On the Interplay Between Behavior Dynamics, Environmental Impacts, and Fairness in the Digitalized Circular Economy with Associated Business Models and Supply Chain Management

Abstract

In contemporary research, the digital transformation of industries and societies has increased the importance of interdisciplinary exploration, particularly when addressing the complex challenges faced by modern organizations and social systems. From the perspective of digitalization, this literature review examines the intricate interactions between three key research domains: behavior dynamics, environmental impact, and fairness. By reviewing a wide range of studies and methodologies, it reveals new insights, challenges, and opportunities that arise at the intersection and through the interdependencies of these areas within digital ecosystems. Through a structured approach covering preliminary background, state-of-the-art methods, and comprehensive analysis, this document seeks to reveal the synergies and divergences among these domains. Special emphasis is placed on their implications in the digitalization of modern circular economy, business models, and supply chain management contexts where these domains converge in meaningful ways. Additionally, through an extensive review of the existing literature, this document highlights the current state of research and identifies notable gaps. These include issues such as ensuring fairness in digitalized sustainable strategies, understanding the role of digital behavior dynamics in promoting environmental management, and managing environmental impacts in new digitally driven business models. By weaving together these diverse elements, this work offers a novel perspective, emphasizing the importance of collaborative and integrative research in shaping a sustainable and equitable digital future.

1. Introduction

In today’s interconnected industrial landscape, where complexity and interdependence continue to grow, the digital transformation of industries and societies has emerged as a critical enabler for understanding the interplay between influencing domains within digitalized ecosystems, which has become essential for advancing sustainable and equitable strategies [1,2,3]. Among essential domains, fairness, environmental impact, and behavior dynamics have become critical focal points as industries navigate growth [4,5]. Understanding the interplay among these three domains plays a crucial role in influencing modern digital industrial strategies. These domains, collectively referred to as the Key Domains, influence and are influenced by crucial industrial and societal contexts, including the circular economy, business models, and supply chain management (referred to as the Key Contexts) [6,7,8].

Fairness ensures that digitalized processes remain just, equitable, and transparent, fostering stakeholder trust. Reducing environmental impact is vital for mitigating climate change and conserving resources, with digital tools playing a key role in optimizing sustainability efforts. Behavior dynamics, in turn, provide insight into how incentives, norms, and policies shape industrial and consumer behaviors toward sustainable outcomes [6,9,10].

These domains are deeply interconnected and play a crucial role in shaping modern industrial strategies within digital ecosystems. Their interactions influence the effectiveness of sustainability initiatives and business operations [2]. For instance, digital incentives can promote circular economy initiatives, while fairness considerations can create more equitable supply chains [8,11].

However, integrating these domains presents challenges. Fairness often conflicts with efficiency and scalability in digital industrial strategies, while sustainability efforts are constrained by existing business models focused on short-term profits [1,2]. Additionally, the complexity of human behavior influences how fairness and environmental goals are perceived and adopted [12].

Addressing these challenges requires a comprehensive approach that considers both the trade-offs and synergies within digital ecosystems. This literature review adopts a digital transformation perspective to examine these intersections and interdependencies, filling gaps in existing research and providing pathways for more integrated industrial strategies [13,14].

Unlike previous studies that focus on these domains separately or in limited combinations, this work systematically analyzes all possible intersections. It prioritizes mechanisms and frameworks that facilitate data sharing within digital ecosystems while excluding broader discussions on digitizing physical operations or raw data collection [2,5,15]. The goal is to offer a structured, interdisciplinary perspective that captures the complexity of these relationships within the broader narrative of digital transformation. Figure 1 illustrates the conceptual framework used in this review, highlighting how fairness, environmental impact, and behavior dynamics intersect across business models, supply chain management, and the circular economy.

This study also provides actionable insights by identifying synergies, conflicts, and research gaps in these domains. It examines how digital incentives can drive circular economy adoption and how fairness principles enhance equity within supply chains. By aligning environmental, social, and behavioral objectives, this work offers strategic guidance for researchers, industry professionals, and policymakers working toward sustainable and equitable industrial transformation [1,8]. To guide this literature review, we formulate the following research questions:

• What are the key barriers to achieving an effective interplay among fairness, environmental impact, and behavior dynamics in digitalized supply chains, business models, and circular economy frameworks? (See Section 3.2, Section 3.3, Section 3.4, Section 5.2.2, Section 5.3.2, and Section 5.4.2 for analysis of key challenges and barriers).
• What mechanisms explain the intersections and interdependency between fairness, environmental impact, and behavior dynamics, and how do these mechanisms shape digital transformation in sustainable industrial practices? (See Section 4.2.4, Section 4.3.4, and Section 4.4.4 for discussion of domain interactions and integration mechanisms).
• What are the best practices and strategies for integrating fairness, environmental sustainability, and behavioral insights in digitalized supply chains and circular business models? (See Section 5.2.3, Section 5.2.4, Section 5.3.3, Section 5.4.3, and Section 5.4.4 for case examples and strategic recommendations).
• What methodological and strategic approaches are most effective for investigating and addressing the interplay among fairness, environmental impact, and behavior dynamics in digitalized industrial systems? (See Section 5.1, Section 5.2, Section 5.3, Section 5.4 and Section 5.5 for methodology and research gap analysis).

The remainder of this paper is organized as follows: Section 2 outlines the methodology employed to synthesize and analyze the literature. Section 3, Motivation and Challenges, provides a foundational understanding of the Key Domains and highlights the complexities involved in addressing their intersections within the digitalized Key Contexts. Section 4 offers a comprehensive review of the state of the art, focusing on advancements across the Key Domains within these digital contexts. Section 5 presents a structured analysis of the interactions among these domains, emphasizing synergies, conflicts, and practical implications within digital transformation. Finally, Section 6 identifies research gaps and proposes directions for future work, with concluding remarks summarizing key findings in Section 7.

2. Methodology

Building on the Introduction, this section outlines a systematic approach to examining the interplay of fairness, environmental impact, and behavior dynamics within digitalized circular economy models, business frameworks, and supply chains. This study adopts a structured literature review approach [16]. The methodology emphasizes recent developments, interdisciplinary perspectives, and the need to address sustainability and equity challenges in digital ecosystems.

2.1. Foundational Concepts and State of the Art

This subsection provides an overview of the literature selection process and theoretical foundations relevant to this study. The literature review was designed to synthesize recent advancements in fairness, environmental impact, and behavior dynamics within digitalized industrial contexts. It categorizes prior research into thematic clusters to identify key trends, synergies, and conflicts that shape digital transformation efforts.

To ensure rigor and transparency, we applied the following inclusion criteria when selecting studies:

• Peer-reviewed journal articles and conference papers published between 2013 and 2023.
• Studies explicitly addressing at least one of the three key domains (behavior dynamics, environmental impact, or fairness), as identified through a set of expanded keyword combinations and synonymous terms, within the contexts of circular economy, business models, or supply chain management.
• Articles discussing digitalization as a key factor influencing these domains.
• Empirical or theoretical studies providing methodological insights or case-based findings relevant to the interplay of these concepts.

Conversely, the exclusion criteria eliminated:

• Non-peer-reviewed materials, such as opinion pieces, editorials, and preprints.
• Studies focusing solely on technical or engineering solutions without direct consideration of behavioral, environmental, or fairness aspects.
• Papers not available in full text or written in languages other than English.

Applying these criteria, our initial search across academic databases (Google Scholar, IEEE Xplore, Wiley, and MDPI) retrieved 172 papers. After screening for relevance, 132 papers were retained for further evaluation. Upon applying the inclusion/exclusion criteria, 118 papers were excluded due to redundancy, lack of empirical or theoretical contributions, or a narrow technical focus. This resulted in a full-text review of 107 papers, from which 99 papers were ultimately selected. These papers provide a balanced representation of theoretical perspectives, methodological approaches, and empirical findings on the interplay of behavior dynamics, environmental impact, and fairness in digitalized sustainability-driven ecosystems.

Keyword Strategy and Synonym Mapping

To ensure a comprehensive and representative literature sample, we used a variety of keyword combinations and synonymous terms across all three key domains (fairness, environmental impact, and behavior dynamics) and the three industrial contexts (circular economy, supply chain management, and business models).

For instance, in the case of environmental impact, we included related terms such as: environmental sustainability, ecological impact, carbon footprint, life cycle assessment (LCA), emissions reduction, resource efficiency, environmental performance. Similarly, for fairness, we searched using terms like: equity, social justice, ethical sourcing, responsible business, inclusive practices, and fair trade. For behavior dynamics, we incorporated: behavioral change, behavioral economics, decision-making, consumer behavior, organizational behavior, social norms, and nudging strategies.

These keywords were used in various combinations with context-specific terms such as digitalization, circular economy, supply chain, business model, and industrial transformation. The inclusion of synonyms aimed to mitigate the risk of sampling bias and ensure a broader and more inclusive representation of the literature.

2.2. Analysis and Practical Implications

The selected studies were systematically analyzed and organized into thematic clusters to identify synergies, conflicts, and emerging patterns in the interplay between behavior dynamics, environmental impact, and fairness within digitalized industrial ecosystems.

This structured approach provided a comprehensive understanding of how digitalization influences sustainability, equity, and behavioral shifts, forming the basis for identifying unresolved challenges and research gaps discussed in the next subsection.

2.3. Research Gaps and Future Directions

This study identified research gaps and proposed future directions through a comparative analysis of theoretical perspectives, methodological approaches, and empirical findings related to fairness, environmental impact, and behavior dynamics in digitalized industrial ecosystems. The goal was to assess where existing studies fall short in fully integrating these domains.

The process of identifying research gaps involved:

• Evaluating inconsistencies and missing links in the literature, particularly in how fairness, environmental impact, and behavior dynamics intersect within digitalized business models and supply chains.
• Assessing theoretical and methodological limitations, identifying areas where interdisciplinary research could provide deeper insights.
• Prioritizing empirical validation needs, particularly in assessing how digital transformation influences sustainability and equity outcomes.

This gap analysis provides a foundation for advancing research on sustainable digital ecosystems and informs future studies aimed at aligning fairness, sustainability, and behavior within industrial and societal frameworks.

3. Motivation and Challenges

The analysis of modern industrial strategies requires a deep understanding of the theoretical foundations that support key areas of concern. This section explores the core domains of fairness, environmental impact, and behavior dynamics, which are critical for evaluating their interactions within digitalized supply chain management, business models, and the circular economy. By examining these domains through the perspective of established theories and concepts, we can better understand the complexities and challenges that arise when these areas intersect in digital ecosystems. Each domain will be explored in terms of its core concepts and ideas, key challenges, and historical and current perspectives, providing a comprehensive frame of reference for the analysis in Section 5 (

Table 1. Overview of Key Domains in the theory section.

