Evaluating sustainability in the textile sector using WASPAS and TOPSIS: A multicriteria decision-making approach

Abstract

The aim of this study is to evaluate the sustainability performance on a yearly basis using the indicators presented in the sustainability reports of a global textile group. For this purpose, the sustainability performance indicators presented in the reports for the specified years have been identified. The inclusion of numerous criteria in this yearly evaluation has made the study suitable for the use of multicriteria decision-making techniques. In the decision-making process of the study, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Weighted Aggregated Sum Product Assessment (WASPAS), which are commonly used and provide reliable results, were applied to determine the best alternative. Results show that the same the ranking order of alternatives was obtained with both proposed approaches, indicating that the decision models reflect consistent outcomes in the evaluation of alternatives. It was concluded that the performance, in the context of the evaluated criteria, showed an increasing trend over the years, with the highest performance observed in 2023.

Keywords

Sustainability assessment textile sector MCDM WASPAS TOPSIS

In recent years, ensuring a sustainable planet has become a focal point for global efforts and initiatives. Industries worldwide play a critical role in this endeavor by integrating their environmental and social performance with economic goals to drive sustainable development.¹ Nearly all companies have recognized the importance of minimizing or eliminating harmful environmental practices to protect the environment while meeting social demands. Sustainability practices not only safeguard the planet and address social needs, but they also enhance organizational performance.² The commitment of companies to achieve the United Nations Sustainable Development Goals (SDGs), introduced as part of “Agenda 2030,” necessitates demonstrating their dedication to meet current stakeholders’ needs without compromising future generations.³ Sustainability reporting (SR) has emerged as a vital tool in reducing information asymmetry between companies and stakeholders, with frameworks such as the Global Reporting Initiative (GRI) gaining prominence globally.⁴

The textiles and apparel sector, responsible for 2% of the global GDP, is considered one of the most environmentally impactful industries, generating 10% of global greenhouse gas emissions.⁵ Its long supply chains, high energy and water consumption, and use of hazardous substances exacerbate its environmental impact, whereas social issues such as low wages and poor working conditions persist. Consequently, the industry faces growing pressure from legislation, nongovernmental organizations (NGOs), and consumers to adopt sustainable practices, requiring a balance among factors to maintain competitiveness.⁶

SR involves disclosing environmental, social, and economic information to stakeholders via formal reports.² Its aim is to deliver detailed financial and nonfinancial performance metrics to shareholders, potential investors, employees, management, customers, suppliers, public entities, creditors, analysts, business advisors, and the wider community, highlighting the company’s economic, environmental, and social performance and commitments.⁷ In addition, SR enables stakeholders to evaluate and compare the sustainability performance of different organizations, aiding in making well-informed decisions about investments, acquisitions, and partnerships. By measuring and disclosing sustainability performance data, organizations can identify areas requiring improvement and set targets to reduce their environmental footprint, enhance social responsibility, and support economic performance.⁷ The practice of SR has increased significantly in recent years. This trend is largely attributed to the observation that organizations disclosing detailed sustainability information enhance their reputation/image, motivate their employees and managers, and improve profitability.⁸ Large companies typically possess the financial and human resource capabilities necessary to report on their sustainability practices.² SR, as highlighted by KPMG’s Sustainability Reporting Survey (2022), indicates that 96% of global firms now report on sustainability, with the use of GRI standards and third-party assurance emerging as significant business practices worldwide.⁹ It has become an established corporate practice, as evidenced by the exponential increase in the number of reports published annually.¹⁰

The effect of SR on firm performance has been evaluated in various studies. A global investigation into the effect of SR on firm performance across seven different sectors revealed that its influence on operational performance (Return on Assets), financial performance (Return on Equity), and market performance (Tobin’s Q) varies among these sectors.¹¹ Another study investigated the relationship between SR and performance in publicly listed companies in Bahrain.¹² The relationship between the level of SR and sectoral energy performance in both developed and emerging economies was examined. The empirical findings of this study demonstrate a significant relationship between Environmental, Social, and Governance (ESG) and operational performance (operating ratio).¹³ The effect of SR quality on the corporate financial performance (CFP) of initial public offerings (IPOs) in Malaysia was investigated. The findings suggest that SR practices can be seen as an initiative to enhance IPO performance.¹⁴ The relationship between the level of SR and the performance (operational, financial, and market) of the retail sectors was examined. The model in this study provides a valuable analytical framework for exploring SR as a driver of performance in the economies of retail sectors.¹⁵ The effect of SR on the firm value of publicly listed oil and gas companies in Nigeria was investigated. The regression results indicate that all three dimensions of SR (social, environmental, and economic) contribute positively to the firm value of publicly listed oil and gas companies in Nigeria.⁷

Multiple criteria decision-making (MCDM) methods provide a structured approach for addressing decision problems with multiple objectives, diverse criteria, and varying preferences. These methods allow decision-makers (DMs) to break down complex problems based on defined criteria, assess alternatives based on these criteria, and make well-informed decisions using clear decision rules. The importance of MCDM methods stems from their capacity to manage the complexities of decision-making processes involving numerous objectives, criteria, and stakeholders.¹⁶ The data shared through SR are highly suitable for use in MCDM methods, as they provide the necessary data on alternatives for use in these models. Utilizing these data allows for the evaluation of performance over a defined time period or facilitates comparisons among specific groups with ease.

Evaluating a company’s sustainable performance based on various ESG criteria is referred to as ESG performance evaluation. From the perspective of operations research, this evaluation process is categorized as a MCDM problem. In ESG performance evaluation, the DM carefully examines multiple interconnected and restrictive criteria.¹⁷ A wide range of decision-making techniques and approaches have developed over the years which support detailed analysis and context-specific outcomes. It is important to acknowledge that some methodologies are designed explicitly for problem domains and may not be applicable or transferable to situations. Therefore, choosing a decision-making methodology requires consideration of various parameters and contextual factors, as each can significantly influence the effectiveness of the decision-making process.¹⁸

In the current study, two MCDM approaches were employed: the Weighted Aggregated Sum Product Assessment (WASPAS) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). These methods are known for their high reliability, simplicity, and ease of use, making them suitable tools for evaluating and ranking alternatives. The WASPAS is an MCDM technique that combines the principles of the weighted sum model (WSM) and the weighted product model (WPM). It works by assigning weights to individual criteria, calculating scores to rank the alternatives.¹⁹ TOPSIS is a MCDM method used to evaluate and rank different alternatives according to specific criteria. This approach measures how closely each option aligns with the ideal solution and the negative-ideal solution, ultimately selecting the option nearest to the ideal solution as the best choice.²⁰ The WASPAS and TOPSIS methods distinguish themselves from other MCDM techniques due to their logical structure, efficiency, and ease of use. They offer a more accurate ranking by providing transparent numerical assessments.¹⁹ Relying on a single MCDM method may not ensure reliable results. Therefore, in this study, comparing the findings obtained from multiple MCDM methods to evaluate the performance of alternatives provides a more reliable assessment.

