Sustainable Entrepreneurship Through Digital Innovation and Green Supply Chain Management Using Systematic Literature Network Analysis

Sustainable Entrepreneurship Through Digital Innovation and Green Supply Chain Management Using Systematic Literature Network Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sustainable Entrepreneurship Through Digital Innovation and Green Supply Chain Management Using Systematic Literature Network Analysis Raya Sulistyowati, Marisa Intan Prawesti, Surya Andhini, Darma Rika Swaramarinda, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8812078/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract This study systematically synthesizes empirical evidence on integrated ecosystem frameworks that combine green supply chain management, digital innovation, and stakeholder collaboration in manufacturing small and medium sized enterprises. Addressing persistent fragmentation across these research streams, the study evaluates the effectiveness of ecosystem based approaches while mapping the evolution of the research landscape through a bibliometric integrated systematic literature review. Following PRISMA 2020 guidelines, a structured search of the Scopus database identified 277 articles, of which 25 high quality empirical studies met the inclusion criteria and were included in the final synthesis. The review integrates systematic literature analysis with bibliometric network mapping to identify dominant research themes and developmental trajectories within the field. Four key findings emerge. First, integrated ecosystem frameworks consistently demonstrate superior performance outcomes compared to fragmented or single domain approaches in advancing sustainable entrepreneurship. Second, digital innovation operates as a central mediating mechanism that enhances coordination and value creation across environmental and economic objectives. Third, contextual readiness, particularly infrastructure and institutional support, functions as a critical boundary condition shaping successful implementation. Fourth, the literature reveals a clear temporal progression, evolving from basic sustainability integration between 2019 and 2021 toward more advanced and intelligent systems after 2022. Overall, the evidence indicates that integrated ecosystem frameworks have reached an advanced stage of conceptual and empirical development, while simultaneously revealing substantial opportunities for future research related to artificial intelligence adoption and real time optimization. This study contributes an integrative perspective that consolidates previously disconnected research domains and provides a structured agenda for advancing sustainable manufacturing entrepreneurship. sustainable entrepreneurship green supply chain management digital innovation bibliometric analysis Manufacturing SMEs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION The global imperative for sustainable development has fundamentally transformed the entrepreneurial landscape, particularly in emerging markets where small and medium-sized enterprises (SMEs) face the dual challenge of economic growth and environmental management. Manufacturing SMEs, which account for more than 90% of businesses worldwide and employ about 50–60% of the global workforce, are increasingly recognized as critical actors in achieving the United Nations Sustainable Development Goals (Qasim et al., 2024 ; Wiratmadja et al., 2025 ; Hasan et al., 2026 ). The manufacturing sector as a whole consumes 54% of global energy and contributes to 20% of carbon emissions, of which SMEs have a significant proportion of this figure (Nasim et al., 2024 ). Paradoxically, the sector also has the greatest potential for sustainable transformation through the adoption of digital technologies and integrated eco-friendly practices, with the potential for a reduction in industrial CO2 emissions of 1.9–3.2 gigatons/year through manufacturing technology changes (Mazhar et al., 2022 ). The urgency of this transformation is further heightened by the 2050 net-zero emissions target and the global commitment to limit global warming to 1.5°C, which necessitates systemic changes in the business models and operations of manufacturing SMEs. The traditional approach of implementing sustainable practices in isolation has proven inadequate to address the complex and interrelated challenges of environmental degradation, resource scarcity, and competitive pressures that characterize the contemporary business environment. The latest developments in digital technology have created transformative opportunities to transform sustainability management from reactive compliance to proactive optimization through the integration of artificial intelligence, blockchain, Internet of Things sensors, and big data analytics (Setyadi et al., 2025 ; Alkhodair & Alkhudhayr, 2025 ). Integrated systems that combine Digital Twins, AI, Blockchain, IoT, and Big Data have demonstrated more than 95% effectiveness in minimizing downtime and improving resource use efficiency, with the potential to reduce energy consumption by up to 30% and reduce material waste by 20% (Nagarajan et al., 2025 ; Setyadi et al., 2025 ). However, the complexity of implementing digital technology in the context of sustainability creates new challenges in the form of financial and technical constraints, data security, and multi-stakeholder coordination that require an integrated ecosystem approach (Sulistyowati et al., 2024 ; Jamwal et al., 2025 ). Despite the growing interest in sustainability integration and digitalization, the current state of knowledge remains fragmented across disciplinary boundaries and methodological approaches. Existing research typically examines green supply chain management, digital transformation, and stakeholder engagement as separate phenomena, limiting the understanding of the potential integration and effectiveness of their combinations (Putri et al., 2025 ; Farfán Chilicaus et al., 2025 ; Setyawati et al., 2024 ). A recent systematic review identified research fragmentation in three main clusters: green supply chain management, implementation aspects, and innovation/adaptation, with significant gaps in integrating emerging technologies with existing systems, particularly in developing regions (Farfán Chilicaus et al., 2025 ). Bibliometric analysis shows that research on "twin transitions" that combine digital strategies with sustainability efforts is still in its early stages, with comprehensive studies addressing all aspects of twin transitions still limited (Radavičiūtė & Meidutė-Kavaliauskienė, 2025 ). This fragmentation has created a significant knowledge gap regarding the optimal combination of components, implementation sequences, contextual adaptation strategies, and performance outcomes of integrated approaches, particularly in emerging market contexts where resource constraints and institutional differences can fundamentally alter implementation dynamics. The limitations of existing reviews also extend to methodological limitations that limit their usefulness to understanding the rapidly evolving research landscape, where traditional systematic reviews struggle to capture emerging themes and evolutionary patterns of research. This study uses a systematic literature review and network analysis (SLNA) approach that is essential to overcome the inherent limitations of single-method research synthesis in a rapidly growing and interdisciplinary field such as sustainable entrepreneurship. The integration of bibliometric network analysis with systematic evidence synthesis allows for the identification of research landscape patterns, emerging themes, and knowledge evolutionary trajectories that cannot be detected by traditional review, while providing strategic insights for researchers and practitioners about current state-of-the-art capabilities and emerging opportunities for advancement (Adamo et al., 2025 ; Ranjan et al., 2025 ). The SLNA approach has proven effective in mapping intellectual evolution and identifying key research clusters in sustainability research, particularly to uncover the relationships between different domains and validate the convergence of research themes (Nirwal & Bhardwaj, 2025 ; Mukhtar et al., 2025 ). The contributions of this research include convergent validation through methodological triangulation that strengthens the confidence of findings beyond what each approach can achieve independently, the development of an evidence-based framework for the implementation of a sustainable ecosystem that addresses the fragmentation of existing knowledge, and the provision of a research roadmap that combines evidence-based effectiveness assessments with strategic research landscape positioning for development sustainable entrepreneurship of the future. This study addresses four specific research questions derived from the PICO framework: RQ1: How effective is an integrated ecosystem framework that combines green supply chain management, green economy transition, and digital innovation in improving sustainable entrepreneurial performance in manufacturing SMEs in emerging markets compared to traditional non-integrated approaches? RQ2: Which components in the ecosystem framework (green supply chain management, digital innovation, stakeholder collaboration, green economy transition) make the most significant contribution to improving the environmental and economic performance of manufacturing SMEs in emerging markets? RQ3: To what extent does digital innovation capabilities act as a mediator in the relationship between green supply chain management practices and sustainable entrepreneurial success in manufacturing SMEs in emerging markets? RQ4: Which emerging market contextual factors (regulatory environment, infrastructure readiness, maturity of stakeholder ecosystems, or cultural factors) most influence the successful implementation of an integrated ecosystem framework in manufacturing SMEs? LITERATURE REVIEW The Sustainable entrepreneurship has become a central theme in emerging economies as firms increasingly integrate environmental and social objectives into their business models (Qasim et al., 2024 ). Recent studies show that small and medium sized manufacturing enterprises contribute significantly to national economic growth, yet face substantial challenges in reducing emissions and improving resource efficiency (Mazhar et al., 2022 ; Nasim et al., 2024 ). Putri et al. ( 2025 ) emphasize that sustainable entrepreneurship requires not only green product innovation but also the integration of sustainability practices across operations, supply chains, and digital systems. Green supply chain management plays a critical role in this transition. Evidence shows that practices such as eco design, green procurement, and waste minimization improve both environmental and economic performance (Qasim et al., 2024 ; Wiratmadja et al., 2025 ). However, adoption among small and medium sized enterprises remains fragmented, with many firms implementing isolated practices without an integrated strategic approach (Jamwal et al., 2021 ; Soomro et al., 2024 ; Rayahu et al., 2024). Farfán Chilicaus et al. ( 2025 ) note that limited managerial capability and financial resources often hinder comprehensive implementation in developing countries. Digital innovation has emerged as an important enabler of sustainability. Technologies such as the Internet of Things, digital twins, and data analytics help firms optimize energy use, reduce waste, and monitor supply chain performance in real time (Nagarajan et al., 2025 ; Setyadi et al., 2025 ). Nasim et al. ( 2024 ) describe this integration as the twin transition, where digital transformation accelerates green transformation. Empirical studies further show that digital innovation can mediate the relationship between green supply chain management and firm performance by enhancing transparency and coordination across the supply chain (Soomro et al., 2024 ; Sakti et al., 2025 ). Stakeholder collaboration is another essential component. Partnerships with suppliers, customers, government agencies, and industry associations provide the technical knowledge and institutional support needed for green transformation (Hoang et al., 2025 ; Mukhtar et al., 2025 ). Studies also show that stakeholder pressure from government and large buyers positively influences the adoption of sustainability practices in small and medium sized enterprises (Fatorachian and Kazemi, 2025 ). Despite growing research, the field remains fragmented. Previous reviews indicate that sustainability, digitalization, and supply chain management are often studied separately rather than as an integrated ecosystem (Adamo et al., 2025 ; Noer et al., 2025 ; Hasan et al., ( 2026 ). Key research gaps include identifying the optimal combination of green supply chain practices and digital capabilities, understanding the sequencing of sustainability investments, and examining the mediating role of digital innovation in a more rigorous way. These gaps highlight the need for systematic and network based analysis to map the evolution of the field and propose an integrated ecosystem framework for sustainable entrepreneurship in small and medium sized manufacturing enterprises in emerging markets. METHOD Research Design This study employs a systematic literature review and network analysis (SLNA) approach that integrates a traditional systematic review with bibliometric network analysis to provide a comprehensive understanding of the effectiveness of ecosystem frameworks in sustainable entrepreneurship. The research design follows a convergent framework, combining evidence-based synthesis with research landscape mapping through keyword co-occurrence analysis, enabling both critical evaluation of empirical findings and identification of dominant themes within the literature (Page et al., 2021 ; Adamo et al., 2025 ; Wibowo et al., 2025 ). This integrated approach allows for convergent validation, whereby insights from the systematic review are corroborated by patterns observed in the bibliometric network structure, while also revealing emerging themes and temporal dynamics that are not readily captured through conventional systematic reviews. Moreover, the SLNA approach facilitates a systematic comparison across disciplinary perspectives, allowing the study to capture conceptual diversity and methodological heterogeneity within the sustainable entrepreneurship literature. This is particularly important given the interdisciplinary nature of ecosystem-based research, which spans entrepreneurship, sustainability science, and digital innovation (Sulistyowati et al., 2026 ). The research process consists of three main stages. First, the systematic review stage includes search strategy formulation, study selection, quality assessment, and data extraction conducted in accordance with the PRISMA 2020 guidelines to ensure transparency, replicability, and methodological rigor (Fig. 1 ). Second, the bibliometric network analysis stage applies keyword co-occurrence analysis using VOSviewer to identify thematic clusters, network centrality, and the temporal evolution of the research field, thereby illustrating how research priorities and conceptual linkages have developed over time. Finally, the convergent synthesis stage integrates evidence from the systematic review with structural insights from the network analysis, enabling triangulation of findings and supporting a more robust and holistic interpretation of ecosystem-based approaches in sustainable entrepreneurship. Research Subject The target population of the research is an academic article that has been peer-reviewed that discusses the implementation of an integrated ecosystem framework for sustainable entrepreneurial performance in manufacturing SMEs in emerging markets. Population inclusion criteria follow the PICO framework (Table 1 ): Population includes manufacturing SMEs (20–200 employees) in emerging markets with the implementation of existing sustainability practices; Interventions in the form of an integrated ecosystem framework that combines at least two components (green supply chain management, digital innovation, stakeholder collaboration, green economy transition); Comparison with traditional/non-integrated approaches or before-to-after implementation comparisons; and Results in the form of performance indicators that can be measured in the environmental, economic, social, and innovation capability dimensions. Table 1 Inclusion and Exclusion Criteria Based on the PICO Framework Component Inclusion Criteria Exclusion Criteria POPULATION (P) Target Organizations Small and Medium Enterprises (SMEs) : a. Manufacturing with 20–200 employees b. $ 300K- $ 10M USD equivalent annual revenue c. Located in emerging markets (Asia, Latin America, Africa, Eastern Europe) d. Manufacturing sector: textile, food, electronics, furniture, or others e. Have existing sustainability practices (environmental management system, green supply chain initiative, or sustainability certification) f. Respondents: CEO, Operations Manager, Supply Chain Manager, Sustainability Officer Non-Target Organizations : a. Large corporations (> 200 employees or > $ 10M revenue) b. Micro enterprises (< 20 employees or < $ 300K revenue) c. Pure service (non-manufacturing) companies d. Only developed countries (USA, Western Europe, Japan) without the context of emerging markets e. Non-business organizations (NGOs, government agencies, academics) f. Without existing sustainability practices g. Non-managerial respondents or without sustainability decision authority INTERVENTION (I) Integrated Ecosystem Framework Integrated Ecosystem Framework (≥ 2 components) : a. Green Supply Chain Management: green procurement, eco-design, waste management, supplier environmental assessment b. Digital Innovation: AI, IoT, blockchain, big data, digital platforms for sustainability monitoring c. Stakeholder Collaboration: multi-stakeholder partnerships, supply chain, collaborative platforms d. Green Economy Transition: circular economy practices, ESG integration, green financing, sustainable business models e. Technology-enabled green practices f. Multi-component sustainability interventions with clear integration strategy Non-Integrated Approach : a. Single-component interventions only b. Study focuses purely financial/accounting without sustainability focus c. Pure technology studies without green supply chain context d. Non-digital traditional approaches only e. Studies without a clear description of the intervention f. Fragmentation of practices without ecosystem integration g. Lack of clear integration between components COMPARISON (C) Comparison Group Comparison Groups yang Valid : a. Traditional/conventional supply chain practices b. Non-integrated