Contextual Relationships of Factors Affecting Sustainability 4.0 in the Textile Industry
影响纺织行业可持续发展 4.0 的要素的情境关系

Abstract  摘要

This study aims to identify the contextual relationships between the impact factors on Sustainability 4.0, through the principles of I4.0 in the textile industry, using interpretive structural modeling (ISM), a qualitative technique that makes it possible to understand the relationship between different factors, classifying them hierarchically based on their interdependencies. The hierarchy of the 16 (sixteen) factors proposed at different levels helps to identify critical areas to focus efforts and investments, providing data to guide strategic business planning. From the result of the Level Partition Chart, four levels were observed for the ISM diagram. The Corporate Social Responsibility factor (FIS7) was considered a dependent variable of all others. The identification of FIS4, FIS10 and FIS14 as factors with high dependence suggests key areas for strategic interventions. Thus, this study provides a solid theoretical basis and practical recommendations that help textile companies adopt sustainable and technologically advanced strategies, promoting an effective transition to Sustainability 4.0.
本研究旨在通过 I4.0 原则在纺织工业中的应用，利用解释结构模型（ISM），一种能够理解不同因素之间关系的定性技术，并基于它们的相互依赖性进行分层分类，来确定影响可持续发展 4.0 的各个影响因素之间的情境关系。在不同层面上提出的 16 个（十六个）因素层次有助于确定关键领域，以集中精力和投资，提供数据以指导战略商业规划。从层次划分图的结果来看，ISM 图观察到了四个层次。企业社会责任因素（FIS7）被视为所有其他因素的依赖变量。将 FIS4、FIS10 和 FIS14 识别为高度依赖的因素，表明了战略干预的关键领域。因此，本研究为纺织公司采用可持续和先进的技术战略提供了坚实的理论基础和实践建议，促进了向可持续发展 4.0 的有效过渡。

1. Introduction  1. 引言

According to data presented by the United Nations Industrial Development Organization [1], Brazil occupies the tenth position in the world ranking of textile production, having generated around USD 9 billion in this industry in 2020. This amount, however, is negligible compared to that generated by China, which leads this production chain and, in the same period, raised more than USD 450 billion [2]. In this scenario, the ascending role of the northeast region of Brazil in the expansion of the textile market and the consequent increase in the trade balance in this sector must be considered [3].
根据联合国工业发展组织提供的数据[1]，巴西在全球纺织生产排名中位居第十，2020 年该行业创造了约 90 亿美元。然而，这一数字与中国的产量相比微不足道，中国在这一产业链中处于领先地位，在同一时期，其产值超过 4500 亿美元[2]。在此背景下，巴西东北部地区在纺织市场扩张中的作用日益上升，以及该部门贸易平衡的相应增加必须予以考虑[3]。

It is worth highlighting, then, the importance of the state of Pernambuco for the development of textile production. Such relevance can be attributed, in large part, to the Clothing Hub of Agreste Pernambucano—formed of the cities of Caruaru, Santa Cruz do Capibaribe, and Toritama—which concentrates multiple production and commercialization activities of clothing items [4]. The responsibility of the activities undertaken in this space to leverage the state’s productive potential has been recognized. However, it is necessary to acknowledge the environmental impacts linked to this context of industrial growth [5]. The authors comment on the close relationship between the textile sector and environmental damage and advocate the need to reflect more deeply on the subject, paying attention to the promotion of sustainability.
值得强调的是，巴伊亚州对纺织生产发展的意义。这种相关性在很大程度上可以归因于阿加斯特·佩尔南布库服装枢纽——由卡鲁阿鲁、圣克鲁斯·多·卡皮巴拉贝和托里塔马等城市组成，它集中了服装产品的多种生产和商业化活动[4]。在此空间开展的活动，旨在发挥该州的生产潜力，其责任已得到认可。然而，有必要承认与这一工业增长背景相关的环境影响[5]。作者评论了纺织行业与环境污染之间的密切关系，并主张更深入地反思这一问题，关注可持续性的推广。

The environmental impacts linked to the textile industry have been a subject of academic investigation for some years, but, according to Schulte and Lopes [6], with the intensification of the debate around sustainable development, the research carried out on the topic has become not only more numerous and dense but also more necessary. This is because the textile industry is among the most polluting productive sectors [7,8,9], information that, related to the evident expansion of this field, highlights the urgency of mapping challenges and thinking about alternatives that mitigate the problem in question.
纺织工业的环境影响已经是一些年来的学术研究课题，但根据舒尔特和洛佩斯[6]的观点，随着关于可持续发展辩论的加剧，对该主题的研究不仅数量和密度增加，而且变得更为必要。这是因为纺织工业是污染最严重的生产部门之一[7, 8, 9]，与这一领域的明显扩张相关联的信息，突显了绘制挑战和思考减轻问题的替代方案的紧迫性。

A phenomenon that directly impacts the dynamics of production and consumption of clothing items is the fast-fashion production sector, which is related to a production chain with a short and accelerated circuit, reflecting consumer trends [10]. This, however, is also associated with a cheaper production chain and the consequent reduction in the useful life of products, a factor that constitutes an obstacle to overcome so that a new horizon of production and consumption of textile products can be established [11].
直接影响服装产品生产和消费动态的现象是快时尚生产部门，这与一个短而快速的生产链相关，反映了消费趋势[10]。然而，这也与更便宜的生产链以及产品使用寿命的相应减少有关，这是一个需要克服的障碍，以便为纺织产品的生产和消费建立一个新的前景[11]。

Considering the conflict between the economic and industrial expansion of the textile sector versus the harm caused to the environment, in large part, by the agents that enable production and profit gains, numerous authors have defended the need to think about a new relationship of consumption with fashion [12,13]. This proposal is linked to the conception of a new stage of industrial production, Industry 4.0 [14,15], which, in turn, necessitates updated reflections on sustainability linked to this new facet of the industry.
考虑到纺织行业经济和工业扩张与生产利润增长所导致的对环境造成的损害之间的冲突，许多作者都主张思考与时尚消费的新关系[12, 13]。这一提议与工业生产新阶段——工业 4.0[14, 15]的概念相联系，而这又反过来需要针对这一行业新方面的可持续性进行更新反思。

It is in this panorama that the concept of Sustainability 4.0 emerges, which consists of integrating the general principles of promoting sustainability with the actions and premises that underlie Industry 4.0. Javaid et al. [16] and Filgueiras et al. [17] explain that this phenomenon is very recent, which is why managers from different companies still recognize weaknesses that hinder the implementation of measures capable of aligning their organizations with the principles of Sustainability 4.0.
在这种背景下，可持续发展 4.0 的概念应运而生，它包括将促进可持续性的基本原则与工业 4.0 背后的行动和前提相结合。Javaid 等人[16]和 Filgueiras 等人[17]解释说，这一现象非常新，这就是为什么不同公司的管理者仍然认识到阻碍其组织与可持续发展 4.0 原则相一致的措施实施的弱点。

Considering the importance of the textile and clothing sector for the economy of Brazil and the world, this article seeks to answer the following question: what are the contextual relationships of impact factors to Sustainability 4.0 based on the use of the principles and pillars of Industry 4.0 in the textile industry? Therefore, this study aims to identify the contextual relationships between the impact factors on Sustainability 4.0 through the principles of Industry 4.0 in the textile industry, using interpretive structural modeling (ISM), which is an appropriate methodology for categorizing and analyzing data into more defined structured mental models [18].
考虑到纺织和服装行业对巴西及世界经济的重要性，本文旨在回答以下问题：基于纺织行业应用工业 4.0 的原则和支柱，影响可持续性 4.0 的因素之间的情境关系是什么？因此，本研究旨在通过解释结构模型（ISM）识别影响可持续性 4.0 的因素之间的情境关系，解释结构模型是一种将数据分类和分析为更明确的结构化思维模型的适当方法[18]。