Due to the potential advantages of MCDM methods, numerous systematic literature reviews have been conducted on their applications in various disciplines for sustainability-oriented applications. The WASPAS method has been applied in sectors such as renewable energy, waste management, and construction, and it has also been used across various other industries for sustainability-focused decision-making.^21-39 While the application of the WASPAS method in the textile sector remains limited in sustainability applications within the textile sector, the TOPSIS method has been utilized extensively across a wide range of studies in this domain. The TOPSIS method has been widely applied in the this sector for various sustainability-related purposes, including wastewater treatment selection, sustainability program assessment, supply chain design, dyeing process optimization, and financial performance evaluation.^40-52 In addition, it has also been utilized in other domains such as software engineering and biomedical waste management in the context of sustainability.^53,54

Although both WASPAS and TOPSIS methods yield reliable results individually, combining them can improve decision accuracy. Several studies have confirmed the effectiveness of integrating both approaches.^55-59 Recent studies in the textile sector employing various MCDM techniques beyond the methods utilized in this study have increasingly focused on sustainability-related issues, with a notable concentration on supplier selection in many of these studies.^60-65

This study fills a significant gap in the literature regarding the application of MCDM methods in the textile sector. Existing uses of MCDM in this field have mostly focused on specific areas such as supply chain management, fabric selection, and supplier evaluation. However, comprehensive assessments involving diverse criteria in the broader and more strategic context of sustainability remain limited.

This study makes an original and meaningful contribution to the field through the following key aspects.

A comprehensive evaluation based on 46 distinct sustainability criteria.

The combined use of the WASPAS and TOPSIS methods, rarely applied together in the textile sector, offering a model framework for future research.

A time-based comparative analysis that uncovers sustainability performance trends.

The integration of ESG standards, regulatory pressures, and sectoral transformation dynamics, providing both academic depth and practical relevance.

Through these contributions, the study introduces a holistic and innovative perspective on MCDM-based sustainability evaluation in the textile industry, while also expanding methodological diversity in the field.

In this study, a MCDM model comprising 46 criteria was developed using data extracted from the annual sustainability reports of a global textile group. The model evaluated four alternative years, allowing for a comparative analysis of sustainability performance over time. The solution phase was carried out by separately applying the TOPSIS and WASPAS methods: two approaches whose application areas have rapidly expanded in recent years.

Methodology

Data Sources

The data used in this study were obtained from the annual sustainability reports published by a global fashion and design company.⁶⁶ The company is a global fast fashion retailer, operating in the apparel and accessories industry. As one of the largest fashion retailers worldwide, it holds a significant position in the global textile and apparel market, particularly due to its large-scale operations, rapid product turnover, and vertically integrated supply chain model. Its role in the industry makes it a relevant and impactful case for studies focusing on decision-making in textile and fashion-related contexts.

The dataset used in this study was constructed based on environmental, social, and governance criteria, which are fundamental components of sustainability. However, since data for some criteria are not available for specified years in the sustainability report, only the criteria with available data across the four selected years have been included in the dataset.

For the study, data categorized under the main headings of “Climate, Chemicals, Materials, Waste and Resource Recirculation, Employees, Workers in Supply Chains, and Supply Chain Management” in the sustainability reports were utilized. A total of 46 sustainability performance indicators were identified as criteria, whereas the years 2020, 2021, 2022, and 2023, to which the data belong, were defined as the alternatives to be evaluated. The large number of criteria makes it difficult to apply methods based on pairwise comparisons. Moreover, since the criteria represent different dimensions of sustainability, assigning subjective priorities among them was not considered methodologically rational. Therefore, equal weighting was applied in the analysis to ensure neutrality and methodological consistency. The maximization (benefit) or minimization (cost) orientation of each criterion was also specified (Table 1).

Table 1.

Criteria of the research model and their orientation.

Criteria	Performance indicator	Unit	Orientation
Factor group: Environmental
- Climate
C1	% renewable electricity in our own operations	-	Max
C2	% change in electricity intensity	-	Max
C3	Energy use within our own operations	GJ	Min
C4	District heating	GJ	Min
C5	Electricity	GJ	Min
C6	Building diesel, natural gas, oil and others	GJ	Min
- Chemicals
C7	Number and % of textile and leather suppliers enrolled in ZDHC program	-	Max
C8	% Manufacturing restricted substances list compliance for chemical inputs	-	Max
C9	% Manufacturing restricted substances list compliance for wastewater	-	Max
- Materials
C10	% recycled or sustainably sourced materials	-	Max
C11	% recycled materials	-	Max
C12	% sustainably sourced materials	-	Max
C13	% of total cotton that is recycled, organic, or sustainably sourced	-	Max
C14	% of total cotton that is recycled	-	Max
C15	% of total cotton that is organic	-	Max
C16	% of total cotton that is sustainably sourced	-	Max
C17	% of polyester from recycled sources	-	Max
C18	% of wool that is Responsible Wool Standard (RWS) compliant	-	Max
C19	% of cashmere that is Good Cashmere Standard (GCS) compliant	-	Max
C20	% of mohair that was Responsible Mohair Standard (RMS) compliant or from recycled sources	-	Max
C21	% of virgin down that is Responsible Down Standard (RDS) compliant	-	Max
C22	% of all leather products produced with chrome-free tanned leather, including vegetable tanned leather and metal-free leather	-	Max
- Waste and resource recirculation
C23	% total product assortment reused or recycled (including charity donations) due to a fault	-	Max
C24	% of total product assortment destroyed (prioritizing incineration for energy recovery) due to the products having failed certain chemical tests, being contaminated by mold, or when there was no viable recycling or downcycling solution available	-	Min
C25	Weight of garments collected through garment collecting initiative	Metric tonnes	Max
C26	% garments collected that are reused as a product	-	Max
C27	% garments collected that are recycled to become products for other industries or made into new fibers	-	Max
C28	% garments collected that, as a last resort, had to be disposed of another way, prioritizing incineration for energy recovery and not sending textiles to landfill	-	Min
C29	% waste handled in H&M Group distribution centers that was recycled or reused	-	Max
Factor group: Social
- Employees
C30	Overall People Engagement Pulses (PEP) employee engagement score (out of 100)	-	Max
C31	% of H&M Group employees agreeing with the statement “I am treated with respect and dignity”	-	Max
C32	% female employees	-	Max
C33	% female employees in management positions	-	Max
C34	% female employees on board of directors	-	Max
C35	Total number of employees that participated in any I&D related training	-	Max
- Workers in our supply chains
C36	% tier 1 supplier factories with trade union representation	-	Max
C37	% tier 1 supplier factories with collective bargaining agreements	-	Max
C38	% of workers in our tier 1 production supply chain that are female	-	Max
C39	% of supervisors in our tier 1 production supply chain that are female	-	Max
C40	% of worker representatives in our tier 1 production supply chain that are female	-	Max
Factor group: Governance
- Supply chain management
C41	Tier 1 supplier factories participating in FEM (number and %)	-	Max
C42	Tier 2 supplier factories participating in FEM (number and %)	-	Max
C43	Tier 1 third-party verifications for FEM (and % of those participating)	-	Max
C44	Tier 2 third-party verifications for FEM (and % of those participating)	-	Max
C45	ETP functionality assessments achieving green grad	%	Max
C46	% of suppliers covered by Higg FSLM regarding H&M Group as a fair business partner	-	Max

MCDM approach

The decision matrix in the study, consisting of 46 criteria for 4 alternative years, encompasses various dimensions of sustainability and represents a multidimensional and complex evaluation structure. Although WASPAS and TOPSIS are not commonly used in the textile industry, they were chosen in this study because they offer a practical and reliable way to evaluate different options based on multiple criteria. WASPAS helps by combining two different evaluation approaches to give more balanced results, whereas TOPSIS identifies the best option by comparing how close each alternative is to the ideal one. Using both methods together provides stronger and more consistent results, which is important in a sector such as textiles where such decision-making tools are emerging application areas.

The methodological framework of the study is illustrated in Figure 1.

Figure 1.

Flowchart for the MCDM process.