approaches (praktik terpisah) c. Before-after implementation comparisons d. Control groups with standard business practices e. Fragmented/isolated sustainability initiatives f. Industry benchmark comparisons g. Low-adoption control groups with minimal ecosystem integration Invalid Comparison : a. Studies without comparison groups or baseline measurements b. Purely descriptive studies without comparative analysis c. Study compares only different digital technologies without sustainability context d. Cross-sectional without comparison element e. Case studies without comparative framework OUTCOMES (O) Performance Indicators Measurable Performance Indicators : a. Environmental: carbon reduction, waste management, energy efficiency, water conservation b. Economic: revenue growth, cost reduction, ROI, profitability, market share c. Social: employee satisfaction, community impact, customer satisfaction, stakeholder relations d. Innovation: green innovation frequency, digital adoption rates, knowledge management effectiveness e. Integrated: sustainability performance indices, triple bottom line outcomes, competitive advantage measure f. Supply chain efficiency dan effectiveness metrics Irrelevant Outcomes : a. Studi measuring only technical performance without business/sustainability metrics b. Pure environmental impact studies without business performance consideration c. Studi without measurable performance indicators d. Outcomes are not related to sustainability or business performance e. Focus only on adoption/implementation without performance outcomes The systematic review sample framework uses a thorough search of the Scopus database with a time frame of 2019–2025 to ensure the up-to-date literature in a rapidly growing field. The search strategy was developed using a combination of controlled vocabulary and free-text terms: ("small medium enterprise" OR "SME" OR "manufacturing firm") AND ("green supply chain" OR "sustainable supply chain") AND ("digital" OR "innovation" OR "ecosystem" OR "technology") AND ("performance" OR "sustainability" OR "competitive advantage"). Data Collection Systematic Review Data Collection The literature search uses the Scopus database by using a search string developed based on the PICO framework in Table 2 . Table 2 Systematic Literature Search Strategy Database Search String Applied Filters Result Date Scopus (Elsevier) TITLE-ABS-KEY (("small medium enterprise" OR "SME" OR "small business" OR "manufacturing firm" OR "medium enterprise") AND ("green supply chain" OR "sustainable supply chain" OR "environmental supply chain" OR "green procurement") AND ("digital" OR "innovation" OR "ecosystem" OR "stakeholder" OR "technology") AND ("performance" OR "sustainability" OR "competitiveness" OR "competitive advantage")) Publication year : 2019–2025 Language : English Document type : Article, Review, Conference Paper Subject area : Business, Management, Environmental Science, Engineering 277 items September 2025 The initial search yielded 277 articles that then went through a two-stage screening process: title/abstract screening by two independent reviewers with 94% inter-reviewer reliability, resulting in 85 articles for full-text grading; and full-text screening with a 91.8% acquisition rate (78 out of 85 articles were successfully accessed), resulting in 25 high-quality studies that met all inclusion criteria with 92.4% inter-reviewer agreement. Data extraction was carried out using a standardized form that included study characteristics (author, year, country, design, sample size), population characteristics (company demographics, industry sector, respondent profile), intervention details (implemented ecosystem components, implementation intensity, duration), characteristics of the comparison group, outcome measurements (environmental, economic, social, innovation indicators), and statistical outcomes (effect size, confidence interval, significance). The dual extraction process was applied in 60% of the studies for quality control with a data entry accuracy of 98.1%. Bibliometric Network Analysis Data Collection The bibliometric dataset uses a complete export of 277 articles from the initial search results, including bibliographic information, author keywords, index keywords, abstracts, and citation data. Keyword preprocessing involves standardization (normalization of single-plural forms, consolidation of synonyms), filtering with a minimum occurrence threshold of 7 occurrences (2.5% of total articles), and exclusion of generic terms to preserve domain-specific terminology. The final keyword dataset includes 75 unique terms out of a total of 1,520 keywords identified, with a network density of 0.23 and 847 co-ocurence links. Data Analysis Systematic Review Analysis Data synthesis uses a convergent mixed method approach that combines quantitative meta-analysis with qualitative thematic analysis. The calculation of the effect size uses Cohen's d for continuous results, with a conversion of the correlation coefficient, t-value, or F-value when the mean and standard deviation are not available. A randomized effects meta-analysis was performed for results with sufficient homogeneous studies (n ≥ 3), with heterogeneity assessment using I² statistics and Cochran's Q test. Subgroup analysis based on geographic area, company size, study design, and quality level to explore sources of heterogeneity. Qualitative synthesis uses systematic thematic analysis to identify implementation mechanisms, success factors, barriers, and contextual influences. The synthesis framework develops a logical model that maps the relationships between ecosystem components, implementation processes, and outcomes based on evidence integration. Publication bias assessment using funnel plot inspection, Egger test, and fail-safe N calculations. Bibliometric Network Analysis The network analysis uses VOSviewer software for co-cocurence analysis with the association strength normalization method. Cluster analysis uses modularity-based community detection algorithms to identify research theme clusters, with optimization of resolution parameters to achieve optimal cluster granularity. Measures of network centrality include centrality of degrees, centrality of betweenness, and eigenvector centrality to identify the key hubs and concepts of bridges connecting different research domains. Temporal analysis uses overlay visualization with the year of publication as the temporal dimension to identify evolutionary patterns from basic concepts (2019–2021) through integration themes (2022–2023) to emerging intelligent systems (2023–2024). Statistical analysis uses R software with bibliometrix packages for performance indicators, collaborative networks, and thematic evolution analysis. Convergent Integration Analysis Synthesis integration uses a combined view and a mixed method matrix to compare quantitative effect sizes with qualitative themes and network structure patterns. The convergence assessment identifies areas of agreement, complementarity, and contradiction between the findings of the systematic review and the insights of the network analysis. Theme validation was carried out by comparing the research clusters from the network analysis with the results of the thematic synthesis from a systematic review to establish the validity of the construct and methodological triangulation. RESULT Selection and Study Characteristics A systematic search of the Scopus database yielded 277 articles relevant to the research topic. The selection process follows the flow chart of PRISMA 2020 (Fig. 1 ) where after the elimination of one duplicate, 276 articles entered the title and abstract screening stage. This stage excludes 191 articles with the main reasons for publication before 2019 (n = 46; 24.1%), research population mismatches (n = 25; 13.1%), and inadequate study design (n = 27; 14.1%). Of the 85 articles assessed for full-text feasibility, 53 articles were excluded due to study design issues (n = 18; 34.0%), population mismatches (n = 12; 22.6%), and inappropriate interventions (n = 15; 28.3%). The final selection process resulted in 25 high-quality studies for synthesis analysis. The quality assessment showed high methodological standards with an average score of 86.2% (SD = 4.8%). A total of 8 8 studies (32.0%) achieved exceptional levels (≥ 90%), 11 studies (44.0%) of excellent quality (85–89%), and 6 studies (24.0%) of good quality (80–84%) as shown in Table 3 . None of the studies failed to meet the minimum quality threshold of 80%, indicating good methodological homogeneity for synthesis. Table 3 Characteristics and Quality of Included Studies (n = 25) Characteristics n % Year of Publication 2025 15 60,0 2024 10 40,0 Study Design Structural equation modeling 8 32,0 Cross-sectional survey 11 44,0 Mixed methods 3 12,0 Longitudinal 2 8,0 Multi-case study 1 4,0 Study Quality Outstanding (≥ 90%) 8 32,0 Excellent (85–89%) 11 44,0 Good (80–84%) 6 24,0 Geographic Distribution East Asia (China) 6 24,0 South Asia (India, Pakistan) 8 32,0 Southeast Asia (Indonesia) 2 8,0 Middle East 4 16,0 Africa 3 12,0 Global/Mixed 2 8,0 Sample Size Average (± SD) 298 ± 67 - Median 289 - Range 198–425 - The characteristics of the participants included 7,448 manufacturing SME organizations from 12 countries in three major geographic regions. The distribution showed the dominance of the Asian context (64.0%) with the strongest representation of China (24.0%), India (20.0%), and Pakistan (12.0%), while the Middle East and Africa region contributed 36.0% of the total studies. Organizational characteristics show the predominance of medium SMEs (51–200 employees; 64.0%) with an average of 87 employees and an average annual revenue of USD 3.2 million. The distribution of the industrial sector was dominated by multi-sector studies (72.0%) with a specific focus on textiles/clothing (12.0%), food/beverages (8.0%), and electronics (8.0%). Bibliometric Network Analysis and Research Evolution Keyword co-currence analysis of 277 articles resulted in a research network with 75 keywords that had a minimum of 7 occurrences, forming a network with a density of 0.23 and 847 co-co-curencies. The network structure reveals four clusters of main research themes as visualized in Fig. 2 : (1) Economic-Environmental Integration (33% of networks) focusing on sustainable economic synergy and environmental protection; (2) Green Manufacturing & Technology Integration (29%) which emphasizes the application of technology in environmentally friendly manufacturing processes; (3) Advanced Digital Innovation & Analytics (21%) that explores AI, blockchain, and big data for sustainability; and (4) Green Innovation & Sustainable Development (16%) which specialises in green innovation systems and sustainable development practices. Source: VosViewer Output, 2025 The centrality analysis identified five of the most central keywords that functioned as hubs in the research network: "supply chain management" (centrality = 0.94) as the universal hub that connects all clusters, "sustainability" (0.87) as the main outcome hub, "sustainable development" (0.79) as the theoretical hub, "green supply chain management" (0.76) as the implementation hub, and "SMEs" (0.72) as the target population hub. Keywords for bridges that connect different clusters include "innovation" that connects the economy and the environment with green innovation, "technology" that connects manufacturing with digital innovation, and "sustainability performance" that bridges implementation with outcomes. The temporal evolution of the research visualized in Fig. 3 reveals three distinct waves of development. The 2019–2021 period (marked in blue) is dominated by foundational concepts such as "sustainable development", "environmental management", and "organizational performance" with a focus on theoretical development and exploration of basic relationships. The 2022–2023 period (green) shows the mastery of integration with the emergence of the themes of "sustainability", "green supply chain management", "innovation", and "digital transformation" which emphasizes technology-sustainability synergy and multi-stakeholder coordination. The current period 2023–2024 (yellow) reveals the emergence of intelligent systems with the themes "artificial intelligence", "big data", "data analytics", and "green innovation" which signals the transformation towards AI-based systems and predictive analytics. Source: VosViewer Output, 2025 Temporal Distribution and Evolution of Research Focus The temporal analysis of 277 publications from 2019 to 2025 reveals a significant upward trajectory in sustainable entrepreneurship research, with publications increasing from 12 articles in 2019 to a peak of 67 articles in 2024, representing a 458% growth rate over the six-year period (Fig. 4 ). The distribution pattern demonstrates three distinct developmental phases as detailed in Table 4 : the Foundation Period (2019–2021) with 58 total publications (20.9% of corpus) establishing core theoretical frameworks; the Integration Period (2022–2023) with 100 publications (36.1%) emphasizing technology-sustainability synergies; and the Intelligence Period (2024–2025) with 119 publications (43.0%) focusing on AI-driven systems and advanced analytics. This temporal progression aligns with the broader digital transformation trends in sustainable business practices and reflects the growing academic interest in integrating environmental economics with technological innovation. Source: Systematic Literature Review Analysis, 2025 Table 4 Temporal Distribution and Characteristics by Research Period Period Publications (n) Dominant Themes Key Focus Areas Foundation (2019–2021) 58 (20.9%) Sustainable development, Environmental management, Organizational performance Theoretical frameworks, Basic environmental practices, CSR integration Integration (2022–2023) 100 (36.1%) Green supply chain, Digital transformation, Innovation, Sustainability performance Technology integration, Multi-stakeholder coordination, Circular economy practices Intelligence (2024–2025) 119 (43.0%) Artificial intelligence, Big data analytics, Green innovation, Blockchain AI-based systems, Predictive analytics, Smart supply chains, Automated sustainability monitoring Source: Systematic Literature Review Analysis, 2025 The keyword frequency analysis across the three periods reveals distinct evolutionary patterns in research focus as illustrated in Fig. 5 . During the Foundation Period (2019–2021), traditional sustainability concepts dominated with 'sustainable development' (frequency index: 85), 'environmental management' (78), and 'organizational performance' (72) representing the core theoretical underpinnings. The Integration Period (2022–2023) witnessed a paradigm shift toward operational implementation, with 'green supply chain management' (82), 'digital innovation' (78), and 'circular economy' (58) emerging as central themes, reflecting the field's transition from conceptual frameworks to practical applications. The current Intelligence Period (2024–2025) demonstrates a technological acceleration, characterized by the prominence of 'artificial intelligence' (92), 'big data analytics' (88), 'green innovation' (85), and 'blockchain technology' (68), signaling the field's evolution toward sophisticated, data-driven sustainability solutions. Source: Systematic Literature Review Analysis, 2025 The heatmap visualization of keyword frequency intensity (Fig. 6 ) demonstrates clear temporal patterns in research priorities. Foundation Period research exhibited strong theoretical orientations with high-frequency indices for conceptual frameworks, while practical implementation keywords showed relatively lower frequencies. The Integration Period marked a transitional phase where both theoretical and practical themes achieved balanced representation, evidenced by moderate-to-high frequency indices across diverse keyword categories. The Intelligence Period shows a decisive shift toward advanced technological applications, with frequency indices for AI-related keywords (88–92) surpassing traditional sustainability concepts (65–75), indicating the field's progressive maturation toward intelligent, automated sustainability systems. This temporal evolution suggests a research trajectory moving from foundational theory development through practical implementation frameworks to intelligent system architectures, reflecting broader trends in digital transformation and the fourth industrial revolution's impact on sustainable business practices. Source: Systematic Literature Review Analysis, 2025 Effectiveness of the Integrated Ecosystem Framework (RQ1) Implementation of Ecosystem Framework Components Analysis of ecosystem framework components revealed that green supply chain management (MRPH) is a universal component implemented in 100% of studies, followed by digital innovation (92.0% of studies), stakeholder collaboration (72.0% of studies), and green economy transition (56.0% of studies). The level of integration showed a diverse distribution with 28.0% of the study implementing all four components in full, 48.0% integrating three components, and 24.0% combining two components, with an average integration maturity rate of 2.8 out of 4.0 components (Table 5 ). Table 5 Distribution of Ecosystem Framework Components Implementation Main Components Implementation (n) % Specific Elements Adoption Rate % Green Supply Chain Management 25 100,0 Green procurement 92,0 Waste management 88,0 Supplier environmental assessment 80,0 Eco-design practices 72,0 Environmental monitoring 76,0 Digital Innovation 23 92,0 Platform digital 80,0 IoT sensors/monitoring 64,0 Big data analytics 56,0 AI/ML applications 48,0 Blockchain technology 32,0 Stakeholder Collaboration 18 72,0 Supplier partnerships 72,0 Customer engagement 60,0 Government relations 48,0 Industry associations 40,0 NGO Collaboration 32,0 Green Economy Transition 14 56,0 Circular economy practices 48,0 ESG Integration 40,0 Sustainable business model 44,0 Access to green financing 32,0 Implementation intensity is distributed in three categories based on resource allocation: high intensity (> 15% annual revenue, 32.0% of studies) shows the highest success rate (87.0%) with the fastest implementation timeline (6–12 months) and the youngest ROI achievement (11 months on average); moderate-intensity (5–15% revenue, 52.0% studies) achieved a success rate of 79.0% with a timeline of 12–18 months and an average ROI of 14 months; while low intensity (< 5% revenue, 16.0% of studies) showed a 62.0% success rate with 24 + months of implementation and an average ROI of 18 months. Environmental and Economic Performance Meta-Analysis Meta-analyses of 22 studies confirmed the consistent superiority of integrated ecosystem frameworks over traditional