2.1. The construction industry and the sustainable development goals

The construction industry is widely recognized as a resource-intensive sector whose operations have profound implications for sustainability. Globally, the sector accounts for 35% of CO₂ emissions and generates between 45% and 65% of landfill waste [15]. Construction activities also contribute 30% of global greenhouse gas emissions, with 18% attributable to material transportation alone [16]. These statistics demonstrate the sector’s substantial environmental footprint, underscoring the urgency of integrating sustainability into construction processes to mitigate climate and resource degradation.

Despite these negative externalities, existing literature highlights the construction industry as a critical enabler of the United Nations Sustainable Development Goals (SDGs). [17] notes that 17% of the SDGs are directly linked to construction activities, while an additional 27% are indirectly connected, emphasizing the sector’s pivotal role in shaping sustainable development outcomes. This influence is further reflected in the centrality of SDGs 6, 7, and 11, which relate to clean water, affordable and clean energy, and sustainable cities to construction sector performance [2]. The evidence suggests that the construction industry’s contributions extend across at least ten SDGs, particularly those associated with health, infrastructure, climate action, and biodiversity conservation. For instance, sustainable and green building initiatives significantly support SDGs 6, 9, 12, 13, and 15 by improving water efficiency, reducing operational energy demand, promoting responsible consumption, mitigating climate impact, and enhancing ecosystem protection [18].

Tools and frameworks have also been developed to align construction practices more systematically with the SDGs. [19] argue that approaches such as design mapping tools facilitate the integration of sustainability considerations throughout project lifecycles, enabling stakeholders to evaluate and optimize project contributions to the SDGs. This reflects a growing shift from viewing sustainability as a compliance requirement to adopting it as an integral component of construction decision-making.

Beyond environmental concerns, the construction industry is deeply embedded within the social and economic dimensions of sustainable development. Employing approximately 7% of the global workforce and contributing 13% to global GDP [20], the sector supports livelihoods and economic resilience. However, persistent challenges continue to hinder progress toward social sustainability. [21] identify issues such as unsafe working conditions, inadequate skills development, and gender inequality as barriers that undermine SDGs 3, 4, 5, and 10. These concerns highlight the need for reforms that address worker welfare, human capital development, and inclusivity.

Overall, the literature emphasizes that achieving sustainable development within the construction sector requires coordinated and deliberate stakeholder efforts. The industry sits at a critical intersection of economic growth, environmental stewardship, and social well-being, and its alignment with the SDGs is essential for balancing these imperatives and advancing global sustainability targets.

2.2. The evolving roles of quantity surveyors in achieving the sustainable development goals

Achieving sustainability in the built environment requires shared responsibility and collective action, as underscored by SDG 17. Within this collaborative landscape, Quantity Surveyors (QSs) hold a strategic and increasingly influential position due to their core competencies in cost management, procurement, risk evaluation, and stakeholder communication [22]. Their remit extends well beyond financial oversight to encompass the evaluation of broader sustainability implications for organizations, investors, communities, and the construction sector. This expanded mandate places QSs at the forefront of efforts to align built environment practices with the United Nations Sustainable Development Goals (SDGs).

QSs perform diverse functions across the construction lifecycle, including cost planning, contract administration, procurement, and environmental advisory services [23,24]. Their early involvement in pre-contract stages enables them to shape sustainability outcomes by guiding design development, advising on procurement routes, and establishing cost-control mechanisms [25]. Tools such as life cycle costing (LCC) and carbon cost planning further strengthen their role in sustainable capital budgeting. By promoting local and environmentally responsible material choices, QSs enhance value for money while embedding environmental considerations into project delivery. However, despite their expansive potential, gaps remain in QSs’ awareness and practice of sustainability principles, especially in carbon cost planning [26]. This challenge is exacerbated by the limited integration of sustainability competencies in QS training programs [27], signaling the need for enhanced knowledge in construction technology, environmental services, ethics, and leadership [24].

