A Hybrid AHP–Fuzzy MOORA Decision Support Tool for Advancing Social Sustainability in the Construction Sector

Abstract

The construction industry plays a key role in economic development but continues to face challenges in promoting employee well-being, particularly mental health and social sustainability. While existing decision-making tools emphasize environmental and economic factors, the social dimension remains largely overlooked, creating a significant gap in both research and practice. To address this, the study develops a decision support tool (DST) to help construction organizations prioritize strategic investments that enhance employee social sustainability. The tool is based on a hybrid multi-criteria decision-making framework, combining the Analytical Hierarchy Process (AHP) with Fuzzy MOORA to integrate both quantitative and qualitative assessments. A literature review, along with findings from a previous empirical study, identified 27 validated criteria, grouped into seven core sustainability alternatives. Additionally, five decision criteria (cost, risk, compatibility, return on investment, and difficulty) were refined through expert interviews. The DST was implemented as a modular Excel-based tool allowing users to input data, conduct pairwise comparisons, evaluate alternatives using linguistic scales, and generate a final ranking through defuzzification. A case study in a private construction company showed Training and Development and Work Environment as top priorities. An online expert focus group confirmed the DST’s clarity, usability, and strategic relevance. By addressing the often-neglected social pillar of sustainability, this tool offers a practical and transparent framework to support decision-making, ultimately enhancing employee well-being and organizational performance in the construction sector.

1. Introduction

The construction industry is widely recognized as a critical pillar of economic development worldwide. As infrastructure projects continue to proliferate to support urbanization and economic growth [1], the industry faces increasing scrutiny not only for its technical and financial performance but also for its impact on human capital. In many regions, particularly in fast-growing economies, construction work is characterized by high physical demands, long working hours, and diverse labor demographics. These factors contribute to occupational stress, safety concerns, and, ultimately, challenges in maintaining employee well-being [2].

Social sustainability in the workplace refers to the development of an environment that supports employee mental health, work–life balance, equitable treatment, and safe working conditions [3,4]. In the context of the construction sector, ensuring social sustainability is essential because it not only improves the quality of life for employees but also enhances overall productivity, reduces turnover, and mitigates the risk of accidents and work-related illnesses [5,6]. Recent studies have further emphasized the significance of social sustainability in driving organizational success. For instance, Papadakis and Tzagkarakis [7] highlighted how public policies influence critical aspects of social sustainability, such as employment, living conditions, healthcare, and education, underscoring their impact on workforce well-being. Similarly, Azeem et al. [8] demonstrated that an organization’s strategic orientation toward social sustainability significantly contributes to sustainable firm performance, illustrating how social sustainability initiatives can align with business objectives. Moreover, Duong et al. [9] revealed that social sustainability practices positively influence supply chain performance, highlighting the broader operational benefits of adopting socially sustainable practices. Research has consistently shown that improved mental health and job satisfaction have a direct impact on performance metrics, which in turn influences a company’s competitive edge [10].

Despite the growing recognition of social sustainability as a critical dimension in the construction sector, there is a clear and underexplored gap in the development of systematic tools that support decision-makers in selecting and prioritizing employee-focused sustainability initiatives. Most existing research has concentrated on identifying individual criteria that influence employee well-being, such as job satisfaction or mental health [2,4,10], but has stopped short of offering integrated, operational frameworks for evaluating these criteria in complex, real-world decision contexts. As a result, many construction organizations continue to rely on fragmented, ad hoc methods that fail to account for the interplay between quantitative considerations (e.g., cost, risk, and ROI) and qualitative dimensions (e.g., organizational support, work-life balance) [11]. Moreover, the absence of standardized, data-driven decision support tools forces managers to rely heavily on subjective judgment, reducing transparency and potentially leading to suboptimal resource allocation [12].

To address this gap, the present research aims to develop a dedicated decision support tool (DST) tailored to enhancing social sustainability within construction organizations. The proposed DST integrates a hybrid multi-criteria decision-making (MCDM) methodology, combining the Analytical Hierarchy Process (AHP) [13] and Fuzzy Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) [14]. This approach enables the systematic evaluation of diverse sustainability initiatives by incorporating both objective metrics and subjective expert judgments, even under conditions of uncertainty. By offering a structured and transparent framework, the DST empowers managers to make informed, balanced decisions that align organizational priorities with employee well-being.

To bridge this gap, the aim of this research is to develop a decision support tool (DST) specifically tailored to enhancing social sustainability in the construction sector. The DST leverages a hybrid multi-criteria decision-making (MCDM) approach that combines the strengths of the Analytical Hierarchy Process (AHP) [13] and Fuzzy Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) [14]. This integration allows for a comprehensive assessment that incorporates both objective numerical data and subjective, linguistic evaluations. Such a tool is designed to assist decision-makers in systematically weighing the benefits and trade-offs of various sustainability initiatives under conditions of uncertainty.

This study is guided by the following main research questions:

• How can a DST that integrates MCDM techniques be developed to enable a holistic evaluation of social sustainability initiatives in construction?
• How effective and user-friendly is the developed DST when applied in a real-world construction context and evaluated through expert feedback?

The significance of this research lies in both its methodological contribution and its practical applicability. By integrating robust MCDM techniques, the proposed DST offers a transparent, repeatable, and scalable framework that accommodates both financial constraints and the intangible aspects of employee well-being. In practical terms, the DST equips decision-makers with a structured approach to evaluating and prioritizing social sustainability initiatives by addressing key investment criteria such as cost, risk, organizational compatibility, return on investment, and implementation difficulty [15]. Importantly, by enabling more informed and balanced decisions, the tool has the potential to improve overall organizational performance through increased employee satisfaction, reduced turnover, and enhanced workplace safety [4,5]. As such, this research contributes not only to the academic discourse on sustainable workforce management but also to practical decision-making in construction organizations and beyond.

The primary objectives of this study are as follows:

• To identify and prioritize the critical decision criteria influencing social sustainability in the construction sector. This involves synthesizing existing literature (e.g., [3,10]) and incorporating insights from expert interviews.
• To develop a robust DST that integrates AHP and Fuzzy MOORA. The tool is designed to aggregate both quantitative data and qualitative assessments, allowing for a holistic evaluation of sustainability initiatives.
• To validate the DST through a case study and expert feedback. A case study in a private construction organization, along with a focus group including industry experts and technology providers, was used to ensure the tool’s practical relevance and user-friendliness.

2. Literature Review

Social sustainability is increasingly recognized as a critical component of organizational success, especially in sectors such as construction where workers face significant physical and psychological challenges. This literature review introduces the key concepts of social sustainability, discusses DSTs for sustainability management, and examines MCDM methods that form the basis for integrated DSTs. In doing so, it identifies limitations in current approaches and highlights the need for a unified tool to guide strategic investments in social sustainability.

2.1. Social Sustainability in the Construction Sector

Social sustainability is broadly defined as the process of creating environments that foster human well-being, fairness, equity, and safety while nurturing supportive organizational cultures [3]. In the construction sector, these principles are particularly vital because workers often operate under conditions that are physically demanding and psychologically stressful. Research dating back to early conceptualizations of mental health as merely the absence of illness [16] has evolved into a more nuanced view that encompasses well-being, psychological flourishing, and life satisfaction [4,17,18].

Empirical studies have underscored the adverse impacts of long working hours, physically strenuous tasks, and job insecurity on construction workers’ mental health [2,19]. For example, Saboor and Ahmed [10] found that in UAE construction firms, initiatives such as enhanced organizational support, fair compensation, and improved work–life balance significantly increased job satisfaction and reduced stress levels. Similarly, Allen et al. [20] reported that supportive work environments lead to lower turnover and higher productivity.

The rapid pace of urbanization and infrastructure development, as documented by Wood [1], further stresses the need for robust social sustainability practices. In regions such as the UAE, where the construction sector is a major employer, addressing social factors is a strategic necessity. Overall, the literature suggests that while the importance of social sustainability is well documented, many construction organizations continue to rely on fragmented or ad hoc methods to evaluate and improve these factors.

A systematic review conducted by Rostamnezhad and Thaheem [21] categorized social sustainability assessment indicators into key themes, including worker well-being, equity, social engagement, and long-term workforce sustainability. Their study emphasized the need for standardized frameworks to assess social sustainability comprehensively, moving beyond traditional occupational safety metrics to include aspects such as mental health, job satisfaction, and organizational culture. Their taxonomy of indicators provides a structured approach for evaluating and improving social sustainability in construction projects.

While the importance of social sustainability is well-documented, there is a pressing need for industry-wide adoption of systematic assessment tools that integrate these diverse factors into decision-making processes. Addressing social sustainability holistically can lead to improved workforce productivity, reduced turnover, and a more resilient construction sector.

