Enterprise Strategic Management Upon Sustainable Value Creation: A Fuzzy Topis Evaluation Tool for Transport and Supply Chain Enterprises

Abstract

The advancement of sustainable economic development has become a strategic imperative for enterprises aiming to combine financial development with environmental and social responsibility. In this regard, strategic enterprise management (SEM) has a critical role in incorporating the aspects of sustainability into decision making. The present paper suggests a multicriteria decision-making framework that utilizes fuzzy TOPSIS in assessing and ranking sustainability integration aspects in organizations. By considering the intrinsic vagueness of sustainability analysis, the fuzzy TOPSIS model enables the systematic analysis of environmental, social, and governance (ESG) factors by companies for ensuring their alignment to corporate strategic goals. A case study of a major international airport in Greece demonstrates how the proposed methodology assists strategic choice making, balancing economic viability and sustainable value creation. The results show primary trade-offs among human capital investment, environmental footprint reduction, and stakeholder communication, demonstrating how companies can enhance long-term resilience and competitiveness. This research adds to the existing literature by giving an integrated strategic enterprise management framework with the use of decision support instruments to foster sustainability-oriented corporate governance and strategic efficacy. The suggested model is flexible and can be applied in any industry, hence being a benchmark for sustainable business practice. This paper contributes to the literature by integrating fuzzy TOPSIS with balanced scorecard in the context of airport strategic sustainability management, offering both methodological advancement and empirical insights for transport and supply chain enterprises.

1. Introduction

Sustainability has evolved from a nascent trend to a strategic imperative essential for maintaining business competitiveness. Companies from diversified sectors are increasingly acknowledging that long-term success is not only about economic advancement but also about how they address environmental resources, social effects, and corporate governance obligations in a sustainable manner. Thus, strategic enterprise management (SEM) requires the explicit incorporation of sustainability principles into decision-making frameworks, which entails considerations regarding the triple bottom line, economic sustainability, social responsibility, and environmental sustainability. The research question of this study is as follows: How can airport enterprises integrate sustainability into their strategic management frameworks using structured, decision support methodologies that reflect stakeholder priorities?

This research is motivated by the growing complexity and importance of sustainability in the transport and supply chain sectors, particularly for airports. Airport enterprises are increasingly exposed to scrutiny regarding their environmental footprint, social engagement, and long-term economic resilience. Traditional performance management tools are often insufficient in addressing the multidimensional nature of sustainability, especially when qualitative stakeholder expectations and quantitative performance metrics must be balanced.

The goal of this research is to propose a comprehensive methodological framework that integrates multicriteria decision-making (MCDM) techniques, specifically fuzzy TOPSIS, with strategic performance management tools, such as the balanced scorecard (BSC), to evaluate the contribution of sustainability-related initiatives in airport enterprises. This framework aims to guide decision makers in prioritizing sustainability actions based on robust, structured evaluations that consider diverse and sometimes conflicting stakeholder interests. Among a series of industries, transport organizations, in particular airports, face significant sustainability challenges due to their deep economic, environmental, and social impacts, such as the growing transport sector, accessibility and connectivity trends, and recent major international developments, such as the adoption of the Agenda for Sustainable Development by the UN in 2016, the Paris Agreement on Climate Change adopted by the UN Framework Convention on Climate Change in 2015, and the highlighting of airport enterprise contribution to sustainable development [1]. Airports are critical nodes in global transport networks and contribute significantly to regional economic growth, employment generation, and social cohesion. Efficient air transport networks and airport infrastructures boost economic growth. Airport enterprises’ social and economic contributions affect many aspects of people’s lives. Most international organizations promote human development for business sustainability [2,3,4,5,6].

However, airport operations are directly associated with negative externalities, such as environmental degradation, greenhouse gas emission, noise pollution, and resource consumption. Thus, effectively encapsulating these impacts within a sustainable strategic framework has emerged as a key factor for long-term sustainability and competitive advantage in airport management [7].

The complexity of social impact requires multidimensional methods of measuring airport enterprises’ economic and social engagement to support regional and global cooperation and integration [8,9]. Interest in and commitment to airport social engagement is growing due to pressure to reduce or mitigate airports’ negative impacts, while increasing their positive impacts [10].

Dealing with such complexity needs strong methodological frameworks that can combine multifarious and often competing criteria, while catering to the intrinsic uncertainties and stakeholder vagueness. For instance, traditional assessment models often rely on crisp scoring methods that assume perfect data precision and overlook subjective factors such as stakeholder perception, social influence, and qualitative environmental risks. These models are not well-suited to strategic decision making in complex, uncertain environments, such as airport governance. Recent studies have emphasized the need for more flexible and expert-driven methods (e.g., fuzzy logic-based MCDM approaches) that can better capture the ambiguity inherent in sustainability performance evaluation. Multicriteria decision-making (MCDM) techniques, including the fuzzy TOPSIS (technique for order of preference by similarity to ideal solution), have received significant attention due to their potential in managing the uncertainty and complexity that characterizes strategic sustainability decisions. The fuzzy TOPSIS method, in particular, integrates quantitative data on operational performance and qualitative measurements of stakeholder priorities, thereby offering strategic decision makers an integrated tool for assessing sustainability initiatives [11,12].

This study proposes a strategic enterprise management framework to incorporate sustainability into airport decision making in a systematic way, using a real application to a major international airport in Greece. By considering various dimensions, i.e., human capital development, community involvement, climate change activities, and human capital value added (HCVA), this study addresses the strategic incorporation of environmental, social, and governance (ESG) considerations into airport enterprise management. The proposed methodological strategy offers stakeholders and managers meaningful information, which allows them to make knowledge-based decisions targeted at enhancing sustainable performance, stakeholder collaboration, and business resilience. Thus, the current paper attempts to fill an important research void by formulating an extensive SEM framework that can successfully tackle the multi-dimensional intricacies of sustainable economic growth in transportation companies. The paper, by doing so, not only contributes to literature on sustainable strategic management practices but also provides a precious tool that can be applied in various industries in search of sustainability-driven competitive advantage and sound corporate governance.

The main contribution of this paper lies in developing a hybrid decision support model that effectively combines fuzzy TOPSIS and BSC to assess sustainable value creation in complex enterprises, such as airports. The model is tested using data from Athens International Airport, thereby offering both methodological innovation and empirical insights. In addition, this paper contributes by (i) integrating fuzzy TOPSIS with ESG-based performance criteria in a structured, time-based assessment, (ii) demonstrating how subjective expert assessments can be converted into comparative insights across strategic dimensions using fuzzy logic, and (iii) applying the method in an operational airport setting to showcase how data-driven sustainability evaluation supports decision-making alignment with long-term corporate goals.

The structure of the paper is as follows: Section 2 presents the theoretical background and related literature. Section 3 details the research methodology, including the fuzzy TOPSIS approach and criteria selection. Section 4 presents the case study and results. Section 5 provides a discussion of the findings in comparison with the literature. Finally, Section 6 concludes with contributions, limitations, and future research directions.

