Analyzing the Benefits of Industry 4.0 Technologies That Impact Sustainability 4.0 in Banking Services

Abstract

The main aim of this paper is to analyze, through the Interpretive Structural Modeling (ISM) methodology, the contextual relationships between the systematization of benefits influenced by Industry 4.0 technologies in the banking services sector from the perspective of Sustainability 4.0. The proposed ISM approach was structured based on 14 benefits capable of influencing Sustainability 4.0 in the services sector through I4.0 technologies. The results showed that Macro (5) and Customer (8) benefits have a direct influence on all other S4.0 benefits in the banking sector. The article presents valuable managerial implications for managers of organizations that intend to or currently use 4.0 technologies in the banking services sector. A priori, the search for economic advantages appears to be a catalyst with greater influence on the model. However, this is not entirely evident, since there appears to be a strong interconnection between social, environmental, and economic benefits. This suggests that the search for other attributes beyond financial aspects can generate advantages for the organization considered essential for the type of business explored.

1. Introduction

Industry 4.0 (I4.0) is a reality of the modern economy, precisely because it is based on the implementation and dissemination of newly developed technologies such as big data, machine learning, the Industrial Internet of Things, blockchain, edge computing, and cloud-based systems [1]. These technologies aim to improve efficiency and decision-making while creating and maintaining a competitive advantage, transforming products, services, and production systems, covering design, processes, and operations [2,3,4]. However, since the emergence of these technologies, little has been explored in sectors other than industrial, although it is recognized that other sectors, such as services, can also benefit significantly from them [5].

Furthermore, Industry 4.0 technologies tend to have significant potential to dramatically influence social and environmental development. However, there is a clear lack of adequate guidance on how these technologies contribute to sustainability, both in the academic literature and in business practice [6,7]. This scenario is further complicated when seeking to understand exactly which benefits can directly impact sustainable development in other fields of the economy, beyond the industrial segment [8,9], mainly aiming for a balance between economic, social, and environmental dimensions—the so-called triple bottom line (TBL).

As the direct relationship between Industry 4.0 (I4.0) technologies and sustainable development became clear, several studies emerged with the aim of exploring this connection, such as Beltrami et al. [10], Strazzullo et al. [11], Khan et al. [12], and Soledispa-Canarte et al. [13]. In their systematic literature review, Filgueira and Melo [14] explore the influence of the emerging concept of Sustainability 4.0 (S4.0) in the services sector, with a focus on the triple bottom line (TBL). During this research, they identified a total of 100 benefits in 14 distinct categories associated with S4.0, which they defined as an ecosystem approach supported by intensive technology integration between organizations and communities. The objective of S4.0 is to improve efficiency in the use of natural resources, promote society’s quality of life, and boost the economic development of organizations.

In service provision, the banking sector plays a fundamental role in financial intermediation and supporting economic activities [15]. Furthermore, it has a significant impact on social and environmental issues [16]. The granting of credit by banks can directly influence economic growth and, indirectly, cause environmental degradation if not managed responsibly. Therefore, it is imperative that banks adopt sustainable practices to minimize these impacts [17,18,19].

The perspective that the banking sector operates separately from environmental concerns does not apply, as financial institutions are responsible for offering credit to a wide range of companies, becoming crucial intermediaries across different sectors of the economy. Because of this, banks must adopt environmental and social responsibilities when providing financing to support their operations [20,21,22].

This paper addresses the following research question: What is the contextual relationship between the systematization of benefits influenced by I4.0 technologies in the banking services sector through Sustainability 4.0? Based on this research problem, the objective of this paper is to analyze the contextual relationships through the Interpretive Structural Modeling (ISM) methodology between the systematization of benefits influenced by I4.0 technologies in the banking services sector from the perspective of S4.0.

The ISM methodology is a modeling tool that allows users to analyze the relationships between specific aspects that define a problem, classifying variables based on their importance and providing managerial inference [23]. In this way, the use of this methodology can contribute valuable information about the topic under study, allowing a deeper understanding and applicability in practical situations [24]. Therefore, the application of ISM to the topic of sustainability is particularly relevant, primarily because it provides insights into understanding the interdependencies and implications of actions aimed at sustainability in the banking services sector.

This paper contributes to the literature by exploring the application of Industry 4.0 technologies in the banking sector, an under-investigated field compared to the industrial sector. The paper suggests how Sustainability 4.0 can be implemented in the banking sector to balance the economic, social, and environmental dimensions. Furthermore, using the ISM methodology, the study offers a detailed analysis of the contextual relationships and specific benefits that I4.0 technologies can provide to the banking sector. This approach provides new perspectives and strategies for the integration of sustainable and technological practices in the sector, expanding our understanding of sustainability in the digital era and filling gaps in existing research on the intersection between advanced technology and sustainability in financial services. By focusing on the banking sector, this paper allows us to explore these complex interactions and provide valuable insights that can be applied to improve economic, social, and environmental sustainability, contributing significantly to existing research.

2. Theoretical Background

2.1. Sustainability and Industry 4.0 Technologies

Sustainable development is a crucial concept that transcends the environmentalist approach and is incorporated into fields previously considered incompatible with its principles, highlighting its broad and ongoing relevance [25]. Its definition began to be adopted after the publication of the Brundtland Commission report on global environment and development in 1987. Furthermore, this was the first comprehensive document to address environmental issues of development from economic, social, and political perspectives, representing a significant advance in this field.

In this way, economic and industrial development provides improvements in the quality of life through products and services, but it is crucial to pay attention to their environmental and social impacts [26]. Although contemporary development models boost wealth and well-being, they have shortcomings by focusing too much on financial success, relegating other important dimensions. These models are responsible for crises and social problems, which makes them unsustainable [27].

Sustainability has become a competitive factor in business, and technological innovation is challenged to be environmentally, socioeconomically, and technologically sustainable [28]. In this sense, sustainability has assumed prominence in recent years, thus becoming a corporate mantra as well as a promising area of research, despite the majority of sustainability studies focusing on environmentally friendly operations. However, sustainability extends beyond environmental performance to also encompass economic and social performance [29]. All three factors combine to form the TBL. The concern with improving social, environmental, and economic standards of living is the basis of the TBL’s link with economic development [30].

The TBL approaches its pillars from the following perspectives. Socially, it seeks to provide access to basic services and global stability. Economically, it aims at financial resilience, long-term investment, and reducing national debt. Environmentally, it faces issues such as climate change, pollution, and species protection, with solutions in renewable energy and recycling [31]. Furthermore, the intersection between the dimensions considered determines the development conditions that can be defined as viable (environmental and economic), equitable (social and economic), and bearable (environmental and social). While these dimensions may seem simple, they are indispensable and appropriate for the full implementation of a widely shared approach to sustainability [32].

Thus, equity involves positive social and economic interactions, such as the eradication of poverty and inequality through a fair distribution of resources. Being bearable involves addressing positive interactions between society and the environment, promoting environmental awareness and the creation of a healthy environment. Being viable involves positive interactions between the environment and the economy, prioritizing environmental issues during economic growth and development [33].

The term Industry 4.0 was first created by the German Economic Development Agency to promote the idea that we are on the brink of the last era of the Industrial Revolution, initiated by the emergence, advancement, and convergence of new technologies [34]. With it comes a new dynamism in companies, which is nothing more than the current business trend in using new knowledge, such as the Internet of Things, big data analysis, blockchain, machine learning, etc. [35].