2.2. Decision Support Tools for Sustainability Management

DSTs are designed to help managers make complex decisions by organizing, analyzing, and synthesizing diverse sets of data under conditions of uncertainty [12]. In the construction industry, DSTs have traditionally been employed for tasks such as project selection, resource allocation, and risk management [22]. However, these tools have often focused primarily on technical and economic dimensions, such as cost efficiency, scheduling, and safety metrics, while largely neglecting the qualitative aspects of social sustainability [23].

A systematic review by Rostamnezhad and Thaheem [21] provides a structured taxonomy of social sustainability assessment indicators in construction projects. Their study underscores that existing decision-making frameworks often lack mechanisms to systematically incorporate workforce well-being, equity, and social resilience into project evaluation. They advocate for integrating these dimensions into DSTs, allowing construction managers to move beyond compliance-based assessments and adopt proactive strategies that foster a socially sustainable workforce. Similarly, Marcher et al. [24] examined the state-of-the-art decision-support methods in construction and found that most existing tools are designed for cost-benefit analysis, energy efficiency optimization, or supply chain decision-making. Their review identified a lack of structured methodologies that incorporate social sustainability indicators within decision frameworks. They emphasize that advanced MCDM methods, such as AHP and fuzzy logic approaches, should be integrated into DSTs to capture both quantitative and qualitative factors in sustainability assessments.

The need for DSTs that integrate social sustainability indicators has grown in recent years. The literature consistently points to a gap: while decision support systems exist to guide technical and financial decisions in construction, there is a lack of integrated DSTs that comprehensively assess social sustainability. Current tools do not fully account for qualitative factors such as work–life balance, employee support, and organizational culture, elements that are critical for long-term sustainability [10,23].

Therefore, an integrated DST that combines quantitative criteria (e.g., cost, risk, ROI) with qualitative assessments through advanced MCDM methods is needed to support strategic decision-making in the construction sector. Such a tool would bridge the gap identified in existing literature, aligning social sustainability considerations with broader financial and operational objectives. By leveraging structured decision-support methodologies, construction firms can enhance workforce well-being, improve retention, and ensure long-term sustainability while maintaining competitive performance.

2.3. Multi-Criteria Decision-Making Methods

MCDM methods provide a systematic approach to evaluating complex decision problems that involve multiple, often conflicting criteria. These techniques allow decision-makers to integrate both quantitative and qualitative factors, ensuring a structured, transparent, and rational decision-making process [11]. MCDM methods are widely applied in engineering, project management, supply chain optimization, and sustainability assessment, among other domains. In the construction industry, where decision-making must balance technical, economic, social, and environmental considerations, MCDM techniques offer a valuable framework for optimizing solutions [25].

MCDM methods can be broadly classified into compensatory and non-compensatory approaches. Compensatory methods allow trade-offs between criteria, meaning that a low score in one criterion can be offset by a high score in another. These methods aggregate scores across criteria to provide an overall evaluation, making them suitable for scenarios where decision-makers need to balance conflicting objectives. Techniques such as TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (VIšekriterijumska Optimizacija i Kompromisno Rešenje), and MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) are examples of compensatory methods [26]. They are particularly useful in situations like minimizing costs while maximizing sustainability [27]. In contrast, non-compensatory methods do not allow trade-offs and instead establish strict thresholds or outranking relationships for decision criteria. Methods such as ELECTRE (Elimination and Choice Expressing Reality) and PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) fall under this category and are typically used when certain criteria must be met without compromise [28].

A comprehensive review by Chen and Pan [29] highlighted the increasing adoption of fuzzy multi-criteria decision-making (FMCDM) in construction management due to the inherent complexity and uncertainty in decision-making. Their study analyzed the relationships between fuzzy sets, MCDM approaches, and construction applications, emphasizing the advantages of fuzzy logic in handling imprecise and ambiguous data. They proposed a network approach for structuring FMCDM methods and applications, demonstrating how different decision-making techniques interact in construction projects. Their findings suggest that hybrid models combining fuzzy logic with traditional MCDM approaches, such as Fuzzy-TOPSIS and Fuzzy-AHP, provide more robust decision-making frameworks by integrating subjective expert opinions with quantitative performance metrics.

Several widely used MCDM techniques were considered in this study, each offering distinct advantages and limitations as summarized in Table 1.

It can be seen from Table 1 that TOPSIS ranks alternatives based on their geometric distance from an ideal solution and a negative-ideal solution. While it allows for weighted criteria, its reliance on geometric distance may makes it less suitable when subjective or qualitative judgments play a significant role [30], whereas VIKOR, on the other hand, is effective for optimizing multi-objective problems through a compromise ranking approach, but it is sensitive to changes in weight assignments, which can significantly influence the results [31]. In addition, ELECTRE is well-suited for handling qualitative assessments through outranking relationships, but its reliance on concordance and discordance indices makes it computationally demanding [32]. ANP (Analytic Network Process) extends AHP by considering interdependencies among criteria, but this increases computational complexity, making it more challenging to apply to large-scale decision problems [22]. DEA (Data Envelopment Analysis) is primarily used for efficiency assessments and is less suited for ranking alternatives based on multiple criteria, as it focuses on measuring relative efficiency rather than comprehensive multi-criteria evaluation [33].

In comparing these methods, it was found that MOORA offers a computationally simple and stable ranking mechanism due to its straightforward ratio-based normalization and aggregation process, making it highly suitable for practical decision-making [34]. Unlike methods such as TOPSIS and VIKOR, MOORA separately considers benefit and cost criteria by subtracting the weighted cost criteria from the weighted benefit criteria, avoiding distortions caused by normalization [35]. Additionally, MOORA is less sensitive to variations in weight assignments compared to methods like VIKOR, making it a more robust approach in many scenarios [35]. However, one of its main limitations is its reliance on crisp numerical values, which may not adequately capture the uncertainty or vagueness in subjective assessments. To address this issue, fuzzy logic can be integrated into MOORA, resulting in Fuzzy MOORA. This extension allows decision-makers to express preferences using linguistic variables such as “Good”, “Moderate”, or “Poor”, which are then converted into fuzzy numbers for a more precise evaluation [36].

Table 1. Key features and performance comparison of main MCDM approaches.

	Simplicity	Calculation	Stability	Transparency	Computational Time	Output
MOORA	High	Low	High	Very High	Very Low	Ranks alternatives by separately evaluating benefit and cost criteria using ratio-based normalization. It ensures robust results with minimal sensitivity to weight variations [34,35].
DEA	Moderate	Moderate	Medium	Moderate	High	Assesses relative efficiency of alternatives by analysing input-output data. Primarily used for performance benchmarking in operational environments [33].
VIKOR	High	Moderate	Medium	High	Low	Provides a compromised solution by ranking alternatives based on proximity to an ideal solution, addressing conflicting criteria for optimal decision-making [31].
ELECTRE	Moderate	High	Medium	Low	High	Uses outranking relations and concordance/discordance indices to identify and exclude unacceptable alternatives, suitable for qualitative assessments [32].
TOPSIS	Moderate	Moderate	Medium	High	Moderate	Ranks alternatives by measuring geometric distance from ideal and negative-ideal solutions, offering a balanced approach to quantitative and qualitative evaluations [30].
ANP	Low	Very High	Low	Low	Very High	Handles complex decision problems by modeling interdependencies among criteria through a network structure, enhancing analytical depth [22].
AHP	Moderate	High	Medium	High	High	Structures decision problems hierarchically; uses pairwise comparisons to derive criterion weights with consistency checks, providing a transparent prioritization process [13,37].

Table 1. Key features and performance comparison of main MCDM approaches.

	Simplicity	Calculation	Stability	Transparency	Computational Time	Output
MOORA	High	Low	High	Very High	Very Low	Ranks alternatives by separately evaluating benefit and cost criteria using ratio-based normalization. It ensures robust results with minimal sensitivity to weight variations [34,35].
DEA	Moderate	Moderate	Medium	Moderate	High	Assesses relative efficiency of alternatives by analysing input-output data. Primarily used for performance benchmarking in operational environments [33].
VIKOR	High	Moderate	Medium	High	Low	Provides a compromised solution by ranking alternatives based on proximity to an ideal solution, addressing conflicting criteria for optimal decision-making [31].
ELECTRE	Moderate	High	Medium	Low	High	Uses outranking relations and concordance/discordance indices to identify and exclude unacceptable alternatives, suitable for qualitative assessments [32].
TOPSIS	Moderate	Moderate	Medium	High	Moderate	Ranks alternatives by measuring geometric distance from ideal and negative-ideal solutions, offering a balanced approach to quantitative and qualitative evaluations [30].
ANP	Low	Very High	Low	Low	Very High	Handles complex decision problems by modeling interdependencies among criteria through a network structure, enhancing analytical depth [22].
AHP	Moderate	High	Medium	High	High	Structures decision problems hierarchically; uses pairwise comparisons to derive criterion weights with consistency checks, providing a transparent prioritization process [13,37].