Application steps of the WASPAS method

The WASPAS method was presented by Zavadskas et al.⁶⁷ and it is an approach that combines WSM and WPM. It can be used to solve dynamically changing problems. Alternatives are ranked based on the combined optimality criteria, but the method also ensures consistency in the rankings by performing sensitivity analysis as part of its process.⁶⁸ Its widespread use and rapid expansion are due to its simple and easy calculation, which gives relatively accurate results in ranking/selecting individual alternatives based on their performance against conflicting criteria.⁶⁹ The stages of the WASPAS algorithm are presented in Table 2.⁷⁰

Table 2.

WASPAS application steps.

Step	Equation	Equation number
Decision matrix	$[\begin{matrix} x_{11} & x_{12} & \dots & x_{1 n} \\ x_{21} & x_{22} & \dots & x_{2 n} \\ ⋮ & ⋮ & ⋱ & ⋮ \\ x_{m 1} & x_{m 2} & \dots & x_{m n} \end{matrix}]$	(1)
Normalization for benefit criterion	${\bar{x}}_{i j} = \frac{x_{i j}}{\max_{j} x_{i j}}$	(2)
Normalization for cost criterion	${\bar{x}}_{i j} = \frac{\min_{j} x_{i j}}{x_{i j}}$	(3)
Weighted normalized value for summation part	${\bar{x}}_{i j, s u m} = w_{j} {\bar{x}}_{i j}$	(4)
Weighted normalized value for multiplication part	${\bar{x}}_{i j, m u l t} = {\bar{x}}_{i j}^{w_{j}}$	(5)
Performance value	$W P S_{i} = (α) \sum_{j = 1}^{n} {\bar{x}}_{i j, s u m} + (1 - α) \prod_{j = 1}^{n} {\bar{x}}_{i j, m u l t}$	(6)

i, alternative, i = 1,2,3,. . .,m; j, factor, j = 1,2,3,. . .,n; x_ij, performance value; ${\bar{x}}_{i j}$ , normalized performance value; w_j, weight; ${\bar{x}}_{i j, s u m}$ , weighted normalized value for the summation part; ${\bar{x}}_{i j, m u l t}$ , weighted normalized value for the multiplication part; α, integration coefficient for summation and multiplication, 0 $\leq α \leq$ 1; WPS_i, performance value for alternative.

Application steps of TOPSIS method

TOPSIS was developed by Hwang Ching-Lai and Yoon in 1981.⁷¹ It is a practical and useful method for ranking and selecting a number of available alternatives by measuring Euclidean distances.⁷² This method analyzes the distances from the ideal and anti-ideal solution. The ideal solution maximizes the beneficial criteria and minimizes the non-beneficial criteria, whereas an anti-ideal solution maximizes the nonbeneficial criteria and minimizes the beneficial criteria or attributes. An ideal alternative is characterized by having the shortest distance to the ideal solution and the longest distance to the anti-ideal solution.⁷³ The entire procedure of the TOPSIS method consisting of several interlinking steps is presented in Table 3.⁷⁴

Table 3.

TOPSIS process.

Step	Equation	Equation number
Decision matrix	$D = [\begin{matrix} x_{11} & x_{12} & \dots & x_{1 n} \\ x_{21} & x_{22} & \dots & x_{2 n} \\ \dots & \dots & \dots & \dots \\ x_{m 1} & x_{m 2} & \dots & x_{m n} \end{matrix}]$	(7)
Normalization	$r_{i j} = \frac{x_{i j}}{\sqrt{\sum_{i = 1}^{m} x_{i j}^{2}}}$	(8)
Weighted normalization	$v_{i j} = w_{j} * r_{i j}$	(9)
Positive ideal solution for benefit criterion	$v_{j}^{+} = \max_{i} v_{i j}$	(10)
Positive ideal solution for cost criterion	$v_{j}^{+} = \min_{i} v_{i j}$	(11)
Negative ideal solution for benefit criterion	$v_{j}^{-} = \min_{i} v_{i j}$	(12)
Negative ideal solution for cost criterion	$v_{j}^{-} = \max_{i} v_{i j}$	(13)
Distance from PIS	$S_{i}^{+} = \sqrt{\sum_{j = 1}^{n} {(v_{i j} - v_{j}^{+})}^{2}}$	(14)
Distance from NIS	$S_{i}^{-} = \sqrt{\sum_{j = 1}^{n} {(v_{i j} - v_{j}^{-})}^{2}}$	(15)
Closeness coefficient	$C_{i} = \frac{S_{i}^{-}}{S_{i}^{-} + S_{i}^{+}}$	(16)

r_ij, normalized value; v_ij, weighted normalized value; v_j⁺, positive ideal solution (PIS); v_j^–, negative ideal solution (NIS); S_i⁺, distance from PIS; S_i^–, distance from NIS; C_i, closeness coefficient.

Implementation of decision models

WASPAS calculations

In the application phase, the WASPAS and TOPSIS methods are used to evaluate the progress in sustainability in terms of years. The values of the criteria identified for use in the initial decision matrix of both methods for the corresponding years are presented in the Table 4.

Table 4.

Decision matrix.

C	2020	2021	2022	2023	C	2020	2021	2022	2023
C1	90	95	92	94	C24	0.03	0.02	0.05	0.03
C2	17	17	23	29	C25	18,800	15,944	14,768	16,855
C3	5,171,889	4,994,444	4,971,267	4,340,035	C26	55	55	65	68
C4	258,458	110,290	91,008	81,733	C27	40	40	22	24
C5	4,641,545	4,575,183	4,607,283	4,007,517	C28	5	5	8	8
C6	271,886	308,971	272,975	250,785	C29	92	92	92	90
C7	553	609	619	577	C30	75	76	76	76
C8	88	95	97	97	C31	80	83	84	82
C9	99.9	99.9	99.9	99.5	C32	74	74	75	74
C10	65	80	84	85	C33	71	71	75	64
C11	6	8	23	25	C34	67	55	55	55
C12	59	62	61	60	C35	13,713	56,618	36,192	34,561
C13	100	100	100	100	C36	32	37	42	37
C14	2	7	11	11	C37	18	27	34	29
C15	20	22	14	13	C38	63	63	62	63
C16	78	71	75	76	C39	24	28	27	27
C17	21	64	74	79	C40	59	62	63	65
C18	12	44	57	66	C41	1,184	894	858	744
C19	13	95	99	99.5	C42	194	337	509	530
C20	100	100	100	100	C43	36	98	99	98
C21	100	100	100	100	C44	46	98	99	98
C22	44	51	75	83	C45	95	88	90	89
C23	0.32	0.42	0.64	0.40	C46	96	98	89	90

For the next step of the method, the entries in the decision matrix are normalized using Equations (2) and (3). Table 5 lists the normalized decision matrix for the WASPAS method. Benefit criteria are normalized by dividing each value by the maximum value in its column, whereas cost criteria are normalized by dividing the minimum value by each value. This ensures that all criteria are adjusted to a comparable scale based on their nature.

Table 5.

Normalized values, Equations (2) and (3).

