Total Interpretive Structural Modeling of Circular Economy Enablers in the Construction Industry: Evidence from Bangladesh

The construction industry is a part of Bangladesh’s emerging economy, playing an important role in economic development. Following recent progress, the government intends to modernize the nation’s infrastructure and has placed emphasis on ongoing mega-development projects [1]. With industrialization, the construction industry has become a prime contributor to waste generation, a concern for emerging economies like Bangladesh. The construction sector suffers from waste overproduction, impacting both the economy and environment and preventing the sector from operating effectively. The building sector is one of the largest contributors to global energy consumption and climate-related emissions [2]. It is noted that building activities alone induce 25% of global emissions, while construction activities contribute an additional 15%, highlighting the substantial environmental footprint of the sector. As environmental concerns intensify, the vision of an environmentally sustainable transition from the linear economy is gaining importance globally to protect the environment, maintain efficiency, and conserve resources for future generations [3]. The world primarily functions under a linear economy model based on the take–make–dispose approach [4]. However, the recycling, reduction, and reuse concept drives the circular economy model, incorporating production, consumption, and waste disposal for new production [5]. The circular economy model can construct a sustainable industry by holding materials in a closed-loop structure to maintain value and reduce waste [6]. Based on the current situation, the construction industry must enforce a circular economy to structure a sustainable economy. Moving towards a circular economy requires a focus on multiple factors, as they follow linear economic patterns [7]. Furthermore, the implementation of circular economy practices is closely aligned with several United Nations Sustainable Development Goals (SDGs), including SDG-6 (Clean Water and Sanitization), SDG-7 (Affordable Clean Energy), SDG-12 (Reasonable Consumption and Production), and SDG-15 (Life on Land). For these SDGs in Bangladesh, research on environmental state and circular economy integration is essential.

Based on the above discussion, this study addresses the following research questions:

i. What are the key enablers that facilitate the implementation of a circular economy in the construction industry?

ii. How do the interrelationships among these enablers form a structured model that can guide this transition?

To answer these questions, this research first identifies the potential enablers of circular economy adoption in the construction industry through a comprehensive review of the existing literature and expert opinion. Subsequently, a modeling approach is employed to analyze and interpret the relationships among these enablers, revealing how they influence and support one another in facilitating circular economy implementation. By developing a structured interpretive model, the study provides a clearer understanding of the hierarchical relationships among the identified enablers. The insights from this research contribute to addressing the implementation gap in emerging economies by offering a systematic framework that can assist industry practitioners, policymakers, and decision-makers in promoting the effective adoption of circular economy practices within the construction sector.

The remainder of this study is organized below. Section 2 discusses the current economic state of Bangladesh’s construction sector, the potential benefits of circular economy implementation, and literature-based identification and screening of circular economy enablers. Section 3 describes the total interpretive structural modeling and MICMAC methodologies, development of the conceptual framework, and survey design used to collect expert opinions. Section 4 reports the results, including the validated total interpretive structural modeling model, MICMAC analysis of enablers, and theoretical and practical implications of the findings. Finally, Section 5 concludes the study by summarizing the key results and outlining the limitations and future research directions.

This section presents reviews of the circular economy, the construction industry, previous research on circular economy implementation in the construction industry and research gaps and contributions.

The current socioeconomic system in Bangladesh operates under a linear economy model, in which resources are used to produce goods that consumers purchase and subsequently discard [8]. This linear model causes waste during and after product lifecycles, along with excessive energy usage and ecological deterioration. With resource depletion evident, the need for a new economic paradigm grows. In response, experts have proposed a circular economy to shift towards environmental sustainability. The circular economy paradigm suggests using waste as resources to produce value through circularity, efficiency, and optimization. A circular economy aims to balance economy, environment, and society by extending product lifespans or reusing resources within the system [4]. As awareness of the circular economy has increased, several frameworks have emerged to operate its principles, such as the 6Rs principle “reuse, reduce, recycle, redesign, refurbish, and repurpose” [9] and the 10Rs principle “refuse, rethink, reduce, reuse, repair, refurbish, remanufacture, repurpose, recycle, and recovery” [10]. These frameworks demonstrate how circular economy practices can address sustainability challenges by simultaneously enhancing environmental performance, supporting social well-being, and promoting economic resilience. Consequently, circular economy activities create positive momentum in economic and environmental conditions [11].

Building on the principles of the circular economy, successful implementation requires a systemic transformation involving multiple stakeholders across the value chain [12]. This includes redesigning products and processes to prioritize durability and ease of disassembly, fostering innovation in material selection toward renewable and non-toxic alternatives, and establishing efficient reverse logistics for material recovery [4]. Policy frameworks and regulatory incentives play a crucial role in accelerating this transition by encouraging sustainable practices and penalizing wasteful behaviors [13].