From a cost management perspective, QSs are instrumental in mainstreaming sustainability within project evaluation. Their expertise informs budgeting, feasibility assessments, and value engineering for green buildings [28,29]. Yet, limited knowledge of green construction costing continues to constrain effective decision-making [30]. Strengthening their capacity in whole-life costing and sustainable material appraisal is therefore essential. As QSs acquire stronger knowledge of green certifications, incentives, and economic instruments, they further support SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure). Their ability to negotiate between affordability and environmental performance positions them as catalysts for cost-effective sustainable development.

Procurement constitutes another major domain in which QSs influence the integration of sustainability. Construction procurement determines the acquisition of goods and services essential to project delivery, making it a key leverage point for embedding environmental, social, and economic considerations [31]. QS roles in contract administration, cost control, BIM coordination, and procurement management allow them to shape sustainable supply-chain decisions [32]. Their expertise aligns closely with sustainable procurement principles, which require balancing cost efficiency with long-term environmental and social value [33,34]. However, entrenched industry preferences for lowest-cost options often hinder the adoption of life cycle performance approaches [35]. Professional training, organizational support, and regulatory frameworks, including green building rating systems, are identified as crucial for overcoming these barriers [36]. Complementary approaches such as Total Quality Management (TQM) and Just-in-Time (JIT) practices can further enhance sustainable supply chain efficiency [28].

Material selection, a critical component of procurement, plays a significant role in advancing the SDGs. Environmentally friendly materials contribute to SDGs 3 (Good Health and Well-being), 6 (Clean Water and Sanitation), and 12 (Responsible Consumption and Production) [37]. While sustainable materials support cleaner water systems and reduce their embodied impacts [38], traditional materials such as copper, lead, and cadmium pose health risks when they leach into water systems [39]. By recommending eco-friendly alternatives, QSs safeguard both environmental performance and public health [40]. Their material decisions thus contribute holistically to sustainability objectives.

The growing emphasis on sustainability has broadened QS responsibilities from traditional cost-focused tasks to more integrative advisory roles. QSs now increasingly engage in evaluating sustainable materials, energy-efficient technologies, and environmentally responsible construction practices [41]. Their involvement at early design stages provides opportunities to integrate sustainability solutions while maintaining cost effectiveness. Through life cycle analysis and evaluation of material durability, reusability, and embodied energy, QSs support long-term environmental performance [42,43]. They also guide investments in energy-saving and water-efficient technologies, directly contributing to SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action) [44].

Nonetheless, significant barriers limit the full realization of QSs’ sustainability potential. Persistent knowledge gaps, inadequate training in green costing, and outdated educational curricula impede their capacity to adopt sustainability practices [27]. These challenges are compounded by procurement cultures that undervalue long-term performance in favour of short-term cost savings [35]. Addressing these constraints requires coordinated efforts to enhance professional capacity, reform QS training, and strengthen institutional and regulatory support.

In summary, QSs are increasingly central to advancing the SDGs through their roles in cost management, procurement, and sustainability advisory services. Their expanding responsibilities, ranging from life cycle cost analysis and material evaluation to sustainable supply chain coordination, position them as key actors in aligning construction practices with global sustainability priorities. However, unlocking their full potential requires continuous professional development, transformative educational reforms, and stronger policy and organizational support. Through these measures, QSs can more effectively drive sustainable development within the construction industry and contribute to achieving the SDGs by 2030. Recent research underscores the importance of examining QS roles in SDG delivery across diverse national contexts. A simulation-based empirical study examining the evolving roles of QS in delivering SDG-aligned construction in Malaysia demonstrates that the correlation between QS practice and the SDG agenda is not unique to just Ghana but reflects a broader professional reorientation occurring across developing economies [45].

2.3. Theoretical framework

The study is anchored within the Role Theory, which conceptualizes professional practice as a dynamic and socially constructed set of functions shaped by organizational expectations, social structure, occupational norms, and evolving societal demands [46]. From this theoretical perspective, the roles of QS are not static but are continually renegotiated in response to emerging social, environmental, and economic vital needs. The SDG framework represents such an imperative for professionals to reorient their practices to conform to sustainability standards.