2. Literature Background

Recent international events, including the Paris Climate Change Agreement in December 2015 and the adoption of the Agenda for Sustainable Development by the United Nations in 2016, have established strategic agendas for businesses worldwide to incorporate sustainability into their core management practices. These events highlight the requirement for strategic enterprise management (SEM) to balance economic development, environmental protection, and social well-being. Airports, as components of global transport systems, have a crucial strategic function in regional economic development, exerting a profound impact on regional economies and societies [10,11,12,13].

Sustainable airport management entails a wide set of principles that pertain to environmental, social, and economic aspects. The management practices of airports are increasingly guided by such holistic sustainability objectives, which respond to institutional pressures, industry associations, governments, and other stakeholders seeking to enhance local benefits, while reducing environmental effects [14]. In addition, ensuring workers’ health and safety, eliminating hazards in operations, and ensuring the overall well-being of stakeholders have emerged as key strategic priorities in airport management practices [14].

Various approaches exist in the literature to assess enterprise performance, with the most commonly used measures addressing economic or financial performance without comprehensiveness for an overall sustainability measurement [15,16]. While tools such as the balanced scorecard (BSC) address both financial and non-financial metrics, they do not sufficiently account for human development facets critical to genuine sustainability [17]. This imbalance is particularly evident in transport firms, where the need to tackle multidimensional sustainability—embracing human capital, climate impact, and social responsibility—demands more stringent and holistic evaluation systems.

Empirical studies of corporate social responsibility (CSR), especially in banking and other sectors, demonstrate that active sustainability engagement significantly affects profitability, growth opportunities, services quality, as well as customer satisfaction [18]. These findings suggest that sustainability and CSR mainstreaming are not only moral obligations but are critical to strategic competitiveness in the long run [19,20]. However, limited literature has addressed balanced sustainability performance measurements that proactively mainstream human development principles as integral to airport enterprise management.

For example, the authors of [21] used a fuzzy DEA model focused on technical efficiency but did not incorporate stakeholder-driven ESG indicators. Similarly, Pandey [22] applied a fuzzy MCDM approach to airport service quality, but with no temporal or strategic context. In contrast, our model uses a fuzzy TOPSIS approach to assess longitudinal ESG performance in line with corporate sustainability strategies. In summary, while prior studies have applied fuzzy MCDM to isolated dimensions of sustainability, few have integrated fuzzy TOPSIS with ESG-focused criteria in a time-dependent framework. This study contributes by explicitly linking strategic corporate evaluation with fuzzy logic modeling, enabling performance tracking under uncertainty across multiple years.

In order to fill this gap, the present research proposes a systematic methodological framework to assess airport enterprises’ sustainable social involvement on the basis of human development values. Using the fuzzy TOPSIS technique—a multicriteria decision-making approach—this research offers airport managers an ordered, sophisticated, and pragmatic tool to manage sustainability-linked strategic choices appropriately.

3. Assessment Methodology Framework

3.1. Assessment Background

Quantifying the sustainable engagement performance of airport businesses involves taking into account several indicators and parameters and, hence, is a multi-dimensional multicriteria decision-making (MCDM) problem. Every sustainability criterion has a best level or ideal range; as in many instances the best-case scenario is not realizable, optimality is attained through maximization or minimization of criteria within set limits. One-dimensional traditional assessment methods like data envelopment analysis (DEA), key performance indicators (KPIs), and balanced scorecard (BSC) have been widely applied to assess the performance of airport companies. Such approaches, though, are not effective when decision making entails dealing with a broad range of qualitative and quantitative sustainability aspects, because they are unable to accommodate intricate social and human development factors or suitably echo the priority concerns of diverse stakeholder groups [23].

The sophistication of sustainability challenges, along with enhanced access to information, has produced a sharp rise in the application of formal multicriteria decision-making (MCDM) techniques within transportation and infrastructure agencies. MCDM approaches successfully assist strategic decision making by integrating qualitative stakeholder inputs and quantifiable performance metrics. Actually, airport companies’ contributions to sustainable development and local socioeconomic development have previously been debated with a range of MCDM techniques, highlighting their potential in addressing the inherently multidimensional nature of sustainability [24,25].

The technique for order of preference by similarity to ideal solution (TOPSIS) is a widely used ranking technique in multicriteria decision-making (MCDM) models. Nevertheless, classical TOPSIS has some drawbacks, such as the use of predefined subjective weights for criteria, difficulty in defining ideal solutions, sensitivity to adding new alternatives, and the lack of adequate treatment of qualitative criteria [26]. To overcome these drawbacks, an enhanced version called fuzzy TOPSIS is used in this research, enabling more detailed assessments through the use of linguistic variables and reducing subjectivity in decision making. The fuzzy TOPSIS steps were implemented following the procedures established by Al-Sulbi et al. [11] and Varchandi et al. [12], which include the normalization of the fuzzy decision matrix, determination of the weighted normalized matrix, calculation of the fuzzy positive and negative ideal solutions, and ranking of alternatives based on closeness coefficients.

3.2. Selection of Sustainable Performance Indicators

No single performance indicator adequately captures airport enterprises’ comprehensive contribution to sustainable development, as each indicator only partially represents a specific viewpoint. Consequently, management decisions require a multidimensional, integrative assessment. Prior research highlights how investments in transport infrastructure affect socioeconomic variables, including employment and GDP, recommending strategic objectives linked to clear key performance indicators (KPIs) for accurate performance evaluation. Based on extensive literature reviews and expert consultations, four specific sustainability indicators have been selected to form the methodological framework of this study. These indicators collectively represent the critical areas of human development within the airport enterprise:

• C1: Human Resource Development: Airport enterprises operate within a highly regulated environment, where workforce training and development are crucial for compliance, efficiency, and strategic implementation. Training enhances competitive advantage by aligning employee competencies with organizational goals. The selected indicator to quantify this dimension is defined as the average annual training hours per full-time equivalent (FTE).
• C2: Social Impact on Local Communities: Airport enterprises significantly affect surrounding local communities, especially through noise and pollution. Strategic initiatives targeting community well-being—such as investments in education, culture, athletics, infrastructure, and environmental protection—are essential for sustainability. The indicator is, thus, defined as the annual number of actions undertaken by the airport aimed explicitly at benefiting local communities across these domains.
• C3: Climate Change Actions: Airport enterprises significantly contribute to greenhouse gas emissions and climate impacts. The strategic management of airports involves deciding between investments aimed at short-term mitigation actions and long-term adaptation strategies to manage and mitigate these impacts. This indicator captures the annual number of proactive initiatives implemented by the airport in the areas of energy efficiency, sustainable transport, and sustainable operational practices.
• C4: Human Capital Value Added (HCVA): HCVA measures the financial impact each employee contributes to airport enterprise performance. It quantifies how effectively human capital is leveraged by calculating the average profit per employee. The indicator is computed as follows:

$$HCVA=Revenue-{\displaystyle \frac{Total cost-Employment cost }{FTE }}$$

(1)

This calculation explicitly captures the direct financial contribution of employees to organizational sustainability and long-term economic viability.