Key Domain	Core Concepts and Ideas	Historical and Current Perspectives	Key Challenges and Considerations
Fairness	- Equity in resource distribution - Ethical sourcing and trade strategies	- Evolved from classical notions of equality - Focus on social justice in global supply chains	- Balancing global trade fairness - Addressing bias in automated decision-making - Ensuring equitable access to resources
Environmental Impact	- Sustainability - Life Cycle Assessment (LCA) - Resource efficiency	- Shift from environmental protection to sustainability - Growing focus on circular economy principles	- Measuring and reducing carbon footprints - Integrating sustainability into traditional business models - Managing resource depletion
Behavior Dynamics	- Behavioral economics - Nudge theory - Decision-making processes	- Rooted in psychology and economics - Recent focus on nudging behaviors towards sustainability	- Understanding consumer resistance to change - Leveraging behavioral insights in business strategies - Promoting sustainable behaviors

).

3.1. Definition of the Key Domains

3.1.1. Definition of Fairness

Fairness is a fundamental principle in digitalized industries, ensuring equitable processes, outcomes, and resource distribution. It plays a critical role in supply chain management, business models, and circular economy strategies, where equity among stakeholders is essential for sustainability and social acceptance. In digital industrial strategies, fairness extends beyond ethical considerations to address algorithmic bias, transparency, and inclusivity [9,17,18].

3.1.2. Definition of Environmental Impact

Environmental impact refers to the effect of industrial and economic activities on the natural environment, including pollution, resource depletion, and habitat destruction. Managing environmental impact is essential for sustainability, requiring industries to balance efficiency with ecological responsibility. In digitalized industrial strategies, minimizing environmental impact is both a regulatory necessity and a corporate social responsibility [6,19]. The relevance of environmental impact extends to digital supply chains, circular economies, and business models, where reducing waste and optimizing resource use are critical objectives [8,20].

3.1.3. Definition of Behavior

Behavior dynamics refer to the evolving interactions and decision-making processes of individuals and groups in industrial and economic settings. These dynamics are influenced by digital systems, organizational structures, and environmental factors, shaping how technologies are adopted and how resources are utilized [21]. In digitalized supply chain management, business models, and the circular economy, behavior dynamics determine the success of sustainability efforts and technological integration [22,23]. Digital platforms, such as search engines and e-commerce sites, rely on behavior dynamics to design intuitive and effective user experiences. Managing these dynamics effectively ensures that technological advancements contribute to sustainable and equitable outcomes, aligning individual and organizational behaviors with broader environmental and social objectives [14,24].

3.2. Fairness

3.2.1. Core Concepts and Ideas in Fairness

Fairness is assessed through key theoretical frameworks. Equity theory suggests individuals evaluate fairness by comparing their inputs and outcomes relative to others [12,25]. Distributive justice ensures resources are allocated equitably, while procedural justice emphasizes transparent, unbiased decision-making [26,27]. These principles are particularly relevant in digitalized decision-making systems, where automated processes increasingly shape industrial policies and stakeholder interactions. Figure 2 presents the fairness integration cycle, outlining how fairness principles are embedded in digital decision-making processes across industrial systems.

3.2.2. Key Challenges and Considerations in Fairness

Ensuring fairness in digitalized industries presents several challenges, and Figure 3 depicts these challenges associated with implementing fairness in digital industries. One major issue is the trade-off between fairness and efficiency—while companies strive for equitable treatment, operational demands often prioritize speed and cost-effectiveness. Additionally, algorithmic bias in AI-driven decision-making can reinforce inequalities if not carefully monitored. Addressing these biases requires ongoing oversight, transparent data practices, and fairness-aware machine learning models [28,29,30].

3.2.3. Historical and Current Perspectives on Fairness

Fairness in industrial strategies has evolved from legal principles of justice to balance profitability and social responsibility [17,31]. The rise of digital automation has introduced new complexities, as businesses and regulators work to ensure fairness in AI-based decision-making, supply chain transparency, and sustainable business models. Current research continues to explore how digital tools can enhance fairness while mitigating bias and inequitable outcomes [32,33].

3.3. Environmental Impact

3.3.1. Core Concepts and Ideas in Environmental Impact

Several established frameworks help industries assess and mitigate environmental impact. Life Cycle Assessment (LCA) evaluates the environmental footprint of products and services from raw material extraction to disposal, allowing organizations to identify areas for improvement [11]. Figure 4 illustrates the Life Cycle Assessment (LCA) process, showing the environmental impact evaluation from raw materials to end-of-life. The Triple Bottom Line (TBL) approach expands business performance metrics to include environmental and social factors alongside financial considerations [6]. The Circular Economy (CE) model promotes waste reduction, resource efficiency, and sustainability-focused design, aiming to extend the life cycle of materials and reduce dependency on finite resources [34]. Digital tools such as AI-driven analytics, IoT-based monitoring, and blockchain-enabled traceability enhance these frameworks, making environmental strategies more precise and actionable.

3.3.2. Key Challenges and Considerations in Environmental Impact

Despite advancements, industries face persistent challenges in integrating sustainability into their digitalized strategies. These obstacles are highlighted in Figure 5. A key issue is balancing profitability with sustainability, as many business models prioritize short-term financial performance over long-term environmental responsibility [7]. Measuring environmental impact also remains difficult due to fragmented data sources and inconsistent reporting standards, which create obstacles for businesses trying to establish reliable sustainability metrics [4]. Regulatory inconsistencies across different countries further complicate compliance, making it challenging for multinational corporations to align their operations with varying environmental laws [35]. Another significant challenge is consumer engagement, as businesses must not only implement sustainable practices but also educate and incentivize consumers to adopt environmentally conscious behaviors [36]. Digital platforms play a crucial role in addressing this issue, leveraging data-driven insights to influence consumption patterns and encourage responsible resource use. Overcoming these challenges requires industry-wide collaboration, regulatory alignment, and greater investment in sustainability-focused digital infrastructure [37].

3.3.3. Historical and Current Perspectives on Environmental Impact

Environmental responsibility has evolved significantly over time. Early industrial policies focused primarily on pollution control and remediation, addressing environmental damage only after it had occurred. By the late 20th century, sustainability principles became more prominent, shifting the focus toward proactive ecological management and integrating environmental considerations into business strategies [38]. In the current era, digital sustainability initiatives have transformed how industries assess and manage their environmental impact. The rise of big data, AI-driven analytics, and IoT-based environmental monitoring allows for real-time tracking of resource consumption and emissions, enabling more effective sustainability efforts. Modern strategies emphasize carbon footprint reduction, renewable energy adoption, and the widespread implementation of circular economy principles [13,39]. As industries continue to digitize, technology-driven sustainability solutions are becoming essential for ensuring long-term ecological and economic viability [7,40].

3.4. Behavior Dynamics

3.4.1. Core Concepts and Ideas in Behavior

Several theoretical frameworks guide the understanding of behavior dynamics in digitalized industries. Behavioral Economics explains how individuals make economic decisions, often deviating from purely rational choices due to cognitive biases [41]. Nudge Theory demonstrates how small modifications in choice architecture can influence decision-making without restricting freedom [42]. Bounded Rationality highlights the limits of human decision-making under constrained information and cognitive resources, leading to adaptable but sometimes suboptimal choices [12]. Social Norms Theory explores how collective behaviors evolve based on societal expectations and peer influences, playing a key role in the adoption of sustainability initiatives and digital transformations [43]. In digital contexts, User Behavior Simulation helps predict how individuals interact with digital systems, improving user experience and engagement. Figure 6 presents a behavioral influence model, identifying key psychological and social drivers that affect sustainable decision-making. These theories provide a foundation for designing strategies that encourage sustainable and ethical decision-making in digitalized industrial ecosystems [44].

3.4.2. Key Challenges and Considerations in Behavior

Industries face significant challenges in managing behavior dynamics, and in Figure 7, these challenges are summarized. One key issue is resistance to change, as individuals and organizations often hesitate to adopt new technologies or sustainability initiatives due to uncertainty, perceived loss of control, or ingrained habits [45]. Another major challenge is the conflict between short-term incentives and long-term sustainability goals, where immediate financial benefits often outweigh ecological and social considerations [46]. Digital platforms also struggle to simulate human behavior accurately, as user interactions are complex and influenced by various external factors. Information overload in digital environments can further hinder effective decision-making, leading to disengagement or unintended behaviors [21]. Additionally, cultural and organizational factors shape how new strategies and technologies are perceived and adopted, making it essential to tailor interventions to specific social and economic contexts [24].

3.4.3. Historical and Current Perspectives on Behavior

The study of behavior dynamics has evolved significantly over time. Early economic models assumed that individuals acted rationally to maximize utility, but behavioral economics later introduced psychological insights to explain real-world decision-making. The rise of nudge theory demonstrated the power of subtle behavioral interventions in shaping long-term outcomes [47]. More recently, social norms and digital interactions have become central to understanding how individuals and organizations respond to sustainability efforts and technological innovations [48]. As industries undergo digital transformation, behavior dynamics continue to shape the success of new business models, supply chain strategies, and circular economy initiatives. Integrating psychology, economics, and digital analytics remains crucial for fostering sustainable and ethical decision-making in modern industrial ecosystems [49].

4. State of the Art

In the current landscape of industrial research and practice, there is a growing emphasis on understanding how key domains (fairness, environmental impact, and behavior dynamics) intersect and influence each other within critical contexts such as the circular economy, business models, and supply chain management. This interplay is essential for designing sustainable, equitable, and resilient industrial strategies in response to global challenges such as resource scarcity, climate change, and social inequality [4,6].

Central to enabling this interplay is the role of digitalization and data sharing, which provide the tools and infrastructure necessary for fostering collaboration, optimizing processes, and enhancing transparency [8,50]. Technologies such as blockchain, artificial intelligence (AI), and the Internet of Things (IoT) are among the most prominent enablers, offering capabilities that address key challenges across these domains and contexts:

• Blockchain enhances traceability and accountability, supporting ethical practices and fostering trust among stakeholders [51,52,53].
• AI provides advanced analytics and predictive insights, enabling resource efficiency and sustainable decision-making [9,27].
• IoT facilitates real-time monitoring of resource flows, waste streams, and product lifecycles, bridging the gap between physical and digital ecosystems [54,55].

While these technologies are transformative, the primary focus of this literature review is to examine the interplay between the three domains and contexts, identifying gaps, synergies, and opportunities for innovation (

Table 2. Comparison of studies across key domains and contexts.