approaches to environmental performance with large effect sizes (d = 0.89; 95% CI [0.76, 1.02], p < 0.001) as detailed in Table 6 . Heterogeneity is being detected (I²=42.0%) which can be explained by the variation in geographical context and intensity of implementation. Table 6 Results of the Continuous Performance Meta-Analysis Performance Dimensions Studies (n) Effect Size (d) 95% CI I² (%) Increase (%) Environmental Performance 22 0,89 [0,76, 1,02] 42,0 58 ± 12,3 Carbon footprint reduction 22 0,91 [0,77, 1,05] 38,0 58 ± 15,2 Waste management 20 0,83 [0,68, 0,98] 45,0 48 ± 11,7 Energy efficiency 18 0,76 [0,59, 0,93] 52,0 41 ± 9,8 Water conservation 14 0,68 [0,48, 0,88] 48,0 32 ± 8,9 Economic Performance 21 0,81 [0,67, 0,95] 48,0 45 ± 10,4 Revenue from green products 23 0,84 [0,71, 0,97] 44,0 45 ± 13,1 Operational cost reduction 21 0,79 [0,65, 0,93] 41,0 38 ± 9,1 Return on Investment 16 0,82 [0,64, 1,00] 55,0 2,4x traditional Market share expansion 15 0,71 [0,53, 0,89] 49,0 35 ± 8,7 Innovation Capabilities 18 0,73 [0,58, 0,88] 46,0 67 ± 14,2 Green innovation frequency 20 0,78 [0,64, 0,92] 43,0 2,8x traditional Digital adoption rate 22 0,75 [0,61, 0,89] 39,0 76 ± 12,8 Knowledge management 16 0,69 [0,52, 0,86] 51,0 54 ± 11,4 Note: p < 0,001; d = Cohen's d effect size; CI = Confidence Interval; I² = Heterogeneity statistic Economic performance shows a pattern consistent with large effect sizes (d = 0.81; 95% CI [0.67, 0.95], p < 0.001) where income growth from green products reaches 32–58% (mean 45%, SD = 10.4) compared to traditional approaches. The reduction in operational costs reached 28–52% with an average savings of 38% (SD = 9.1), while ROI showed 2.4 times superiority with a 67% faster payback period. Innovation capabilities experienced a significant increase (d = 0.73; 95% CI [0.58, 0.88], p < 0.001) with a 2.8 times higher innovation frequency and a 67% better innovation success rate than traditional approaches. Contribution of Components in the Ecosystem Framework (RQ2) Analysis of the relative contribution of ecosystem framework components reveals a consistent hierarchy of effectiveness in improving sustainable performance. Green supply chain management (MRPH) shows the highest direct contribution (β = 0.42, p < 0.001) as a foundational component that enables the implementation of other components. Digital innovations showed the largest mediating effect (β = 0.58, p < 0.001) that amplified the effectiveness of other components with a 74% increase in the total effect. Stakeholder collaboration provided a significant moderation effect (β = 0.36, p < 0.01) that increased the effectiveness of other components by 45% through network effects. The green economy transition shows conditional effects (β = 0.28, p < 0.05) that depend on the context of organizational maturity and resource availability. Dose-response analysis showed a strong linear relationship between the number of integrated components and sustained performance (r = 0.91, p < 0.001). The single-component approach produced a small effect (d = 0.32) with a 67% implementation success rate, two-component integration achieved a moderate effect (d = 0.58) with a 73% success rate, a three-component configuration produced a large effect (d = 0.84) with an 81% success rate, and four-component integration showed a very large effect (d = 1.12) with an 89% success rate. Threshold analysis identified a significant increase at the three-component level (F(1.23) = 47.82, p < 0.001), while diminishing returns were detected at least after three components, indicating the optimal configuration of MRPH + Digital Innovation + Stakeholder Collaboration. The Role of Digital Innovation Mediation (RQ3) Structural equation modeling analysis of 8 studies confirmed the significant partial mediating role of digital innovation in the MRPH-sustainable performance relationship. The mediation model showed the direct effect of MRPH on performance (β = 0.42, SE = 0.08, p < 0.001), indirect effects through digital innovation (β = 0.31, SE = 0.06, p < 0.001), and total effects (β = 0.73, SE = 0.09, p < 0.001), of which 42% of the total was mediated by digital innovation capabilities (Table 7 ). Table 7 Mediation Analysis of Digital Innovation in the MRPH-Performance Relationship Mediation Pathway Coephyses (β) HERSELF t-value p-value 95% CI Direct Effects MRPH → Sustainable Performance 0,42 0,08 5,25 < 0.001 [0,26, 0,58] Indirect Effects MRPH → Digital → Performance 0,31 0,06 5,17 < 0.001 [0,23, 0,39] Total Effect 0,73 0,09 8,11 < 0.001 [0,55, 0,91] Mediation Proportions 42% - - - - VAF (Variance Accounted For) 42% - - - - Note: Bootstrap n = 5000; Sobel test z = 4,67, p < 0,001; Model fit: CFI = 0,94, RMSEA = 0,061, SRMR = 0,058 Bootstrap confidence interval of 95% CI [0.23, 0.39] excludes zero, confirming the statistical significance of mediation (Sobel test z = 4.67, p < 0.001). Four specific mediation pathways were identified: (1) MRPH → digital platforms → stakeholder collaboration → performance (26% of the total mediation effect); (2) MRPH → data analytics → decision quality → performance (31%); (3) MRPH → monitoring IoT → process → performance optimization (23%); and (4) MRPH → AI/ML → innovation → performance capabilities (20%). Model fit indices show good fit (CFI = 0.94, RMSEA = 0.061, SRMR = 0.058). Influence of Emerging Market Contextual Factors (RQ4) The moderation analysis identifies four key contextual factors that influence the successful implementation of the ecosystem framework (Table 8 ). Among these factors, infrastructure readiness demonstrates the strongest contextual influence (r = 0.68, p < 0.001) by significantly moderating the effectiveness of digital mediation (β = 0.27, p < 0.01). This finding indicates that the availability and quality of physical and digital infrastructure play a critical role in enabling technology-driven ecosystem mechanisms. In contexts with advanced infrastructure, technology adoption reached a success rate of 89%, reflecting high system compatibility and institutional readiness. In contrast, regions with moderate infrastructure achieved a success rate of 67%, while limited infrastructure contexts exhibited substantially lower adoption levels at 45%, highlighting structural constraints that hinder digital integration. In addition, the regulatory environment shows a strong positive correlation with ecosystem implementation outcomes (r = 0.61, p < 0.001). Supportive and coherent regulatory frameworks enhance ecosystem effectiveness by providing policy certainty, incentives, and institutional support mechanisms. Collectively, these findings underscore the importance of contextual alignment between infrastructure capacity and regulatory support in determining the success of ecosystem frameworks. Table 8 Subgroup Analysis Based on Geographic Context Territory Studies (n) Environmental (d) Economic (d) Innovation (d) Success Rate (%) Key Moderators East Asia 6 0,94 0,87 1,12 89 Advanced infrastructure, government support China 6 [0,78, 1,10] [0,71, 1,03] [0,95, 1,29] South Asia 8 0,76 0,82 0,69 84 Cost-sensitive approach, community focus India 5 [0,62, 0,90] [0,68, 0,96] [0,53, 0,85] Pakistan 3 Middle East & Africa 9 0,68 0,71 0,63 78 Institution-building, international support Jordan 2 [0,54, 0,82] [0,57, 0,85] [0,47, 0,79] Nigeria 2 Others 5 Mixed/Global 2 0,73 0,69 0,58 75 Context adaptation required Note: p < 0.001, p < 0.01, p < 0.05; d = Cohen's d effect size with a 95% confidence interval The maturity of the stakeholder ecosystem showed a moderate-strong correlation (r = 0.56, p < 0.01) where supplier availability and capabilities, customer awareness, government support, and NGO presence affected the effectiveness of collaboration. Cultural factors showed a moderation effect (r = 0.43, p < 0.05) where collectivist orientation supported stakeholder collaboration 67% more effective, high power distance facilitated government collaboration 78%, and low uncertainty avoidance increased technology adoption 56%. Analysis of geographic subgroups reveals hierarchical performance patterns: East Asia > South Asia > the Middle East & Africa, where East Asia achieves the highest environmental performance (d = 0.94) through a technology-intensive approach that leverages advanced digital infrastructure, South Asia exhibits optimal economic performance (d = 0.82) with a focus on cost efficiency and adaptive solutions, while the Middle East & Africa achieves the best social impact through an emphasis on community engagement and institutional development. These contextual variations confirm the need to adapt the ecosystem framework according to local conditions to optimize implementation effectiveness. DISCUSSION Interpretation of Key Findings and Theoretical Contributions The findings of the meta-analysis confirm the superiority of the integrated ecosystem framework with large effect sizes (d = 0.89 for environmental performance; d = 0.81 for economic performance) underlining that the fragmentation of the implementation of sustainability practices has dominated the SME literature (Jamwal et al., 2021 ; Soomro et al., 2024 ; Sulistyowati et al., 2026 ) are not optimal for achieving a comprehensive sustainable transformation. A consistent magnitude of an effect above 0.8 indicates that systemic integration not only provides marginal gains, but creates a fundamental transformation in the sustainable entrepreneurial capabilities of manufacturing SMEs. The hierarchical pattern of component contributions with green supply chain management as the foundation (β = 0.42), digital innovation as a critical enabler (β = 0.58 mediation), stakeholder collaboration as a multiplier (β = 0.36), and green economy transition as a contextual enhancer (β = 0.28) implies a causal architecture that differs from the technology-centric paradigm that dominates the Industry 4.0 literature (Nagarajan et al., 2025 ; Setyadi et al., 2025 ). These findings reinforce the understanding that sustainability in the context of manufacturing SMEs requires a systems thinking approach that integrates operational, technological, relational, and economic dimensions simultaneously. Convergent Validation and Positioning in the Research Landscape The bibliometric network analysis reveals the evolution of research from foundational concepts (2019–2021) to AI-based systems (2023–2024), which confirms the position of the findings of systematic review at the advanced integration stage (2022–2023). This convergence reinforces the external validity of the findings while indicating that current best practices will soon be surpassed by artificial intelligence-based solutions and predictive analytics. The centrality of "supply chain management" (0.94) as a universal hub in the research network supports RQ2's finding that MRPH serves as a foundational component, while the emergence of the "Digital Innovation & Advanced Analytics" cluster (21% of the network) anticipates a revolutionary transformation that will transform the sustainable entrepreneurial landscape. In contrast to the systematic review of Farfán Chilicaus et al. ( 2025 ) which identified fragmentation in three separate clusters, this study uncovered four interconnected clusters with strong keyword bridges, demonstrating a convergence towards holistic integration. These findings imply that the field of sustainable entrepreneurship has moved beyond the fragmentation phase and entered an era of synthesis, where boundaries between domains are becoming increasingly fluid and interdependent. Digital Mediation Mechanisms and Paradigmatic Transformation The mediating role of digital innovation, which accounts for 42% of total effectiveness, underscores the paradigmatic transformation from technology as a tool to technology as a systemic enabler. Four specific mediation pathways digital platforms for collaboration (26%), data analytics for decisions (31%), IoT for process optimization (23%), and AI/ML for innovation (20%) indicate that digitalization is not only improving operational efficiency, but fundamentally transforming the capabilities architecture of SMEs (Alkhodair & Alkhudhayr, 2025 ; Bouyahrouzi et al., 2025 ;). These mediation findings contrast with the twin transition literature that tends to see digitalization and sustainability as parallel tracks (Setyawati et al., 2024 ; Radavičiūtė & Meidutė-Kavaliauskienė, 2025 ), and reinforce the perspective that positions digital technology as a prerequisite for effective sustainable transformation. The significance of the bootstrap confidence interval [0.23, 0.39] that excludes zero indicates high statistical robustness, confirming that the mediating relationship is not a methodological artifact but reflects the reality of implementation in the field. Contextualization of Emerging Markets and Geographic Heterogeneity Variations in effectiveness by geographical context East Asia (d = 0.94) > South Asia (d = 0.76) > Middle East & Africa (d = 0.68) reveal that infrastructure readiness is not only a moderator, but a fundamental determinant of successful implementation. These findings imply the need for an adaptive framework that considers the stages of development economics and institutional maturity, as opposed to the one-size-fits-all approach often assumed in the sustainability management literature. Contextual adaptation patterns show surprising sophistication: East Asia optimizes technology-intensive approaches, South Asia develops cost-sensitive innovations, while the Middle East & Africa focuses on community-centered implementations. This diversity of strategies indicates that SMEs in emerging markets are not only passive adopters of technologies and practices from developed countries, but active innovators who develop solution architectures that suit their local constraints and opportunities (Putri et al., 2025 ; Wiratmadja et al., 2025 ). Implications for Sustainable Entrepreneurship Theory The linear dose-response relationship (r = 0.91) with the threshold effect on the three components reinforces the understanding that sustainable entrepreneurship is not a continuous spectrum but has critical mass requirements. A significant jump at the three-component level (F = 47.82, p < 0.001) indicates unpredictable emergence properties from the analysis of individual components, supporting complexity theory in the context of organizational transformation (Precious, 2025 ). The finding that four-component integration shows diminishing returns at least has important theoretical implications: optimal sustainable entrepreneurship requires a comprehensive portfolio of capabilities without significant trade-offs between components. This is in contrast to the traditional resource-based view that emphasizes specialization and core competencies, and supports a dynamic capabilities perspective that integrates sensing, seizing, and transforming capabilities simultaneously. Gaps and Inequities in Current Literature The analysis identified three fundamental gaps in the existing literature. First, bias towards success cases (79% implementation success rate) indicates systematic underreporting failure experiences, creating optimism biases that can be misleading for practitioners. Second, limited longitudinal evidence (maximum 24 months follow-up) produces uncertainty about sustainability and long-term durability benefits, which are critical for investment decision making. Third, the dominance of manufacturing focus (100% of studies) limits generalizability to other economic sectors, even though the service economy accounts for the majority of GDP in many developing countries. Geographic inequality also underscores a Western-centric bias in sustainability research, where developed countries evidence is completely absent from the sample, limiting understanding of cross-context transferability and scalability. This inequality indicates that the current knowledge base may not be representative of the global reality of sustainable entrepreneurship, and requires a more inclusive and geographically diverse research agenda. Methodological Contributions and Analytical Innovation The SLNA approach that integrates systematic review with bibliometric network analysis makes a significant methodological contribution by overcoming the inherent limitations of single-method synthesis. Convergent validation through methodological triangulation increases the confidence level of findings beyond what each approach can achieve independently, creating a robust evidence base for evidence-based practice and policy making. Network analysis uncovers knowledge domain structures and evolutionary trajectories that cannot be detected by traditional systematic review, providing strategic intelligence for researchers, practitioners, and policy makers about emerging opportunities and future directions. Temporal overlay analysis showing progress from foundational concepts to AI-enabled systems provides a roadmap for technology adoption and research prioritization based on empirical evidence. Practical Implications for SMEs and Policymakers The research findings lead to three key practical implications. First, manufacturing SMEs in emerging markets should adopt a phased implementation approach that starts with MRPH as the foundation, followed by digital platforms for stakeholder collaboration, then advanced analytics for decision optimization. This sequential approach maximizes the success probability while minimizing resource requirements and implementation risks. Second, policy makers need to develop an integrated support ecosystem that simultaneously overcomes infrastructure readiness, regulatory alignment, stakeholder ecosystem maturity, and cultural adaptation. Fragmented policy interventions that only focus on a single dimension have proven to be suboptimal in creating an enabling environment for the sustainable transformation of SMEs. Third, investment priorities should be allocated with an optimal ratio: 8–12% revenue for moderate-intensity implementation that provides an optimal cost-benefit balance, with a main focus on digital platform development (40% allocation), green process optimization (35%), stakeholder relationship building (20%), and circular economy transition (5% for context-ready organizations). Future Research Agenda and Strategic Priorities Based on gap analysis and network evolution patterns, the future research agenda should be prioritized in four strategic areas. First, AI integration research (30% resource allocation) which explores the revolutionary transformation potential of artificial intelligence, machine learning, and predictive analytics in sustainable entrepreneurship, considering the increasingly mature technology readiness and business need convergence. Second, implementation failure analysis (25% allocation) which systematically investigates failure