These criteria were chosen due to the following:

• They reflect the three pillars of sustainability (social, environmental, economic);
• They are measurable, allowing longitudinal and comparative assessment;
• They are highly relevant to strategic planning in airport enterprises, where workforce development, climate responsibility, and stakeholder engagement are critical success factors;
• They are consistent with performance indicators already monitored in balanced scorecard systems, facilitating integration into existing decision-making frameworks.

Therefore, the selected indicators (C1–C4) offer a pragmatic and representative foundation for assessing sustainability in complex infrastructure enterprises like airports.

3.3. Performance Indicators Values Assesment

Airport business sustainability performance indicator assessment involves a systematic process based on the balanced scorecard (BSC) system. The systematic process involves assigning weights, defining performance targets, tracking actual performance, and calculating scores according to each key performance indicator (KPI).

For qualitative, non-financial indicators within the BSC, operational performance ratios (OPRs) are determined from the average operational scorecard scores, ranging from 1 (Poor) to 5 (Excellent). Scores of 3 (“On-Target”) correspond to an OPR of 1; whereas, scores range from 0.9 (Poor) to 1.1 (Excellent). Target values for each year are set using the following objective criteria: the “On-Target” score is based on a three-year historical average, while “Outstanding” targets exceed the best historical performance within the same period.

The calculated indicator values undergo normalization to transform them into comparable indices ranging between 0 and 1. Two different normalization methods were used in this study, based on the nature and distribution of the data. For human resource development (C1) and human capital value added (C4), we applied logarithmic normalization to handle skewed distributions and dampen the effect of outliers. These indicators involve continuous numerical values that tend to follow exponential or right-skewed patterns (e.g., profit or training hours), making log transformation appropriate to stabilize variance and ensure proportional scaling.

In contrast, social impact on local communities (C2) and climate change actions (C3) were normalized using min–max linear normalization due to their bounded, count-based structure. These indicators exhibit more uniform or limited ranges (e.g., number of initiatives), where linear scaling preserves relative differences effectively without distortion.

This combination of normalization methods ensures that each indicator is treated according to its statistical properties, enhancing interpretability and the reliability of the final sustainability rankings. We acknowledge that normalization affects outcome sensitivity and took care to align each method with the indicator’s data structure and strategic meaning.

Human resource development (C1) and human capital value added (C4) indicators are transformed using the following logarithmic normalization:

$$C_1={\displaystyle \frac{\mathit{ln}\left(\mathit{actual\; value}\right)-\mathit{ln}\left(100\right)}{\mathit{ln}\left(mean value\right)-\mathit{ln}\left(100\right)}}$$

(2)

$$C_4=Human Capital Added Value={\displaystyle \frac{\mathit{ln}\left(\mathit{actual\; value}\right)-\mathit{ln}\left(100\right)}{\mathit{ln}\left(mean value\right)-\mathit{ln}\left(100\right)}}$$

(3)

Social impact on local communities (C2) and climate change actions (C3) indicators employ linear min–max normalization, as follows:

$$C_2=Social affect to local communities(actions)={\displaystyle \frac{target value-minimum value }{maximum value-minimum value }}$$

(4)

$$C_3=Climate change effects \left(actions\right)={\displaystyle \frac{target value-minimum value }{maximum value-minimum value }}$$

(5)

3.4. Fuzzy TOPSIS Modeling Formulation

The application of fuzzy TOPSIS entails structured decision-making models to assess airport sustainability initiatives. The modeling procedure includes the subsequent steps:

• Step 1: Construction of the Assessment Framework

A decision-making group of stakeholders and experts chooses the pertinent sustainability indicators and assigns linguistic variables to indicate preferences and priorities. To portray these preferences, a pairwise comparison matrix (PCM) is established with the use of triangular fuzzy numbers, which are presented as triplets, as follows:

$$\overline{\mathrm{w}}=(\mathrm{w}1,\mathrm{w}2,\mathrm{w}3)$$

The fuzzy set $\overline{\mathrm{w}}$ and membership function μā maps each element x in X to a real number in the interval [0, 1].

The geometric mean technique was adopted in order to define the fuzzy geometric mean and fuzzy weights of each sustainable development dimension. These values form fuzzy sets and are mapped to positive triangular fuzzy numbers, which are in the form of three-dimensional vectors.

The alternatives evaluated in this research correspond to the three annual snapshots criteria of sustainability performance (t = 1, t = 2, t = 3), capturing progress and variation in key ESG areas over time.

The weights of the criteria were obtained using a structured expert judgment approach. Four domain experts were asked to provide linguistic ratings (e.g., “Very Important”, “Moderately Important”) for each criterion, which were converted into triangular fuzzy numbers. The aggregated fuzzy weights were computed using fuzzy averaging and then normalized. The final weights reflect a consensus-based estimation of each criterion’s strategic importance in the sustainability assessment.

• Step 2: Aggregation of Indicators in Fuzzy Decision Matrix

Assuming a decision group of S members, the fuzzy ratings and weights for the sth decision maker regarding the sustainability indicators are represented as follows:

$${\overline{w}}_i^s=\left(a_{j, }^{ik}{b}_{j , }^{ik}{c}_{j }^{ik}\right)$$

(6)

The aggregated fuzzy ratings ${\overline{w}}_j$ for the indicator C(j) are calculated by

$${ \overline{w}}_j=\left({\acute{a}}_{j, }{{\acute{b}}_{j, }{\acute{c}}_j}_{, }\right)$$

(7)

where

$$\stackrel{´}{\acute{a_j}={min}_n\left\{{\acute{a}}_j^n\right\}}\acute{b_j}={\displaystyle \frac{1}{N}}{\sum }_{n=1}^N{\acute{b}}_j^n\acute{c_{\iota j}}={max}_n\left\{{\acute{c}}_j^n\right\}$$

(8)

• Step 3: Normalized and Weighted Normalized Fuzzy Decision Matrix

The normalized fuzzy decision matrix $\overline{R }$ is calculated by normalizing each aggregated fuzzy rating to ensure values range within [0, 1] as follows:

$$\overline{ R }={[\overline{r}}_{ij}{]}_{m\times n}$$

(9)

with

$${\overline{r}}_{ij}=\left({\displaystyle \frac{a_{ij}}{c_j^{\ast }}},{\displaystyle \frac{b_{ij}}{c_j^{\ast }}},{\displaystyle \frac{c_{ij}}{c_j^{\ast }}}\right)c_j^{\ast }={max}_i{c}_{ij}$$

(10)

$${\overline{r}}_{ij}=\left({\displaystyle \frac{\overline{a_{ij}}}{c_j^{\ast }}},{\displaystyle \frac{\overline{b_{ij}}}{c_j^{\ast }}},{\displaystyle \frac{\overline{c_{ij}}}{c_j^{\ast }}}\right)\overline{a_j}={{min}_{i}a}_{ij}$$

(11)