Paper/Study	SCM	Business Models	Circular Economy	Fairness	Env. Impact	Behavior Dynamics
Velenturf & Purnell [4]			√		√
Ali et al. [17]				√
Jesse & Jannach [14]						√
Ghobakhloo [13]	√		√		√
Voukkali et al. [34]			√		√
Mehrabi et al. [5]				√
Reike et al. [6]			√		√
White et al. [10]						√
Geissdoerfer et al. [7]		√	√
Booth et al. [26]				√
Legros & Cislaghi [57]						√
Bhutta et al. [39]	√				√
Rosa et al. [58]		√	√
Sarkis et al. [8]	√				√
Kouhizadeh et al. [59]	√		√
Kraus et al. [60]		√
Biswas et al. [35]	√

). Digital tools are discussed in this context as mechanisms that operationalize and amplify the benefits of this interplay. By framing the analysis around these domains and contexts, this section provides a comprehensive overview of recent advancements, key trends, and ongoing challenges in driving sustainable industrial practices [13,56].

Although circular economy, supply chain management, and business models share multiple interconnections both in academia and industry, we examine them separately to allow for a focused analysis of their distinct contributions to behavior dynamics, environmental impacts, and fairness. This also allows for the analysis of these overlapping topics using different viewpoints to find insights that are not easily identified by studying only one of them. Circular economy represents a macro-level systemic shift focused on resource regeneration and waste minimization, while supply chain management primarily deals with the operational logistics of material and product flows. Meanwhile, business models serve as the strategic framework guiding how firms create and capture value, often integrating both circular economy principles and supply chain innovations. Examining these domains separately enables a structured exploration of their unique theoretical foundations, methodologies, and implications in digitalized industries. However, we acknowledge their overlaps and, where relevant, highlight their synergies in the discussion.

4.1. Digitalization and Data Sharing

The rapid advancement of digital technologies and the increasing importance of data sharing have become central to modern industrial ecosystems. These innovations provide the foundation for addressing challenges related to fairness, environmental sustainability, and behavior dynamics, enabling collaboration, optimization, and transparency across industries [61,62].

4.1.1. Key Frameworks and Initiatives

Digitalization and data-sharing efforts are driven by several key frameworks. The International Data Spaces Association (IDSA) and IDS Reference Architecture Model (IDS RAM) provide globally recognized standards for secure data exchange, ensuring interoperability, data sovereignty, and compliance with ethical and legal regulations. Similarly, GAIA-X and Catena-X facilitate federated data infrastructures, with GAIA-X supporting cross-sectoral data availability in Europe and Catena-X enabling enhanced traceability and efficiency within the automotive industry. The European Union’s Ecodesign for Sustainable Products Regulation (ESPR) and Digital Product Passports (DPPs) further integrate sustainability by enabling lifecycle tracking of products and optimizing environmental performance [15,61,63].

Beyond these large-scale frameworks, industry-specific initiatives enhance data-sharing applications. The healthcare sector utilizes data spaces to improve patient care and medical research, while manufacturing industry applies real-time data-sharing technologies to optimize production efficiency and reduce resource waste [13,64].

4.1.2. Challenges and Opportunities

Despite these advancements, several challenges persist. Data security and privacy concerns continue to hinder widespread adoption, as organizations must ensure compliance with legal and regulatory standards. Additionally, managing intellectual property rights presents obstacles, as businesses weigh the risks and benefits of sharing proprietary data. Organizational resistance to data-sharing initiatives also remains a key challenge, with companies hesitant to transition from traditional, isolated systems to open, collaborative frameworks [14,25].

4.1.3. Scope Clarification

It is essential to distinguish between data sharing and broader concepts such as data collection or industrial automation. This paper focuses on the mechanisms and frameworks enabling data sharing within digital ecosystems. It deliberately excludes broader processes, such as digitizing physical operations or collecting data at the source. By narrowing the focus, this discussion underscores the strategic importance of collaboration and interoperability in achieving sustainable industrial strategies [8,65].

4.1.4. Integration with Key Contexts

Integrating data-sharing frameworks into circular economy models, business strategies, and supply chain management has significant potential. In circular economies, data-sharing technologies facilitate material tracking, optimizing resource use and reducing waste. Within business models, transparent data-sharing practices strengthen stakeholder trust and encourage collaborative innovation. In supply chains, real-time data exchange enhances operational efficiency while ensuring compliance with ethical and sustainable standards [15,55,62,66].

By embedding data-sharing technologies within digitalized industrial systems, industries can enhance sustainability, improve transparency, and drive collaborative, data-driven decision-making.

4.2. Circular Economy

The circular economy is a transformative approach to economic activity that prioritizes reuse, recycling, and sustainable resource management. Figure 8 illustrates the closed-loop system of the circular economy, where resources are continually reused to reduce waste. Unlike the traditional take-make-dispose model, the circular economy seeks to extend resource lifecycles, maximize value extraction, and minimize waste. As industries and governments respond to environmental challenges and resource scarcity, the circular economy has gained prominence as a pathway to a more sustainable and resilient economic system. Within digitalized contexts, circular economy principles are strengthened by digital tools and technologies, which enable efficient resource tracking, data-driven decision-making, and process optimization [34,67].

4.2.1. Fairness in Circular Economy

Fairness in the circular economy involves equitable resource distribution, ethical labor practices, and social inclusion throughout supply chains. As circular strategies expand, it is essential that their implementation addresses systemic inequities rather than reinforces them. Digital technologies, such as blockchain, artificial intelligence (AI), and the Internet of Things (IoT), are playing an increasingly important role in embedding fairness into circular systems by enhancing transparency, accountability, and traceability [9,32].

Blockchain enables secure verification of fair labor practices and ethical sourcing, fostering trust and compliance across supply chains. Digital Product Passports (DPPs), introduced under the European Union’s Ecodesign framework, offer lifecycle data that support traceability and equitable resource management. AI contributes by identifying underserved areas and optimizing resource allocation, while IoT facilitates real-time tracking of material flows to improve access and reduce inefficiencies [59,68].

Ensuring fair labor conditions is particularly important in recycling and remanufacturing sectors, where vulnerable workers may be at risk of exploitation. Recent studies emphasize the value of integrating marginalized communities into circular strategies to ensure that initiatives like recycling and resource recovery support inclusivity. While digital tools offer promising pathways for operationalizing fairness, their widespread adoption remains limited. Future frameworks must more deliberately leverage these technologies to align fairness with broader sustainability objectives [67,69,70].

4.2.2. Environmental Impact in Circular Economy

Minimizing environmental impact is a core objective of the circular economy, achieved through waste reduction, resource efficiency, and product life extension. By keeping materials in circulation and designing products for reuse, the circular economy addresses critical environmental challenges, such as pollution, resource depletion, and climate change [56,71].

Digital technologies play a pivotal role in optimizing circular economy strategies. IoT-based monitoring, blockchain-enabled traceability, and AI-driven analytics enhance environmental performance by enabling real-time tracking of resource flows and optimizing recycling efficiency [53]. AI assists Life Cycle Assessments (LCAs) by providing real-time environmental footprint evaluations, improving decision-making on waste reduction and emissions management. Studies demonstrate how AI-driven analytics optimize recycling processes, supply chain emissions tracking, and resource allocation, ensuring more effective environmental management [72,73].

IoT technologies further improve environmental monitoring, offering real-time insights into material use, energy consumption, and waste patterns. For instance, IoT-enabled sensors track product lifecycles, facilitating precise resource allocation and minimizing environmental impact. Blockchain ensures transparent and immutable reporting of sustainability performance, fostering trust among stakeholders and verifying compliance with environmental standards [55].

While digital tools offer significant environmental benefits, challenges remain in scaling their adoption across industries. High upfront investment costs, concerns over data privacy, and the digital divide hinder widespread implementation. Integrating these technologies into existing industrial and regulatory frameworks is necessary to fully achieve the circular economy’s environmental objectives [74].

4.2.3. Behavior Dynamics in Circular Economy

Behavior dynamics are essential for circular economy adoption, as they influence how consumers, businesses, and governments engage with sustainable practices. Encouraging behaviors such as reuse, recycling, and responsible consumption requires a deep understanding of behavioral economics, psychology, and social incentives. Research highlights that consumer engagement strategies, financial incentives, and regulatory interventions play a critical role in shaping circular economy participation [21,75].

Digital tools enhance behavior dynamics by leveraging AI-driven personalization, gamification, and real-time feedback mechanisms. AI-powered platforms can tailor circular economy initiatives to consumer preferences, while gamification strategies encourage recycling and sustainable purchasing habits. Studies have shown that digital tools increase recycling rates and engagement by making sustainability more accessible, rewarding, and interactive. IoT applications provide real-time insights into waste reduction and energy efficiency, reinforcing circular behaviors at the individual and organizational levels [3,50].

Blockchain and AI also contribute to ethical decision-making within circular systems. Blockchain fosters trust and transparency, ensuring that businesses uphold sustainable sourcing and recycling commitments. AI optimizes supply chain resource flows, aligning production with sustainability goals. However, widespread behavioral shifts require systemic incentives, including policy-driven interventions, corporate commitments, and consumer education programs [37,59].

Despite advancements, barriers such as digital access inequality, consumer skepticism, and behavioral inertia must be addressed to ensure broader adoption of circular economy practices. Digital ecosystems have the potential to drive these behavioral changes, aligning consumer actions with long-term sustainability objectives [74,76].

4.2.4. Intersections and Interdependencies: Emerging Themes

The circular economy is deeply interconnected with fairness, environmental sustainability, and behavior dynamics, forming an integrated framework where these domains reinforce one another. Achieving environmental sustainability within circular systems depends on equitable resource distribution and consumer engagement [6,77].

Blockchain-enabled traceability and lifecycle data tools enhance transparency, accountability, and social equity, ensuring that circular strategies effectively reduce environmental impact while addressing systemic inequalities. Emerging research highlights the importance of inclusive infrastructure, where digital transformation supports fair and accessible resource recovery systems [59].

Behavior dynamics further shape the effectiveness of circular initiatives, as sustainable outcomes depend on widespread adoption of recycling, reuse, and waste reduction behaviors. AI-driven analytics and gamification platforms are demonstrating significant potential in promoting large-scale behavioral shifts toward circular consumption [72].

A comprehensive approach that integrates fairness, sustainability, and behavioral insights is needed to maximize the circular economy’s impact. Addressing challenges in accessibility, policy alignment, and stakeholder collaboration is critical to ensuring circular strategies are socially inclusive, environmentally effective, and economically viable. Digital technologies remain a key enabler in achieving these objectives, supporting a collaborative, multi-stakeholder approach to circular economy transformation [34,74].