This apparatus demonstrates a positive causal relationship between the TBL and sustainable development practices, with more and more studies revealing this interconnection [31,36,37]. Technologies such as big data, for example, can improve the use of resources and the training of models based on Artificial Intelligence (AI) for effective decision-making, in addition to generating large-scale processing of customer and product data for ethical and sustainable operations [38]. Artificial Intelligence (AI) through machine learning algorithms has enormous potential to revolutionize service organizations such as healthcare, banking, and e-commerce, thus making them more sustainable [39]. Meanwhile, blockchain technology offers an innovative, decentralized, and transparent transaction mechanism capable of generating computational trust and impacting the security, sustainability, and resilience of organizations [40]. Furthermore, the Internet of Things (IoT) can enhance resource efficiency and encourage sustainable energy and transparency, contributing to the sustainable development of society [41].

Therefore, despite these technologies emerging from the demands of the industrial sector, it is undeniable that they have also begun to influence other sectors of the economy. This is presented in the work of Parida et al. [42], who carried out the PRISMA methodology to identify the benefits that Industry 4.0 technologies bring to the healthcare services sector, which range from the capacity to expand this segment to the ability to train employees more efficiently. Similarly, Bilan et al. [43] studied the factors of online financing services that use Industry 4.0 technologies in traditional banks. To do this, he used the correlation analysis method and thus managed to identify that the most modern credit services were able to increase the financial inclusion and innovation levels in a country.

Furthermore, like Bilan et al. [43], other research has begun to try to understand the impact that this new technological device can have on the banking sector. In the analysis by Shkodina et al. [44], for example, the authors sought to understand the opportunities and threats that arise because of the implementation of new digital technologies in the banking sector. They used varied methodologies and recommended that traditional banks transform their businesses according to the trends of these new technologies.

The study by Mehdiabadi et al. [45] investigated the technologies used in Industry 4.0 in internationally renowned banks. To achieve this, the authors conducted a comprehensive systematic literature review (SLR). They concluded that the basis for evaluating fourth-generation banks (Banks 4.0) is centered on promoting collaboration between financial institutions and their customers. Therefore, the focus must be directed towards improving service provision from the customer’s point of view, so that banks can identify the best ways to enhance their consumers’ experience.

Research conducted by Abdul-Rahim et al. [46] employed an online approach to investigate the impact of benefit and risk perception on consumers’ adoption of Industry 4.0 (I4.0) technology-based banking services. Furthermore, the study explored the contributory potential of these technologies to sustainability. The results revealed a significant influence of the benefits perceived by customers in adopting new technologies, also highlighting the substantial impact of adopting these technologies in promoting sustainability.

The study by Mazurchenko et al. [47] utilized descriptive statistics, a literature review, and semi-structured interviews to explore the concept of Banks 4.0, providing a theoretical framework for digitalization and its drivers in the financial sector. As a result, the need for banks and their employees to develop more modern technological knowledge was highlighted. They also emphasize that digitalization is a catalyst for change, and drives innovation, automation, and the modernization of technological systems in the financial services sector.

Finally, in the work of Amiri et al. [48], researchers combine a literature review with the opinion of experts to determine the criteria for implementing digital banks in the context of Industry 4.0. The results demonstrate that human resources, rules and regulations, and customer satisfaction are the most important criteria for implementing digital banking.

Thus, the growing importance of digital transformation in the financial sphere becomes evident. The analyses carried out in the research highlight the recent adoption of Industry 4.0 technologies by banks. Additionally, scientific studies highlight the strong positive influence of customers’ perception of benefits in adopting these innovations, as well as the potential impact of these technologies in promoting sustainability within the banking sector.

2.2. Sustainability 4.0

Industry 4.0 technology represents a fusion of production processes, incorporating new advances in information technologies and improved manufacturing techniques [31]. These technological innovations have made it possible to achieve increasing levels of efficiency in industrial production and in the services sector [6,42,43]. The convergence of these technological transformations with sustainability goals can drive the path towards a more responsible and resilient industry, capable of meeting society’s current and future demands.

Furthermore, many companies adopt I4.0 technologies to enable operational sustainability against the increasing effects of climate change, decreasing natural resources, and increasing consumer awareness of environmental issues [34]. In this sense, I4.0 offers significant potential to contribute to sustainability in organizations by supporting managerial decision-making, generating increased financial income, and reducing fuel consumption and CO₂ emissions [6]. Similarly, studies demonstrate a strong relationship between I4.0 technologies and sustainability performance [31]. However, there is still a scarcity of research that simultaneously addresses the assessment of readiness and maturity of the concepts of 4.0 technologies and sustainability [34]. Therefore, to effectively promote sustainability through I4.0, it is essential to fill these gaps by developing a more complete and holistic understanding of the relationship between this apparatus and the sustainable objectives of organizations.

Despite this, researchers have encountered challenges in understanding sustainability due to its ambiguity and polysemy. Inoperative and contradictory definitions make it difficult to select an adequate definition, resulting in studies that avoid an explicit definition of the term, which can be considered a methodological error [49]. Contrary to this perspective, Vogt and Weber [50] encourage a debate about this concept based on seven dimensions: (1) Ecological: society and environment; (2) Political: sustainability as politics; (3) Ethical: responsibility between generations; (4) Socioeconomic: sustainability in the economy; (5) Democracy: pluralism and sustainability; (6) Cultural: redefining models of wealth; and (7) Theological: Emphasizing belief in creation.

In a similar way, Pazienza et al. [51] stimulate understanding of the concept of corporate sustainability by carrying out a reflective analysis of the different concepts in the literature and defining it as a new business paradigm that requires attention to environmental, social, and economic dimensions to be able to benefit current and future generations. In Tollefsen’s [52] view, the concept of sustainability entails both positive and negative consequences. On the one hand, the popularity of the idea of sustainability has enabled a broad debate on the responsible use of natural resources. On the other hand, the concept has also become susceptible to various strategic misuses, ranging from deceptive marketing practices by companies to extreme criticism.

In this context, all authors highlight the ambiguity and polysemy of the concept of sustainability, making it difficult to select a conclusion about the definition of sustainability. Perhaps the main reason for this is the fact that sustainability has a nature that adapts to the new contexts in which it is inserted. Thus, the advent of I4.0 may have caused new ramifications for this theme, presenting disruptive perspectives that were previously not possible [6,31,36,37,38,39,40,41,42,43,44,45,46,47,48].

Thus, the connection between sustainability, the TBL, and I4.0 technologies leads to a trend of observed practices that can promote elements of sustainable development when using these tools. This can be observed through the results of academic studies in recent years that have addressed this topic.

Bai et al. [6] investigated the relationship between I4.0 technologies and their implications for sustainability. To evaluate this impact, several attributes of the TBL were incorporated. The study used a hybrid decision method, which integrated techniques such as Fuzzy sets, cumulative prospect theory, and VIKOR. The results revealed that mobile technology was shown to have the greatest impact on sustainability across all industries assessed. Furthermore, in the automotive, electronics, food, and beverage industries, as well as the textile, apparel, and footwear industries, nanotechnology, mobile technology, simulation, and drones have stood out as the technologies with the greatest positive impact on sustainability.

El Hamdi and Abouabdellah [53] analyzed the connection between I4.0 and its impact on the evolution of logistics in a bibliographic study that identified some benefits of this combination. These benefits include better visibility and connectivity of information, in combination with a physical network of fast and reliable delivery options, as well as efficiency in logistics productivity and a reduced impact on the environment. Finally, the authors also highlighted the lack of worker qualifications and the absence of cybersecurity skills as significant risks to be considered more seriously.

