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Review

Competitiveness in the Era of Circular Economy and Digital Innovations: An Integrative Literature Review

by
Ibrahim M. Awad
1,*,
Hasan Nuseibeh
1 and
Alaa A. Amro
2
1
Faculty of Business and Economics, Al-Quds University, East Jerusalem 20002, Palestine
2
Department of Research and Market Studies, Smart University College for Modern Education, Hebron 777, Palestine
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(10), 4599; https://doi.org/10.3390/su17104599
Submission received: 22 March 2025 / Revised: 24 April 2025 / Accepted: 28 April 2025 / Published: 17 May 2025
(This article belongs to the Special Issue Digitalisation, Entrepreneurship and Innovation for Sustainable Business Management)
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Abstract

:
This study explores the intersection of competitiveness in a circular economy and the role of digital innovations through an integrative literature review. Synthesizing quantitative and qualitative research identifies gaps and offers insights on how these trends shape competitive strategies. The review emphasizes three main areas: technological enablers, operational challenges, and the role of policy and collaboration. It highlights the interrelationship among the circular economy, digital innovations, and competitiveness in promoting sustainable practices. The research suggests that policymakers should support small- and medium-sized enterprises (SMEs) with financial assistance for digital tool adoption and establish regional digital innovation hubs for technology access and training. Standardized data-sharing protocols are crucial for effective circular economy practices and cybersecurity. Ultimately, the review identifies key research opportunities at the nexus of digital innovations and the circular economy, aiming to enhance theoretical knowledge and inform sustainable business model development.

1. Introduction

Competitiveness is the ability of companies and productive sectors to increase their market share by successfully competing with foreign goods in both domestic and international markets. This capacity may arise from cost leadership, superior product quality, innovation, or brand strength. Ref. [1] expanded this definition, describing competitiveness as the capacity of a nation’s citizens to attain higher living standards, achieved through sustained innovation and productivity growth. Cost competitiveness is generally evaluated through cost indicators, efficiency metrics, and labor productivity measures. Ref. [2] propose that clusters can indirectly boost competitiveness by focusing on the economic, social, and environmental benefits associated with agricultural clusters, rather than through a direct impact. Additionally, refs. [3,4] explored the role of industrial clusters in enhancing competitiveness and fostering economic development. Ref. [5] further argues that competitiveness is a relative concept that must be assessed compared to peers, whether firms or nations, and that productivity and workforce utilization should be emphasized as key levers of economic performance.
Against this backdrop, the current global economy is undergoing a systemic transformation driven by environmental constraints, resource depletion, and digital disruption. As a response, the circular economy (CE) has emerged as a regenerative model that contrasts the traditional linear “take–make–dispose” paradigm. According to [6] CE, it is designed to restore natural systems and eliminate waste through restorative loops such as reuse, remanufacturing, and recycling. CE is increasingly seen not only as an environmental imperative but also as an engine for economic innovation and renewal, with strong policy momentum from the European Union and international organizations [7,8].
Concurrently, digital technologies—including the Internet of Things (IoT), big data analytics, and artificial intelligence—have enabled new production, logistics, and consumption capabilities. These technologies enhance resource efficiency and traceability, facilitating the implementation of circular business models and strengthening firms’ ability to respond to market and regulatory pressures. As Ref. [9] note, industrial actors are central to operationalizing CE strategies, and digital technologies provide the data-driven tools necessary for this transition. Ref. [10] also highlights how digital innovations contribute to small and medium enterprise (SME) growth by enabling smarter, circular business approaches.
The convergence of CE and DI is increasingly recognized as a potential driver of competitive advantage. Firms that successfully integrate circular practices with digital capabilities may improve operational efficiency, build more resilient supply chains, and differentiate their offerings through innovation Ref. [11]. However, despite the growing interest in CE and DI as separate domains, their combined influence on competitiveness remains insufficiently explored in the literature.
The primary aim of this study is to shed light on the dynamics of competitiveness within the intertwined contexts of circular economy and digital innovations. To achieve this, the paper conducts an integrative literature review, synthesizing research across multiple disciplines to understand how these evolving paradigms influence competitive strategies, organizational capabilities, and policy design. By identifying intersections, gaps, and emerging patterns, this review contributes to both academic theory and practical guidance for firms and policymakers navigating the transition to a digitally enabled circular economy.

2. Review Methodology

2.1. Review Design and Rationale

An integrative literature review (ILR) method was adopted to meet the study objectives. ILR is particularly suited for synthesizing diverse bodies of knowledge, including both empirical and theoretical research, to provide a broader and deeper understanding of complex phenomena [12,13,14]. It enables conceptual synthesis by incorporating studies with varied methodologies and epistemological orientations. This approach is especially appropriate when exploring multidisciplinary fields such as the circular economy (CE), digital innovations (DI), and competitiveness, where theoretical frameworks, methodological tools, and practical applications differ significantly [15,16].
Compared to systematic or bibliometric reviews, which typically apply rigid inclusion criteria and emphasize quantitative mapping techniques, ILRs support broader conceptual exploration. This method allows for integrating both experimental and non-experimental literature, addressing theoretical and methodological gaps by uncovering patterns, contradictions, and emerging trends. It is well-aligned with the study’s aim of identifying how CE and DI jointly influence competitiveness while providing a basis for future policy and research implications.
In response to the methodological complexity of research at the intersection of circular economy (CE), digital innovations (DI), and competitiveness, this study adopts an enhanced integrative literature review (ILR) approach. While ILRs are well suited for synthesizing diverse empirical and conceptual sources [12,14], we extend this design by incorporating selected elements of bibliometric mapping—specifically, author affiliation, methodological diversity, and publication geography—following the evolving best practices in sustainability and innovation review literature (e.g., [17,18]. This methodological refinement supports a more structured classification of prior reviews and empirical works and enables the identification of underexplored themes and regional gaps.
Moreover, the review is theory-informed, drawing on the Resource-Based View (RBV), Dynamic Capabilities (DC), and Innovation Diffusion Theory (IDT) to interpret emerging mechanisms linking CE and DI to competitiveness. This combined strategy supports the development of a synthesis that is both conceptually coherent and practically applicable across policy and firm-level contexts.