The originality of this study lies in the search for identifying the contextual relationships of impact factors to Sustainability 4.0 based on the principles and pillars of Industry 4.0 within the textile industry through the application of the interpretive structural modeling methodology. Furthermore, it is worth noting that there is still a scarcity of specific studies in the literature that report research in this direction.
本研究的特点在于通过应用解释结构模型方法，在纺织行业内寻找识别影响可持续性 4.0 的因素之间的情境关系，基于工业 4.0 的原则和支柱。此外，值得注意的是，在文献中关于这一方向的特定研究仍然很少。

This study offers contributions to the advancement of scientific knowledge, of theoretical and practical value, and can be used as a guide both for organizations in the textile sector and for agents involved in the sector’s production chain regarding implementation, also because the concept of Sustainability 4.0 (S4.0) is under construction. Therefore, this study, in addition to providing integration between Sustainability and Industry 4.0, offers a valuable conceptual structure and significant practical guidance for leaders and managers of organizations that implement 4.0 technologies in the textile and clothing sector.
本研究为科学知识的进步做出了贡献，具有理论和实践价值，可以作为纺织行业组织和参与生产链的代理商在实施方面的指南，也因为在构建可持续性 4.0（S4.0）的概念。因此，本研究除了提供可持续性与工业 4.0 的整合外，还为在纺织和服装行业实施 4.0 技术的组织领导者和经理提供了有价值的概念结构和重要的实践指导。

2. Theoretical Background
2. 理论背景

2.1. Industry 4.0 and Sustainability
2.1. 工业 4.0 与可持续性

According to Pereira et al. [19], the term Industry 4.0 would have been used to designate a new period of transformation of production dynamics, characterizing a Fourth Industrial Revolution, in which digitalization and globalized culture would be considered structuring elements of production processes and the distribution of goods. In this sense, the authors state that it is an integration between human beings and digital technologies to optimize purchasing, selling, and manufacturing networks—in other words, the production chain in general. It is a phenomenon of the reconfiguration of production that impacts different areas of knowledge and professional activity, such as engineering, administration, economics, computational sciences, among others.
根据佩雷拉等人[19]的研究，术语“工业 4.0”原本是用来指代生产动态的新一轮变革时期，标志着第四次工业革命，其中数字化和全球化文化被视为生产过程和商品分配的结构性要素。在这种情况下，作者们认为，这是人类与数字技术之间的整合，以优化采购、销售和制造网络——换句话说，就是整个生产链。这是一种影响不同知识领域和专业活动的生产重构现象，如工程、管理、经济学、计算科学等。

Thus, according to Tessarini Junior and Saltorato [20], the developments of Industry 4.0 are not limited to industrial and commercial issues, covering dimensions of human life more broadly. In their study, the researchers highlight the quality of intelligent forms of production, which integrate cyber–physical systems and two dynamics related to the internet, the Internet of Things (IoT) and the Internet of Services (IoS). Both phenomena group the digital interconnections of information relating to products and services to expand the horizon of possibilities of the citizen-consumer, predicting and directing consumption trends, associating the sharing of data between different dimensions, etc.
因此，根据 Tessarini Junior 和 Saltorato[20]的研究，工业 4.0 的发展不仅限于工业和商业问题，更广泛地涵盖了人类生活的各个方面。在他们研究中，研究人员强调了智能生产形式的质量，这些形式整合了网络物理系统以及与互联网相关的两种动态，即物联网（IoT）和互联网服务（IoS）。这两种现象将有关产品和服务的信息的数字互联分组，以扩大公民-消费者的可能性范围，预测和引导消费趋势，将不同维度之间的数据共享等。

It is an understanding of intelligent and optimized forms of production, aimed at satisfying consumer needs in a personalized way and, simultaneously, favoring environmental and social issues. This is because of the discourse that warns about the relevance of developing more sustainable forms of production, linked to minimizing environmental damage caused by the production process [21,22]. The authors conclude with the increased productivity of academic works that articulate the themes of Sustainability and Industry 4.0.
这是一种对智能和优化生产形式的理解，旨在以个性化的方式满足消费者需求，同时有利于环境和社交问题。这是因为有关发展更可持续生产形式的论调，与最小化生产过程造成的环境损害相关[21, 22]。作者以阐述可持续性和工业 4.0 主题的学术作品的增加生产力作为结论。

The authors, however, highlight challenges for implementing sustainability coupled to Industry 4.0. Some of them include increased investments in the acquisition and maintenance of digital technological resources, which makes it difficult to establish this new dimension. Another obstacle also stands out: while optimizing factories, whether through mechanical or digital automation, speeds up operations between companies, it tends to hinder the expansion of the workforce.
作者然而，强调了实施可持续性与工业 4.0 相结合的挑战。其中一些包括增加对数字技术资源的获取和维护的投资，这使得建立这一新维度变得困难。另一个障碍也凸显出来：在优化工厂时，无论是通过机械还是数字自动化，虽然可以加快公司间的运营速度，但往往阻碍劳动力的扩张。

It is also worth highlighting the plurality of meanings that are attributed to the term sustainability [23]. It involves the construction of a tripartite structure, which combines social, economic, and environmental interests. The researchers reinforce the relevance of establishing a macro-sustainability policy that is integrated, articulating the interests of the different entities that make up the three spheres mentioned, hence the importance of national and global initiatives, such as the PNRS [24] and Our Common Future, a document also known as the Brundtland Report—after the surname of the Prime Minister of Norway, Gro Harlem Brundtland, who chaired the discussion on sustainability by the United Nations [25].
它还值得强调，人们对“可持续性”这一术语赋予了多种含义[23]。它涉及构建一个三方结构，将社会、经济和环境利益结合起来。研究人员强调了建立综合的宏观可持续性政策的相关性，阐述构成所提及三个领域的不同实体的利益，因此国家及全球倡议的重要性，例如 PNRS[24]和《我们的共同未来》，这份文件也被称为布伦特兰报告——以挪威首相格罗·哈莱姆·布伦特兰的姓氏命名，她主持了联合国关于可持续性的讨论[25]。

Sugahara and Rodrigues [26] highlight the defining role of the Brundtland Report as guidelines for the construction of reflections and measures for the institution of a sustainable development practice. However, they warn of the fact that there is a current dispute over the co-option of the environmental preservationist discourse so that it serves economic interests, which would weaken the implementation of a developmental policy that truly prioritizes environmental conditions. It remains, therefore, to think about how this discussion is based on studies that interrelate the textile industry and sustainability.

When it comes to the textile industry, between the years 1960 and 2015, there was an increase of around 811% in waste produced by the textile industry [27], which highlights not only the boom in this field but the environmental impact that comes attached to it. These data are linked to the extract–transform–discard model, which impacts the logic of production and consumption, as it makes the production and distribution processes of inputs and parts cheaper but harms their durability to accelerate the disposal and procurement cycle of new products, moving the profit produced by the sector. As Barros [28] highlights, there is a close relationship between the culture of consumerism and industrial production, such that products are created with an increasingly shortened useful life cycle, a process that directly contributes to the generation of waste and emission of polluting gases.

