Perceptions of lean construction for waste management in the UAE building sector: a partial least squares structural equation modeling approach

Abstract

Despite having a significant socio-economic impact on a country’s growth, one of the biggest contributors to waste generation is the construction sector, thus increasing the necessity for innovative construction waste management strategies. Lean Construction (LC) is a much-adopted methodology that helps manage construction waste efficiently. This study investigates the barriers to LC implementation in the UAE’s building construction sector, with a specific focus on its potential for effective construction waste management. Recognizing the urgent need to adopt sustainable practices in the face of escalating environmental concerns, the study employs a quantitative exploratory approach combining literature synthesis, expert validation, descriptive statistical analysis, Relative Importance Index (RII) ranking, and Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine stakeholder perceptions across eight critical categories: Knowledge/Awareness, Attitude, Management, Government, Financial, Material/Resource, Technical, and Other contextual factors. The RII analysis ranks these barriers based on stakeholder responses, while the PLS-SEM approach models the strength and significance of their interrelationships within a validated structural framework. The findings reveal that while all eight categories significantly influence stakeholder perceptions, Attitude and Management factors exhibit the strongest impact, highlighting the importance of behavioral and organizational readiness in enabling LC adoption. The key deliverable of this research is a validated and empirically supported structural model that provides a strategic roadmap for overcoming resistance to LC implementation. By offering actionable insights into which barriers matter most and how they interact, the study equips policymakers, contractors, and industry stakeholders with evidence-based guidance to design targeted interventions. Ultimately, this research contributes to the growing discourse on sustainable construction by positioning LC as a viable pathway for reducing construction waste and improving efficiency in the UAE context.

1 Introduction

The construction industry is crucial for the socio-economic development of a nation as it significantly helps improve its residents’ standards of living by providing fundamental and enhanced infrastructure, such as roads, hospitals, schools and others. However, this industry is also one of the most significant contributors toward waste, thus, also affecting the need for new and innovative methods of effective waste management.

In this regard, the construction industry is held accountable for its various unsustainable practices including the use of non-renewable resources, the production of waste, energy and water consumption, and pollution, the most detrimental being the construction waste generation (Sadi et al., 2012). The United States Environmental Protection Agency (EPA) (US EPA-OLEM, 2016) defines construction and demolition (C&D) waste as the waste produced during the construction, refurbishment, and destruction of buildings, roads, and bridges. It contains a wide variety of materials such as concrete, bricks, wood, glass, metals, plastic, dust and more.

As per an article by British Broadcasting Corporation (BBC) (Miller, 2021), a third of the world’s waste and at least 40% of its carbon dioxide emissions are estimated to come from the construction sector. Further, according to the EPA’s (US EPA-OLEM, 2016) 2018 waste characterization report, the United States generated 600 million tons of construction and demolition waste in 2018, which was more than twice that of municipal solid waste. Likewise, in 2020, more than 37% of all waste produced in the European Union came from C&D operations (Statista, 2021).

Additionally, in the United Arab Emirates (UAE), the waste generated from the construction industry accounts for the largest share of the total waste produced in the country. In 2021, the Emirate of Abu Dhabi alone produced about 10.4 million tons of solid waste, of which, 37% of the non-hazardous solid waste was solely from the C&D sector (Environmental Environmental Agency—Abu Dhabi, 2022). Similarly, with an estimated 77 million metric tons produced in 2022, construction waste was, by far, the most prevalent kind of waste produced in Dubai (Saleh, 2023). Consequently, a significant amount of collected waste, including those from the C&D industry, eventually ends up in dumps or landfills outside of major cities. These landfills or dumpsites might not be designed to handle the greenhouse gas (GHG) emissions, waste sludge, odor, and soil and water contamination that follow, hence, leading to disastrous consequences on the environment and human health. Thus, in light of these increasing threats posed by the C&D waste, implementation of new and effective construction waste management approaches is of utmost importance. The Lean Construction (LC) approach is one such method.

Furthermore, the adoption of LC aligns closely with the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production) (United Nations, 2015). By promoting process efficiency, minimizing resource waste, and optimizing material flow throughout the project lifecycle, LC contributes directly to sustainable urban development and responsible production practices. Within the UAE’s construction context, where waste minimization is a key national priority, enhancing LC adoption supports not only operational efficiency but also the broader transition toward sustainable and circular built-environment practices.

While LC has demonstrated strong global uptake in countries such as the United Kingdom, United States, among others, driven by advanced procurement systems, robust training ecosystems, and integrated project delivery models, its application within the distinct regulatory and industrial environment of the UAE remains relatively limited. Despite the UAE’s strategic emphasis on sustainability through initiatives such as Vision 2030 and Net Zero by 2050, LC has not yet been widely institutionalized within mainstream construction practices. The UAE’s construction sector presents a unique set of challenges. These contextual differences limit the direct transferability of global LC solutions and necessitate locally grounded research. Moreover, although international literature emphasizes LC’s benefits for waste reduction, empirical studies investigating how UAE stakeholders perceive its barriers and applicability are scarce, especially using quantitative frameworks. This study addresses this gap by exploring how key organizational, managerial, and contextual factors influence perceptions of LC implementation in the UAE’s building sector, using a structural modeling approach to uncover patterns unique to this regional context. Accordingly, this study seeks to answer the following research questions:

What are the main barriers to implementation of LC methodologies for waste management in the UAE building construction sector, and how are these barriers associated with specific construction and Lean waste types across project phases?

How do stakeholders in the UAE’s building construction sector perceive these barriers, and which of these barriers exert the most significant influence on their perceptions of LC adoption?

What are the most commonly found construction waste types in the UAE building construction sector, and how do stakeholder-perceived barriers translate into construction waste generation patterns across different construction project phases?

To address this, a dual-method approach was adopted, integrating Partial Least Squares Structural Equation Modeling (PLS-SEM) and Relative Importance Index (RII) techniques to quantify both conceptual influences and practical waste manifestations. This enables a systems-level understanding of how various factors interact to shape LC implementation and its impact on waste management outcomes.

2 Literature review

2.1 Lean construction

2.1.1 Origin

The term “Lean” was originally used in 1988 to refer to the management concept of lean manufacturing, which has its roots in Toyota’s manufacturing System of the 1930s (Howell and Ballard, 1998). With its origins in the lean production system, LC emerged in the early 1990s by approaching the building process from three angles: transformation, flow, and value. Figure 1 gives an overview of the development of the “Lean Construction (LC)” concept over the years.

Figure 1

The idea of lean thinking and its benefits are realized through the adherence to five fundamental principles of lean production (Eskander and Hafez, 2013):

• The first principle involves specifying the customer’s value, which requires a thorough understanding and identification of the client’s wants, focusing solely on what the client desires.

• The second principle is understanding the value stream, which refers to the sequence of actions that, when executed correctly and in the right order, result in the product or service that the client values. Activities are categorized as superfluous and wasteful (which should be eliminated), supporting value-adding activities (which should be minimized as much as possible), and client value-adding (which should be continuously enhanced).

• The third principle is about improving the flow of work to ensure it is consistent and uninterrupted from one value-adding or supporting activity to the next. This flow significantly accelerates processing, and all efforts should be made to eliminate obstructions and bottlenecks that impede this flow.

• The fourth principle involves implementing a pull strategy to respond to client demand. Unlike non-lean businesses where work is pushed, resulting in the creation of unneeded outputs, most lean services respond to consumer demand and thus pull work through the system.

• The fifth and final principle is perfection. Based on the previous four principles, organizations should strive to gain a deeper understanding of the system and provide recommendations for further improvement. An excellent process delivers just the right amount of value to the client. In an ideal process, each stage is value-adding, capable (produces a good outcome every time), available (produces the intended output, not just the desired quality, every time), adequate (does not cause delay), adaptable, and linked by continuous flow. If any of these elements fail, some waste is inevitably generated.

2.1.2 Suitability of lean principles for the construction industry

The primary objective of implementing Lean ideas in the construction industry is to eliminate the significant waste from its materials, processes, and workflows (Aslam et al., 2020). LC employs a suite of tools designed to identify and systematically reduce waste across various domains, including time, inventory, space, labor, equipment, and money. This approach ultimately enhances quality while reducing both production time and costs. Traditionally, sustainable construction efforts have prioritized the building’s operational performance, often overlooking the waste generated during the construction process itself. Nonetheless, integrating Lean thinking into construction practices offers the potential to substantially boost productivity and minimize waste generation. By focusing on adding value and eliminating inefficiencies, Lean promotes continuous and incremental improvements in both processes and outcomes (Waite, 2020).

