A novel neutrosophical approach in stakeholder analysis for sustainable fashion supply chains

Abstract

Purpose

The purpose of this study is proposing a novel neutrosophical stakeholders' analysis approach for sustainable fashion supply chain (SFSC), presenting a supply chain members and objectives in order to conduct a sustainable business, investigating the roles and positions of these stakeholders, determining the contribution levels of these stakeholders to the sustainability objectives, and accordingly identifying the convergence and divergence among the stakeholders in terms of realization of the objectives.

Design/methodology/approach

A novel neutrosophic set-based stakeholders' analysis Method of ACTors, Objectives, strength Reports (MACTOR) approach is proposed considering the uncertain and indeterminate opinions of decision-makers. In order to obtain the mutual opinions of decision-makers, Delphi technique is employed.

Findings

The analysis results of this research emphasizes that although the manufacturers can be thought as the foremost actor is SFSC by producing the main product, they have no superior power on conducting the business. Besides, the government, customer and fashion firms are the key players shaping the fashion industry. Retailers and distribution centers can be interpreted as an intermediary in between the other stakeholders. Moreover, the eco-friendly packaging providers have not gained an important role that they were supposed to in terms of the sustainability objectives.

Research limitations/implications

The application phase of the research includes the possibility of subjective judgments of the participants as a limitation. Therefore, Delphi technique is applied to overcome this challenge by multiple rounds of interviews for panel of participants in order to combine the benefits with elements of the wisdom of people.

Practical implications

Examining a multi-echelon supply chain is a practical implication providing the mutual opinions of experts such as designers, stylists, journalists, consultants, procurement managers, entrepreneurs, activists etc. for sustainability in the fashion industry. One can derive from the findings to determine which sub-echelon requires more attention, or which business is more important to focus on most, or which branch of activity influences others most.

Originality/value

This is one of the few articles that focuses on the sustainability objective and highlights the active roles of all members of the supply chain. Besides, this is the first study deploying neutrosophic sets for MACTOR analysis.

Citation

Karadayi-Usta, S. (2023), "A novel neutrosophical approach in stakeholder analysis for sustainable fashion supply chains", Journal of Fashion Marketing and Management, Vol. 27 No. 2, pp. 370-394. https://doi.org/10.1108/JFMM-03-2022-0044

1. Introduction

Fast fashion is a trend that is making possible to buy the latest fashion products via Internet in a quick way at affordable prices, providing easy return option, creating excitement among people and putting customers in a style/elegance race (Brydges, 2021). This trend has been spread rapidly both globally and domestically, exposed the ready-made clothing market to the invasion of powerful retail giants. The main harms brought by fast fashion are: shopping unnecessarily, creating a shopping addiction in the consumer, increasing the frequency of logistics and thus carbon emissions, the wasteful use of plastic packaging materials and causing environmental pollution. Under the title of “sustainable consumption and production”, which is among the sustainable development goals that the United Nations plans to achieve by 2030, the negative consequences of fast consumption are detailed. The conscious consumption, actions changing the lifestyle, the circular fashion movement, empowering the local value chains through transparency and traceability, innovation and collaborations are all highlighted (United Nations, 2015).

Despite providing an easy access, abandoning the fast fashion trend and keeping up with the “sustainable fashion” movement is essential for the future of the world and the resources. Sustainable fashion (SF) begins with the questioning whether an individual really needs what he/she buys, continues with calculating how many years he/she can use that product, questions whether the product fabric has a natural content such as linen, cotton, silk, etc., and then aims to turn expired products back into yarn or fabric, or with a future-friendly environmentalist approach, sells the product to someone else and makes the product's lifecycle longer (Testa et al., 2021).

In case of abandoning fast fashion consumption, the opportunity to get rid of about 90 tons of waste every year will be achieved, the water resources polluted by chemical textile dyes will become cleaner, the annual consumption of 70 trillion liters of water will be prevented, fair payments to the farmers will be provided, the exploitation of resources will be obstructed, and the working conditions of the workers will be improved. Next, the world gains time to renew itself, more reasonable payment conditions for manufacturers and designers, much better quality clothes with less production will be obtained: (1) by making the SF more economical, (2) by letting the dissolution of the natural garment in a biological way without an environment harm, (3) by transforming the garment into a new product, which is known as up-cycling, (4) by selling the garment to someone else (Bloomberg, 2020; Benson, 2020; Bayav, 2021).

This significant background information motivates this research to conduct sustainable fashion research in supply chain/supply network detail.

Current SF literature discusses sustainable practices and financial performance in fashion firms (Medcalfe and Miralles Miro, 2022), measuring consumer's perception of SF (Grazzini et al., 2021), fashion 4.0 transformation (de Haro and Wang, 2021), analysis of barriers to SF (Brandão and da Costa, 2021), SF from the perspective of customers and companies (Puspita and Chae, 2021), SF perception in social media (Orminski et al., 2021), SF system (Heinze, 2020), positioning luxury brands for SF (Bandyopadhyay and Ray, 2020).

Fashion supply chain is labor-intensive and sensitive to environment and society, it is significant to set a sustainable supply chain for fashion companies. Sustainable fashion supply chain (SFSC) covers eco-material preparation, sustainable manufacturing, green distribution, green retailing, and ethical consumers (Shen et al., 2014). An SF product is manufactured in an eco- and socio-friendly environment through the supply chain that contains raw material supply, manufacturing, distribution, and retailing (de Brito et al., 2008). Reuse and recycling of materials, such as old clothes, manufacturing scraps and bottles also can be the material of SF (Anson, 2012).

Beside of the fashion item consumption, the apparel production is also significant to reach a sustainable fashion supply chain. As the major incident of the fashion industry, the disaster of the Rana Plaza building on 24 April 2013 emphasizes the importance of being ethical and sustainable in fashion industry by pushing companies to change the behavior of apparel sourcing regarding building safety (ILO, 2021). Besides, since the fashion industry incurs many negative impacts on environment in all stages of garment manufacturing, sustainability is a must along the supply chain (de Brito et al., 2008).