C	2020	2021	2022	2023	C	2020	2021	2022	2023
C1	0.9474	1.0000	0.9684	0.9895	C24	0.6667	1.0000	0.4000	0.6667
C2	0.5862	0.5862	0.7931	1.0000	C25	1.0000	0.8481	0.7855	0.8965
C3	0.8392	0.8690	0.8730	1.0000	C26	0.8088	0.8088	0.9559	1.0000
C4	0.3162	0.7411	0.8981	1.0000	C27	1.0000	1.0000	0.5500	0.6000
C5	0.8634	0.8759	0.8698	1.0000	C28	1.0000	1.0000	0.6250	0.6250
C6	0.9224	0.8117	0.9187	1.0000	C29	1.0000	1.0000	1.0000	0.9783
C7	0.8934	0.9838	1.0000	0.9321	C30	0.9868	1.0000	1.0000	1.0000
C8	0.9072	0.9794	1.0000	1.0000	C31	0.9524	0.9881	1.0000	0.9762
C9	1.0000	1.0000	1.0000	0.9960	C32	0.9867	0.9867	1.0000	0.9867
C10	0.7647	0.9412	0.9882	1.0000	C33	0.9467	0.9467	1.0000	0.8533
C11	0.2400	0.3200	0.9200	1.0000	C34	1.0000	0.8209	0.8209	0.8209
C12	0.9516	1.0000	0.9839	0.9677	C35	0.2422	1.0000	0.6392	0.6104
C13	1.0000	1.0000	1.0000	1.0000	C36	0.7619	0.8810	1.0000	0.8810
C14	0.1818	0.6364	1.0000	1.0000	C37	0.5294	0.7941	1.0000	0.8529
C15	0.9091	1.0000	0.6364	0.5909	C38	1.0000	1.0000	0.9841	1.0000
C16	1.0000	0.9103	0.9615	0.9744	C39	0.8571	1.0000	0.9643	0.9643
C17	0.2658	0.8101	0.9367	1.0000	C40	0.9077	0.9538	0.9692	1.0000
C18	0.1818	0.6667	0.8636	1.0000	C41	1.0000	0.7551	0.7247	0.6284
C19	0.1307	0.9548	0.9950	1.0000	C42	0.3660	0.6358	0.9604	1.0000
C20	1.0000	1.0000	1.0000	1.0000	C43	0.3636	0.9899	1.0000	0.9899
C21	1.0000	1.0000	1.0000	1.0000	C44	0.4646	0.9899	1.0000	0.9899
C22	0.5301	0.6145	0.9036	1.0000	C45	1.0000	0.9263	0.9474	0.9368
C23	0.5000	0.6563	1.0000	0.6250	C46	0.9796	1.0000	0.9082	0.9184

After the normalization process, the total relative importance value for each alternative is first calculated using the WSM with Equation (4) and then using the WPM with Equation (5), as presented in Tables 6 and 7, respectively.

Table 6.

Weighted normalized values for the summation part, Equation (4).

C	2020	2021	2022	2023	C	2020	2021	2022	2023
C1	0.0206	0.0217	0.0211	0.0215	C24	0.0145	0.0217	0.0087	0.0145
C2	0.0127	0.0127	0.0172	0.0217	C25	0.0217	0.0184	0.0171	0.0195
C3	0.0182	0.0189	0.0190	0.0217	C26	0.0176	0.0176	0.0208	0.0217
C4	0.0069	0.0161	0.0195	0.0217	C27	0.0217	0.0217	0.0120	0.0130
C5	0.0188	0.0190	0.0189	0.0217	C28	0.0217	0.0217	0.0136	0.0136
C6	0.0201	0.0176	0.0200	0.0217	C29	0.0217	0.0217	0.0217	0.0213
C7	0.0194	0.0214	0.0217	0.0203	C30	0.0215	0.0217	0.0217	0.0217
C8	0.0197	0.0213	0.0217	0.0217	C31	0.0207	0.0215	0.0217	0.0212
C9	0.0217	0.0217	0.0217	0.0217	C32	0.0214	0.0214	0.0217	0.0214
C10	0.0166	0.0205	0.0215	0.0217	C33	0.0206	0.0206	0.0217	0.0186
C11	0.0052	0.0070	0.0200	0.0217	C34	0.0217	0.0178	0.0178	0.0178
C12	0.0207	0.0217	0.0214	0.0210	C35	0.0053	0.0217	0.0139	0.0133
C13	0.0217	0.0217	0.0217	0.0217	C36	0.0166	0.0192	0.0217	0.0192
C14	0.0040	0.0138	0.0217	0.0217	C37	0.0115	0.0173	0.0217	0.0185
C15	0.0198	0.0217	0.0138	0.0128	C38	0.0217	0.0217	0.0214	0.0217
C16	0.0217	0.0198	0.0209	0.0212	C39	0.0186	0.0217	0.0210	0.0210
C17	0.0058	0.0176	0.0204	0.0217	C40	0.0197	0.0207	0.0211	0.0217
C18	0.0040	0.0145	0.0188	0.0217	C41	0.0217	0.0164	0.0158	0.0137
C19	0.0028	0.0208	0.0216	0.0217	C42	0.0080	0.0138	0.0209	0.0217
C20	0.0217	0.0217	0.0217	0.0217	C43	0.0079	0.0215	0.0217	0.0215
C21	0.0217	0.0217	0.0217	0.0217	C44	0.0101	0.0215	0.0217	0.0215
C22	0.0115	0.0134	0.0196	0.0217	C45	0.0217	0.0201	0.0206	0.0204
C23	0.0109	0.0143	0.0217	0.0136	C46	0.0213	0.0217	0.0197	0.0200

Table 7.

Weighted normalized values for the multiplication part, Equation (5).

C	2020	2021	2022	2023	C	2020	2021	2022	2023
C1	0.9988	1.0000	0.9993	0.9998	C24	0.9912	1.0000	0.9803	0.9912
C2	0.9885	0.9885	0.9950	1.0000	C25	1.0000	0.9964	0.9948	0.9976
C3	0.9962	0.9970	0.9971	1.0000	C26	0.9954	0.9954	0.9990	1.0000
C4	0.9753	0.9935	0.9977	1.0000	C27	1.0000	1.0000	0.9871	0.9890
C5	0.9968	0.9971	0.9970	1.0000	C28	1.0000	1.0000	0.9898	0.9898
C6	0.9982	0.9955	0.9982	1.0000	C29	1.0000	1.0000	1.0000	0.9995
C7	0.9976	0.9996	1.0000	0.9985	C30	0.9997	1.0000	1.0000	1.0000
C8	0.9979	0.9995	1.0000	1.0000	C31	0.9989	0.9997	1.0000	0.9995
C9	1.0000	1.0000	1.0000	0.9999	C32	0.9997	0.9997	1.0000	0.9997
C10	0.9942	0.9987	0.9997	1.0000	C33	0.9988	0.9988	1.0000	0.9966
C11	0.9695	0.9755	0.9982	1.0000	C34	1.0000	0.9957	0.9957	0.9957
C12	0.9989	1.0000	0.9996	0.9993	C35	0.9696	1.0000	0.9903	0.9893
C13	1.0000	1.0000	1.0000	1.0000	C36	0.9941	0.9972	1.0000	0.9972
C14	0.9636	0.9902	1.0000	1.0000	C37	0.9863	0.9950	1.0000	0.9965
C15	0.9979	1.0000	0.9902	0.9886	C38	1.0000	1.0000	0.9997	1.0000
C16	1.0000	0.9980	0.9991	0.9994	C39	0.9967	1.0000	0.9992	0.9992
C17	0.9716	0.9954	0.9986	1.0000	C40	0.9979	0.9990	0.9993	1.0000
C18	0.9636	0.9912	0.9968	1.0000	C41	1.0000	0.9939	0.9930	0.9900
C19	0.9567	0.9990	0.9999	1.0000	C42	0.9784	0.9902	0.9991	1.0000
C20	1.0000	1.0000	1.0000	1.0000	C43	0.9782	0.9998	1.0000	0.9998
C21	1.0000	1.0000	1.0000	1.0000	C44	0.9835	0.9998	1.0000	0.9998
C22	0.9863	0.9895	0.9978	1.0000	C45	1.0000	0.9983	0.9988	0.9986
C23	0.9850	0.9909	1.0000	0.9898	C46	0.9996	1.0000	0.9979	0.9982

In the WASPAS method, the effect of α is examined to improve the accuracy and efficiency of the decision-making process. The effect of α on the ranking is calculated using Equation (6), and the performance values and the corresponding rankings are presented in Tables 8 and 9.