Another theory complementary to the Role Theory is Institutional Isomorphism Theory, which holds that organizations and professionals adopt practices in response to regulatory, normative, and cognitive pressures from their institutional environment [47]. In the Ghanaian construction industry, institutional pressures in the form of the national sustainability policy and professional guidelines from the Ghana Institute of Surveyors (GhIS) form the normative and cognitive environment within which QS practice is formed. These pressures in one way or another shape and influence how QSs engage with sustainability goals, including the 17 SDGs. These two theories provide a dual-lens framework that guides the empirical analysis and interpretation of the study’s findings.

3.1. Research approach and strategy

This study adopted a quantitative research approach. The survey strategy was employed as a result of it being well-suited for acquiring a more generalized perspective on a phenomenon, especially on the critical roles of quantity surveyors in achieving SDGs. According to [48], quantitative research typically asks the “what”, “how much”, and “how many” questions, whereas qualitative research typically asks the “why” question. Considering the subject matter under investigation concerning what critical roles quantity surveyors play in achieving Sustainable Development Goals (SDGs), it was evident that the research question can be answered explicitly to achieve the goal of the study by gathering quantitative data.

3.2. Survey design and administration

A structured questionnaire was employed to solicit the views of respondents and was organized into three sections. Section 1 captured respondents’ demographic characteristics. Section 2 required respondents to rate, on a five-point Likert scale ranging from 1 (Not Prioritized) to 5 (Highly Prioritized), the Sustainable Development Goals (SDGs) they prioritize in the performance of their professional roles as Quantity Surveyors (QSs). Section 3 assessed respondents’ level of agreement with the role of QSs in achieving the 17 SDGs, using a five-point Likert scale ranging from 1 (Highly Disagree) to 5 (Highly Agree).

Each of the 17 SDGs was translated into a corresponding first-person role statement reflecting how practicing QSs enact a specific goal in professional practice. As seen in Table 2, SDG1 (Zero Poverty) has been operationalized as R1, "I ensure through my activities that poverty is ended in all its forms everywhere”; SDG13 (Climate Action) has been reworded as R17 “My role as a surveyor allows me to take urgent action to combat climate change and its impacts”. This approach was adopted from the extant literature on QS roles in sustainable construction [22–24].Five items were dropped during the EFA due to insufficient factor loadings, resulting in a final set of 18 items used in the analysis.

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Table 2. Rotated component matrix.

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Prior to the main survey, the questionnaire was piloted over a two-week period among three professional QSs who hold Fellow membership of the Ghana Institute of Surveyors (GhIS). Following an explanation of the questionnaire contents, all pilot participants confirmed the suitability of the instrument. However, they recommended rephrasing some statements to address ambiguities. These suggestions were incorporated, and the questionnaire was subsequently finalized for administration.

The target population comprised professional QSs working in quantity surveying firms registered with the GhIS and in good standing in Ghana. Records from the GhIS Secretariat indicated that, as of the end of 2024, there were 77 such firms operating nationwide. Given that the researchers had access to the full sampling frame, a census sampling technique was adopted. It is important to note that the census approach was applied at the firm level, meaning that all 77 registered firms constituted the sampling frame. Two QSs were selected from each firm, resulting in a total of 154 questionnaires administered. The 57.14% response rate, therefore, reflects the proportion of individual questionnaires returned from across all sampled firms and does not contradict the census claim, which pertains to the complete enumeration of firms rather than individual respondents.

Participant recruitment and data collection commenced on 15 February 2025 and concluded on 21 May 2025. Participation was voluntary, and all respondents provided informed written consent prior to completing the questionnaire. Out of the 154 questionnaires distributed, 88 valid responses were received, representing a response rate of 57.14%.