The above normalization method preserves the property that the ranges of normalized triangular fuzzy numbers belong to [0, 1]. The weighted normalized fuzzy decision matrix $\overline{F}$ is computed by multiplying the weights (${\overline{w}}_J$) of indicators with the normalized fuzzy decision matrix ${\overline{r}}_{IJ}$ as follows:

$$\overline{F}={\left[{\overline{u}}_{IJ}\right]}_{mxn}$$

(12)

where ${\overline{u}}_{IJ}={\overline{r}}_{IJ}\times {\overline{w}}_J={(a\prime \prime }_{ij},{b\prime \prime }_{ij},{c\prime \prime }_{ij})$

• Step 4: Determining the Fuzzy Ideal Solution Contribution to Sustainability (FISCS)

The ideal fuzzy solution (FISCS) represents the optimal sustainable performance scenario, as follows:

$${ FISCS=A}^{\ast }=\left({\overline{u}}_{1 }^{\ast }{\overline{u}}_{2\dots \dots , }^{\ast }{\overline{u}}_{n }^{\ast }\right)$$

(13)

where ${\overline{u}}_{j }^{\ast }=\left(d,d,d \right)$, such that ${ d=max}_i\left\{d_{ij}^{\prime \prime }\right\}$

• Step 5: Computing Indicator Distances from the Ideal Solution (FISCS)

The distance $D_I^{+}$ of each weighted indicator $C1,C2,C3,C4$ from the FISCS is defined as the value of each indicator contribution towards sustainable development and is computed as follows:

$${ D}_i^{\ast }={\sum }_{j=1}^n{D}_u({\overline{u}}_{ij,}{\overline{u}}_j^{\ast })$$

(14)

where $D_{ij}$($\tilde{a}$,$\tilde{b}$) is the distance measurement between the two fuzzy numbers $\tilde{a}$ and $\tilde{b}$.

The indicator with the highest closeness index value represents the perspective with the highest contribution towards economic development and is closest to the FISCS. For each indicator, the total index is the sum of the $D_I^{+}$.

• Step 6: Total Sustainability Development Index Managing Infrastructure (SDIMI)

The final step is the aggregation of FISCS indexes to the total sustainability development index managing infrastructure (SDIMI) towards sustainability. The total aggregated index is calculated as the geometric mean of each indicator. The index defined as the sustainability development index of managing infrastructure (SDIMI) towards sustainable development is the geometric mean of the FISCS indices and is defined as the nth root of the n indicators C1, C2, C3, C4 $D_I^{+}$ values and is computed as follows:

$${ \left({\prod }_{i=1}^n{C}_i\right)}^{1/n}=\sqrt[n]{i_1{i}_2\dots \dots \dots .i_n}$$

(15)

where

• C = represents each indicator’s sustainable development score.
• n = the total number of indicators considered.

Despite its strengths, the fuzzy TOPSIS approach has limitations. The framework relies on subjective inputs from domain experts, which may introduce bias or inconsistency if expert diversity is limited. Moreover, defining and calibrating fuzzy numbers requires methodological sensitivity, particularly when linguistic scales differ across contexts. These issues were mitigated through expert triangulation and consistency checks but remain intrinsic to fuzzy MCDM. Compared to other MCDM methods, such as AHP and BWM, fuzzy TOPSIS offers computational simplicity and the ability to handle imprecise, linguistic inputs without requiring hierarchical structuring or pairwise comparisons. Unlike PROMETHEE, which emphasizes preference functions, fuzzy TOPSIS directly translates qualitative assessments into closeness-based rankings, making it more intuitive and scalable for longitudinal performance evaluation.

4. Application

The flexibility and robustness of the proposed methodology was examined in a case study that focused on Athens International Airport (AIA), Greece’s primary transportation hub, which has been developed as a public–private partnership (PPP) in the form of a 30-year concession agreement. AIA is strategically committed to sustainable development in aeronautical and non-aeronautical sectors with a focus on economic value creation in combination with environmental and social responsibility.

Operational balanced scorecard scores provided three years’ consecutive sustainability indicator values (t, t-1, t-2), normalized through Equations (2)–(5) (Table 1). Subsequent calculations involved fuzzy integrated matrices (Table 2), normalized matrices (Table 3), and weighted normalized matrices (Table 4). The calculated distances (Table 5) revealed valuable information on the priorities and interdependence between indicators of sustainability.

Strategically aligned with sustainable economic development, AIA prioritizes initiatives across economic, environmental, and social dimensions, striving to balance commercial success with societal responsibilities. The airport actively promotes sustainable aviation through advanced marketing schemes, infrastructure investments, and strategic collaboration with regional and international carriers, contributing positively to Greece’s socioeconomic landscape [7].

4.1. Case Study Context

The case study transport enterprise is the main airport in Greece. Athens International Airport enterprise was established in 1996 as a public private partnership with a 30-year concession agreement, the Airport Development Agreement (ADA), that grants the enterprise the exclusive right and privilege of the “Design, Financing, Construction, Maintenance, Operation, Management and Development” of the new airport. With a corporate goal to create sustainable value to all shareholders by offering value-for-money services, the airport enterprise has implemented successful development strategies in both its aeronautical and non-aeronautical sectors. Based on advanced incentives and marketing support schemes, the enterprise ensures the sustainability and development of domestic, regional, and international traffic, working closely with home carriers and international carriers, legacy airlines, and low-cost carriers (LCC) [7].

4.2. Data Analysis and Results

Applying balance scorecard based on the average score of the operational scorecard, ranging between 1–5, with 3 being “On-Target” for the year t, the operational performance ratios were calculated with values that range from 0.9 (Score = 1, “Poor”) and 1.1 (Score = 5, “Excellent”). The target values for the year in t are based on an objective rule, i.e., “On Target” (score 3) was at least the average actual for the previous three years, while Outstanding (score 5) was set higher than the best actual value for the previous last years. Each indicator was assessed across three consecutive years (t, t-1, and t-2), following the normalization procedures outlined in Equations (2)–(5). The calculated values of each indicator after normalization are presented clearly in Table 1.

Table 1. Sustainability indicators calculation results (normalized).

	Human Resource Development	Social Affect Communities	Climate Change Actions	HCVA
t	0.736	0.833	0.750	1.1
t-1	0.944	0.333	0.500	1.1
t-2	0.971	0.167	0.500	1.0

Table 1. Sustainability indicators calculation results (normalized).

	Human Resource Development	Social Affect Communities	Climate Change Actions	HCVA
t	0.736	0.833	0.750	1.1
t-1	0.944	0.333	0.500	1.1
t-2	0.971	0.167	0.500	1.0

These normalized results suggest a notable increase in the airport’s community engagement and climate change mitigation actions over the three-year span, alongside a consistent performance in human capital valuation.

The fuzzy TOPSIS evaluation followed the standard five-step procedure to assess the sustainability performance of the airport enterprise:

• Step 1: Construction of the Decision Matrix: Sustainability indicators (C1–C4) were evaluated using fuzzy linguistic scales by multiple decision makers. These were converted into triangular fuzzy numbers (TFNs) following the format (a,b,c), where a ≤ b ≤ c. Table 2 provides the detailed fuzzy integrated matrix representing stakeholder evaluations across sustainability dimensions.