Table 8.

Performance values, Equation (6).

α	2020	2021	2022	2023
0	0.6700	0.8671	0.8938	0.9068
0.1	0.6786	0.8688	0.8952	0.9079
0.2	0.6871	0.8706	0.8965	0.9091
0.3	0.6957	0.8723	0.8979	0.9103
0.4	0.7042	0.8740	0.8993	0.9115
0.5	0.7128	0.8758	0.9006	0.9126
0.6	0.7213	0.8775	0.9020	0.9138
0.7	0.7298	0.8792	0.9034	0.9150
0.8	0.7384	0.8809	0.9048	0.9162
0.9	0.7469	0.8827	0.9061	0.9173
1	0.7555	0.8844	0.9075	0.9185

Table 9.

Ranks.

α	2020	2021	2022	2023
0	4	3	2	1
0.1	4	3	2	1
0.2	4	3	2	1
0.3	4	3	2	1
0.4	4	3	2	1
0.5	4	3	2	1
0.6	4	3	2	1
0.7	4	3	2	1
0.8	4	3	2	1
0.9	4	3	2	1
1	4	3	2	1

The coefficient specified here is a parameter that determines the influence of the summation and multiplication parts in the WASPAS method. To ensure an equal reflection of both parts’ effects, this coefficient is commonly set to 0.5. The highest performance score among these values indicates the optimal alternative according to the WASPAS method.⁷⁵

According to the results obtained through the WASPAS method, the year 2023 was identified as the optimal alternative in terms of overall sustainability performance based on the sustainability criteria considered in the study.

TOPSIS calculations

Following the construction of the decision matrix (Table 4) for the first stage of the TOPSIS method, the normalized decision matrix (r_ij) is calculated using Equation (8) and presented in Table 10.

Table 10.

Normalized values, Equation (8).

C	2020	2021	2022	2023	C	2020	2021	2022	2023
C1	0.4851	0.5120	0.4959	0.5066	C24	0.4376	0.2917	0.7293	0.4376
C2	0.3852	0.3852	0.5211	0.6571	C25	0.5643	0.4786	0.4433	0.5059
C3	0.5299	0.5118	0.5094	0.4447	C26	0.4506	0.4506	0.5325	0.5571
C4	0.8433	0.3599	0.2970	0.2667	C27	0.6129	0.6129	0.3371	0.3677
C5	0.5197	0.5123	0.5159	0.4487	C28	0.3748	0.3748	0.5996	0.5996
C6	0.4909	0.5578	0.4928	0.4528	C29	0.5027	0.5027	0.5027	0.4918
C7	0.4686	0.5160	0.5245	0.4889	C30	0.4950	0.5016	0.5016	0.5016
C8	0.4665	0.5036	0.5142	0.5142	C31	0.4862	0.5045	0.5106	0.4984
C9	0.5005	0.5005	0.5005	0.4985	C32	0.4983	0.4983	0.5050	0.4983
C10	0.4119	0.5069	0.5323	0.5386	C33	0.5045	0.5045	0.5330	0.4548
C11	0.1694	0.2259	0.6495	0.7060	C34	0.5753	0.4722	0.4722	0.4722
C12	0.4875	0.5123	0.5040	0.4958	C35	0.1786	0.7372	0.4713	0.4500
C13	0.5000	0.5000	0.5000	0.5000	C36	0.4305	0.4977	0.5650	0.4977
C14	0.1164	0.4076	0.6404	0.6404	C37	0.3259	0.4889	0.6156	0.5251
C15	0.5659	0.6225	0.3961	0.3678	C38	0.5020	0.5020	0.4940	0.5020
C16	0.5197	0.4731	0.4997	0.5064	C39	0.4521	0.5275	0.5086	0.5086
C17	0.1647	0.5020	0.5804	0.6197	C40	0.4736	0.4977	0.5057	0.5218
C18	0.1219	0.4471	0.5792	0.6706	C41	0.6337	0.4785	0.4592	0.3982
C19	0.0765	0.5589	0.5824	0.5853	C42	0.2333	0.4054	0.6122	0.6375
C20	0.5000	0.5000	0.5000	0.5000	C43	0.2068	0.5629	0.5687	0.5629
C21	0.5000	0.5000	0.5000	0.5000	C44	0.2607	0.5555	0.5612	0.5555
C22	0.3370	0.3906	0.5744	0.6356	C45	0.5246	0.4860	0.4970	0.4915
C23	0.3474	0.4560	0.6948	0.4343	C46	0.5143	0.5250	0.4768	0.4822

Equal weights were assigned to all criteria. Subsequently, using Equation (9), each element in the normalized decision matrix was multiplied by the corresponding weight value to obtain the weighted normalized decision matrix, which is presented in Table 11.

Table 11.

Weighted normalized values, Equation (9).

C	2020	2021	2022	2023	C	2020	2021	2022	2023
C1	0.0105	0.0111	0.0108	0.0110	C24	0.0095	0.0063	0.0159	0.0095
C2	0.0084	0.0084	0.0113	0.0143	C25	0.0123	0.0104	0.0096	0.0110
C3	0.0115	0.0111	0.0111	0.0097	C26	0.0098	0.0098	0.0116	0.0121
C4	0.0183	0.0078	0.0065	0.0058	C27	0.0133	0.0133	0.0073	0.0080
C5	0.0113	0.0111	0.0112	0.0098	C28	0.0081	0.0081	0.0130	0.0130
C6	0.0107	0.0121	0.0107	0.0098	C29	0.0109	0.0109	0.0109	0.0107
C7	0.0102	0.0112	0.0114	0.0106	C30	0.0108	0.0109	0.0109	0.0109
C8	0.0101	0.0109	0.0112	0.0112	C31	0.0106	0.0110	0.0111	0.0108
C9	0.0109	0.0109	0.0109	0.0108	C32	0.0108	0.0108	0.0110	0.0108
C10	0.0090	0.0110	0.0116	0.0117	C33	0.0110	0.0110	0.0116	0.0099
C11	0.0037	0.0049	0.0141	0.0153	C34	0.0125	0.0103	0.0103	0.0103
C12	0.0106	0.0111	0.0110	0.0108	C35	0.0039	0.0160	0.0102	0.0098
C13	0.0109	0.0109	0.0109	0.0109	C36	0.0094	0.0108	0.0123	0.0108
C14	0.0025	0.0089	0.0139	0.0139	C37	0.0071	0.0106	0.0134	0.0114
C15	0.0123	0.0135	0.0086	0.0080	C38	0.0109	0.0109	0.0107	0.0109
C16	0.0113	0.0103	0.0109	0.0110	C39	0.0098	0.0115	0.0111	0.0111
C17	0.0036	0.0109	0.0126	0.0135	C40	0.0103	0.0108	0.0110	0.0113
C18	0.0027	0.0097	0.0126	0.0146	C41	0.0138	0.0104	0.0100	0.0087
C19	0.0017	0.0121	0.0127	0.0127	C42	0.0051	0.0088	0.0133	0.0139
C20	0.0109	0.0109	0.0109	0.0109	C43	0.0045	0.0122	0.0124	0.0122
C21	0.0109	0.0109	0.0109	0.0109	C44	0.0057	0.0121	0.0122	0.0121
C22	0.0073	0.0085	0.0125	0.0138	C45	0.0114	0.0106	0.0108	0.0107
C23	0.0076	0.0099	0.0151	0.0094	C46	0.0112	0.0114	0.0104	0.0105

At this stage, the positive and negative ideal solutions (PIS and NIS) are determined using Equations (10)–(13) and the results are presented in Table 12.

Table 12.