Moreover, the construction industry encompasses a wide range of core activities, such as regulation, design, planning, production, construction, and maintenance. However, the complexity of these activities—marked by fragmented responsibilities and poorly defined interactions—often obscures a clear understanding of the industry’s structure and the challenges it faces (Hughes et al., 2015). The sector is often characterized by low productivity, cost overruns, delayed schedules, flaws, a bad reputation, insufficient safety precautions, and a scarcity of competent workers (Thomassen and Andreas, 2004). Additionally, the sector exhibits distinct characteristics that set it apart from other industries—the physical nature of its products, on-site project execution on the client’s premises, the uniqueness of most designs, the division of design and construction phases, the sequential nature of construction processes, and the pricing mechanisms used. These factors collectively contribute to the unique challenges and opportunities for improvement within the construction industry.

2.1.3 Application of lean principles in construction

As previously mentioned, LC is the constant process of eliminating waste, meeting or exceeding all client needs, focusing on the full value stream, and striving for perfection in the execution of building projects (Diekmann et al., 2004). To create a lean workplace, construction could implement a set of lean concepts and best practices, including customer focus, culture/people, workplace standardization, waste elimination, and continuous improvement/built-in quality.

Lean concepts have been introduced into the construction industry in various nations, including Australia, Brazil, Denmark, Ecuador, the UK, the USA Peru, and Finland (Fernández-Solís, 2008). However, transitioning to Lean is not a quick or isolated process; it involves a fundamental transformation of the entire organization. Lean is not merely a set of tools or rules—it is a shared philosophy and behavioral system that must be integrated throughout the value chain. Achieving Lean requires a long-term commitment to cultural and operational change.

Given the complex, dynamic, and often unpredictable nature of construction projects, Lean principles must be contextually understood and applied. This necessitates a deep understanding of the construction system as an interdependent network of processes and stakeholders (Diekmann et al., 2004). Lewis (2000) emphasized that while Lean offers significant benefits, its success in implementation heavily depends on the organizational context and prevailing culture.

Accordingly, the tenets of lean thinking in construction can be encapsulated into these key points (Xue et al., 2014). First and foremost, it emphasizes decreasing the shares of non-value-adding activities. This is followed by increasing the value of output by providing systematic configurations that cater to the needs of the customer. It also focuses on decreasing the variability that may occur in the process. Simplifying the different number of steps and parts is another crucial aspect. Furthermore, it aims at increasing the flexibility of output and enhancing the transparency of the process. Concentrating on the completion of the process is another significant point. It also underscores the importance of providing continuous improvements in the process and maintaining a balance between flow and conversion improvement. Lastly, establishing a benchmark is an integral part of these principles. These points collectively form the foundation of lean thinking.

Furthermore, the application of a lean system offers a multitude of benefits to companies (Abo-Zeid and Othman, 2018; Aslam et al., 2024). It enables a significant reduction in waste, cuts production costs, allows for a decrease in manufacturing cycle times while simultaneously reducing labor and maintaining or even increasing throughput. Furthermore, it aids in reducing inventory, which in turn increases customer service levels. The capacity in current facilities can be increased through the implementation of this system. It also plays a crucial role in improving quality and increasing profits. The system’s flexibility is enhanced, allowing it to react swiftly to changes in requirements. It also helps in creating a better strategic focus. Lastly, it improves cash flow by increasing shipping and billing frequencies. Thus, lean production methods provide a comprehensive solution for enhancing operational efficiency and profitability.

2.1.4 Lean tools for prevention and/or reduction of waste

Lean waste refers to any activity or element that adds cost without delivering value to the product or the end customer (Klein et al., 2020). In this context, eight classic categories of waste have been identified: transportation, motion, overproduction, inventory, waiting, over-processing, defects, and underutilized talent. A critical step in progressing toward a more efficient and leaner organizational state is the identification of the most prevalent or most detrimental forms of waste that should be addressed first. Furthermore, for successful Lean implementation, organizations must systematically identify, evaluate, and prioritize these waste types, focusing initially on those that offer the greatest potential for rapid improvement or quick, visible gains in productivity. The eight lean waste types are listed in Table 1 along with their respective description.

Several Lean strategies play a vital role in preventing/reducing waste within construction projects (Xue et al., 2014):

• Process Analysis is a well-known tool for waste reduction in building projects. Each organization has its criteria and standards, which results in a unique type of process and analysis for its initiatives. This tool is essential for defining project objectives clearly and systematically, allowing teams to analyze workflows and pinpoint inefficiencies that contribute to waste.

• Pull Technique is another effective Lean strategy, which ensures that materials and resources are delivered to the construction site only as needed, based on project demands and timelines. Proper implementation of this technique enhances planning accuracy, reduces excess inventory, and eliminates unnecessary handling—thereby cutting waste and improving overall workflow efficiency.

• Mistake-proofing techniques is a proactive approach that improves project quality by preventing errors before they occur. It helps minimize rework, reduce delays, and ensure consistency in performance. By limiting the likelihood and consequences of errors in both products and processes, mistake-proofing significantly contributes to waste reduction and productivity gains.

In addition, there are numerous other Lean tools available, each supported by structured guidelines for adoption. Among the most prominent are 5S, Plan Do Check Act (PDCA), Continuous Improvement Cycle (CIP), and Kaizen. These methodologies share common philosophical principles, emphasizing iterative cycles and continuous improvement. These also require a bidirectional flow of information—communicating management goals and decisions while incorporating feedback from frontline personnel. This feedback loop enables teams to align improvements with organizational objectives and fosters a culture of proactive problem-solving. 5S, PDCA, CIP and Kaizen are depicted in Figure 2 (Waite, 2020) to show their underlying philosophies of iterative and cyclical processes seeking continual improvement.

Figure 2

Ultimately, the value of Lean lies not in the mechanical application of tools, but in embracing its core strategies of waste elimination, transparent communication, and relentless pursuit of improvement.

2.2 Impact of lean construction on construction waste management

A number of literatures point to the fact that LC is highly beneficial in the reduction of waste produced during the life of a construction project, right from its inception to even after its demolition. This section briefly looks through some of these literatures.

To begin with, a case study carried out by Nowotarski et al. (2019) present the benefits of LC toward waste reduction at an office building construction site. Lean Management was implemented for the performance of three different processes in the project and it was concluded that LC approach helped significantly reduce (by about half) the waste generated at the site. Furthermore, Agyekum et al. (2013) examined the application of LC approach for minimizing material wastage in the construction industry in Ghana. Likewise, Abo-Zeid and Othman (2018) investigated the role of Lean approach for reduction of waste in the Egyptian construction industry. A comprehensive understanding of various aspects regarding construction waste, its causes and impacts as well as the challenges and benefits of the implementation of LC in a construction project was highlighted. Additionally, the perception and application of LC for the reduction of construction waste was emphasized upon in this study. Similarly, a study by Waite (2020), lays stress on the use of Lean Management Principles to reduce waste and achieve sustainability in the construction sector. Furthermore, a case study by Berroir et al. (2023) investigated the applicability of LC and Artificial Intelligence (AI) and their potential synergy to aid in on-site construction waste management. In addition, Maraqa et al. (2023) provides strategies for waste reduction in construction projection using lean principles. Also, Karaz and Teixeira (2020) conducted a systematic literature review on construction waste management using LC and Building Information Modeling (BIM), providing useful insights into the relevance of LC for effective construction waste management.

2.3 Lean construction implementation in the UAE’S construction sector

Despite its numerous proven advantages for construction waste management globally, very few studies emphasize on the applicability of LC in the UAE’s construction sector (Small et al., 2017). A study by Bakry (2022) analyzed the suitability of lean tools for effective application in the UAE’s construction industry, by comparing the features of the regional construction market with an assessment of Lean tool capabilities. Al-Aomar (2012) carried out an analysis of LC practices in the construction industry within the Emirate of Abu Dhabi, with the aim of developing a framework for effective application of the approach in the C&D industry. According to the report, only 32% of the organizations surveyed within Abu Dhabi had either used or are currently aware of LC approaches. Warid and Hamani (2023) conducted a case study to investigate the suitability of applying the Last Planner System of LC in UAE construction projects and concluded its successful implementation in the industry. Additionally, Shurrab and Hussain (2018) carried out an empirical investigation to examine the manner in which lean methodology affects the performance of medium and large construction businesses in the UAE. Furthermore, Small et al. (2017) examined the opportunities for integrating LC methods into the construction industry in Dubai by providing insights into specific techniques for overcoming institutional resistance. Additionally, Kanafani (2015) investigated the barriers to the application of Lean principles in the UAE’s construction sector and concluded that lack of commitment from stakeholders overall and from management in particular was the greatest barrier. Another study by Wajahat (2022) also highlighted the barriers toward Lean implementation in the UAE construction industry, offering potential solutions from a Swedish context. Also, Watfa and Sawalha (2021) conducted an empirical study on the critical factors for the successful application of LC in the UAE construction sector.

In conclusion, numerous studies have demonstrated the effectiveness of LC (LC) methodologies in reducing construction waste. However, adoption of these practices within the UAE’s construction sector remains limited, and research on this subject in the region is relatively scarce. Notably, there is a lack of studies that explore stakeholders’ perceptions of LC specifically in the context of construction waste reduction, particularly within the UAE’s building construction sector.