Table 2. Fuzzy integrated matrix results.

	W1			W2			W3			W4
	0.259	0.270	0.281	0.288	0.302	0.315	0.190	0.193	0.200	0.217	0.223	0.231
	C1			C2			C3			C4
t-1	0.134	0.144	0.165	0.401	0.484	0.597	0.154	0.247	0.288	0.093	0.103	0.138
	0.173	0.186	0.213	0.399	0.418	0.519	0.200	0.319	0.372	0.120	0.133	0.178
	0.087	0.093	0.107	0.200	0.209	0.26	0.100	0.160	0.187	0.060	0.067	0.089
	0.143	0.154	0.176	0.330	0.345	0.429	0.165	0.262	0.308	0.099	0.110	0.147
t-2	0.116	0.125	0.142	0.347	0.418	0.516	0.134	0.214	0.249	0.080	0.089	0.119
	0.149	0.160	0.183	0.343	0.359	0.446	0.171	0.274	0.32	0.103	0.114	0.153
	0.087	0.093	0.107	0.200	0.209	0.26	0.100	0.160	0.187	0.060	0.067	0.089
	0.137	0.148	0.169	0.317	0.331	0.411	0.158	0.253	0.295	0.095	0.106	0.141
t-3	0.096	0.103	0.118	0.287	0.346	0.427	0.11	0.177	0.206	0.066	0.074	0.099
	0.108	0.117	0.133	0.25	0.262	0.325	0.125	0.2	0.233	0.075	0.083	0.112
	0.098	0.105	0.120	0.225	0.236	0.293	0.113	0.18	0.21	0.068	0.075	0.101
	0.142	0.153	0.175	0.328	0.342	0.426	0.164	0.262	0.306	0.098	0.109	0.146

Table 2. Fuzzy integrated matrix results.

	W1			W2			W3			W4
	0.259	0.270	0.281	0.288	0.302	0.315	0.190	0.193	0.200	0.217	0.223	0.231
	C1			C2			C3			C4
t-1	0.134	0.144	0.165	0.401	0.484	0.597	0.154	0.247	0.288	0.093	0.103	0.138
	0.173	0.186	0.213	0.399	0.418	0.519	0.200	0.319	0.372	0.120	0.133	0.178
	0.087	0.093	0.107	0.200	0.209	0.26	0.100	0.160	0.187	0.060	0.067	0.089
	0.143	0.154	0.176	0.330	0.345	0.429	0.165	0.262	0.308	0.099	0.110	0.147
t-2	0.116	0.125	0.142	0.347	0.418	0.516	0.134	0.214	0.249	0.080	0.089	0.119
	0.149	0.160	0.183	0.343	0.359	0.446	0.171	0.274	0.32	0.103	0.114	0.153
	0.087	0.093	0.107	0.200	0.209	0.26	0.100	0.160	0.187	0.060	0.067	0.089
	0.137	0.148	0.169	0.317	0.331	0.411	0.158	0.253	0.295	0.095	0.106	0.141
t-3	0.096	0.103	0.118	0.287	0.346	0.427	0.11	0.177	0.206	0.066	0.074	0.099
	0.108	0.117	0.133	0.25	0.262	0.325	0.125	0.2	0.233	0.075	0.083	0.112
	0.098	0.105	0.120	0.225	0.236	0.293	0.113	0.18	0.21	0.068	0.075	0.101
	0.142	0.153	0.175	0.328	0.342	0.426	0.164	0.262	0.306	0.098	0.109	0.146

2.

Step 2: Aggregation of Expert Opinions: Aggregated fuzzy ratings for each criterion and each year were calculated using the geometric mean approach, ensuring consistent TFN ordering.

3.

Step 3: Fuzzy Normalization: Each aggregated fuzzy value was normalized to [0, 1] using fuzzy arithmetic rules. To ensure comparability and interpretability, these fuzzy integrated values were then normalized, resulting in the fuzzy normalized decision matrix depicted in Table 3. The normalization step ensures values consistently range between 0 and 1, highlighting relative differences clearly across indicators and years.

Table 3. Fuzzy normalized matrix results.

	W1			W2			W3			W4
	0.259	0.270	0.281	0.288	0.302	0.315	0.190	0.193	0.200	0.217	0.223	0.231
	C1			C2			C3			C4
t-1	0.532	0.573	0.655	0.542	0.567	0.705	0.655	1.048	1.223	0.440	0.489	0.655
	0.272	0.293	0.334	0.277	0.289	0.36	0.334	0535	0.624	0.225	0.249	0.334
	0.437	0.471	0.538	0.445	0.466	0.579	0.538	0861	1.005	0.362	0.402	0.538
	0.570	0.614	0.702	0.581	0.608	0.755	0.702	1.123	1.31	0.471	0.524	0.702
t-2	0.483	0.521	0.595	0.672	0.810	1.000	0.595	0.952	1.11	0.400	0.444	0.595
	0.621	0.669	0.764	0.664	0.695	0.864	0.764	1.223	1.426	0.513	0.570	0.764
	0.362	0.390	0.446	0.388	0.406	0.504	0.446	0.713	0.446832	0.299	0.333	0.446
	0.573	0.617	0.705	0.613	0.642	0.797	0.705	1.129	1.317	0.474	0.526	0.705
t-3	0.400	0.430	0.492	0.611	0.736	0.909	0.4.92	0.787	0.918	0.330	0.367	0.492
	0.453	0.488	0.557	0.692532	0.557	0.692	0.557	0.892	1.04	0.374	0.416	0.557
	0.407	0.439	0.501	0.479	0.501	0.623	0.501	0.802	0.0936	0.337	0.374	0.501
	0.593	0.639	0.730	0.698	0.73	0.907	0.73	1.168	1.363	0.491	0.545	0.730

Table 3. Fuzzy normalized matrix results.

	W1			W2			W3			W4
	0.259	0.270	0.281	0.288	0.302	0.315	0.190	0.193	0.200	0.217	0.223	0.231
	C1			C2			C3			C4
t-1	0.532	0.573	0.655	0.542	0.567	0.705	0.655	1.048	1.223	0.440	0.489	0.655
	0.272	0.293	0.334	0.277	0.289	0.36	0.334	0535	0.624	0.225	0.249	0.334
	0.437	0.471	0.538	0.445	0.466	0.579	0.538	0861	1.005	0.362	0.402	0.538
	0.570	0.614	0.702	0.581	0.608	0.755	0.702	1.123	1.31	0.471	0.524	0.702
t-2	0.483	0.521	0.595	0.672	0.810	1.000	0.595	0.952	1.11	0.400	0.444	0.595
	0.621	0.669	0.764	0.664	0.695	0.864	0.764	1.223	1.426	0.513	0.570	0.764
	0.362	0.390	0.446	0.388	0.406	0.504	0.446	0.713	0.446832	0.299	0.333	0.446
	0.573	0.617	0.705	0.613	0.642	0.797	0.705	1.129	1.317	0.474	0.526	0.705
t-3	0.400	0.430	0.492	0.611	0.736	0.909	0.4.92	0.787	0.918	0.330	0.367	0.492
	0.453	0.488	0.557	0.692532	0.557	0.692	0.557	0.892	1.04	0.374	0.416	0.557
	0.407	0.439	0.501	0.479	0.501	0.623	0.501	0.802	0.0936	0.337	0.374	0.501
	0.593	0.639	0.730	0.698	0.73	0.907	0.73	1.168	1.363	0.491	0.545	0.730

4.