4.3. Business Models

Business models define how organizations create, deliver, and capture value, aligning their strategies with stakeholder needs and market demands. Figure 9 breaks down the components of modern business models and their influence on sustainability and long-term success. While the circular economy focuses on resource optimization and supply chain management emphasizes logistics, business models serve as the strategic frameworks that integrate these concepts into operational and financial structures [78].

By embedding fairness, environmental sustainability, and behavior dynamics, they drive innovation and contribute to global sustainability goals. Recent research highlights the importance of sustainable business models that balance financial performance with environmental and social impact, ensuring long-term value creation for businesses and society [79,80].

4.3.1. Fairness in Business Models

Fairness in business models centers on the equitable distribution of value among workers, suppliers, customers, and marginalized communities. Achieving this involves implementing fair pricing, ethical sourcing, and inclusive innovation practices that build consumer trust and strengthen stakeholder collaboration [4].

In digitalized environments, technologies like blockchain and artificial intelligence (AI) enhance fairness by increasing transparency and enabling data-driven accountability. Blockchain supports the verification of ethical labor practices and compensation mechanisms, while AI-driven analytics identify disparities in supply chain operations and optimize resource allocation to address systemic imbalances [17,53].

Corporate social responsibility (CSR) and inclusive innovation have become integral to modern business models, particularly in sectors marked by power asymmetries. Platform-based models and AI-augmented services provide opportunities to expand access to goods and services, while blockchain-enabled systems ensure traceability and stakeholder equity. When strategically integrated, these tools enable firms to align fairness with broader goals of sustainability and inclusion [26].

4.3.2. Environmental Impact in Business Models

Minimizing environmental impact is a key objective for modern business models as companies strive to balance profitability with sustainability. Organizations are under increasing pressure to reduce their ecological footprint and contribute to global sustainability goals, driving the adoption of strategies such as resource efficiency, waste reduction, and renewable energy integration [40].

Digital tools play a pivotal role in achieving these goals. IoT-based monitoring systems, AI-driven lifecycle assessments (LCAs), and predictive analytics provide real-time insights into resource use, energy consumption, and emissions, enabling businesses to optimize operations and reduce environmental impact. Additionally, blockchain-enabled transparency ensures accurate tracking of sustainability efforts, fostering accountability across supply chains [80].

The circular economy has emerged as a highly effective model for minimizing environmental impact by extending product lifecycles and optimizing resource utilization. Research highlights the integration of circular strategies into business models, demonstrating how companies can prioritize durability, facilitate product reuse, and improve resource efficiency. Sustainable business model archetypes, when combined with digital innovations, enhance environmental performance while maintaining economic viability [20,72].

By embedding circular economy principles into their business strategies and leveraging digital tools, companies can reduce waste, optimize resources, and drive long-term resilience in a dynamic global market [81].

4.3.3. Behavior Dynamics in Business Models

Behavioral dynamics play a crucial role in shaping business strategies and sustainability efforts, influencing both internal decision-making and consumer engagement. By leveraging behavioral insights, businesses can adapt operations, develop innovative models, and align with evolving market demands. AI-driven tools, such as behavioral analytics and personalization algorithms, help companies analyze consumer preferences and design sustainability-focused products and services, fostering deeper engagement and long-term customer relationships [22,24].

Unlike societal-scale behavioral trends observed in the circular economy, business models emphasize internal organizational behavior. Implementing sustainability-focused strategies often requires cultural shifts and operational adjustments. Research highlights that organizations incorporating nudge-based approaches, sustainable workplace cultures, and predictive analytics are more likely to succeed in scaling circular practices. Data-driven simulations further enable businesses to anticipate and influence behavioral trends, ensuring alignment with sustainability objectives [42,45].

Digital platforms enhance real-time engagement by allowing businesses to track and respond to consumer behavior dynamically. These platforms integrate incentive mechanisms, such as subscription models, take-back programs, and gamification strategies, to drive consumer participation in circular economy practices. Understanding behavior dynamics within organizations is also critical to successfully implementing new business models, particularly those requiring cultural or operational changes. Companies that embed behavioral insights into their strategic frameworks not only strengthen stakeholder relationships but also drive innovation and resilience in dynamic markets [21,75].

4.3.4. Intersections and Interdependencies: Emerging Themes

Business models integrate fairness, environmental sustainability, and behavior dynamics, fostering innovation and aligning profitability with sustainability goals. The connection between fairness and sustainability is evident in business models that prioritize ethical value chains, equitable resource distribution, and environmental responsibility. Blockchain and AI technologies enhance transparency and accountability, ensuring that sustainable initiatives benefit all stakeholders [72,81].

Behavioral dynamics further influence the success of business models by shaping consumer and organizational engagement. AI-driven personalization, gamification strategies, and incentive mechanisms encourage participation in circular initiatives, such as recycling programs and sustainable consumption incentives. However, the effectiveness of these strategies depends on their adaptability across diverse markets and industries [82].

Emerging research highlights the transformative potential of business models that fully integrate these principles. Companies that embed environmental and social considerations into their strategies enhance resilience and stakeholder trust while improving long-term sustainability outcomes. Digital tools, including predictive analytics and real-time monitoring, allow businesses to respond dynamically to evolving market demands [58].

The integration of fairness, sustainability, and behavior insights also drives inclusive innovation, ensuring that business models benefit a broad range of stakeholders, including traditionally underserved communities. By leveraging technological advancements and behavioral insights, businesses can create economically viable, socially responsible, and environmentally sustainable frameworks that address modern economic challenges [13,83].

4.4. Supply Chain Management

Supply chain management (SCM) involves the planning, coordination, and oversight of goods, services, and information from raw material suppliers to end consumers. Figure 10 shows the interconnectivity of different supply chain stages and their relevance to fairness, sustainability, and behavioral alignment. As global supply chains grow increasingly complex, organizations must balance efficiency, transparency, and sustainability. This section explores how fairness, environmental impact, and behavior dynamics shape modern SCM strategies, ensuring ethical, sustainable, and resilient supply networks [13].

4.4.1. Fairness in Supply Chain Management

Fairness in supply chain management entails equitable treatment of all stakeholders, workers, suppliers, and communities, through ethical labor practices, responsible sourcing, and inclusive participation. As global supply networks become increasingly complex, particularly in outsourced or multi-tiered industries, promoting fairness requires deliberate strategies to address systemic disparities [84,85].

Digital technologies play a pivotal role in advancing fair supply chain practices. Blockchain enables transparent tracking of sourcing and labor conditions, while smart contracts automate compliance and reduce the risk of exploitation. These technologies strengthen trust and accountability across supplier relationships [72,86].

AI-driven tools further support fairness by analyzing patterns in resource distribution and supplier engagement, helping businesses identify and correct imbalances. By making inequities visible and actionable, such tools improve inclusivity and ensure that sustainability practices extend across all tiers of the supply chain [55,59].

Recent studies also emphasize the need for structured frameworks that embed ethical principles into contracts, negotiations, and resource planning. These frameworks are essential for institutionalizing fairness and guiding the development of transparent, socially responsible, and sustainable supply chain strategies [87].

4.4.2. Environmental Impact on Supply Chain Management

Environmental sustainability is integral to modern supply chain management (SCM), requiring businesses to reduce emissions, waste, and resource consumption while ensuring efficiency. As industries shift toward green supply chain management (GSCM), companies must integrate circular principles and data-driven decision-making to enhance sustainability efforts [36].

Digital technologies play a pivotal role in reducing environmental footprints. IoT-enabled monitoring systems and AI-powered optimization tools provide real-time data and predictive analytics, allowing companies to track energy consumption, minimize waste, and optimize logistics. For instance, IoT sensors monitor material flows and environmental performance, while AI-driven analytics optimize routes and inventory to reduce emissions and resource use [88,89].

Adopting green supply chain practices, such as closed-loop logistics, lifecycle assessments (LCAs), and sustainable transportation, is essential for long-term sustainability. LCAs help businesses identify environmental impact hotspots, guiding strategic interventions to enhance sustainability across supply chains. These strategies not only minimize ecological footprints but also improve supply chain resilience by addressing resource scarcity and regulatory compliance [90].

Integrating circular economy principles into SCM extends the lifespan of materials and products, fostering efficient and sustainable supply chains. Approaches such as reuse, remanufacturing, and recycling, when supported by digital platforms and predictive modeling, provide actionable insights to optimize supply chain sustainability and long-term resource management [34].

4.4.3. Behavioral Dynamics in Supply Chain Management

Behavioral dynamics shape decision-making, collaboration, and compliance in supply chain management (SCM), influencing how businesses adopt sustainable practices. Understanding how human behavior interacts with digital tools and organizational systems is essential for optimizing supply chain performance and achieving sustainability goals [32].

AI-powered behavioral analytics, gamification platforms, and predictive models help align stakeholder behaviors with circular supply chain models. For instance, businesses can incentivize greener supplier practices and encourage consumer participation in reverse logistics, enhancing sustainable supply chain strategies. Real-time feedback systems further support these efforts by providing actionable insights into behavioral trends [24].

The organizational culture within supply chains also plays a critical role. Research underscores the importance of collaboration, innovation, and addressing decision-making biases to ensure the successful adoption of sustainability initiatives. Encouraging proactive engagement fosters resilient and adaptable supply chains capable of navigating sustainability challenges [21,75].

In digitalized SCM, real-time behavioral analytics enhance risk anticipation, collaboration, and compliance with sustainable practices. By integrating behavioral insights with digital technologies, organizations can create supply chains that are both economically viable and environmentally responsible, ensuring long-term sustainability [33].

4.4.4. Intersections and Interdependencies: Emerging Themes

Supply chain management (SCM) integrates fairness, environmental sustainability, and behavior dynamics to create efficient, ethical, and resilient systems. Blockchain and IoT technologies enhance transparency and accountability, ensuring equitable resource distribution while minimizing environmental impacts. These digital tools foster trust and collaboration among stakeholders, reinforcing sustainable supply chain practices [54,73].

Behavioral dynamics further shape supply chain sustainability by driving collaboration, compliance, and green strategies such as closed-loop logistics and resource efficiency. AI-powered decision-making systems, gamification platforms, and digital incentives help align stakeholder behaviors with sustainability goals, promoting widespread adoption of eco-friendly supply chain models [72,81].

Emerging research highlights the need for integrated digital frameworks to enhance stakeholder alignment and accessibility. Ethical AI and real-time analytics are crucial for adapting supply chain practices to evolving social and environmental demands. Additionally, circular supply chain models demonstrate how economic efficiency and environmental sustainability can be effectively combined to create scalable, resilient systems [7,91].