Among the criteria-weighting methods, AHP was selected for its ability to structure complex decision problems into a hierarchical model. AHP enables decision-makers to prioritize decision criteria through pairwise comparisons, ensuring a transparent and systematic assignment of weights [13]. Unlike simple weighting methods, AHP incorporates expert judgment and provides a consistency check, reducing the risk of bias in weight assignments [37]. While AHP is computationally intensive when dealing with large datasets, its ability to handle both qualitative and quantitative factors makes it ideal for sustainability-related decision-making [12].

By integrating AHP and Fuzzy MOORA, this study leverages the strengths of both methods to develop a robust and efficient DST. AHP is used to determine the relative importance of decision criteria, while Fuzzy MOORA ranks alternatives by incorporating both objective performance indicators and subjective human factors. This hybrid approach ensures that sustainability decisions are made with a balance between economic feasibility and employee well-being, providing organizations with a transparent, adaptable, and data-driven framework for strategic decision-making in the construction sector.

Nonetheless, we acknowledge that alternative weighting and ranking methods, such as the Best–Worst Method (BWM), Evaluation Based on Distance from Average Solution (EDAS), and advanced fuzzy extensions like Z-numbers, offer promising capabilities. Future research could explore and compare these methods with our current framework to further enhance decision-making accuracy, manage uncertainty more effectively, and assess the robustness of results across different MCDM techniques.

2.3.1. The Analytical Hierarchy Process (AHP)

The AHP, developed by Thomas Saaty in the 1970s [13], is one of the most widely used MCDM techniques. It is designed to decompose complex decision problems into a hierarchical structure, making them easier to analyze and compare. AHP enables decision-makers to structure problems into a hierarchy of criteria, sub-criteria, and alternatives, facilitating a systematic and logical approach to decision-making [38]. This hierarchical breakdown allows for an in-depth evaluation of each factor, ensuring that all relevant aspects are considered in decision-making processes.

AHP follows a step-by-step approach to quantify subjective judgments, ensuring transparency and consistency in decision-making. The process involves the following key steps:

• Defining the Decision Problem: The first step in AHP involves clearly defining the objective or goal of the decision-making process. This could be selecting the best alternative, prioritizing projects, or evaluating sustainability measures.
• Establishing the Hierarchical Structure: The decision problem is broken down into multiple levels, starting with the overall goal at the top, followed by criteria and sub-criteria, and finally, the alternatives at the bottom. This hierarchical structure allows decision-makers to systematically compare the elements at each level.
• Pairwise Comparisons of Criteria: Decision-makers perform pairwise comparisons among criteria and alternatives using Saaty’s scale (ranging from 1 to 9). This scale captures the relative importance of each criterion based on expert judgment, allowing for an intuitive comparison of decision factors.
• Constructing the Comparison Matrix and Deriving Weights: The pairwise comparisons are structured into a comparison matrix, where the principal eigenvector is used to calculate normalized weights for each criterion. These weights reflect the relative significance of each factor in the decision-making process as shown below:

The pairwise comparison matrix established by adopting Saaty’s scale is represented by Matrix A:

$$\mathrm{A}=\left[\begin{array}{ccc}1 & \dots & {\mathit{a}}_{\mathbf{1}\mathit{n}}\\ ⋮& \ddots & ⋮\\ {\mathit{a}}_{\mathit{n}\mathbf{1}}& \dots & 1\end{array}\right]$$

(1)

where $a_{ij}$ defines the relative importance of criteria i in comparison with j and $a_{ji}$ = ${\displaystyle \frac{1}{a_{ij}}}$ is the relative importance of criteria j in comparison with i.

Let’s suppose there are n alternatives/elements (A_i, i = 1, …, n) and r number of decision-makers D_k, (k = 1, …, r), were invited. In this case, let A[k] = (a_ij [k]) be the judgment matrix provided by the kth number of decision-makers when comparing n elements so that β_k will be the weight assigned that the k-th decision-maker has in forming the group decision. For this study, the weight of the kth decision-maker was considered equal. Therefore, for the aggregation of the judgments of the decision makers, Equations (2) and (3) will be adopted:

$${\mathrm{a}}_{\mathrm{ij}} \left[\mathrm{G}\right]={\prod }_{k=1}^r ({{\mathrm{a}}_{\mathrm{ij}} \left[\mathrm{k}\right])}^{{\beta }_{\mathrm{k}}}$$

(2)

where a_ij = 1/a_ji, for i ≠ j, and a_ii = 1, all i.

$${\mathrm{a}}_{\mathrm{ij}}=a_{ij}/{\sum }_{i=1}^n{a}_{ij},\mathrm{n}\text{-}\mathrm{dimensional}\mathrm{column}\mathrm{vector}$$

(3)

Furthermore, the weights of each criterion given by the decision-makers is determined by calculating the principal eigenvectors of the decision comparison matrix as shown in the equation below:

$$\left[\begin{array}{ccc}1& \dots & {\mathit{a}}_{\mathbf{1}\mathit{n}}\\ ⋮& \ddots & ⋮\\ {\mathit{a}}_{\mathit{n}\mathbf{1}}& \dots & 1\end{array}\right]\begin{array}{c}{w}_1\\ ⋮\\ w_n\end{array}={\lambda }_{max} \begin{array}{c}{w}_1\\ ⋮\\ w_n\end{array}$$

(4)

where ($w_1, w_2, \dots \dots w_n)$^T is the maximum eigenvector of matrix A and ${\lambda }_{max}$ is the max eigen value of comparison matrix A. The weights of each criterion can be determined.

• Consistency Check: AHP incorporates a Consistency Ratio (CR) to ensure that the decision-maker’s judgments are reliable. If the CR is less than 0.1, the judgments are considered consistent; otherwise, the comparisons must be revised (Waris et al., 2019 [37]). This step enhances the robustness of AHP by minimizing subjective biases using the following equation as mentioned below:

$$CR={\displaystyle \frac{CI}{RI}} and CI-{\displaystyle \frac{{\mathsf{\lambda }}_{MAX}-n}{n-1}}$$

(5)

where n is the number of elements/criteria adopted by AHP analysis to conduct comparison, and the RI is the random index. For the results to be consistent and acceptable, the Consistency ratio must be less than 0.1.

• Ranking Alternatives and Decision Selection: The final step involves aggregating the weighted scores and ranking the alternatives based on their overall priority. The alternative with the highest score is considered the optimal choice for the decision-making problem.

AHP offers several key benefits, making it a powerful tool for decision-making in complex and multi-faceted environments:

• AHP provides a well-organized framework that enhances clarity and accountability in decision-making.
• AHP allows for the evaluation of both numerical values and subjective judgments, making it particularly useful in domains where human perception plays a role, such as sustainability assessments and workforce management.
• The pairwise comparison process ensures that multiple stakeholders can contribute to the decision, making it ideal for group decision-making scenarios.
• AHP is adaptable to a wide range of applications, including project prioritization, resource allocation, supplier selection, and risk assessment.

Despite its strengths, classical AHP has some inherent limitations:

• AHP relies on exact numerical values to represent judgments, which may not fully capture vagueness or uncertainty in qualitative assessments. This limitation is particularly significant when evaluating organizational culture, employee perceptions, and work-life balance, where subjective factors influence decision-making.
• As the number of criteria and alternatives increases, the number of pairwise comparisons grows exponentially, leading to a more complex and time-consuming analysis.
• The results of AHP can be influenced by inconsistent judgments or subjective biases, making the consistency check a critical component of the method.

By integrating AHP into the DST, organizations can strategically prioritize employee well-being initiatives based on the relative importance of each factor. This method ensures that social sustainability decisions align with both organizational objectives and employee needs.

2.3.2. The Fuzzy MOORA Method

The MOORA method is a widely used MCDM technique that provides a systematic approach for evaluating and ranking alternatives based on multiple, often conflicting, criteria. It was originally developed by Brauers and Zavadskas [39] and has been successfully applied in various domains, including project management, material selection, supplier evaluation, and economic sustainability [39]. The strength of MOORA lies in its ability to handle complex decision problems efficiently, ensuring transparency and computational simplicity.