PIS and NIS values, Equations (10)–(13).

C	PIS	NIS	C	PIS	NIS
C1	0.0111	0.0105	C24	0.0063	0.0159
C2	0.0143	0.0084	C25	0.0123	0.0096
C3	0.0097	0.0115	C26	0.0121	0.0098
C4	0.0058	0.0183	C27	0.0133	0.0073
C5	0.0098	0.0113	C28	0.0081	0.0130
C6	0.0098	0.0121	C29	0.0109	0.0107
C7	0.0114	0.0102	C30	0.0109	0.0108
C8	0.0112	0.0101	C31	0.0111	0.0106
C9	0.0109	0.0108	C32	0.0110	0.0108
C10	0.0117	0.0090	C33	0.0116	0.0099
C11	0.0153	0.0037	C34	0.0125	0.0103
C12	0.0111	0.0106	C35	0.0160	0.0039
C13	0.0109	0.0109	C36	0.0123	0.0094
C14	0.0139	0.0025	C37	0.0134	0.0071
C15	0.0135	0.0080	C38	0.0109	0.0107
C16	0.0113	0.0103	C39	0.0115	0.0098
C17	0.0135	0.0036	C40	0.0113	0.0103
C18	0.0146	0.0027	C41	0.0138	0.0087
C19	0.0127	0.0017	C42	0.0139	0.0051
C20	0.0109	0.0109	C43	0.0124	0.0045
C21	0.0109	0.0109	C44	0.0122	0.0057
C22	0.0138	0.0073	C45	0.0114	0.0106
C23	0.0151	0.0076	C46	0.0114	0.0104

In the TOPSIS method, the Euclidean distance approach is utilized to determine the distances of each alternative’s criterion value from the PIS and NIS sets. In calculating the relative closeness (C_i^*) of each alternative to the ideal solution, the distance from the PIS and NIS are utilized. Distance from PIS and NIS, closeness coefficient [Equations (14)–(16)] and rankings of the alternatives are given in Table 13.

Table 13.

Distance from PIS, distance from NIS, closeness coefficient, and ranks, Equations (14)–(16).

Alternative	$S_{i}^{+}$	$S_{i}^{-}$	$C_{i}$	Rank
2020	0.0365	0.0127	0.2586	4
2021	0.0182	0.0291	0.6149	3
2022	0.0161	0.0325	0.6689	2
2023	0.0144	0.0342	0.7033	1

In the final stage of the TOPSIS method, the calculated C_i values range between 0 and 1, with values closer to 1 indicating that the alternative best meets the expected conditions.⁷⁶ According to the results, the year 2023 represents better performance among the alternative years. The ranking results obtained from both methods are presented together in the graph shown in Figure 2.

Figure 2.

Comparison of WASPAS and TOPSIS rankings over years.

Discussion

The findings of this study provide critical insights into the assessment of sustainability performance in the textile industry using the WASPAS and TOPSIS methods. The analysis, based on sustainability reports from a global textile company, highlights a clear trend of improving sustainability performance over the evaluated years, with 2023 emerging as the best-performing year. This improvement reflects the growing importance of sustainability within the industry and the increasing adoption of practices that align with global sustainability standards.

The highest sustainability performance observed in 2023 can be attributed to the strengthening orientation toward sustainability within the textile sector, shaped by evolving regulatory frameworks and increasing stakeholder expectations. This indicates that firms have restructured their strategic priorities around sustainability and implemented tangible improvements reflected in performance metrics. Accordingly, for companies in the textile industry, adopting more transparent reporting practices, cleaner production technologies, and circular economy strategies is critical to maintaining or enhancing sustainability performance over time.

One of the key takeaways from this research is the effectiveness of MCDM methods in assessing sustainability performance. The application of both WASPAS and TOPSIS methodologies ensures robustness and consistency in the evaluation, as demonstrated by the alignment of their ranking results. This dual-method approach mitigates potential biases and errors associated with single-method evaluations, providing a more comprehensive assessment of sustainability performance.

Similar studies have also shown that MCDM methods are effective in assessing sustainability in the textile sector.^77-80 For example, analyses using different types of MCDM methods have contributed to the development of more structured sustainability strategies in companies. This highlights the importance of integrating MCDM approaches into decision-making and performance evaluating processes in industries such as textiles.

The study also underscores the significance of data-driven decision-making in the textile sector. By leveraging sustainability reports, companies can track their progress, identify areas for improvement, and align their operations with regulatory requirements and stakeholder expectations. The ability to quantify sustainability performance through well-defined indicators enables firms to set achievable sustainability targets and monitor their progress effectively.

Moreover, the research contributes to the broader discourse on sustainable development by showcasing how SR can serve as a valuable tool for performance assessment. As companies face increasing pressure from regulators, consumers, and investors to adopt sustainable practices, transparent and detailed SR becomes a critical factor in maintaining corporate credibility and competitiveness.

Conclusion

This study has highlighted the applicability of MCDM techniques in evaluating sustainability performance in the textile industry. The use of MCDM tools with data derived from sustainability reports provides an objective and systematic approach to performance evaluation. The findings highlight the positive trajectory of sustainability efforts within the sector and the role of systematic evaluation in fostering continuous improvement. As the industry advances toward a responsibility framework encompassing all dimensions of sustainability, adopting comprehensive and objective assessment methods will be crucial for ensuring long-term sustainability and competitiveness. The integration of sustainability metrics into decision-making models not only facilitates a more structured evaluation but also enables companies to align their strategies with global sustainability goals, such as the UN’s SDGs. These methods support companies in achieving sustainability goals, making strategic decisions, and gaining competitive advantages, while also establishing a scientific foundation for building a more sustainable future.

This study is limited to the data of a single company; however, the applicability of the model to multicompany comparisons or the analysis of sustainability performance in the textile sector across different geographical regions could be considered a valuable direction for future research. Another limitation of this study concerns the predefined set of sustainability criteria. Future research could extend the analysis by incorporating a broader range of sustainability indicators to evaluate the performance of other globally operating textile companies and to monitor their progress over time. In addition, in the context of criteria weighting, future studies focusing on a specific sustainability domain could incorporate expert evaluations to determine the relative importance of the criteria, which may then be systematically integrated into the applied decision-making methods. In addition, integrating other MCDM methods could provide deeper insights and further validate the robustness of sustainability assessments.

Footnotes

Declaration of conflicting interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Eda Acar

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

Sarker

Ali

Paul

Munim

. Measuring sustainability performance using an integrated model. Measurement 2021; 184: 109931.

Appiah-Kubi

. Management knowledge and sustainability reporting in SMEs: The role of perceived benefit and stakeholder pressure. J Cleaner Prod 2024; 434: 140067.

WCED. World Commission on Environment and Development. Brundtland Commission. Our Common Future. Oxford: Oxford University Press, 1987.

Calabrese

Costa

Ghiron

Menichini

. Materiality analysis in sustainability reporting: a method for making it work in practice. Eur J Sustain Dev 2017; 6: 439.

Tolentino-Zondervan

DiVito

. Sustainability performance of Dutch firms and the role of digitalization: The case of textile and apparel industry. J Cleaner Prod 2024; 459: 142573.

Saygılı

Yargı

. An analysis of sustainability disclosures of textile and apparel companies in Turkey. Text Apparel 2019; 29: 189.

Ukoh

Nduokafor

Nworie

. Sustainability Reporting among Oil and Gas Firms: A Strategic Tool for Enhanced Firm Value. Int J Econ Financial Manage 2024; 9: 144.

Bananuka

Tauringana

Tumwebaze

. Intellectual capital and sustainability reporting practices in Uganda. J Intellect Capital 2023; 24: 487.