By embedding digital technologies with fairness, sustainability, and behavior insights, SCM can evolve into a flexible, adaptive model that meets global challenges while ensuring long-term economic, social, and environmental viability.

5. Analysis and Practical Applications

In this section, we will focus on examining the practical applications, challenges, successes, and future prospects of the key domains within the key contexts. Additionally, we will identify gaps in current research and suggest areas where further study is needed.

5.1. An Integrated Framework for Circular Economy, Business Models, and Supply Chain Management

The previous sections have examined behavior dynamics, environmental impacts, and fairness as they apply to circular economy, business models, and supply chain management independently. However, these domains do not function in isolation; rather, they intersect dynamically, influencing industrial and economic strategies in an increasingly digitalized world. This section presents an integrated framework that synthesizes these relationships, offering a structured approach to aligning sustainability objectives across industrial applications.

Figure 11 illustrates how these three key areas, circular economy, business models, and supply chain management, form a mutually reinforcing system that promotes long-term sustainability and fairness.

5.1.1. Circular Economy as the Systemic Foundation

• The circular economy provides a macro-level sustainability strategy, ensuring resource efficiency and waste reduction.
• It influences business models by necessitating value chain adaptations that align with regenerative production and consumption cycles.
• Within supply chains, it requires green logistics, closed-loop systems, and digital tracking tools to optimize resource use.

5.1.2. Business Models as the Strategic Enabler

• Sustainable business models act as the bridge between circular economy principles and supply chain operations.
• They define how companies integrate fairness and environmental goals while ensuring profitability.
• Digitalization and innovation (e.g., product-as-a-service, sharing economy models) facilitate sustainable transitions.

5.1.3. Supply Chain Management as the Operational Driver

• Supply chains serve as the implementation mechanism, translating sustainability strategies into practice.
• Transparency, digital tracking, and blockchain-enabled traceability ensure fairness in labor practices and ethical sourcing.
• Behavior dynamics, including consumer engagement and supplier collaboration, drive supply chain adaptability.

This integrated framework highlights the interdependence of these domains, showing how changes in one domain, such as fairness initiatives, can directly influence environmental outcomes and behavioral shifts. These mutual influences underscore the systemic nature of sustainable transformation in digitalized industries. The following section discusses research gaps and future directions to advance this interdisciplinary perspective.

5.2. Circular Economy

Building on the integrated framework presented in Section 5.1, this subsection explores the application of fairness, environmental impact, and behavior dynamics within circular economy strategies. By applying the framework to the circular economy context, we highlight key areas where digital technologies and stakeholder behaviors intersect to support sustainability, equity, and systemic transformation.

5.2.1. Integration of Key Domains

Fairness plays a critical role in the circular economy by ensuring that the benefits of strategies such as recycling, remanufacturing, and resource efficiency are equitably distributed among all stakeholders (

Table 3. Matrix of Key Domain impacts on Circular Economy elements.

Element	Fairness	Environmental Impact	Behavior Dynamics
Resource Use	Equitable Access to Resources	Minimizing Resource Depletion	Encouraging Sustainable Consumption
Product Lifecycle	Fair Labor Strategies in Manufacturing	Design for Longevity and Recyclability	Promoting Product Returns for Recycling
Waste Management	Socially Responsible Waste Disposal	Waste Reduction and Reuse Strategies	Behavioral Incentives for Waste Reduction
Economic Models	Inclusive Economic Participation	Circular Business Models	Consumer Engagement in Circular Initiatives
Policy and Governance	Fair Policy Frameworks	Environmental Regulations	Public Awareness and Education

). This includes addressing social equity by providing fair access to resources and opportunities, particularly for marginalized communities that are often disproportionately affected by environmental degradation. Fair division approaches, as discussed in sustainability and biodiversity impact studies, provide valuable insights into equitable resource distribution [26,58].

The primary objective of the circular economy is to minimize environmental impact by maintaining resources in use for as long as possible, reducing waste, and designing products with longer lifecycles. Strategies such as recycling, remanufacturing, and designing for end-of-life recovery aim to create closed-loop systems that reduce raw material extraction and waste generation, ultimately mitigating the environmental footprint of industrial activities. This aligns with principles of sustainability and resource conservation [4,70].

Understanding behavior dynamics is essential for the successful adoption and implementation of circular economy initiatives. Consumer, organizational, and governmental behaviors evolve and interact in response to these initiatives, requiring strategies that promote sustainable practices. Encouraging behaviors such as recycling, reducing consumption, and purchasing sustainable products necessitates targeted interventions that address the complexity of behavior dynamics and social norms [10,71].

5.2.2. Challenges

• Scaling Circular strategies: One of the primary challenges in the circular economy is scaling circular strategies across industries and geographies. Implementing circular models often requires significant upfront investment in infrastructure, technology, and education, which can be a barrier for many companies, especially smaller ones [40,92].
• Ensuring Social Equity: Ensuring that the circular economy is fair and inclusive is a significant challenge. Often, the benefits of circular strategies are not equitably distributed, with marginalized communities either being excluded from these benefits or disproportionately bearing the negative impacts of circular initiatives (e.g., e-waste recycling) [34,67].
• Behavioral Resistance: Changing consumer and organizational behaviors to support the circular economy is challenging. Consumers may resist circular strategies due to a lack of awareness, convenience, or perceived cost barriers. Similarly, businesses may be slow to adopt circular models due to concerns about profitability or operational complexity. Effective strategies are needed to influence behavior dynamics at all levels to support the transition to a circular economy [10,57].

5.2.3. Case Studies in Circular Economy

One notable example of the successful application of circular economy principles is the remanufacturing processes implemented in the automotive industry. Companies have increasingly embraced closed-loop production models where used vehicle components are collected, refurbished, and reintegrated into the market as high-quality alternatives to new parts. This approach significantly reduces waste, conserves raw materials, and minimizes energy consumption, aligning with sustainability and circular economy principles [7,86].

In the realm of waste management and recycling, inclusive recycling programs have emerged as a transformative strategy for promoting both environmental sustainability and social equity. These programs create employment opportunities in marginalized communities while ensuring the efficient recovery and reuse of materials. By integrating social inclusion into circular strategies, such initiatives demonstrate the potential for circular economy models to address broader socio-economic challenges beyond environmental concerns [34,67].

Consumer participation is another critical factor in the success of circular economy initiatives. Companies have implemented behavioral incentives to encourage customers to return used products for refurbishment or recycling. Such strategies help extend product life cycles and reduce waste generation. Research has shown that consumer behavior dynamics, supported by targeted incentives and digital engagement, play a crucial role in fostering sustainable consumption patterns and promoting circularity [75].

5.2.4. Future Prospects and Research Needs

Future research should focus on developing frameworks and strategies to ensure that the circular economy is inclusive and equitable. This includes identifying ways to involve marginalized communities in circular initiatives and ensuring that they benefit from the transition to circularity. Addressing fairness in the circular economy requires targeted interventions that prevent social inequalities and ensure fair access to sustainable resources [26].

The adoption of circular economy principles in supply chain management is expected to grow, with more companies exploring closed-loop systems that prioritize resource efficiency and waste reduction. Research is needed to identify the best strategies for scaling these models across different industries. There is significant potential for innovation in circular design and manufacturing, particularly in developing materials and processes that facilitate the disassembly, recycling, and repurposing of products at the end of their lifecycle. Advances in sustainable manufacturing and supply chain strategies, alongside the integration of digital technologies such as blockchain and IoT, can further enhance the feasibility of circular business models [40,77].

Further research is also needed to explore how behavior dynamics influence the adoption and success of circular economy strategies. Understanding the role of incentives, policies, and cultural factors in shaping consumer and organizational behavior is crucial to accelerating the shift toward circularity. Social norms, behavioral nudges, and digital platforms have been identified as key factors in influencing sustainable consumption and production patterns, making them important areas for future study [69,93].

5.3. Business Models

Following the integrated framework established in Section 5.1, this subsection explores how business models can strategically align fairness, environmental impact, and behavior dynamics. By examining these domains within digitalized business strategies, we illustrate how organizations can design inclusive, sustainable, and adaptive value creation approaches that support circular and ethical innovation.

5.3.1. Integration of Key Domains

Fairness in business models is essential for creating value in a way that benefits all stakeholders, including customers, employees, suppliers, and communities (

Table 4. Matrix of Key Domain impacts on Business Model elements.

Element	Fairness	Environmental Impact	Behavior Dynamics
Value Proposition	Ethical Sourcing, Social Equity	Sustainable Products	Behavioral approach for Engagement
Revenue Streams	Inclusive Financial Models	Pay-Per-Use Systems	Reward Systems for Sustainable Behavior
Customer Segments	Serving Underserved Markets	Eco-Conscious Consumers	Tailored Marketing Based on Behavior
Key Activities	Fair Trade Partnerships	Recycling, Remanufacturing	Customer Feedback Loops
Cost Structure	Investment in Social Programs	Cost of Sustainable Strategies	Costs for Behavior Analysis

). This involves ensuring the equitable distribution of profits, fair pricing strategies, and ethical sourcing. As businesses increasingly integrate social responsibility into their core strategies, fairness has become a key element in designing sustainable and inclusive business models. Incorporating fairness into business models also contributes to long-term competitive advantage by fostering trust and stakeholder engagement [79,94].

Modern business models are progressively being designed with environmental sustainability at their core. This includes adopting strategies that minimize waste, reduce carbon footprints, and utilize renewable resources. Companies are also exploring innovative models, such as the circular economy, which inherently reduces environmental impact by keeping resources in use for as long as possible. The integration of environmental sustainability into business models is not only a response to regulatory pressures but also a strategic approach to meeting consumer demand for greener products and services. Research highlights that integrating circular and green strategies into business models is a key driver for long-term corporate resilience and success [20].

Behavior dynamics play a crucial role in shaping and adapting business models, particularly in how companies respond to and influence customer behavior. Understanding consumer behavior is essential for developing business models that not only meet current market needs but also encourage sustainable consumption patterns. Additionally, internal behavior dynamics within organizations influence how business models are implemented and sustained, particularly when new, more sustainable strategies are introduced. The adoption of sustainable business models often requires shifts in organizational culture, employee engagement, and market perceptions, all of which are deeply connected to behavior dynamics [57,60].