However, classical MOORA relies on crisp numerical values, which may not adequately capture subjective judgments, uncertainty, and qualitative factors such as employee perceptions, organizational culture, or social sustainability initiatives. To address this limitation, Fuzzy MOORA integrates fuzzy logic into the traditional MOORA framework, allowing decision-makers to incorporate both quantitative and qualitative assessments in a structured and mathematically sound manner. This adaptation is particularly valuable in human-centered and sustainability-related decisions, where subjective evaluations and linguistic terms play a significant role.

The integration of fuzzy logic into MOORA offers several advantages, particularly in domains where decision-makers must balance objective financial constraints with subjective human factors. Unlike classical MOORA, which requires precise numerical inputs, Fuzzy MOORA enables decision-makers to express evaluations in linguistic terms such as Excellent, Good, Moderate, Poor. These verbal assessments are then converted into triangular fuzzy numbers (TFNs), which help model the inherent imprecision in qualitative judgments.

The Fuzzy MOORA method is particularly useful in decision scenarios with the following circumstances:

• Involve subjective assessments, such as employee perceptions of fairness, satisfaction, or well-being.
• Require balancing conflicting objectives, such as maximizing employee engagement while minimizing implementation costs.
• Need a structured yet flexible approach, allowing decision-makers to consider both tangible (cost, risk) and intangible (organizational support, morale) factors.

The Fuzzy MOORA approach follows a structured decision-making process that enables organizations to evaluate alternatives comprehensively:

• Constructing the Decision Matrix: A decision matrix is created, where various alternatives (e.g., employee training programs, workplace wellness initiatives, work-life balance policies) are evaluated against multiple criteria (e.g., cost, return on investment, impact on employee morale). However, instead of relying solely on numerical data, decision-makers provide linguistic evaluations, which are then converted into TFNs as shown below (Linguistic scale are used for this transformation [40]):

$$\begin{gathered} \begin{array}{ccc}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\mathrm{m}\mathrm{a}\mathrm{x}\dots & \mathrm{m}\mathrm{i}\mathrm{n}\dots & \mathrm{m}\mathrm{a}\mathrm{x}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}{C}_1\dots & C_j\dots & C_n\end{array} \\ {\left[DM\right]}_{mxn}=\begin{array}{c}{A}_1\\ ⋮\\ A_m\end{array}[\begin{array}{ccc}{x}_{11k}\dots & x_{1jk}\dots & x_{1nk}\\ ⋮& ⋮& ⋮\\ x_{m1k}& x_{mjk}& x_{nmk}\end{array}] \end{gathered}$$

(6)

where $x_{ijk}$ represents the performance rating of ith alternative with respect to jth criteria by kth decision-makers and expressed as $x_{ij}^{~}$ = ${(x}_{ij1}^{~}$, $x_{ij2}^{~}$, $x_{ij3}^{~}$). For this case study, The DST includes. m = 7 alternatives (Organizational Support, Work-Life Balance, Equity Factor, Work Control, Work Environment, Training and Development, Contract Type), n = 5 decision criteria (Cost of improvement/Budget, Risk, Compatibility with Organization, Return on Investment, and Degree of Difficulty) and, k = 3 decision-makers.

The responses of the decision-makers are aggregated into the decision matrix by adopting Fuzzy Weighted Averaging (FWA):

$$x_{ij}^{~}={\displaystyle \frac{\sum _{k=1}^K{w}_k^{~}{x}_{ij}^{~k}}{\sum _{k=1}^K{w}_k^{~}}}$$

(7)

where $w_k^{~}$ represents the fuzzy coefficient of the significance of the kth decision-makers. The aggregated response of alternatives is calculated as $x_{ij}^{~}$ = ${(x}_{ij1}^{~}$, $x_{ij2}^{~}$, $x_{ij3}^{~}$).

• Normalization of data: Since different criteria have different measurement units (e.g., financial costs in dollars, job satisfaction on a survey scale), normalization ensures all values are comparable. Additionally, this step differentiates between benefit and cost criteria:

  ○

  Benefit-type criteria (e.g., employee satisfaction, engagement, productivity) should be maximized.

  ○

  Cost-type criteria (e.g., financial cost, risk, implementation difficulty) should be minimized.

$$x_{ij}^{~}\times w_{ij}^{~}=r_{ij}^{~}$$

(8)

$$r_{ij}^{~}=r_{ij1}^{~}, r_{ij2}^{~}, r_{ij3}^{~}=({\displaystyle \frac{r_{ij1}^{~}}{\surd \sum _{i=1}^m{r}_{ij1}^2}} , {\displaystyle \frac{r_{ij2}^{~}}{\surd \sum _{i=1}^m{r}_{ij2}^2}} , {\displaystyle \frac{r_{ij3}^{~}}{\surd \sum _{i=1}^m{r}_{ij3}^2}}) \mathrm{for} \mathrm{all} \mathrm{i}, \mathrm{j}$$

(9)

where $r_{ij}^{~}$ is the weighted normalized performance index ith alternative with respect to jth criteria i = 1, 2, …, m and j = 1, 2, …, n.

• Assigning weights to criteria: To reflect their relative importance, weights are assigned to each criterion. These weights, derived using the Analytical Hierarchy Process (AHP), ensure that decision-makers prioritize key factors in the evaluation process. For example, if employee well-being is a higher priority than cost savings, it will be given a higher weight in the decision model.
• Aggregation of weighted scores: Once weights are assigned, the weighted performance values of each alternative are computed. This ensures that the ranking process considers both numerical and subjective assessments in a balanced manner.
• Ranking of alternatives: The weighted scores are aggregated for each alternative, producing an overall ranking. The alternative with the highest fuzzy score is considered the most optimal choice, as it offers the best balance between cost-effectiveness and sustainability impact.
• Defuzzification for final decision-making: Since fuzzy values represent ranges rather than exact numbers, a defuzzification process is applied to convert them into a single crisp numerical score for each alternative. This final step simplifies decision-making by ensuring a clear and interpretable ranking of options:

$$y_i^{~}=(y_{i1}^{~} , y_{i2}^{~}, y_{i3}^{~})$$

(10)

$${BNP}_{{\mathit{y}}_{\mathit{i}}^{~}}=( {\displaystyle \frac{\left({\mathit{y}}_{\mathit{i}\mathbf{3}}^{~}-{\mathit{y}}_{\mathit{i}\mathbf{1}}^{~}\right)+\left({\mathit{y}}_{\mathit{i}\mathbf{2}}^{~}-{\mathit{y}}_{\mathit{i}\mathbf{1}}^{~}\right)}{3}}+{\mathit{y}}_{\mathit{i}\mathbf{1}}^{~})$$

(11)

• Decision-making and implementation: The final ranking enables decision-makers to select the best alternative while considering both financial constraints and workforce well-being. This structured yet flexible approach ensures transparency, accuracy, and fairness in evaluating social sustainability initiatives.

Finally, the alternative ranking by using the Fuzzy Ratio system is calculated as the ordinal ranking of ${BNP}_{{\mathit{y}}_{\mathit{i}}^{~}}$ that has the highest and maximum assessment value by using this equation:

$$A_{FRS}^{*}=\left[A_i\right| {max}_i {BNP}_{y_i^{~}}]$$

(12)

This allows the organization to determine the optimal solution of significant influence and performance of alternatives on social sustainability, keeping in mind the decision criteria and objectives to enhance the employee’s social sustainability.

The Fuzzy MOORA approach is particularly well-suited for decision-making in human-centered and sustainability-focused domains, offering several advantages:

• Effectively handles subjectivity and uncertainty:

  ○

  Allows decision-makers to express evaluations in linguistic terms rather than forcing rigid numerical scores.

  ○

  Ensures that subjective perceptions (e.g., employee satisfaction, cultural fit) are quantitatively incorporated into decision-making.

• Balances Conflicting Criteria:

  ○

  Integrates cost considerations and social sustainability goals, ensuring holistic decision-making.

  ○

  Provides a structured framework for optimizing both financial efficiency and employee well-being.

• Computationally simple yet powerful:

  ○

  Unlike more complex MCDM methods (e.g., ANP, TOPSIS), Fuzzy MOORA is computationally efficient while maintaining high accuracy.

  ○

  Can handle large decision problems with multiple alternatives and criteria.

• Improves transparency and justifiability of decisions:

  ○

  Provides a clear ranking mechanism, reducing bias and subjectivity in sustainability-related decision-making.

  ○

  Ensures that decisions are data-driven and aligned with strategic organizational goals.

While Fuzzy MOORA is highly effective in ranking alternatives, it does not inherently determine the weights of decision criteria. To overcome this limitation, it is combined with AHP, which has the following effects:

• Provides a structured framework for deriving criterion weights based on expert judgment.
• Ensures that sustainability initiatives align with organizational priorities.
• Allows decision-makers to assign priority to factors such as employee well-being, financial feasibility, and operational efficiency.