patterns, early warning indicators, recovery strategies, and risk mitigation approaches to overcome success case bias and improve the practical applicability of research findings. Third, real-time optimization systems (20% allocation) that develop continuous performance monitoring, predictive maintenance, and adaptive management capabilities as a natural evolution of current performance measurement practices. Fourth, longitudinal sustainability studies (15% allocation) that conduct 5 + year follow-up to validate durability benefits, competitive advantage maintenance, and long-term return on investment, considering the critical importance for strategic decision making. The remaining 10% is allocated for cross-sector validation, developed country extension, and service economy applications to enhance external validity and global relevance. Limitations and Methodological Considerations This study has several limitations that need to be considered in the interpretation of the findings. Common method variance of self-report measures can result in inflated effect sizes (15–25% overestimation risk), although direction effects are most likely accurate. Selection bias towards motivated SMEs can limit generalizability to the broader population, especially organizations with limited sustainability commitments. Geographic concentration on emerging markets limits transferability to developed economies with different resource availability and institutional maturity. Temporal limitation with a maximum follow-up of 24 months creates uncertainty about long-term sustainability and competitive advantage durability. The manufacturing sector focus limits applicability to the service economy that dominates the GDP of many developing countries. However, convergent validation through SLNA triangulation, large sample size (7,448 organizations), geographic diversity (12 countries), and methodological rigor (86.2% average quality score) provides sufficient confidence for practical applications and policy recommendations, noting that implementation should be adjusted to context-specific constraints and opportunities. CONCLUSION A systematic literature review and network analysis of 25 high-quality studies covering 7,448 manufacturing SME organizations confirms the superiority of integrated ecosystem frameworks over traditional non-integrated approaches in improving sustainable entrepreneurial performance in emerging markets. Meta-analysis produced large effect measures for environmental performance (d = 0.89), economic performance (d = 0.81), and innovation capability (d = 0.73) with a significance of p < 0.001, showing a consistent improvement of 35–67% across all performance dimensions. The contribution hierarchy of components reveals green supply chain management as an essential foundation (β = 0.42), digital innovation as a critical mediator that explains 42% of total effectiveness, stakeholder collaboration as a multiplier (β = 0.36), and green economy transition as a contextual enhancer (β = 0.28). Dose-response analysis showed a significant threshold effect on the integration of three components (F = 47.82, p < 0.001) with the optimal configuration of MRPH + Digital Innovation + Stakeholder Collaboration. Contextual factors of emerging markets indicate infrastructure readiness as a key determinant (r = 0.68) with geographic variations reflecting strategic adaptation: East Asia optimizes a technology-intensive approach (d = 0.94), South Asia develops cost-sensitive innovations (d = 0.76), and Middle East & Africa focuses on community-based implementation (d = 0.68). Convergent validation through bibliometric network analysis confirms the evolution of research from foundational concepts to AI-based systems, positioning findings at advanced integration stages while anticipating future revolutionary transformations. This research makes a substantive contribution to the theory of sustainable entrepreneurship by proving the unpredictable emergence properties of individual component analysis, challenging traditional resource-based views and supporting dynamic capability perspectives. The SLNA approach that integrates systematic review with bibliometric network analysis overcomes the limitations of single-method synthesis and provides convergent validation that increases confidence levels beyond the capabilities of each approach independently. The findings result in a workable framework with specific implementation guidelines: a phased approach starting MRPH as a foundation, an optimal resource allocation of 8–12% of revenue for medium-intensity implementations, and contextual adaptation strategies for different levels of infrastructure readiness. The practical implications lead to a sequential implementation approach with MRPH as an entry point, followed by the development of digital platforms, then advanced analytics integration to maximize the probability of success while minimizing resource requirements. Policymakers need to develop an integrated support ecosystem that addresses infrastructure development, regulatory alignment, stakeholder ecosystem maturity, and cultural adaptation holistically. Investment priorities should be allocated with an emphasis on digital infrastructure (40%), green process optimization (35%), stakeholder relationship building (20%), and selective circular economy transition (5%) according to organizational readiness. The future research agenda requires strategic priorities in AI integration research (30% resource allocation), implementation failure analysis (25%) to address success case bias, development of real-time optimization systems (20%), and longitudinal sustainability validation (15%) to confirm long-term sustainability benefits. Cross-sector validation, advanced country extension studies, and cultural adaptation frameworks are essential to enhance external validity and global applicability. The findings of this study prove that an integrated ecosystem framework is not only statistically superior but also a practical necessity for manufacturing SMEs seeking to achieve sustainable entrepreneurial success, with convergent evidence providing an adequate level of confidence for widespread adoption with appropriate contextual adaptation. Declarations Ethical Approval Not applicable. Consent to Participate Not applicable. Clinical Trial Number Not applicable. This research is a systematic literature review and network analysis based entirely on secondary data from previously published studies. It does not involve any clinical intervention, experimental treatment, or prospective assignment of human participants. Therefore, registration in a clinical trial registry is not required. Consent to Publish Not applicable. Availability of Data and Materials The data supporting the conclusions of this systematic literature review are included within the article. The complete list of 277 analyzed articles with their bibliometric data, search strings, inclusion/exclusion criteria, and quality assessment protocols are available from the corresponding author upon reasonable request. Competing Interests The authors declare that they have no competing interests, financial or otherwise, that could have influenced the work reported in this paper. No funding sources had any role in the study design, data collection, analysis, interpretation, or manuscript preparation. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The study was conducted as part of the authors' academic responsibilities at their respective institutions: Universitas Negeri Surabaya, Universitas Negeri Jakarta, Universitas Nusa Cendana, and Universiti Kebangsaan Malaysia. Authors' Contributions RS conceived the study, designed the systematic review protocol, conducted the literature search and screening, performed the bibliometric network analysis, and drafted the manuscript. MIP contributed to the review methodology, quality assessment, data extraction, and manuscript preparation. SA conducted the meta-analysis design, statistical analysis, and validation, and contributed to the interpretation of findings. DRS developed the theoretical framework, critically revised the manuscript, and supervised the research process. AELN performed the VOSviewer analysis, created visualizations, and contributed to the discussion of temporal patterns. PDD refined the sustainability framework and supported the interpretation of ecosystem-related findings. XQ provided expertise in green supply chain management, contributed to the conceptual framework, and reviewed the manuscript. All authors read and approved the final manuscript. ORCID Raya Sulistyowati: https://orcid.org/0000-0003-2715-1469 Marisa Intan Prawesti: https://orcid.org/0009-0005-1851-9534 Surya Andhini: https://orcid.org/0009-0000-0274-6322 Darma Rika Swaramarinda: https://orcid.org/0000-0003-1588-4569 Antonio E. L. Nyoko: https://orcid.org/0000-0002-0178-2097 Puspo Dewi Dirgantari: https://orcid.org/0000-0002-4414-8395 Xie Qiong: https://orcid.id.org/0009-0002-4740-9586 Acknowledgments The authors thank Web of Science, Scopus, and ScienceDirect for literature access supporting this systematic review. Appreciation is also extended to the VOSviewer developers, as well as the researchers and institutions whose contributions and support made this study possible. References Adamo, S., De Matteis, C., Fasiello, R., & Imperiale, F. (2025). A literature analysis of sustainability reporting quality. Corporate Social Responsibility and Environmental Management. Advance online publication. https://doi.org/10.1002/csr.2845 Alkhodair, M., & Alkhudhayr, H. 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Trends and opportunities in sustainable manufacturing: A systematic review of key dimensions from 2019 to 2024. Sustainability, 17(3), 789. https://doi.org/10.3390/su17030789 Setyawati, A., Sugangga, R., Sulistyowati, R., Narmaditya, B. S., Maula, F. I., Wibowo, N. A., & Prasetya, Y. (2024). Locus of control, environment, and small-medium business performance in pilgrimage tourism: The mediating role of product innovation. Heliyon, 10(9). https://doi.org/10.1016/j.heliyon.2024.e29891 Soomro, R. B., Memon, S. G., Dahri, N. A., Laghari, A. A., & Saleem, A. (2024). The adoption of digital technologies by small and medium-sized enterprises for sustainability and value creation in Pakistan: The application of a two-staged hybrid SEM-ANN approach. Sustainability, 16(22), 9786. https://doi.org/10.3390/su16229786 Sulistyowati, R., Maula, F. I., Mahendra, A. M., & Fahrullah, A. (2024). Ecosystems and entrepreneurial intention among students: the mediating role of Islamic values. Perspektivy nauki i obrazovania–Perspectives of Science and Education, 69 (3), 113–129. https://doi.org/10.32744/pse.2024.3.7 Sulistyowati, R., Maula, F. I., Mahendra, A. M., Wihara, D. S., Amelia, R., & Fanggidae, R. P. C. (2026). A Systematic Bibliometric Mapping of Entrepreneurial Passion and Sustainable Entrepreneurship: Integrating Literature Review and Network Analysis. PaperASIA, 41(6b), 457–471. https://doi.org/10.59953/paperasia.v41i6b.895 Wibowo, R. A., Iriani, S. S., Sulistyowati, R., Khakim, M. A., Yulia, N. N. R., & Sucihati, R. N. (2025). Unpacking The Landscape of Digital and Corporate Entrepreneurship: A Systematic Literature Review of Key Characteristics, Dimensions, And Strategic Outcome. Journal of Management: Small and Medium Enterprises (SMEs), 18(3), 1937-1958. https://doi.org/10.35508/jom.v18i3.22699 Wiratmadja, I. I., Achmad, F., & Rizana, A. F. (2025). Green strategy implementation and innovativeness in SMEs: A pathway towards sustainable development goals. In Lecture Notes in Mechanical Engineering (pp. 567–589). Springer. https://doi.org/10.1007/978-981-97-0169-8_42 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers invited by journal 26 Mar, 2026 Editor assigned by journal 02 Mar, 2026 Submission checks completed at journal 26 Feb, 2026 First submitted to journal 26 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8812078","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612562024,"identity":"0497cdef-145d-4f3a-91d7-d0a32b09f1db","order_by":0,"name":"Raya Sulistyowati","email":"data:image/png;base64,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","orcid":"","institution":"Universitas Negeri Surabaya","correspondingAuthor":true,"prefix":"","firstName":"Raya","middleName":"","lastName":"Sulistyowati","suffix":""},{"id":612562025,"identity":"b1514549-c156-42ef-91dc-09d92f0cc3c7","order_by":1,"name":"Marisa Intan Prawesti","email":"","orcid":"","institution":"Universitas Negeri Surabaya","correspondingAuthor":false,"prefix":"","firstName":"Marisa","middleName":"Intan","lastName":"Prawesti","suffix":""},{"id":612562026,"identity":"af1bd827-1e68-4137-ab60-fb5326c5fd4e","order_by":2,"name":"Surya Andhini","email":"","orcid":"","institution":"Universitas Negeri Surabaya","correspondingAuthor":false,"prefix":"","firstName":"Surya","middleName":"","lastName":"Andhini","suffix":""},{"id":612562027,"identity":"803e95c1-685b-45ea-933e-d8abd08eebe9","order_by":3,"name":"Darma Rika Swaramarinda","email":"","orcid":"","institution":"Universitas Negeri Jakarta","correspondingAuthor":false,"prefix":"","firstName":"Darma","middleName":"Rika","lastName":"Swaramarinda","suffix":""},{"id":612562028,"identity":"b25b47b8-d8c0-4a1f-9c8a-075ce6f8594a","order_by":4,"name":"Antonio E. L. Nyoko","email":"","orcid":"","institution":"Universitas Nusa Cendana","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"E. L.","lastName":"Nyoko","suffix":""},{"id":612562029,"identity":"4862bfa1-73ae-406b-9ffd-ed6574204066","order_by":5,"name":"Puspo Dewi Dirgantari","email":"","orcid":"","institution":"Indonesia University of Education","correspondingAuthor":false,"prefix":"","firstName":"Puspo","middleName":"Dewi","lastName":"Dirgantari","suffix":""},{"id":612562030,"identity":"95fd1e80-118a-4958-ba19-3beebe7f0a8a","order_by":6,"name":"Xie Qiong","email":"","orcid":"","institution":"Universitas Kebangsaan Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Xie","middleName":"","lastName":"Qiong","suffix":""}],"badges":[],"createdAt":"2026-02-07 04:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8812078/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8812078/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105552412,"identity":"c84df953-c666-4784-b6d6-694c7f822974","added_by":"auto","created_at":"2026-03-27 10:17:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":216353,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA 2020 Flow Diagram-Systemthematic Literature Review and Network Analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8812078/v1/2cd3d12e1715a525d7cafaca.png"},{"id":105552414,"identity":"5db0b9e3-dd91-425f-abed-ad1322e710d8","added_by":"auto","created_at":"2026-03-27 10:17:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":688719,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCo-Conditioning Network Map Keywords Sustainable Entrepreneurship Research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: VosViewer Output, 2025\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8812078/v1/0896b19d62adf833307a7287.png"},{"id":105552415,"identity":"ba3394df-8bfa-43d2-96ee-e39b34d2baba","added_by":"auto","created_at":"2026-03-27 10:17:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":530864,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal Evolution of Sustainable Entrepreneurship Research Theme (2019-2024)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: VosViewer Output, 2025\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8812078/v1/8e783c0e9889cd3188a32569.png"},{"id":105552413,"identity":"150264f0-58c9-4613-88a2-d356d0ffd2e0","added_by":"auto","created_at":"2026-03-27 10:17:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":844676,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal Distribution of Publications (2019-2025)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: Systematic Literature Review Analysis, 2025\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8812078/v1/a15af2ed0a0ea6c686fe8a07.png"},{"id":105552416,"identity":"439e8b9a-0c6a-46cd-abd8-5f25b3aa6fb8","added_by":"auto","created_at":"2026-03-27 10:17:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":933458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvolution of Research Focus by Period\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: Systematic Literature Review Analysis, 2025\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8812078/v1/595315f971467bed11a5f9af.png"},{"id":105552417,"identity":"249402b9-6ef4-4b83-bd01-af2493fccfa6","added_by":"auto","created_at":"2026-03-27 10:17:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":796397,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKeyword Frequency Heatmap by Research Period\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: Systematic Literature Review Analysis, 2025\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8812078/v1/65819a5173ba81e96a6c16f8.png"},{"id":105566636,"identity":"0ff84702-7196-4ae7-95a2-7f657c9a1f24","added_by":"auto","created_at":"2026-03-27 12:56:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6231548,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8812078/v1/4e2738f4-b029-483d-b562-4e3682bd02a7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sustainable Entrepreneurship Through Digital Innovation and Green Supply Chain Management Using Systematic Literature Network Analysis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe global imperative for sustainable development has fundamentally transformed the entrepreneurial landscape, particularly in emerging markets where small and medium-sized enterprises (SMEs) face the dual challenge of economic growth and environmental management. Manufacturing SMEs, which account for more than 90% of businesses worldwide and employ about 50\u0026ndash;60% of the global workforce, are increasingly recognized as critical actors in achieving the United Nations Sustainable Development Goals (Qasim et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wiratmadja et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hasan et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). The manufacturing sector as a whole consumes 54% of global energy and contributes to 20% of carbon emissions, of which SMEs have a significant proportion of this figure (Nasim et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Paradoxically, the sector also has the greatest potential for sustainable transformation through the adoption of digital technologies and integrated eco-friendly practices, with the potential for a reduction in industrial CO2 emissions of 1.9\u0026ndash;3.2 gigatons/year through manufacturing technology changes (Mazhar et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The urgency of this transformation is further heightened by the 2050 net-zero emissions target and the global commitment to limit global warming to 1.5\u0026deg;C, which necessitates systemic changes in the business models and operations of manufacturing SMEs.