Maqbool et al. [54] studied the adoption of I4.0 and Internet of Things (IoT) technologies in the construction industry in Ghana, including the associated benefits. The methodology they used involved an extensive literature review to identify key variables, followed by the creation of a closed-ended questionnaire administered in interviews with 154 construction experts and researchers. The results showed that the most significant benefit of these technologies is the introduction of sustainable policy requirements in tenders, due to the lack of talent and skills in using I4.0 and IoT technologies. This study highlights the need for training and skills development to make the most of these innovations in the construction industry in Ghana.

Pandya and Kumar [39] sought to understand which I4.0 technologies are capable of achieving sustainability in medium and small service companies. To achieve this, two methods were adopted: the FDM (Fuzzy Delphi Method) and the AHP (Analytic Hierarchy Process). They reached the conclusion that to overcome the lack of financial resources in these companies, it is essential to understand the specific needs of each organization. The results indicated the order of importance of I4.0 technologies for sustainability, in descending order, as Artificial Intelligence (AI), Advanced Data Analysis (BDA), Internet of Things (IoT), Cyber-Physical Systems, and Computing in the Cloud, among others. These technologies were evaluated based on their ability to contribute to improving the sustainability of medium and small service companies.

However, some studies have not only highlighted the benefits of Industry 4.0 technologies but also explored their risks. For example, Birkel et al. [55] investigated the risks associated with adopting these technologies based on the TBL approach through a literature review and 14 interviews with experts. They identified economic risks such as inadequate investment, threats to existing business models, and increased competition from new entrants. In the environmental sphere, they discussed the increase in waste and energy consumption, along with possible environmental risks of the “batch size one” concept. In the social aspect, they mentioned concerns about job losses, challenges in reorganizing companies and training employees, as well as internal resistance to change.

Abdul-Hamid et al. [56] analyzed barriers to implementing I4.0 and proposed a model to understand the challenges of Industry 4.0 in the circular economy, aiming to obtain social, economic, and environmental benefits in practice. Thirty significant challenge factors related to I4.0 in the circular economy were identified. The study applied the Fuzzy Delphi Method to address qualitative information and translate linguistic preferences. The study identified 18 essential challenges in Industry 4.0 in the circular economy. The most important challenges include the lack of virtualization of automation systems, unclear economic benefits of digital investments, lack of process design, unstable connectivity between companies, and disruptions in employability.

It was from this perspective that Filgueiras and Melo [14] conducted an SLR 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 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. By dividing the 100 benefits into 14 categories according to the TBL, the concepts that will be applied to each of these benefits that are related to I4.0 Technologies and the services sector in general were established. These benefits are detailed in

Table 1. Benefits of using I4.0 technology that impacts S4.0 in the services sector.

Benefits	Concept
1—Efficiency	A comprehensive advantage that addresses various aspects of a service organization’s operations and is related to improving its efficiency, reliability, and competitiveness based on I4.0 technologies.
2—Innovation	An advantage that allows an organization to promote modern and transformative advances in the services sector, especially in financial and technological services, in the context of I4.0.
3—Performance	A benefit that increases an organization’s ability to achieve and maintain high levels of quality and error reduction in a variety of services and sectors, using the I4.0 technologies.
4—Financial	An advantage linked to I4.0 is that it allows an organization to increase its financial performance, measuring its ability to generate revenue, optimize costs, and improve financial efficiency.
5—Macro	This benefit is related to the use of I4.0 to generate advances in an organization in the macroeconomic environment, measuring its ability to influence and contribute to economic growth and development at a broad level.
6—Management	This indicator generates improvements in management practices in an organization based on the use of I4.0. It seeks to ensure that the organization is equipped to make informed decisions, optimize its resources, and respond to market demands in an agile manner.
7—Employee	An advantage that impacts human resource management practices in an organization, focusing on the well-being, development, and satisfaction of employees, through the I4.0 apparatus. This ensures that employees are valued, have opportunities for growth, and are encouraged to contribute meaningfully to the company’s goals.
8—Client	A benefit linked to the use of I4.0 to improve the quality of the relationship between the organization and its customers, measuring the company’s ability to meet the needs and expectations of its consumers. The Customer Indicator is crucial to the success of an organization in building solid and lasting relationships with your target audience, seeking to ensure that the services provided meet their needs, resulting in satisfaction, loyalty, and positive impacts on consumers’ quality of life.
9—Workplace	A benefit that improves the quality of the physical and psychosocial environment where employees carry out their activities. It is related to improving working conditions and the health and well-being of employees, as well as the safety of the services provided. It seeks to ensure that the workplace is safe, ethical, and conducive to the effective performance of work activities.
10—Society	An advantage that enhances an organization’s contributions to society in general, measuring how its actions and services benefit the community and promote positive social changes. It seeks to ensure that the organization’s actions have a positive impact on society, driving changes and improvements that benefit the community at large.
11—Energy Yield	A benefit capable of improving the environmental and economic impact of an organization’s energy use practices. It seeks to promote the transition to cleaner and more efficient energy sources, helping to conserve natural resources and reduce carbon emissions.
12—Inputs	An advantage capable of evolving responsibility in the use of raw materials, resources, and materials by a service organization, measuring how its practices impact the environment and waste management.
13—Environmental impact	A benefit that improves the company’s accuracy in assessing the effect of its operations and activities on the environment, allowing greater precision in its ability to monitor and manage the environmental impact it generates.
14—Organizational Effects	An advantage related to improving an organization’s practices and policies to improve its performance and environmental impact, measuring the company’s ability to promote sustainability and environmental responsibility in its operations. In short, it seeks to enhance sustainability and environmental responsibility at the core of the organization’s operations, promoting benefits for both the company as well as the environment and society in general.

Source: Adapted from [14].

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A visual representation of these categories can be seen in Figure 1, which provides a systematized view of the contributions of Sustainability 4.0 in the service sector of the economy. The 14 benefits were subdivided based on the TBL. First, the economic benefits stand out. These encompass efficiency, innovation, performance, financial, macro, and management, each contributing in a specific way to improving the sustainability of the services sector, through S4.0. Next, we have social benefits, comprising employees, customers, the work environment, and society. These areas are in line with the positive social impacts generated by the adoption of S4.0 in the services sector. Finally, the environmental advantages stand out, covering energy yield, inputs, environmental impact, and organizational effects. Such benefits highlight the role of S4.0 in promoting environmental responsibility and minimizing negative impacts.

2.3. Interpretive Structural Modeling (ISM) Applied to Sustainability

The ISM methodology emerges as a powerful analysis tool that aims to identify and describe the interrelationships between specific characteristics that define a particular challenge, assigning them a hierarchy based on the importance of their effects. This approach makes it possible to obtain valuable managerial inferences [23]. Furthermore, ISM is considered an effective method for representing causal relationships between elements, playing a crucial role in the decision-making process by providing a hierarchical structure for the model under analysis [57].