2.2. Analytical Procedure

The review process followed a structured analytical procedure adapted from established integrative review frameworks [12,19]. Four sequential steps were employed:
Step 1—Defining Key Concepts and Their Interrelationships: The initial step involves establishing clear and consistent definitions for CE, DI, and competitiveness. CE is conceptualized as a regenerative system to minimize waste and maximize resource utilization through closed-loop systems and restorative practices [6,20,21]. DI encompasses a wide range of technologies, including IoT, AI, blockchain, and big data, which enable CE by facilitating improved data collection, analysis, process optimization, and traceability [10,22,23]. DI is understood as a sociotechnical phenomenon incorporating technological and social transformations [15]. In this context, competitiveness refers to businesses and economies’ ability to achieve sustainable growth and enhanced market share through the strategic adoption of CE principles and DI [24]. This is achieved by increasing resource productivity, reducing operational costs, enhancing product differentiation, and creating innovative circular business models [1,8].
Step 2—Reviewing Relevant Theoretical Frameworks: To guide the synthesis of literature and interpret emerging themes, this study draws on three theoretical frameworks that collectively offer a multidimensional perspective on the relationship between circular economy, digital innovation, and competitiveness. These frameworks were selected based on their conceptual relevance to the central themes of this review. Specifically, the Resource-Based View (RBV) offers a foundation for understanding how firms leverage valuable, rare, and inimitable resources—including those enabled by digital innovations—for competitive advantage [25]. The Dynamic Capabilities framework complements this by explaining how firms reconfigure internal and external resources to respond to shifting technological and environmental conditions [26]. Within this context, DI enables organizations to sense opportunities, seize them by developing innovative business models, and transform their operations accordingly [27]. Finally, Innovation Diffusion Theory (IDT) provides a behavioral and organizational perspective on how CE and DI practices are adopted and disseminated across industries, underscoring the role of early adopters and institutional support [28,29].
Step 3—Identifying Research Gaps and Methodological Challenges: A comprehensive review of the existing literature identifies significant gaps and challenges. Many studies have limited sectoral and geographical coverage, often focusing on specific industries or regions, reducing generalizability [30,31]. Methodological inconsistencies are evident, with studies employing diverse approaches such as case studies, surveys, and quantitative modeling complicating cross-comparison [32]. Additionally, there is limited empirical research on the long-term socio-economic impacts of CE initiatives enabled by DI. Challenges faced by SMEs, such as resource constraints, lack of expertise, and resistance to change, remain underexplored [33,34]. Standardization and interoperability of digital technologies in CE contexts are critical issues that require further attention [32].
Step 4—Synthesizing and Analyzing the Literature: The final step synthesized findings into a thematic framework aligned with the theoretical perspectives selected in Step 2. However, detailed results of this synthesis—such as identified themes, recurring patterns, and sectoral insights—are presented in the Discussion section to maintain methodological clarity and follow academic conventions. [35] framework guided the thematic analysis, which supports identifying conceptual relationships across diverse literature. Methodological rigor was assessed based on transparency in data collection, analytical technique, and theoretical contribution, ensuring reliability in theme development.

2.3. Data Sources

A structured integrative literature review focused on three central dimensions of the study: the interlinkages and intersections among circular economy (CE), digital innovations (DI), and competitiveness. This process involved examining the relevant literature that integrates these three areas.
The integrative literature review utilized three primary academic databases: Web of Science, Scopus, and Google Scholar. These platforms were selected for their extensive coverage of peer-reviewed articles across various disciplines, ensuring a comprehensive inclusion of relevant literature. To capture the multifaceted dimensions of competitiveness, circular economy, and digital innovation, a systematic search was conducted using a combination of keywords, including “competitiveness”, “circular economy”, “digital innovation”, and “integrative literature review”. Boolean operators were applied to refine the search accuracy (e.g., “competitiveness AND circular economy AND digital innovation”).
The following inclusion and exclusion criteria were established to ensure the relevance and quality of the studies incorporated in the review:
Inclusion Criteria:
  • Studies published in peer-reviewed journals or reputable academic conferences.
  • Articles published in English to ensure consistency in interpretation and review.
  • Research focusing on the intersection of CE, DI, and competitiveness.
  • Studies employing qualitative, quantitative, or mixed methods to explore CE or DI as drivers of competitiveness.
  • Papers published primarily between 2015 and 2024 reflect the contemporary evolution of digital technologies and the circular economy.
Exclusion Criteria:
  • Studies focus solely on technical algorithms, modeling, or optimization without linking them to competitiveness or CE.
  • Non-peer-reviewed sources like white papers, blogs, or opinion pieces.
  • Articles without explicitly focusing on CE, DI, or competitiveness as central themes.
  • Papers primarily discuss linear economic models without considering circular approaches or innovations.

2.4. Data Collection and Administration

Following the identification and selection of 71 relevant studies, data extraction focused on capturing core themes, research methods, theoretical lenses, sectoral focus, and geographic coverage. A data extraction template was designed to ensure consistency and alignment with the study objectives.
Thematic synthesis followed [35] six-phase framework: familiarization with data, generation of initial codes, theme searching, theme reviewing, theme definition/naming, and report production. An inductive–deductive approach was used to ensure that emerging themes remained grounded in the literature while aligning with the conceptual framework developed in Step 2 of the Analytical Procedure.
The actual findings and detailed themes derived through this process are presented in the Discussion section to maintain clarity between methodological execution and interpretive analysis.
Data have been extracted from the included studies and synthesized into an integrative literature review framework for the study, as shown in Table 1 below.

3. Literature Mapping and Analytical Insights

This section provides an overview and meta-level analysis of the 71 studies included in the integrative literature review. It responds directly to reviewer suggestions by mapping existing literature’s volume, scope, methodologies, and geographic trends. The goal is to offer a clear empirical foundation that contextualizes the thematic findings presented in the next section.

3.1. Existing Literature Reviews and Their Limitations

The table below (Table 2) organizes previous literature reviews into five key types and synthesizes their contributions. It follows best practices in integrative reviews by combining descriptive insights, critical appraisals, and practical relevance:

3.2. Study Volume and Thematic Scope

To understand the landscape of research linking circular economy (CE), digital innovations (DI), and competitiveness, we analyzed 72 selected studies across a range of publication years, geographic focuses, methods, and thematic priorities. These studies include both empirical and theoretical work published between 2012 and 2024.
Much of the literature focuses on the manufacturing and industrial sectors, with notable emerging work in construction, logistics, and SME strategy. Thematically, digital enablers such as IoT, big data, AI, and blockchain reoccur as key technologies linked to CE transitions, with competitiveness explored through productivity, innovation, cost-efficiency, and adaptability metrics.
Methodologically, a mix of qualitative, quantitative, mixed-methods, and conceptual approaches was found. This diversity underscores the need for an integrative lens capable of synthesizing across epistemological boundaries.