In the context of Bangladesh, integrating circular economy principles into the construction industry demands tailored approaches that consider local economic constraints, technological capabilities, and social dynamics. Capacity building, awareness campaigns, and collaboration between government, industry players, and communities are essential to overcome barriers such as limited infrastructure for recycling and prevailing linear consumption habits. Ultimately, embedding circularity within the socioeconomic fabric can drive resource optimization, reduce environmental impact, and support long-term sustainable development goals [14].

The construction industry is widely recognized as one of the most resource-intensive sectors, consuming large quantities of natural resources while generating substantial waste and environmental impacts. As a result, the adoption of circular economy principles has become increasingly important for improving sustainability within the sector. The traditional linear model causes resource depletion, excessive waste, and energy use, impeding sustainable development and economic efficiency [15]. In contrast, circular economy practices reduce material consumption, enhance resource recovery, and minimize environmental harm through recycling, reuse, and reduction tailored to construction lifecycles [16].

Globally, the construction industry is one of the largest and rapidly expanding sectors, significantly impacting Bangladesh's progress as a developing nation [2]. According to the construction industry, it plays a vital role in Bangladesh's infrastructure development and has great potential to contribute to economic growth and foreign investment. Infrastructure remains crucial for industrial and economic prosperity in developing nations like Bangladesh [17]. The country has undertaken several megaprojects, with the Dhaka Metro Rail and Padma Multipurpose Bridge being recent accomplishments. Additional projects, i.e., the Dohazari–Ghundum Railway, the Karnaphuli Tunnel, the Dhaka Elevated Expressway, and the Bus Rapid Transit corridor from Dhaka Airport to Gazipur, are also expected to significantly enhance the country’s transportation and infrastructure system [18].

Despite this rapid growth, the construction sector continues to face sustainability challenges related to inefficient resource use, waste generation, and environmental management. While circular economy approaches offer promising solutions through green building practices, sustainable materials, and improved waste management strategies, their adoption in the construction industry remains limited. Key gaps in the construction industry’s circular economy transition include entrenched linear practices, limited awareness and expertise, weak policy frameworks, and inadequate waste management infrastructure. Integration of the circular economy with local economic, social, and regulatory contexts remains underdeveloped, especially in emerging economies like Bangladesh, restricting the sector’s sustainability contributions aligned with SDGs [19]. Despite challenges, the construction industry is gradually adopting the circular economy through green building innovations, sustainable materials, and waste reduction programs. Emerging economies increasingly integrate the circular economy into quality, waste, and safety management [16]. Pilot projects and policies promote stakeholder collaboration, resource-efficient technologies, and resilient supply chains. However, broader adoption requires enhanced capacity building, regulatory support, and mainstreaming the circular economy in construction to realize sustainable infrastructure development [14].

To curtail its global impact and safeguard resources, the construction industry should embrace circular economy principles, shifting from a linear economy to minimize resource depletion and environmental impact while promoting sustainable practices. Researchers are accentuating the need for circular economy integration, including ways to determine indicators for circular economy success in the construction industry. Table 1 provides a summary of the literature on achieving a circular economy in the construction industry.

As shown in Table 1, prior studies have made important contributions to advancing the circular economy in the construction industry by examining challenges, sustainability frameworks, bibliometric trends, digital technologies, reverse logistics, business models, and regulatory barriers. However, most of these studies are primarily descriptive, review-based, or focused on specific dimensions of circular economy adoption, and therefore do not sufficiently explain how the key enablers of circular economy implementation interact in a hierarchical and contextual manner, particularly in the construction sector of emerging economies such as Bangladesh. In addition, previous research has not adequately integrated the technological, organizational, environmental, and human dimensions within a single analytical framework to identify, distinguish, and prioritize the enablers of circular economy implementation [32]. This fragmented approach limits the ability to comprehensively understand how these dimensions interact and collectively influence the success of circular economy initiatives. By integrating the technological, organizational, environmental, and human dimensions within a unified analytical framework, it becomes possible to systematically identify the specific enablers that drive circular economy adoption, distinguish their relative importance, and prioritize actions that effectively address multiple dimensions simultaneously [33]. Such a holistic perspective acknowledges the complex interdependence between technology readiness, organizational culture and structure, environmental pressures, and human factors such as skills and motivation. Moreover, a consolidated technological, organizational, environmental, and human framework enables organizations and policymakers to tailor strategies that leverage synergies among these dimensions, thereby enhancing the feasibility and impact of circular economy practices [34].

Addressing this gap requires an approach that goes beyond listing factors and instead reveals their direct and indirect interrelationships. Therefore, this study adopts total interpretive structural modeling to develop a structured and interpretive hierarchy of circular economy enablers, while MICMAC analysis is used to classify them according to their driving and dependence power. By combining the technological, organizational, environmental, and human perspective with total interpretive structural modeling and MICMAC, this study offers a more holistic and practically useful understanding of circular economy implementation in the construction industry and contributes context-specific evidence from an emerging economy. This integrated approach extends the existing circular economy in the construction industry literature by providing not only a categorized set of enablers, but also a validated structural explanation of how those enablers influence one another and where decision-makers should intervene first.