In this sense, as Mesacasa and Zanette [29] state, the circular economy applied to fashion has emerged as an alternative for transforming a production model that is more harmful to the environment. Since it is related to the conscious use of natural resources and the production of durable goods to promote a lasting use of consumed goods, this proposal for an economic system, therefore, represents a paradigmatic change in production and consumption relations. Therefore, it is proposed that new meanings be given to all stages of the production chain, from the selection of raw materials to consumption and disposal, avoiding producing as much waste as possible.

As Rathore [30] explains, the theme of sustainability has been taken as a point of reflection in the textile industry since the 1960s, and the concepts and premises that guide the promotion of sustainable production have been updated, since, for example, issues how carrying out ethical and non-exploitative work in the production process also came to be considered a dimension of sustainability, expanding the idea that it would be exclusively about taking care of resources that did not harm the environment, which does not mean that environmental issues are the core of the discussion. The author comments that large companies, such as Nike and Patagonia, have been directly committed to minimizing environmental damage through measures, such as the use of renewable energy, reducing the use of synthetic materials, reducing the use of water, among other actions that would contribute to a transformation in the industry that would bring it closer to the concept of Sustainability 4.0.

Fletcher and Grose [31] commented on how the materials used by the textile industry are related to various environmental problems, such as water pollution and the imbalance of hydrological cycles, climate change, the compromise of ecosystems, the creation of human health problems, among many other issues. Thus, with the growth in social and institutional awareness regarding these issues, companies have been compelled to rethink their practices, assuming positions that represent the values of a consumer who is concerned with the demands of the environment and understands the need to take measures for its maintenance.

Authors have already developed studies on possibilities for action in the textile industry, with the aim of promoting transformations that reduce environmental impacts. Schulte et al. [32], for example, discuss the use of biomaterials—such as spider web fibers, to which the authors place some emphasis—in the composition of clothing items, which have characteristics such as high durability, biodegradability, and non-release of microplastics, factors that favor their use in the manufacture of textile pieces. Furthermore, Amaral and Spers [33] present the use of orange pomace to compose an alternative fiber to silk but warn that the production process of this biomaterial is still costly but promising. Thus, it appears that there is a clear conception regarding the need to invest in new ways of producing to align the demands of the consumer, society, and the environment with the interests of the industry.

Furthermore, it is worth mentioning the consensus between studies dealing with alternatives to produce sustainable materials in the textile industry, which is very incipient and needs to be expanded. Silva, Silva and Ruthschilling [4], in their study on technological innovations in textile production, mention the emergence of fab labs—an abbreviated term from the English fabrication laboratory—aimed at the textile industry, which are spaces for the experimental creation of materials and practices that could transform this field of production.

As shown by Ahmad et al. [34], there are challenges in implementing Sustainability 4.0 measures in the textile industry. The authors state that accelerating the production process in a way that minimizes waste generation or uses resources and practices suited to Sustainability 4.0 is still a challenge to overcome. Therefore, according to them, the shortening of time and the increase in speed are phenomena that need mechanisms to be implemented in line with sustainable principles, and BISs (business intelligence systems) have been recognized as essential resources to speed up consumer demands with short-term processes, favoring the supply chain and enhancing delivery and advertising methods.

From another perspective, it must be made clear that there is a misunderstanding regarding the understanding that technology and sustainability are antagonistic elements and that it is essential to invest not only in the research of new resources and practices but also in the preparation of fashion professionals. Therefore, as Soares [35] says, the mechanisms and procedures that promote sustainability must be worked on in training courses for professionals who work in fashion.

In this way, we recognize an intrinsic relationship between the concepts of Industry 4.0 and sustainability, a central foundation for proposing the concept of Sustainability 4.0 as a new paradigm for sustainable development in the 21st century. This concept is engendered by the principles of promoting sustainability and technologies linked to the Fourth Industrial Revolution, adapting to the demands and problems that shape the dynamics of operation of contemporary productive sectors to offer viable solutions to overcome obstacles to their growth or functioning [36].

In this sense, as proposed by Filgueiras and Melo [37], Sustainability 4.0 can be interpreted, especially in the services sector, as a multidimensional and integrative approach, aimed at the articulation of economic, environmental, and social instances to promote development in these areas’ three scopes. In this context, using contemporary technological resources, we seek to optimize the implementation of the most varied processes.

Therefore, for the intensification of Sustainability 4.0 linked to the textile industry to occur, it is essential to build a project that involves companies, training spaces, and society, promoting a new culture of production and consumption of materials and garments.

It was from this perspective that Silva and Melo [36] conducted a systematic literature review (RSL) with the objective of understanding how I4.0 affects the services sector and how this combination can contribute to Sustainability 4.0, considering the principles of the Triple Bottom Line (TBL). They identified a total of 14 general benefits related to this intersection, which were further categorized into 54 benefits for the economic dimension, 25 for the social dimension, and 21 for the environmental dimension. These benefits are detailed in Table 1.

Table 1. Self-interaction structural matrix for sustainability impact factors 4.0.

	j	FSI1	FSI2	FSI3	FSI4	FSI5	FSI6	FSI7	FSI8	FSI9	FSI10	FSI11	FSI12	FSI13	FSI14	FSI15	FSI16
i
FIS1		-	X	V	A	V	V	V	A	V	A	A	A	V	A	V	V
FIS2			-	A	A	V	X	V	V	O	A	A	O	V	A	A	X
FIS3				-	A	A	A	V	X	V	A	X	O	V	A	A	O
FIS4					-	V	V	O	O	O	V	V	O	V	O	V	V
FIS5						-	A	V	V	V	A	V	V	O	A	V	O
FIS6							-	V	V	O	A	V	V	O	A	V	O
FIS7								-	A	O	A	O	O	A	A	O	A
FIS8									-	A	A	V	A	O	A	A	O
FIS9										-	A	V	V	O	A	A	O
FIS10											-	V	V	O	X	V	V
FIS11												-	X	O	A	V	O
FIS12													-	O	O	V	V
FIS13														-	A	A	X
FIS14															-	V	V
FIS15																-	V
FIS16																	-

Once the concept of Sustainability 4.0 was given and under the research perspective of Silva and Melo [36], they conducted a systematic literature review (RSL) with the aim of identifying the factors that affect Sustainability 4.0 in the fashion industry through a systematic review of the literature based on the premises of the Triple Bottom Line (TBL). From the RSL by Silva and Melo [36], 16 (sixteen) factors that impact Sustainability 4.0 in the textile industry were listed, with 8 (eight) factors being identified for the economic dimension, 4 (four) for the social dimension, and 4 (four) for the environmental dimension. The identification of these factors is essential to achieving the objective of this study, that is, identifying the contextual relationships between the impact factors on sustainability in Industry 4.0. These sustainability impact factors 4.0 (FIS) will be used in the methodological model proposed in this study and are presented below.