3.3. Research Methods and Data Types

The methods employed by the studies in this review vary widely. Based on a reclassification of all 70 studies using the verified master dataset, the updated methodological distribution is as follows (Table 3):
This refined breakdown reflects a deeper engagement with the methodological diversity of CE-DI-competitiveness research. With all studies now successfully classified, the field demonstrates a balance between qualitative and mixed-methods insights, systematic reviews, and emerging quantitative analysis. The low number of integrative and conceptual studies suggests opportunities for further synthesis and theory development, while the presence of bibliometric and general reviews points to growing interest in mapping and framing this interdisciplinary domain.
This distribution reflects significant methodological heterogeneity across the literature. While systematic reviews remain the most common, many studies do not clearly indicate a defined method and remain unclassified. These may include hybrid approaches, opinion pieces, or reports that lack explicit methodological labeling. Their presence underscores the importance of employing an integrative lens to accommodate diverse knowledge sources while also pointing to the need for more transparent methodological reporting across the field.

3.4. Geographic and Sectoral Distribution and Bibliometric Trends

The reviewed studies span multiple continents, with clusters of contributions from Europe (notably Germany, UK, The Netherlands), Asia (China, India, Japan), and emerging input from Latin America and the Middle East. Sectoral manufacturing dominates the dataset, particularly Industry 4.0-linked studies, followed by construction, logistics, waste management, and SMEs.
This distribution reflects both the industrial relevance of CE and DI and the academic interest in technologically mediated sustainability transitions. However, the geographic skew toward high-income regions highlights a research gap in understanding CE-DI-competitiveness linkages in the Global South.

3.5. Author, Institutional, and Country-Level Trends in CE-DI-Competitiveness Research

Recent bibliometric studies have helped illuminate the distribution of scholarly activity across authors, institutions, and countries in the CE-DI-competitiveness nexus:
  • Geographic Concentration: Research output is concentrated in Europe (notably Germany, the UK, and Italy), with significant contributions also from China and the USA, reflecting their active innovation ecosystems.
  • Institutional Leadership: Leading institutions include the University of Cambridge, Delft University of Technology, and Tsinghua University, often collaborating across sustainability, industrial engineering, and digital innovation faculties.
  • Author Collaboration Networks: Co-authorship analyses reveal strong regional collaborations within Europe and Asia. Intercontinental research partnerships, however, remain limited, suggesting opportunities for expanding global collaboration networks.
These findings reinforce the need for more inclusive research representation and encourage multi-institutional, cross-border partnerships that can deepen understanding and increase the applicability of CE and DI strategies across diverse economic contexts.

4. Findings and Discussion

The results of the integrative literature review synthesize insights across three key dimensions: technological enablers, operational challenges, and opportunities for policy and collaboration. This synthesis highlights the crucial interplay between the circular economy (CE), digital innovations (DI), and competitiveness, demonstrating how these elements converge to foster sustainable and competitive business practices. Additionally, the findings are explicitly linked to the theoretical frameworks as discussed below.

4.1. Technological Enablers

Digital technologies facilitate the transition to a CE, creating valuable and potentially inimitable resources for firms, aligning with the Resource-Based View (RBV) [25]. IoT and AI empower real-time monitoring, predictive analytics, and process automation, enabling companies to optimize resource consumption, minimize waste generation, and extend product lifecycles [59,63,81]. For instance, IoT sensors allow companies to proactively maintain equipment, minimizing downtime and enhancing customer satisfaction [81]. This directly addresses improved resource efficiency, a key driver of CE and competitiveness [1]. Blockchain technology enhances transparency and traceability across supply chains, addressing accountability challenges and fostering stakeholder trust [55,57,75]. Additionally, blockchain is highlighted as a key enabler of secure and transparent transaction processes, positively influencing CE practices [75]. This aligns with the Dynamic Capabilities framework, enabling firms to sense and seize opportunities for circular business models [27]. Big data analytics facilitates data-driven decision-making, enabling stakeholder collaboration and optimizing industry resource flows [68]. For example, digital platforms like those examined by [64] streamline CE implementation by improving resource monitoring and supply chain efficiency. These technologies illustrate how DI can enhance operational efficiency and create new value propositions, contributing to enhanced competitiveness.

4.2. Operational Challenges

Despite the transformative potential of DI, several operational challenges hinder its widespread adoption within CE. A significant obstacle for many organizations, particularly SMEs, is the lack of appropriate digital infrastructure and capacity to adopt and integrate new digital tools [71,91]. This relates to the Innovation Diffusion Theory, as organizations with limited resources may be slower to adopt new technologies [28]. SMEs often face unique challenges, including financial constraints, lack of skilled personnel, and limited access to advanced technologies [30,31]. Resource constraints, including these factors, are significant barriers to their adoption of DI. Furthermore, organizational resistance to change and the presence of legacy systems complicate the integration of new technologies [71]. Data interoperability and standardization remain critical issues; the lack of uniform protocols for data exchange hinders effective collaboration between stakeholders [33,64]. Knowledge integration is also critical, particularly within SMEs, where balancing innovation with existing organizational practices poses a challenge [71]. This highlights the need for targeted support mechanisms, such as digital innovation hubs [74], to assist SMEs in overcoming these barriers. Additionally, the role of Chief Digital Officers (CDOs) in navigating organizational structures and implementing digital strategies is pivotal for larger enterprises [85].

4.3. Policy and Collaboration Opportunities

Effective policy frameworks and collaborative initiatives are crucial for promoting the adoption of DI to advance CE principles and enhance competitiveness. Supportive regulations, incentives for innovation, and public–private partnerships are essential for driving investment in DI for CE [11,34]. For example, the Smart CE framework proposed by [33] demonstrates how digital technologies can align with circular strategies to optimize resource efficiency. Establishing international data sharing and interoperability standards is critical to facilitating cross-border collaboration and enhancing the scalability of CE solutions [55]. Collaborative platforms and ecosystems foster knowledge sharing, joint development of solutions, and the creation of shared value among stakeholders [68,80]. Additionally, government support fosters innovation ecosystems, including effective institutional regulations, digital players, and technological progress [72]. Targeted initiatives like regulatory sandboxes can encourage businesses to experiment with circular business models [88]. The role of corporate incubators in fostering digital innovation, particularly in sectors like the automotive industry, highlights opportunities for scaling CE initiatives [92]. Government policies should also prioritize the development of digital innovation hubs to facilitate knowledge sharing and collaboration among SMEs, enabling them to compete more effectively in a CE framework [74].
Given the study findings, the integrative literature review provides critical insights into the interplay between the circular economy (CE), digital innovations (DI), and competitiveness. By synthesizing existing studies, this section contextualizes the findings within established theoretical frameworks and highlights practical implications while addressing gaps in the literature.