Considering the significant environmental effects of the built environment, adopting a circular economy might help address sustainability issues. Integrating environmental performance evaluation into the construction industry evaluation is crucial for successful implementation [35]. While working to support a circular economy in the construction industry, it is vital to view improvements through the lens of the United Nations SDGs [36]. Despite its potential to drive sustainable development and boost the economy, the circular economy remains underutilized in the construction industry. The circular economy is viewed as one of the best solutions for reconciling the conflict between economic progress and long-term societal development in the current economic climate [37]. This concept has the potential to completely transform the construction industry of Bangladesh. The enablers for this study were estimated based on a thorough literature review. To ensure sustained benefits, the chosen parameters were balanced from a technological, organizational, environmental, and human perspective. Google Scholar, Science Direct, Elsevier, Web of Science, Emerald Insight, Springer, and Taylor & Francis resources were used to compile literature reviews. Articles were retrieved using keywords including circular economy, sustainability, construction industry, critical factors/enablers, life cycle assessment, resource efficiency, waste management, recycling, remanufacturing, government policies, construction management, reverse logistics, and supply chain stakeholders in the construction industry. Relevant books, publications, and journal articles were reviewed. These keywords were used separately and with Boolean operators “AND” and “OR”. Since keywords could lead to unrelated topics, titles determined relevance. Abstracts were examined for content, removing irrelevant information. Relevant papers were included after full review and elimination of those not meeting standards. The final screening papers listed major enablers influencing circular economy implementation in the construction industry. These enablers were concentrated in technological, organizational, environmental, and human categories. The enablers, generated through reviews and expert opinions, are described in the section below. Figure 1 shows the literature selection and filtering process.

This section reviews the literature on the theoretical framework for implementing a circular economy in the construction industry based on a four-dimensional model of technical, organizational, environmental, and human aspects [38]. In addition, enablers are derived from the existing literature, operational and industrial experts' insights, and academic feedback. In this study, a four-dimensional technological, organizational, environmental, and human model is used to facilitate the implementation of a circular economy in the construction industry. Enablers are proposed through an extensive literature review and finalized based on feedback from academic and industry professionals.

(i) Viable remanufacturing process

Remanufacturing has been incorporated into circular economy models as a strategy for achieving sustainable practices and maintaining product value [37]. The main obstacles to the circular economy business model are financial uncertainty and complex procedures. A comparison of the circular economy business model and the linear business model has shown that the circular economy business model carries higher financial risk due to costly remanufacturing and refurbishing designs. Manufacturing new products is often less costly than labor-intensive remanufacturing processes, which yields material savings inadequately to cover labor expenses. Therefore, developing a workable remanufacturing model with lower costs would promote the circular economy business model [39].

(ii) Building information modeling integrated life cycle assessment

Sustainable building practices emphasize resource reuse, recycling and minimal environmental impacts. The life cycle assessment evaluates products, processes and services based on environmental impact over their lifespan. Implementing the life cycle assessment is challenging due to data collection requirements, often performed after the design phase, when they have less influence on decisions [40]. Building information modeling enables early correction of design flaws and material selection problems and reduces data collection complexity for the life cycle assessment. Primary data for the life cycle assessment can be gathered from case-specific building information modeling design. This integration increases efficiency and accuracy of the life cycle assessment while decreasing complexity of sustainable building design [30].

(iii) Integration of the Internet of Things

Digital technologies support circular economy implementation in businesses. The Internet of Things, big data, and analytics enable the circular economy business model by improving product design, attracting customers, monitoring product activity, providing technical support and maintenance, optimizing usage, upgrading products, and enhancing end-of-life activities. The main applications of the Internet of Things in construction include preventive maintenance, reduced administration, real-time observation, construction management, tracking labor, safety, locating materials, monitoring interference, security, and insurance protection [41].

(iv) Acknowledgement of technological advancements

Technology is crucial for circular economy implementation. The application of a circular economy is inhibited in many industries by inferior technology [42]. Enterprises may optimize circular economy implementation through digital manufacturing technologies. Predictive maintenance strategies can be improved using technology that offers real-time data. The more advanced technology is available, the more sustainable circular economy practices an organization can achieve [43].

(i) Collaborative supply chain

In Bangladesh, achieving a circular economy requires breaking from linear economic strategies and collaboration among supply chain actors [16]. A circular economy demands commitment from every player in the supply chain. To implement circular economy practices, it is crucial to develop and integrate reverse network management, circular business model innovation, circular design, and system enablers, such as collaborative initiatives and value chain development [38]. The importance of higher-order actor learning, shared vision formation, and mutual trust in fostering collaboration among supply chain participants has been emphasized [44].

(ii) Interdisciplinary circular economy policy investigation

Different nations' circular economy transitions have received varying amounts of attention, and further studies have been conducted to determine how governmental policies affect circular economy adoption. Governmental organizations that intentionally create plans and policies as external forces for the circular economy transition must work with industrial players and promote circular innovation [45].