ISM is a qualitative approach generated from collaboration with experts and has found application in research across multiple disciplines, including being a useful approach for understanding the complex relationships between hierarchies in the context of adopting sustainable practices [58]. This methodology provides experts with more than one way of collecting information, making it possible to use individual face-to-face interviews, apply the Delphi method, conduct discussion sessions, apply matrix-based questionnaires with a selection of alternatives (V, A, O, and X) for each pair of relationships through software/questionnaires, and the generation of ideas [59]. Janssen et al. [60] and Perwez and Kundu [61] define the stages of ISM analysis as follows:

• Step 1: Identify the variables relevant to the study. In the first step of the ISM methodology, the variables relevant to the study are identified. In the case of this paper, the 14 benefits generated by I4.0 technologies in the sustainability of the service sector were identified based on research by Filgueiras [14].
• Step 2: Establish contextual relationships. The establishment of contextual relationships is carried out based on a structured script that aims to elicit the relationships of influence of one benefit on others or vice versa in the context of banking services.
• Step 3: Build a self-interaction structural matrix. Based on expert opinion, it is possible to develop a self-interaction structural matrix that represents the perceptions of the directed relationships between variables. For this purpose, the notations V, A, X, and O are used to indicate the types of relationships between benefits. For the “V” symbol, the respondent indicates that there is a relationship between benefit i and benefit j, but no relationship between benefit j and benefit i. For symbol “A”, the respondent indicates that there is a relationship between benefit j and benefit i, but no relationship between benefit i and benefit j. The respondent indicates that the symbol “X” indicates that there is a bidirectional relationship between both benefits at the same time, whether from i to j or from j to i. For the “O” symbol, the respondent indicates that the relationship between benefits is non-existent, whether between i and j or j with i.
• Step 4: Develop the Accessibility Matrix. This matrix reflects the accessibility relationships between the variables. In this way, with the completion of the self-interaction structural matrix, it is possible to convert it to a binary matrix, replacing the values of V, A, X, and O in the numbers 0 and 1. Thus, to establish the initial reachability matrix from the self-interaction structural matrix, different classifications are applied to the corresponding entries (i, j):

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  If the entry (i, j) is identified as “V” in the self-interaction structural matrix, the corresponding entry in the initial reachability matrix will be assigned the value 1, while the corresponding entry (j, i) will be defined as 0.

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  Similarly, if the rating is “A”, the entry (i, j) in the initial reachability matrix will be 0, and the corresponding entry (j, i) will be 1.

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  For the “X” classification, the entries (i, j) and (j, i) in the Initial Accessibility Matrix will be set to 1.

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  On the other hand, if the rating is “O”, both entries (i, j) and (j, i) in the initial reachability matrix will be set to 0.

• Step 5: Check the transitivity of the matrix. Since the Initial Accessibility Matrix is formed from the conversion of binary numbers 1 and 0, the search for transitivity between factors occurs based on the following idea: if an attribute “1” is linked to an attribute “2 “ and that same attribute “2” is linked to an attribute “3”, there is likely a relationship between “1” and “3”.
• Step 6: Partition the variable hierarchy. When defining levels, the identified links are grouped according to their nature and relative relevance. This helps to establish a cascade structure that mirrors the connections between benefits at different levels of influence. Each level represents a different degree of impact or dependence between the elements. To carry out this process, it is necessary to define the sets of accessibility and antecedents corresponding to each category, as well as the intersection of these sets. These iterations are crucial for systematically building this hierarchy and understanding the relationships between system components. For its creation, first, the “Set of Background” (set A) is formed, where the elements of the system are identified and listed. This set serves as a starting point for understanding the relationships between the elements. Next, we create the “Intersection Set” (set R), where for each pair of elements, we determine which ones have a direct relationship. This set contains pairs of elements that influence each other. These sets are then combined based on analyses of the relationships between the elements. The intensity of influences between components is reviewed and corrected as necessary. From these iterations, a “Common Level” emerges that was established in the previous step. This common level serves as the basis for determining the “Top Level” in the hierarchy. Based on this higher level, we formulate the “Level Partitioning Interactions”. This means that influences between different levels and hierarchical relationships are mapped, resulting in a clear representation of the structure of interconnections in the system. These iterations continue until a solid hierarchical structure is built, which illustrates the relationships and interactions between the system’s components.
• Step 7: Develop a MICMAC analysis chart. This analysis is used to facilitate decision-making by highlighting which benefits exert the greatest influence, especially by identifying the most susceptible attributes and understanding their behavior in a complex system. This results in the possibility of categorizing them into four groups based on driving and dependency power as follows:

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  Cluster I (Autonomous Variables): This group is characterized by having low dependency power and weak driving power.

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  Cluster II (Dependent Variables): This group has high dependency power and low driving power. This region is characterized by the fact that its elements depend on each other but have little power to influence other benefits.

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  Cluster III (Linking Variables): This group is made up of elements with high dependency power and high driving power. The benefits in this region can influence the other benefits, in addition to being influenced.

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  Cluster IV (Independent Variables): This group presents attributes with low dependency power and high driving power, with a high capacity to influence other benefits in a stable manner.

Therefore, with its ability to assist in the analysis of complex and ambiguous data, ISM stands out as a relevant approach for both academic research and the management of practical issues. This methodology is also well applied to the approach to sustainability, as demonstrated by some studies carried out by academics.

The study by Narayanan et al. [62] aimed to identify and prioritize relevant barriers to implementing sustainable practices in the rubber product manufacturing industry in India. Initially, the authors conducted a systematic literature review (SLR) and identified 11 important barriers, which were submitted to the ISM methodology. The results suggest that the lack of commitment from senior management, the lack of motivation, the lack of government initiatives, and the high initial cost are some of the main barriers to implementing sustainable practices in organizations.

The study carried out by Vafadarnikjoo et al. [63] used Interpretive Structural Modeling (ISM) and classification (HF-MICMAC) methodologies to identify the interrelationships between social sustainability criteria. The main objective of the study was to develop a social sustainability assessment framework that could guide decision-making in developing economies. The results demonstrated that community rights and employment practices are the most critical social sustainability criteria, suggesting that the local community and its involvement can be important drivers for achieving sustainability beyond the walls of the focal company.

In the paper by Chauhan et al. [64], the authors emphasize the crucial importance of supplier selection in supply chain management and its strategic impact on the competitiveness of organizations. The study consisted of a literature review where 21 sustainable supplier selection criteria were identified and grouped into three dimensions: economic, environmental, and social. To analyze these criteria and their interrelationships, the authors used the ISM and developed an explanatory hierarchical model. The results demonstrated that “service provision”, “Ecological Design” and “stakeholder rights” are the most significant criteria in the ISM model of sustainability, strongly influencing other criteria because they have a stronger driving power.

The study by Bux et al. [65] examines the barriers to implementing Corporate Social Responsibility (CSR) in Pakistan’s manufacturing industry. Through the use of ISM and MICMAC techniques, they identify and prioritize the main barriers that affect the adoption of CSR in this sector. The results of the study provide valuable insights into the challenges that developing countries face in implementing sustainability, as the data indicates that “lack of resources”, “lack of regulations and standards”, and “lack of political incentives” are the most critical barriers hindering the implementation of CSR in Pakistan’s manufacturing industry.

The work carried out by Gholami et al. [66] aimed to contribute to sustainability in higher education through the implementation of sustainable practices on campus. These practices are considered essential elements in Higher Education Institutions (HEIs). To this end, a literature review was used to investigate the implementation of these practices on campus, focusing on the analysis of existing barriers. Thus, the contextual relationships between the main barriers identified were studied using an ISM-based approach, with the intention of improving the understanding of the interactions and correlations between them. Among the main findings, ‘lack of awareness’, ‘lack of knowledge’, ‘resistance to change’, and ‘inefficient communication’ were identified as the dominant barriers, with high driving power and low dependence.

Chen et al. [67] conducted an analysis to identify barriers related to sociopolitical sustainability in the banking sector supply chain in emerging economies, with a focus on India. Using ISM and MICMAC techniques, they identified significant barriers such as “anti-social considerations”, “unstable political climate”, and “lack of political cohesion”. The study highlights the importance of addressing these barriers to achieving sociopolitical sustainability and improving customer satisfaction.