• FIS1: Production on demand—a production model that develops goods and merchandise that already have their own demand, a trend in the business market that, as we know, is constantly changing and looking for ways to adapt to generate a more effective result in less time, reducing costs and minimizing waste; after all, only what was already demanded for final consumption/sale will be produced [23,30].
• FIS2: Technological innovation—in a scenario of continuous digital transformation, this innovation consists of implementing resources, based on technology and its tools (AI, IoT, BigData, Blockchain, Machine Learning, etc.), in order to produce positive results for the purpose and processes of the organization, resulting in increased quality and productivity in order to effectively contribute to organizational development [5,38].
• FIS3: Optimization of resources (e.g., energy efficiency)—use of resources, whether inputs, human, or financial, in a more effective and efficient way, reducing waste and costs throughout the execution of processes and enhancing production capacity [39].
• FIS4: Remanufacturing or reverse manufacturing—the industrial process of reconstructing the product by reusing the components/materials that offer technical conditions, aiming to create a new product with the same characteristics and functionalities as the original product [40,41].
• FIS5: Adherence to the Circular Economy—unlike the traditional linear model, where resources are extracted, used, and discarded, the circular economy promotes the reuse and recycling of materials, closing the cycle and reducing dependence on finite resources, seeking to redesign, produce, and market products intelligently, ensuring the efficient use and recovery of resources [42].
• FIS6: Process improvement—continuous improvement in quality and productivity with increased effectiveness and efficiency in the execution of production processes using technologies such as, for example, artificial intelligence, Internet of Things, and robotics [43].
• FIS7: Corporate Socio-Environmental Responsibility—organizations reaffirm their ethical commitment to social, environmental, and economic development, with all their processes guided by the objective of promoting this sustainable production management [44].
• FIS8: Adherence to the Sharing Economy—structured according to new consumer trends, the sharing economy is a business model that is based on the sharing of products and services, as well as the reuse of goods, stimulating the generation of new sources of income and extending the useful life of disused materials, including technology, acting as a catalyst for this economic model, supporting the countless networks that help and drive this model [45].
• FIS9: More conscious consumption—we are facing a generation that is increasingly moving towards conscious and questioning consumption. This consumer, who seeks to understand from the origin of the raw material of that piece of clothing to the end it will have, believes that understanding the entire process and life cycle of a product is fundamental in times when it is necessary to rethink daily habits and how we can adapt them to a planet in need of change [46,47].
• FIS10: Focus on customer experience—taking advantage of technologies such as artificial intelligence, for example, offering solutions to improve customer satisfaction through qualified and relevant experiences of excellence, which have a positive impact; after all, the better the experience, the tendency is for the satisfaction to be greater [48].
• FIS11: Strengthening sustainable fashion—sustainable fashion is related to the generation of an experience associated with social and environmental commitment, uniting the pillars of production and consumption with awareness and commitment to societal issues, considering the entire life cycle of the product, from design to production/manufacturing [49].
• FIS12: Personalization (3D Clothes, Virtual Fitting Rooms, etc.)—technology that facilitates the adaptation of services to the needs of each client, using technologies such as augmented reality and artificial intelligence through, for example, 3D clothes and virtual fitting rooms [4].
• FIS13: Waste reduction—waste management as well as the reduction in its generation together have the responsibility to reduce the environmental impacts arising from industrial production processes, including, for example, practices such as the reuse of inputs and reusing waste, preventing raw materials from being completely discarded [50,51].
• FIS14: Extension of the product’s useful life cycle—the implementation of the extension of the useful life of products is urgent to advance towards more sustainable production and consumption patterns. Extending the useful life of already manufactured products contributes to reducing the use of natural resources and the generation of waste, fundamental factors for accelerating the transition of businesses to a more circular economy [52].
• FIS15: Reduction in the environmental footprint (e.g., reduction in carbon emissions)—the reduction in impacts caused to the biosphere, with the environmental footprint being an indicator of sustainability that monitors the relationship between the biocapacity (or regenerative capacity) of the planet and the demand for natural resources necessary to produce consumer goods and services [53,54].
• FIS16: Use of sustainable materials as raw materials—their production/making process causes a significantly lower impact on the environment, as is the case, for example, with organic fabrics, produced from elements of natural origin and free from pesticides [55,56].

The factors mentioned are fundamental for Industry 4.0 and Sustainability 4.0, as they integrate advanced technologies and sustainable practices to promote efficient and responsible production. Models such as on-demand production and remanufacturing increase efficiency and reduce waste, while technological innovation and resource optimization improve quality and productivity. Adherence to the circular economy and conscious consumption, together with corporate socio-environmental responsibility, promotes the reuse of materials and minimizes the environmental impact. These factors combine technological advances with sustainability, ensuring balanced and ecological economic development.

When thinking about the textile industry, the choice of the 16 factors reflects a strategic integration of advanced technologies and sustainable practices to face the contemporary challenges of Industry 4.0 and Sustainability 4.0. They highlight the importance of technological innovation, resource optimization, and customization to increase efficiency and productivity. Customer experience and social sustainability are also prioritized. Alternatives such as the economy of decarbonization, cyber security, and inclusion can be considered to complement and update these strategies.

In this way, Sustainability 4.0 impact factors provide specific actions on how I4.0 technologies can be used to achieve sustainability objectives in the textile industry, thereby facilitating the understanding of potential risks and benefits involved in adopting I4.0 technologies.

2.2. Interpretative Structural Modeling (ISM)

The interpretative structural modeling (ISM) methodology was proposed by John N. Warfield [57] and later improved by Rakesh K. Sage [58] with the aim of identifying the contextual relationships between previously identified factors, criteria, variables, or foundations. Through the ISM methodology procedure, it is possible to validate the contextual relationship between the factors studied by applying Graph Theory, which consists of a mathematical assumption that studies the relationships between elements representing them through combinatorial objects, called “graphs” [59]. The ISM evolution phases comprise the following steps:

• Identification and exhaustive listing of all factors that will be the subject of study; the identification of factors is the first step in the ISM methodology. For this study, 16 (sixteen) factors that impact Sustainability 4.0 in the textile industry were listed, with 8 (eight) factors being identified for the economic dimension, 4 (four) for the social dimension, and 4 (four) for the environmental dimension [36].
• Establishment of contextual relationships between factors—for this step, a structured script is used with the aim of establishing the influence relationships between the factors that impact Sustainability 4.0 in the textile industry.
• Preparation of the structural self-interaction matrix referring to the factors under analysis—to identify the contextual relationships that are the subject of research, it is necessary to develop a structural self-interaction matrix based on classification symbols (V, A, X, O) so that the direction between bases i and j can be demonstrated. The “V” classification indicates that there is a relationship only between factor “i” and factor “j”. The “A” classification indicates that there is a relationship only between factor “j” and factor “i”. The “X” classification indicates that there is a relationship in both directions, whether from “i” to “j” or from “j” to “i”. And, finally, the “O” classification points to a lack of relationship in both directions, whether between “i” and “j” or “j” with “i” [60,61].
• Formulation of the Binary Initial Accessibility Matrix for the factors considered—once the structural self-interaction matrix has been developed for the factors that impact Sustainability 4.0 in the textile industry, the Binary Initial Accessibility Matrix must be developed using the classification V, A, X, O, which will be inter-crossed through the relationships between lines and columns according to the following rule [61]:

  ○

  If in the Matrix the entry (i, j) is classified as V, it will be represented by 1 in the entry (i, j) and 0 in the entry (j, i).

  ○

  If the entry (i, j) in the Matrix is classified as A, it will be represented by 0 entry (i, j) and 1 entry (j, i).

  ○

  If the entry (i, j) in the Matrix is classified as X, it will be represented by 1 in the entries (i, j) and (j, i).

  ○

  If the entry (i, j) in the Matrix is classified as O, it will be represented by 0 in the entries (i, j) and (j, i).

  ○

  In the structural self-interaction matrix, diagonal entries will be represented by 1.