The related current SFSC literature focuses on circular fashion from the young consumers' perspective (Kovacs, 2021), SFSC risk mitigation (Hsu et al., 2021), sustainable planning strategies in SFSC (Fung et al., 2020), asymmetric relationships of small suppliers in SFSC (Talay et al., 2020), operational transparency and environmental efforts in SFSC (Guo et al., 2021), fashion vlogger attributes in SFSC (Tran, 2020), sustainability performance appraisal in SFSC (Karaosman et al., 2017). An overall stakeholder analysis including indeterminacy of decision-makers' opinions is missing in the field of SFSC study.

Hence, there is an obvious gap in analyzing the stakeholders of the SFSC with the objectives of this specific supply chain in terms of sustainability. There is an obvious research gap in stakeholders' analysis of SFSC literature in terms of considering the uncertain and indeterminate opinions of decision-makers (Santos et al., 2020).

Stakeholders are the key supply chain members in fashion industry's sustainability. Especially consumers have an increasing awareness of sustainability which is remarkable in marketing and branding, since it can strengthen customer interest and loyalty (Muntean and Stremtan, 2010). Customers' awareness triggers the manufacturers to produce in a sustainable way. Moreover, ethical practices such as offering recycling service and recyclable product in stores make green retailing and manufacturing stakeholders more and more important (Chan and Wong, 2012).

Prospective stakeholder analysis is paramount of importance in terms of developing a circular economy around a specific value chain. Stakeholders' economic and cultural commitment is crucial to overcome the barriers in transforming to circular economy in order to be sustainable and economically viable (Fiallos-Cárdenas et al., 2022; Föll and Thiesse, 2021).

All of the aforementioned issues in SFSC constitute a research question focusing on the SFSC stakeholders with their roles/positions in the supply chain, sustainability objectives and contribution levels to these defined sustainability objectives (Kovacs, 2021). Therefore, the research questions of this study focuses on what are the sustainability objectives of a fashion supply chain, who are the key stakeholders in this network and what are their roles.

Hence, the purpose of this study is proposing a novel neutrosophical stakeholders' analysis approach for SFSC, and presenting supply chain members and objectives in order to conduct a sustainable business, investigating the roles and positions of these stakeholders, determining the contribution levels of these stakeholders to the sustainability objectives, and accordingly identifying the convergence and divergence among the stakeholders in terms of realization of the objectives. Therefore, the proposed methodology presents a novel neutrosophical stakeholders' analysis approach for the defined indeterminate factors.

The Method of ACTors, Objectives, strength Reports (MACTOR) technique proposes a stakeholder investigation approach that permits taking the richness and complexity of connections between decision-makers into account. By characterizing the position/role of every SFSC member for the defined objectives and the relations of power between them, the technique allows investigating their inevitable alliances and anticipated future attitudes. It contributes at last to the plan of key and strategic recommendations to improve the viability of a production network (Fetoui et al., 2021).

In order to handle the uncertainty, the neutrosophic sets are chosen in this paper depends on their benefits. For example, neutrosophic sets can quantify every membership of the three truth, falsity and, indeterminacy autonomously, and shows how one impacts one another. In case of applying intuitionistic fuzzy sets, if membership increases, the certainty the sum of other two measure diminishes. In the intuitionistic fuzzy sets, the membership and non-membership degrees are dependent, and their sum ought to be less than or equal to 1. In neutrosophic sets, all sources are independent, and they do not communicate or do not know the response of each other (Smarandache, 2021; Karadayi-Usta, 2022).

The current neutrosophy literature addresses supplier selection problem (Pamucar et al., 2020), facility location and routing (Mohammadi et al., 2020), production processes (De et al., 2020), linear programming (Nozari et al., 2022; Khatter, 2020), multi-criteria decision making (De et al., 2022; Gül, 2020), time series models (Zhao et al., 2020; Singh and Huang, 2019), sentiment analysis (Kandasamy et al., 2020). However, a neutrosophical approach in MACTOR is missing in the literature.

The specific context of this study contains mainly the Turkish textile industry via the point of view of fashion designers, procurement experts, sustainable fashion consultants/coordinators and entrepreneurs. Also, experts from Italy, Portugal, France, UK and Spain have contributed in shaping the analysis findings.

Key findings of this research emphasized the specific actors and their roles in SFSC, while the practical contribution is about determining which sub-echelon requires more attention, or which business is more important to focus on most, or which branch of activity influences others most. Additionally, this is the first study deploying neutrosophic sets for MACTOR analysis.

The following sections present the literature review of SFSC stakeholders and objectives, preliminaries of neutrosophic sets, the proposed neutrosophical MACTOR approach, application and findings, discussion and conclusion parts.

2. Literature review

Sustainability has three fundamentals as economic, social and environmental dimensions. Environmental sustainability could be achieved through (1) sourcing, (2) production, (3) delivery. Economical sustainability depends on (a) operating expenditures, (b) recycling, (c) production quality, and (d) time efficiency. Social sustainability has four pillars as (1) philanthropy, (2) labor equity, (3) workplace safety and health, (4) medical benefit (Poh and Liang, 2017).

SF is an umbrella term including slow fashion, ethical fashion and eco-friendly fashion (Gurova and Morozova, 2018). The European Apparel and Textile Confederation (EURATEX) defines the SFSC as a network of producing fibre, yarn and fabric sequentially to generate garments and technical textiles, next trading and recycling these garments in a joint cross-functional involvement of apparel, recycle, machinery and technology, chemical fibre industries and service providers (Figure 1).

A fashion supply network member must follow ethical attitudes towards its business partners in terms of daily and sufficient payments. Also the manufacturers must fund its workers with living wages (LeBaron et al., 2022). Safety, security and ergonomic working conditions must be provided throughout the whole supply network.

Secondly, the fashion network must adopt a slow fashion manner. Local garment ateliers and micro organizations must be supported, and the fast access of apparels must be avoided (Henninger et al., 2015). Furthermore, biodegradable fabrics with natural content must be selected for the apparels (Majumdar et al., 2020).