5. Conclusions, Theoretical and Policy Implications

5.1. Conclusions

This integrative literature review has highlighted the transformative potential of digital innovations (DI) in advancing circular economy (CE) practices and enhancing competitiveness. This study provides a nuanced understanding of how DI and CE converge to drive sustainable and competitive business practices by synthesizing findings across technological enablers, operational challenges, and policy opportunities. The interplay between these elements is underpinned by established theoretical frameworks, such as the Resource-Based View (RBV), Dynamic Capabilities, and Innovation Diffusion Theory (IDT), which collectively inform the practical and policy recommendations outlined.
The findings underscore the critical role of DI technologies, such as IoT, AI, blockchain, and big data, in enabling CE transitions. These technologies optimize resource flows, enhance supply chain transparency, and facilitate innovative business models, contributing to firms’ competitive advantage and sustainability. However, significant operational challenges persist, particularly for SMEs, including limited financial resources, lack of technical expertise, and organizational resistance to change. These barriers highlight the importance of targeted interventions, such as financial incentives, digital innovation hubs, and capacity-building initiatives, to support broader DI adoption.
Policy frameworks and collaborative ecosystems emerge as pivotal mechanisms for addressing these challenges. Establishing data-sharing standards, public–private partnerships, and regulatory incentives can accelerate the diffusion of DI-enabled CE practices. Investments in digital infrastructure and workforce development are also crucial to ensuring equitable access to the benefits of DI and CE, particularly in underserved regions and sectors.
Despite the progress made, several research gaps remain. Future studies should focus on quantifying the long-term socio-economic impacts of CE, such as job creation and environmental benefits, to strengthen the case for DI investments. SME-specific challenges warrant deeper investigation to design tailored support mechanisms, particularly in developing economies. Ethical and social considerations, including data privacy, equitable technology access, and the potential for job displacement, must also be addressed to ensure an inclusive transition to CE.
In conclusion, the convergence of DI and CE represents a significant opportunity to foster sustainable and competitive economies. By addressing the identified challenges, leveraging collaborative opportunities, and pursuing targeted research agendas, stakeholders can unlock the full potential of DI for CE. This transition will require coordinated efforts among policymakers, businesses, and researchers to create a resilient, circular, and sustainable future.

5.2. Theoretical Model

This review proposes a theoretical model in which digital innovation (DI) enables circular economy (CE) capabilities that, in turn, enhance competitiveness. This model is grounded in three well-established frameworks. The Resource-Based View (RBV) explains how digital technologies become valuable, inimitable resources that underpin CE transformation and sustainable advantage. The Dynamic Capabilities (DC) perspective positions DI as a trigger for sensing opportunities, seizing circular innovations, and reconfiguring firm resources for resilience and adaptability. Meanwhile, Innovation Diffusion Theory (IDT) clarifies how DI–CE practices spread across firms and sectors, with adoption patterns shaped by perceived complexity, compatibility, and observable advantage. These theories explain how DI and CE co-evolve to strengthen long-term competitiveness systematically (Figure 1).
Digital technologies like IoT, blockchain, and big data align with RBV by creating valuable, rare, and non-substitutable resources. For example, IoT enables real-time monitoring and predictive maintenance, directly enhancing resource efficiency and lifecycle extension [63,81]. Blockchain increases transparency and accountability across supply chains [75]. These capabilities illustrate how DI contributes to competitive advantage by optimizing resources and enabling differentiation through CE practices [68].
From a Dynamic Capabilities’ perspective, DI allows firms to sense opportunities for circular business models, seize them through innovation, and reconfigure internal and external competencies to align with sustainability goals [27]. AI-driven analytics, for example, can identify inefficiencies and opportunities for CE integration, thereby reinforcing organizational adaptability and resilience [30].
IDT further clarifies the pace and pattern of CE-DI adoption, especially in SMEs, which often face resource constraints and institutional inertia [28]. Early adopters, supported by innovation units, play a pivotal role in diffusing new technologies and circular practices across industries [29,33].
By weaving these theoretical perspectives together, this review constructs a layered explanation for how DI enables CE transitions that ultimately contribute to both firm-level and systemic competitiveness.

5.3. Practical Implications

The findings of this review offer actionable guidance for firms of all sizes aiming to integrate digital innovation (DI) into circular economy (CE) strategies.
For small- and medium-sized enterprises as it discusses ICT policy in developing countries, with a focus on digital innovation and competitiveness (SMEs): SMEs often face structural barriers to adopting DI, including limited financial resources, technical capabilities, and organizational capacity. The role of ICT in sustainable development, as explored by [93,94], highlights how effective infrastructure and policy decisions can enhance economic competitiveness, particularly in digital innovation and the circular economy. Their framework offers policymakers practical steps to strengthen the digital economy in developing countries, emphasizing the need to address resource constraints and promote institutional collaboration to achieve sustainable growth. Policymakers and industry leaders should prioritize the development of digital innovation hubs and collaborative platforms that grant SMEs access to technical expertise, shared infrastructure, and funding opportunities [74]. In parallel, targeted training and capacity-building programs are essential to enhance digital readiness among smaller firms [71]. Promoting access to affordable, modular digital tools—tailored to SME needs—can further lower entry barriers and support scalable CE adoption.
For large enterprises: Larger firms are strategically positioned to lead CE transitions by integrating DI at scale. Chief Digital Officers (CDOs) are pivotal in aligning digital strategy with sustainability goals and navigating organizational complexity [85]. Moreover, corporate incubators and accelerators can support innovation pipelines by partnering with startups whose technologies align with CE principles [92]. Larger enterprises can also serve as ecosystem anchors, using their resources and visibility to pilot CE business models and set sectoral standards that enable diffusion across supply chains.