• Assessment of transitivity in the structural self-interaction matrix—In this step, we seek to evaluate transitivity to check whether there is conformity in the relationships between different opposing factors [62]. It is important to check the possibility of indirect relationships between the crossed elements in the matrix, the relationships between the two attributes A and B and B and C, respectively, and indicate a relationship between attributes A and C [60,61,62].
• Definition of partition levels in the Final Accessibility Matrix creation of the diagram ISM—After verifying the transitivity of the matrix, it is necessary to calculate the Power of Direction and Dependence Matrix, in which the summed values in the rows and columns will be represented. It is fundamental for creating a diagram that encompasses the entire ISM model, as well as its fragmentation into different levels [63]. By creating the matrix, it is possible to draw the hierarchical ISM model using a diagram.
• Carrying out MICMAC analysis in relation to the factors examined—finally, the MICMAC analysis is used to segment the impact factors on Sustainability 4.0 into clusters according to the following classification:

  ○

  Cluster I (Autonomous Variables): the elements of this set are characterized by having low powers of dependence and direction [64].

  ○

  Cluster II (Dependent Variables): the elements of this set are characterized by having high power of dependence and low power of direction. In this group, the elements depend on each other, despite having low power of influence over other factors, therefore representing little relevance [65].

  ○

  Cluster III (Linkage Variables): the elements that make up this set are characterized by having high power of dependence and high power of direction. The factors located in this group influence the other factors, in addition to being influenced themselves [66].

  ○

  Cluster IV (Independent Variables): the elements that compose it are characterized by factors with low power of dependence and high power of direction, that is, it has a high capacity to influence other factors in a stable way [67].

• Review of potential inconsistencies in the ISM Model.

Some studies around the theme of sustainability using ISM were developed. The study by Rafiq et al. [68] focused on management practices related to the sustainable energy business, so the authors used the ISM methodology to recognize and interrelate elements that would enhance performance in this market, especially in the period after the COVID-19 pandemic. In the work, the emergence of the clean energy market stands out as one of the sustainability trends of the 21st century, which highlights the possibility of ISM being used to group and relate management practices of different organizations working in the energy market. Through the application of this model, the authors were able to verify that new management practices have been the basis for enhancing operations in the renewable energy niche.

Elhidaoui et al. [69] investigated the field of “green energy”, another name used to refer to energy matrices that are less aggressive to the environment. ISM was used as a research methodology carried out by them to analyze the CSFs (Critical Success Factors) based on the adoption of blockchain technology in a logistics chain of an energy company. Through the adopted method, it was possible to identify elements of this technology that needed to be implemented better so that this resource worked more adequately.

Sathvik, Krishnaraj, and Awuzie [70] used ISM to identify and interpret the causes of risky behaviors of workers working in the construction sector. From a literature review, the authors recognized fifteen factors that were frequently listed as triggering situations that harmed workers’ safety. The structural interpretation model was used to intertwine these factors so that elements that were at the root of these problems could be recognized. Through the scheme created by the researchers, it was possible to observe that elements, such as age, quality of sleep, degree of interaction, and the skills of each professional, that would directly impact the adoption of behaviors could risk the safety of these workers.

In another study, Santos et al. [61] applied the ISM to identify the contextual relationships between sustainable solutions in the context of Industry 4.0 in law firms, which highlights the versatility of this methodology for analyzing sustainability measures in different areas. The authors observed, according to the data they collected, that there was an awareness on the part of the management of the office studied regarding the importance of promoting sustainable measures. According to the researchers, the office analyzed work to promote sustainable solutions relevant to Industry 4.0, such as the implementation of smart services and flexibility in the provision of services, which had the consequence of building a better image of the office before authorities of society who had a relationship with that company, as potential investors and suppliers.

In this study, the interpretative structural model (ISM) is used to verify the relationships between the indicated factors. Through this method, it becomes possible for a group of factors to be structured into a defined systematic model. Thus, ISM is a methodological tool applied by researchers and academics in search of understanding complex relationships between some factors in the most diverse fields of study [71].

3. Materials and Methods

The research developed in this study is classified as exploratory and qualitative: exploratory in terms of its objective since the methods used include data collection through experience surveys; qualitative in terms of its approach, as it works with data seeking their meaning, based on the perception of the phenomenon within the context.

Due to criteria of relevance and convenience in local operations within the textile and clothing production scope, the ISM methodology chosen for this study was guided by the opinions of experts from a large company located in the Local Production Arrangement of Clothing, located in the northeast of Brazil.

The ISM model assumes that a manager (decision maker in the sector) estimates the relationships between factors, and then this relationship is validated by a group of experts. For Sangari and Dashtpeyma [72] and Melo and Medeiros [60], there must be at least 8 specialists. In this regard, the methodology was applied between June and July 2023, with the collaboration of the manager of an organization in the textile segment under study and another 15 (fifteen) specialists from different areas of the sector, including several experienced actors in the textile segment and academics.

Therefore, to begin applying the ISM methodology, the manager of an organization in the textile segment was asked, using a structured script, to establish the relationships of influence between the factors that impact Sustainability 4.0 in the textile industry. After establishing relationships, a message exchange group was created with the 15 experts to validate the relationships estimated by the manager. At first, there were divergences in the answers, but after explaining the manager’s rationality, the relationships were accepted by the group, and the model was validated. Figure 1 illustrates a flowchart of the structuring phases of the ISM process proposed in this work.

Figure 1. Steps of the ISM methodology.

The operations of the company studied began in 1996, in the city of Santa Cruz do Capibaribe, Agreste region of Pernambuco, Brazil. Its main activity is the manufacture of clothing items, including exports, and is currently a successful case and reference in business and people management for the entire state. For reasons of confidentiality, the organization in question will be treated as a study company, and, therefore, its name is omitted. In this sense, studies on the textile industry in Santa Cruz do Capibaribe are essential due to its economic relevance, generating jobs and income in the region. The city is a textile production hub, with a production chain that ranges from fabric production to clothing production. Analysis of local success stories provides valuable insights into good management and innovation practices. Furthermore, these studies contribute to regional development, identifying challenges and opportunities, and promoting the adoption of Industry 4.0 technologies and sustainable practices. They can also direct professional training programs and improve working conditions, positively impacting the community, in addition to the power of replicability of the models developed in any region due to the characteristics of the sector.

4. Results

As the ISM method process directs towards the construction of the ISM diagram, the starting point for its application is an interview with the manager of the company. He was asked to identify the contextual relationships between the 16 sustainability impact factors 4.0 (FIS), namely: production on demand (FIS1), technological innovation (FIS2), resource optimization (FIS3), remanufacturing or reverse manufacturing (FIS4), adherence to the circular economy (FIS5), process improvement (FIS6), Corporate Social and Environmental Responsibility (FIS7), adherence to the shared economy (FIS8), more conscious consumption (FIS9), focus on customer experience (FIS10), strengthening of sustainable fashion (FIS11), personalization (FIS12), reducing waste (FIS13), extending the product life cycle (FIS14), reducing the environmental footprint (FIS15), using sustainable materials as raw materials (FIS16). In this first step, the manager of an organization in the textile segment estimates the relationships between factors that impact Sustainability 4.0 in the textile industry.

Once the relationships between the factors have been structured, it is necessary to validate the relationships through the process of agreement between the experts involved. In this context, the 15 (fifteen) experts were placed in a group on a free messaging platform so that they could exchange information and validate the relationships previously identified in the interview of the manager of an organization in the textile segment, based on their knowledge and experiences. Initially, there was no agreement on some relationships between factors that affect Sustainability 4.0 in the textile industry. At this point, the manager explained his rationality process, and the experts reached a consensus between the contextual relationships. According to Melo and Medeiros [60], disagreement in the relationship validation process is common.