5.3.2. Challenges

• Balancing Profitability and Sustainability: One of the central challenges in integrating environmental impact into business models is finding a balance between profitability and sustainability. Many companies struggle with the initial costs of adopting sustainable strategies and the long-term return on investment. While sustainability-driven business models offer competitive advantages, organizations often face financial and operational barriers in transitioning to greener practices [20,39].
• Incorporating Fairness: Designing business models that are fair and inclusive is challenging, especially in global markets with diverse cultural and economic conditions. Ensuring that all stakeholders benefit equitably from the business model requires careful planning and execution. Factors such as ethical sourcing, fair labor practices, and inclusive economic participation play a crucial role in business fairness [26].
• Behavioral Change and Adoption: Encouraging consumers to adopt new, sustainable products or services often require significant behavioral change. Companies need to design their business models in a way that makes it easy and appealing for customers to make sustainable choices. Research suggests that consumer education, incentives, and nudging strategies can help drive sustainable consumption patterns [24].

5.3.3. Case Studies in Business Models

Subscription-based models have emerged as a successful business approach that aligns with circular economy principles by promoting product reuse and extending the lifecycle of goods. Many companies in industries such as fashion and electronics have adopted subscription-based business models, reducing waste and encouraging sustainable consumption patterns. These models focus on customer engagement and value delivery while fostering circularity. By shifting from traditional ownership to access-based consumption, businesses can reduce environmental impact and create long-term customer relationships [7,60].

Inclusive business models have also gained prominence as a means of integrating fairness into business practices. By addressing social and economic inequalities, these models create value for both businesses and marginalized communities. Microfinance institutions, for example, have demonstrated how financial inclusion can drive social equity. These models ensure that underserved populations gain access to financial services, allowing them to participate in economic activities that improve their livelihoods. Such approaches exemplify achieving social equality [42].

Behavioral economics plays a crucial role in digital service business models, where companies leverage consumer insights to enhance engagement and retention. Businesses in sectors such as streaming services and e-commerce employ behavioral nudges, personalized recommendations, and data-driven decision-making to align their services with consumer preferences. These strategies help create value by optimizing user experience and encouraging sustainable consumption behaviors, demonstrating the power of behavioral insights in modern business models [21,57].

5.3.4. Future Prospects and Research Needs

As sustainability becomes a key driver of business success, there is a growing need for research into innovative business models that integrate environmental and social considerations at their core. Future studies could explore how businesses can create value while minimizing their environmental footprint and maximizing social impact. Sustainable business model innovation requires the development of frameworks that balance economic growth with responsible resource management, emphasizing long-term resilience and stakeholder value creation [20].

While inclusive business models have demonstrated success in small-scale applications, further research is needed to explore how these models can be effectively scaled to benefit larger populations without losing their focus on equity and inclusiveness. Scaling sustainable and fair business models requires overcoming challenges related to policy alignment, financial viability, and maintaining stakeholder engagement across diverse regions [83].

There is also significant potential for further research on how behavioral dynamics in economics can be leveraged to enhance customer engagement in sustainable business models. Understanding the most effective strategies for nudging consumers toward more sustainable behaviors is essential for increasing the adoption of green products and services. Research on social influence, behavioral economics, and digital marketing strategies can provide valuable insights into designing effective customer engagement initiatives that drive sustainable consumption patterns [95].

5.4. Supply Chain Management

Building on the integrated framework introduced in Section 5.1, this subsection analyzes how fairness, environmental sustainability, and behavior dynamics are operationalized within digitalized supply chain strategies. By applying the framework to supply chain contexts, we identify key mechanisms that support ethical sourcing, environmental optimization, and behavior-driven logistics.

5.4.1. Integration of Key Domains

Fairness in supply chain management focuses on ensuring equitable treatment across all stages of the supply chain, from suppliers to end consumers (

Table 5. Matrix of Key Domain impacts on Supply Chain Management elements.

Element	Fairness	Environmental Impact	Behavior Dynamics
Supplier Relations	Fair Trade Certification, Equitable Sourcing	Sustainable Sourcing Strategies	Incentives for Ethical Sourcing
Logistics	Equitable Distribution Networks	Green Logistics	Behavioral Insights for Efficiency
Inventory Management	Fair Access to Inventory	Eco-Friendly Inventory Strategies	Behavioral Patterns in Inventory Usage
Customer Fulfillment	Fair Pricing and Access	Eco-Conscious Delivery Methods	Customer Engagement in Sustainability
Risk Management	Fair Risk Mitigation Strategies	Environmental Risk Management	Behavioral Risk Assessment

). This involves fair pricing, ethical sourcing, and the equitable distribution of benefits, particularly in global supply chains where disparities often arise. Addressing fairness in supply chains is critical to mitigating issues related to labor exploitation, unethical sourcing, and economic inequalities among supply chain stakeholders. Companies that integrate fairness principles into their supply chain strategies not only enhance their corporate social responsibility but also improve long-term supplier relationships and market competitiveness [54,89].

Supply chains are significant contributors to environmental degradation, making the integration of environmentally sustainable strategies essential. Companies are increasingly adopting green supply chain management (GSCM) approaches, such as life cycle assessment (LCA), to minimize their carbon footprint and reduce waste. The implementation of closed-loop supply chains, circular logistics, and green procurement strategies has been shown to enhance both environmental sustainability and operational efficiency. Research highlights that sustainable supply chain practices not only contribute to ecological conservation but also provide long-term cost savings and compliance with evolving regulatory frameworks [39,90].

Understanding and influencing behavior dynamics within the supply chain is key to optimizing performance and ensuring the adoption of sustainable strategies. This involves analyzing how supplier and consumer behaviors evolve over time and in response to various incentives or pressures. Effective management of behavior dynamics can lead to more resilient and adaptable supply chains that are better equipped to handle disruptions and meet sustainability objectives. Factors such as supplier collaboration, customer demand shifts, and regulatory policies play a significant role in shaping supply chain behaviors. Companies that actively manage these dynamics can enhance supply chain agility and sustainability performance [10,60].

5.4.2. Challenges

• Ensuring Global Fairness: Implementing fairness in global supply chains is complex, as it requires navigating diverse legal, cultural, and economic environments. Ensuring that all stakeholders benefit equitably remains a persistent challenge. Addressing fair labor practices, ethical sourcing, and equitable profit distribution in international supply chains requires continuous monitoring and strong governance frameworks [26].
• Environmental Management Complexity: Managing the environmental impact across a global supply chain is inherently challenging. Companies must address issues like carbon emissions, waste management, and resource depletion at every stage, often with limited visibility or control over their supply chains. The integration of sustainable supply chain management practices, such as green logistics and carbon footprint reduction initiatives, remains a priority for organizations looking to balance profitability with sustainability [39,96].
• Motivating Behavioral Change: Encouraging sustainable behaviors among suppliers and consumers is challenging, particularly in markets where cost and convenience often take precedence over sustainability. Developing effective strategies to influence behavior dynamics is essential for driving the adoption of green strategies and achieving long-term environmental goals. Research indicates that consumer education, supplier incentives, and regulatory support play crucial roles in promoting sustainable supply chain behaviors [21,57].

5.4.3. Case Studies in Supply Chain Management

A notable example of fairness integration in supply chain management is the adoption of fair trade certification. Fair trade initiatives ensure that producers, particularly in developing countries, receive fair compensation and operate under ethical working conditions, thereby promoting equity in global trade networks. These programs have led to improved wages, better working conditions, and greater economic stability for producers in industries such as coffee, textiles, and agriculture. Fair trade models continue to gain traction as businesses recognize the importance of ethical sourcing and social responsibility in supply chain practices [94].

The implementation of circular supply chain models has also proven to be highly effective in reducing waste and promoting sustainability. Many companies have embraced closed-loop supply chain systems, where used materials are collected, recycled, and reintegrated into the production process. Such models minimize environmental impact by extending product lifecycles and reducing reliance on raw materials. Research highlights that circular supply chains not only contribute to sustainability but also improve operational efficiency and reduce costs over time, making them a viable strategy for businesses aiming to achieve environmental and economic goals simultaneously [7,35].

Behavioral economics has also played a crucial role in supply chain sustainability by influencing both corporate and consumer behaviors. Companies have successfully integrated behavioral insights into their sustainability initiatives to encourage responsible consumption and production practices. Strategies such as nudging consumers toward sustainable choices, offering incentives for responsible sourcing, and using digital platforms for transparent supply chain tracking have contributed to measurable environmental benefits. Studies show that behavioral-driven approaches in supply chain management can lead to greater compliance with sustainability standards and long-term shifts toward greener business practices [21,75].

5.4.4. Future Prospects and Research Needs

Future research should focus on developing frameworks and tools that further integrate fairness and sustainability into supply chain management. This includes exploring new models for equitable resource distribution and ethical sourcing, particularly in emerging markets where supply chain disparities are more pronounced. Ensuring fairness across global supply chains requires stronger governance, transparency, and mechanisms that prevent exploitative labor practices while promoting fair trade and sustainable business practices [54,84].

There is also a growing need for continued research into innovative Green Supply Chain Management (GSCM) strategies that can be effectively scaled across global supply chains. Advancements in digital technologies, such as blockchain, artificial intelligence, and the Internet of Things (IoT), have the potential to enhance transparency and traceability in sustainable supply chains. These technologies can help organizations track environmental impact, improve waste management, and ensure compliance with sustainability standards. Research on the integration of digital tools into sustainable supply chain frameworks will be crucial in the coming years [39].

Understanding the behavior dynamics of supply chain stakeholders is another critical research area. Supplier and consumer behaviors evolve in response to sustainability initiatives, and developing strategies to influence these dynamics effectively is essential for achieving long-term sustainability goals. Research suggests that behavioral economics, policy interventions, and corporate incentives can play a crucial role in encouraging sustainable practices among businesses and consumers. Future studies should examine how social and economic factors shape supply chain decisions and how companies can design effective engagement strategies to drive sustainable change [11,97].

5.5. Research Gaps and Future Research Directions

While the integrated framework introduced in Section 5.1 provides a structured lens for aligning fairness, environmental impact, and behavior dynamics across digitalized industrial contexts (

Table 6. Comparison of Key Domains across Circular Economy, Business Models, and Supply Chain Management applications.

Key Domain	Supply Chain Management	Circular Economy	Business Models
Fairness	Fair Trade Certification, Equitable Sourcing	Inclusive Recycling, Social Equity in Circular strategies	Inclusive Finance, Micro-lending
Environmental Impact	Green Supply Chains, LCA	Waste-to-Resource, Remanufacturing	Sustainable Product-Service Systems
Behavior	Consumer Approach for Sustainability	Behavioral Incentives for Recycling	Personalized Services, Subscription Models

), several critical research gaps still prevent its full operationalization. Identifying and addressing these gaps is essential for advancing the interdisciplinary understanding and practical implementation of sustainable, equitable strategies within supply chains, circular economies, and business models. This section outlines key limitations observed in the literature and suggests future research directions that could strengthen and extend the framework’s applicability.