Khatri

Kjærland

. Sustainability reporting practices and environmental performance amongst nordic listed firms. J Cleaner Prod 2023; 418: 138172.

10.

Roszkowska-Menkes

Aluchna

Kamiński

. True transparency or mere decoupling? The study of selective disclosure in sustainability reporting. Crit Perspect Account 2024; 98: 102700.

11.

Al Hawaj

Buallay

. A worldwide sectorial analysis of sustainability reporting and its impact on firm performance. J Sustain Finance Invest 2022; 12: 62.

12.

Almashhadani

. The Impact of Sustainability Reporting on Promoting Firm performance. Int J Business Manage Invent 2023; 12: 101.

13.

Alhawaj

Buallay

Abdallah

. Sustainability reporting and energy sectorial performance: developed and emerging economies. Int J Energy Sect Manage 2023; 17: 739.

14.

Abbas

Mehmood

Ali

Aman-Ullah

. Sustainability reporting and corporate financial performance of IPOs: witnessing emerging market. Environ Sci Pollut Res 2023; 30: 85508.

15.

Buallay

. Sustainability reporting and retail sector performance: worldwide evidence. Int Rev Retail Distrib Consum Res 2022; 32: 311.

16.

Sahoo

Goswami

. A comprehensive review of multiple criteria decision-making (MCDM) Methods: advancements, applications, and future directions. Decis Making Adv 2023; 1: 25.

17.

Chen

Wang

Mardani

. An integrated MCDM framework for evaluating the environmental, social, and governance (ESG) sustainable business performance. Ann Operat Res 2024; 342: 987.

18.

Wang

Yang

TMN

Nguyen

VTT

Singh

. Enhancing efficiency and cost-effectiveness: A groundbreaking bi-algorithm MCDM approach. Appl Sci 2023; 13: 9105.

19.

Solanki

Sarkar

Shah

. Evaluation of factors affecting the effective implementation of Internet of Things and cloud computing in the construction industry through WASPAS and TOPSIS methods. Int J Constr Manage 2024; 24: 226.

20.

Acar

Kılıç

Güner

. Measurement of sustainability performance in textile industry by using a multi-criteria decision making method. Text Apparel 2015; 25: 3.

21.

Singh

. Entropy weighted WASPAS and MACBETH approaches for optimizing the performance of solar water heating system. Case Stud Therm Eng 2024; 53: 103922.

22.

Ayvaz

Tatar

Sağır

Pamucar

. An integrated Fine-Kinney risk assessment model utilizing Fermatean fuzzy AHP-WASPAS for occupational hazards in the aquaculture sector. Process Saf Environ Prot 2024; 186: 232.

23.

Menekşe

Akdağ

. Medical waste disposal planning for healthcare units using spherical fuzzy CRITIC-WASPAS. Appl Soft Comput 2023; 144: 110480.

24.

Kaya

Kundu

Görçün

ÖF

. Evaluation of container port sustainability using WASPAS technique using on type-2 neutrosophic fuzzy numbers. Mar Pollut Bull 2023; 190: 114849.

25.

Huang

Solangi

Magazzino

Solangi

. Evaluating the efficiency of green innovation and marketing strategies for long-term sustainability in the context of Environmental labeling. J Cleaner Prod 2024; 450: 141870.

26.

Dehshiri

SJH

Amiri

Mostafaeipour

. Evaluation of renewable energy projects based on sustainability goals using a hybrid pythagorean fuzzy-based decision approach. Energy 2024; 297: 131272.

27.

Solangi

Jianguo

. Examining challenges and solutions for environmental and natural resource management with a focus on mineral resources. Resources Policy 2023; 86: 104085.

28.

Akgün

Yapıcı

Özkan

Günkaya

Banar

. A combined multi-criteria decision-making approach for the selection of carbon-based nanomaterials in phase change materials. J Energy Storage 2023; 60: 106619.

29.

Ocampo

Cabigas

Jones

Labib

. An integrated three-way decision methodology for sustainability of wastewater circularity in thermal power plants. Appl Soft Comput 2024; 151: 111111.

30.

Husain

Hasan

Khan

Asjad

. A robust decision-making approach for the selection of an optimal renewable energy source in India. Energy Convers Manage 2024; 301: 117989.

31.

Dehshiri

SSH

Firoozabadi

. A new multi-criteria decision making approach based on wins in league to avoid rank reversal: A case study on prioritizing environmental deterioration strategies in arid urban areas. J Cleaner Prod 2023; 383: 135438.

32.

Zolfani

Mosharafiandehkordi

Kutut

. A pre-planning for hotel locating according to the sustainability perspective based on BWM-WASPAS approach. Int J Strat Prop Manage 2019; 23: 405.

33.

Meng

Shaikh

. Evaluating environmental, social, and governance criteria and green finance investment strategies using fuzzy AHP and fuzzy WASPAS. Sustainability 2023; 15: 6786.

34.

Turskis

Goranin

Nurusheva

Boranbayev

. A fuzzy WASPAS-based approach to determine critical information infrastructures of EU sustainable development. Sustainability 2019; 11: 424.

35.

Jayant

Chandan

Singh

. Sustainable supplier selection for battery manufacturing industry: A MOORA and WASPAS Based Approach.J Phys Conf Ser 2019; 1240: 1.

36.

Bathrinath

Mohan

Koppiahraj

Bhalaji

RKA

Santhi

. Analysis of factors affecting sustainable performance in construction sites using fuzzy AHP-WASPAS methods. Mater Today Proc 2022; 62: 3118.

37.

Keshavarz-Ghorabaee

Govindan

Amiri

Zavadskas

Antucheviciene

. An integrated type-2 fuzzy decision model based on WASPAS and SECA for evaluation of sustainable manufacturing strategies. J Environ Eng Landscape Manage 2019; 27: 187.

38.

Ighravwe

Oke

. A multi-criteria decision-making framework for selecting a suitable maintenance strategy for public buildings using sustainability criteria. J Build Eng 2019; 24: 100753.

39.

Klumbytė

Bliūdžius

Medineckienė

Fokaides

. An MCDM model for sustainable decision-making in municipal residential buildings facilities management. Sustainability 2021; 13: 2820.

40.

Pattnaik

Dangayach

. Sustainability of textile waste-water management by using an integrated fuzzy AHP-TOPSIS method: a case study. Int J Environ Sustain Dev 2021; 20: 105.

41.

Lombardi Netto

Salomon

Ortiz-Barrios

Florek-Paszkowska

Petrillo

De Oliveira

. Multiple criteria assessment of sustainability programs in the textile industry. Int Trans Oper Res 2021; 28: 1550.

42.

Gonca

ŞG

Çağdaş

. Performance evaluation of sustainability in a textile firm via multi-criteria decision-making method. Industria Textila 2023; 74: 426.

43.

Bathrinath

Bhalaji

RKA

Saravanasankar

. Risk analysis in textile industries using AHP-TOPSIS. Mater Today Proc 2021; 45: 1257.

44.

Ersoy

. AHP ve TOPSIS yöntemleri kullanılarak tekstil sektöründe personel seçimi. Kafdağı 2021; 6: 60.

45.

Hadizadeh

Khodaparast

Ghasemi

Fakhrzad

. Designing an Anti-fragile Supply Chain in the Textile Industry under Conditions of Uncertainty Using the Fuzzy BWM and TOPSIS. J Text Polym 2023; 11: 3.

46.

Akgül

Bahtiyari

Kizilkaya Aydoğan

Benli

. Use of TOPSIS method for designing different textile products in coloration via natural source “Madder”. J Nat Fibers 2022; 19: 8993.

47.