By leveraging AHP for criteria weighting and Fuzzy MOORA for alternative ranking, the proposed decision-support model provides a balanced, transparent, and efficient solution for prioritizing employee sustainability measures in the construction industry.

2.4. Research Gap and Rationale

While extensive research has highlighted the importance of social sustainability in enhancing employee well-being and overall organizational performance [2,3,10], there remains a significant gap in the development of integrated decision support systems that holistically address social sustainability. Current DSTs in construction tend to focus on technical and economic dimensions [22,23], while neglecting qualitative aspects such as employee support, work–life balance, and organizational culture.

Moreover, previous studies have typically employed either AHP or fuzzy MCDM methods independently, rather than combining these approaches to capture both the precision of quantitative data and the uncertainty of qualitative judgments. This fragmentation results in decision-making processes that are less transparent and may fail to provide a comprehensive evaluation of social sustainability initiatives.

In response to these challenges, the present study proposes the development of a DST for Social Sustainability that employs a hybrid AHP–Fuzzy MOORA framework. This tool is designed to achieve the following effects:

• Integrate quantitative and qualitative criteria: Combining AHP for precise weighting with Fuzzy MOORA for handling uncertainty in qualitative assessments.
• Enhance decision transparency: Providing a systematic, traceable process that clearly shows how each criterion influences the final ranking.
• Facilitate strategic resource allocation: Enabling construction managers to prioritize investments in sustainability initiatives that effectively balance cost, risk, and intangible benefits.

By addressing this research gap, the proposed DST is expected to offer a robust framework for decision-makers in the construction industry, thereby improving overall social sustainability and employee well-being.

3. Methodology

To develop and validate a DST for enhancing social sustainability in the construction sector, this study followed a structured three-phase methodology (Figure 1). The methodology integrates qualitative and quantitative research techniques to ensure rigor, practical relevance, and a strong empirical foundation for decision-making.

• Pre-Development Phase

The pre-development phase was based on the findings of a previous study by the same authors (Saboor and Ahmed, 2024 [10]). It involved a systematic literature review of 76 publications (2000–2022) related to employee well-being and social sustainability in construction, followed by qualitative and quantitative analysis.

In the previous study, a set of 32 initial sustainability criteria was identified using content analysis. These were then refined through semi-structured interviews with 10 industry experts, resulting in a validated list of 27 contextually grounded criteria. A quantitative survey (506 participants) and Structural Equation Modeling (SEM) were subsequently used to group these criteria into seven core sustainability alternatives based on their impact on employee motivation and well-being.

• Development Phase

The development phase focused on translating the validated criteria into a functional DST. This included the design of a hybrid AHP–Fuzzy MOORA model embedded in a modular Excel-based tool. First, a holistic case study was conducted in a UAE-based construction firm employing over 400 staff to validate and weight five decision criteria: Cost, Risk, Compatibility, Return on Investment, and Difficulty.

The AHP module was used to determine the weight of these decision criteria via pairwise comparisons by three key decision-makers (HR, finance, and operations heads). These weights were then used in the Fuzzy MOORA module to evaluate and rank the seven sustainability alternatives based on linguistic assessments using triangular fuzzy numbers.

• Decision Support Tool Phase

The final DST, structured in input–processing–output layers, was subjected to expert validation in two ways:

• ○

  DST Testing by decision-makers (validation interviews): The tool was tested by the same decision-makers from the case study, who assessed its clarity, usability, and feasibility in supporting sustainability-related decision-making.

  ○

  Focus group validation: A separate expert focus group (six participants) including professionals from construction and digital technology sectors (IoT, blockchain) was convened through an online session to validate the DST’s interface, usability, and real-world applicability. Their feedback confirmed the tool’s practicality and suggested future integration with emerging technologies.

This methodological framework ensured that the DST is grounded in rigorous empirical research, validated by practitioners, and adaptable for real-world application in the construction sector.

4. Decision Support Tool for Social Sustainability Development

The DST for social sustainability is designed to assist construction organizations in systematically evaluating and prioritizing initiatives that enhance employee well-being. This tool builds upon prior research findings and incorporates a structured multi-criteria decision-making framework to address the complexities of social sustainability. The DST integrates both qualitative and quantitative factors, allowing decision-makers to balance financial, operational, and well-being considerations when selecting sustainability initiatives. The following subsections detail the development phases of the DST, including its foundational criteria, methodological framework, and validation through real-world case studies.

4.1. Pre-Development Phase

The DST for social sustainability is built upon the findings of a prior study by Saboor and Ahmed [10], which explored the key factors influencing employees’ social sustainability and their effect on job satisfaction in the U.A.E. construction sector. This foundational study established a structured framework for evaluating social sustainability, which serves as the conceptual and analytical basis for the current research.

In the pre-development phase (conducted as a part of the previous study), a systematic literature review was conducted covering publications from 2000 to 2022, using databases such as Scopus, Web of Science, and Google Scholar. The inclusion criteria focused on empirical and conceptual studies that addressed employee well-being, social sustainability, and job satisfaction, particularly in the construction and broader built environment sectors. Studies unrelated to workplace sustainability or outside these domains were excluded. In total, 76 relevant papers were reviewed.

The identification of initial factors was carried out using qualitative content analysis, where themes and patterns were coded to extract key criteria influencing employee social sustainability. This process yielded 32 underpinning criteria spanning organizational, personal, social, and environmental dimensions. Examples include management involvement, work–life balance, equitable compensation, and workplace safety.

To refine and contextualize these criteria, semi-structured interviews were conducted with ten senior industry experts occupying a range of leadership roles: Chief Financial Officer (CFO), Director of National Capabilities, Regional Chief Human Officer (CHO), General Manager, Senior Group Human Resources Manager, Head of HR Department (three participants), Head of Quality and Materials Management, and HR Executive. These professionals brought between 5 and 32 years of experience in the construction sector, with an average of 18.4 years. Their diverse expertise and strategic responsibilities enabled a comprehensive evaluation of the proposed criteria. Based on their feedback, redundant or overlapping items were eliminated, resulting in a streamlined and contextually grounded list of 27 criteria [10].

Further validation was performed through a quantitative survey conducted with 506 participants from the UAE construction sector. Participants were included based on their active employment in the construction industry, encompassing both blue- and white-collar roles. A combination of convenience and snowball sampling was employed, and the survey was disseminated to ensure broad accessibility. The inclusion criterion was current employment within the sector, without restrictions on level or position, to capture a diverse range of insights. The final sample included employees across various hierarchical levels, entry, mid-level, senior, and managerial, with a wide range of experience (13.7% had 1–5 years; 86.3% had more than 5 years). Educational backgrounds were also varied, including diploma holders, graduates, postgraduates, and doctorates. This diversity ensured a representative and balanced sample for evaluating the relevance and impact of social sustainability criteria in the construction industry [10]. SEM was used to explore the relationships between these criteria and their impact on employee motivation and well-being. The analysis led to the consolidation of the 27 criteria into seven core sustainability alternatives, which now form the basis for decision-making within the DST framework [10]. These alternatives are presented in

Table 2. Description of underpinning criteria for social sustainability in the workplace (Saboor and Ahmed, 2024 [10]).

Underpinning Criteria/Alternatives	Description
Organizational Support	Defines the organization’s commitment and culture in providing social support to employees through management involvement and supervisor relationships, ensuring social sustainability.
Work-Life Balance	Reflects the organization’s commitment to managing work schedules effectively by allocating shifts and hiring adequate staff. It ensures employees can maintain a healthy work-life balance through proper staffing, reasonable work hours, and flexible shift systems.
Equity Factor	Evaluates salary satisfaction and pay equity, ensuring employees’ financial well-being and fulfillment of their needs, which positively impacts social sustainability and employee motivation and well-being.
Contract Type	Assesses job security and stability, ensuring employees feel satisfied and sustainable in their employment.
Training and Development	Highlights the organization’s dedication to fostering employee growth and career development through continuous training and professional advancement opportunities.
Work Environment	Ensures a safe and healthy working environment by implementing appropriate health and safety measures to enhance employees’ well-being and overall work conditions.
Work Control	Examines the extent to which employees are empowered by being given autonomy in choosing their work roles, teams, projects, and flexible work timings while ensuring that job demands are not physically or psychologically overwhelming.

.

While care was taken during expert interviews and the SEM-based validation process to minimize overlaps and ensure conceptual clarity among the 27 criteria, we acknowledge that some interdependencies may remain. This is inherent to the complex and multidimensional nature of social sustainability constructs. Addressing these inter-relationships through advanced modeling techniques, such as the Analytic Network Process (ANP), Fuzzy Cognitive Maps (FCM), DEMATEL (Decision-Making Trial and Evaluation Laboratory), or Interpretive Structural Modeling (ISM), represents a valuable direction for future research to further enhance the robustness and accuracy of the DST framework.