(iii) Eco-industrial development

Eco-industrial development is a business-to-business business model that allows traditionally separate groups and businesses to collaborate and share resources with each other [46]. This promotes sustainability and has a positive impact on the economy, society, and environment. Through this procedure, waste or by-products from one industry or industrial process might be the basis for another. The solid waste from the construction industry may also be used as raw materials by other sectors. The implementation of this concept encourages the growth of a circular economy and ensures sustainable material usage [47].

(iv) Regulatory framework

Comprehensive regulatory frameworks and effective policies enable forward movement towards the circular economy. Such a framework supporting the implementation of a circular economy in the construction industry can overcome the disadvantages of current laws and regulations that support the linear structure of the economy [48].

(v) Government support & policy framework

Bottom-up supply chain initiatives require top-down governmental support, which indicates the government's obligation to create reasons and encourage supply chain levels to perform accordingly [49]. Most adjustments are the government's responsibility, including developing a circular economy, increasing awareness, including recycling in life cycle assessments, and adopting legislation. Governmental entities that design plans and policies as external factors in the circular economy transition must collaborate with business stakeholders to advance circular innovation [6].

(i) Circular material curation

Using recyclable materials in the building construction enables reuse, refurbishment, and remanufacturing of parts, materials, and by-products, reducing solid waste from the construction industry [50]. Scrap steel can be recycled into usable secondary steel. Recycled concrete can be crushed and used as aggregate in new concrete items [51]. Using circular materials for construction offers benefits, including cost savings, significantly impacting emerging economies [52].

(ii) Strategic deconstruction

The first step in strategic deconstruction is removing harmful waste like asbestos, then removing usable parts or materials (such as metals) [53]. Strategic deconstruction involves removing building installations while protecting and sorting components to encourage reuse and quality recovery of materials. By recovering usable materials, strategic deconstruction prevents needless destruction of resources [53].

(i) Effectual information brokerage

Effective information sharing is crucial for a circular economy. Information sharing promotes strong relationships in the supply chain, avoids duplication, lowers risks, and improves circular economy performance by connecting scattered parties [54]. The construction industry has been criticized for being convoluted and fragmented. The building life cycle involves stakeholders with various specialties, most operating in silos with their own interests. Because links between the construction industry actors are temporary and detached, collaboration and knowledge gathering remain problematic [55]. The concept of “information brokers” emerges as intermediaries who produce, interpret, organize, or communicate information to specific groups to achieve goals. Understanding information brokers and their functions is essential for filling structural gaps where information lacks in supply chains [56].

(ii) Top management commitment

A comprehensive circular economy framework, followed by a practical implementation process, is possible only with management support and a devoted strategy. The assistance of lower- and middle-level managers is also influential in adopting successful circular measures in the linear economic structure of the construction industry. With appropriate management, the system can prosper [57]. Management’s commitment to adopting circular economy initiatives can be influenced by an industrial organization's strategy, planning, and employment practices. To improve knowledge of the circular economy, it is crucial that key management personnel are involved [58].

(iii) Advanced knowledge and awareness of the circular economy

It is essential to overcome the lack of understanding of the circular economy among the construction industry stakeholders. To transform perspectives towards a circular economy, it is essential to increase awareness and comprehension among all actors, and disclose outcomes to stakeholders through education, training, and progressive thinking [59]. Training is crucial for players to understand the circular economy's goals, indicators, frameworks, rules, and policies [60]. Circular economy efforts are determined by awareness levels. Incorporating sustainable business practices requires awareness of social and environmental accountability. Circular economy involves not only technical and financial competencies but also motivation and knowledge of its implications [6]. Table 2 shows the categories and enablers for circular economy implementation in the construction industry.

The primary phase of this study’s research framework involves identifying the key enablers for implementing a circular economy in the construction industry. Initially, fourteen potential enablers were identified through an extensive literature review. Following this, a questionnaire survey was used to extract and finalize the most relevant enablers with the help of academic and industry experts. The questionnaire, presented in Appendix A of the supplementary materials, was sent to interested participants. In the first part of the questionnaire, respondents’ personal and professional information was collected, while in the second part they were asked to provide their judgments on the proposed enablers based on their experience. Questionnaire 1 was distributed to 132 professionals, of whom 45 responded. The demographic and professional profile of the respondents is presented in Table 3, based on the survey data; the corresponding detailed profile is also provided in Appendix B (Table B1).

The respondents were selected purposively from both academia and industry based on their professional involvement in the construction sector and their familiarity with sustainability- and circular economy-related practices. To ensure the credibility of the expert input, priority was given to individuals occupying relevant academic, technical, managerial, and policy-related roles. Accordingly, the respondent group included professors, associate professors, and lecturers from academia, as well as construction managers, project managers, construction engineers, project planning engineers, structural engineers, architects, site inspectors, and policymakers from practice. The respondents also had substantial professional experience, with 42.2% having 5–9 years of experience, 37.8% having 10–15 years, and 20.0% having more than 15 years of experience.