5.5.1. Developing Comprehensive Frameworks for Fairness

While the integrated framework introduced in Section 5.1 provides a conceptual foundation for aligning fairness with environmental impact and behavior dynamics, a significant gap remains in developing operational, cross-domain fairness models that can be applied across industries. Much of the current literature focuses on isolated aspects of fairness, such as ethical sourcing, labor conditions, or consumer equity, without addressing how these elements can be integrated systematically into digitalized circular strategies [26,92].

Future research should build upon the framework by designing and validating fairness mechanisms that are adaptable to different industrial contexts, responsive to cultural and regulatory diversity, and compatible with digital infrastructures, such as blockchain or AI-based traceability systems. Such efforts are essential for ensuring that fairness is not just a theoretical ideal but a practical, enforceable component of sustainable digital transformation [60].

5.5.2. Measurement and Management of Environmental Impact

Although the integrated framework in Section 5.1 highlights the role of digital technologies in advancing environmental sustainability, there remains a substantial gap in the standardization, scalability, and integration of tools used to measure and manage environmental impact across industries. Current methodologies such as Life Cycle Assessment (LCA) provide valuable insights but often lack the flexibility needed to address the complexities of global supply chains, the varied requirements of circular business models, and the real-time demands of digitalized systems. Moreover, many sectors, particularly in manufacturing and finance, remain underserved by existing tools [39,86,96].

Future research should aim to extend the environmental dimension of the framework by developing interoperable, digital-first tools that incorporate AI-powered analytics, blockchain-enabled traceability, and real-time environmental monitoring. These innovations can enable more actionable, transparent, and context-sensitive assessments, helping industries operationalize sustainability commitments in alignment with strategic and regulatory goals [35,92].

5.5.3. Understanding and Influencing Behavior Dynamics

As emphasized in the integrated framework presented in Section 5.1, behavior dynamics are essential for translating digital tools and strategic goals into real-world sustainability outcomes. However, current research still lacks a nuanced understanding of how individual and organizational behaviors evolve in response to sustainability initiatives, particularly in diverse cultural, industrial, and regulatory contexts. Many interventions continue to rely on models of rational decision-making, which often overlook psychological biases, social norms, and structural constraints [46,98].

To strengthen the behavioral layer of the framework, future research should explore longitudinal behavioral studies, context-specific nudging strategies, and the use of digital behavior modeling platforms that capture feedback loops between stakeholders and technologies. Understanding how behaviors are shaped, and how they can be influenced at scale, is critical for ensuring the lasting impact of digital sustainability strategies [24,42].

5.5.4. Applying Integrated Approaches in Real-World Practice

Although the integrated framework introduced in Section 5.1 offers a conceptual basis for aligning fairness, environmental sustainability, and behavior dynamics, there remains a critical gap in its practical implementation across industries. Most current strategies address these domains in silos, resulting in suboptimal solutions that do not harness the potential synergies between social, environmental, and behavioral interventions. Despite growing recognition among businesses and policymakers of the value of comprehensive approaches, real-world examples of fully integrated strategies are scarce. Barriers such as regulatory fragmentation, financial constraints, and the absence of actionable interdisciplinary frameworks continue to hinder progress [63,92].

Future research should prioritize the development and documentation of case studies that demonstrate integrated approaches in diverse contexts, showcasing how organizations can address fairness, reduce environmental impact, and harness behavior dynamics simultaneously. In particular, studies should examine the enabling role of digital innovations, transparent supply chain systems, and consumer engagement strategies in operationalizing these concepts. Such evidence can help organizations move beyond high-level sustainability commitments toward scalable, integrated, and data-driven practices that align ethics, profitability, and environmental responsibility [60,97].

5.5.5. Understanding and Managing Cross-Domain Impacts and Trade-Offs

As the integrated framework in Section 5.1 illustrates, aligning fairness, environmental sustainability, and behavior dynamics requires navigating complex interdependencies that often result in trade-offs. Despite widespread agreement on the need for integration, current research offers limited insight into the cross-domain consequences of sustainability strategies. For example, implementing stricter environmental regulations may inadvertently increase operational costs, reduce affordability, or strain small suppliers, raising equity concerns. Similarly, advancing fairness in labor practices may demand financial adjustments that affect business efficiency or consumer access [7].

To strengthen the practical relevance of integrated sustainability strategies, future research should investigate these cross-domain tensions in both theoretical and applied settings. This includes analyzing where and how trade-offs emerge, as well as identifying strategies to balance or even convert them into synergies. Interdisciplinary approaches drawing from behavioral economics, sustainability science, and ethics are essential to model these dynamics effectively. Moreover, digital technologies such as blockchain for supply chain transparency, AI for predictive optimization, and smart contracts for fair transactions offer promising avenues for mitigating trade-offs and enabling more agile, adaptive decision-making. Understanding how to anticipate and manage these cross-domain interactions is key to translating sustainability ideals into actionable strategies [97,99].

5.6. Comparative Overview of Case Studies

To illustrate how fairness, environmental sustainability, and behavior dynamics interact in practical contexts, this section provides a comparative overview of the case studies discussed in Section 5.2.3, Section 5.3.3 and Section 5.4.3. These examples highlight successful applications of interdisciplinary strategies across the circular economy, business models, and supply chain management. The selected cases were chosen for their relevance to digital transformation, their operational maturity, and their ability to integrate cross-domain insights in real-world settings.

Table 7. Comparative overview of case studies across domains.

Case Study	Fairness	Environmental Impact	Behavior Dynamics
Automotive Remanufacturing	Indirect benefit through extended product access; supports economic equity	Minimizes raw material use, waste, and energy via closed-loop systems	Consumer incentives encourage return of used components for refurbishment
Inclusive Recycling Programs	Creates employment in marginalized communities; promotes social equity	Enhances recovery and reuse of materials; reduces landfill and emissions	Community-based engagement and incentives foster recycling behavior
Behavioral Incentives for Circularity	Supports equitable participation through consumer-focused programs	Reduces waste generation and extends product life cycles	Uses targeted incentives and digital platforms to promote sustainable consumption
Subscription-Based Business Models	Promotes fair access through use-over-ownership models	Lowers waste and product turnover; supports circular economy goals	Builds long-term user engagement; uses personalization to support sustainable habits
Inclusive Finance/Microfinance	Addresses social and economic inequality through financial inclusion	Indirectly supports sustainable enterprises and inclusive growth	Encourages economic participation and behavior change via access to capital
Behavioral Economics in Digital Business	Optimizes services through fairness-aware personalization	Supports eco-friendly choices via usage models and AI recommendations	Uses nudges, feedback, and personalization to shape consumer behavior
Fair Trade Supply Chains	Ensures ethical sourcing and fair compensation for producers	Promotes sustainable procurement and responsible sourcing	Builds consumer trust and shapes ethical purchasing behavior
Circular Supply Chain Models	Supports equitable material flow and reverse logistics opportunities	Minimizes waste and extends product/material lifecycle	Uses behavior-informed tracking to improve compliance and efficiency
Behavioral Economics in SCM	Informs equitable supplier decision-making and relationships	Boosts sustainability via behavior-driven logistics optimization	Applies incentives, gamification, and feedback loops to drive alignment

summarizes each case study, showing how key domains are addressed. By presenting these cases side-by-side, we aim to clarify the synergies and trade-offs that emerge when implementing equitable, sustainable, and behaviorally informed strategies in industrial ecosystems.

This comparative analysis reveals that while each domain prioritizes different operational challenges, certain enabling patterns recur across contexts. For example, the use of digital incentives (e.g., nudges, gamification, personalization) appears critical to encouraging circular behavior and improving system compliance. Similarly, fairness-enhancing mechanisms, such as inclusive financing or fair trade certification, not only improve social equity but often complement environmental objectives.

By synthesizing these observations, the table provides a reference point for both researchers and practitioners aiming to replicate or scale similar initiatives. It also serves as a foundation for the research gaps and future directions identified in the following section.

5.7. Implications for Academia and Industry

Building upon the integrated framework and research gaps outlined above, this section reflects on the key implications for academic research and industrial practice.

5.7.1. Academic Implications

The identified gaps emphasize the need for interdisciplinary research that bridges the fields of technology, ethics, and sustainability. Academia should prioritize collaborative research efforts that address these challenges, particularly those involving the ethical implications of artificial intelligence, the social dimensions of the circular economy, and the integration of behavioral economics into business models. Addressing these topics through interdisciplinary studies will help create comprehensive frameworks that guide both policy and industry practice [10,60].

Developing new theoretical frameworks and empirical studies that focus on these areas will contribute significantly to the fields of supply chain management, circular economy, and business models. These contributions will not only advance academic knowledge but also offer practical applications for industries aiming to implement sustainable and ethical strategies. The role of digital innovation, policy interventions, and corporate governance in these domains is another promising area for academic exploration. These efforts will also serve to refine and validate integrative models like the one proposed in this study, strengthening their relevance to both theory and practice [1,7].

5.7.2. Industry Implications

For industry, addressing these research gaps is essential to remaining competitive and socially responsible in an increasingly complex global market. Companies that engage with these areas will be better positioned to implement innovative, ethical, and sustainable strategies that align with evolving consumer expectations and regulatory requirements. Businesses that fail to integrate fairness, sustainability, and behavior-driven insights into their models risk reputational damage, loss of consumer trust, and difficulties in adapting to future policy shifts [98].

Industries that adopt ethical AI frameworks, inclusive circular economy strategies, and scalable, sustainable business models will likely experience improved consumer trust, regulatory compliance, and long-term profitability. The integration of emerging technologies, such as blockchain for supply chain transparency and AI-driven analytics for sustainability monitoring, presents significant opportunities for companies to enhance operational efficiency while maintaining ethical and environmental commitments. Research-backed strategies will be crucial in ensuring that these innovations are implemented effectively, balancing profitability with corporate responsibility [46].

6. Discussion

The interplay among fairness, environmental impact, and behavior dynamics, across circular economy, business models, and supply chain management, reveals both synergistic opportunities and persistent barriers in the path toward sustainable digital transformation. The review shows that:

• Fairness: Functions as a critical driver of inclusivity, promoting ethical practices and equitable outcomes across digitalized systems. However, systemic inequities in global supply chains and access disparities present ongoing challenges, particularly for scalability and adaptability.
• Environmental Impact: Is increasingly shaped by digital tools such as IoT and AI, which support data-driven optimization of resource use. Yet, the lack of standardized digital frameworks in some industries limits full integration.
• Behavior: Plays a pivotal role in aligning both consumer and organizational actions with sustainability objectives. While personalization and gamification help foster engagement, resistance to change and misaligned incentives remain key barriers to long-term impact.