Consequently, this gap underscores the necessity for the present study, which aims to assess the perceptions of professionals in the UAE construction industry regarding the application of Lean Construction. The objective is to gain insight into the attitudes, beliefs, and behaviors of stakeholders, and to identify the key factors influencing the adoption of LC practices. By doing so, the study seeks to inform the development of effective strategies for the successful implementation of LC in the UAE, thereby enhancing construction waste management with a focused lens on the building construction sector.

Tables 2, 3 present the foundational insights derived from the literature, detailing the primary types of construction waste and their prevalence across different project phases, respectively. These insights contextualize the relevance of Lean principles in addressing specific waste streams within the construction industry. Table 4 builds upon this by synthesizing a comprehensive list of LC implementation barriers, grouped into thematic constructs identified through content analysis of the literature.

Based on this synthesis, the following section develops a set of hypotheses to test the influence of these identified constructs on stakeholder perceptions of LC adoption. These hypotheses form the conceptual framework evaluated through the PLS-SEM approach employed in this study.

2.4 Hypotheses development

The hypotheses tested in this study were developed based on a thematic synthesis of individual barriers LC adoption identified in prior literature. Rather than relying on a pre-existing framework, the study first compiled a comprehensive list of LC implementation barriers drawn from numerous scholarly sources. These barriers were then categorized into conceptually coherent constructs based on observed patterns and similarities in their descriptions. The process involved inductive grouping of barriers into eight overarching categories namely, Knowledge/Awareness, Attitude, Management, Government, Financial, Material/Resources, Technical, and Other, each representing a distinct dimension of influence on stakeholder perceptions.

Table 4 presents the detailed mapping of individual indicators (barriers) under each construct, along with citations to the respective sources from which these barriers were extracted. This data-driven categorization process enabled the formulation of hypotheses that align both with observations from previous studies and with the structural requirements of the proposed research model.

Based on this thematic structuring, the following hypotheses are proposed to assess how these eight categories of barriers influence stakeholder perceptions of LC adoption in the UAE building construction sector (illustrated in Figure 3):

Figure 3

H1: Knowledge/awareness factor (K) has a significant impact on the perception of stakeholders toward LC implementation.

H2: Attitude factor (A) has a significant impact on the perception of stakeholders toward LC implementation.

H3: Management factor (M) has a significant impact on the perception of stakeholders toward LC implementation.

H4: Government factor (G) has a significant impact on the perception of stakeholders toward LC implementation.

H5: Financial factor (F) has a significant impact on the perception of stakeholders toward LC implementation.

H6: Material requirement/Resources factor (R) has a significant impact on the perception of stakeholders toward LC implementation.

H7: Technical factor (T) has a significant impact on barriers on the perception of stakeholders toward LC implementation.

H8: Other factor (O) has a significant impact on the perception of stakeholders toward LC implementation.

Testing these hypotheses helps validate the conceptual framework proposed in this study, which integrates stakeholder perceptions with operational challenges in LC implementation. By empirically examining how distinct categories of barriers impact stakeholder perception toward adoption of LC, the study contributes to addressing the research gap concerning systemic limitations to LC adoption in developing construction markets such as the UAE.

3 Materials and methods

3.1 Research design

This study employed a quantitative exploratory research design. In the first phase, a comprehensive literature review and expert validation were conducted to identify and refine the barriers influencing LC adoption in the UAE. The outcome of this phase was a set of 49 validated barriers grouped into eight categories. In the second phase, a structured questionnaire was developed based on these barriers and distributed to construction industry professionals. The collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to investigate barriers to LC implementation and perceptions of its effectiveness in the context of the UAE’s building construction sector. The overall flow diagram of the research methodology is illustrated in Figure 4. This diagram provides a visual summary of the methodological stages, clarifying the logical progression from barrier identification and validation through quantitative analysis and interpretation. It enhances transparency and replicability by outlining each key step in the data collection and analysis process. This section gives a detailed description of the various phases of this study.

Figure 4

3.2 Identification and expert validation of barriers to adoption of lean construction in the UAE

To support the development of the theoretical framework, a targeted literature search was carried out using academic databases such as Scopus, ScienceDirect, and Google Scholar. Search terms included “Lean Construction,” “construction waste,” “barriers to Lean implementation,” and “UAE construction sector,” with a focus on recent peer-reviewed publications. Sources were selected based on conceptual relevance and citation impact to ensure practical alignment with the study objectives. An initial literature review was conducted to identify the types of construction waste commonly generated, the stages of the construction process during which they arise, and potential obstacles hindering LC adoption. This foundational research informed the development of key research instruments and is summarized in Tables 2–4, which, respectively, outline the types of construction waste, construction phase-wise waste generation, and recognized LC implementation barriers. To strengthen the analytical connection between the identified LC implementation barriers and their practical implications on waste management, a column was included within Table 4 linking each barrier to its corresponding Lean waste and construction waste category. This mapping was developed through content analysis of prior studies and cross-referencing with the eight classical Lean waste types and 10 major construction waste streams summarized in Tables 1, 2. The inclusion of this mapping enables a clearer understanding of how specific organizational and technical barriers contribute to distinct forms of waste generation within the UAE’s building sector.

The gathering of data was an essential and crucial part of this study as it helped to determine how stakeholders in the UAE understands LC and construction waste generation. The distribution of a questionnaire to professionals actively engaged in the building construction industry in the UAE was the chosen quantitative data collecting strategy for obtaining the perspectives and understandings of the stakeholders.

To ensure contextual relevance and validity, the survey underwent expert validation by a panel of six experienced professionals from diverse roles within the UAE construction industry. Their expertise spanned a spectrum of projects including residential villas, residential buildings (ranging from G + 5 to G + 10), commercial developments (such as malls and offices), industrial facilities, and infrastructure ventures. In an authoritative capacity, the expert panel undertook the validation of the existing barriers. During the expert validation phase, each of the initially identified 49 barriers was evaluated based on three criteria: (1) contextual relevance to the UAE construction sector, (2) frequency of occurrence in practice, and (3) perceived impact on LC implementation. Their feedback led to refinements in the questionnaire, including the removal of specific indicators such as “Inflation in material prices due to unsafe market conditions for construction” (G4), “Corruption due to bribery, extortion and fraud” (G5), and “Inaccurate and incomplete designs” (T6), while retaining others that received neutral responses—“Inconsistent governmental policies (not constant and steady) over time” (G2), “Government bureaucracy and instability” (G3) and “Complexity of LC implementation” (T5)—but were deemed relevant in the local context. Barriers such as corruption, inflation instability, and governmental inconsistency were excluded as experts unanimously confirmed that these issues are largely mitigated in the UAE through strict legal enforcement, transparent tendering systems, and stable macroeconomic conditions. Their exclusion strengthened the contextual validity of the study instrument and ensured that the final barrier set accurately reflects the practical realities of LC adoption in the UAE. The conceptual integrity of the affected constructs was maintained, as the remaining items continued to represent the same underlying dimensions influencing LC implementation in the UAE building industry. Table 5 presents the background details of the expert panel involved in the validation process.

3.3 Data collection

3.3.1 Survey design

Following expert validation, the final questionnaire was prepared and structured into three distinct sections to enable a comprehensive understanding of stakeholder perceptions and waste management practices. Section 1 focused on gathering demographic data about the respondents, such as their job roles, years of experience, and sector of involvement. Section 2 was designed to assess stakeholder perceptions regarding the adoption of LC, using the set of barriers and indicators previously identified in Table 4 after the inclusion of modifications suggested by the expert panel. Section 3 aimed to understand waste generation practices and trends within the UAE’s building construction industry, capturing information about the types and sources of construction waste encountered in practice. All questions in the latter two sections collected responses based on Likert scale—Extremely High (5), High (4), Moderate (3), Low (2), and Extremely Low (1). The conversion from the qualitative scale, of Extremely High to Extremely Low, to numerical scale, of 5–1 respectively, was carried out manually using Microsoft Excel. A 5-point Likert scale was selected as it offers a balanced range of responses while maintaining simplicity for respondents, thereby enhancing survey completion rates.

3.3.2 Sampling

The survey was administered online, over a period of 1 month, to ensure broad accessibility and outreach. A combination of purposive and snowball sampling methods was adopted. Initially, responses were collected from professionals working directly within the UAE building construction sector, following which, participants were also encouraged to forward the survey to colleagues in similar roles to enhance participation. In total, 77 valid responses were obtained, including five from individuals currently working in infrastructure development but with relevant prior experience in building construction. These responses were included due to their relevance to the study. All responses obtained were complete, resulting in 100% valid response rate. Thus, no data cleaning or exclusions were required.