Following the flow predicted by the methodology, after conceiving the consensus regarding contextual relationships, the structural self-interaction matrix (SSIM) was then developed using the symbols V, A, X, O, as shown in Table 1.

When the “V” classification was chosen, the manager stated that there is a relationship only between factor “i” and factor “j”. When the “A” classification was chosen, the manager stated that there is a relationship only between factor “j” and factor “i”. When the “X” classification was chosen, the manager stated that there is a relationship in both directions, whether from “i” to “j” or from “j” to “i”. And, when the “O” classification was chosen, the manager stated that there is no relationship in both directions, whether between “i” and “j” or “j” with “i”. Once the structural self-interaction matrix was created, it was possible to convert the V, A, X, O symbology into a system of binary values (0, 1), creating the Initial Accessibility Matrix, presented in Table 2.

Table 2. Initial Accessibility Matrix for sustainability impact factors 4.0.

	j	FSI1	FSI2	FSI3	FSI4	FSI5	FSI6	FSI7	FSI8	FSI9	FSI10	FSI11	FSI12	FSI13	FSI14	FSI15	FSI16
i
FIS1		1	1	1	0	1	1	1	0	1	0	0	0	1	0	1	1
FIS2		1	1	0	0	1	1	1	1	0	0	0	0	1	0	0	1
FIS3		0	1	1	0	0	0	1	1	1	0	1	0	1	0	0	0
FIS4		1	1	1	1	1	1	0	0	0	1	1	0	1	0	1	1
FIS5		0	0	1	0	1	0	1	1	1	0	1	1	0	0	1	0
FIS6		0	1	1	0	1	1	1	1	0	0	1	1	0	0	1	0
FIS7		0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
FIS8		1	0	1	0	0	0	1	1	0	0	1	0	0	0	0	0
FIS9		0	0	0	0	0	0	0	1	1	0	1	1	0	0	0	0
FIS10		1	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1
FIS11		1	1	1	0	0	0	0	0	0	0	1	1	0	0	1	0
FIS12		1	0	0	0	0	0	0	1	0	0	1	1	0	0	1	1
FIS13		0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1
FIS14		1	1	1	0	1	1	1	1	1	1	1	0	1	1	1	1
FIS15		0	1	1	0	0	0	0	1	1	0	0	0	1	0	1	1
FIS16		0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1

Next, an analysis of the transitivity of the Initial Accessibility Matrix was carried out to create the Final Accessibility Matrix. To achieve this, the search for transitivity is based on a fundamental principle: if FIS1 is related to FIS2 and, in turn, FIS2 is related to FIS3, then FIS1 is necessarily related to FIS3. Symbolically, in the Final Accessibility Matrix, when the transitive relationships are identified, the relationships previously valued as “0” in the Initial Accessibility Matrix are assigned a value “1 *”. Table 3 shows the Final Accessibility Matrix after transitivity verification.

Table 3. Final Accessibility Matrix for sustainability impact factors 4.0.

	j	FSI1	FSI2	FSI3	FSI4	FSI5	FSI6	FSI7	FSI8	FSI9	FSI10	FSI11	FSI12	FSI13	FSI14	FSI15	FSI16
i
FIS1		1	1	1	0	1	1	1	1 *	1	0	1 *	1 *	1	0	1	1
FIS2		1	1	1 *	0	1	1	1	1	1 *	0	1 *	1 *	1	0	1 *	1
FIS3		1 *	1	1	0	1 *	1 *	1	1	1	1 *	1	1 *	1	0	1 *	1 *
FIS4		1	1	1	1	1	1	1 *	1 *	1 *	1	1	1 *	1	1 *	1	1
FIS5		1 *	1 *	1	0	1	1 *	1	1	1	0	1	1	1 *	0	1	1 *
FIS6		1 *	1	1	0	1	1	1	1	1 *	0	1	1	1 *	0	1	1 *
FIS7		0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
FIS8		1	1 *	1	0	1 *	1 *	1	1	1 *	0	1	1 *	1 *	0	1 *	1 *
FIS9		1 *	1 *	1 *	0	1 *	1 *	1 *	1	1	0	1	1	1 *	0	1 *	1 *
FIS10		1	1	1	0	1	1	1	1	1	1	1	1	1 *	1	1	1
FIS11		1	1	1	0	1 *	1 *	1 *	1 *	1 *	0	1	1	1 *	0	1	1 *
FIS12		1	1 *	1 *	0	1 *	1 *	1 *	1	1 *	0	1	1	1 *	1 *	1	1
FIS13		1 *	1 *	1 *	0	1 *	1 *	1	1 *	1 *	0	1 *	1 *	1	0	1 *	1
FIS14		1	1	1	0	1	1	1	1	1	1	1	1 *	1	1	1	1
FIS15		1 *	1	1	0	1 *	1 *	1 *	1	1	0	1 *	1 *	1	0	1	1
FIS16		1 *	1	1 *	0	1 *	1 *	1	1 *	1 *	0	1 *	1 *	1	0	1 *	1

The relationships between the impact factors on Sustainability 4.0 that obtained a “1 *” rating in the Final Accessibility Matrix are results of the Initial Accessibility Matrix transformed through transitivity verification. After the development of the Final Accessibility Matrix, the Power of Direction and Power of Dependency Matrix was formulated.

Steering power relates to the ability of an impact factor to influence another factor, and this is represented by the horizontal sum of the factors, that is, the sum of the rows in the table. Dependency Power is linked to the ability of an impact factor to be achieved without affecting the reach of other factors. This indicator can be evaluated by the vertical sum of the factors, that is, by the sum of the table columns.

Therefore, the factors must be categorized according to their influence and dependence and will serve to form the groupings in the Partition of Levels [60,61] according to the result explained, through Table 4.

Table 4. Matrix of Power of Direction and Dependency for sustainability impact factors 4.0.

	j	FSI1	FSI2	FSI3	FSI4	FSI5	FSI6	FSI7	FSI8	FSI9	FSI10	FSI11	FSI12	FSI13	FSI14	FSI15	FSI16	Driving Power
i
FIS1		1	1	1	0	1	1	1	1 *	1	0	1 *	1 *	1	0	1	1	13
FIS2		1	1	1 *	0	1	1	1	1	1 *	0	1 *	1 *	1	0	1 *	1	13
FIS3		1 *	1	1	0	1 *	1 *	1	1	1	1 *	1	1 *	1	0	1 *	1 *	13
FIS4		1	1	1	1	1	1	1 *	1 *	1 *	1	1	1 *	1	1 *	1	1	16
FIS5		1 *	1 *	1	0	1	1 *	1	1	1	0	1	1	1 *	0	1	1 *	13
FIS6		1 *	1	1	0	1	1	1	1	1 *	0	1	1	1 *	0	1	1 *	13
FIS7		0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1
FIS8		1	1 *	1	0	1 *	1 *	1	1	1 *	0	1	1 *	1 *	0	1 *	1 *	13
FIS9		1 *	1 *	1 *	0	1 *	1 *	1 *	1	1	0	1	1	1 *	0	1 *	1 *	13
FIS10		1	1	1	0	1	1	1	1	1	1	1	1	1 *	1	1	1	15
FIS11		1	1	1	0	1 *	1 *	1 *	1 *	1 *	0	1	1	1 *	0	1	1 *	13
FIS12		1	1 *	1 *	0	1 *	1 *	1 *	1	1 *	0	1	1	1 *	1 *	1	1	13
FIS13		1 *	1 *	1 *	0	1 *	1 *	1	1 *	1 *	0	1 *	1 *	1	0	1 *	1	13
FIS14		1	1	1	0	1	1	1	1	1	1	1	1 *	1	1	1	1	15
FIS15		1 *	1	1	0	1 *	1 *	1 *	1	1	0	1 *	1 *	1	0	1	1	13
FIS16		1 *	1	1 *	0	1 *	1 *	1	1 *	1 *	0	1 *	1 *	1	0	1 *	1	13
Dependence power		15	15	15	1	15	15	16	15	15	3	15	15	15	3	15	15

* Transitivity check.