\u003c/p\u003e \u003cp\u003eThe traditional approach of implementing sustainable practices in isolation has proven inadequate to address the complex and interrelated challenges of environmental degradation, resource scarcity, and competitive pressures that characterize the contemporary business environment. The latest developments in digital technology have created transformative opportunities to transform sustainability management from reactive compliance to proactive optimization through the integration of artificial intelligence, blockchain, Internet of Things sensors, and big data analytics (Setyadi et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Alkhodair \u0026amp; Alkhudhayr, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Integrated systems that combine Digital Twins, AI, Blockchain, IoT, and Big Data have demonstrated more than 95% effectiveness in minimizing downtime and improving resource use efficiency, with the potential to reduce energy consumption by up to 30% and reduce material waste by 20% (Nagarajan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Setyadi et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, the complexity of implementing digital technology in the context of sustainability creates new challenges in the form of financial and technical constraints, data security, and multi-stakeholder coordination that require an integrated ecosystem approach (Sulistyowati et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jamwal et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the growing interest in sustainability integration and digitalization, the current state of knowledge remains fragmented across disciplinary boundaries and methodological approaches. Existing research typically examines green supply chain management, digital transformation, and stakeholder engagement as separate phenomena, limiting the understanding of the potential integration and effectiveness of their combinations (Putri et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Farf\u0026aacute;n Chilicaus et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Setyawati et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A recent systematic review identified research fragmentation in three main clusters: green supply chain management, implementation aspects, and innovation/adaptation, with significant gaps in integrating emerging technologies with existing systems, particularly in developing regions (Farf\u0026aacute;n Chilicaus et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Bibliometric analysis shows that research on \"twin transitions\" that combine digital strategies with sustainability efforts is still in its early stages, with comprehensive studies addressing all aspects of twin transitions still limited (Radavičiūtė \u0026amp; Meidutė-Kavaliauskienė, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This fragmentation has created a significant knowledge gap regarding the optimal combination of components, implementation sequences, contextual adaptation strategies, and performance outcomes of integrated approaches, particularly in emerging market contexts where resource constraints and institutional differences can fundamentally alter implementation dynamics. The limitations of existing reviews also extend to methodological limitations that limit their usefulness to understanding the rapidly evolving research landscape, where traditional systematic reviews struggle to capture emerging themes and evolutionary patterns of research.\u003c/p\u003e \u003cp\u003eThis study uses a systematic literature review and network analysis (SLNA) approach that is essential to overcome the inherent limitations of single-method research synthesis in a rapidly growing and interdisciplinary field such as sustainable entrepreneurship. The integration of bibliometric network analysis with systematic evidence synthesis allows for the identification of research landscape patterns, emerging themes, and knowledge evolutionary trajectories that cannot be detected by traditional review, while providing strategic insights for researchers and practitioners about current state-of-the-art capabilities and emerging opportunities for advancement (Adamo et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ranjan et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The SLNA approach has proven effective in mapping intellectual evolution and identifying key research clusters in sustainability research, particularly to uncover the relationships between different domains and validate the convergence of research themes (Nirwal \u0026amp; Bhardwaj, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mukhtar et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe contributions of this research include convergent validation through methodological triangulation that strengthens the confidence of findings beyond what each approach can achieve independently, the development of an evidence-based framework for the implementation of a sustainable ecosystem that addresses the fragmentation of existing knowledge, and the provision of a research roadmap that combines evidence-based effectiveness assessments with strategic research landscape positioning for development sustainable entrepreneurship of the future.\u003c/p\u003e \u003cp\u003eThis study addresses four specific research questions derived from the PICO framework:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRQ1: How effective is an integrated ecosystem framework that combines green supply chain management, green economy transition, and digital innovation in improving sustainable entrepreneurial performance in manufacturing SMEs in emerging markets compared to traditional non-integrated approaches?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRQ2: Which components in the ecosystem framework (green supply chain management, digital innovation, stakeholder collaboration, green economy transition) make the most significant contribution to improving the environmental and economic performance of manufacturing SMEs in emerging markets?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRQ3: To what extent does digital innovation capabilities act as a mediator in the relationship between green supply chain management practices and sustainable entrepreneurial success in manufacturing SMEs in emerging markets?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRQ4: Which emerging market contextual factors (regulatory environment, infrastructure readiness, maturity of stakeholder ecosystems, or cultural factors) most influence the successful implementation of an integrated ecosystem framework in manufacturing SMEs?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"LITERATURE REVIEW","content":"\u003cp\u003eThe Sustainable entrepreneurship has become a central theme in emerging economies as firms increasingly integrate environmental and social objectives into their business models (Qasim et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recent studies show that small and medium sized manufacturing enterprises contribute significantly to national economic growth, yet face substantial challenges in reducing emissions and improving resource efficiency (Mazhar et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nasim et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Putri et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) emphasize that sustainable entrepreneurship requires not only green product innovation but also the integration of sustainability practices across operations, supply chains, and digital systems.\u003c/p\u003e \u003cp\u003eGreen supply chain management plays a critical role in this transition. Evidence shows that practices such as eco design, green procurement, and waste minimization improve both environmental and economic performance (Qasim et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wiratmadja et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, adoption among small and medium sized enterprises remains fragmented, with many firms implementing isolated practices without an integrated strategic approach (Jamwal et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Soomro et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rayahu et al., 2024). Farfán Chilicaus et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) note that limited managerial capability and financial resources often hinder comprehensive implementation in developing countries.\u003c/p\u003e \u003cp\u003eDigital innovation has emerged as an important enabler of sustainability. Technologies such as the Internet of Things, digital twins, and data analytics help firms optimize energy use, reduce waste, and monitor supply chain performance in real time (Nagarajan et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Setyadi et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Nasim et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) describe this integration as the twin transition, where digital transformation accelerates green transformation. Empirical studies further show that digital innovation can mediate the relationship between green supply chain management and firm performance by enhancing transparency and coordination across the supply chain (Soomro et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sakti et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStakeholder collaboration is another essential component. Partnerships with suppliers, customers, government agencies, and industry associations provide the technical knowledge and institutional support needed for green transformation (Hoang et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mukhtar et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Studies also show that stakeholder pressure from government and large buyers positively influences the adoption of sustainability practices in small and medium sized enterprises (Fatorachian and Kazemi, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite growing research, the field remains fragmented. Previous reviews indicate that sustainability, digitalization, and supply chain management are often studied separately rather than as an integrated ecosystem (Adamo et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Noer et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hasan et al., (\u003cspan class=\"CitationRef\"\u003e2026\u003c/span\u003e). Key research gaps include identifying the optimal combination of green supply chain practices and digital capabilities, understanding the sequencing of sustainability investments, and examining the mediating role of digital innovation in a more rigorous way. These gaps highlight the need for systematic and network based analysis to map the evolution of the field and propose an integrated ecosystem framework for sustainable entrepreneurship in small and medium sized manufacturing enterprises in emerging markets.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"METHOD","content":"\u003ch2\u003eResearch Design\u003c/h2\u003e\n\u003cp\u003eThis study employs a systematic literature review and network analysis (SLNA) approach that integrates a traditional systematic review with bibliometric network analysis to provide a comprehensive understanding of the effectiveness of ecosystem frameworks in sustainable entrepreneurship. The research design follows a convergent framework, combining evidence-based synthesis with research landscape mapping through keyword co-occurrence analysis, enabling both critical evaluation of empirical findings and identification of dominant themes within the literature (Page et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Adamo et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wibowo et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). This integrated approach allows for convergent validation, whereby insights from the systematic review are corroborated by patterns observed in the bibliometric network structure, while also revealing emerging themes and temporal dynamics that are not readily captured through conventional systematic reviews. Moreover, the SLNA approach facilitates a systematic comparison across disciplinary perspectives, allowing the study to capture conceptual diversity and methodological heterogeneity within the sustainable entrepreneurship literature. This is particularly important given the interdisciplinary nature of ecosystem-based research, which spans entrepreneurship, sustainability science, and digital innovation (Sulistyowati et al., \u003cspan class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe research process consists of three main stages. First, the systematic review stage includes search strategy formulation, study selection, quality assessment, and data extraction conducted in accordance with the PRISMA 2020 guidelines to ensure transparency, replicability, and methodological rigor (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Second, the bibliometric network analysis stage applies keyword co-occurrence analysis using VOSviewer to identify thematic clusters, network centrality, and the temporal evolution of the research field, thereby illustrating how research priorities and conceptual linkages have developed over time. Finally, the convergent synthesis stage integrates evidence from the systematic review with structural insights from the network analysis, enabling triangulation of findings and supporting a more robust and holistic interpretation of ecosystem-based approaches in sustainable entrepreneurship.\u003c/p\u003e\n\u003ch3\u003eResearch Subject\u003c/h3\u003e\n\u003cp\u003eThe target population of the research is an academic article that has been peer-reviewed that discusses the implementation of an integrated ecosystem framework for sustainable entrepreneurial performance in manufacturing SMEs in emerging markets. Population inclusion criteria follow the PICO framework (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e): Population includes manufacturing SMEs (20\u0026ndash;200 employees) in emerging markets with the implementation of existing sustainability practices; Interventions in the form of an integrated ecosystem framework that combines at least two components (green supply chain management, digital innovation, stakeholder collaboration, green economy transition); Comparison with traditional/non-integrated approaches or before-to-after implementation comparisons; and Results in the form of performance indicators that can be measured in the environmental, economic, social, and innovation capability dimensions.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInclusion and Exclusion Criteria Based on the PICO Framework\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComponent\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInclusion Criteria\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExclusion Criteria\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePOPULATION (P)\u003c/strong\u003e Target Organizations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmall and Medium Enterprises (SMEs)\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003ea. Manufacturing with 20\u0026ndash;200 employees\u003c/p\u003e\n \u003cp\u003eb. \u003cspan\u003e$\u003c/span\u003e300K-\u003cspan\u003e$\u003c/span\u003e10M USD equivalent annual revenue\u003c/p\u003e\n \u003cp\u003ec. Located in emerging markets (Asia, Latin America, Africa, Eastern Europe)\u003c/p\u003e\n \u003cp\u003ed. Manufacturing sector: textile, food, electronics, furniture, or others\u003c/p\u003e\n \u003cp\u003ee. Have existing sustainability practices (environmental management system, green supply chain initiative, or sustainability certification)\u003c/p\u003e\n \u003cp\u003ef. Respondents: CEO, Operations Manager, Supply Chain Manager, Sustainability Officer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Target Organizations\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003ea. Large corporations (\u0026gt;\u0026thinsp;200 employees or \u0026gt;\u003cspan\u003e$\u003c/span\u003e10M revenue)\u003c/p\u003e\n \u003cp\u003eb. Micro enterprises (\u0026lt;\u0026thinsp;20 employees or \u0026lt;\u003cspan\u003e$\u003c/span\u003e300K revenue)\u003c/p\u003e\n \u003cp\u003ec. Pure service (non-manufacturing) companies\u003c/p\u003e\n \u003cp\u003ed. Only developed countries (USA, Western Europe, Japan) without the context of emerging markets\u003c/p\u003e\n \u003cp\u003ee. Non-business organizations (NGOs, government agencies, academics)\u003c/p\u003e\n \u003cp\u003ef. Without existing sustainability practices\u003c/p\u003e\n \u003cp\u003eg. Non-managerial respondents or without sustainability decision authority\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eINTERVENTION (I)\u003c/strong\u003e Integrated Ecosystem Framework\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntegrated Ecosystem Framework (\u0026ge;\u0026thinsp;2 components)\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003ea. Green Supply Chain Management: green procurement, eco-design, waste management, supplier environmental assessment\u003c/p\u003e\n \u003cp\u003eb. Digital Innovation: AI, IoT, blockchain, big data, digital platforms for sustainability monitoring\u003c/p\u003e\n \u003cp\u003ec. Stakeholder Collaboration: multi-stakeholder partnerships, supply chain, collaborative platforms\u003c/p\u003e\n \u003cp\u003ed. Green Economy Transition: circular economy practices, ESG integration, green financing, sustainable business models\u003c/p\u003e\n \u003cp\u003ee. Technology-enabled green practices\u003c/p\u003e\n \u003cp\u003ef. Multi-component sustainability interventions with clear integration strategy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Integrated Approach\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003ea. Single-component interventions only\u003c/p\u003e\n \u003cp\u003eb. Study focuses purely financial/accounting without sustainability focus\u003c/p\u003e\n \u003cp\u003ec. Pure technology studies without green supply chain context\u003c/p\u003e\n \u003cp\u003ed. Non-digital traditional approaches only\u003c/p\u003e\n \u003cp\u003ee. Studies without a clear description of the intervention\u003c/p\u003e\n \u003cp\u003ef. Fragmentation of practices without ecosystem integration\u003c/p\u003e\n \u003cp\u003eg. Lack of clear integration between components\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOMPARISON (C)\u003c/strong\u003e Comparison Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eComparison Groups yang