5.4. Policy Implications

Strategic policy interventions enable DI-driven circular transitions and enhance systemic competitiveness. Based on the synthesized findings, we identify six key areas for intervention:
  • SME Enablement: Support mechanisms such as grants, tax credits, and subsidized technology programs can offset the upfront costs of DI adoption for SMEs [30,31]. Regional digital innovation hubs (DIHs) should be scaled up to offer SMEs shared access to infrastructure, mentoring, and training [74], acting as knowledge brokers that link academia, government, and industry.
  • Standardization and Data Governance: A lack of interoperability and common standards hinders CE collaboration. Policymakers must coordinate the development of standardized data-sharing protocols across industries [33]. Open data initiatives and clear data ownership and protection frameworks are essential to building trust and ensuring transparency in digital circular ecosystems [55].
  • Digital Infrastructure Investment: Robust and inclusive digital infrastructure, especially high-speed internet, cloud services, and cybersecurity, is foundational to DI adoption, particularly in underserved regions [91]. Public investments should prioritize equity and resilience, ensuring that both rural and urban firms can participate in digital CE transformations.
  • Regulatory and Market Signals: Regulatory sandboxes can allow businesses to experiment with CE practices in controlled environments, helping regulators learn in parallel [88]. Public procurement policies should be revised to favor circular solutions, creating stable demand for sustainable products and services. Tax incentives for CE-aligned digital investments can further encourage adoption [34].
  • Collaboration and Ecosystem Governance: Policymakers should promote multi-stakeholder platforms and public–private partnerships (PPPs) to co-create CE-DI solutions [68]. These collaborations can address complex challenges such as resource recovery or circular logistics by pooling expertise and distributing risk [80]. International cooperation can accelerate alignment on global CE standards and policy benchmarks.
  • Workforce Development and Social Equity: A digitally fluent workforce is critical for executing CE strategies. Policymakers should invest in education, vocational training, and reskilling programs that prepare workers for emerging roles in digital circular systems [69]. At the same time, social safeguards are needed to address potential job displacement, data ethics, and access inequality. Regulatory frameworks should guarantee data privacy, while social policies should ensure that the CE transition is inclusive and just.

6. Addressing Research Gaps

Effective policy frameworks are crucial for promoting DI adoption. Standardization of data-sharing protocols and interoperability frameworks can address key operational challenges and foster cross-sector collaboration [33]. Financial incentives, such as tax breaks and subsidies, can encourage businesses to invest in DI and circular practices [34]. Moreover, establishing clear regulatory guidelines can reduce uncertainties and facilitate smoother transitions to CE practices. This review highlights critical gaps that future research must address to advance the integration of DI and CE:
  • Long-Term Impacts: Limited studies explore the socio-economic and environmental outcomes of DI-enabled CE practices. Quantitative metrics are needed to assess the long-term benefits, such as job creation and resource conservation [32]. Additionally, longitudinal studies can provide deeper insights into the sustained impacts of DI on CE transitions.
  • Sectoral and Geographical Variations: Research often focuses on specific industries or regions, limiting the generalizability of findings. Future studies should examine diverse sectors and geographies to provide a comprehensive understanding of DI’s role in CE. Cross-comparative studies across developed and developing economies could uncover unique challenges and opportunities.
  • Ethical and Social Considerations: Issues such as data privacy, equitable access to technology, and the implications of job displacement remain underexplored. Addressing these concerns is critical for ensuring inclusive and sustainable CE transitions [55]. Furthermore, the ethical implications of using advanced technologies like AI and blockchain in CE must be carefully examined to prevent unintended consequences.
  • Opportunities for Future Development: The convergence of CE and DI presents unique opportunities for innovation and collaboration.
    • Collaborative Ecosystems: Digital platforms can facilitate partnerships between businesses, governments, and research institutions, enabling the co-development of circular solutions [68]. These ecosystems can act as innovation hubs, fostering creativity and shared value creation.
    • Advanced Technologies: Emerging technologies such as AI and blockchain hold significant potential for enhancing resource efficiency and enabling new circular business models. Research should explore their scalability and practical applications across industries [63,75]. Innovations such as digital twins and predictive analytics can further streamline operations and improve CE outcomes.
    • Policy Integration: Policymakers must align incentives and regulations to support CE and DI convergence, creating an enabling environment for innovation and sustainability [72]. Additionally, international cooperation on CE standards can be fostered, and practices can enhance global impact and scalability.
However, integrating digital innovation (DI) within circular economy (CE) frameworks presents a transformative opportunity to improve competitiveness and sustainability. Stakeholders can fully unlock the potential of this paradigm shift by addressing identified gaps and leveraging available opportunities. Collaboration among businesses, policymakers, and researchers will be crucial for successfully implementing DI-enabled CE practices.

7. Future Research Directions

This review has highlighted significant areas for future research at the intersection of Digital innovations (DI) and circular economy (CE). Addressing these gaps will advance theoretical understanding, inform policy development, and guide practical implementation to achieve sustainable and competitive business models.
Future research should prioritize the development of quantitative metrics to assess the socio-economic and environmental impacts of DI-enabled CE practices. Metrics examining job creation, resource conservation, and long-term financial performance are critical for substantiating the benefits of circular models. Studies employing econometric modeling and scenario analyses could provide deeper insights into the scalability and sustainability of CE initiatives [32]. By quantifying these impacts, researchers can offer evidence-based guidance to policymakers and practitioners.
Another vital area is the exploration of sectoral and geographical variations in DI adoption. Industry characteristics and regional contexts significantly influence the effectiveness of DI in advancing CE. Comparative studies across sectors—such as manufacturing, construction, and services—can uncover unique barriers and opportunities. Similarly, investigating DI adoption in developed versus developing economies could yield insights for tailoring interventions to specific contexts [30,31]. Understanding these variations can enable more targeted and impactful strategies.
The ethical and social implications of integrating DI into CE frameworks also demand further investigation. Data privacy, equitable access to technology, and the potential for job displacement must be addressed to ensure that CE transitions are inclusive and equitable. For instance, technologies like AI and blockchain present opportunities and challenges, particularly in safeguarding privacy and promoting accessibility [55]. Research into these societal dimensions can help mitigate risks while fostering stakeholder trust and engagement.
Longitudinal studies are essential to understanding the sustained impact of DI on CE transitions over time. These studies could explore how businesses evolve their circular practices with continued DI integration, identifying factors that drive or hinder long-term success. Additionally, longitudinal analyses can refine theoretical frameworks such as RBV and Dynamic Capabilities to better reflect the complexities of CE transitions [27]. Such research would contribute to both academic knowledge and practical applications.
Emerging technologies—including digital twins, predictive analytics, and quantum computing—hold significant potential for advancing CE practices. Future research should explore their scalability and practical applications across various industries. For example, case studies and pilot projects can provide valuable insights into integrating these technologies into real-world contexts. By focusing on innovation, researchers can uncover new pathways for achieving CE goals [63,75,95].
Collaboration and policy frameworks remain pivotal to the success of DI and CE integration. Research should examine the role of public–private partnerships, international collaborations, and regulatory sandboxes in fostering innovation. Identifying the best practices in these areas can guide policymakers in creating supportive environments for DI-enabled CE transitions [68]. Moreover, studies focusing on collaborative ecosystems can show how different stakeholders can co-develop solutions and share best practices.
Finally, interdisciplinary approaches are crucial for addressing the multifaceted challenges at the intersection of DI and CE. Integrating perspectives from environmental science, economics, sociology, and engineering can uncover synergies and promote holistic solutions. Interdisciplinary research can bridge gaps between technology and sustainability, advancing comprehensive strategies for CE transitions.
By addressing these future research directions, scholars and practitioners can deepen the understanding of DI’s transformative role in advancing CE. These efforts will strengthen theoretical frameworks and provide actionable insights for businesses and policymakers striving to build sustainable and competitive economies.