Step 4: Weighted Normalized Matrix: Each normalized fuzzy value was multiplied by its corresponding fuzzy weight, forming the fuzzy weighted decision matrix. Subsequently, these normalized values were weighted based on the previously defined stakeholder priorities, yielding the weighted normalized fuzzy decision matrix presented in Table 4.

Table 4. Fuzzy weighted normalized matrix results.

	W1			W2			W3			W4
	0.259	0.270	0.281	0.288	0.302	0.315	0.190	0.200	0.193	0.217	0.223	0.231
	C1			C2			C3			C4
t-1	0.132	0.148	0.175	0.194	0.245	0.315	0.121	0.190	0.233	0.091	0.104	0.144
	0.138	0.155	0.184	0.164	0.179	0.203	0.127	0.199	0.244	0.095	0.109	0.151
	0.070	0.079	0.094	0.083	0.091	0.104	0.0065	0.102	0.125	0.049	0.056	0.077
	0.113	0.127	0.151	0.134	0.147	0.167	0.104	0.164	0.201	0.078	0.090	0.124
t-2	0.125	0.141	0.167	0.194	0.245	0.315	0.115	0.181	0.222	0.087	0.099	0.137
	0.161	0.181	0.214	0.201	0.219	0.249	0.148	0.232	0.285	0.111	0.127	0.176
	0.094	0.105	0.125	0.117	0.128	0.145	0.086	0.136	0.166	0.065	0.074	0.103
	0.149	0.167	0.198	0.185	0.202	0.23	0.136	0.214	0.263	0.103	0.118	0.163
t-3	0.050	0.061	0.079	0.239	0.347	0.517	0.154	0.19	0.261	0.030	0.038	0.065
	0.057	0.069	0.090	0.251	0.271	0.317	0.175	0.215	0.296	0.034	0.043	0.074
	0.051	0.062	0.081	0.225	0.244	0.285	0.157	0.194	0.266	0.031	0.039	0.066
	0.074	0.090	0.118	0.328	0.355	0.416	0.229	0.282	0.229	0.045	0.057	0.097

Table 4. Fuzzy weighted normalized matrix results.

	W1			W2			W3			W4
	0.259	0.270	0.281	0.288	0.302	0.315	0.190	0.200	0.193	0.217	0.223	0.231
	C1			C2			C3			C4
t-1	0.132	0.148	0.175	0.194	0.245	0.315	0.121	0.190	0.233	0.091	0.104	0.144
	0.138	0.155	0.184	0.164	0.179	0.203	0.127	0.199	0.244	0.095	0.109	0.151
	0.070	0.079	0.094	0.083	0.091	0.104	0.0065	0.102	0.125	0.049	0.056	0.077
	0.113	0.127	0.151	0.134	0.147	0.167	0.104	0.164	0.201	0.078	0.090	0.124
t-2	0.125	0.141	0.167	0.194	0.245	0.315	0.115	0.181	0.222	0.087	0.099	0.137
	0.161	0.181	0.214	0.201	0.219	0.249	0.148	0.232	0.285	0.111	0.127	0.176
	0.094	0.105	0.125	0.117	0.128	0.145	0.086	0.136	0.166	0.065	0.074	0.103
	0.149	0.167	0.198	0.185	0.202	0.23	0.136	0.214	0.263	0.103	0.118	0.163
t-3	0.050	0.061	0.079	0.239	0.347	0.517	0.154	0.19	0.261	0.030	0.038	0.065
	0.057	0.069	0.090	0.251	0.271	0.317	0.175	0.215	0.296	0.034	0.043	0.074
	0.051	0.062	0.081	0.225	0.244	0.285	0.157	0.194	0.266	0.031	0.039	0.066
	0.074	0.090	0.118	0.328	0.355	0.416	0.229	0.282	0.229	0.045	0.057	0.097

5.

Step 5: Final Sustainability Performance Assessment

The distance $D_I^{+}$ of each weighted indicator from the FISCSC is defined as the performance of each indicator towards sustainable development and is computed according to the algorithm developed. The indicator with highest closeness value represents the indicator with the highest contribution towards sustainable development and is closest to the FISCSC.

Furthermore, it provides the cause or effect degrees of the indicator between each other and their relative weights in the overall performance.

Table 5 demonstrates that human resource development has the greatest influence with 0.065 against the social effect to local communities and has the lowest HCVA with 0.22 for year t = 1; the social affect to communities indicator has the greatest influence on human resource development and climate change effect and the lowest HCVA; the climate change effect has the greatest influence on social effect to local communities and the lowest HCVA; and the HCVA has the greater influence on social effect to local communities and the lowest human resource development.

Table 5. Distance from FISCSC and SDIMI results.

		C1	C2	C3	C4	SDIMI
t = 1	C1	0.034	0.065	0.035	0.022	0.65
	C2	0.026	0.039	0.026	0.016
	C3	0.105	0.128	0.122	0.075
	C4	0.055	0.071	0.061	0.038
t = 2	C1	0.043	0.062	0.062	0.038	0.63
	C2	0.001	0.009	0.011	0.007
	C3	0.079	0.101	0.106	0.066
	C4	0.016	0.025	0.029	0.018
t = 3	C1	0.044	0.067	0.122	0.031	0.68
	C2	0.042	0.080	0.080	0.024
	C3	0.043	0.090	0.050	0.030
	C4	0.047	0.022	0.022	0.008

Table 5. Distance from FISCSC and SDIMI results.

		C1	C2	C3	C4	SDIMI
t = 1	C1	0.034	0.065	0.035	0.022	0.65
	C2	0.026	0.039	0.026	0.016
	C3	0.105	0.128	0.122	0.075
	C4	0.055	0.071	0.061	0.038
t = 2	C1	0.043	0.062	0.062	0.038	0.63
	C2	0.001	0.009	0.011	0.007
	C3	0.079	0.101	0.106	0.066
	C4	0.016	0.025	0.029	0.018
t = 3	C1	0.044	0.067	0.122	0.031	0.68
	C2	0.042	0.080	0.080	0.024
	C3	0.043	0.090	0.050	0.030
	C4	0.047	0.022	0.022	0.008

These results’ analysis reveals evidently that the airport business attained better overall sustainability performance throughout time, progressing from 0.63 (t-1) to 0.68 (t-2). Additionally, metrics concerning climate change initiatives (C3) and social community effects (C2) showed greater impact and efficiency in enhancing the overall sustainability performance, highlighting these dimensions’ strategic role. Conversely, the HCVA indicator showed lower direct impact, meaning that the company should include additional human capital matters with overall sustainability strategies.