The end of life or old-fashioned undesired clothes can be mended and used for a longer time, or recycled to a new clothing or to a new fabric, or up-cycled by changing its function to an another item, or donated, or sold as second-hand. Therefore, reverse logistics is a must to generate a sustainable supply chain for the fashion industry (Nagurney et al., 2015).

Sustainable fashion practices also vary by location/place/country. As it is highlighted in the literature, the researches focuses on Turkish textile industry provide insides from fabric manufacturing (Alkaya and Demirer, 2014), while a Brazilian case focusing on reverse logistics implementations (Bouzon and Govindan, 2015), a Swedish case conducting a circular economy practice via a closed loop supply chain structure (Brydges, 2021), a case of Hong Kong emphasizing the mass market second-hand clothing retail operations (Chan et al., 2015), ICT Adoption in India (Chopra and Desai, 2019), practices of sustainable clothing designers in Helsinki (Gurova and Morozova, 2018), green fashion marketing of Romanian organizations (Muntean and Stremtan, 2010), and sustainable trends in Vietnam's fashion supply chain (Nayak et al., 2019).

In addition, eco-friendly packaging (Srivastava et al., 2022), producing zero carbon emission (Sakamoto et al., 2022), being cruelty-free (Ervin and Mallet, 2012) are the other vital issues in establishing sustainability in fashion supply chains. The following section addresses the SFSC stakeholders and objectives in detail.

2.1 Sustainable fashion supply chain stakeholders

A SFSC consists of fashion firms, manufacturing facilities/plants, storages, distribution centers, demand markets, disposal service provider, multiple shipment modes. Especially the intermodal shipment mode enables greater flexibility, which may, in turn, depending on the firms' decisions, be good for consumers and also for the environment (Nagurney et al., 2015). Nagurney et al. (2015) explains the intermodal shipment's correlation with respect to SF as the flexibility ability of the green shipment alternatives. Moreover, Henninger et al. (2015) points out sustainable supply chain members (suppliers, agents, and other micro-organizations) and stakeholders (employees, consumers) in the slow-fashion industry. Shi et al. (2017) only focuses on manufacturer, retailers, consumers; while Poh and Liang (2017) just emphasizes the suppliers (sourcing), fashion company, retailers (channel). Hence, this study defines sustainable fashion supply chain stakeholders as;

1. Farmers producing cotton, linen, hemp, silk, etc. (Nayak et al., 2019)

2. Biodegradable natural-content fabrics manufacturers (Shen, 2014; Wankowicz, 2016; Nayak et al., 2019). For instance, newly announced sustainable cruelty-free vegan leather out of cactus leaves (Webber, 2021), out of mushroom (Bolt Projects, 2021), plant-based leather from apple waste (Prev, 2021).

3. Manufacturers (producing outfits, capable of producing recycled outfits) (Wankowicz, 2016; Fung et al., 2021)

4. Fashion firms with ethical attitudes, i.e. brand owners designing new seasons in a sustainable way (Nayak et al., 2019; Lee, 2021)

5. Shipment providers (Wankowicz, 2016; Nayak et al., 2019)

6. Distribution centers, storage (Shen, 2014; Lee, 2021)

7. Retailers, channel to customers (Wankowicz, 2016; Fung et al., 2021)

8. Reverse logistics service providers (Khurana and Ricchetti, 2016; Nayak et al., 2019; Fung et al., 2021)

9. Eco-friendly packaging providers (Alkaya and Demirer, 2014; Fung et al., 2021)

10. Government, i.e. legal sanctions and environmental taxes (Khurana and Ricchetti, 2016; Choi and Luo, 2019)

11. Conscious consumers (Wankowicz, 2016; Fung et al., 2021; Lee, 2021)

The following section introduces the SFSC objectives.

2.2 Sustainable fashion supply chain objectives

Sustainable development goals that are adopted by all United Nations Member States in 2015 in order to end poverty, protect the planet and ensure the peace and prosperity by 2030 with integrated 17 defined goals (United Nations, 2015). Particularly, responsible production and consumption goal, reduced inequality goal, and partnership for goals are the fundamentals for a SFSC. The fashion industry specific SFSC objectives as a result of a detailed literature review are addressed below:

1. Raising awareness of the target customer group, informing about SF concept (Nagurney et al., 2015; Khurana and Ricchetti, 2016)

2. Using natural ingredients, biodegradable raw materials (Nagurney et al., 2015; Nayak et al., 2019)

3. Execution of eco-design activities (Bouzon and Govindan, 2015; Nayak et al., 2019)

4. Widespread use of reverse logistics applications, creating an environment enabling the recycling (discount or coupon application for recycled products) (Bouzon and Govindan, 2015; Nayak et al., 2019)

5. Producing quality, durable, long-lasting products, creating a suitable basis for second-hand garment shopping; philantrophic activities (Dickson and Chang, 2015; Fung et al., 2021)

6. Providing non-product services, personalized designs and repair activities to the customers; increasing the slow fashion trend (Saricam et al., 2014; Henninger et al., 2015)

7. Respecting the law regarding health and safety at work (Khurana and Ricchetti, 2016; Moretto et al., 2018), employing qualified personnel and paying workers a living wage (Dickson and Chang, 2015; Nayak et al., 2019)

8. Ensuring traceability and transparency with block chain applications (Khurana and Ricchetti, 2016; Guo et al., 2021; Caldarelli et al., 2021; Lee, 2021)

9. Advanced sustainability reporting, knowledge sharing with suppliers and actors of the supply chain (Khurana and Ricchetti, 2016; Moretto et al., 2018)

Following these objectives, preliminaries for neutrosophic sets are stated.

3. Proposed methodology

3.1 Preliminaries for neutrosophic sets

Neutrosophic sets are proposed by Smarandache (1998) as a general form of fuzzy sets and intuitionistic fuzzy sets. This is a widely-used technique to deal with incomplete, indeterminate and inconsistent information of the real world implementations (Broumi et al., 2018; Karadayi-Usta, 2022).