The sample size was considered adequate for this study because the objective of the total interpretive structural modeling is not statistical generalization, but the identification and structuring of contextual relationships among key enablers based on informed expert judgment. In the first stage, the 45 responses were used to screen and finalize the most relevant enablers from the literature-derived list. In the second stage, the finalized enablers were used to construct and validate the total interpretive structural modeling framework through expert-based pairwise interpretation and link validation. The reliability and validity of the findings were strengthened through several measures, including literature-based identification of enablers, purposive selection of knowledgeable respondents, expert consultation for screening, structured pairwise comparison of enablers, transitivity checking in the reachability matrix, and a separate validation stage in which the interpreted links were assessed by experts. The finalized enablers were then used to construct the total interpretive structural modeling framework.

The primary objective of this study is to identify the enablers and assess the interrelationships among different enablers to implement a sustainable circular economy in the construction industry. Figure 2 depicts the successive steps of the total interpretive structural modeling method.

Step 1: Identifying and defining enablers

With the help of an existing literature review on the desired topic and further taking feedback from professionals, multiple crucial enablers were identified.

Step 2: Defining contextual relationships between enablers

To achieve contextual relationships among enablers, structured and semi-structured interviews with construction industry experts were conducted, followed by brainstorming sessions. And based on the outcome of these sessions, contextual relationships were designed.

Step 3: Interpretation of relationships

Each enabler was compared with others to interpret “How an enabler will help achieve another enabler.” By responding to this question, the relationship would be interpreted for each set of enablers to make implicit knowledge explicit. All comparison sets were discussed with experts. For each set, if most experts' responses were positive, it was scored “yes”; otherwise, “no”. All “yes” replies indicating contextual relationships were analyzed, and experts' interpretations were used to create combined interpretation statements in the explanation column. The pairwise comparison with explanations formed the interpretive logic base [61].

Step 4: Reachability matrix and transitivity test

The initial reachability matrix shows the interrelationships among enablers. It is translated from the interpretive logic-knowledge base. Then transitivity is checked between enablers. If enabler A is related to enabler B and enabler B has a relation with enabler C, then according to the transitivity rule, enabler A must be related to enabler C. These types of links are called transitive links. The initial reachability matrix is updated to the reachability matrix by denoting transitive links in respective cells as “1*.”

Step 5: Level partitioning on the final reachability matrix

The most important factors were determined based on their effect on other factors. This iterative process levels enablers according to their impact. The final reachability matrix was divided into reachability, antecedent, and interaction sets. The reachability set comprises the enabler and those it influences. The antecedent set includes the enabler and those influencing it. The intersection set consists of enablers present in both reachability and antecedent sets. Enablers with identical reachability and intersection sets reach the top total interpretive structural modeling hierarchy levels. The enablers at the top cannot reach any enablers above their level. The top-level enablers are distinguished from others. This approach finds the next level of enablers, continuing until each enabler's level is determined [62].

Step 6: Developing a digraph

Digraph was built by placing the enablers according to their level and was further updated with the help of the reachability matrix.

Step 7: Binary interaction matrix

The binary interaction matrix was derived from the digraph. The interactions are represented as “1” in their respective cells.

Step 8: Interpretive matrix

To create the interpretative matrix, each cell with the value “1” was interpreted with the respective interpretation drawn from the interpretive logic-knowledge base.

Step 9: Final total interpretive structure modeling

The total interpretive structural modeling framework was derived from the digraph and the interpretive matrix and further validated by industrial experts. The nodes in the digraph were substituted by interpreting the enablers contained in the box. The interpretation in the cells of the interpretive matrix is represented in the structural model by depicting the interpretation by the side of the respected links in the digraph. This results in a complete interpretation of the structural model, including both its nodes and links.

Step 10: Validation of the total interpretive structural modeling framework

It is necessary to validate the model structure’s enablers and relationships. For model structure validation, it is crucial that the following questions be addressed, whether all relevant enablers are included and whether the interpretation of relations is correct [63].

MICMAC analysis helps understand mutual interactions among enablers and find their driving and dependence power. The factors were divided into four clusters based on their driving power and dependence. The driving and dependence power of each enabler are calculated from the reachability matrix by summing binary values row-wise and column-wise. By plotting these values, enablers are categorized into four clusters. Cluster A contains autonomous enablers with weak driving power and weak dependence, having little system connection. Cluster B contains dependent enablers with weak driving power and strong dependence, where changes to other variables impact them. Cluster C contains linkage enablers with both strong driving power and dependence, making them extremely unstable and requiring special care. Cluster D contains independent enablers with strong driving power and weak dependence, having the most influence on other enablers.

After receiving feedback from the surveys (see details in Chapter 3), twelve enablers were finalized, as shown in Table 4.