Author Contributions

Conceptualization, I.M.A. and H.N.; methodology, I.M.A. and H.N.; validation, I.M.A., H.N. and A.A.A.; formal analysis, I.M.A. and H.N.; resources, I.M.A., H.N. and A.A.A.; data curation, I.M.A., H.N. and A.A.A.; writing—original draft preparation, I.M.A. and H.N.; writing—review and editing, I.M.A., H.N. and A.A.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Digital Innovation and Circular Economy as Enablers of Competitiveness: A Theoretical Model.
Figure 1. Digital Innovation and Circular Economy as Enablers of Competitiveness: A Theoretical Model.
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Table 1. Synthesis of the key arguments about circular economy, digital innovations, and competitiveness.
Table 1. Synthesis of the key arguments about circular economy, digital innovations, and competitiveness.
Study, YearObjectiveKey InsightContributionDataMethodology
[36], 2024Explore how AI, blockchain, and digital twins can transform the built environment towards circularity.Material passports, BIM-linked digital twins, and blockchain-enabled supply-chain tracking are key enablers for closed-loop construction.Provides practice-ready frameworks for integrating digital tools into circular building practice.Multidisciplinary literature synthesis and illustrative case studiesQualitative framework analysis
[37], 2024DTs facilitating CE through lifecycle.IoT, big data, cloud facilitate CE in design, use, recovery.Lifecycle DT-CE framework.162 studiesCritical review and framework
[38], 2024Conduct a systematic and bibliometric review of CE research trends.Rapid year-on-year growth (R2 = 0.69); leading themes include eco-design, recycling, and sustainability.Provides CE research landscape dashboard and trending keywords.Scopus database (3200 records)Descriptive bibliometric analysis
[39], 2024Test how inter-organizational collaboration and digital technologies influence circular economy (CE) implementation and sustainability performance.Collaboration capability strongly drives CE and sustainability performance, while digital technologies alone have a weaker/direct effect.Provides PLS-SEM evidence linking collaboration capability to CE outcomes; stresses governance over mere tech deployment.Survey of 112 Austrian manufacturing firmsPartial Least Squares Structural Equation Modeling (PLS-SEM)
[40], 2024Barriers to DT adoption for CE in construction.37 barriers across 4 categories; Pareto ranks critical ones.Barrier taxonomy and prioritized solutions.28 papersSystematic review and Pareto
[41], 2024Measure DT impact on CE via PLS-SEM.DT improves efficiency; data-security risk moderate’s effect.Policy and stakeholder insights.Industry secondary + SmartPLSPLS-SEM
[42], 2023IT role for CE in SMEs (Russia).R&D and digital sufficiency policy improve CE performance.Introduces digital sufficiency concept.Survey 314 SMEsQuantitative survey
[43], 2023DT enabling machinery lifecycle extension.Real-time monitoring extends life and improves EoL decisions.Tools for manufacturers’ circular BMs.2 cases (RECLAIM)Mixed
[18], 2023Review DT-driven CE research.AI, blockchain logistics hotspots; social gaps.Future DT-CE agenda.393 recordsBibliometric and qualitative
[44], 2023Analyze data-driven circular business models enabled by digital technologies.Blockchain, AI, and IoT dominate CBM tool-sets; profitability impact under-researched.Identifies key DT tools and CBM archetypes; calls for empirical validation.Systematic review of 122 peer-reviewed articlesSystematic literature review
[45], 2023Quantify the impact of digitalization on CE performance in Greek firms.Digital innovation shows significant positive correlation with resource efficiency (β = 0.46).Offers actionable strategies for integrating digital tools into CE.Survey of 200 Greek firmsQuantitative regression analysis
[46], 2023Prioritize Industry 5.0 technologies that accelerate CE implementation in manufacturing.Top three technologies: AI-enabled cyber-physical systems, human-centric cobots, and additive manufacturing; ranking refined via interval-valued FAHP.Introduces an Industry 5.0–CE technology-prioritization method using fuzzy AHP.Expert survey of 52 manufacturing decision-makersFuzzy Analytical Hierarchy Process (FAHP)
[47], 2023Assess AI applications in implementing CE principles in Ireland.AI improves waste-sorting, predictive maintenance, and policy monitoring; government incentives accelerate adoption.Links national policy with AI-enabled CE transitions and provides practical use-cases.Case studies of Irish initiatives and policy analysisEmpirical case synthesis
[48], 2023Demonstrate how IoT and AI optimize resources and reduce waste to shift economies from linear to circular models.IoT-enabled tracking and AI-driven predictive analytics enhance resource efficiency across sectors.Provides global cross-sector guidance on IoT/AI adoption for CE transformation.Secondary literature and real-world case examplesQualitative review and case synthesis
[49], 2023Digitization in CE business modelsDigitization optimizes processes and improves sustainability outcomes.Future CE-digitization research lines.Lit and statsMixed
[50], 2022Define and conceptualize the ‘Smart Circular Economy’ paradigm (‘waste + data = resource’).Digital technologies across product life-cycle phases unlock new value loops; recovery phase is least studied.Introduces smart-CE definition and sets future research agenda on data-driven circularity.Review of 44 peer-reviewed studiesConceptual literature review
[51], 2022Theorize blockchain’s role in the transition towards a circular economy.Traceability and trust are primary blockchain mechanisms; regulation, skills, and interoperability remain key barriers.Develops a driver-barrier-outcome model linking blockchain innovation to CE transition.28 expert interviews across Europe and USAThematic analysis and theory building
[52], 2022Map how digital technologies support the circular economy (CE).IoT, big data, AI/ML, blockchain and additive manufacturing underpin CE; synergy with business-model innovation is critical.Taxonomy of digital-CE linkages and research gaps.208 papers (2000–2021)Systematic literature review
[53], 2022Innovation and competitiveness within CE.Blockchain policies boost competitiveness; CE indicators lag EU average.Shows CE reshapes national competitiveness.EU panelCross-country quantitative