The advantages of neutrosophic sets are independently measuring ability of all three truth, indeterminacy, and falsity memberships, and showing how one influences to the other one in decision-making. In the case of intuitionistic fuzzy sets, when membership rises, the sum of the other two measures falls. The membership and non-membership degrees are dependent in intuitionistic fuzzy sets, and their sum should be less than or equal to 1. All sources in neutrosophic sets are autonomous, and they do not communicate with one another or know each other's responses (Smarandache and Pramanik, 2016; Smarandache, 2021). Basic definitions and operations of neutrosophic sets are as follows:

Definition 1.

A neutrosophic set A in E, where E is the universe, is defined by a truth-membership function TA(x), an indeterminacy-membership function IA(x), and a falsity-membership function FA(x) where x∈E. A is characterized as A={〈x, TA(x), IA(x),FA(x),|x∈E〉} where  TA(x), IA(x),FA(x)∈]0−, 1+[ such that 0≤ TA(x), IA(x),FA(x)≤3+.

Definition 2.

A single-valued neutrosophic set A is a subclass of neutrosophic sets and is defined as A={〈x, TA(x), IA(x),FA(x),|x∈E〉} where TA, IAFA: X→[0, 1] such that 0≤ TA(x)+IA(x)+FA(x)≤3. In particular, if E has only 1 element, A is called a simplified neutrosophic number (SNN), which is represented as A=〈TA, IA, FA〉 (Wang et al., 2010).

Definition 3.

Let A=〈TA, IA, FA〉 be a SNN. The score function s(A)=(2+TA−IA−FA)/3 of a SNN (Broumi et al., 2018). In order to rank two SNN A and B, this function is used to defuzzify the neutrosophic sets into crisp ones.

All of the above definitions will be applied to the proposed methodology in the following sections.

3.2 Proposed neutrosophical MACTOR approach

The MACTOR technique enables stakeholders to decide how to implement a given policy based on alliances and conflicts (Godet, 2004). This methodology enhances data processing for every stakeholder, particularly in terms of their vision concerning strategic objectives. The technique identifies the relations' power between stakeholders in order to determine the dominant relationships and autonomous stakeholders (Godet, 2000a, b). It assesses significant relationships, hence determines which of supply chain members are the key stakeholders. A direct influence matrix is defined in the scope of the analysis, stakeholders are characterized and classified according to the position they occupy in the system. Then, a stakeholder-objective matrix is defined in order to establish the position of a stakeholder concerning the objectives, as well as the possibility to develop partnerships to achieve them. Finally, a stakeholder convergence graph is designed (Godet, 2004; Godet, 2000a, b).

MACTOR is implemented in various field of studies such as supply and value chain management (Ramírez-Gómez and Rodríguez-Espinosa, 2022; Bozkurt and Kadaifci, 2021), tourism industry sustainability (Mafruhah et al., 2020), flood risk reduction (Isa et al., 2019), ICT adoption (Chopra and Desai, 2019), migrant worker policy (Mafruhah et al., 2019), integrated farming (Suryawati et al., 2021), and cooperation in restoration techniques (Fetoui et al., 2021).

The methodology addresses the convergence and divergence among the stakeholders in terms of realization of the objectives. It allows determining both the relationships among the stakeholders, and the role and power of them on the objective. This is an analytical tool of examining the stakeholders' movements, plans, power balance, and stance towards the implementation of projects (Serdarasan and Kadaifci, 2020). The proposed neutrosophical MACTOR approach involves the following steps:

1. Identify the objectives

2. Determine the important stakeholders

3. Evaluate the stakeholder × stakeholder relationships, take the input values as neutrosophic sets (obtain MDI: Matrix of Direct Influences)

4. Calculate the score function values for defuzzification, apply the Definition 3 procedure.

5. Take the square of MDI matrix to generate the indirect relationships. (obtain MDII: Matrix of Direct and Indirect Influences)

6. Obtain the balance of power coefficients with Equation (1) as

(1)ri=Mi∑i=1nMi×MiMi+Di

and Equation (2) as

(2)ri∗=riri¯

where n is the total number of the stakeholders, Mi is the direct and indirect influence of the ith stakeholder, and Di is the direct and indirect dependence of the ith stakeholder. ri∗ is the balance of power coefficient among the whole stakeholders.

1. Draw the positioning map of the stakeholders.

2. Evaluate the stakeholder × objective (MSO: Matrix of Stakeholder and Objective) relationships, take the input values as neutrosophic sets. Again apply Definition 3 procedure to get score function values.

3. Calculate the weighted matrix by multiplying the “MSO” and “balance of power coefficients” matrices.

4. Draw the convergence and divergence graphs, make strategic recommendations for each stakeholder.

In the following section, the proposed methodology will be applied to SFSC to highlight the fashion industry specific roles and objectives.

4. Application and findings

The first step as “identifying the objectives” and the second one as “determining the important stakeholders” were carried out in the previous literature review. Hence, the third step “evaluating the stakeholder × stakeholder relationships, taking the input values as neutrosophic sets” is the next phase of the study. The applied methodology steps are stated in Figure 2.

The participants were got in touch via LinkedIn in December, 2021 for the possible evaluations. They were informed about the aim and the scope of the research. 112 people were asked to join the evaluation, and 16 of them were able to participate. The whole participants (Table 1) joined in two-hours online meeting in English on January 6th, 2022. The most difficult part of the meeting was finding a suitable time for everyone, thus a day-time session was arranged. Since the participants have been informed previously about the scope of the meeting, the individual evaluations were ready at the beginning of the session.

Delphi technique is applied to gather their mutual opinions about the relationships between the defined factors. The Delphi technique is a prediction framework based on the results of multiple rounds of interviews for panel of experts. The experts are presented with an aggregated summary of evaluation, allowing each expert to adjust their answers according to the group response. This process combines the benefits of expert analysis with elements of the wisdom of crowds (Twin, 2021).