Gani et al. [68] managed to identify 15 crucial indicators of environmental sustainability to assess the impact of manufacturing activities of small and medium-sized companies on the environment. To achieve this objective, the study employed different methodological techniques, including the ISM (Interpretive Structural Modeling) approach. The main results highlight the design of green products as a determining factor in influencing other sustainability indicators.

Paul et al. [69] also used ISM partially to assess supply chain sustainability in the Bangladesh timber industry. The objective was to use this methodology to determine the driving power and dependence of critical success factors for sustainability in this segment. The results reveal that research and development, supplier relationships, and the use of eco-friendly technology are the most significant critical success factors of the Bangladesh timber industry.

In the study conducted by Jangre et al. [70], social, economic, and environmental factors were considered to analyze the long-term viability of a biodiesel plant based on used cooking oil in India. For this analysis, the ISM methodology was used to prioritize and evaluate factors related to the sustainability of biodiesel plants. Additionally, the study suggests that ‘legal and regulatory compliances’, ‘political restrictions’, ‘international relations’, ‘health and education’, and ‘public safety and security’ are the five most influential factors that must be addressed for sustainability in biodiesel power plants.

Although there are ongoing studies on the adoption of Industry 4.0 technologies, research that seeks to propose an ISM to guide the prioritization of these technologies in companies in the banking sector, with a focus on Sustainability 4.0, is still limited. Furthermore, the scarcity of similar models aimed specifically at the banking sector accentuates the lack of specific approaches in this context. To verify this statement, a search was carried out in the Web of Science database using the keywords “Industry 4.0 (all fields)”, “financial services (all fields)”, and “bank (all fields)”, with a time range covering 2019 to 2024 and with open access. The results of the initial search produced a set of 18 results, of which 10 studies were selected after an initial review. These selected studies were later systematized and are presented in

Table 2. Bibliographical Survey of Research related to Industry 4.0 and the banking sector.

Author	Objective	Method	Results
Bilan [43]	Study the factors of online financing services as an alternative to traditional financial intermediaries (including banks).	Correlation Analysis	(1) The significant impact of the country’s economic development on the degree of alternative financial development; (2) The strong direct influence of financial inclusion and the country’s level of innovation on the volume of alternative financing; (3) The lack of direct influence of information technology and the absence of state regulatory influence on the development of the alternative financial market.
Shkodina [44]	Consider the opportunities and threats that arise as a result of global banks implementing new digital technologies.	Statistical analysis combined with observation and generalization	(1) The digitalization of the banking sector does not occur uniformly in all regions; (2) There is a tendency to use 4.0 technologies in the banking sector, including everyday interactions with customers and advanced analysis of unstructured data. This technological adoption, on the one hand, intensifies market competition and efficiency, but on the other hand, introduces new systemic risks that can affect financial stability and integrity; (3) The main obstacles to digital transformation are not technological in nature, but rather related to the difference in organizational cultures between traditional banks and fintech companies. Furthermore, there are divergences in strategic management visions and a lack of qualified personnel to drive this transformation.
Lee e Lee [71]	(1) Examine the evolution of customer-centric service and offloading through a literature review. (2) Explore the drivers of the emergence of “untact” as a new service strategy.	Literature review	(1) Financial services seek to simplify and improve financial transactions for customers. This includes facilitating payments, money transfers, and investments, as well as providing greater convenience and security through digital solutions such as banking apps and online payment systems. (2) Customers began to trust more services offered by chatbots and Artificial Intelligence in the banking sector.
Wellalage et al. [72]	Understand the relationship between ICT and Financial Inclusion (FI) of entrepreneurs in African countries.	Cross-sectional data from the World Bank Business Survey (WBES) database for African economies	(1) Confirm the important role that technological advancement plays in advancing Africa’s financial inclusion and the potential for broader applicability to other developing economies. (2) The growth of ICT represents the potential to reduce information asymmetry, which in turn provides other macroeconomic benefits, including stimulating economic growth and employment rates and ensuring overall financial sector stability.
Mazurchenko et al. [47]	Provide a theoretical framework for digitalization and its drivers in the financial sector, present the phenomenon of Banking 4.0 in relation to the necessary skills, and identify gaps and barriers for faster and more effective development.	Literature review and analysis of selected primary and secondary data. Descriptive statistics and Spearman’s correlation coefficient, as well as semi-structured interviews with experts	(1) Digitalization is a catalyst for change and drives innovation, automation, and the modernization of technology systems in the financial services sector. (2) There will be a tendency for I4.0 technologies to be the main drivers of the global banking sector in the next 5 years. (3) The data presented in the article confirm that companies in the financial sector see digitalization as important and realize the need to regularly develop the digital skills of their employees, considering it a strategic aspect of their future development.
Svitlana et al. [73]	Summarize existing global trends in banking informatization of the financial sector and assess the impact of selected macroeconomic indicators on them.	Primary and secondary data; multifactor correlation and regression model	(1) Information technologies in the field of banking services have the strategic effect of increasing the customer base and reducing the cost of banking operations at the optimal level of operational risk and operating costs. (2) The main global trends in the development of banking informatization include the close relationship between banks and customers, the integration of banks into the IT sector, the interaction of banks with social networks, and the adoption of new technologies.
Saputra et al. [74]	Estimate maximum potential losses for digital banking transaction risks	Semi-structured interviews in Fintech companies	(1) The risk related to the workforce (inadequate management or absence) does not directly affect the company’s financial results. (2) Despite the high digitalization of Fintech businesses, the greatest influence on a company’s financial results is the company’s governance.
Abdul-Rahim et al. [46]	(1) Examine whether perceived benefits and risks affect the adoption of FinTech services; (2) test the role of COVID-19 fear in FinTech adoption; and (3) investigate whether the adoption of FinTech contributes to sustainability.	Structural Equation Modeling (SEM)	(1) Perceived benefits significantly influence the adoption of digital banks, while perceived risk does not; (2) Fear of COVID-19 moderates the relationship between perceived benefits and the adoption of digital banks and fully mediates the relationship between perceived risk and their adoption; (3) The adoption of digital banks significantly affects sustainability.
Pellegrino e Abe [75]	Contribute to the literature on digital finance by studying the link between digital finance tools to support MSMEs, especially in times of crisis.	Bibliometric Study	(1) Digital financing can be an important solution to increase medium and small businesses’ access to financial services, especially in developing economies. (2) Digital literacy is a fundamental challenge for governments seeking to help medium and small businesses with innovative tools, highlighting the need for awareness campaigns to demonstrate the benefits of using digital technologies.
Mhlanga [76]	The objectives of this study were to analyze how blockchain technology has contributed to including previously underserved populations in the conventional financial system and to highlight best practices and lessons learned in relation to sustainable development.	Systematic literature review	(1) Sustainable development can be promoted in several areas if the technology behind blockchains is successfully used to improve financial inclusion. (2) Governments, especially in developing countries, must prioritize investments in blockchain if they want to increase citizens’ access to financial services.

Source: The authors (2024).

.

3. Materials and Methods

In this study, the focus is on applying the ISM methodology to analyze the contextual relationships influenced by Industry 4.0 (I4.0) technologies in the banking services sector from the perspective of Sustainability 4.0 (S4.0). Based on the systematic literature review proposed by Filgueiras and Melo [14], we identified 14 potential benefits capable of influencing sustainability within the services sector through I4.0 technologies. Therefore, a group of sustainability experts from the banking sector was invited to guide the contextual relationships of these identified advantages.