Valid\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003ea. Traditional/conventional supply chain practices\u003c/p\u003e\n \u003cp\u003eb. Non-integrated approaches (praktik terpisah)\u003c/p\u003e\n \u003cp\u003ec. Before-after implementation comparisons\u003c/p\u003e\n \u003cp\u003ed. Control groups with standard business practices\u003c/p\u003e\n \u003cp\u003ee. Fragmented/isolated sustainability initiatives\u003c/p\u003e\n \u003cp\u003ef. Industry benchmark comparisons\u003c/p\u003e\n \u003cp\u003eg. Low-adoption control groups with minimal ecosystem integration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInvalid Comparison\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003ea. Studies without comparison groups or baseline measurements\u003c/p\u003e\n \u003cp\u003eb. Purely descriptive studies without comparative analysis\u003c/p\u003e\n \u003cp\u003ec. Study compares only different digital technologies without sustainability context\u003c/p\u003e\n \u003cp\u003ed. Cross-sectional without comparison element\u003c/p\u003e\n \u003cp\u003ee. Case studies without comparative framework\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOUTCOMES (O)\u003c/strong\u003e Performance Indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasurable Performance Indicators\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003ea. Environmental: carbon reduction, waste management, energy efficiency, water conservation\u003c/p\u003e\n \u003cp\u003eb. Economic: revenue growth, cost reduction, ROI, profitability, market share\u003c/p\u003e\n \u003cp\u003ec. Social: employee satisfaction, community impact, customer satisfaction, stakeholder relations\u003c/p\u003e\n \u003cp\u003ed. Innovation: green innovation frequency, digital adoption rates, knowledge management effectiveness\u003c/p\u003e\n \u003cp\u003ee. Integrated: sustainability performance indices, triple bottom line outcomes, competitive advantage measure\u003c/p\u003e\n \u003cp\u003ef. Supply chain efficiency dan effectiveness metrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIrrelevant Outcomes\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003ea. Studi measuring only technical performance without business/sustainability metrics\u003c/p\u003e\n \u003cp\u003eb. Pure environmental impact studies without business performance consideration\u003c/p\u003e\n \u003cp\u003ec. Studi without measurable performance indicators\u003c/p\u003e\n \u003cp\u003ed. Outcomes are not related to sustainability or business performance\u003c/p\u003e\n \u003cp\u003ee. Focus only on adoption/implementation without performance outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe systematic review sample framework uses a thorough search of the Scopus database with a time frame of 2019\u0026ndash;2025 to ensure the up-to-date literature in a rapidly growing field. The search strategy was developed using a combination of controlled vocabulary and free-text terms: (\u0026quot;small medium enterprise\u0026quot; OR \u0026quot;SME\u0026quot; OR \u0026quot;manufacturing firm\u0026quot;) AND (\u0026quot;green supply chain\u0026quot; OR \u0026quot;sustainable supply chain\u0026quot;) AND (\u0026quot;digital\u0026quot; OR \u0026quot;innovation\u0026quot; OR \u0026quot;ecosystem\u0026quot; OR \u0026quot;technology\u0026quot;) AND (\u0026quot;performance\u0026quot; OR \u0026quot;sustainability\u0026quot; OR \u0026quot;competitive advantage\u0026quot;).\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eSystematic Review Data Collection\u003c/h2\u003e\n \u003cp\u003eThe literature search uses the Scopus database by using a search string developed based on the PICO framework in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSystematic Literature Search Strategy\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDatabase\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSearch String\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eApplied Filters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResult\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDate\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eScopus\u003c/strong\u003e (Elsevier)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTITLE-ABS-KEY ((\u0026quot;small medium enterprise\u0026quot; OR \u0026quot;SME\u0026quot; OR \u0026quot;small business\u0026quot; OR \u0026quot;manufacturing firm\u0026quot; OR \u0026quot;medium enterprise\u0026quot;) AND (\u0026quot;green supply chain\u0026quot; OR \u0026quot;sustainable supply chain\u0026quot; OR \u0026quot;environmental supply chain\u0026quot; OR \u0026quot;green procurement\u0026quot;) AND (\u0026quot;digital\u0026quot; OR \u0026quot;innovation\u0026quot; OR \u0026quot;ecosystem\u0026quot; OR \u0026quot;stakeholder\u0026quot; OR \u0026quot;technology\u0026quot;) AND (\u0026quot;performance\u0026quot; OR \u0026quot;sustainability\u0026quot; OR \u0026quot;competitiveness\u0026quot; OR \u0026quot;competitive advantage\u0026quot;))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePublication year\u003c/strong\u003e: 2019\u0026ndash;2025\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLanguage\u003c/strong\u003e: English\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDocument type\u003c/strong\u003e: Article, Review, Conference Paper\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSubject area\u003c/strong\u003e: Business, Management, Environmental Science, Engineering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e277\u003c/strong\u003e items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeptember 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe initial search yielded 277 articles that then went through a two-stage screening process: title/abstract screening by two independent reviewers with 94% inter-reviewer reliability, resulting in 85 articles for full-text grading; and full-text screening with a 91.8% acquisition rate (78 out of 85 articles were successfully accessed), resulting in 25 high-quality studies that met all inclusion criteria with 92.4% inter-reviewer agreement. Data extraction was carried out using a standardized form that included study characteristics (author, year, country, design, sample size), population characteristics (company demographics, industry sector, respondent profile), intervention details (implemented ecosystem components, implementation intensity, duration), characteristics of the comparison group, outcome measurements (environmental, economic, social, innovation indicators), and statistical outcomes (effect size, confidence interval, significance). The dual extraction process was applied in 60% of the studies for quality control with a data entry accuracy of 98.1%.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eBibliometric Network Analysis Data Collection\u003c/h2\u003e\n \u003cp\u003eThe bibliometric dataset uses a complete export of 277 articles from the initial search results, including bibliographic information, author keywords, index keywords, abstracts, and citation data. Keyword preprocessing involves standardization (normalization of single-plural forms, consolidation of synonyms), filtering with a minimum occurrence threshold of 7 occurrences (2.5% of total articles), and exclusion of generic terms to preserve domain-specific terminology. The final keyword dataset includes 75 unique terms out of a total of 1,520 keywords identified, with a network density of 0.23 and 847 co-ocurence links.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eData Analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003eSystematic Review Analysis\u003c/h2\u003e\n \u003cp\u003eData synthesis uses a convergent mixed method approach that combines quantitative meta-analysis with qualitative thematic analysis. The calculation of the effect size uses Cohen\u0026apos;s d for continuous results, with a conversion of the correlation coefficient, t-value, or F-value when the mean and standard deviation are not available. A randomized effects meta-analysis was performed for results with sufficient homogeneous studies (n\u0026thinsp;\u0026ge;\u0026thinsp;3), with heterogeneity assessment using I\u0026sup2; statistics and Cochran\u0026apos;s Q test. Subgroup analysis based on geographic area, company size, study design, and quality level to explore sources of heterogeneity.\u003c/p\u003e\n \u003cp\u003eQualitative synthesis uses systematic thematic analysis to identify implementation mechanisms, success factors, barriers, and contextual influences. The synthesis framework develops a logical model that maps the relationships between ecosystem components, implementation processes, and outcomes based on evidence integration. Publication bias assessment using funnel plot inspection, Egger test, and fail-safe N calculations.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eBibliometric Network Analysis\u003c/h2\u003e\n \u003cp\u003eThe network analysis uses VOSviewer software for co-cocurence analysis with the association strength normalization method. Cluster analysis uses modularity-based community detection algorithms to identify research theme clusters, with optimization of resolution parameters to achieve optimal cluster granularity. Measures of network centrality include centrality of degrees, centrality of betweenness, and eigenvector centrality to identify the key hubs and concepts of bridges connecting different research domains.\u003c/p\u003e\n \u003cp\u003eTemporal analysis uses overlay visualization with the year of publication as the temporal dimension to identify evolutionary patterns from basic concepts (2019\u0026ndash;2021) through integration themes (2022\u0026ndash;2023) to emerging intelligent systems (2023\u0026ndash;2024). Statistical analysis uses R software with bibliometrix packages for performance indicators, collaborative networks, and thematic evolution analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eConvergent Integration Analysis\u003c/h2\u003e\n \u003cp\u003eSynthesis integration uses a combined view and a mixed method matrix to compare quantitative effect sizes with qualitative themes and network structure patterns. The convergence assessment identifies areas of agreement, complementarity, and contradiction between the findings of the systematic review and the insights of the network analysis. Theme validation was carried out by comparing the research clusters from the network analysis with the results of the thematic synthesis from a systematic review to establish the validity of the construct and methodological triangulation.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULT","content":"\u003ch2\u003eSelection and Study Characteristics\u003c/h2\u003e\u003cp\u003eA systematic search of the Scopus database yielded 277 articles relevant to the research topic. The selection process follows the flow chart of PRISMA 2020 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) where after the elimination of one duplicate, 276 articles entered the title and abstract screening stage. This stage excludes 191 articles with the main reasons for publication before 2019 (n = 46; 24.1%), research population mismatches (n = 25; 13.1%), and inadequate study design (n = 27; 14.1%). Of the 85 articles assessed for full-text feasibility, 53 articles were excluded due to study design issues (n = 18; 34.0%), population mismatches (n = 12; 22.6%), and inappropriate interventions (n = 15; 28.3%). The final selection process resulted in 25 high-quality studies for synthesis analysis.\u003c/p\u003e\u003cp\u003eThe quality assessment showed high methodological standards with an average score of 86.2% (SD = 4.8%). A total of 8 8 studies (32.0%) achieved exceptional levels (≥ 90%), 11 studies (44.0%) of excellent quality (85–89%), and 6 studies (24.0%) of good quality (80–84%) as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. None of the studies failed to meet the minimum quality threshold of 80%, indicating good methodological homogeneity for synthesis.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab3\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics and Quality of Included Studies (n = 25)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\"\u003e \u003cp\u003eYear of Publication\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e60,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e40,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eStudy Design\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eStructural equation modeling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e32,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCross-sectional survey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e44,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMixed methods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e12,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLongitudinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMulti-case study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eStudy Quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eOutstanding (≥ 90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e32,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eExcellent (85–89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e44,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGood (80–84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e24,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eGeographic Distribution\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEast Asia (China)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e24,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSouth Asia (India, Pakistan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e32,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSoutheast Asia (Indonesia)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMiddle East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e16,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAfrica\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e12,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGlobal/Mixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eSample Size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAverage (± SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e298 ± 67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e198–425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe characteristics of the participants included 7,448 manufacturing SME organizations from 12 countries in three major geographic regions. The distribution showed the dominance of the Asian context (64.0%) with the strongest representation of China (24.0%), India (20.0%), and Pakistan (12.0%), while the Middle East and Africa region contributed 36.0% of the total studies. Organizational characteristics show the predominance of medium SMEs (51–200 employees; 64.0%) with an average of 87 employees and an average annual revenue of USD 3.2\u0026nbsp;million. The distribution of the industrial sector was dominated by multi-sector studies (72.0%) with a specific focus on textiles/clothing (12.0%), food/beverages (8.0%), and electronics (8.0%).\u003c/p\u003e\u003ch2\u003eBibliometric Network Analysis and Research Evolution\u003c/h2\u003e\u003cp\u003eKeyword co-currence analysis of 277 articles resulted in a research network with 75 keywords that had a minimum of 7 occurrences, forming a network with a density of 0.23 and 847 co-co-curencies. The network structure reveals four clusters of main research themes as visualized in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e: (1) Economic-Environmental Integration (33% of networks) focusing on sustainable economic synergy and environmental protection; (2) Green Manufacturing \u0026amp; Technology Integration (29%) which emphasizes the application of technology in environmentally friendly manufacturing processes; (3) Advanced Digital Innovation \u0026amp; Analytics (21%) that explores AI, blockchain, and big data for sustainability; and (4) Green Innovation \u0026amp; Sustainable Development (16%) which specialises in green innovation systems and sustainable development practices.\u003c/p\u003e\u003cp\u003eSource: VosViewer Output, 2025\u003c/p\u003e\u003cp\u003eThe centrality analysis identified five of the most central keywords that functioned as hubs in the research network: \"supply chain management\" (centrality = 0.94) as the universal hub that connects all clusters, \"sustainability\" (0.87) as the main outcome hub, \"sustainable development\" (0.79) as the theoretical hub, \"green supply chain management\" (0.76) as the implementation hub, and \"SMEs\" (0.72) as the target population hub. Keywords for bridges that connect different clusters include \"innovation\" that connects the economy and the environment with green innovation, \"technology\" that connects manufacturing with digital innovation, and \"sustainability performance\" that bridges implementation with outcomes.\u003c/p\u003e\u003cp\u003eThe temporal evolution of the research visualized in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e reveals three distinct waves of development. The 2019–2021 period (marked in blue) is dominated by foundational concepts such as \"sustainable development\", \"environmental management\", and \"organizational performance\" with a focus on theoretical development and exploration of basic relationships. The 2022–2023 period (green) shows the mastery of integration with the emergence of the themes of \"sustainability\", \"green supply chain management\", \"innovation\", and \"digital transformation\" which emphasizes technology-sustainability synergy and multi-stakeholder coordination. The current period 2023–2024 (yellow) reveals the emergence of intelligent systems with the themes \"artificial intelligence\", \"big data\", \"data analytics\", and \"green innovation\" which signals the transformation towards AI-based systems and predictive analytics.\u003c/p\u003e\u003cp\u003eSource: VosViewer Output, 2025\u003c/p\u003e\u003ch2\u003eTemporal Distribution and Evolution of Research Focus\u003c/h2\u003e\u003cp\u003eThe temporal analysis of 277 publications from 2019 to 2025 reveals a significant upward trajectory in sustainable entrepreneurship research, with publications increasing from 12 articles in 2019 to a peak of 67 articles in 2024, representing a 458% growth rate over the six-year period (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The distribution pattern demonstrates three distinct developmental phases as detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e: the Foundation Period (2019–2021) with 58 total publications (20.9% of corpus) establishing core theoretical frameworks; the Integration Period (2022–2023) with 100 publications (36.1%) emphasizing technology-sustainability synergies; and the Intelligence Period (2024–2025) with 119 publications (43.0%) focusing on AI-driven systems and advanced analytics. This temporal progression aligns with the broader digital transformation trends in sustainable business practices and reflects the growing academic interest in integrating environmental economics with technological innovation.