Table 2 shows the agreed opinions of the participant about stakeholder × stakeholder relationships with neutrosophic sets based input values. Here, the truth-membership TA stands for “the possibility in which the statement is true”, the indeterminacy-membership IA is “the degree in which he/she is not sure”, and the falsity-membership FA means that “the statement is false” (Ye, 2014). In other words, participants state that stakeholder 1 affects stakeholder 8 with 0,2 power degree, but they are 90% sure that the statement is true. For example, the participants agree that stakeholder 3 has an influence on stakeholder 1 with 0.9 power degree, and they are 90% sure that the statement is true.

The 4th step of the proposed approach is “calculating the score function values. Therefore, following the evaluations of the experts, Table 2 is updated to indicate the required function values. According to the Definition 3, the defuzzification is also obtained by turning the neutrosophic sets into crisp ones.

The fifth step is obtaining the MDII matrix (see Tables 3 and 4).

These values in MDII matrix represent direct and indirect influences between stakeholders. The higher the value, the more influence the stakeholder has on the other. M_i is the degree of direct and indirect influence of each stakeholder (by summing the lines of the MDII matrix), where D_i is the degree of direct and indirect dependence of each stakeholder (by summing the columns of the MDII matrix), r_i is the competitiveness of a stakeholder considering its maximum influences, direct and indirect dependence, and feedback. Finally, r_i* is the “balance of power coefficient” among the whole stakeholders. Hence, the 6th step of the proposed methodology is fulfilled, and the competitiveness situation is stated in Figure 3 with balance of power coefficients. Next, the 7th step focuses on drawing the positioning map of the stakeholders (Figure 4).

According to the positioning map of the stakeholders, government is an influential actor, while the customer is a key player leading the rest of stakeholders. In other words, the government has the power to force the whole supply chain to do what needs to be done for sustainability, the customer is a triggering position to shape the supply network as the demand generator.

Shipment providers, reverse logistics service providers and eco-friendly packaging providers are autonomous stakeholders that are contributing to the supply network but not strictly dependent on that specific industry. The rest of the supply chain members are dependent each other. Any bullwhip effect may lead to excessive results throughout the supply chain.

The 8th step requires the stakeholder × objective relationships with input values as neutrosophic sets. Again the Definition 3 is applied (See Table 5). Participants are not against the objective, thus there is no negative value in these tables.

Next, MSO_(1x11) and “balance of power coefficients” _(11x9) matrices were multiplied to obtain the weighted matrix as it is stated in Table 6.

Accordingly, the 7th objective (respecting the law regarding health and safety at work, employing qualified personnel and paying workers a living wage) is the most prominent one among all. Secondly, raising the awareness of the customers is significant, while the third remarkable point is reverse logistics. Next, natural ingredient use, then the traceability and transparency are important.

The remaining importance levels follow as advanced sustainability reporting, providing non-product-services, producing quality products, and eco-design activities, respectively.

The final step of the proposed methodology is drawing the convergence and divergence graphs (See Figure 5–7). According to the stakeholders' convergence map, the tenth and eleventh stakeholders (government and customer) are in a leading position having the most influential role in the service network in terms of their attitudes towards the objectives. Next, the fourth player as fashion firms is noteworthy in the realization of the objectives. Then the eight player (reverse logistics) is a major actor in accomplishing sustainability. The seventh one as retailer is also important for actualization of the sustainable fashion supply chains. In addition, the remaining stakeholders are those contributing the least to the achievement of the objectives.

Net distances between objectives demonstrates that “respecting the law regarding health and safety at work, employing qualified personnel and paying workers a living wage” objective is the most important one among others. “Raising awareness of the target customer group, informing about SF concept”, “widespread use of reverse logistics applications”, “using natural ingredients, biodegradable raw materials”, “ensuring traceability and transparency with block chain applications”, and “advanced sustainability reporting, knowledge sharing with suppliers and actors of the supply chain” are the second level significant objectives for SFSC, respectively. In accordance with the participants' evaluations, “providing non-product services, personalized designs and repair activities to the customers; increasing the slow fashion trend”, “producing quality, durable, long-lasting products, creating a suitable basis for second-hand garment shopping”, and finally “eco-design activities” are the last three important objectives.

Figure 7 illustrates which stakeholder is more influential to actualize which SFSC objective. Accordingly, stakeholder 10 and 11 (government and conscious customers) affect most the objective I, II, IV and VII. If this situation is interpreted, consciousness can be raised by these stakeholders most in terms of using natural ingredients in garments and reverse logistics applications. Besides, the government is responsible to check compliance to the health and safety laws, and has to ensure that workers are paid a living wages. Moreover, objective III “eco-design activities” can be fulfilled by fashion firms. Objective V “producing quality, durable, long-lasting products” can be obtained via the cooperation of the fashion firms and reverse logistics service providers. Objective VI “providing non-product services” can be obtained under the cooperation of fashion firms, retailers and conscious customers. Furthermore, last two objectives “ensuring traceability and transparency” and “advanced sustainability reporting, knowledge sharing” depends on the integrated running of customers, fashion firms, reverse logistics service providers, shipment providers and distribution centers. As a result of these converge and divergence maps, it is obvious that the importance of the “eco-friendly packaging” is underestimated. Retailers and distribution centers can be interpreted as an intermediary in between the other stakeholders. Although the manufacturers can be thought as the foremost actor is SFSC by producing the main product, they have no superior power on conducting the business. The government, customer and fashion firms are the key players shaping the fashion industry.

The following section of the research discusses the findings of this paper and the articles' results stated in the literature.

5. Discussion

In this part of the research, the conceptual comparisons with the literature, and methodological contributions will be addressed. For example, Choi et al. (2019) builds a two-stage framework to achieve SFSC, focuses on achieving coordination and integration, managing risks, employing technologies, choosing the quicker manufacturer, managing leftovers, demand uncertainty, and over-stocked fabric. However, the research does not identify key actors of supply network, and does not mention about ethical attitudes of the network members. Similar to this research's reverse logistics emphasis, the leftovers and over-stocked fabrics were contextualized within the scope of the paper.