The invitations were extended to three banking institutions in the Metropolitan Region of Recife (Northeast Brazil): two public banks and one private bank, for several strategic and practical reasons. The selection of public banks is based on their representativeness and significant impact on the local economy, as well as their extensive scope in providing financial services to the community. These institutions play a pivotal role in the economic and social development of the region, offering a comprehensive and robust perspective on the topic under investigation.

On the other hand, the inclusion of a private bank provides a complementary perspective, particularly regarding its differentiated management and sustainability practices and policies. This diversity among the participating banks enables a more comprehensive comparison and analysis of the strategies adopted by various types of financial institutions in response to the challenges and opportunities posed by Industry 4.0 and Sustainability 4.0.

Furthermore, the convenience of invitations is related to the accessibility and availability of the institutions selected to collaborate in the study. The criteria for participation were: (I) at least an undergraduate degree, (II) a significant professional trajectory with a minimum of five years of experience in the banking sector, (III) direct or indirect involvement in any sustainability policy of the institution, and (IV) availability to participate in the study.

The methodology was applied through online consultations with the help of employees from these organizations. Initially, 25 experts were invited to participate in this work, of whom 14 agreed to make themselves available. Among the selected experts, 10 were employees of public banks, including 3 involved in decision-making activities within their institutions, while the others had at least seven years of experience in the field. The remaining 4 were employees of private banks familiar with the proposed topic.

To achieve the objectives of this study, we established an online environment where experts could exchange messages and develop a consensus on the model. In this space, study participants shared information, asked questions, and proposed suggestions that aligned with the study’s objectives. A detailed outline of the model development process can be found in Figure 2.

4. Results

In this study, the ISM methodology was applied to analyze the contextual relationships between the benefits of Industry 4.0 in the banking sector from the perspective of Sustainability 4.0. Fourteen influential sustainability benefits were identified by banking sector experts.

The experts, selected with specific criteria, were consulted through a message exchange platform. In total, 14 experts participated, including employees from public and private banks with relevant experience. To achieve the objectives of the study, an online environment was created where experts could collaborate, exchange information, and reach a consensus on the model. This process of interaction and collaboration was fundamental to the development of the model. After reaching a consensus on contextual relationships, the self-interaction structural matrix was then developed, as shown in

Table 3. Self-interaction Structural Matrix.

(j)	1	2	3	4	5	6	7	8	9	10	11	12	13	14
Benefits
(i)
1—Efficiency		V	V	A	A	V	V	A	V	V	V	V	V	V
2—Innovation			V	A	A	V	V	A	O	V	A	V	X	V
3—Performance				A	A	X	X	A	A	A	A	A	V	V
4—Financial					O	V	V	O	V	V	V	V	X	V
5—Macro						V	V	O	V	V	V	V	V	V
6—Management							X	A	A	A	A	A	V	V
7—Employee								A	A	A	A	A	V	V
8—Client									V	V	O	V	V	V
9—Workplace										V	A	V	V	V
10—Society											A	A	A	V
11—Energy Yield												V	X	V
12—Inputs													X	V
13—Environmental impact														V
14—Organizational Effects

Source: The authors (2024).

.

Table 3 presents the contextual relationship between the benefits elicited by the experts. Benefits in which there is a relationship between i and j but no relationship between j and i were classified as “V”. Benefits in which there is a relationship between j and i but no relationship between i and j were classified as “A”. Benefits in which there is a relationship between i and j as well as j and i were classified as “X”. Finally, benefits where there is no relationship were classified as “O”. With the creation of the Structural Self-Interaction Matrix, it is possible to create the Initial Accessibility Matrix. To do this, simply start converting the notation V, A, X, O into a matrix of binary values (0, 1). Therefore, the Initial Accessibility Matrix can be viewed in

Table 4. Initial Accessibility Matrix.

Benefits	1	2	3	4	5	6	7	8	9	10	11	12	13	14	Driving Power
1—Efficiency	1	1	1	0	0	1	1	0	1	1	1	1	1	1	11
2—Innovation	0	1	1	0	0	1	1	0	0	1	0	1	1	1	8
3—Performance	0	0	1	0	0	1	1	0	0	0	0	0	1	1	5
4—Financial	1	1	1	1	0	1	1	0	1	1	1	1	1	1	12
5—Macro	1	1	1	0	1	1	1	0	1	1	1	1	1	1	12
6—Management	0	0	1	0	0	1	1	0	0	0	0	0	1	1	5
7—Employee	0	0	1	0	0	1	1	0	0	0	0	0	1	1	5
8—Client	1	1	1	0	0	1	1	1	1	1	0	1	1	1	11
9—Workplace	0	0	1	0	0	1	1	0	1	1	0	1	1	1	8
10—Society	0	0	1	0	0	1	1	0	0	1	0	0	0	1	5
11—Energy Yield	0	1	1	0	0	1	1	0	1	1	1	1	1	1	10
12—Inputs	0	0	1	0	0	1	1	0	0	1	0	1	1	1	7
13—Environmental impact	0	1	0	1	0	0	0	0	0	1	1	1	1	1	7
14—Organizational Effects	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
Dependence power	4	7	12	2	1	12	12	1	6	10	5	9	12	14	-

Source: The authors (2024).

.

From the V, A, X, and O matrix, the Initial Accessibility Matrix was developed following these rules: For the “V” classification in the self-interaction structural matrix, the corresponding entry in the initial reachability matrix will be assigned the value 1, while the corresponding entry (j, i) will be defined as 0. For the “A” classification, the entry (i, j) in the initial reachability matrix will be 0, and the corresponding entry (j, i) will be 1. For the “X” classification, the entries (i, j) and (j, i) in the Initial Accessibility Matrix will be set to 1. For the “O” classification, both entries (i, j) and (j, i) in the initial reachability matrix will be set to 0.

Next, it is necessary to verify the transitivity of the Matrix to create the Final Accessibility Matrix. To this end, the search for transitivity is carried out based on the fundamental principle: when a benefit “1” is interconnected with a benefit “2” and, in turn, this benefit “2” is associated with a benefit “3”, this strongly suggests the existence of a relationship between “1” and “3”. In simple terms, the Final Accessibility Matrix replaces relationships previously classified as “0” in the Initial Accessibility Matrix with “1*” in the Final Accessibility Matrix, when the transitive relationships that are established are identified. The result obtained in the final accessibility matrix can be obtained below in

Table 5. Final Accessibility Matrix.

Benefits	1	2	3	4	5	6	7	8	9	10	11	12	13	14	Driving Power
1—Efficiency	1	1	1	1*	0	1	1	0	1	1	1	1	1	1	12
2—Innovation	1*	1	1	1*	0	1	1	0	1*	1	1*	1	1	1	12
3—Performance	1*	1*	1	1*	0	1	1	0	1*	1*	1*	1*	1	1	12
4—Financial	1	1	1	1	0	1	1	0	1	1	1	1	1	1	12
5—Macro	1	1	1	1*	1	1	1	0	1	1	1	1	1	1	13
6—Management	1*	1*	1	1*	0	1	1	0	1*	1*	1*	1*	1	1	12
7—Employee	1*	1*	1	1*	0	1	1	0	1*	1*	1*	1*	1	1	12
8—Client	1	1	1	1*	0	1	1	1	1	1	1*	1	1	1	13
9—Workplace	1*	1*	1	1*	0	1	1	0	1	1	1*	1	1	1	12
10—Society	1*	1*	1	1*	0	1	1	0	1*	1	1*	1*	1*	1	12
11—Energy Yield	1*	1	1	1*	0	1	1	0	1	1	1	1	1	1	12
12—Inputs	1*	1*	1	1*	0	1	1	0	1*	1	1*	1	1	1	12
13—Environmental impact	1*	1	1*	1	0	1*	1*	0	1*	1	1	1	1	1	12
14—Organizational Effects	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
Dependence power	13	13	13	13	1	13	13	1	13	13	13	13	13	14

Source: The authors (2024).