3.4 Data analysis

The data obtained from the three sections of the questionnaire were systematically analyzed using both descriptive and inferential statistical techniques to address the study objectives. Data from section 1 (respondent demographics) were analyzed to provide an overview of the sample composition in terms of age, experience, professional role, and prior exposure to LC. Data from sections 2 and 3 were subsequently subjected to inferential analysis using two complementary techniques: Partial Least Squares Structural Equation Modeling (PLS-SEM) and the Relative Importance Index (RII). The PLS-SEM method was employed to evaluate the hypothesized relationships among latent constructs and to assess the reliability and validity of the measurement and structural models. In parallel, the RII method was used to prioritize the identified barriers to LC adoption and to analyze the perceived frequency and significance of different construction waste types and sources. The integration of these analytical approaches enabled both theoretical validation and practical ranking of the factors influencing LC implementation and construction waste management within the UAE’s building sector.

3.4.1 Partial least squares-structural equation modeling analysis

Partial Least Squares Structural Equation Modeling (PLS-SEM) is a robust statistical technique designed to examine complex relationships between observed (manifest) variables and latent constructs within a reflective measurement model (Hair et al., 1998). Quantitative data collected for section 2 were mainly analyzed using SmartPLS 4 employed for PLS-SEM analysis. The sample size of 77 respondents falls below commonly recommended thresholds for complex PLS-SEM models. However, unlike covariance-based structural equation modeling (CB-SEM), PLS-SEM is particularly well-suited for exploratory research, smaller sample sizes, and theory development rather than theory confirmation (Huang, 2021; Henseler and Sarstedt, 2013). Also, Henseler and Sarstedt (2013) states that the sample size requirements in PLS-SEM is usually independent of the overall model complexity. Additionally, a separate G*Power calculation was conducted to check for the adequacy of the sample size obtained. With 77 responses, 8 predictor constructs, an alpha level of 0.05, and an assumed medium effect size (f² = 0.15), the resulting statistical power was 0.81, exceeding the conventional threshold of 0.80 recommended for ensuring statistical reliability (Cohen, 1977). Hence, PLS-SEM is the chosen methodology for this study, given its exploratory nature and the under-researched context of LC barriers in the UAE (Hair et al., 1998). The SEM path model is shown in Figure 3.

The analysis proceeded in two stages: (1) assessment of the measurement model, and (2) assessment of the structural model. The first stage involved evaluating indicator reliability, internal consistency, convergent validity, and discriminant validity to ensure that the reflective constructs were measured accurately and consistently. In the second stage, the structural relationships among the latent constructs were estimated using path analysis and bootstrapping procedures to determine the strength and statistical significance of each hypothesized path.

Model quality was further evaluated using common PLS-SEM criteria such as the Standardized Root Mean Square Residual (SRMR) for model fit, the Variance Inflation Factor (VIF) to test for multicollinearity, and R² and f² statistics to assess explanatory power and effect size. The details of these results, along with the corresponding indices, are presented and discussed in section 4.2.1.

This structured approach ensured that both the measurement and structural aspects of the model were rigorously tested in line with established PLS-SEM practices (Hair et al., 1998; Fornell and Larcker, 1981; Hu and Bentler, 1998; Henseler et al., 2015; Huang, 2021).

3.4.2 Relative importance index analysis

To complement the PLS-SEM findings, RII method was used to rank both the identified barriers (section 2) and the types of construction waste (section 3) based on stakeholder perceptions of their significance. The RII method is commonly utilized to determine the relative significance of various criteria or indicators as perceived by respondents. This approach enables a comparative assessment of stakeholder priorities or perceptions regarding specific factors. The RII values range from 0 to 1 (excluding 0), with higher values indicating greater perceived importance of a given criterion within the context of the survey. The RII formula used is (Aravindh et al., 2022):

Where, n1—number of respondents who selected “Extremely Low.”

n2—number of respondents who selected “Low.”

n3—number of respondents who selected “Moderate.”

n4—number of respondents who selected “High.”

n5—number of respondents who selected “Extremely High.”

A—highest value of the scale = 5 in this study.

N—total number of respondents.

This analysis enabled the identification of the most critical barriers affecting LC adoption in the UAE, complementing the PLS-SEM analysis by offering a practical ranking of barrier significance. The combined use of PLS-SEM and RII provided both statistical validation and managerial prioritization, ensuring that the study findings were theoretically grounded and practically relevant.

Furthermore, in addition to ranking the identified barriers to LC adoption in the UAE, RII method was also applied to assess and rank construction waste generation patterns across three distinct dimensions (analysis carried out on data obtained from section 3 of the survey):

• By Construction Waste Type: Respondents rated the frequency of occurrence for 10 common categories of construction waste given in Table 2, derived from literature and previous regional studies.

• By Lean Waste Category: Participants assessed the frequency of waste occurrence corresponding to the eight Lean waste types from Table 1.

• By Construction Project Phase: Respondents indicated the project stages mentioned in Table 3, during which waste generation is most prevalent.

This combined RII approach enabled both diagnostic and comparative evaluation of waste generation trends, helping to identify the most critical sources and phases of waste within UAE construction projects. The detailed results of this analysis are presented in section 4.2.3.

Integration of Barrier–Waste Analysis for Developing Recommendations.

In addition to the above-mentioned quantitative analyses, the study included an interpretive step that connected the identified barriers to their corresponding Lean and construction waste types, as summarized in Table 4. This linkage was established through literature synthesis and cross-referencing, ensuring theoretical consistency with established LC principles. The purpose of this association was to clarify how specific implementation barriers contribute to distinct waste streams within the UAE’s building construction context.

Furthermore, this interpretive step was extended through the integration of results from the PLS-SEM and RII analyses in section 4.3. While the PLS-SEM model revealed the relative influence of barrier constructs, such as managerial, attitudinal, and knowledge-related factors, on stakeholder perceptions toward LC adoption, the RII analysis identified the most critical barriers and waste types as perceived by practitioners. Synthesizing these findings allowed for a deeper understanding of how conceptual barriers translate into operational inefficiencies and waste patterns.

The results of this analytical integration directly informed the development of the strategic recommendations presented in section 4.4. By identifying which barriers are most closely tied to particular forms of waste and how these relationships manifest statistically and practically, the recommendations were designed to address both the root causes of waste management inefficiency and their outcomes. This integration ensured that the proposed strategies for enhancing LC adoption were explicitly aligned with waste reduction objectives and provided the analytical basis for the synthesized interpretation presented in section 4.3, which in turn guided the development of the strategic recommendations in section 4.4.

Overall, this methodological design provides a rigorous and context-specific evaluation of how LC is perceived in the UAE, offering insights into key implementation barriers and informing strategic interventions for effective construction waste management in the country’s building sector using LC.

4 Results and discussion

4.1 Demographic information

The first section of the survey was designed to gather general demographic information about the respondents, enabling a contextual understanding of their professional backgrounds and the characteristics of their respective organizations. This information provides essential context for interpreting the subsequent responses related to LC implementation and perceptions. A summary of the demographic data is presented in Table 6.

This demographic data reveals that a significant majority of respondents (81.8%) were between 25 and 35 years old, indicating a relatively young professional cohort. Most respondents held at least a Master’s degree (55.8%), and over 79% were affiliated with contracting or consulting firms. The predominant involvement was in residential construction, especially mid-rise buildings (G + 5), followed by high-rise and villa projects. Additionally, only 20.8% reported that LC principles were already implemented in their organizations.

This demographic composition accurately represents the current profile of the UAE’s building construction workforce, which is characterized by a relatively young and operationally engaged professional base. Importantly, the low proportion of respondents with LC experience aligns with the study’s focus on identifying barriers to adoption rather than evaluating mature implementation. The perspectives of these professionals therefore provide direct insight into the practical, managerial, and cultural factors that currently limit LC diffusion across the sector. However, as most participants are general construction practitioners rather than LC specialists, the results are intended to represent industry-wide perceptions rather than expert evaluations.

4.2 Perception toward lean construction implementation in the UAE’S building construction sector

The responses collected from section 2 of the survey were utilized to perform both Partial Least Squares Structural Equation Modeling (PLS-SEM) and Relative Importance Index (RII)-based analyses, while responses from section 3 were analyzed using RII alone. Participants rated each item using a five-point Likert scale, ranging from “Extremely High” to “Extremely Low.” For analytical purposes, these qualitative responses were converted to corresponding numerical values on a scale from 5 to 1, respectively. The transformed data were then employed to assess the perceived significance and interrelationships of the identified indicators and barriers related to LC adoption within the UAE construction sector.

4.2.1 Partial least squares-structural equation modeling analysis

The responses obtained from this section of the survey were imported into SmartPLS 4 for conducting the Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis. The relatively small sample size limits the generalizability of the results. The study should thus be considered exploratory, providing initial insights to guide future large-scale research.

This method is employed to determine the existence and strength of statistically significant linear relationships among constructs, making it highly effective for developing and testing theoretical frameworks. In the current study, PLS-SEM was utilized to investigate the interconnections among the identified constructs and to evaluate the validity of the proposed research model. The analysis involved repeated iterations using the PLS algorithm and bootstrapping procedures to compute path coefficients and assess their statistical significance. These results provide insights into the relationships and relative influence among the factors under investigation. The hypotheses presented in section 2.5 (and Figure 3) were tested using PLS-SEM to evaluate the relationships between the identified barrier constructs and LC implementation perceptions.