Blockchain adoption in SFSC (Caldarelli et al., 2021; Guo et al., 2021) is the topic discussed frequently. However, these papers address that there is a need of high technology understanding and extensive communication with network to achieve successful integration. These papers focus on what needs to be done, make a list of obstacles encountered, but do not clearly show the root causes and main actors, remains hypothetical.

In order to compare the proposed approach with the existing MACTOR technique, the neutrosophical approach makes possible to make more precise measurements of decision-makers. For instance, the traditional MACTOR uses 0, 1, 2 and 3 evaluation degrees (Serdarasan and Kadaifci, 2020), while the neutrosophical approach involves membership degrees in between 0 and 1 for the strength, and for the indeterminacy of the decision-makers' evaluations.

In comparison with the intuitionistic fuzzy sets, neutrosophical sets present truth, falsity, indeterminacy membership degrees independently. In case of intuitionistic fuzzy sets implementation, an increase in membership degrees resulted in a decrease of certainty. Since the membership and non-membership degrees are dependent in intuitionistic fuzzy sets, neutrosophic sets provided better results (Karadayi-Usta, 2022).

In addition, a follow-up one-hour online meeting in April 19th, 2022 is arranged with the practitioners, whose details are stated in Table 1, to discuss and expand the findings of the research. The fashion designers and stylists indicate that the “eco-design activities” is surprisingly have a lower-than-expected level of importance. All participants supported the “paying workers a living wage” objective as the most important issue in developing a sustainable fashion supply chain. The fashion activist participant emphasizes the second-hand garment shopping's significance, while consultants and procurement managers focus on “biodegradable raw materials”. In brief, each industry representative comments on the findings at just their own point of view. But they arrive the same point that human resource management is the core of fashion industry to be sustainable. Hence, by triangulating the results with fashion industry representatives, several stakeholders' perspectives are presented.

6. Theoretical contribution and managerial implications

As it is previously detailed in methodology section, there is a research gap in stakeholders' analysis MACTOR in literature in terms of considering the uncertain and indeterminate opinions of decision-makers. Therefore, neutrosophic sets are implemented as a theoretical contribution of this study. The reason behind why the neutrosophic sets are deployed in this novel approach is that the ability of quantifying truth, falsity and, indeterminacy autonomously, and determining how one impacts one another.

This is one of the few articles that focuses on the sustainability objective and highlights the active roles of all members of the supply chain. Besides, this is the first study deploying neutrosophic sets for MACTOR analysis as a theoretical contribution.

In addition, as a managerial implication, a multi-echelon supply chain is examined to provide the mutual opinions of experts such as designers, stylists, journalists, consultants, procurement managers, entrepreneurs, activists etc. for the fashion industry sustainability. One can derive from the findings to determine which sub-echelon requires more attention, or which business is more important to focus on most, or which branch of activity influences others most.

The analysis illustrate that government is an influential stakeholder, while the customer is a key player triggering and shaping the whole network as a demand generator. The government has the power to force the whole supply chain members to comply with the laws, while the conscious customers' requirements creates a bullwhip effect towards suppliers of fashion firms. Besides, respecting the law regarding health and safety at work, employing qualified personnel and paying workers a living wage are the most prominent objectives among all. The government and customers are in a leading position, and then the fashion firms, reverse logistics, retailer are noteworthy in actualizing the SFSC.

7. Conclusion

SFSC is labor-, environment- and society-intensive business involving several stakeholders, in particular, supply network levels. Prospective stakeholder analysis is paramount of importance in terms of developing a circular economy around the SFSC. Hence, the purpose of this study is proposing a novel neutrosophical stakeholders' analysis approach for SFSC, presenting a supply chain members and objectives, investigating the roles and positions of these stakeholders, determining the contribution degrees of these stakeholders to the sustainability objectives, and accordingly identifying the convergence and divergence among the stakeholders in terms of realization of the objectives. Since, there is a research gap in stakeholders' analysis MACTOR in literature in terms of considering the uncertain and indeterminate opinions of decision-makers, neutrosophic sets are implemented.

The reason behind why the neutrosophic sets are deployed in this novel approach is that the ability of quantifying truth, falsity and, indeterminacy autonomously, and determining how one impacts one another. In comparison with the intuitionistic fuzzy sets, in the intuitionistic fuzzy sets, the membership and non-membership degrees are dependent, and their sum ought to be less than or equal to 1. In neutrosophic sets, all sources are independent, and they do not communicate or do not know the response of each other.

The findings of the analysis illustrate that government is an influential stakeholder, while the customer is a key player triggering and shaping the whole network as a demand generator. The government has the power to force the whole supply chain members to comply with the laws, while the conscious customers' requirements create a bullwhip effect towards suppliers of fashion firms. Shipment, reverse logistics and eco-friendly packaging providers are autonomous stakeholders that are contributing to the supply network but not strictly dependent on this specific industry. The rest of the supply chain members are dependent each other. Any bullwhip effect may lead to excessive results throughout the supply chain.

According to the stakeholders × objectives analysis, respecting the law regarding health and safety at work, employing qualified personnel and paying workers a living wage are the most prominent ones among all. Next, raising the awareness of the customers is significant, while the third remarkable point is reverse logistics. Then, natural ingredient use, and the traceability and transparency are remarkable. Finally, the remaining importance levels follow as advanced sustainability reporting, providing non-product-services, producing quality products, and eco-design activities, respectively.

The stakeholders' convergence map in terms of realizing the objectives reveals that government and customers are in a leading position, and then the fashion firms, reverse logistics, retailer are noteworthy in actualizing the SFSC. The remaining stakeholders are those contributing the least to the achievement of the objectives.

Net distances between objectives demonstrates that “respecting the law regarding health and safety at work, employing qualified personnel and paying workers a living wage” objective is the most important one among others. “Raising awareness of the target customer group, informing about SF concept”, “widespread use of reverse logistics applications”, “using natural ingredients, biodegradable raw materials”, “ensuring traceability and transparency with block chain applications”, and “advanced sustainability reporting, knowledge sharing with suppliers and actors of the supply chain” are the second level significant objectives for SFSC, respectively. In accordance with the participant's evaluations, “providing non-product services, personalized designs and repair activities to the customers; increasing the slow fashion trend”, “producing quality, durable, long-lasting products, creating a suitable basis for second-hand garment shopping”, and finally “eco-design activities” are the last three important objectives.