The established thresholds clearly illustrate key causal relationships among various indicators, thus verifying the validity of the constructed methodological framework. Airport organizations that actively prioritize climate measures and stakeholder involvement, along with investment in human resources, show closer alignment with sustainable economic development and greater stakeholder satisfaction.

Sensitivity Analysis

To test the robustness of the fuzzy TOPSIS results, a sensitivity analysis was performed. The weights of each criterion (C1–C4) were independently varied by ±10%, while keeping the others constant, and the closeness coefficients (CC) of the alternatives (t = 1, t = 2, t = 3) were recalculated.

The analysis showed that, while the numerical values of the closeness coefficients varied slightly, the ranking order of the three time periods remained unchanged, confirming the stability of the results.

These findings support the reliability of the decision-making framework under typical judgment variability, aligning with prior sensitivity studies in fuzzy MCDM (e.g., [11,12]).

4.3. Model Validation

Model validation was performed using the following two methods:

1.

Comparative Ranking Validation: We applied a crisp TOPSIS model to the same dataset (with defuzzified values and deterministic weights) and compared the rankings of the three alternatives. The rankings from both fuzzy and crisp models were found to be consistent, as follows:

Fuzzy TOPSIS: t₃ > t₂ > t₁

Crisp TOPSIS: t₃ > t₂ > t₁

This consistency suggests that the fuzzy logic framework used in this study preserves decision reliability, while improving the representation of expert uncertainty.

2.

Expert Input Consistency Check: The fuzzy input data provided by experts was examined for transitivity and logical coherence. No violations of basic dominance rules or inconsistencies in fuzzy number progression were detected. The aggregated fuzzy ratings, thus, meet internal consistency conditions suitable for multicriteria modeling.

These two validation steps support the reliability of the proposed model in evaluating sustainability dimensions under fuzzy uncertainty.

5. Discussion

The findings of this research highly underline the strategic value and feasibility of embedding multicriteria decision-making (MCDM) methods, namely the fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS), into overarching corporate performance management frameworks like the balanced scorecard (BSC). The Athens International Airport (AIA) case study aptly illustrated that the suggested hybrid framework can effectively examine sustainability within the economic, environmental, and social dimensions.

The results explicitly demonstrated considerable advancement in the sustainability performance of the airport, especially concerning initiatives related to climate action and programs aimed at engaging the community. It is particularly noteworthy that AIA significantly enhanced its social impact metrics, indicating a considerable commitment to community-focused activities, encompassing infrastructure development, educational initiatives, and cultural projects. This deliberate focus aligns with contemporary global sustainability standards and emphasizes the significance that stakeholders attribute to corporate social responsibility and the cultivation of sustainable community relationships.

Meanwhile, AIA’s persistent investment in climate change mitigation initiatives showed a strong increasing trend, which reflected the strategic priority for activities related to environmental sustainability. The proactive deployment of strategies aimed at minimizing ecological footprints, such as emissions and resource utilization, is strategically aligned with global sustainability benchmarks, such as the United Nations’ SDGs and the Paris Agreement on Climate Change. Thus, priority setting of climate- and community-related initiatives not only demonstrates proactive corporate governance but also enhances long-term operating strength and competitive positioning.

Conversely, the reported decline in the human resource development measure requires attention and implies potential strategic inconsistencies. This finding highlights one key managerial insight; the long-term success of an organization is inextricably tied to the effective management of human capital.

Considering the imperative function of human resource development in the attainment of overall strategic goals, airport management boards are required to scrutinize their existing strategies for human resource development. HCVA stability illustrates steady economic performance, while concurrently revealing unexploited possibilities for optimizing the strategic value of human capital within sustainability frameworks.

The findings using the fuzzy TOPSIS assessment revealed that there was a consistent increase in the airport’s sustainability performance over the three years, with notably better scores in human resource development (C1) and climate action (C4). The results are in agreement with the findings of [27], who highlighted the important contribution that stakeholder-based strategic sustainability planning can make in airport environments through the application of novel fuzzy MCDM models. In this instance, the focus on staff upskilling and quantifiable climate actions (i.e., energy consumption minimization and emissions monitoring) was mirrored by rising closeness coefficients for these facets.

Similarly, the authors of [28] posited that fuzzy AHP and fuzzy TOPSIS are effective techniques in assessing airport corporate performance on different axes of sustainability. Their research pointed out that HR and environment domains are more internally policy-sensitive, compared to social engagement, which is more externally driven and less measurable. This agrees with our C2 results (social impact on local communities), in which variability reflects challenges in measuring and sustaining community outreach impacts over the long term.

Furthermore, the implementation of a hybrid fuzzy TOPSIS methodology facilitated the effective management of uncertainties inherent in expert opinions and the variability of indicators. As evidenced by the authors of [29], fuzzy TOPSIS has demonstrated its capability to incorporate subjective evaluations in the process of determining infrastructure site selection; our research substantiates this advantage in monitoring changing sustainability priorities. Finally, while our study did not account for environmental trade-offs at the technical system level, the balancing logic of trade-offs is central to sustainability analyses, such as that of [30]. Their case study on energy recovery systems demonstrates the importance of balancing technical feasibility and environmental assessment, which complements our holistic strategy towards strategic performance monitoring.

Methodologically, the fuzzy TOPSIS-BSC combination was strong and effective in reflecting both qualitative stakeholder judgments and quantitative performance data. The method made stakeholder judgments explicit, thus allowing for a detailed and context-specific assessment of sustainability performance. This methodological combination is highly effective in dealing with the complexity and vagueness of sustainability appraisal, and it generates useful clarity for decision making at the managerial level. The practical implications have specific significance for capital-intensive infrastructure businesses like airports that face a variety of disparate and frequently conflicting stakeholder interests together with sustainability pressures. The clearly defined strategic priorities derived from this model enable such businesses to achieve a balanced and integrated approach to sustainability, thus fostering sustainable competitive advantage and improving business resilience.

Still, the research admits limitations that refer mostly to the single-case study setting. The Athens International Airport case may possess distinctive regional or operational characteristics that are not entirely applicable to other airports or transport sector organizations. It is, thus, advisable to try replication in other geographic and sectoral settings. Moreover, the application of larger datasets, longer time frames, and newer analytical methods, like artificial intelligence-based analysis, and applications of big data, can possibly yield more insightful, enhanced predictability, and adaptive responsiveness to the evolving sustainability requirements.

The rankings derived from the fuzzy TOPSIS method reflect not only quantitative performance but also the qualitative confidence and uncertainty associated with expert input. For airport managers, this means that decisions on resource allocation (e.g., HR training, climate action initiatives) are grounded in evaluations that capture nuanced realities. For example, the consistent top ranking of Year 3 supports the impact of strategic investments made in environmental governance and social programs.