For identifying contextual relationships among enablers, the professionals were consulted with by following Step 2 of Section 3.4. To find the relationship among enablers, yes/no questions were asked to the professionals. Each enabler was compared with every other enabler to interpret “Factor 1 will influence or enhance Factor 2”. A total of 12 enablers was used in this study. Therefore, the total number of pairwise-comparison sets was 12 * 11 = 132. Thus, these 132 rows of pairwise comparison along with explanation columns for those sets which have a contextual relationship formed an interpretive logic-knowledge base, shown in Appendix C, Table C1 of supplementary materials using Step 3. Then, the initial reachability matrix was built based on the response in the interpretive logic-knowledge base using Step 4. For each ($i$, $j$) cell, if the knowledge base response is “yes,” then binary value “1” is allotted in that cell; otherwise, binary value “0” is allotted, as shown in Table C2 of supplementary materials.

Then, transitivity was checked between enablers. The initial reachability matrix was updated to the reachability matrix (Table 5) by denoting transitive links in respective cells as “1*,” as shown in Table 6, by using Step 4.

Following Step 5, level partitioning was conducted which required a total of six iterative processes, as shown in Table C3, and the summary of level partitioning is shown in Table 6.

Digraphs were then built by placing the enablers according to their level using Step 6. Directed links were connected according to the relationship depicted in the reachability matrix. And not all transitive links were drawn in the digraph; only significant transitive links were drawn [61]. The digraph is shown in Figure 3. Then the binary interaction matrix was derived from the digraph using Step 7, as shown in Table C4. Binary interaction was then converted into an interpretive matrix by incorporating an interpretive logic-knowledge base using Step 8, as shown in Table C5. Digraph was further updated with the interpretive matrix to generate an initial total interpretive structural modeling framework, as shown in Figure 4 using Step 9.

A questionnaire was then prepared to obtain expert opinions on the interpreted links of enablers. Eight experts were interviewed for validation with their feedback; their profiles are shown in Table C8. The questionnaire is shown in Table C6, and expert scores on individual interpretive links are shown in Table C7. Opinions were collected on a scale of 1–5, with 5 being most relevant and 1 least relevant. The average score is shown in Table C9. Links scoring above three marks on average remained in the final validated total interpretive structural model [62]. The experts scored F5-F6 and F9-F1 less than three on average. Therefore, all links except F5-F6 and F9-F1 remained in the final validated total interpretive structural modeling framework, as shown in Figure 5.

In this step, factors were clustered into four groups, as depicted in Figure 6, based on their driving power and dependence.

Cluster A: Autonomous enablers

As shown in Figure 6, no enabler falls in this cluster. Therefore, there are no enablers having weak driving power and weak dependence.

Cluster B: Dependent enablers

From Figure 6, it is observed that five enablers, i.e., building information modeling-integrated life cycle assessment (F2), collaborative supply chain (F5), strategic deconstruction (F9), eco-industrial development (F6), and circular material curation (F8), fall in this cluster, having weak driving power and strong dependence plotted in this cluster.

Cluster C: Linkage enablers

Enablers in this cluster have both strong driving power and strong dependence. No enablers fall in this cluster.

Cluster D: Independent enablers

In this cluster, government support & policy framework (F7), top management commitment (F11), advanced knowledge and awareness of the circular economy (F12), acknowledgement of technological advancements (F4), integration of the Internet of Things (F3), and effectual information brokerage (F10) are present, exhibiting strong driving power and weak dependence.

One of the main objectives of this study is to develop the total interpretive structural modeling framework (Figure 5), providing the levels of enablers to implement a circular economy in the construction industry, as well as to classify the enablers with the help of the MICMAC analysis (Figure 6). In the total interpretive structural modeling graph, interactions among the enablers are analyzed which provide substantial information to implement a sustainable circular economy in the construction industry. The MICMAC analysis presents relations among enablers based on their interdependence and degree to which enablers impact the successful implementation. In the six-level total interpretive structural modeling framework, Level 1 consists of only one enabler sitting on the topmost layer of the total interpretive structural modeling framework, i.e., the viable remanufacturing process (F1), which has the least influential effects on other enablers but is influenced the most by other enablers. For successful implementation of a circular economy in the construction industry, a viable remanufacturing process (F1) is very important as it ensures financial certainty over the linear business model.

Level 6, placed on the lowest level of the total interpretive structural modeling framework, consists of three enablers, i.e., government support & policy framework (F7), top management commitment (F11), and advanced knowledge and awareness of the circular economy (F12), having the most influential power over other enablers directly and indirectly. Top management commitment (F11) and advanced knowledge and awareness of the circular economy (F12) influence each other. In the view of implementing a circular economy, governmental support from the top down is necessary for enforcing the practice of sustainability in the building sector. Governmental organizations that consciously design strategies and policies to act as external factors in the circular economy transition must collaborate with business stakeholders and promote circular innovation. The right incentives from the government can influence the implementation of a circular economy and new technical advancements in the right direction. On the other hand, top management commitment can enable a practical implementation process. The assistance of management is influential in adopting successful circular measures in the linear economic structure of the construction industry. Through education, training, and forward-thinking, it is crucial to raise all actors' understanding of a circular economy and their knowledge of its importance, as well as their disclosure of the results to various stakeholders. Therefore, the realization of social and environmental accountability ensures sustainable circular economy business practices, and it requires advanced knowledge and awareness of the circular economy.