Firstly, the measuremental model was evaluated for its indicator reliability and validity. Reliability pertains to the consistency and stability of the measurement instruments employed in a study. In the context of PLS-SEM, reliability is assessed through two key components: individual item reliability and internal consistency (Hair et al., 2021a; Hair et al., 1998). Individual item reliability is typically evaluated using factor loadings, with values exceeding 0.60 considered acceptable (Fornell and Larcker, 1981), while the internal consistency is assessed through Composite Reliability (CR) and Cronbach’s alpha. Cronbach’s alpha measures the extent to which items within a construct are interrelated, providing an estimate of the shared variance among the indicators relative to the total variance (Collins, 2007; DeVellis, 2005). Values of Cronbach’s alpha range between 0 and 1, with values above 0.70 generally indicating a high level of internal consistency of a factor.

Conversely, validity refers to the degree to which the measurement instrument accurately captures the intended constructs. Two primary forms of validity assessed in this study are convergent validity and discriminant validity. Convergent validity reflects the extent to which multiple indicators of the same construct are correlated, and is evaluated using Average Variance Extracted (AVE). An AVE value above 0.50 is recommended to establish adequate convergent validity (Hair et al., 2021a; Bagozzi and Yi, 1988). Discriminant validity, on the other hand, assesses the extent to which a construct is truly distinct from other constructs. It is commonly evaluated using the Heterotrait–Monotrait (HTMT) criterion (Fornell and Larcker, 1981), which compares the average correlations between indicators of different constructs (heterotrait) with those of the same construct (monotrait) (Hair et al., 2021a; Henseler et al., 2015). When the HTMT ratio is below a specified threshold, typically 0.85 or 0.90, it indicates good discriminant validity, confirming that the constructs are distinct and not capturing the same underlying concept.

As presented in Table 7, the factor loadings for all items in this study exceed 0.70, thereby satisfying the threshold for individual item reliability. Furthermore, both the Cronbach’s alpha and Composite Reliability values for all constructs are above 0.70, indicating strong internal consistency. Additionally, the AVE values for all constructs are greater than 0.50, confirming acceptable convergent validity. These results collectively demonstrate that the measurement model exhibits robust reliability and validity.

In addition to AVE and composite reliability, discriminant validity was confirmed using the Heterotrait–Monotrait (HTMT) criterion. All HTMT ratios ranged between 0.42 and 0.81, remaining below the conservative threshold of 0.85. These results confirm that each construct is empirically distinct from the others, satisfying the discriminant validity requirement for SEM analysis. The validity results further confirmed that the refined constructs retained theoretical coherence following expert validation.

Furthermore, the Standardized Root Mean Square Residual (SRMR) is a widely used goodness-of-fit index in PLS-SEM to evaluate the adequacy of the overall model. The SRMR value ranges between 0 and 1, with a value below 0.08 generally indicating a satisfactory model fit (Hu and Bentler, 1998). In this study, the SRMR value obtained for the model is 0.0601, thereby confirming that the model demonstrates a good fit. The model fit indices, including the SRMR value, are presented in Table 8. It is important to note that the SRMR value was derived directly from the SmartPLS 4 output using the bootstrapped PLS algorithm. Although an SRMR of 0.0601 indicates a satisfactory model fit, caution is warranted when interpreting this value given the relatively small sample size. In small-sample PLS-SEM models, fit indices can be somewhat optimistic due to limited data variability and reduced model complexity (Hair et al., 2021b). Therefore, the reported SRMR should be viewed as indicative rather than conclusive evidence of overall model adequacy. Despite this limitation, the consistency of other model diagnostics, such as high factor loadings, and strong reliability and validity measures, supports the robustness of the model.

In addition to model fit, assessing multicollinearity is essential in Structural Equation Modeling (SEM) to ensure the validity of the parameter estimates. Multicollinearity is examined using the Variance Inflation Factor (VIF), where values less than 5 are indicative of acceptable levels of collinearity (Huang, 2021). All VIF values in this study fall well below the threshold of 5, as shown in Table 8, confirming the absence of multicollinearity among the latent constructs.

Next, the results from the path analysis were utilized to examine and explain the structural model verification. In the path analysis, the T-value is used to determine whether the hypothesis is valid or not (Huang, 2021). The path coefficient (β) represents the strength and direction of the relationship between each independent variable (K, A, M, G, F, R, T, O) and the dependent variable (stakeholder perception). A higher β value indicates a stronger positive influence. In this study, as seen in Table 9, all β values range from 0.871 to 0.944, reflecting strong positive relationships between each category of barrier and stakeholder perceptions of LC. Further, to evaluate the statistical significance of each relationship, T-values and p-values were analyzed. The T-value determines the statistical significance of the relationship between variables: a T-value greater than 1.96 corresponds to a significance level of 0.05, greater than 2.58 corresponds to 0.01, and greater than 3.29 indicates a highly significant relationship at 0.001. In this study, all eight hypothesized paths (H1 through H8) reported T-values significantly greater than 3.29 and p-values less than 0.001, confirming that each hypothesis is statistically significant and valid.

These findings reinforce the robustness of the proposed model and confirm that various factors—including knowledge, awareness, managerial, governmental, financial, resource-based, technical, and other barriers—significantly shape stakeholder perceptions toward LC adoption in the UAE’s building construction sector. The results underscore the multifaceted nature of resistance toward LC adoption and validate the importance of addressing these dimensions holistically. The validated structural model, as generated using SmartPLS 4, is illustrated in Figure 5.

Figure 5

In addition, the coefficient of determination (R²) and the effect size (f²) are key indicators used in PLS-SEM to assess the explanatory power of the structural model constructs (Huang, 2021). The R² value, which ranges from 0 to 1, indicates the proportion of variance in the endogenous (dependent) variable explained by the exogenous (independent) constructs. An R² value of approximately 0.50 is considered to reflect moderate explanatory power, whereas values closer to 1.0 denote strong explanatory capacity. As shown in Table 10, the R² values obtained in this study are consistently high, suggesting that the structural model provides a high degree of explanation of the variance in stakeholder perceptions toward LC implementation in the UAE building construction sector.

Furthermore, the effect size (f²) is used to evaluate the impact of individual exogenous variables on the endogenous construct. According to established guidelines, an f² value between 0.02 and 0.15 represents a small effect, between 0.15 and 0.35 indicates a medium effect, and values above 0.35 denote a large effect. As detailed in Table 10, the constructs of Attitude (f² = 3.208) and Management (f² = 1.510) exhibit large effect sizes, indicating a substantial contribution to stakeholders’ perception of LC. In contrast, Knowledge/Awareness (f² = 0.179) and Government (f² = 0.287) demonstrate medium-level explanatory power. Other factors such as Financial, Resource, Technical, and Other aspects reflect lower f² values, suggesting smaller effect sizes in the model.

It may be mentioned here that, although all eight hypothesized paths (H1–H8) were found to be statistically significant (p < 0.5), this uniform significance pattern is consistent with theoretical expectations rather than indicative of model artifacts. LC adoption is a multidimensional process that depends on the concurrent functioning of managerial, technical, financial, policy and attitudinal enablers. Hence, the presence of consistently significant relationships across constructs reflects the integrated nature of these barriers in influencing stakeholder perceptions. Moreover, the variation observed in the standardized path coefficients and effect size (f²) values confirms that while all relationships are significant, their relative strength differs. Together with low VIF values (<5.0), these results demonstrate that the model is neither overfitted nor multicollinear, but rather captures the systemic and interrelated dynamics of LC implementation in the UAE context.

4.2.2 Relative importance index and ranking of barriers to LC adoption in the UAE building construction sector

The numerically scaled responses obtained from section 2 of the survey were subsequently employed to conduct a Relative Importance Index (RII) analysis, followed by an RII-based ranking of the indicators.

The analysis of Table 11, with barriers ranked individually within each construct, reveals key impediments to LC implementation in the UAE. In the Knowledge/Awareness category, the most pressing issue is K1: Lack of knowledge about lean concepts within public procurement, emphasizing a critical institutional knowledge gap. K2: Lack of awareness programs follows, suggesting limited initiatives to disseminate lean knowledge. Within the Attitude construct, A6: Inability to change organizational culture ranks first, indicating that entrenched cultural norms pose a major obstacle. Other attitudinal concerns include A2: Resistance to new ideas and A1: Selfishness among professionals, which reflect behavioral inertia within project teams. These findings underline the need for both top-down policy reform and bottom-up mindset shifts.