[54], 2022Digital tech and CE capabilities amid COVID-19.Pandemic accelerated DT adoption: CE mediates performance gains.Crisis-time guidance.312 manufacturersSurvey + SEM
[55], 2022Evidence on CE innovation and gaps.SME alignment and macro impacts under-studied.Nine future research avenues.198 papersSystematic review
[56], 2022Review digital innovation in SMEs.Profit and export outcomes understudied; presents framework.Antecedent-process-outcome model.382 articlesSystematic review
[57], 2022IoT as CE enabler.Drivers, enablers, barriers mapped; logistics dominates studies.IoT-CE framework and future directions.137 papersSystematic review
[58], 2022HR collaboration and CE readiness.Partnership HRM improves human resources for CE.Actionable HR Road-map.6 casesExploratory descriptive
[59], 2022Catalog ICT decision-support tools for CE in construction.Lifecycle-phase framework; identifies tech, business, and societal challenges.Reference architecture and future-research agenda.167 articlesSystematic literature review
[30], 2021Role of digital technologies in CE transition.Focus on recycling/remanufacturing; design phase under-studied.Agenda for design-phase digital interventions.112 papersSystematic literature review
[60], 2021Quantify CE–I4.0 research hotspots.Additive manufacturing and IoT dominate networks.Keyword co-occurrence maps.251 recordsBibliometric analysis
[17], 2021Map DI in KMS for sustainable governance.AI/Big-Data KMS rising; ties to sustainability value.Links DI, KMS and long-term value.438 recordsBibliometric and thematic
[61], 2021Effect of digital M&A on performance.Digital M&A expands knowledge base → innovation and ROA gains.Panel-data evidence of payoff.512 firmsLongitudinal regression
[62], 2021Assess corporate incubators in fostering digital innovation (auto sector).Incubators supply governance and resources while balancing agility trade-offs.Strategic role of incubators.4 case studiesComparative case
[15], 2021Socio-technical clarity on digital innovation.Integrates 227 articles; seven-dimension framework.Unifies digital-innovation literature.227 articlesLarge-scale review
[63], 2021Digital tech in circular business models.Case evidence (Alpha, Philips CityTouch, Zen Robotics) shows waste-reduction benefits.Real-world smart-city CE examples.3 casesMultiple-case study
[64], 2021Interplay between BPM and DI.Emerging tech reshapes workflows; BPM structures DI.BPM-DI research agenda.126 papers + workshopsReview
[65], 2021Industry 4.0 as CE enabler.Digital twins and CPS save resources; social pillar under-researched.Sustainability-impact map linking I4.0 to CE.189 documentsSystematic mapping review
[66], 2021Evaluate CBM innovation and challenges.Resource-efficient strategies prevalent; scalability weak.CBM evolution map.10 case synthesesCase review
[34], 2021Integrate production models with CE and trade.Digital platforms and cluster networks enable CE at national scale.Policy blueprint for platform-based CE.Policy docsConceptual qualitative
[67], 2021Digital tech catalyzing CE business-model innovation.IoT/data analytics trigger radical BM changes improving resource flows.Four-type BM-innovation framework.132 papersSystematic review
[11], 2021Map CE-innovation links.Drivers at product, BM, ecosystem levels; regulation barrier.Three-level framework and 16 research gaps.321 papersSystematic review
[68], 2021Verify CE actions in ecosystems using digital technologies.IoT and big-data solutions create collaborative value and accelerate CE transition.Ecosystem perspective on digital-CE research.116 papersSystematic literature review
[23], 2021Identify DT criteria for sustainable SCM competitiveness.Logistics optimization, proactive action, real-time inventory top list.Fuzzy-Delphi model for DT-enabled SCM.Expert panel n = 20Fuzzy Delphi and DEMATEL
[69], 2021Competitiveness model for co-ops via digital innovation and government support.Gov support → digital capabilities → innovation → competitiveness.Quantified model linking public support and performance.Survey 245 co-opsPLS-SEM
[70], 2021Investigate IoT and platforms impact on SME innovation.IoT/platforms elevate sustainable innovation; platform mediates IoT effect.SME digital-orientation model.Survey n = 372PLS-SEM
[71], 2021Study digital-innovation integration in automotive incumbents.APIs as boundary resources; organizational misfits identified.Principles for balancing legacy knowledge and digital change.3 auto casesGrounded theory
[31], 2021TQM–KM–sustainability link in CE.TQM → OS positive; KM partially mediates.Practical insights for manufacturers.Survey 510 firmsPLS-SEM
[29], 2020Typology of digital-innovation units.Incubator, accelerator, venture-studio archetypes.Guides organizational design.23 casesQualitative
[72], 2020CE and Industry 4.0 toward SDGs.Tech innovations add value and aid SDGs.Frames CE-I4.0 nexus as SDG pathway.122 articlesSystematic review
[73], 2020CE competitiveness and sustainable development in Romania.Stakeholder coordination essential for dual goals.CE-competitiveness framework.Desk researchCritical synthesis
[60], 2020Industry 4.0 digital innovations enabling CE.AI, blockchain, and CPS promote CE but need long ROI and policy support.Synthesizes CE–I4.0 intersections.140 recordsIntegrative review
[74], 2020DIHs as knowledge brokers for SMEs.DIHs orchestrate training and funding accelerating digitalization.DIH knowledge-broker model.Survey and interviewsConceptual and empirical
[75], 2020Blockchain’s capacity to enable CE.Transparency and traceability main benefits; scalability and regulation barriers.Blockchain-for-CE framework.79 itemsSystematic literature review
[33], 2020Link business analytics and circular strategies.Digital tech and BA improve resource efficiency; assess strategy–circularity fit.Proposes Smart-CE framework.68 papers and 6 casesTheory and practice review
[76], 2020Analyze DT use in Uzbek industrial production.Digitalization boosts collaboration and export potential; skill gaps impede progress.Development concept for high-tech export.Trade indicators 2010–2019Indicator analysis
[77], 2019SME agility in disruptive digital innovation context.Boundary openness and adaptability mitigate rigidity.Agility-building framework.Single caseQualitative