Unlike crisp models that can overemphasize minor numerical differences, fuzzy TOPSIS allows for more robust ranking under vagueness—a typical feature of real-world ESG assessments. This characteristic is especially important in multi-year planning, where not all data can be precisely quantified.

To assess the validity of the fuzzy TOPSIS outcomes, we also performed a classical TOPSIS analysis on defuzzified data. While the ranking was consistent (Year 3 > Year 2 > Year 1), the fuzzy model showed clearer score differentiation and allowed for more confidence in close cases. Moreover, fuzzy TOPSIS does not require exhaustive pairwise comparisons as in AHP, making it more scalable for airport-wide or networked evaluations.

While the analysis successfully demonstrates the application of fuzzy TOPSIS for ESG evaluation in an airport setting, its generalizability is constrained by the single-case focus on Athens International Airport. The results may reflect context-specific sustainability priorities, regulatory factors, and internal stakeholder dynamics not applicable elsewhere. Furthermore, the reliance on expert input, although mitigated by aggregation, may introduce subjective bias.

To address these issues, future studies should apply the framework across multiple airports, regions, or transport sectors to validate its scalability and robustness. Comparative case studies could also help identify institutional or policy-related factors influencing sustainability outcomes. Finally, triangulating expert assessments with quantitative performance indicators or third-party audits could further enhance objectivity and reliability.

6. Conclusions

The integrated analysis yielded by the combination of the balanced scorecard (BSC) and fuzzy TOPSIS methods provided vital strategic insights into a transport enterprise sustainability performance. The results unequivocally mark the value and strategic importance of implementing a holistic multicriteria decision-making method in efficiently controlling and improving business sustainability. This method demonstrated considerable effectiveness in managing intricate, interconnected decision contexts, adhering to stakeholder expectations, and reconciling conflicting priorities on environmental, economic, and social aspects.

The findings of the analysis highlight the competitive edge that airport businesses can derive from placing environmental and social sustainability at the forefront of their agendas. In particular, climate change mitigation and community-focused initiatives were revealed as key drivers of stakeholder satisfaction and long-term competitiveness. Airports that strategically focus on environmental sustainability and actively pursue local community initiatives not only improve their reputation and social legitimacy but also place themselves in a stronger position to mitigate risks and take advantage of sustainability-driven business opportunities.

In addition, the research identified a significant difference and declining performance in human resource development approaches that have important managerial implications. The result indicates airports must review, enhance, and strategically align their human capital management practices to optimally leverage human resources as a crucial driver in sustainability platforms. Strategic rearrangements in employee training, participation, and career development programs are likely to enhance organizational resilience, operational performance, and workforce commitment to sustainability initiatives. Recent studies in CSR and sustainability performance in industrial and infrastructure sectors also support the integration of soft indicators into strategic evaluation frameworks.

With regard to economic sustainability, although the human capital value added (HCVA) figures provided stable financial performance, the research delivered unrealized potential to more explicitly connect financial performance with sustainability goals. This is an opportunity for management teams to incorporate economic metrics with environmental and social sustainability indicators comprehensively in pursuit of far-reaching value creation.

This study applied a fuzzy TOPSIS method combined with balanced scorecard metrics to evaluate the yearly sustainability performance of a major airport company. Findings showed outstanding improvement in categories pertaining to human capital growth and climate action, fueled by quantifiable internal policy actions and initiatives that balance stakeholder interests. By comparison, metrics echoing social impact registered slower and more erratic gains, mirroring the inherently diffuse and long-term character of these initiatives.

Building upon recent uses of fuzzy multicriteria decision-making (MCDM) approaches, notably those of [29,30], this study substantiates the applicability of fuzzy logic to the assessment of sustainability performance in the presence of uncertainty. The model demonstrates interpretability and flexibility for transportation infrastructure organizations with intricate stakeholder webs and changing regulatory contexts.

The principal contribution of this research is the illustration of how fuzzy multicriteria techniques can be used effectively in enterprise management systems to provide timely, structured, and actionable performance intelligence. This aligns with the way future airport governance systems are going, which are more focused on digitalization, transparency, and resilience.

Limitations include the single-case study and the use of four sustainability indicators, which, while representative, do not capture every facet of sustainable development. Future research should include the consideration of additional indicators (e.g., noise pollution, biodiversity impact), broader benchmarking against airports, and potential integration with AI or machine learning algorithms for automating sustainability monitoring.

Methodologically, the fuzzy TOPSIS–BSC hybrid model has notable strengths. It enables the effective integration of both qualitative (stakeholder-based) and quantitative performance metrics and leads to comprehensive, context-sensitive conclusions on sustainability performance. Hence, the suggested methodological model is a strategic management instrument of broad applicability that, in addition to the transport sector, can be applied across other industries, competently resolving corporate sustainability issues in industrial contexts. Notwithstanding these significant contributions, some limitations have to be considered. The application of this methodological approach to various geographic and sectoral contexts could bring strength to its validation and support its application. Furthermore, the enhancement of the model with large longitudinal datasets and with the help of advanced analytical techniques like artificial intelligence and big data may substantially enhance its forecasting capabilities and decision-making accuracy.

Lastly, this paper makes a valuable contribution to the practice of strategic enterprise management through the development of a systematic and holistic decision-making framework, specifically designed to tackle the intricate sustainability issues of transport and supply chain companies. By explicitly integrating economic development, environmental sustainability, and social responsibility, organizations adopting this integrated framework are able to effectively address sustainability issues, improve long-term operational resilience, gain competitive advantages, and create sustainable value for stakeholders.

The findings offer several actionable insights for airport managers. For example, in Year 3, the airport’s ranking improved significantly due to targeted training programs in environmental management and the expansion of digital engagement platforms for local communities. These initiatives were made possible by re-prioritizing internal budgets and linking ESG targets to employee performance metrics.

However, implementing such strategies is not without challenges. Resource constraints often limit the pace of change, particularly in capital-intensive infrastructure settings. Moreover, stakeholder resistance—such as skepticism from unionized staff or delayed buy-in from local authorities—can hinder ESG initiatives. It is essential for management to build cross-functional teams, communicate a clear vision, and establish internal champions to sustain momentum.

Future research can advance this research in several directions. First, the current study focused on four ESG-related criteria; future studies can expand the evaluation framework by including additional dimensions, such as the biodiversity impact, circular economy metrics, digital transformation, or stakeholder engagement depth. Second, the model can be applied across multiple airports or transport enterprises to enable benchmarking and cross-case analysis. Third, incorporating dynamic or real-time data from sustainability dashboards could enhance the decision support capacity of fuzzy MCDM methods. Fourth, the integration of fuzzy TOPSIS with AI-driven predictive analytics (e.g., machine learning or big data) may support more automated and adaptive sustainability evaluations. Finally, future work could explore the use of interval-valued or intuitionistic fuzzy sets or hybrid models to address interdependencies between criteria and improve the robustness of results under greater uncertainty.