In the intermediate levels, acknowledgement of technological advancements (F4), integration of the Internet of Things (F3), and effectual information brokerage (F10) all directly influence building information modeling-integrated life cycle assessment (F2). The life cycle assessment technique provides a thorough environmental evaluation of products, processes, and even services based on their life cycle by maximizing design, reusing, and recycling resources and minimizing the impact on the environment, achieving sustainable construction. Building information modeling enables the early correction of design flaws, material selection problems, and other issues. Most importantly, it can lessen the complexity of data collection needed for the life cycle assessment. Effectual information brokerage (F10) influences building information modeling-integrated life cycle assessment (F2) by providing necessary data to simulate the building information model. A collaborative supply chain (F5) is a crucial enabler as the supply chain actors of the construction industry are very dispersed, creating structural holes due to lack of collaboration and knowledge gathering. Effectual information brokerage (F10) overcomes this problem and achieves a collaborative supply chain (F5) by introducing information brokers who are involved in producing, interpreting, organizing, or communicating information to dispersed actors of the supply chain. Collaborative supply chain (F5) facilitates circular material curation (F8). Circular material curation (F8) and strategic deconstruction (F9) directly influence each other. Strategic deconstruction (F9) prevents the complete demolition of building components, parts, and materials. It ensures the conservation of reusable building material with substantial quality, thus helping to achieve circular material curation (F8). Eco-industrial development (F6) helps to achieve both circular material curation (F8) and a viable remanufacturing process (F1). Creating industrial associations and collaborating with industrial networks help to build eco-industrial development. Government support & policy framework (F7) can initiate the process of creating industrial associations and facilitate it further by implementing flexible policies and regulations.

The MICMAC analysis reveals that no enablers are present in Cluster A, showing that no enablers are disconnected from the model. All the enablers have a significant impact on circular economy implementation in the construction industry. Cluster B consists of building information modeling-integrated life cycle assessment (F2), collaborative supply chain (F5), strategic deconstruction (F9), eco-industrial development (F6), and circular material curation (F8). These enablers fall to Level 2 and Level 3 in the hierarchy level. These enablers have weak driving power but a high dependency level. As these enablers have strong dependence, any changes made to other variables would have an impact on these. Integration of the Internet of Things (F3), acknowledgement of technological advancements (F4), government support & policy framework (F7), effectual information brokerage (F10), top management commitment (F11), and advanced knowledge and awareness of the circular economy (F12) fall in Cluster D, having the most influence over other enablers but a low dependency level. These are the most vital enablers with influence over all other enablers and contribution levels. Thus, it requires careful attention and handling. Enablers in Cluster C have both strong driving power and strong dependence. One of the significant characteristics of the enablers of this cluster is that they are extremely unstable. No enablers fall in this cluster in this study.

This study contributes to the circular economy and construction industry literature by systematically categorizing enablers into four dimensions, i.e., technological, environmental, organizational, and human, thereby extending existing fragmented perspectives into an integrated framework. In doing so, it provides a structured foundation for future theoretical investigations linking circular economy implementation with the SDGs. Unlike prior studies that primarily identify enablers in isolation [36], [37], [59], this research advances theory by revealing the hierarchical and contextual relationships among enablers using the total interpretive structural modeling approach. This enables a deeper understanding of how different categories of enablers interact and influence each other within a complex system.

The findings further offer an important theoretical insight by highlighting the dominant role of human and organizational enablers in driving circular economy adoption, suggesting that technological and environmental factors alone are insufficient without strong managerial commitment and awareness. This challenges the commonly technology-centric view in circular economy literature and emphasizes the need for a more socio-organizational perspective. Methodologically, the application of total interpretive structural modeling demonstrates its potential as a theory-building tool for analyzing interdependencies among sustainability enablers, which can be extended to other domains and industries. Future research could build upon this work by exploring the dynamic interactions among these enablers and validating the proposed relationships using quantitative approaches, thereby strengthening the theoretical robustness of circular economy implementation frameworks.

The United Nations approved the SDGs in 2015 as a call to action to eradicate poverty, protect the planet, and ensure peace and prosperity for all by 2030 [35]. The enablers identified in this study directly impact several SDGs. A viable remanufacturing model reduces costs, impacting SDG8 (Decent Work and Economic Growth). Selecting circular materials and establishing eco-industrial development can reduce natural resource consumption, waste generation, water contamination, and pollution, helping achieve SDG6 (Clean Water and Sanitation), SDG11 (Sustainable Cities and Communities), and SDG12 (Responsible Consumption and Production). Selective demolition helps remove building fixtures, fittings, and materials, supporting SDG11. Building information modeling-integrated life cycle assessment enables early correction of design flaws through environmental evaluation, aiding SDG9 (Industry, Innovation, and Infrastructure). Internet of Things, big data, and analytics support usage-focused business models to improve efficiency, extend product lifespan, and close loops, helping achieve SDG9 and SDG11. Collaborative supply chains and information brokerage bring innovations. Technical advancement acknowledgement helps establish sustainable cities (SDG11). Government support, top management commitment, and advanced knowledge and circular economy awareness each contribute to sustainable development objectives.