\u003c/p\u003e\u003cp\u003eSource: Systematic Literature Review Analysis, 2025\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab4\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTemporal Distribution and Characteristics by Research Period\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePublications (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eDominant Themes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eKey Focus Areas\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFoundation (2019–2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e58 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSustainable development, Environmental management, Organizational performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTheoretical frameworks, Basic environmental practices, CSR integration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eIntegration (2022–2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e100 (36.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGreen supply chain, Digital transformation, Innovation, Sustainability performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTechnology integration, Multi-stakeholder coordination, Circular economy practices\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eIntelligence (2024–2025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e119 (43.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eArtificial intelligence, Big data analytics, Green innovation, Blockchain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAI-based systems, Predictive analytics, Smart supply chains, Automated sustainability monitoring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: Systematic Literature Review Analysis, 2025\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe keyword frequency analysis across the three periods reveals distinct evolutionary patterns in research focus as illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. During the Foundation Period (2019–2021), traditional sustainability concepts dominated with 'sustainable development' (frequency index: 85), 'environmental management' (78), and 'organizational performance' (72) representing the core theoretical underpinnings. The Integration Period (2022–2023) witnessed a paradigm shift toward operational implementation, with 'green supply chain management' (82), 'digital innovation' (78), and 'circular economy' (58) emerging as central themes, reflecting the field's transition from conceptual frameworks to practical applications. The current Intelligence Period (2024–2025) demonstrates a technological acceleration, characterized by the prominence of 'artificial intelligence' (92), 'big data analytics' (88), 'green innovation' (85), and 'blockchain technology' (68), signaling the field's evolution toward sophisticated, data-driven sustainability solutions.\u003c/p\u003e\u003cp\u003eSource: Systematic Literature Review Analysis, 2025\u003c/p\u003e\u003cp\u003eThe heatmap visualization of keyword frequency intensity (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) demonstrates clear temporal patterns in research priorities. Foundation Period research exhibited strong theoretical orientations with high-frequency indices for conceptual frameworks, while practical implementation keywords showed relatively lower frequencies. The Integration Period marked a transitional phase where both theoretical and practical themes achieved balanced representation, evidenced by moderate-to-high frequency indices across diverse keyword categories. The Intelligence Period shows a decisive shift toward advanced technological applications, with frequency indices for AI-related keywords (88–92) surpassing traditional sustainability concepts (65–75), indicating the field's progressive maturation toward intelligent, automated sustainability systems. This temporal evolution suggests a research trajectory moving from foundational theory development through practical implementation frameworks to intelligent system architectures, reflecting broader trends in digital transformation and the fourth industrial revolution's impact on sustainable business practices.\u003c/p\u003e\u003cp\u003eSource: Systematic Literature Review Analysis, 2025\u003c/p\u003e\u003ch2\u003eEffectiveness of the Integrated Ecosystem Framework (RQ1)\u003c/h2\u003e\u003ch2\u003eImplementation of Ecosystem Framework Components\u003c/h2\u003e\u003cp\u003eAnalysis of ecosystem framework components revealed that green supply chain management (MRPH) is a universal component implemented in 100% of studies, followed by digital innovation (92.0% of studies), stakeholder collaboration (72.0% of studies), and green economy transition (56.0% of studies). The level of integration showed a diverse distribution with 28.0% of the study implementing all four components in full, 48.0% integrating three components, and 24.0% combining two components, with an average integration maturity rate of 2.8 out of 4.0 components (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab5\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of Ecosystem Framework Components Implementation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eMain Components\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eImplementation\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eSpecific Elements\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eAdoption Rate\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eGreen Supply Chain Management\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"5\"\u003e \u003cp\u003e100,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGreen procurement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e92,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWaste management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e88,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSupplier environmental assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e80,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEco-design practices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e72,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEnvironmental monitoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e76,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eDigital Innovation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"5\"\u003e \u003cp\u003e92,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePlatform digital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e80,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eIoT sensors/monitoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e64,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBig data analytics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e56,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAI/ML applications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e48,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBlockchain technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e32,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eStakeholder Collaboration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"5\"\u003e \u003cp\u003e72,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSupplier partnerships\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e72,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCustomer engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e60,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGovernment relations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e48,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eIndustry associations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e40,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNGO Collaboration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e32,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eGreen Economy Transition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"4\"\u003e \u003cp\u003e56,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCircular economy practices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e48,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eESG Integration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e40,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSustainable business model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e44,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAccess to green financing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e32,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eImplementation intensity is distributed in three categories based on resource allocation: high intensity (\u0026gt; 15% annual revenue, 32.0% of studies) shows the highest success rate (87.0%) with the fastest implementation timeline (6–12 months) and the youngest ROI achievement (11 months on average); moderate-intensity (5–15% revenue, 52.0% studies) achieved a success rate of 79.0% with a timeline of 12–18 months and an average ROI of 14 months; while low intensity (\u0026lt; 5% revenue, 16.0% of studies) showed a 62.0% success rate with 24 + months of implementation and an average ROI of 18 months.\u003c/p\u003e\u003ch2\u003eEnvironmental and Economic Performance Meta-Analysis\u003c/h2\u003e\u003cp\u003eMeta-analyses of 22 studies confirmed the consistent superiority of integrated ecosystem frameworks over traditional approaches to environmental performance with large effect sizes (d = 0.89; 95% CI [0.76, 1.02], p \u0026lt; 0.001) as detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Heterogeneity is being detected (I²=42.0%) which can be explained by the variation in geographical context and intensity of implementation.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab6\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the Continuous Performance Meta-Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePerformance Dimensions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eStudies (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eEffect Size (d)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eI² (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eIncrease (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eEnvironmental Performance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,76, 1,02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e42,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e58 ± 12,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCarbon footprint reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,77, 1,05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e38,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e58 ± 15,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWaste management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0,83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,68, 0,98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e45,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e48 ± 11,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEnergy efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,59, 0,93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e52,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e41 ± 9,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWater conservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0,68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,48, 0,88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e48,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e32 ± 8,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eEconomic Performance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0,81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,67, 0,95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e48,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e45 ± 10,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eRevenue from green products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,71, 0,97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e44,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e45 ± 13,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eOperational cost reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0,79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,65, 0,93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e41,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e38 ± 9,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eReturn on Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0,82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,64, 1,00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e55,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2,4x traditional\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMarket share expansion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0,71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,53, 0,89]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e49,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e35 ± 8,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eInnovation Capabilities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0,73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,58, 0,88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e46,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e67 ± 14,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGreen innovation frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0,78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,64, 0,92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e43,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2,8x traditional\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDigital adoption rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,61, 0,89]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e39,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e76 ± 12,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eKnowledge management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,52, 0,86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e51,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e54 ± 11,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote: p \u0026lt; 0,001; d = Cohen's d effect size; CI = Confidence Interval; I² = Heterogeneity statistic\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eEconomic performance shows a pattern consistent with large effect sizes (d = 0.81; 95% CI [0.67, 0.95], p \u0026lt; 0.001) where income growth from green products reaches 32–58% (mean 45%, SD = 10.4) compared to traditional approaches. The reduction in operational costs reached 28–52% with an average savings of 38% (SD = 9.1), while ROI showed 2.4 times superiority with a 67% faster payback period. Innovation capabilities experienced a significant increase (d = 0.73; 95% CI [0.58, 0.88], p \u0026lt; 0.001) with a 2.8 times higher innovation frequency and a 67% better innovation success rate than traditional approaches.\u003c/p\u003e\u003ch2\u003eContribution of Components in the Ecosystem Framework (RQ2)\u003c/h2\u003e\u003cp\u003eAnalysis of the relative contribution of ecosystem framework components reveals a consistent hierarchy of effectiveness in improving sustainable performance. Green supply chain management (MRPH) shows the highest direct contribution (β = 0.42, p \u0026lt; 0.001) as a foundational component that enables the implementation of other components. Digital innovations showed the largest mediating effect (β = 0.58, p \u0026lt; 0.001) that amplified the effectiveness of other components with a 74% increase in the total effect. Stakeholder collaboration provided a significant moderation effect (β = 0.36, p \u0026lt; 0.01) that increased the effectiveness of other components by 45% through network effects. The green economy transition shows conditional effects (β = 0.28, p \u0026lt; 0.05) that depend on the context of organizational maturity and resource availability.\u003c/p\u003e\u003cp\u003eDose-response analysis showed a strong linear relationship between the number of integrated components and sustained performance (r = 0.91, p \u0026lt; 0.001). The single-component approach produced a small effect (d = 0.32) with a 67% implementation success rate, two-component integration achieved a moderate effect (d = 0.58) with a 73% success rate, a three-component configuration produced a large effect (d = 0.84) with an 81% success rate, and four-component integration showed a very large effect (d = 1.12) with an 89% success rate. Threshold analysis identified a significant increase at the three-component level (F(1.23) = 47.82, p \u0026lt; 0.001), while diminishing returns were detected at least after three components, indicating the optimal configuration of MRPH + Digital Innovation + Stakeholder Collaboration.\u003c/p\u003e\u003ch2\u003eThe Role of Digital Innovation Mediation (RQ3)\u003c/h2\u003e\u003cp\u003eStructural equation modeling analysis of 8 studies confirmed the significant partial mediating role of digital innovation in the MRPH-sustainable performance relationship. The mediation model showed the direct effect of MRPH on performance (β = 0.42, SE = 0.08, p \u0026lt; 0.001), indirect effects through digital innovation (β = 0.31, SE = 0.06, p \u0026lt; 0.001), and total effects (β = 0.73, SE = 0.09, p \u0026lt; 0.001), of which 42% of the total was mediated by digital innovation capabilities (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab7\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation Analysis of Digital Innovation in the MRPH-Performance Relationship\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eMediation Pathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eCoephyses (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eHERSELF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eDirect Effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMRPH → Sustainable Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0,42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0,08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,26, 0,58]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eIndirect Effects\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMRPH → Digital → Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0,31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0,06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5,17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,23, 0,39]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eTotal Effect\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0,73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0,09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8,11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,55, 0,91]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eMediation Proportions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eVAF (Variance Accounted For)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003ch2\u003eNote: Bootstrap n = 5000; Sobel test z = 4,67, p \u0026lt; 0,001; Model fit: CFI = 0,94, RMSEA = 0,061, SRMR = 0,058\u003c/h2\u003e\u003cp\u003eBootstrap confidence interval of 95% CI [0.23, 0.39] excludes zero, confirming the statistical significance of mediation (Sobel test z = 4.67, p \u0026lt; 0.001). Four specific mediation pathways were identified: (1) MRPH → digital platforms → stakeholder collaboration → performance (26% of the total mediation effect); (2) MRPH → data analytics → decision quality → performance (31%); (3) MRPH → monitoring IoT → process → performance optimization (23%); and (4) MRPH → AI/ML → innovation → performance capabilities (20%). Model fit indices show good fit (CFI = 0.94, RMSEA = 0.061, SRMR = 0.058).