Despite the transformative potential of emerging technologies, their implementation remains uneven across domains. Fragmentation in digital infrastructure and strategy inhibits the cross-domain alignment necessary for systemic progress. Bridging these gaps requires cohesive frameworks that account for industrial diversity, integrate ethical and behavioral dimensions, and embed fairness as a design principle rather than an afterthought.

6.1. Implications of the Integrated Framework

While existing research often addresses fairness, environmental sustainability, and behavioral dynamics in isolation, the integrated framework introduced in Section 5.1 repositions these domains as interdependent components of a unified system. The model demonstrates how these domains interact dynamically across different levels of industrial systems, offering a more comprehensive approach to sustainable design.

A key contribution of this framework is its ability to bridge theoretical insights with practical application. It outlines a layered structure that connects:

• The circular economy as a macro-level enabler of systemic sustainability goals.
• Business models as the strategic link between organizational values and sustainable innovation.
• Supply chain management as the operational layer where fairness and environmental objectives are implemented in practice.

This structure supports better navigation of trade-offs. For example, it shows how blockchain and IoT can enhance transparency and fairness in global sourcing, while simultaneously improving environmental tracking. Likewise, behavioral incentives, such as nudging or gamification, can encourage sustainable choices without compromising operational efficiency.

6.2. Why an Integrated Framework Is Necessary

The demand for an integrated model arises from persistent silos in sustainability research and practice. While prior work has generated valuable domain-specific insights, it often neglects the feedback loops and trade-offs that emerge when these systems interact. The proposed framework helps fill this gap by:

• Delivering a comprehensive lens through which fragmented research findings can be synthesized.
• Offering practical tools for designing strategies that align ethical, environmental, and behavioral goals across industrial contexts.
• Identifying potential conflicts and synergies, for example, where traceability tools may increase efficiency but risk excluding smaller suppliers from digital systems.

By operationalizing this framework, both researchers and practitioners can move beyond isolated metrics or single-domain interventions toward truly interdisciplinary, system-level change. The next section builds on these findings to propose directions for future investigation.

7. Conclusions

This literature review has provided a comprehensive analysis of the integration of Fairness, Environmental Impact, and Behavior within the key contexts of Supply Chain Management, Circular Economy, and Business Models. The analysis has highlighted the critical role these domains play in shaping sustainable and equitable industrial strategies, as well as the challenges and opportunities associated with their integration.

Key findings underscore that fairness, as operationalized through transparency and inclusivity, is central to creating equitable and resilient systems. Environmental sustainability requires not only innovative business models but also the active engagement of digital tools to optimize resource use and lifecycle management. Behavior dynamics emerge as a pivotal factor in aligning individual, organizational, and societal actions with sustainability goals. These insights are consolidated within the integrated framework proposed in Section 5.1, which serves as a guiding structure for bridging ethical, environmental, and behavioral objectives across digitalized industrial systems.

To consolidate the key findings of this review,

Table 8. Research questions mapped to sections and key insights.

Research Question	Related Sections	Key Insights
RQ1. What are the key barriers to achieving an effective interplay among fairness, environmental impact, and behavior dynamics in digitalized supply chains, business models, and circular economy frameworks?	Section 3.2, Section 3.3, Section 3.4, Section 5.1.2, Section 5.2.2 and Section 5.3.2	Trade-offs between fairness and efficiency; lack of scalable circular practices; fragmented accountability and behavioral resistance to sustainability transitions.
RQ2. What mechanisms explain the intersections and interdependency between fairness, environmental impact, and behavior dynamics, and how do these mechanisms shape digital transformation in sustainable industrial practices?	Section 4.2.4, Section 4.3.4 and Section 4.4.4	Digital technologies (AI, IoT, blockchain) act as enablers; behavior dynamics and data-driven feedback loops enhance convergence across domains.
RQ3. What are the best practices and strategies for integrating fairness, environmental sustainability, and behavioral insights in digitalized supply chains and circular business models?	Section 5.1.3, Section 5.2.3 and Section 5.3.3	Identifies successful models: inclusive recycling, fair trade certifications, subscription-based services, reverse logistics, and gamified consumer engagement.
RQ4. What methodological and strategic approaches are most effective for investigating and addressing the interplay among fairness, environmental impact, and behavior dynamics in digitalized industrial systems?	Section 5.4, Section 5.5 and Section 5.6	Highlights interdisciplinary methods (e.g., behavioral economics, life cycle assessment). Discusses practical implications for academia and industry, and introduces an integrated framework for cross-domain sustainability.

maps each research question to its corresponding sections and summarizes the key insights derived from the analysis. This structure provides a clear overview of how the study addresses its core objectives and highlights the theoretical and practical relevance of the results.

Key Contributions:

• Broad Perspective: By examining the intersection of these domains across multiple contexts, this literature review offers an extensive perspective on the complexities of modern industrial strategies.
• Identification of Research Gaps: The literature review has identified key research gaps, particularly in areas such as ethical AI, the social dimensions of the circular economy, and the scalability of sustainable business models.
• Practical Implications: The findings have significant implications for both academia and industry, providing actionable insights that can inform future research and guide the development of more sustainable and equitable strategies.

Together, these practical contributions are underpinned by a deeper theoretical advancement that emerges from the interdisciplinary synthesis developed in this study.

Theoretical Contributions: This review makes a distinct theoretical contribution by proposing an integrated framework that unites fairness, environmental sustainability, and behavior dynamics within the context of digital transformation. Unlike previous studies that explore these domains in isolation, this work synthesizes them into a cohesive lens for analyzing sustainable industrial systems. The paper advances theory by showing how digital technologies, such as AI, blockchain, and behavioral data, can operationalize ethical and environmental objectives simultaneously. Furthermore, the review contributes to the theoretical understanding of cross-domain interplay by demonstrating how the alignment of normative goals (fairness), ecological imperatives (sustainability), and individual-level drivers (behavior) forms a foundation for comprehensive industrial innovation. This interdisciplinary synthesis offers a scalable theoretical model that can inform future research in sustainability, digital ethics, and industrial transformation.

Final Reflection: Despite significant advancements, this paper identifies critical research gaps in integrating these domains comprehensively. Future studies should explore how emerging technologies can address systemic inequities, foster deeper collaboration across stakeholders, and enhance the scalability of sustainable models.

The interplay of fairness, sustainability, and behavioral insights presents a robust framework for addressing the challenges of digital transformation. By bridging theoretical constructs and practical applications, this paper lays the groundwork for advancing research and industrial practices in an era defined by global sustainability imperatives.

Importantly, this review also emphasizes that these domains are not only connected by intersections but shaped by dynamic interdependencies. Changes or interventions in one domain, such as fairness initiatives, can have cascading effects on environmental outcomes and behavioral dynamics. Recognizing and analyzing these mutual influences is essential for designing integrated and adaptive strategies for sustainable industrial systems.

7.1. Implications and Research Directions

7.1.1. Implications for Industry and Academia

Industry: Companies that effectively integrate these domains into their operations will likely see significant benefits, including enhanced sustainability, improved stakeholder relations, and increased operational efficiency. However, they must also navigate challenges such as ethical considerations in AI, the costs of sustainable strategies, and the need for inclusive approaches that address social equity. The integrated framework presented in this review offers a strategic lens for guiding these transformations, helping firms move from isolated sustainability efforts to comprehensive, value-aligned innovation.

Academia: There is a clear need for interdisciplinary research that bridges technology, ethics, and sustainability. Academic research should focus on developing frameworks and empirical studies that address the identified gaps, particularly in areas such as ethical AI, scalable sustainable business models, and the social dimensions of the circular economy. Collaborative research across domains will also be crucial for testing and refining integrated approaches like the one proposed in this study.

7.1.2. Future Research Directions

Integrative Framework Development: A key challenge in advancing fairness, sustainability, and behavioral strategies is the fragmented implementation of these concepts across industries. Many existing approaches operate in isolation, missing opportunities for synergy and comprehensive optimization. Future research should focus on refining and applying integrative frameworks, such as the one proposed in this review, to enable cross-sector alignment. These frameworks should be tested in various industries to assess their scalability, adaptability, and effectiveness in optimizing sustainability, efficiency, and fairness simultaneously.

Ethical AI and Blockchain Deployment: Despite their potential to enhance transparency and fairness, AI and blockchain remain underutilized in addressing systemic inequities and environmental accountability. AI models risk perpetuating biases, while blockchain technologies face adoption barriers due to regulatory and technical complexities. Future research should aim to develop ethical AI guidelines and scalable blockchain frameworks that enhance traceability and fairness in supply chains. Addressing regulatory challenges and improving interoperability will be crucial for ensuring widespread adoption and real-world impact. These technologies also represent key enablers for operationalizing the integrated framework across global industrial systems.

Behavioral Insights for Sustainability: A significant gap in sustainability research is the limited understanding of how behavioral dynamics influence industrial and consumer decision-making. While behavior-driven interventions have shown promise, their long-term effectiveness and scalability remain uncertain. Future research should explore how behavioral nudges can be systematically integrated into circular business models and supply chain incentives. Additionally, studies should assess how these behavioral approaches can drive sustained change in both consumer habits and corporate strategies. Behavioral integration is also critical for aligning digital tools and strategic goals with real-world action, as emphasized in the framework.

Industry-Specific Strategies: Generalized sustainability and digitalization strategies often fail to address the unique challenges faced by different industries. Sectors such as heavy manufacturing, retail, and emerging markets have distinct constraints that require tailored solutions. Future research should prioritize sector-specific studies that identify unique barriers and opportunities for digital and sustainable transformations. Developing industry-adapted strategies will ensure that digital and environmental advancements are practical, effective, and aligned with each sector’s operational realities. Applying the integrated framework across varied sectors can also reveal opportunities for adaptive design and customization.

Digital Collaboration Ecosystems: Limited cross-industry collaboration on data-sharing and sustainability frameworks continues to hinder global progress. The absence of integrated digital platforms restricts knowledge exchange and innovation. Future research should focus on fostering shared digital ecosystems, such as GAIA-X, to promote seamless collaboration. These ecosystems should facilitate transparent data-sharing, establish common ethical standards, and support cross-sector innovation, ultimately enabling businesses and policymakers to implement framework-driven, sustainability-oriented digital transformation on a global scale.