the construction industry is facing growing pressure to become more sustainable, reduce waste, and build climate resilience. Hence, construction organisations can no longer ignore CE, and the role of CEBM and key resources. Since CEBM adoption depends on the resources that help deliver circular value, key resources are central to success. They also offer a way for firms to gain a competitive edge. The descriptive statistics of the study showed that sustainable construction sites, operating in energy-efficient buildings, energy from renewable sources, direct visualisation of materials through digitalisation, and substituting resources with better-performing materials were the most important key resource attributes. However, professionals had different views on the key resource attributes based on their professional roles. Nevertheless, there was general agreement on using digital tools for material visualisation. In addition, the CFA confirmed that all seven key resource attributes strongly influenced the adoption of CEBM in construction organisations.

This study gives insights into the key resource attributes influencing CEBM in construction organisations. It helps improve the understanding of how construction organisations can better adopt CEBM through key resources. Implementing CEBM within construction organisations is a significant approach to advancing CE and meeting the sustainable development goals in the industry. The findings present a structured approach to the key resources required for organisations to deliver circular value propositions to different client segments. In addition, the findings provide a strong base for future research on CEBM within the construction sector. Since this study was conducted in South Africa, future studies could explore other regions to compare the results with those of this study. Also, the study focused on contracting organisations. Hence, future studies could be conducted to get the perspectives of consulting firms. Furthermore, future studies could consider focusing on how circular resource attributes affect project outcomes over time. Lastly, the findings offer useful insights but may be influenced by local policies, economic conditions, and industry practices. Hence, caution should be exercised in applying the findings to other geographical or organisational contexts without considering these differences.