Although the initial study was conducted in the UAE, introducing a potential geographic bias, the comprehensive validation process (combining literature review, expert interviews, and empirical modeling) ensures the robustness and broader relevance of the identified criteria.

4.2. Development Phase Results

The DST was implemented as an Excel-based application using a hybrid AHP–Fuzzy MOORA framework. The development phase involved two key analytical modules (AHP and Fuzzy MOORA), the creation of a user interface, and a case study application to validate the tool.

The AHP module was used to calculate the weights of the decision criteria identified in the pre-development phase through literature review, expert interviews, and case studies. The Fuzzy MOORA module then facilitated decision-making by evaluating the performance and influence of the seven sustainability alternatives in relation to the five decision criteria. This approach allowed decision-makers to systematically assess trade-offs between benefit-type and non-benefit-type criteria, ensuring a balanced decision-making process (Figure 2).

This structured methodology provided organizations with an optimal solution for ensuring employee social sustainability, incorporating multiple decision perspectives while accounting for organizational constraints.

4.2.1. Case Study for DST Decision Criteria Validation

To validate the DST, a case study was conducted in a private medium-to-high construction organization that employs over 400 employees. The goal was to confirm the decision criteria, assess their significance, and evaluate the applicability of the DST in real-world decision-making scenarios.

The study approached the three key decision-makers from the organization, each with extensive experience in financial, human resource, and operational management (

Table 3. Profile of participants for DST decision criteria validation.

Code	Role	Years of Experience
P1	Supervisor (Line Manager)	23
P2	Chief Financial Officer (CFO)—Financial	37
P3	Head of Human Resource	25

):

These decision-makers were invited to discuss and validate the five core decision criteria (Cost of Improvement, Risk, Compatibility with Organization, Return on Investment, and Degree of Difficulty) as essential considerations for sustainability initiatives in the construction sector.

All participants confirmed the relevance of these criteria and highlighted their direct impact on organizational decision-making processes. This validation step reinforced the importance of integrating these criteria into the DST, ensuring its practicality for industry adoption.

The final set of decision criteria, along with their respective objectives and descriptions, is summarized in

Table 4. Decision criteria for evaluating social sustainability initiatives in the DST.

Decision Criteria	Objective	Description
Cost of Improvement	Minimize	Evaluates the financial investment required to implement a sustainability initiative, ensuring that costs are kept to a minimum while maximizing the benefits to social sustainability and EMW.
Risk Associated	Minimize	Assesses the level of uncertainty and potential negative impact of implementing a sustainability initiative, ensuring that risks are minimized while maintaining a reasonable level of performance and influence on social sustainability and EMW.
Compatibility with Organization	Maximize	Determines the extent to which a sustainability initiative aligns with the organization’s culture, vision, and internal environment, ensuring that compatibility is maximized to enhance organizational effectiveness and employee well-being.
Return on Investment	Maximize	Measures the financial and non-financial gains resulting from implementing a sustainability initiative, ensuring that returns are maximized to justify the allocation of resources toward social sustainability and EMW.
Degree of Difficulty	Minimize	Evaluates the complexity and operational challenges associated with implementing a sustainability initiative, ensuring that the level of difficulty is minimized to facilitate smooth adoption and integration within the organization.

. These criteria provide a structured approach to evaluating sustainability initiatives by balancing financial feasibility, strategic alignment, and implementation complexity. Specifically, the emphasis on minimizing cost, risk, and difficulty ensures that sustainability initiatives remain viable and practical, while maximizing compatibility and return on investment aligns them with long-term organizational goals.

By structuring the decision-making process around these objectives, the DST ensures an optimal balance between financial, operational, and strategic considerations in sustainability-related investments. The clear definition of each criterion ensures transparency and consistency in decision-making, allowing organizations to prioritize initiatives that align with their sustainability goals while considering constraints related to cost, risk, and feasibility.

4.2.2. Integration of AHP and Fuzzy MOORA

The DST framework integrates the AHP and Fuzzy MOORA methods to provide a structured, data-driven approach to decision-making in social sustainability initiatives. This integration ensures that both quantitative (e.g., cost, risk, return on investment) and qualitative (e.g., employee well-being, organizational compatibility) factors are systematically evaluated, leading to an optimal selection of sustainability initiatives.

The hybrid framework follows a three-step approach:

• AHP module for prioritization of decision criteria: The AHP method [13] is employed to determine the relative importance (weights) of the decision criteria. Decision-makers conduct pairwise comparisons using Saaty’s 1 to 9 scale, and the aggregated judgments form a consensus comparison matrix. The normalized eigenvector of this matrix represents the final weights assigned to each criterion. The Consistency Ratio (CR) is calculated to ensure logical consistency, with a threshold of CR < 0.10 being acceptable. The weighted criteria derived from AHP serve as inputs for the subsequent evaluation in Fuzzy MOORA.
• Fuzzy MOORA module for evaluation of sustainability alternatives: Since decision-making in social sustainability involves linguistic and uncertain judgments, the Fuzzy MOORA method is used to capture this subjectivity. Decision-makers rate the performance of each alternative against the decision criteria using a predefined linguistic scale (e.g., “Very Poor” to “Very Good”). These linguistic terms are converted into triangular fuzzy numbers, forming a fuzzy decision matrix. The steps involved in the Fuzzy MOORA evaluation are the following:

  ○

  Normalization of the fuzzy decision matrix to ensure comparability across different measurement scales.

  ○

  Weighting of criteria using the AHP-derived values.

  ○

  Aggregation of scores, where benefit criteria values are summed, and cost criteria values are subtracted.

  ○

  Defuzzification using the Best Non-Fuzzy Performance (BNP) function, converting fuzzy scores into crisp values for ranking.

• Optimal selection for ranking and decision-making: The final output of the hybrid AHP–Fuzzy MOORA approach is a ranked list of sustainability alternatives based on their decision scores. This ranking enables organizations to prioritize initiatives that best align with their social sustainability goals while considering financial, operational, and strategic constraints.

  ○

  The integration of AHP and Fuzzy MOORA within the DST ensures a structured and transparent decision-making process, balancing both objectivity (quantitative factors) and subjectivity (qualitative assessments). By adopting this approach, organizations can systematically evaluate social sustainability initiatives, mitigate risks, and optimize resource allocation, ultimately enhancing employee well-being and organizational performance.

4.2.3. System Architecture and User Interface

The DST was developed as a modular, Excel-based system to ensure accessibility and user-friendliness for decision-makers in the construction sector. The architecture follows a three-layer structure, which is visually demonstrated in Figure 3, Figure 4, Figure 5 and Figure 6. This setup ensures streamlined data flow and efficient processing from input to output.

• Input Layer:

Users begin by logging into the system, where they are prompted to define the five decision criteria, such as cost and risk, and the seven sustainability alternatives identified earlier. Customization features, as shown in Figure 3, allow organizations to tailor the tool to their specific needs. This panel provides fields for entering decision criteria, setting objective functions (minimize or maximize), and assigning weights. The design ensures that even users with minimal technical expertise can navigate the setup process, making it adaptable across different sectors or project types.

• Processing Layer:

This layer manages the core computational processes:

• ○

  AHP Module: Facilitates pairwise comparisons among the decision criteria. As depicted in Figure 4, decision-makers compare criteria using a simplified scale (1 to 9) to determine relative importance. This interface captures comparative judgments systematically, ensuring consistency and accuracy in weight calculations.

  ○

  Fuzzy MOORA Module: Converts subjective assessments into quantifiable data. Figure 5 shows how alternatives are evaluated against criteria using a linguistic scale, translating qualitative judgments into a fuzzy performance matrix. The integration of AHP-generated weights ensures that these assessments reflect the organization’s strategic priorities, enhancing the robustness of the decision-making process.

• Output Layer:

The output layer visualizes the decision outcomes. Figure 6 presents a dual-panel display, with the left panel illustrating the prioritization of decision criteria through bar charts, while the right panel shows the optimal solution ranking for the sustainability alternatives. This clear visualization helps decision-makers quickly grasp the implications of their inputs and adjustments, fostering more informed and confident decisions. The ability to export these results into detailed reports further supports transparency and accountability in organizational decision-making.

The DST’s design focuses on usability and clarity, ensuring that even complex decision-making scenarios are navigable for users with varying levels of expertise. The incorporation of visual aids, clear instructions, and logical workflows enhances the tool’s practicality and relevance in real-world applications, particularly within the construction sector.