From the management category, M10: Unclear project definitions and outcomes emerges as the top barrier, demonstrating how ambiguous goals hinder LC execution. It is followed by M6: Slow decision-making and M11: Centralized authority, showing that hierarchical bottlenecks significantly impede agility. In the Government construct, G1: Lack of supportive laws and regulations is the foremost barrier, highlighting the policy vacuum around LC adoption. Financially, F1: Lack of funding is the most critical, underscoring the investment risk firms perceive. The Material and Equipment category is led by R1: Scarcity of materials suitable for LC, and the Technical barriers by T1: Poor performance measurement strategies. Lastly, in the Other barriers group, O4: Lack of integrity in the production chain takes the top spot, pointing to trust and coordination deficits across stakeholders. This structured view shows that critical barriers vary across domains but consistently reflect systemic, managerial, and cultural weaknesses that must be targeted simultaneously.

Meanwhile, Table 12, which presents a consolidated ranking of the 46 identified barriers based on their RII value, provide a sharper lens on the specific factors most obstructing LC implementation in the UAE.

The top-ranked barrier is K1: Lack of knowledge about lean concepts including LC within the public procurement procedure (RII = 0.8052). This highlights a systemic issue—lean principles are not yet embedded in the country’s tendering, contract design, or procurement regulations. Public sector projects in the UAE tend to emphasize lowest-cost bids over long-term efficiency or value-driven delivery, limiting opportunities for LC-aligned practices.

Closely following is O4: Lack of integrity in the production chain (RII = 0.7896), which reflects the UAE’s heavily subcontracted and fragmented construction ecosystem. With a multilayered supply chain often involving consultants, subcontractors, and imported materials, misalignment across stakeholders obstructs the continuous flow and transparency required by lean frameworks.

Next, R1: Scarcity of materials suitable for LC (RII = 0.7870), further complicates implementation. The UAE’s reliance on imported construction materials, combined with limited local standardization and availability of modular or prefabricated components, restricts the feasibility of lean-based inventory and scheduling approaches.

Interestingly, two barriers—F1: Lack of funding and T1: Poor performance measurement strategies, tie in the fourth position, point to practical organizational challenges. Many contractors in the UAE operate on tight margins and prioritize short-term project delivery, lacking both the financial buffers and performance KPIs (Key Performance Indicators) needed for sustained lean transformation.

Several Management-related issues such as M10: Unclear project definition and outcomes, M6: Complex decision-making, and M13: Poor leadership, M9: Lack of support from top management and M15: Poor communication among participants, also appear within the top 15, confirming the pivotal role that managerial processes play in the region. The sixth-ranked barrier, M10 (RII = 0.7766), is especially relevant in the UAE, where fast-track project approvals and shifting client requirements often result in scope ambiguity. This undermines the upfront planning essential for lean success. Meanwhile, M6 (RII = 0.7662) rounds out the top 10 barriers to LC implementation in the UAE’s building construction sector. Hierarchical, top-down structures common in both public and private construction firms hinder agile decision-making, a core requirement of lean environments.

Further, O3: Long implementation period (RII = 0.7714) signals organizational hesitance to commit to lean due to perceived delays in seeing returns. In a highly competitive and fast-paced construction market like the UAE, this perception becomes a significant deterrent.

Tied for eighth are K2: Lack of awareness programs and G1: Lack of supportive government laws and regulations (RII = 0.7688). These point to macro-level gaps—although national strategies like Vision 2030 and Net Zero 2050 highlight sustainability, they do not mandate or incentivize lean practices specifically, and formal training remains rare across firms.

Finally, at the bottom of the ranking are barriers like A5: Lack of self-criticism and R2: Inadequate equipment, which are perceived to have lesser impact, possibly due to their more indirect influence or sector-specific nature.

Overall, the rankings reveal that the most critical barriers span across Knowledge, Organizational, Financial, Technical, and Management domains, implying that successful LC implementation requires a multi-dimensional approach. Targeted interventions such as training programs, production chain optimization, better material planning, and strategic leadership initiatives will be essential to address the highest-impact barriers against LC implementation in the UAE construction sector.

4.2.3 Relative importance index and ranking of waste types generated in the UAE building construction sector

The final section of the survey focused on identifying the types of construction waste commonly encountered at the respondents’ workplaces. Similar to the previous sections, responses were collected using a five-point Likert scale ranging from “Extremely High” (assigned a value of 5) to “Extremely Low” (assigned a value of 1). These numerical responses were subsequently utilized for Relative Importance Index (RII) analysis and corresponding RII-based rankings. This section helped explore multiple aspects related to construction waste, including the frequency of various waste types generated and the construction phases during which waste generation is most prevalent. Additionally, respondents’ perceptions regarding the frequency of waste types, as categorized under LC principles, were also analyzed.

The first question required participants to rank common types of construction waste based on their frequency of occurrence in their respective projects. A summary of the responses is shown in Figure 6. An RII-based ranking of the same was also conducted as shown in Table 13.

Figure 6

The RII-based analysis of construction waste types, as shown in Figure 6 and Table 13, highlights “packaging waste” as the most frequently encountered form of waste on construction sites, with the highest RII value of 0.787, supported by nearly 38% of respondents rating its occurrence as “Extremely High.” “Demolition waste” follows closely, also receiving a high proportion of “Extremely High” responses (39%), underscoring its significance in the construction waste stream. Off-cuts and trim waste ranked third and received the highest proportion of “High” responses (54.5%), indicating consistent but slightly less intense generation. Land clearing and structural waste occupied the mid-range of the rankings, suggesting moderate occurrence. In contrast, non-hazardous, hazardous, and excavation wastes had lower RII values, reflecting less frequent generation according to respondents. The least frequently occurring waste types were electrical/electronic waste (E-waste) and mechanical/plumbing waste, corresponding with a high proportion of “Extremely Low” and “Low” responses.

These findings emphasize that packaging and demolition activities are the predominant sources of construction waste in the UAE context, necessitating targeted interventions. In particular, proper disposal of packaging materials, along with efficient pre-planning to ensure only necessary and high-quality materials are brought on-site, can significantly reduce packaging waste generation. While some amount of demolition waste is unavoidable, its environmental impacts may be mitigated through reuse or recycling of components, the selection of sustainable and eco-friendly materials, and the adoption of prefabricated or modular construction systems, which facilitate component reuse across multiple projects.

The subsequent question focused on assessing the frequency of waste generation in relation to LC-recognized waste categories within building construction projects in the UAE. A summary of the respondents’ feedback is presented in Figure 7, while an RII-based ranking of these waste types is provided in Table 14.

Figure 7

This analysis reveals that “waiting” waste emerged as the most frequently occurring, with an RII of 0.784 and the greatest proportion of respondents rating it as “Extremely High” or “High” (67.6%). This type of waste typically results from delays in material deliveries, equipment unavailability, or inefficient scheduling, and must be addressed through more robust planning and supply chain coordination to prevent project slowdowns. Transportation waste (ranked second) was also commonly encountered, with 58.5% of respondents identifying it as “High” or “Extremely High,” suggesting logistical inefficiencies and poor site layout as contributing factors. Inventory waste followed closely, indicating overstocking or poor inventory control mechanisms. Similarly, motion waste was frequently reported, further underscoring the need for ergonomic and process-optimized site layouts. Defects, overprocessing, and overproduction received moderate RII scores, indicating recurring issues related to rework, redundant processes, and producing more than what is required—symptoms of inadequate quality control and communication. Interestingly, the least ranked was underutilized talent, with 54.6% of respondents rating its frequency as “Low” or “Extremely Low,” which points to a potential gap in recognizing or fully deploying workforce capabilities. Overall, the findings highlight critical inefficiencies that can be strategically addressed through targeted lean interventions, particularly in planning, logistics, and workforce utilization.

The final question in this section sought to identify the specific phase of a construction project during which waste generation is most significant. Respondents’ perceptions on this aspect are presented in Figure 8, with an RII-based ranking provided in Table 15.

Figure 8

The data indicate that the “finishing phase” of construction projects is perceived as the stage during which the greatest volume of waste is generated, with 36.4% of respondents rating it as “Extremely High,” corresponding to the highest RII. This finding reflects the complex material usage and labor-intensive nature of this terminal phase, where suboptimal coordination and execution inefficiencies often result in material surplus and waste. The maintenance and repair phase was also identified as a significant contributor to waste generation, receiving a “High” rating by 44.2% of respondents, underscoring the implications of material durability and upkeep practices on cumulative waste output. The design phase registered a moderate waste frequency, signifying the critical importance of integrating waste mitigation strategies early in project planning to reduce downstream material loss. Conversely, the construction and procurement phases were associated with comparatively lower waste generation, as evidenced by lower RII values and higher percentages of respondents indicating “Extremely Low” waste levels (27.3 and 31.2%, respectively). These outcomes highlight the imperative to intensify waste reduction efforts during the finishing and maintenance phases through meticulous project scheduling, enhanced quality control, and material efficiency. Furthermore, the adoption of durable and environmentally sustainable materials, coupled with proactive and preventive maintenance regimes—such as performing upkeep prior to the actual end-of-life of components—can extend asset longevity and substantially diminish waste production throughout the building lifecycle.