Actionable policy and managerial recommendations can be derived from the validated total interpretive structural modeling and MICMAC results. Since government support & policy framework (F7), top management commitment (F11), and advanced knowledge and awareness of the circular economy (F12) are identified as the most influential enablers, policymakers and industry leaders should prioritize these as foundational intervention points. First, government agencies in emerging economies such as Bangladesh should develop circular economy-oriented construc-tion guidelines, incentives for recycled and reusable materials, and regulatory provisions that encourage deconstruction, material recovery, and industrial collaboration rather than conventional demolition and disposal practices. Second, construction firms should translate top management commitment into internal circular economy roadmaps by setting measurable sustainability targets, allocating resources for circular economy adoption, and integrating circular practices into procurement, project planning, and operational decision-making. Third, targeted awareness and capacity-building programs should be introduced for managers, engineers, architects, contractors, and policymakers to improve knowledge of circular economy principles and their practical application in the construction sector.

In addition, the findings suggest that integration of the Internet of Things (F3), acknowledgement of technological advancements (F4), and effectual information brokerage (F10) act as important enabling mechanisms for operationaliz-ing circular practices. Therefore, organizations should invest in building information modeling-integrated life cycle assessments, digital monitoring systems, and information-sharing platforms that support collaboration across fragmented supply chain actors. Industry associations and public agencies may also facilitate shared databases, technical guidelines, and cross-stakeholder coordination platforms to strengthen information flow and material traceability. Furthermore, because collaborative supply chain (F5), circular material curation (F8), strategic deconstruction (F9), and eco-industrial development (F6) are more dependent enablers, decision-makers should treat them as implementation outcomes that can be improved once the stronger driving enablers are activated. In this way, the hierarchy developed in this study can serve as a practical roadmap: begin with policy support, managerial commitment, and awareness building, then strengthen technological and information systems, and finally scale operational circular economy practices across the construction value chain.

Bangladesh is a promising least-developed country with the potential to develop further. One of Bangladesh's top-rising sectors is the construction sector, which has experienced strong economic and infrastructural growth. This growth has led to a substantial increase in waste production and emissions globally. This situation has prompted a reassessment of the linear economy and the adoption of a sustainable circular approach. Therefore, this study aimed to identify the enablers of circular economy implementation in the construction industry and formulate a contextual relationship among them, along with a hierarchical model using the total interpretive structural modeling method. A systematic literature review was conducted to achieve this goal. Forty-five articles were included in the review after a progressive screening process. The findings were then justified by feedback from industrial and academic experts. This process developed a four-dimensional (technological, organizational, environmental, and human) model of crucial enablers. The total interpretive structural modeling was then used to examine the structural frameworks of these enablers for implementing a sustainable circular economy. According to driving power and dependence, all enablers were categorized into four clusters using the MICMAC analysis.

The results reveal that viable remanufacturing processes, building information modeling-integrated life cycle assessment, integration of the Internet of Things, acknowledgement of technological advancements, collaborative supply chains, eco-industrial development, government support and policy frameworks, circular material curation, selective demolition and deconstruction, effectual information brokerage, top management commitment, and advanced knowledge and awareness of the circular economy are among the most significant enablers for circular economy implementation in the construction industry. The identified relationships and dependencies among these enablers provide valuable insights for industry practitioners, enabling more informed decision-making and contributing to enhanced economic and environmental sustainability.

Despite these contributions, this study has some limitations which also open avenues for future research. According to the assessment of industrial and academic experts, only 12 of the many enablers from a thorough literature evaluation were used in this study. In future investigations, additional enablers may be added. A range of experts confirmed the key enablers and verified the final hierarchical total interpretive structural model. Experts were selected based on their backgrounds in relevant fields of study or industry. Forty-five professionals commented on the final enablers, while eight experts validated the interactions among the enablers. These remarks may be biased and produce inconsistent findings.

To address these limitations, the model can be further validated by carrying out a thorough statistical analysis to confirm the existing links and relationships among the enablers, discuss and refuse some of the links that already exist, or justify the omission of some plausible links that are not considered in the model. Other techniques, such as the fuzzy total interpretive structural modeling, can be employed to verify the accuracy of the total interpretive structural model. The structural equation modeling approach may be utilized to validate the theoretical model developed in this study. Moreover, expanding the diversity and number of experts involved in future investigations would enhance the reliability of the results. Future studies could incorporate a larger spectrum of experts into experiments. In this study, enablers were categorized using the MICMAC analysis. Future studies can utilize different analytic techniques, such as fuzzy MICMAC. This study has exclusively focused on implementing a circular economy only in the construction industry. Any other industry that desires to find enablers that affect a sustainable circular economic model can use the same research approaches.