3.3. Data analyses

The data collected from the survey were input into IBM SPSS Statistics version 27 for analysis and subsequent interpretation. Both descriptive statistics (frequencies and means) and inferential statistics (specifically, fuzzy synthetic evaluation analysis) were conducted on the data. Exploratory Factor Analysis was adopted to group the correlated SDGs into clusters by categorizing them into smaller groups. The fuzzy synthetic analysis was utilized at the latter stage to establish the overall indices of the critical roles of QSs in achieving the SDGs. The Cronbach’s Alpha Test was used to verify the data’s reliability by examining the scales’ internal consistency in rating the 17 SDGs that QSs prioritize when carrying out their roles. [49] defined an internally consistent rating scale as one with a score of 0.70 or higher. The 0.949 level of agreement on the role in prioritizing and achieving the 17 SDGs indicates that the data used in the analyses is reliable.

4.1. Respondents’ profile

Table 1 presents data on the respondents’ profiles. The sample consisted of 88 professional QSs, all affiliated with the Ghana Institute of Surveyors. Most respondents (61.4%) are Senior Quantity Surveyors, with the majority (84.1%) working as industry practitioners. The level of experience of the respondents is well-distributed, with the majority having 6 years to over 15 years of experience. This suggests a mature professional field, which is consistent with findings on the development of quantity surveying in Ghana [66]. Notably, 87.5% of the respondents have postgraduate qualifications, with 45.5% holding Master’s degrees.

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Table 1. Respondents’ profile.

https://doi.org/10.1371/journal.pone.0342601.t001

4.2. Exploratory Factor Analysis of the critical roles

Exploratory Factor Analysis (EFA) is used for data reduction, capable of grouping correlated variables into clusters. It is commonly employed for structure detection in research involving quantitative evaluation of multiple factors. However, certain conditions must be met before applying factor analysis to a dataset. This primary requirement is the reliability of the dataset. A Cronbach’s Alpha of 0.949 confirmed that this initial criterion was satisfied. Next, the Kaiser-Meyer-Olkin (KMO) Test for Sampling Adequacy was performed, yielding a statistic of 0.874, which exceeds the 0.6 recommended [60]. This result suggests that the dataset is suitable for EFA. Bartlett’s test of sphericity was then conducted to determine if the factors were related and appropriate for structure detection. This test produced an approximate Chi-square of 1410.084 and a p-value less than 0.000, indicating that correlations between variables are sufficient for factor analysis [60]. While there are other techniques to assess a dataset’s suitability for factor analysis, the consistent affirmative results from these statistical indicators are considered sufficient to justify proceeding with factor analysis.

The EFA was performed using Principal Component Analysis (PCA) for factor extraction and Varimax with Kaiser Normalization for factor rotation. Although PCA is sometimes distinguished from common factor extraction methods such as Principal Axis Factoring, its use in this study is consistent with established practice in construction management research employing EFA for data reduction purposes [50,51]. PCA was selected over common factor extraction due to the fact that the primary objective of the factor analysis in this study was not to model latent casual structures underlying the observed variables but rather to reduce the 18 retained items into a smaller number of interpretable groupings for subsequent FSE computation. Varimax rotation was selected because it simplifies interpretation by representing the principal component factor with a small number of variables [67]. The obtained data passed both the KMO and Bartlett’s tests, which supported the factor analysis. Exposure was determined where the principal component analysis with varimax rotation was used with an eigenvalue of 1 and above. Other criteria used for item identification resulted in the exclusion of items with factor loadings less than 0.50. Five items failed to achieve this requirement or clarify the factor structure. Consequently, there was a reduction to 18 items, on which analysis showed seven factors. The rotation converged in 4 iterations and produced a 4-factor solution, which formed the basis for the FSE analysis. The results of the EFA are presented in Table 2. In the subsequent sections, the factor groupings were labelled as critical roles CR1 (Environmental Sustainability and Resource Management), CR2 (Social Development and Education), CR3 (Economic Growth and Infrastructure Development), and CR4 (Poverty Alleviation and Food Security) in the study. It is worth noting that item R3 “Through my activities, there is an improvement in nutrition and sustainable agriculture” belongs to CR1 rather than CR4, as might be intuitively implied given its proximity to SDG2. This placement is consistent with both statistical and substantive reasoning. Statistically, the EFA assigns items to factors based on patterns of shared variance across respondents, and R3 exhibited its strongest covariance with the environmental sustainability cluster. Also, this alignment is well grounded in literature that presents sustainable agriculture as fundamentally linked to responsible land use, ecosystem conservation, climate-resilient resource management, and biodiversity protection [1,19].