Based on the Power of Direction and Dependence Matrix for impact factors on Sustainability 4.0, the level partition stage was then developed, as shown in Table 5, Table 6, Table 7 and Table 8. A level partition table identifies the accessibility and antecedent sets. The accessibility set is the set that represents the impact factors in Sustainability 4.0 that influence how the objective is achieved, while the antecedent sets are characterized by the sustainability impact factors that the accessibility set influences [60]. The intersection of these sets indicates the interdependence between them. By comparing the accessibility set with the antecedent set, it is possible to classify the ISM hierarchy in relation to critical factors. Level I in the ISM hierarchy is reached when the accessibility and antecedent sets are identical; that is, they have the same elements [61]. Once the level in the ISM hierarchy has been classified, it is crucial to understand its meaning, as this allows us to interpret that the elements contained at each level do not affect the achievement of the objectives of any other elements below the same level. After level I classification, all elements classified at that level are removed and discarded, and the procedure is repeated until all elements have been classified at an appropriate level. Once the classification of all levels has been completed, it is then possible to construct the ISM flow diagram.

Table 5. Level partition–iteration I for sustainability impact factors 4.0.

FIS	Reachability Set	Antecedent Set	Intersection Set	Level
FIS1	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 4, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS2	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 4, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS3	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 4, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS4	4	4	4	I
FIS5	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 4, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS6	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 4, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS7	7	1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16	7
FIS8	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 4, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS9	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 4, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS10	10, 14	4, 10, 14	10, 14
FIS11	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 4, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS12	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 4, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS13	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 4, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS14	10, 14	4, 10, 14	10, 14
FIS15	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 4, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 4, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16

Table 6. Level partition–iteration II for sustainability impact factors 4.0.

FIS	Reachability Set	Antecedent Set	Intersection Set	Level
FIS1	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS2	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS3	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS5	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS6	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS7	7	1, 2, 3, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16	7
FIS8	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS9	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS10	10, 14	10, 14	10, 14	II
FIS11	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS12	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS13	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS14	10, 14	10, 14	10, 14	II
FIS15	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16
FIS16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 10,11, 12, 13, 14, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16

Table 7. Level partition–iteration III for sustainability impact factors 4.0.

FIS	Reachability Set	Antecedent Set	Intersection Set	Level
FIS1	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11, 12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	III
FIS2	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11, 12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	III
FIS3	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11, 12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	III
FIS5	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11, 12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	III
FIS6	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11, 12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	III
FIS7	7	1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16	7
FIS8	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11, 12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	III
FIS9	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11, 12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	III
FIS11	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11, 12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	III
FIS12	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11, 12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	III
FIS13	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11, 12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	III
FIS15	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11, 12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	III
FIS16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11, 12, 13, 15, 16	1, 2, 3, 5, 6, 8, 9, 11,12, 13, 15, 16	III

Table 8. Level partition–iteration IV for sustainability impact factors 4.0.

FIS	Reachability Set	Antecedent Set	Intersection Set	Level
FIS7	7	1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16	7	IV

From the Level Partition Chart results, four levels were observed for the ISM diagram. The Corporate Social and Environmental Responsibility (FIS 7) was classified at level IV but is related to level III. The factors classified at level III are related to level II, including production on demand (FIS 1), technological innovation (FIS 2), resource optimization (FIS 3), adherence to the circular economy (FIS 5), process improvement (FIS 6), adherence to the sharing economy (FIS 8), more conscious consumption (FIS 9), strengthening sustainable fashion (FIS 11), personalization (FIS 12), reducing waste (FIS 13), reducing the environmental footprint (FIS 15), and use of sustainable materials as raw materials (FIS 16).

These impact factors, in turn, are related to the factors focus on customer experience (FIS 10) and extension of the product’s useful life cycle (FIS 14), which were also classified at level III. All the aforementioned factors relate to and influence the remanufacturing or reverse manufacturing factor (FIS 4), classified at level I, to achieve its objective. Figure 2 illustrates the impact factors on Sustainability 4.0, classified into levels and developed through the ISM methodology, obtained through interaction data, which made it possible to classify the levels that helped in the construction of the ISM diagram.

Figure 2. Results of forming the ISM diagram for impact factors in Sustainability 4.0.

The level partition tables indicated four levels for the ISM model. The logical analysis and understanding of the ISM structural model have its flow of interpretation reading from lower to higher levels, so that each lower level relates to the higher level, to exert influence on it.

Corporate Social and Environmental Responsibility (FIS 7) is associated with level IV, the lowest in the ISM diagram. The elements that make up this level have a direct influence on all other factors, indicating an important steering power. Production on demand (FIS 1), technological innovation (FIS 2), resource optimization (FIS 3), adherence to the circular economy (FIS 5), process improvement (FIS 6), adherence to the sharing economy (FIS 8), more conscious consumption (FIS 9), strengthening sustainable fashion (FIS 11), personalization (FIS 12), reducing waste (FIS 13), reducing the environmental footprint (FIS 15), using sustainable materials as raw materials (FIS 16) were factors classified at level III. Focus on customer experience (FIS 10) and extension of the product life cycle (FIS 14) were located at level II.

These last fifteen previously mentioned impact factors deeply influence the performance of the factor at the highest level of the model, the remanufacturing or reverse manufacturing impact factor (FIS4). Therefore, the impact factors on Sustainability 4.0 classified at levels II, III, and IV demand greater attention from the management of companies in the textile and clothing sector that wish to control the risks associated with the impact process on Sustainability 4.0.

The impact factor remanufacturing or reverse manufacturing (FIS4) has a high dependence power and does not have the power to boost other impact factors on Sustainability 4.0. Improvement actions in favor of FIS4 will not influence other factors due to its strong dependency power. Any improvement action promoted for factors positioned at levels II, III, and IV will influence the impact factor positioned at level I.

Finally, in the final stage of correlation analysis, there is MICMAC analysis (Cross-Impact Matrix, Multiplication Applied to Classification), which selects the variables associated directly and indirectly in the object system, which, in this case, are the impact factors in Sustainability 4.0 [73], categorizing them in terms of their influence and interdependence, by their degrees of autonomy within the system. Therefore, the variables were described based on the influence that each variable exerts on the other. The impact factors on Sustainability 4.0 were then subdivided into 4 (four) clusters, in this order: I—Autonomous Variables; II—Dependent Variables; III—Linkage Variables; IV—Independent Variables.

Thus, to help classify the impact factors in Sustainability 4.0 and in the adoption of organizational strategies, Figure 3 shows a graph with the classification of impact factors according to the Power of Direction (X axis) and the Power of Dependence (Y axis), derived from the MICMAC analysis, as shown in the detailed graph below, in Figure 3.