4.3. Validation Phase Results

The validation phase was conducted to assess the DST in a real-world setting, ensuring that it aids decision-makers in selecting optimal sustainability initiatives. The DST was implemented within a private construction organization in the UAE, employing over 400 workers, and was evaluated based on employee perceptions, managerial input, and decision-making efficiency. The validation process aimed to align employee insights with managerial priorities, ensuring that the DST is not only methodologically robust but also practically relevant for enhancing social sustainability.

The case study was conducted within a UAE-based construction company engaged in both private and government projects. The organization was selected due to its large workforce, the complexity of its operations, and its active sustainability initiatives. The construction sector is highly dynamic, and maintaining employee well-being is critical yet challenging, making this an ideal setting for evaluating the DST’s effectiveness. The multi-departmental structure, including finance, HR, and operations, provided a multi-dimensional perspective on decision-making.

To evaluate employees’ perceptions of critical social sustainability factors, an AHP-based survey was conducted before deploying the DST. The survey aimed to rank sustainability factors based on employee preferences. The survey was distributed to 400 employees across various departments within the organization, and 175 valid responses were received, resulting in a response rate of 42.5%. This sample size provides a reasonable basis for analysis and offers meaningful insights into employee priorities regarding workplace sustainability and well-being. The results were structured using the AHP hierarchy framework (Figure 7) and quantitatively analyzed in

Table 5. Ranking of social sustainability factors based on employee perceptions.

Level 0	Level 1	Weight	Rank	Level 2	Weight	Rank
Prioritization	Organizational Support	0.093	4	Association with Supervisor	0.60	5
				Social Support	0.23	10
				Workload	0.17	12
	Work-Life Balance	0.171	3	Time Management	0.42	6
				Burnout at job	0.24	9
				Sleep Quality	0.33	8
	Equity Factor	0.073	6	Salary Satisfaction	0.890	2
				Pay Equity	0.11	13
	Contract Type	0.058	7	Satisfaction with contract	0.68	3
				Self-commitment	0.33	8
	Training and Development	0.204	2	Employee Empowerment	0.20	11
				Development	0.42	6
				Career Growth	0.38	7
	Work Environment	0.32	1	Health and Safety	0.89	1
				Air/Noise quality	0.11	13
	Work Control	0.081	5	Flexible work	0.67	4

, which prioritizes sustainability criteria based on weighted rankings.

The AHP analysis in Table 5 highlights the prioritization of social sustainability factors based on employee perceptions. Work Environment emerged as the most critical factor, with Health and Safety ranking highest, emphasizing the importance of safe and supportive work conditions. Training and Development followed closely, with career growth and development opportunities being key motivators. Work-Life Balance was also significant, particularly in managing burnout and maintaining time efficiency. Organizational Support, particularly through supervisor relationships, played a crucial role in workplace well-being. Equity Factor ranked lower overall, yet salary satisfaction was one of the most important determinants of job motivation. Contract Type and Work Control, while relevant, were less influential in comparison to other factors. The findings validate that prioritizing workplace safety, employee development, and balanced workloads is essential in enhancing social sustainability in the construction sector.

The weighted prioritization results (Table 5) were used as a benchmark to validate the DST’s effectiveness in aligning employee concerns with managerial decision-making.

Following the AHP analysis, the DST was introduced to decision-makers to evaluate sustainability alternatives systematically. The DST interface facilitated a step-by-step approach using Hybrid AHP–Fuzzy MOORA, ensuring a structured decision-making process:

• Step 1: Defining decision criteria and participants—Decision-makers entered the five core decision criteria and seven sustainability alternatives into the system using the Interface Main Panel (Figure 3), allowing them to customize the decision-making framework to fit organizational needs.
• Step 2: Pairwise comparison of decision criteria—Saaty’s AHP scale (1–9) was used to compare decision criteria, as shown in Figure 4. This process ensured that weight assignments were consistent and aligned with strategic objectives.
• Step 3: Fuzzy MOORA-based alternative evaluation—Decision-makers assessed the performance of sustainability alternatives using a linguistic scale ranging from “Very Poor” to “Very Good” (Table 2). The Alternative Evaluation Interface (Figure 5) allowed for structured qualitative assessments.
• Step 4: Computing the optimal solution—The system normalized fuzzy scores and integrated them with AHP-derived weights to generate a final ranking of sustainability alternatives. The decision panel (Figure 6) visually presents the results in two parts:
• The left-side chart titled “Decision Criteria Prioritization” displays the relative importance (weights) of each decision criterion, such as cost, risk, and ROI, helping decision-makers understand the criteria that most influence the final evaluation.
• The right-side chart titled “Optimal Solution” ranks the seven sustainability alternatives (e.g., Training and Development, Work Environment) based on their aggregated fuzzy scores. This visualization provides a clear and intuitive comparison, guiding users in selecting the most impactful and feasible initiatives.

The DST evaluation confirmed that “Cost of Improvement” was the most influential decision criterion, significantly shaping investment decisions related to social sustainability initiatives. This highlights the critical role of financial feasibility in determining which actions receive priority within organizations.

The final ranking of sustainability alternatives was based on the input of three senior decision-makers who evaluated the alternatives using the hybrid AHP–Fuzzy MOORA framework embedded in the DST. According to their assessments, “Training and Development” emerged as the top priority, followed by “Work Environment”, “Equity Factor”, and “Contract Type”, emphasizing the importance of structured growth, workplace safety, fair compensation, and job stability from a strategic and managerial standpoint. “Organizational Support”, “Work-Life Balance”, and “Work Control” followed in subsequent positions, reflecting their perceived operational significance.

In contrast, employee preferences were gathered through a separate AHP-based survey conducted prior to the DST implementation (Table 5), which ranked “Work Environment” and “Training and Development” as the top priorities, particularly emphasizing workplace safety and professional growth.

The clear alignment between the two perspectives, employees and decision-makers, strengthens the validity and practical relevance of the DST. However, the slight differences in prioritization underscore the value of incorporating both viewpoints in decision-making. The structured, data-driven approach of the DST facilitates this integration, ensuring that workforce concerns are balanced with managerial priorities.

The DST provides multiple strategic benefits:

• Aligning employee needs with organizational goals;
• Enhancing transparency in decision-making;
• Reducing bias through structured evaluation;
• Supporting investment decisions based on cost-effectiveness and social impact.

The validation phase successfully demonstrated that the Hybrid AHP–Fuzzy MOORA framework embedded in the DST provides an objective, transparent, and systematic decision-making approach. The integration of employee priorities and managerial perspectives was operationalized by using employee survey results (Table 5) as a reference benchmark to compare with the DST outcomes generated through decision-maker evaluations. This comparative analysis highlighted both convergence and divergence in priorities, informing potential alignment strategies. By explicitly acknowledging these complementary perspectives, the DST helps bridge the gap between workforce needs and organizational goals, supporting more inclusive, sustainable, and high-impact decision-making.

4.4. Expert Focus Group Validation

The expert focus group validation was conducted to assess the DST. This process aimed to ensure that the DST is aligned with industry needs, user-friendly, and practically applicable in real-world construction environments. By incorporating perspectives from both industry professionals and technology specialists, the evaluation provided a comprehensive assessment of the DST’s feasibility, effectiveness, and potential for integration with emerging digital technologies.

The focus group comprised six experts drawn from both the construction industry and the technology sector. On the construction side, three senior professionals, namely the Chief Financial Officer (CFO), the Head of the Human Resources Department, and the Head of Quality and Materials Management, were included. These professionals had previously contributed to the case study phase and brought extensive expertise in strategic planning, workforce development, compliance, and organizational sustainability practices within large construction firms.

The remaining three participants represented a UAE-based technology company specializing in IoT and Blockchain applications for workforce monitoring. This group included the company’s Chief Executive Officer (CEO) and two senior technical specialists with strong backgrounds in systems integration, real-time data tracking, and smart compliance technologies. Their role in the focus group was to assess the feasibility of enhancing the DST through digital integration and to evaluate its potential for deployment in technology-enabled environments.

The focus group session was conducted online, allowing participants to test the DST and provide structured feedback on its effectiveness, usability, feasibility, and alignment with industry requirements. Participants assessed the DST based on five key evaluation dimensions: clarity of guidelines, ease of navigation, relevance for decision-making, user-friendliness, and feasibility of adoption within existing organizational workflows. Each expert interacted with the DST interface, explored its decision-making functions, and rated these dimensions using a structured evaluation form.

The results, presented in

Table 6. Expert evaluation of the DST for usability and feasibility indicated by * sign.