4.3. Fuzzy synthetic evaluation analysis to identify the critical roles

4.3.1. Determining the weighing of each of the variables under each construct.

The weighting for each variable was computed following equation (1), and the results indicating the weighted scores of the critical roles are presented in Table 3. This weighting shows the relative importance of each variable based on the respondent’s grading of the variables. Given the mean values, the weightings are computed based on an equation [54].

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Table 3. Weighting scores of the critical roles.

https://doi.org/10.1371/journal.pone.0342601.t003

(1)

Where is the weighting of each variable in each role, is the mean value of each variable, is the summation of the average mean of each role grouping. The weighting of each variable and its critical role are represented in Table 4. For example, CR1 (Critical Role 1) has a mean value of 3.84, whereas the total mean for environmental sustainability and resource management is 25.807. The weighting for the CR was computed based on equation (2)

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Table 4. Membership functions of Latent Variables (Level 2) and Observed variables (Level 1).

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(2)

The weighing of each variable was computed using the same procedure above. Following this, the total weight for each construct was computed using a similar approach. For instance, the total mean for environmental sustainability and resource management was 25.807. Equation (3) shows how the total weighting was computed.

(3)

4.3.3. Determining the Membership Function of each variable.

In determining the membership function (MF) of the latent variables and observed variables, [60] suggest that the basic element of three variables can be derived as depending on the Likert scale. Thus, in this study, the Likert scale used is given as 1 – Highly Disagree; 2 – Disagree; 3 – Neutral; 4 – Agree; and 5- Highly Agree. The MF for each variable was computed using equation (4), and the results are presented in Table 4. Taking the CR1, the percentage shows 3.90% were assigned to highly disagree, 7.95% to disagree, 27.60% for neutral, 36.69% agree, and 23.86% for highly agree. This MF is derived as follows in equation (4).

(4)

This can also be written as . Adopting a similar approach, the membership function of the remaining variables for the competencies was computed. After establishing the membership function of level 2 for all the variables, the next step is to compute the MF level, here dealing with each construct based on equation (5) as suggested by Tseng et al. (2019).

(5)

where represent the weighting for all the variables. For example, all the and is the function matrix for each CR as an example. Using the CR1, the MF level 1 is expressed as:

Hence, the membership function of the CR1 of seven variables is computed as follows:

This is given by equation (6)

(6)

Where D represents the MF of a given construct, for example, CR and E denote the scaling grade here (1,2,3,4,5). Using the equation, the Indices of each construct of the critical role were calculated. This is shown in Table 5.

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Table 5. Overall indices of the critical roles.

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4.3.4. Evaluating the overall competency index.

The last step is the evaluation of the overall critical role indexes for each of the constructs under each parameter in assessing the critical role of the quantity surveyor in achieving the SDGs.

To assess the robustness of the FSE results, a sensitivity analysis was conducted using RStudio 2024.09.0 by systematically varying the weights assigned to the four critical role constructs (CR1–CR4). RStudio was employed to perform the sensitivity analysis due to its strong capability for reproducible, matrix-based computations and systematic simulation of parameter variations within fuzzy-based analytical frameworks, as demonstrated in similar construction research integrating FSE with advanced statistical tools [58]. As [68] further demonstrated, robustness in quantitative multi-criteria analyses can be assessed by examining the stability of outcomes under varying computational parameters. The weights derived from the mean score normalization process were perturbed by ±10% and ±20%, one at a time, followed by renormalization to ensure that the total weight remained equal to one. The membership functions and evaluation grading scale were held constant, and the fuzzy evaluation vectors and corresponding criticality indices were recomputed using the same procedure outlined in Equations 4–6. The recalculated indices under each scenario were compared with the base-case results to examine the stability of the rankings. The analysis revealed that variations in the weights did not lead to any change in the ranking order of the constructs. Social Development and Education (CR2) consistently ranked first, followed by Environmental Sustainability and Resource Management (CR1), Poverty Alleviation and Food Security (CR4), and Economic Growth and Infrastructure Development (CR3). This stability indicates that the FSE model is robust to moderate variations in input weights, thereby enhancing the reliability of the study’s findings [68].