.

Table 5 also presents data relating to driving power and dependence power. Driving Power relates to the ability of an attribute to influence the realization of another benefit, and this is represented by the sum of the benefits across the lines. On the other hand, dependence power is linked to the ability of a benefit to be achieved without affecting the achievement of other benefits. This indicator can be evaluated by the vertical sum of the attributes; that is, by the columns in the table.

The Economic benefits 1—Efficiency, 2—Innovation, 3—Performance, 4—Financial, and 6—Management presented a driving power of “12” and a dependence power of 13. Similarly, the social benefits 7—employee, 9—Workplace, and 10—Society obtained the same numbering. Finally, the environmental attributes 11—Energy Yield, 12—Input, and 13—Environmental Impact were classified in the same way. Therefore, the highlights are the benefits: 5—Macro, 8—Customer, and 14—Organizational Effects. The “Macro” and “Client” benefits had a driving power of “13” and a dependency power of “1”, while the “Organizational Effects” benefit had a driving power of “1” and a dependency power of “14”.

In this way, the benefits must be categorized according to their influence and dependence and will serve to form the groupings in the Level Partition (PN) [77]. The results can be seen in

Table 6. Level partition.

Benefits	Reachability Set	Antecedent Set	Intersection Set	Level
1—Efficiency	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	2
2—Innovation	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	2
3—Performance	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	2
4—Financial	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	2
5—Macro	5	5	5	3
6—Management	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	2
7—Employee	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	2
8—Client	8	8	8	3
9—Workplace	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	2
10—Society	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	2
11—Energy Yield	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	2
12—Inputs	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	2
13—Environmental impact	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,	1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13,	2
14—Organizational Effects	14	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14	14	1

Source: The authors (2024).

.

After defining the Level Partitioning that groups elements with similar influences, we use it as a basis to establish the “Top Level” in the hierarchy. From this starting point, “Level Partitioning Interactions” are created, as shown in Table 6, to map the influences and relationships between the different levels and elements. This iterative process persists until a solid hierarchical structure is developed, allowing for continuous adjustments, and improving the representation of the intricate relationships and interactions between system components. This step helps to unveil the complexity of these relationships and offers valuable insights for understanding the system. It is also necessary to obtain the higher level between several levels, being a conditioning process for formulating the Conic Matrix [58].

In this way, the Initial Accessibility Matrix is rearranged by levels in a conical matrix, grouping elements of the same level, and forming a digraph. Removing transitivity transforms the digraph into the ISM model, as explained in the ISM technique [58,59].

Table 7. Conic Matrix.

Benefits	14	1	2	3	4	6	7	9	10	11	12	13	5	8	Driving Power	Level
14—Organizational Effects	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
1—Efficiency	1	1	1	1	1*	1	1	1	1	1	1	1	0	0	12	2
2—Innovation	1	1*	1	1	1*	1	1	1*	1	1*	1	1	0	0	12	2
3—Performance	1	1*	1*	1	1*	1	1	1*	1*	1*	1*	1	0	0	12	2
4—Financial	1	1	1	1	1	1	1	1	1	1	1	1	0	0	12	2
6—Management	1	1*	1*	1	1*	1	1	1*	1*	1*	1*	1	0	0	12	2
7—Employee	1	1*	1*	1	1*	1	1	1*	1*	1*	1*	1	0	0	12	2
9—Workplace	1	1*	1*	1	1*	1	1	1	1	1*	1	1	0	0	12	2
10—Society	1	1*	1*	1	1*	1	1	1*	1	1*	1*	1*	0	0	12	2
11—Energy Yield	1	1*	1	1	1*	1	1	1	1	1	1	1	0	0	12	2
12—Inputs	1	1*	1*	1	1*	1	1	1*	1	1*	1	1	0	0	12	2
13—Environmental impact	1	1*	1	1*	1	1*	1*	1*	1	1	1	1	0	0	12	2
5—Macro	1	1	1	1	1*	1	1	1	1	1	1	1	1	0	13	3
8—Client	1	1	1	1	1*	1	1	1	1	1*	1	1	0	1	13	3
Dependence Power	14	13	13	13	13	13	13	13	13	13	13	13	1	1
Level	1	2	2	2	2	2	2	2	2	2	2	2	3	3

Source: The authors (2024).

demonstrates the result.

Once all the ISM methodology procedures were completed, it became feasible to create a model with a comprehensive list of benefits. Thus, employing the fundamental principles of directed graph theory, an ISM model is created to illustrate the hierarchical representations of the benefits generated by S4.0 in the banking services sector, as depicted in Figure 3.

The results demonstrate that the Organizational Effects benefit (14) is at level I, while the benefits Efficiency (1), Innovation (2), Performance (3), Financial (4), Management (6), Employee (7), Workplace (9), Society (10), Energy Yield (11), Inputs (12), and Environmental Impact (5) were selected at Level II. Finally, the Macro (5) and Customer (8) advantages were categorized at Level III.

Level II attributes represent advantages that can impact the benefit in Level I and are influenced by those in Level III. Therefore, they do not indicate the initial advantages when adopting S4.0 in the banking sector but should be prioritized to mature these practices in these organizations, mainly due to their relationship with the attributes in Level III.

Macro (5) and Customer (8) benefits are associated with level III. This means that attributes at this level have a direct influence on all other benefits of S4.0 in the banking sector. This implies the importance of these two economic advantages for S4.0 in the banking sector as classified in the TBL.

The MICMAC analysis (Figure 4) is related to subdividing the benefits of S4.0 in the banking services sector into four clusters. The first is the cluster of autonomous variables with low dependency power and weak driving power; the second is the cluster of dependent variables with high dependence power and low driving power; and the third is the cluster of linking variables with the benefits of high dependence power and high driving power. Finally, the fourth cluster, with the independent variables, is characterized by low dependence and high driving power.

No benefit from this work was categorized in the cluster of autonomous variables, which is an indication that the 14 advantages studied have some degree of interconnection. The benefit of Organizational Effects (14) was defined in the cluster of dependent variables, which makes it the only indicator characterized by dependence on others and with little capacity to influence other variables. The advantages of Efficiency (1), Innovation (2), Performance (3), Financial (4), Management (6), Employee (7), Workplace (9), Society (10), Energy Performance (11), Inputs (12) and Environmental Impact (5) were better defined in the cluster of linking variables, which makes them capable of influencing other benefits without losing the characteristic of also being influenced. Finally, in the cluster of independent variables, meaning those capable of influencing other benefits in a stable way and which banking organizations should pay greater attention to, the Macro (5) and Customer (8) advantages were defined.

5. Discussion

After viewing the arrangement of each benefit by ISM hierarchy level, it is necessary to understand their meanings. Initially, the relevance found in the Macro (5) and Customer (8) benefits stands out. These were the benefits with greater driving power and lower dependency power and were classified as independent variables and associated with level III, with a greater ability to generate other benefits. They were therefore considered a priority for achieving S4.0 in the banking services sector.