Evaluation Criteria/Experts		Expert 1	Expert 2	Expert 3	Expert 4	Expert 5	Expert 6
Clarity of the guideline	Vague
	Appropriate
	Clear	*	*	*	*	*	*
Navigation through the tool	Difficult
	Moderate
	Easy	*	*	*	*	*	*
Relevance for decision-making	Not relevant
	Quite Relevant		*	*
	Relevant	*			*	*	*
User Friendly	Not Friendly
	Somewhat friendly						*
	Very Friendly	*	*	*	*	*
Feasibility of the tool	Not feasible
	Somewhat Feasible
	Feasible	*	*	*	*	*	*
Adoption of tool	No
	Likely	*				*

, highlight the overall positive reception of the DST. All participants found the guidelines clear, ensuring that users could easily understand and navigate the tool without requiring extensive training. Ease of navigation was another strong aspect, with every participant rating it as “Easy”, reinforcing the DST’s intuitive workflow. In terms of decision-making relevance, four out of six experts rated the DST as “Highly Relevant”, demonstrating its effectiveness in supporting strategic decision-making related to employee sustainability. Five out of six experts found the tool to be very user-friendly, with one suggesting minor improvements to the graphical user interface (GUI) for enhanced usability. Additionally, all experts confirmed that the DST is feasible for integration within their decision-making workflows, with four expressing a commitment to adopting the tool in their organizations and two showing strong interest in potential future integration.

The findings from the expert focus group reinforced the DST’s value as an industry-ready tool for structured decision-making in social sustainability. The evaluation confirmed that the DST provides a clear and structured decision-making framework, is highly user-friendly, and is well-suited for adoption within construction organizations.

5. Conclusions, Discussion, Limitations, and Future Work

This study aimed to develop and validate a DST that integrates a hybrid MCDM framework, combining AHP and Fuzzy MOORA, to assist construction organizations in enhancing employees’ social sustainability. Building on a comprehensive literature review and the work of Saboor and Ahmed (2024) [10], 27 underpinning criteria were identified and categorized into seven core sustainability alternatives, while five key decision criteria—cost, risk, compatibility, ROI, and difficulty—were established to guide the evaluation process.

The DST was designed as a modular Excel-based tool, structured with input, processing, and output layers. The AHP module derived consistent criteria weights, while the Fuzzy MOORA module processed linguistic evaluations to generate a ranking of sustainability alternatives. The case study conducted in a private construction organization demonstrated the DST’s effectiveness, prioritizing Training and Development and Work Environment as the most critical factors for improving social sustainability. An expert focus group validation further confirmed that the DST is clear, user-friendly, relevant, and practical for adoption in real-world decision-making.

This research fills a significant gap in the literature, as traditional decision support systems in the construction sector have primarily focused on technical and economic factors, often overlooking the social dimension of sustainability, such as employee well-being, work environment, and organizational support. Our study addresses this gap by offering a holistic, empirically validated DST that integrates both quantitative (AHP pairwise comparisons) and qualitative (Fuzzy MOORA linguistic assessments) within a unified framework to assess social sustainability.

The findings from the pre-development phase confirmed the multi-dimensional nature of social sustainability. The clustering of 27 validated criteria into seven core sustainability alternatives aligns closely with the work of Vallance et al. [3], who emphasized the need for structured approaches to capture diverse aspects of social sustainability in the built environment. Our results further support Saboor and Ahmed [10], whose empirical work laid the foundation for the current DST.

In the development phase, the AHP module emphasized decision criteria such as cost control and risk minimization, reflecting organizational priorities. Simultaneously, the Fuzzy MOORA module successfully managed qualitative uncertainties, enabling the nuanced inclusion of expert judgment, something often missing in previous tools. The final ranking of alternatives, which prioritized “Training and Development” and “Work Environment”, closely aligns with prior studies, reaffirming that investment in human capital and workplace safety remains central to enhancing job satisfaction and workforce sustainability.

At the same time, our study introduces a more comprehensive and operational framework than those seen in earlier literature. Factors like Work Control and Contract Type, while often only briefly mentioned or generalized in past research, are treated here as distinct and influential components of social sustainability, highlighting our expanded conceptualization and providing more actionable insights for practitioners.

From a methodological perspective, while other studies have used AHP or fuzzy logic individually for sustainability assessments, the hybrid application in a socially focused, sector-specific context is novel. The DST’s Excel-based modular structure, with clearly defined input, processing, and output layers, ensures transparency, usability, and real-world applicability, bridging the gap between theoretical models and decision-making tools actually deployable in organizational contexts.

The expert validation conducted through focus groups further strengthens the tool’s practical utility. Participants emphasized the tool’s clarity, relevance, and intuitive design, reinforcing its potential for broader industry adoption.

In terms of managerial implementation, the DST provides clear, actionable guidance for decision-makers seeking to enhance social sustainability in the construction sector. Its modular, Excel-based design allows practitioners, including HR managers, safety officers, and project directors, to easily input organizational data, assess the impact of various initiatives, and prioritize investments based on multiple criteria. The tool facilitates structured evaluation processes that improve transparency, align with strategic goals, and are adaptable to a wide range of managerial contexts. Moreover, by incorporating both quantitative and qualitative judgments, the DST enables managers to balance cost and feasibility with employee-centric outcomes. The practicality and relevance of the tool were reinforced during expert validation sessions, where industry professionals confirmed its suitability for real-world use in project planning, HR policy formulation, and performance monitoring.

This study not only aligns with the trajectory of prior research in identifying key factors influencing social sustainability but also extends the field by delivering a validated, structured, and implementable solution. By focusing explicitly on the social dimension—a long-underrepresented area in sustainability assessments—and by incorporating robust MCDM techniques, our research makes a significant theoretical and practical contribution to both construction management literature and organizational decision-making practice.

Despite the promising results, some limitations should be acknowledged. The case study was conducted in a single private construction organization with only three decision-makers, which may limit the generalizability of the findings to other organizations or different sub-sectors within the construction industry. Additionally, the DST assumes a homogeneous weightage among decision-makers, as the participants shared similar backgrounds. However, in practice, different stakeholders—such as frontline workers, middle management, and senior executives—may have divergent perspectives that could influence the relative importance of decision criteria. Another limitation is the scope of sustainability dimensions, as this study primarily focused on social sustainability, while comprehensive sustainable development also includes economic and environmental aspects. Future iterations of the DST could benefit from incorporating a broader set of sustainability criteria. Furthermore, the DST currently functions as a static tool, requiring periodic re-evaluation (quarterly or annually) rather than incorporating real-time data integration or dynamic updating features. Lastly, while the study discussed the potential integration of IoT and blockchain, these technologies were not empirically implemented within the DST, and further research and development will be necessary to enable their full incorporation.

Building on the current study, several specific avenues for future research and development are suggested:

• Validate the DST across diverse geographic regions and organizational scales (e.g., small and medium-sized enterprises, public sector projects) to improve external validity and identify context-specific factors affecting social sustainability.
• Expand stakeholder participation in decision-making by incorporating varied weighting schemes for different roles (e.g., workers, supervisors, HR, and executives), using participatory approaches such as Delphi or consensus workshops.
• Integrate additional sustainability pillars, including environmental and economic dimensions, to evolve the tool into a comprehensive model supporting the full triple bottom line.
• Enhance the tool’s functionality through digital transformation by incorporating IoT-based sensors for real-time monitoring of workplace conditions and blockchain for secure, tamper-proof tracking of compliance and labor practices.
• Apply artificial intelligence techniques, such as fuzzy neural networks (FNNs), to forecast the long-term organizational impact of different sustainability initiatives and optimize intervention planning.
• Develop a cloud-based or mobile app version of the DST to support accessibility, remote collaboration, and real-time data input from field operations.
• Improve the user interface based on iterative testing with practitioners to enhance usability, adaptability across sectors, and integration with existing enterprise systems (e.g., HR or ERP platforms).
• Explore alternative MCDM and fuzzy approaches such as BWM, EDAS, Z-numbers, and D-numbers to potentially enhance decision accuracy and address known limitations of AHP and triangular fuzzy numbers. Comparative analyses between the current AHP–Fuzzy MOORA model and these emerging methods can identify trade-offs in terms of transparency, computational complexity, and practical usability.
• Investigate potential interdependencies among decision criteria using advanced modeling techniques such as ANP, FCM, DEMATEL, or ISM, to enhance the accuracy and structural validity of the DST framework.

This study contributes meaningfully to advancing social sustainability in the construction sector by introducing a validated DST based on a hybrid AHP–Fuzzy MOORA framework. The tool bridges the gap between theory and practice by enabling systematic prioritization of workforce-centered sustainability initiatives. Its transparent structure, empirical foundation, and positive expert validation highlight its value in guiding strategic organizational decisions. While the study has limitations, it sets a strong groundwork for future enhancements, including dynamic integration, broader stakeholder input, and extension to other sustainability dimensions.