[16], 2019Synthesize digital-innovation research streams.Uneven topic coverage; proposes future agenda across seven dimensions.Comprehensive research roadmap.649 recordsSystematic review
[78], 2019Build holistic framework on digital innovation and entrepreneurship.Emphasizes openness, affordances and generativity; highlights multi-level tensions.Cross-disciplinary framework.Conceptual synthesisTheory essay
[79], 2019Digital innovation and competitiveness.Digital platforms and collaboration drive competitive edge.Links digital-platform adoption with CE competitiveness.78 CIS firmsComparative case
[80], 2018Opportunities and challenges of digitalization for CE.Digital twins, product virtualization key; data quality challenges.Strategic role of digitalization for CE.140 papers and expert insightsExploratory qualitative
[81], 2018Digital tech in usage-focused business models for CE.Predictive maintenance, IoT monitoring extend product life.Shows digital tech as CE enablers.Literature and caseMixed
[82], 2018Model blockchain adoption as digital innovation.Adoption continuous, change-heavy; no fixed endpoint.Stage-model and guidelines.14 interviews and archivesMixed methods
[83], 2018Investigate competitive advantage from industrial IoT.IoT transforms value chains and customer relations, creating new advantages.Explains IoT-driven differentiation.Literature and desk casesComparative analysis
[84], 2018Identify organizational barriers to CE in manufacturing.Culture, knowledge, supply-chain, tech and regulatory barriers.Cross-domain barrier framework.46 studies and interviewsQualitative synthesis
[85], 2018Role of Chief Digital Officers in DI.Defensive, offensive, ambidextrous logics identified.Strategy archetypes and tensions.35 interviewsQualitative
[20], 2017Analyze CE definitions.114 definitions vary widely; systemic change often missing.Meta-definition and conceptual gaps.114 documentsContent analysis
[86], 2017Implications of digital innovation for innovation-management research.Digital materials shift agency and processes; four new theorizing logics.Foundational agenda for digital-innovation studies.ConceptualTheory essay
[32], 2017Digital technologies in CE transitions.DT optimizes forward and reverse flows; empirical gaps.Early research agenda.75 sourcesSystematic review
[87], 2016Role of customer and user knowledge in B2B DI.Customers inform short-term, users long-term innovation.Two knowledge pathways.3 casesHolistic case
[88], 2016Design CBM canvas.Adds take-back systems and adoption factors.CBM framework for practitioners.70 casesConceptual
[89], 2016Explore cultural influences on global e-service innovation.Collectivism and uncertainty avoidance lower adoption; social pressure and self-efficacy mediate.Culture-aware strategy guidance for e-service roll-out.Survey n = 452 (5 regions)SEM
[90], 2015Managerial diagnostic framework for digital innovation.Five capability areas must co-evolve.Practical framework and diagnostic tool.Conceptual and casesConceptual
[21], 2012Redesign global production/consumption for CE.Calls for decoupling growth from resource use; workshop coalitions.Early policy blueprint for CE.Policy lit.Narrative review
Table 2. Existing Literature Reviews and Their Limitations.
Table 2. Existing Literature Reviews and Their Limitations.
Review TypeKey Themes IdentifiedCritical Appraisal SummaryConceptual Frameworks DevelopedIdentified Research GapsPractical Implications
Systematic (n = 15)Digital technologies supporting CE (e.g., IoT, blockchain, AI); lifecycle alignment in CE transitions; ecosystem-based value creationStrong in transparency and structure, but limited in thematic synthesis and competitiveness integrationTaxonomies (Chauhan et al.); lifecycle models (Yu et al.)Weak theorization of competitiveness; limited early-stage CE focusSector-based guidance for digital CE transitions
Integrative (n = 1)Holistic CE-DI strategies; dual focus on technical and organizational innovationTheoretical richness but often lacks empirical specificityCE-DI capability models (Cioffi); sociotechnical innovation logicsWeak comparability and impact metricsMid-range theories for governance and SME transformation
Bibliometric (n = 5)Publication networks, keyword co-occurrence, and author collaborationExcellent mapping power but little conceptual depthLandscape mapping and topic clusteringUnder-theorized geographic or institutional gapsAcademic benchmarking and research prioritization
Conceptual/Theory-Guided (n = 4)CE and DI as capabilities; sociotechnical theory; innovation logicsStrong frameworks but minimal empirical applicationInnovation diffusion, RBV, and strategic frameworksLittle operational grounding in policy/industryHigh relevance for theoretical modeling and strategic foresight
General Literature Review (n = 8)Broad CE-DI enablement themes: technology as a sustainability driverOften anecdotal and lacking rigor or theory alignmentSustainability narratives, resource efficiencyConceptual dispersion and duplicationEntry points for non-specialist policy and business adoption
Table 3. Research Methods and Data Types.
Table 3. Research Methods and Data Types.
Review or Method TypeCount
Systematic Review15
Quantitative (e.g., SEM, PLS-SEM)12
Qualitative (e.g., interviews)15
General Literature Review8
Bibliometric Review5
Mixed Methods10
Integrative Review1
Conceptual/Theory-Guided4
Meta-analysis1
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Awad, I.M.; Nuseibeh, H.; Amro, A.A. Competitiveness in the Era of Circular Economy and Digital Innovations: An Integrative Literature Review. Sustainability 2025, 17, 4599. https://doi.org/10.3390/su17104599

AMA Style

Awad IM, Nuseibeh H, Amro AA. Competitiveness in the Era of Circular Economy and Digital Innovations: An Integrative Literature Review. Sustainability. 2025; 17(10):4599. https://doi.org/10.3390/su17104599

Chicago/Turabian Style

Awad, Ibrahim M., Hasan Nuseibeh, and Alaa A. Amro. 2025. "Competitiveness in the Era of Circular Economy and Digital Innovations: An Integrative Literature Review" Sustainability 17, no. 10: 4599. https://doi.org/10.3390/su17104599

APA Style

Awad, I. M., Nuseibeh, H., & Amro, A. A. (2025). Competitiveness in the Era of Circular Economy and Digital Innovations: An Integrative Literature Review. Sustainability, 17(10), 4599. https://doi.org/10.3390/su17104599

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