The Macro (5) benefit, as identified in the research by Filgueiras and Melo [14], is directly linked to the use of I4.0 to drive improvements in the macroeconomic context of an organization. This attribute contains essential characteristics for the existence of the banking sector, such as how to develop long-term customer loyalty and increase the organization’s share price.

Furthermore, the high driving power of this indicator also suggests the need for financial resources combined with organizational stability to make the most of the benefits of I4.0 in banking sustainability, as suggested in the studies by Pandya and Kumar [39], Birkel et al. [55], Narayanan et al. [62], Bux et al. [65], and Pellegrino and Abe [75]. Furthermore, this benefit is congruent with the findings of Wellalage et al. [72]. Therefore, it is important to highlight the importance of business maturation in the use of these technologies, when aiming to reach S4.0 in the banking services sector, suggesting that perhaps medium and small companies may have greater difficulties in achieving all the benefits exemplified in this work, which is in line with studies by Pandya and Kumar [39] and Pellegrino and Abe [75]. Additionally, countries facing a lack of precise regulations, political instability, or developmental inconsistencies may be a barrier to S4.0 in the banking sector, as seen in the studies by Bilan et al. [43], Shkodina et al. [44], Bux et al. [65], Jangre et al. [70], and Chen et al. [78].

In this sense, the implementation of I4.0 technologies, such as Artificial Intelligence, big data, and the Internet of Things (IoT), allows banks to optimize their operations, reducing operational costs and increasing efficiency. This, in turn, can result in greater profitability, which is reflected in the appreciation of the organization’s shares in the market. Furthermore, I4.0 facilitates the personalization of banking services, improving the customer experience and, consequently, long-term loyalty. Another important dimension is the ability to respond to economic and regulatory changes. I4.0 allows banks to be more agile and adaptable, reacting quickly to new challenges and opportunities in the global market. This adaptability is crucial to maintaining relevance and competitiveness in the financial sector.

Regarding the Customer (8) benefit, this result is congruent with the banking adage that its customers are the foundation of financial institutions. At RSL, this benefit is related to the improvement of service provision to its consumers and the increase in their satisfaction. To prosper, banks must place their customers’ interests and needs at the center of their operations and strategies [22]. Furthermore, the adoption of innovative technologies and the personalization of financial services are crucial to meeting the growing expectations of modern consumers, thus ensuring a competitive advantage in the financial market.

Therefore, it is natural that the benefits that will arise from S4.0 in this segment initially begin by reflecting on how to benefit their consumers, whether through the expansion of services as demonstrated in the studies by Parida et al. [42] and Bilan et al. [43], through more innovative services as evidenced by Abdul-Rahim et al. [46], or in the provision of more modern, simple, and safe services, as pointed out by Lee and Lee [71]. In addition, this finding is congruent with the study by Svitlana et al. [73]. Therefore, it is consistent to expect that the advantages related to the customers of service organizations are drivers of the other indicators, as observed by Demir et al. [34].

The analysis also demonstrated that the benefits categorized in Level II make up 11 of the 14 advantages presented. As these variables have a high power of dependence and a high power of influence, it is suggested that there is a complex and highly interconnected system so that an action carried out on specific variables will have an impact not only on these variables but also on others, creating a cascade effect due to the high connectivity and interdependence between them. This observation reveals the interactive complexity of the system’s elements, capable of generating a complex and dynamic environment [59], meaning that the operational level of the organization needs to consider possible instabilities in achieving these benefits. Furthermore, this characteristic can make it difficult to identify key elements in the system, which can create barriers to determining which benefits are most relevant to the organization. This is partially in line with the results found by Pandya and Kumar [39]. However, there were no benefits categorized in Cluster I of Autonomous Variables, which is a strong suggestion that all attributes studied are relevant to S4.0 in the banking sector.

In this group, there is a mix of benefits in the three dimensions of the TBL. This is a strong indication that the use of the I4.0 apparatus in the banking sector is strongly related to sustainability, as highlighted by Abdul-Rahim et al. [46]. Furthermore, it shows, according to the results of other studies, how the use of these technologies can bring direct benefits to the financial services sector. For example, they benefit social indicators, such as for employees, as stated by Mazurchenko et al. [47]; they benefit society, as pointed out by Wellalage et al. [72] and Mhlanga [76]; and the work environment, as partially discussed by Shkodina et al. [44]. Additionally, they enhance economic attributes.

6. Conclusions

This paper presents the benefits brought about by Industry 4.0 in the services sector from the perspective of the TBL to demonstrate the relationship between sustainability and Industry 4.0, as discussed by Bai et al. [6] and Ghaithan et al. [31]. As a result of using the ISM methodology, it was possible to carry out a contextual analysis and rank the 14 proposed benefits. Thus, the Macro (5) and Customer (8) benefits were identified with greater power of influence and less power of dependence on the others categorized in Level III.

The analytical method applied is not exactly new, but the area of application is original and the derived results can clearly be useful for the related area. Thus, when using the ISM methodology to identify the benefits of S4.0 in this sector, it is possible to find valuable findings through the ISM/MICMAC analysis on the topic: 1—it was possible to identify the most prominent benefits in order of relevance; 2—the proposed model appears to have a high level of interconnection, which suggests that the system is more complex than it appears; and 3—the MICMAC results demonstrated the Macro (5) and Customer (8) benefits as the main determinants of S4.0 in the services sector, being a strong indication for the banking segment. Therefore, the application of ISM to the topic of sustainability is particularly relevant, mainly because it provides insights into understanding the interdependencies and implications of actions aimed at sustainability in the banking services sector. Furthermore, the study contributes to understanding the relationship between sustainability and I4.0 technologies.

This study significantly contributes to both academia and practical applications by elucidating the benefits of Industry 4.0 technologies within the banking sector through the lens of Sustainability 4.0 and the Triple Bottom Line. Academically, it fills a critical gap in the literature by applying the ISM methodology to systematically analyze and rank the contextual relationships of these technologies in enhancing sustainability. The findings underscore the interconnectedness of economic, social, and environmental benefits, providing a nuanced understanding that can inform future research in sustainability-driven technological adoptions. Practically, the study offers valuable insights for banking institutions aiming to integrate Industry 4.0 innovations, emphasizing the need for holistic approaches that balance financial gains with broader societal and environmental impacts. By aligning with UN SDGs such as industry, innovation, and infrastructure (SDG 9) and responsible consumption and production (SDG 12), this research provides a structured framework for decision-makers to navigate complexities and foster sustainable development within their organizations.

Despite its contributions, this study has several limitations that warrant consideration. Firstly, the research relied on a relatively small sample size of experts from specific banking institutions in the Metropolitan Region of Recife, which may limit the generalizability of findings to other regions or types of financial institutions. Additionally, the use of the ISM methodology, while robust, depends on subjective expert opinions, which can introduce biases and variations in interpretation. Future research could address these limitations by expanding the study to include a broader and more diverse sample of banks across different geographical regions. Moreover, incorporating longitudinal studies or case analyses could provide deeper insights into the long-term impacts of Industry 4.0 technologies on sustainability in banking. Furthermore, exploring the specific challenges and opportunities faced by different types of banks (e.g., small vs. large, regional vs. multinational) in implementing Sustainability 4.0 initiatives could offer nuanced perspectives. Furthermore, it is suggested to use multiple methods to confirm the current conclusions, which will strengthen the credibility of the prepared investigation. Lastly, examining the potential ethical and social implications of these technologies, such as data privacy concerns and workforce displacement, would enrich our understanding of their holistic impact on society and the environment.