Mishra

Pundir

Ganapathy

. Evaluation and prioritisation of manufacturing flexibility alternatives usin g integrated AHP and TOPSIS method: Evidence from a fashion apparel firm. Benchmarking: Int J 2017; 24: 1437.

48.

Tetteh

. Delphi, Entropy and TOPSIS Analysis of Political, Economic and Cultural Characteristics that Affect Textile and Apparel Industry. World Acad J Business Appl Sci 2013; 50-61.

49.

Silobrit

Jureviciene

. Assessing circular textile ındustry development. Econ Cult 2023; 20: 55.

50.

Yen

PTH

Tien-Chin

Hoa

NTH

Anh

NTBN

. An evaluation of financial performance of Vietnam textile and apparel industry using the entropy-TOPSIS method. J Int Econ Manage 2023; 23: 14.

51.

Chen

. Selection of cotton fabrics using Pythagorean fuzzy TOPSIS approach. J Nat Fibers 2022; 19: 9085.

52.

Bansal

Sikka

Choudhary

. Optimization of parameters for needle cut index using TOPSIS method. Indian J Fibre Text Res 2022; 46: 319.

53.

Anbarkhan

. A fuzzy-TOPSIS-based approach to assessing sustainability in software engineering: an industry 5.0 perspective. Sustainability 2023; 15: 13844.

54.

al-Sulbi

Chaurasia

Attaallah

, et al. A fuzzy TOPSIS-based approach for comprehensive evaluation of bio-medical waste management: advancing sustainability and decision-making. Sustainability 2023; 15: 12565.

55.

Šiožinytė

Antuchevičienė

. Solving the problems of daylighting and tradition continuity in a reconstructed vernacular building. J Civ Eng Manage 2013; 19: 873.

56.

Miç

Antmen

. A decision-making model based on TOPSIS, WASPAS, and MULTIMOORA methods for university location selection problem. Sage Open 2021; 11: 21582440211040115.

57.

Xiong

Zhong

Liu

Zhang

. An approach for resilient-green supplier selection based on WASPAS, BWM, and TOPSIS under intuitionistic fuzzy sets. Math Prob Eng 2020; 2020: 1761893.

58.

Shivade

Sapkal

. Selection of optimum plant layout using AHP-TOPSIS and WASPAS approaches coupled with Entropy method. Decis Sci Lett 2022; 11: 545.

59.

Pratama

Iswara

Nababan

. Decision support system in determining the achievement of Widyaiswara value using WASPAS and TOPSIS algorithm. In: 2022 1st IEEE International Conference on Technology Innovation and Its Applications (ICTIIA), Indonesia, September 2022.

60.

Dehshiri

SJH

. Sustainable supplier selection based on a comparative decision-making approach under uncertainty. Spect Operat Res 2026; 3: 238.

61.

Maden

. Low-carbon supplier selection using a scenario-based ıntegrated FCM-EDAS approach: A textile company case study. Int J Fuzzy Syst 2025; 1-20.

62.

Pamucar

Ulutaş

Topal

Karamaşa

Ecer

. Fermatean fuzzy framework based on preference selection index and combined compromise solution methods for green supplier selection in textile industry. Int J Syst Sci Operat Logist 2024; 11: 2319786.

63.

Phan Ha

Nguyen

STQ

. Sustainable supplier selection in the apparel industry: an integrated AHP-TOPSIS model for multi-criteria decision analysis. Res J Text Apparel 2024. https://doi.org/10.1108/RJTA-04-2024-0056

64.

Sagnak

Berberoglu

Kazancoglu

. Sustainable performance assessment of textile and apparel ındustry in a circular context. Sustainable Manufacturing Practices in the Textiles and Fashion Sector. Cham: Springer Nature Switzerland, 2024, p. 199.

65.

Gbolarumi

Wong

. Assessment of environmental performance criteria in textile industry using the best-worst method. In: The International Workshop on Best-Worst Method. Cham: Springer International Publishing, 2021; p. 160.

66.

H&M Group. Sustainability Disclosure 2023. https://hmgroup.com/wp-content/uploads/2024/03/HM-Group-Sustainability-Disclosure-2023.pdf (accessed 10 May 2024).

67.

Zavadskas

Turskis

Antucheviciene

Zakarevicius

. Optimization of weighted aggregated sum product assessment. Elektron Elektrotech 2012; 122: 3.

68.

Yücenur

Ipekçi

. SWARA/WASPAS methods for a marine current energy plant location selection problem. Renew Energy 2021; 163: 1287.

69.

Singh

. Entropy weighted WASPAS and MACBETH approaches for optimizing the performance of solar water heating system. Case Stud Therm Eng 2024; 53: 103922.

70.

Keleş

Işıldak

Özdağoğlu

. Isparta Süleyman Demirel Havalimanı’nın CİLOS-WASPAS bütünleşik yaklaşımıyla yıllara göre performansının değerlendirilmesi. 14th International Congress of Social Sciences with Current Research, Turkey, October 2021.

71.

Hwang

Yoon

. Multiple attribute decision making: Methods and applications. New York: Springer-Verlag, 1981.

72.

Sianaki

. Intelligent decision support system for energy management in demand response programs and residential and ındustrial sectors of the smart grid. Doctoral dissertation, Curtin University, 2015.

73.

Sokolović

Stanujkić

Štirbanović

. Selection of process for aluminium separation from waste cables by TOPSIS and WASPAS methods. Minerals Eng 2021; 173: 107186.

74.

Özdagoglu

. The effects of different normalization methods to decision making process in TOPSIS. Ege Acad Rev 2013; 13: 245.

75.

Özdağoğlu

Keleş

Eren

. Evaluation of macroelisa equipment alternatives in a univercity hospital with WASPAS and SWARA. Süleyman Demirel Univ J Fac Econ Admin Sci 2019; 24: 319.

76.

Shih

. Incremental analysis for MCDM with an application to group TOPSIS. Eur J Oper Res 2008; 186: 720.

77.

Alexander

Thomas

. A hybrid MCDM approach for sustainable yarn supplier selection in Bangladesh’s apparel industry. Int J Adv Eng Technol Innovations 2025; 1: 246.

78.

Aytekin

Okoth

Korucuk

Karamaşa

Tirkolaee

. A neutrosophic approach to evaluate the factors affecting performance and theory of sustainable supply chain management: application to textile industry. Manage Decis 2023; 61: 506.

79.

Roy

Sen

Pal

. An integrated green management model to improve environmental performance of textile industry towards sustainability. J Cleaner Prod 2020; 271: 122656.

80.

Yıldırım Söylemez

Kayabaşı

Demirağ

. Prioritization and relational analysis of green supplier evaluation criteria in the textile industry. J Enterp Inf Manage 2025; 38: 849.

α	2020	2021	2022	2023
0	4	3	2	1
0.1	4	3	2	1
0.2	4	3	2	1
0.3	4	3	2	1
0.4	4	3	2	1
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0.6	4	3	2	1
0.7	4	3	2	1
0.8	4	3	2	1
0.9	4	3	2	1
1	4	3	2	1

α	2020	2021	2022	2023
0	4	3	2	1
0.1	4	3	2	1
0.2	4	3	2	1
0.3	4	3	2	1
0.4	4	3	2	1
0.5	4	3	2	1
0.6	4	3	2	1
0.7	4	3	2	1
0.8	4	3	2	1
0.9	4	3	2	1
1	4	3	2	1

α	2020	2021	2022	2023
0	4	3	2	1
0.1	4	3	2	1
0.2	4	3	2	1
0.3	4	3	2	1
0.4	4	3	2	1
0.5	4	3	2	1
0.6	4	3	2	1
0.7	4	3	2	1
0.8	4	3	2	1
0.9	4	3	2	1
1	4	3	2	1