Figure 3. MICMAC diagram for impact factors on Sustainability 4.0.

The quadrant that integrates the dependent variables (II) contains focus on customer experience (FIS 10), extension of the product life cycle (FIS 14), and remanufacturing or reverse manufacturing impact factor (FIS4), reinforcing the idea that these impact factors on Sustainability 4.0 are primordial and strongly influenced by other factors in the system.

The factors production on demand (FIS 1), technological innovation (FIS 2), resource optimization (FIS 3), adherence to the circular economy (FIS 5), process improvement (FIS 6), adherence to the sharing economy (FIS 8), more conscious consumption (FIS 9), strengthening sustainable fashion (FIS 11), personalization (FIS 12), reducing waste (FIS 13), reducing the environmental footprint (FIS 15), using sustainable materials as raw materials (FIS 16) were classified in Cluster III; that is, they are considered linkage factors and indicate a strong power of direction and dependence, and, due to this, these fundamentals are considered unstable. The factors classified in this cluster have a mutual influence on each other and can be influenced by other factors in the system.

The factor Corporate Social and Environmental Responsibility (FIS 7) was classified in Cluster IV, indicating its independence in relation to other factors that influence Sustainability 4.0. In this sense, FIS 7 has a high capacity to influence other factors in a stable way. It is worth noting that no factor influencing Sustainability 4.0 was classified in Cluster I; that is, the model does not have autonomous foundations. In this sense, the system does not have any factor with weak power of direction and dependence isolated from the system.

Discussion

The interpretation of the classification of impact factors in Sustainability 4.0 into levels (1 to 4) provides a clear understanding of the influence and interdependence between the factors. Remanufacturing or reverse manufacturing factor (FIS 4), positioned at level 1 of the ISM diagram, is positively considered as a critical factor, indicating that this is a fundamental aspect in the implementation of sustainable business in the clothing industry, as evidenced in a study by Thorisdottir and Johannsdottir [44]. The factors positioned at level 3 of the diagram are important factors, considered relevant, however, not critical like FIS4. In this sense, there is a need for organizations to take an active role in the development of production and commercialization models in line with Sustainability 4.0, as business action constitutes an essential factor for the effectiveness of these sustainable practices, something already evidenced in other studies [74,75,76].

The factors grouped at level 2 of the diagram are considered relevant, however, not as impactful as those classified at levels 1 and 4. Therefore, Corporate Social and Environmental Responsibility (FIS 7) was considered a high-impact factor, which may indicate an opportune area for investment or improvement in favor of the evolution of organizational sustainability indexes. In this way, there is evidence from other works [41,42,77,78] about the importance of developing initiatives for encouraging Corporate Social and Environmental Responsibility in the textile industry, as well as expanding research that measures the impact of this specific factor for structuring an industry shaped by the precepts of Sustainability 4.0.

Making investments and constant reinforcement towards improvements related to remanufacturing or reverse manufacturing initiatives can be strategic, just as deepening Corporate Social Responsibility practices can attract significant benefits for sustainability and organizational efficiency. However, it is also necessary to address issues related to the other two elements present in the cluster that encompasses dependent variables: the customer experience and the product life cycle. These aspects have stood out, especially in digital media, as evidenced by studies that emphasize the relevance of using digital technologies such as artificial intelligence and the metaverse [79] and the use of virtual platforms as interaction spaces that encourage sales of second-hand products in e-commerce spaces, which contributes to the expansion of their life cycle [80], and consumption among consumers themselves, creating a community of customers who become loyal to the company [81].

From a comparative perspective between the models, in both results, both ISM and MICMAC point to a consistency in the importance of the socio-environmental responsibility factor (FIS7). Regarding the autonomy of the factors, the MICMAC method indicates that the sustainability impact factors 4.0 FIS4, FIS10, and FIS14 have greater dependence. This may point to the ability of these factors to have an impact, without being directly influenced by others.

The results of this study highlight the need for strategic actions focused on specific areas of Sustainability 4.0 for the textile and clothing industry. Investment in remanufacturing or reverse manufacturing factor (FIS4) is critical, as improvements in this aspect can have a substantial impact on the overall sustainability of the industry. Companies must invest in technologies and processes that facilitate the remanufacturing and recycling of products, such as systems for collecting and reusing used materials. Furthermore, developing robust Corporate Social and Environmental Responsibility (FIS7) programs is essential, including reducing the carbon footprint and using sustainable materials. Implementing digital technologies such as artificial intelligence and metaverse can improve the customer experience and encourage the purchase of sustainable products. Creating e-commerce platforms that facilitate the sale of second-hand products and reuse can increase the durability of products and reduce waste. Measures such as regular environmental audits and sustainability certifications are also recommended.

The identification of FIS4, FIS10, and FIS14 as factors with high dependence suggests key areas for strategic interventions. Focusing on initiatives that directly address these factors can ensure that improvements are sustainable and aligned with industry best practices. Developing specific performance indicators for sustainability, such as reducing CO2 emissions and increasing the use of recycled materials, is fundamental. Real case studies within the Brazilian textile industry can provide valuable insights and best practices for other companies. Ongoing training and development programs for employees on sustainability practices are also beneficial.

5. Conclusions

The analysis of impact factors in Sustainability 4.0, using the principles of Industry 4.0 in the textile industry, is essential to understand how these factors interact and influence each other. The importance of this analysis lies in the need to identify contextual relationships and dependencies between factors, which are fundamental for the implementation of sustainable practices. By applying the Structural Interpretive Modeling (ISM) methodology, this study clearly categorized and structured these factors, allowing for a deeper understanding of the dynamics involved.

In this sense, the analysis answered the proposed research question by mapping how the impact factors on Sustainability 4.0 and how the factors that influence Sustainability 4.0 relate to the principles of Industry 4.0. These principles include digitalization, system integration, and the use of advanced technologies. For example, the application of digital technologies and e-commerce platforms to the customer experience and the extension of the product life cycle demonstrates the direct connection with Industry 4.0. Thus, this study provides a solid theoretical basis and practical recommendations that will help textile companies adopt sustainable and technologically advanced strategies, promoting an effective transition to Sustainability 4.0.

The interpretation after ISM/MICMAC analysis regarding the factors focus on customer experience (FIS10) and extension of the product’s useful life cycle (FIS14) demonstrate the necessary concern in achieving a balance between customer needs and the sustainability of products.

Another consideration that must be considered is the occurrence of possible limitations in the Real Impact Assessment, given that the model can indicate the relative importance of factors but may not offer concrete measures of the real impact on performance or practical implementation of strategies.

Finally, it is concluded that the method provides a very useful framework for understanding the interrelationships between key factors. However, it is important to remember that it is a simplified representation, and there may be additional nuances and real complexities that may not be fully captured by the model. Therefore, it is essential to recognize that from the moment that the experts involved in the research may have different perceptions and possible trends in relation to the points under analysis, it becomes necessary to conduct future studies, in search of greater validation and reinforcement of the results obtained in this study. Furthermore, it is important to reiterate that discussions surrounding S4.0 remain under constant debate and demand more robust examinations.

As a suggestion for future research, the expansion of data sources is considered, in the search for expanding samples at the local level, as well as in the sector of productive arrangements in the textile sector in question, nationally speaking, since there appears to be a strong interconnection between factors from the three dimensions of social, environmental, and economics of the Triple Bottom Line.