As a result of the converge maps, it is obvious that the importance of the “eco-friendly packaging” is underestimated. Retailers and distribution centers can be interpreted as an intermediary in between the other stakeholders. Although the manufacturers can be thought as the foremost actor is SFSC by producing the main product, they have no superior power on conducting the business. The government, customer and fashion firms are the key players shaping the fashion industry.

The application phase of the research includes the possibility of subjective judgments of the participants as a limitation. Therefore, Delphi technique is applied to overcome this challenge by multiple rounds of interviews for panel of participants in order to combine the benefits with elements of the wisdom of people.

Further research studies may employ different perspectives of neutrosophic sets or other fuzzy sets to develop a different approach in MACTOR analysis. Also the analysis type can be changed, or decision-makers can be diversified to validate or compare the findings of this study.

Figures

Figure 1

Sustainable fashion supply chain

Figure 2

The applied methodology steps

Figure 3

Histogram of the MDII's competitiveness, balance of power coefficients

Figure 4

Positioning map of the stakeholders

Figure 5

Stakeholders' convergence map

Figure 6

Net distances between objectives

Figure 7

Detailed convergence and divergence of stakeholders according to each objective

Table 1

Participant profiles

#	Institution	Role in the institution	Country
1	H Textile	Fashion Designer	Turkey
2	A Textile	Sustainable Fashion and Textile Designer	Italy
3	R Group	Procurement Expert Assistant	Turkey
4	U Consultancy	Sustainable Textile Consultant	Turkey
5	FD Company	Sustainable Fashion Designer	Portugal
6	C Textile	Sustainable Fashion Entrepreneur	Turkey
7	Freelance	Sustainable Fashion Activist	Italy
8	L Fashion	Fashion Stylist	Italy
9	Freelance	Upcycling and Sustainable Fashion Expert	France
10	Self-employed	Sustainable Fashion Journalist	UK
11	S Fashion	Sustainability Coordinator	Turkey
12	B Textile	Interior Brand Director	UK
13	Freelance	Brand Designer and Interior Stylist	Turkey
14	G Eco Fashion	Sustainable Fashion Studio Assistant	Portugal
15	K Fashion	Buying Manager	Turkey
16	Freelance	Sustainable Fashion Consultant	Spain

Table 2

Stakeholder × stakeholder relationships with neutrosophic sets based input values < T_A I_A F_A >, and score function values S*

	S1	S*	S2	S*	S3	S*	S4	S*	S5	S*	S6	S*	S7	S*	S8	S*	S9	S*	S10	S*	S11	S*
S1	<0 0 0>	0	<1 0 0>	1	<0.9 0.1 0.1>	0.9	<0.8 0.1 0.2>	0.83	<0 0 0>	0.67	<0 0 0>	0.67	<0.2 0.1 0.8>	0.43	<0.2 0.1 0.8>	0.43	<0 0 0>	0.67	<0 0 0>	0.67	<0.7 0.2 0.3>	0.77
S2	<1 0 0>	1	<0 0 0>	0	<0.8 0.1 0.2>	0.83	<0.7 0.2 0.3>	0.73	<0 0 0>	0.67	<0 0 0>	0.67	<0 0 0>	0.67	<0 0 0>	0.67	<0 0 0>	0.67	<0 0 0>	0.67	<0.7 0.2 0.3>	0.77
S3	<0.9 0.1 0.1>	0.9	<0.9 0.1 0.1>	0.9	<0 0 0>	0	<0.9 0.1 0.1>	0.9	<0 0 0>	0.67	<0 0 0>	0.67	<0.9 0.1 0.1>	0.9	<0 0 0>	0.67	<0.9 0.1 0.1>	0.9	<0 0 0>	0.67	<0.7 0.2 0.3>	0.77
S4	<0.8 0.1 0.2>	0.83	<0.9 0.1 0.1>	0.9	<1 0 0>	1	<0 0 0>	0	<0.9 0.1 0.1>	0.9	<1 0 0>	1	<1 0 0>	1	<0.2 0.1 0.8>	0.43	<0 0 0>	0.67	<0 0 0>	0.67	<1 0 0>	1
S5	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<0 0 0>	0.67	<0 0 0>	0	<1 0 0>	1	<1 0 0>	1	<0 0 0>	0.67	<0 0 0>	0.67	<0 0 0>	0.67	<0.5 0.2 0.5>	0.6
S6	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<0 0 0>	0.67	<1 0 0>	1	<0 0 0>	0	<1 0 0>	1	<1 0 0>	1	<0 0 0>	0.67	<0 0 0>	0.67	<0 0 0>	0.67
S7	<0 0 0>	0.67	<0 0 0>	0.67	<0.5 0.2 0.5>	0.6	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<0 0 0>	0	<1 0 0>	1	<1 0 0>	1	<0 0 0>	0.67	<1 0 0>	1
S8	<0 0 0>	0.67	<0.7 0.2 0.3>	0.73	<0.7 0.2 0.3>	0.73	<1 0 0>	1	<0 0 0>	0.67	<1 0 0>	1	<1 0 0>	1	<0 0 0>	0	<0.7 0.2 0.3>	0.73	<0 0 0>	0.67	<1 0 0>	1
S9	<0 0 0>	0.67	<0.6 0.2 0.4>	0.67	<0.8 0.1 0.2>	0.83	<0 0 0>	0.67	<0 0 0>	0.67	<0 0 0>	0.67	<1 0 0>	1	<0 0 0>	0.67	<0 0 0>	0	<0 0 0>	0.67	<1 0 0>	1
S10	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<0 0 0>	0	<1 0 0>	1
S11	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<0 0 0>	0