The empirical results directly address the three research questions guiding this study. RQ1 asked: What SDGs do professional QSs prioritize in the performance of their roles? The descriptive analysis in Section 2 of the questionnaire revealed that respondents consistently prioritized SDGs associated with economic performance and infrastructure delivery (SDG8, 9, 11). In contrast, SDGs related to marine ecosystems (SDG14), terrestrial biodiversity (SDG15), and hunger (SDG2) received comparatively lower prioritization scores, suggesting that QSs in Ghana are yet to integrate environmental and food-system goals into their professional roles. RQ2 was directly addressed by the FSE results presented in Table 5, which identified the 4 critical roles all exceeding the 3.0 significance threshold. In addressing RQ3: What areas of QS practice are most strongly aligned with SDG attainment in the Ghanaian construction industry? The FSE indices revealed that Social Development and Educational practices (CR2, index 4.300) are most strongly aligned with SDG attainment.

The conceptual framework in Fig 1 below provides the pictorial guide of the study’s empirical analysis. The dual-lens framework comprising the Role Theory and Institutional Isomorphism Theory used for the study establish that QS professional roles are not static occupational functions but are continually reoriented in response to societal and environmental demands, shaped by the normative, regulatory, and cognitive pressures within the Ghanaian construction industry. Based on data from 88 GhIS registered QS professionals, the 18 SDG role items were reduced to four latent constructs by EFA and further yielded four critical roles that collectively translate into the social, economic, environmental, and poverty-related dimensions of the 2030 Agenda of UN. These roles comprising of, Social Development and Education, Environmental Sustainability and Resource Management, Poverty Alleviation and Food Security, and Economic Growth and Infrastructure Development, are directly linked to a cluster of specific SDGs. Collectively, these are critical roles of QS practice. It is observed also that to some extent, QS perceive their roles in attaining Agenda 2030 goals as critical within these four aspects of their practice.

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Fig 1. Conceptual Framework.

Source: Author’s construct (2026).

https://doi.org/10.1371/journal.pone.0342601.g001

5.1. Limitations and directions for further research

The study acknowledges some limitations that affect the results and findings. A key limitation of this study relates to the sample size used for the EFA. Although the study achieved 88 valid responses, representing a commendable 57.14% response rate from registered QS firms in Ghana, this sample size remains relatively modest for factor analysis involving an initial pool of over 18 items. Smaller samples may affect the stability, robustness, and replicability of the extracted factor structure. While the adequacy of the dataset was supported by strong statistical indicators, including KMO value and Cronbach’s alpha, caution should be exercised in generalizing the factor solution. Future studies are therefore encouraged to employ larger samples to enhance the reliability and external validity of the EFA results and to further validate the identified factor structure.

Also, the study measures QS perceptions of role prioritization rather than actual practice outcomes. The index values reflect how respondents rate their engagement with SDG-related activities, not whether such engagement produces quantifiable sustainability impacts. This distinction is important for understanding the scope of the study’s contributions. Future research employing mixed methods, including qualitative case studies and project-level outcome data would usefully complement the quantitative evidence presented and provide a more complete picture of the relationship between QS practice and SDG attainment. This study is centered and based on Ghana-specific data, thus restricting the relevance of the study to other regions. The high index for Social Development may be as a result of specific normative pressures within Ghana’s professional environment rather than a universal feature of QS practice globally. Comparative research involving QS professionals in other regions, particularly in South Asia, America, and other parts of sub-Saharan Africa, would provide valuable insights into whether the findings of this study are extrapolatable to other regional contexts.