4.3 Integrated interpretation of data analysis using PLS-SEM and RII

This subsection integrates the statistical insights derived from the PLS-SEM analysis with the practical prioritization outcomes obtained through RII analysis, providing a cohesive interpretation of how stakeholder perceptions, barrier significance, and waste generation patterns interrelate within the context of LC implementation in the UAE building sector. While the PLS-SEM analysis identified the strength and direction of relationships between the main barrier constructs and stakeholder perceptions, the RII results revealed the relative prominence of specific individual barriers and waste types as perceived by practitioners. Together, these analyses present a comprehensive empirical picture, linking what influences stakeholder perceptions (from PLS-SEM) with what practitioners’ experience as the most frequent or critical barriers and waste sources (from RII).

The PLS-SEM results established that attitudinal and managerial dimensions have the strongest and most significant influence on stakeholder perceptions of LC adoption, as evidenced by their high path coefficients (β values) and large effect sizes (f² values). This highlights that LC implementation is driven less by financial or technical capacity and more by human and organizational factors, specifically leadership engagement, team motivation, and openness to process change. Constructs such as knowledge/awareness and government support demonstrated medium effects, suggesting that institutional alignment and education are supportive enablers but not the primary determinants of LC success. These findings collectively indicate that the key to LC implementation lies in transforming organizational culture and management practice rather than merely improving technical procedures.

Meanwhile, the RII-based analysis highlighted which barriers are most frequently encountered in practice and which types of waste occur most often on UAE building construction sites. Managerial and operational barriers, such as poor communication, lack of training, inadequate pre-planning, and insufficient supply chain integration, were ranked highest, demonstrating the industry’s functional inefficiencies. Similarly, the RII analysis of waste types revealed that waiting, transportation, and inventory wastes, as well as packaging and demolition wastes, were the most prevalent, especially during the finishing and maintenance phases of projects. These waste categories reflect time losses, excessive movement, and overstocking, all of which can be traced to weaknesses in managerial planning, logistical coordination, and on-site supervision.

The results of both analytical methods thus complement one another: PLS-SEM explains why LC implementation faces obstacles (by identifying the influential conceptual dimensions), while RII shows where and how these barriers manifest operationally (in material handling, workflow, and project phases). Attitudinal and managerial deficiencies, shown as dominant constructs in the structural model, translate in practice into recurrent wastes linked with scheduling delays, miscommunication, and inefficient logistics. Likewise, technical and knowledge-based barriers identified in the RII analysis correspond to defect, underutilized talent and overprocessing waste types, where rework, redundant effort, or poor-quality outputs occur due to inadequate technical competence or unclear work procedures.

This integrated interpretation strengthens the explanatory validity of the study by connecting behavioral, managerial, and operational dimensions to observable patterns of construction waste. It underscores that LC implementation and waste management are inherently interdependent, systemic organizational weaknesses manifest as physical and process-level wastes. Addressing such barriers through enhanced training, collaborative decision-making, digital resource monitoring, and Lean-aligned planning practices can therefore yield measurable reductions in material waste and improve overall project efficiency.

Thus, the integration of PLS-SEM and RII results provides a holistic understanding of the current state of LC adoption in the UAE. It confirms that the successful reduction of construction waste cannot be achieved solely through technological or procedural improvements but requires a parallel transformation in organizational culture, leadership behavior, and stakeholder engagement.

The findings of this study align with and extend the insights of previous research on LC adoption, particularly within developing and Middle Eastern contexts. The strong influence of managerial and attitudinal barriers (high effect sizes) observed in the UAE reflects observations by Sarhan and Fox (2012), who emphasized that cultural resistance, poor collaboration, and lack of leadership are key impediments to LC implementation. These results affirm that the success of LC is contingent on the organizational mindset and leadership engagement.

Moreover, the moderate role of governmental and educational barriers in this study is consistent with findings by Musa et al. (2023), who emphasized that awareness and training are necessary precursors for successful Lean implementation. However, the present study builds on this by suggesting that such enablers, while important, may not be sufficient in the UAE context without concurrent managerial commitment and attitudinal change. This points to a broader systems-level challenge, where improving knowledge must be accompanied by leadership-driven organizational transformation to create an enabling environment for LC.

In terms of waste types, the high frequency of packaging and demolition waste, as well as waiting and transportation waste, reflects broader logistical and planning inefficiencies often noted in UAE construction projects (Al-Hajj and Hamani, 2011). The observed concentration of waste in the finishing and maintenance phases also further suggests that LC interventions in the UAE should be extended beyond early project stages to ensure full life-cycle efficiency.

In contrast, Bajjou and Chafi (2019), examining the Moroccan construction industry, highlighted lack of knowledge about Lean practices, insufficient financial resources, and unskilled labor as the most critical barriers to LC implementation. While these factors were also found to be significant in the present study, they did not exert the strongest influence on stakeholder perceptions in the UAE. Instead, the UAE context suggests that even when a technically skilled workforce and financial resources are moderately available, attitudinal inertia and managerial inefficiencies may still obstruct LC adoption. This divergence underscores the context-specific nature of LC challenges and the necessity for locally grounded strategies.

Overall, the current study extends prior research by offering an integrated, empirically validated model that combines structural equation modeling with practical ranking analyses. This dual-method approach enhances understanding of how theoretical constructs and operational waste patterns co-exist and interact within the UAE construction sector. These synthesized insights form the empirical foundation for the strategic recommendations outlined in the following section.

4.4 Enhancing lean construction implementation for waste management in the UAE building sector: practical insights

As shown in Table 4, each barrier was mapped to its corresponding Lean and construction waste types to illustrate the mechanisms through which challenges in LC implementation translate into waste management inefficiencies. Building on this mapping and the comprehensive analysis of stakeholder perceptions, several targeted recommendations are proposed to facilitate the effective adoption of LC principles in the UAE’s building construction sector. The analysis revealed that barriers span multiple dimensions, including knowledge gaps, attitudinal resistance, managerial inefficiencies, technical limitations, financial constraints, and systemic issues across different project phases. To overcome these multifaceted obstacles, a strategic and coordinated approach is essential, one that integrates improvements in project management, organizational culture, workforce capacity, and regulatory support. These insights collectively provide the empirical foundation for the recommendations presented in this section. By addressing the specific barriers that lead to distinct waste types, particularly those barriers that have been ranked higher in stakeholder concern within this study, the following recommendations operationalize LC principles as a practical framework for reducing material, time, and process waste in UAE construction projects.

Firstly, enhancing communication among all stakeholders is critical. Clear, timely, and transparent communication reduces uncertainties, improves coordination, and fosters a culture of trust and collaboration across disciplines. This is especially important given the prevalence of waiting and transportation wastes, which are symptomatic of poor scheduling, unclear task dependencies and logistical inefficiencies. These challenges are closely tied to O4 (lack of integrity in the production chain), one of the highest-ranked barriers in the study, which reflects systemic fragmentation and poor integration across clients, subcontractors, and suppliers. As such, contractors and consultants must take the lead in implementing digital collaboration and visualization platforms such as Building Information Modeling (BIM) dashboards and Last Planner^® System (LPS) interfaces to improve transparency in task sequencing and resource allocation. In addition, regular short “lean huddle” meetings and shared digital progress trackers can synchronize on-site activities, reduce idle time, and enhance workflow reliability, while also empowering team members in decision-making, and further enhancing engagement and shared accountability.

Secondly, the construction industry in the UAE should pursue a culture of continuous improvement to drive sustained enhancements in productivity, cost-efficiency, and quality. This is especially necessary given the high-priority barriers related to K1 (lack of lean knowledge in procurement) and K2 (lack of awareness programs). To achieve this culture, construction managers must be proactive change agents, showing commitment to LC implementation by allocating resources for training and facilitating cultural transformation. Structured LC training programs should incorporate hands-on modules on waste identification and measurement, enabling workers to recognize defects, overproduction, and motion waste on-site. Incorporating digital learning tools and simulation-based training (like 4D BIM or process-mapping apps) can further reinforce understanding of waste sources and counter-measures. Establishing waste performance indicators, such as rework percentage or material yield ratios, within employee evaluation systems can sustain accountability and reinforce continuous improvement.

Thirdly, organizational strategies must align with client expectations to ensure that project outcomes are demand-driven rather than supply-driven, minimizing overprocessing and overproduction. This recommendation will also help address M10 (unclear project definition and outcomes), a top-ranked managerial barrier that is particularly critical in the UAE, where fast-tracked projects and limited early-stage stakeholder engagement often result in scope creep and material waste. Consultants and project planners should prioritize early engagement with clients and end-users during the design and planning stages enables value stream mapping, ensuring that all processes add measurable value to the final output. Adopting BIM-integrated cost and material forecasting tools can prevent over-ordering and unnecessary specification changes, which often generate surplus materials and packaging waste. Aligning procurement schedules with real-time demand through just-in-time material delivery systems ensures that resources are supplied only when required, reducing inventory waste and improving cash flow efficiency.