Table 3

MDI matrix

	S1	S2	S3	S4	S5	S6	S7	S8	S9	S10	S11
S1	0	1	0.9	0.83	0	0	0.43	0.43	0	0	0.77
S2	1	0	0.83	0.73	0	0	0	0	0	0	0.77
S3	0.9	0.9	0	0.9	0	0	0.9	0	0.9	0	0.77
S4	0.83	0.9	1	0	0.9	1	1	0.43	0	0	1
S5	1	1	1	0	0	1	1	0	0	0	0.6
S6	1	1	1	0	1	0	1	1	0	0	0
S7	0	0	0.6	1	1	1	0	1	1	0	1
S8	0	0.73	0.73	1	0	1	1	0	0.73	0	1
S9	0	0.67	0.83	0	0	0	1	0	0	0	1
S10	1	1	1	1	1	1	1	1	1	0	1
S11	1	1	1	1	1	1	1	1	1	1	0

Table 4

MDII matrix and balance of power coefficient calculations

	S1	S2	S3	S4	S5	S6	S7	S8	S9	S10	S11	Mi	ri	ri*
S1	3.27	2.64	3	3.17	1.95	2.46	2.84	1.56	2.32	0.77	3.15	23.86	0.026	0.516
S2	2.12	3.17	2.4	2.35	1.43	1.5	2.68	1.51	1.52	0.77	2.14	18.41	0.015	0.306
S3	2.42	3.08	4.51	3.07	2.48	2.57	2.96	2.44	1.67	0.77	4.09	25.55	0.025	0.505
S4	4.7	4.94	5.31	4.68	3	3.33	4.59	3.36	3.21	1	4.07	37.51	0.052	1.061
S5	3.5	3.5	3.93	4.06	2.6	1.6	2.93	3.03	2.5	0.6	3.31	28.96	0.042	0.85
S6	2.9	3.63	4.06	4.46	1	3	3.33	1.43	2.63	0	4.91	28.35	0.038	0.768
S7	4.37	5.84	5.56	2.54	2.9	4	6.54	2.43	2.27	1	4.06	34.97	0.045	0.915
S8	4.22	4.05	4.81	3.19	3.9	3	4.39	3.43	2.66	1	3.85	35.06	0.056	1.14
S9	2.42	1.75	2.16	3.24	2	2	1.75	2	2.75	1	2.16	20.46	0.025	0.505
S10	5.73	7.2	7.89	5.46	3.9	5	7.33	3.86	3.63	1	6.91	56.91	0.141	2.85
S11	5.73	7.2	7.89	5.46	3.9	5	7.33	3.86	3.63	0	7.91	50	0.078	1.584
Di	38.1	43.83	47.01	37	26.45	30.46	40.11	25.48	26.04	6.91	38.65	360.05

Table 5

Stakeholder × objective relations with neutrosophic sets based input values <T_A I_A F_A>, and score function values S*

	M		II		III		IV		V		VI		VII		VII		IX
	TA IA FA	S*	TA IA FA	S*	TA IA FA	S*	TA IA FA	S*	TA IA FA	S*	TA IA FA	S*	TA IA FA	S*	TA IA FA	S*	TA IA FA	S*
S1	<0.8 0.1 0.2>	0.83	<1 0 0>	1	<0 0 0>	0	<0 0 0>	0	<1 0 0>	1	<0.8 0.1 0.2>	0.83	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1
S2	<0.9 0.1 0.1>	0.9	<1 0 0>	1	<0 0 0>	0	<0.8 0.1 0.2>	0.83	<1 0 0>	1	<0.8 0.1 0.2>	0.83	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1
S3	<1 0 0>	1	<1 0 0>	1	<0 0 0>	0	<0.9 0.1 0.1>	0.9	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1
S4	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<0 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1
S5	<0 0 0>	0	<0 0 0>	0	<0 0 0>	0	<0 0 0>	0	<0 0 0>	0	<0 0 0>	0	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1
S6	<0 0 0>	0	<0 0 0>	0	<0 0 0>	0	<0 0 0>	0	<0 0 0>	0	<0 0 0>	0	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1
S7	<1 0 0>	1	<0 0 0>	0	<0 0 0>	0	<0.9 0.1 0.1>	0.9	<0 0 0>	0	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1
S8	<1 0 0>	1	<1 0 0>	1	<0 0 0>	0	<1 0 0>	1	<0.8 0.1 0.2>	0.83	<0 0 0>	0	<1 0 0>	1	<1 0 0>	1	<1 0 0>	1
S9	<1 0 0>	1	<1 0 0>	1	<0 0 0>	0	<0.8 0.1 0.2>	0.83	<0 0 0>	0	<0 0 0>	0	<1 0 0>	1	<1 0 0>	1	<0 0 0>	0
S10	<1 0 0>	1	<1 0 0>	1	<0 0 0>	0	<1 0 0>	1	<0 0 0>	0	<0 0 0>	0	<1 0 0>	1	<0 0 0>	0	<0 0 0>	0
S11	<1 0 0>	1	<1 0 0>	1	<0 0 0>	0	<1 0 0>	1	<0 0 0>	0	<0.9 0.1 0.1>	0.9	<0.8 0.1 0.2>	0.83	<1 0 0>	1	<0.8 0.1 0.2>	0.83

Table 6

Weighted matrix

	I	II	III	IV	V	VI	VII	VIII	IX
S1	0.43	0.52	0	0	0.52	0.43	0.52	0.52	0.52
S2	0.28	0.31	0	0.25	0.31	0.25	0.31	0.31	0.31
S3	0.51	0.51	0	0.45	0.51	0.51	0.51	0.51	0.51
S4	1.06	1.06	1.06	1.06	1.06	1.06	1.06	1.06	1.06
S5	0	0	0	0	0	0	0.85	0.85	0.85
S6	0	0	0	0	0	0	0.77	0.77	0.77
S7	0.91	0	0	0.82	0	0.91	0.91	0.91	0.91
S8	1.14	1.14	0	1.14	0.95	0	1.14	1.14	1.14
S9	0.51	0.51	0	0.42	0	0	0.51	0.51	0
S10	2.85	2.85	0	2.85	0	0	2.85	0	0
S11	1.58	1.58	0	1.58	0	1.43	1.32	1.58	1.32