In terms of technical and logistical barriers, firms should develop standardized construction components and invest in technologies, such as digital material tracking and logistics management systems, thereby reducing inventory and motion waste. These recommendations directly respond to R1 (scarcity of materials suitable for LC implementation) and T1 (poor performance measurement strategies), both identified among the top five barriers to LC implementation in the UAE context. Addressing barrier R1 requires contractors and suppliers to prioritize locally adaptable lean-compliant materials and collaborate with manufacturers to develop regionally available alternatives to imported or unsustainable options. Additionally, consultants and digital teams can support this effort by introducing technologies such as BIM and radio-frequency identification (RFID), to facilitate real-time monitoring of material flow, ensuring that supplies are procured, delivered, and utilized efficiently. Emerging technologies such as IoT-enabled smart bins and AI-driven waste analytics platforms can automatically monitor the type and quantity of waste generated in real time, enabling data-based decision-making and facilitating continuous improvement in waste management practices. Additionally, implementation of just-in-time (JIT) delivery protocols and prefabrication or modular construction methods can further minimize on-site material storage, motion, and rework. Integrating Design for Disassembly (DfD) principles during early project stages allows components to be easily dismantled and reused, reducing demolition waste and extending material lifecycles, while also addressing another top barrier to LC implementation in the UAE—O3 (long implementation period).

Moreover, firms should establish on-site waste segregation systems with clearly designated zones for recyclable, reusable, and hazardous materials. This recommendation also aligns with O4 (lack of integrity in the production chain) by promoting structured workflows and improving inter-organizational collaboration on material handling. The introduction of circular economy-oriented practices, such as the reuse of demolition materials, recycling of concrete aggregates, and recovery of packaging materials, should be institutionalized within project execution plans, especially by contractors and construction managers. To support his transition and to promote material reuse and transparency, government agencies can mandate the adoption of Digital Waste Passports, which record material composition, reuse potential, and recycling history, enhancing traceability across the entire construction lifecycle and further aligning with global circular economy initiatives. Additionally, construction stakeholders must ensure proper planning during finishing and maintenance phases, which were identified as critical periods of high waste generation in this study. This includes deploying sustainable and durable materials, modular components, and conducting preventive maintenance to extend the life cycle of construction components and minimize repair-related waste, particularly in rapidly urbanizing regions such as the UAE.

At the policy and governance level, government agencies must support LC adoption through enabling regulations and incentives for waste-efficient practices. This will also ensure addressal of yet another barrier to LC implementation the UAE—G1 (lack of supportive regulations). These might include the development of national standards for lean-compliant practices, such as waste segregation and tracking, subsidies for adopting digital waste monitoring technologies, requirements for training certification in public procurement contracts, and the integration of LC and waste management criteria into public procurement requirements. Public–private partnerships could also support the creation of centralized recycling facilities and digital waste databases for benchmarking performance across projects. Further, integrating Lean–Green performance metrics, such as waste diversion rates, carbon footprint reduction, and energy intensity, into project key performance indicators (KPIs) can align LC adoption with sustainability and environmental management objectives.

At a regional level, developing digital waste exchange platforms can facilitate industrial symbiosis by allowing surplus materials from one project to be repurposed in another, thereby reducing landfill dependency and supporting the UAE’s zero-waste goals.

In parallel, management must address systemic organizational barriers, such as rigid hierarchical cultures or risk-averse cultures, that hinder information sharing, innovation and lean thinking (also tackling barrier M6—low decision-making due to hierarchy). By fostering open communication and empowering cross-functional teams, firms can strengthen collaboration, enhance knowledge flow, and ensure that lean-based waste reduction practices are sustained across projects. Finally, firms must also monitor economic variables, such as inflation and market volatility, to maintain cost stability, a critical enabler for long-term LC adoption. By integrating lean operational practices with waste-specific management tools, stakeholders in the UAE’s building construction industry can systematically dismantle barriers to LC implementation, and significantly reduce both process and material waste. This dual approach not only enhances project efficiency and profitability but also advances the national sustainability agenda toward a circular and resource-efficient construction sector. These recommendations not only advance the operational adoption of LC but also support the achievement of broader sustainability objectives. In particular, by reducing resource waste, improving energy efficiency, and optimizing material use, LC directly contributes to SDG 12 (Responsible Consumption and Production). Moreover, through enhanced planning, communication, and stakeholder collaboration, LC fosters safer, more inclusive, and sustainable urban development, resonating with SDG 11 (Sustainable Cities and Communities). Embedding LC principles within the UAE’s construction practices thus provides a practical pathway for aligning national development strategies with global sustainability commitments.

5 Conclusion

Construction industry, despite being crucial for the socio-economic development of a nation, is also one of the largest contributors to waste generated in a country, especially in the UAE. Thus, innovative solutions to efficient management of this waste is critical. Hence, this study undertook an in-depth understanding of the perceptions of stakeholders in the UAE’s building construction industry, that act as barriers toward implementation of LC for the effective management of construction waste.

Initially, this study provided a comprehensive assessment of the current state of LC (LC) implementation within the UAE’s building construction sector, with a particular focus on identifying the prevailing barriers, stakeholders’ perceptions, and waste generation trends. Through an integrated approach that combines descriptive analysis, Relative Importance Index (RII) ranking, and robust PLS-SEM modeling, the study uncovers both the perceived significance and the structural interrelations of the factors that influence stakeholder acceptance of LC. The findings reveal that although there is a growing awareness of LC principles, their practical application remains limited due to a combination of organizational, managerial, technical, financial, and policy-related challenges.

In this regard, the PLS-SEM analysis provided a rigorous validation of the proposed structural model, confirming that all eight hypothesized factors—knowledge/awareness, attitude, management, government, financial, material/resource, technical, and other contextual influences—significantly shape stakeholder perceptions toward LC implementation in the UAE building construction sector. The high factor loadings, strong reliability and validity indicators, and robust model fit metrics (SRMR = 0.0601) affirm the integrity of the measurement model. Moreover, the high path coefficients (β > 0.87) and statistically significant T-values (p < 0.001) highlight the strength of the interrelationships between latent constructs and stakeholder perceptions. Particularly, the constructs of attitude and management demonstrated the largest effect sizes, underscoring their critical roles in influencing LC adoption. The substantial R² values further demonstrate that the model accounts for a considerable proportion of the variance in stakeholder perceptions, suggesting that targeted strategies addressing these key constructs could effectively enhance LC adoption for improved construction waste management in the UAE. This empirical evidence reinforces the need for holistic, multi-dimensional interventions that integrate policy support, managerial commitment, and cultural transformation to drive sustainable change in the industry.

Furthermore, the analysis of construction waste types and phases highlighted that packaging and demolition wastes are the most frequently generated, indicating opportunities for intervention through material optimization, reuse, and modular construction techniques. Furthermore, waste types commonly recognized in LC, particularly waiting, transportation, inventory, and motion, emerged as key inefficiencies that require targeted process improvements. The finishing and maintenance phases were identified as critical stages with high waste generation, suggesting the need for better planning, resource allocation, and long-term sustainability strategies.

The study concludes that achieving effective LC implementation in the UAE requires an integrated approach involving organizational reform, capacity building, stakeholder alignment, and supportive regulatory frameworks. The recommendations outlined in this study serve as a roadmap for construction professionals, firms, and policymakers to address systemic inefficiencies and promote a culture of continuous improvement. By embracing lean thinking, the UAE construction industry can move toward enhanced productivity, reduced waste, and more sustainable project outcomes aligned with national development goals. Furthermore, the study’s findings demonstrate a clear linkage between LC practices and the advancement of the United Nations Sustainable Development Goals, particularly SDG 11 and SDG 12. By embedding LC within construction processes, the UAE can promote responsible resource use and foster sustainable urban growth consistent with global sustainability frameworks.

5.1 Limitations and future research

While this study offers important insights into LC adoption and waste management practices in the UAE, several limitations should be acknowledged. First, the research relies on self-reported data from stakeholders, which may introduce subjective bias or variations in interpretation across respondents. Although the use of PLS-SEM and RII helped triangulate perceptions with robust statistical modeling, future studies could incorporate observational or project-level performance data to validate stakeholder assessments.

Second, the sample was limited to the UAE’s building construction sector, which, while significant, may not fully represent practices in other segments such as infrastructure, industrial, or residential retrofitting projects. Expanding the scope to include these sectors could offer a more holistic view of LC adoption across the construction ecosystem.

Third, the cross-sectional nature of the study captures perceptions at a single point in time. Longitudinal research would help evaluate how LC adoption evolves in response to changing policies, market dynamics, or organizational learning.

Finally, despite several recent studies that have explored waste management through frameworks such as the Circular Economy (CE), Material Flow Analysis (MFA), and digital waste-tracking systems, the present research remains grounded in the LC paradigm. LC provides an established theoretical basis for examining process and material inefficiencies in construction, emphasizing continuous improvement and waste minimization. Future research could extend this exploratory work by integrating LC with CE or BIM-based material tracking approaches to establish a more comprehensive and data-driven framework for waste management in the UAE construction context.

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