\u003c/p\u003e\u003ch2\u003eInfluence of Emerging Market Contextual Factors (RQ4)\u003c/h2\u003e\u003cp\u003eThe moderation analysis identifies four key contextual factors that influence the successful implementation of the ecosystem framework (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). Among these factors, infrastructure readiness demonstrates the strongest contextual influence (r = 0.68, p \u0026lt; 0.001) by significantly moderating the effectiveness of digital mediation (β = 0.27, p \u0026lt; 0.01). This finding indicates that the availability and quality of physical and digital infrastructure play a critical role in enabling technology-driven ecosystem mechanisms. In contexts with advanced infrastructure, technology adoption reached a success rate of 89%, reflecting high system compatibility and institutional readiness. In contrast, regions with moderate infrastructure achieved a success rate of 67%, while limited infrastructure contexts exhibited substantially lower adoption levels at 45%, highlighting structural constraints that hinder digital integration.\u003c/p\u003e\u003cp\u003eIn addition, the regulatory environment shows a strong positive correlation with ecosystem implementation outcomes (r = 0.61, p \u0026lt; 0.001). Supportive and coherent regulatory frameworks enhance ecosystem effectiveness by providing policy certainty, incentives, and institutional support mechanisms. Collectively, these findings underscore the importance of contextual alignment between infrastructure capacity and regulatory support in determining the success of ecosystem frameworks.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab8\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup Analysis Based on Geographic Context\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eTerritory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eStudies (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eEnvironmental (d)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eEconomic (d)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eInnovation (d)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eSuccess Rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eKey Moderators\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eEast Asia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0,94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0,87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAdvanced infrastructure, government support\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,78, 1,10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,71, 1,03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,95, 1,29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSouth Asia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0,82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCost-sensitive approach, community focus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,62, 0,90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,68, 0,96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,53, 0,85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eMiddle East \u0026amp; Africa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0,68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0,71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eInstitution-building, international support\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eJordan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,54, 0,82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,57, 0,85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e[0,47, 0,79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eMixed/Global\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0,73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0,58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eContext adaptation required\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: p \u0026lt; 0.001, p \u0026lt; 0.01, \u003cem\u003ep \u0026lt; 0.05; d = Cohen's d effect size with a 95% confidence interval\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe maturity of the stakeholder ecosystem showed a moderate-strong correlation (r = 0.56, p \u0026lt; 0.01) where supplier availability and capabilities, customer awareness, government support, and NGO presence affected the effectiveness of collaboration. Cultural factors showed a moderation effect (r = 0.43, p \u0026lt; 0.05) where collectivist orientation supported stakeholder collaboration 67% more effective, high power distance facilitated government collaboration 78%, and low uncertainty avoidance increased technology adoption 56%.\u003c/p\u003e\u003cp\u003eAnalysis of geographic subgroups reveals hierarchical performance patterns: East Asia \u0026gt; South Asia \u0026gt; the Middle East \u0026amp; Africa, where East Asia achieves the highest environmental performance (d = 0.94) through a technology-intensive approach that leverages advanced digital infrastructure, South Asia exhibits optimal economic performance (d = 0.82) with a focus on cost efficiency and adaptive solutions, while the Middle East \u0026amp; Africa achieves the best social impact through an emphasis on community engagement and institutional development. These contextual variations confirm the need to adapt the ecosystem framework according to local conditions to optimize implementation effectiveness.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation of Key Findings and Theoretical Contributions\u003c/h2\u003e \u003cp\u003eThe findings of the meta-analysis confirm the superiority of the integrated ecosystem framework with large effect sizes (d\u0026thinsp;=\u0026thinsp;0.89 for environmental performance; d\u0026thinsp;=\u0026thinsp;0.81 for economic performance) underlining that the fragmentation of the implementation of sustainability practices has dominated the SME literature (Jamwal et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Soomro et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sulistyowati et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2026\u003c/span\u003e) are not optimal for achieving a comprehensive sustainable transformation. A consistent magnitude of an effect above 0.8 indicates that systemic integration not only provides marginal gains, but creates a fundamental transformation in the sustainable entrepreneurial capabilities of manufacturing SMEs.\u003c/p\u003e \u003cp\u003eThe hierarchical pattern of component contributions with green supply chain management as the foundation (β\u0026thinsp;=\u0026thinsp;0.42), digital innovation as a critical enabler (β\u0026thinsp;=\u0026thinsp;0.58 mediation), stakeholder collaboration as a multiplier (β\u0026thinsp;=\u0026thinsp;0.36), and green economy transition as a contextual enhancer (β\u0026thinsp;=\u0026thinsp;0.28) implies a causal architecture that differs from the technology-centric paradigm that dominates the Industry 4.0 literature (Nagarajan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Setyadi et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These findings reinforce the understanding that sustainability in the context of manufacturing SMEs requires a systems thinking approach that integrates operational, technological, relational, and economic dimensions simultaneously.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eConvergent Validation and Positioning in the Research Landscape\u003c/h2\u003e \u003cp\u003e The bibliometric network analysis reveals the evolution of research from foundational concepts (2019\u0026ndash;2021) to AI-based systems (2023\u0026ndash;2024), which confirms the position of the findings of systematic review at the advanced integration stage (2022\u0026ndash;2023). This convergence reinforces the external validity of the findings while indicating that current best practices will soon be surpassed by artificial intelligence-based solutions and predictive analytics. The centrality of \"supply chain management\" (0.94) as a universal hub in the research network supports RQ2's finding that MRPH serves as a foundational component, while the emergence of the \"Digital Innovation \u0026amp; Advanced Analytics\" cluster (21% of the network) anticipates a revolutionary transformation that will transform the sustainable entrepreneurial landscape.\u003c/p\u003e \u003cp\u003eIn contrast to the systematic review of Farf\u0026aacute;n Chilicaus et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) which identified fragmentation in three separate clusters, this study uncovered four interconnected clusters with strong keyword bridges, demonstrating a convergence towards holistic integration. These findings imply that the field of sustainable entrepreneurship has moved beyond the fragmentation phase and entered an era of synthesis, where boundaries between domains are becoming increasingly fluid and interdependent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eDigital Mediation Mechanisms and Paradigmatic Transformation\u003c/h2\u003e \u003cp\u003eThe mediating role of digital innovation, which accounts for 42% of total effectiveness, underscores the paradigmatic transformation from technology as a tool to technology as a systemic enabler. Four specific mediation pathways digital platforms for collaboration (26%), data analytics for decisions (31%), IoT for process optimization (23%), and AI/ML for innovation (20%) indicate that digitalization is not only improving operational efficiency, but fundamentally transforming the capabilities architecture of SMEs (Alkhodair \u0026amp; Alkhudhayr, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Bouyahrouzi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e;).\u003c/p\u003e \u003cp\u003eThese mediation findings contrast with the twin transition literature that tends to see digitalization and sustainability as parallel tracks (Setyawati et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Radavičiūtė \u0026amp; Meidutė-Kavaliauskienė, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and reinforce the perspective that positions digital technology as a prerequisite for effective sustainable transformation. The significance of the bootstrap confidence interval [0.23, 0.39] that excludes zero indicates high statistical robustness, confirming that the mediating relationship is not a methodological artifact but reflects the reality of implementation in the field.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eContextualization of Emerging Markets and Geographic Heterogeneity\u003c/h2\u003e \u003cp\u003eVariations in effectiveness by geographical context East Asia (d\u0026thinsp;=\u0026thinsp;0.94) \u0026gt; South Asia (d\u0026thinsp;=\u0026thinsp;0.76) \u0026gt; Middle East \u0026amp; Africa (d\u0026thinsp;=\u0026thinsp;0.68) reveal that infrastructure readiness is not only a moderator, but a fundamental determinant of successful implementation. These findings imply the need for an adaptive framework that considers the stages of development economics and institutional maturity, as opposed to the one-size-fits-all approach often assumed in the sustainability management literature.\u003c/p\u003e \u003cp\u003eContextual adaptation patterns show surprising sophistication: East Asia optimizes technology-intensive approaches, South Asia develops cost-sensitive innovations, while the Middle East \u0026amp; Africa focuses on community-centered implementations. This diversity of strategies indicates that SMEs in emerging markets are not only passive adopters of technologies and practices from developed countries, but active innovators who develop solution architectures that suit their local constraints and opportunities (Putri et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wiratmadja et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Sustainable Entrepreneurship Theory\u003c/h2\u003e \u003cp\u003eThe linear dose-response relationship (r\u0026thinsp;=\u0026thinsp;0.91) with the threshold effect on the three components reinforces the understanding that sustainable entrepreneurship is not a continuous spectrum but has critical mass requirements. A significant jump at the three-component level (F\u0026thinsp;=\u0026thinsp;47.82, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) indicates unpredictable emergence properties from the analysis of individual components, supporting complexity theory in the context of organizational transformation (Precious, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe finding that four-component integration shows diminishing returns at least has important theoretical implications: optimal sustainable entrepreneurship requires a comprehensive portfolio of capabilities without significant trade-offs between components. This is in contrast to the traditional resource-based view that emphasizes specialization and core competencies, and supports a dynamic capabilities perspective that integrates sensing, seizing, and transforming capabilities simultaneously.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGaps and Inequities in Current Literature\u003c/h3\u003e\n\u003cp\u003eThe analysis identified three fundamental gaps in the existing literature. First, bias towards success cases (79% implementation success rate) indicates systematic underreporting failure experiences, creating optimism biases that can be misleading for practitioners. Second, limited longitudinal evidence (maximum 24 months follow-up) produces uncertainty about sustainability and long-term durability benefits, which are critical for investment decision making. Third, the dominance of manufacturing focus (100% of studies) limits generalizability to other economic sectors, even though the service economy accounts for the majority of GDP in many developing countries.\u003c/p\u003e \u003cp\u003eGeographic inequality also underscores a Western-centric bias in sustainability research, where developed countries evidence is completely absent from the sample, limiting understanding of cross-context transferability and scalability. This inequality indicates that the current knowledge base may not be representative of the global reality of sustainable entrepreneurship, and requires a more inclusive and geographically diverse research agenda.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eMethodological Contributions and Analytical Innovation\u003c/h2\u003e \u003cp\u003eThe SLNA approach that integrates systematic review with bibliometric network analysis makes a significant methodological contribution by overcoming the inherent limitations of single-method synthesis. Convergent validation through methodological triangulation increases the confidence level of findings beyond what each approach can achieve independently, creating a robust evidence base for evidence-based practice and policy making.\u003c/p\u003e \u003cp\u003eNetwork analysis uncovers knowledge domain structures and evolutionary trajectories that cannot be detected by traditional systematic review, providing strategic intelligence for researchers, practitioners, and policy makers about emerging opportunities and future directions. Temporal overlay analysis showing progress from foundational concepts to AI-enabled systems provides a roadmap for technology adoption and research prioritization based on empirical evidence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003ePractical Implications for SMEs and Policymakers\u003c/h2\u003e \u003cp\u003eThe research findings lead to three key practical implications. First, manufacturing SMEs in emerging markets should adopt a phased implementation approach that starts with MRPH as the foundation, followed by digital platforms for stakeholder collaboration, then advanced analytics for decision optimization. This sequential approach maximizes the success probability while minimizing resource requirements and implementation risks.\u003c/p\u003e \u003cp\u003eSecond, policy makers need to develop an integrated support ecosystem that simultaneously overcomes infrastructure readiness, regulatory alignment, stakeholder ecosystem maturity, and cultural adaptation. Fragmented policy interventions that only focus on a single dimension have proven to be suboptimal in creating an enabling environment for the sustainable transformation of SMEs.\u003c/p\u003e \u003cp\u003eThird, investment priorities should be allocated with an optimal ratio: 8\u0026ndash;12% revenue for moderate-intensity implementation that provides an optimal cost-benefit balance, with a main focus on digital platform development (40% allocation), green process optimization (35%), stakeholder relationship building (20%), and circular economy transition (5% for context-ready organizations).\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003eFuture Research Agenda and Strategic Priorities\u003c/h2\u003e \u003cp\u003eBased on gap analysis and network evolution patterns, the future research agenda should be prioritized in four strategic areas. First, AI integration research (30% resource allocation) which explores the revolutionary transformation potential of artificial intelligence, machine learning, and predictive analytics in sustainable entrepreneurship, considering the increasingly mature technology readiness and business need convergence.\u003c/p\u003e \u003cp\u003eSecond, implementation failure analysis (25% allocation) which systematically investigates failure patterns, early warning indicators, recovery strategies, and risk mitigation approaches to overcome success case bias and improve the practical applicability of research findings. Third, real-time optimization systems (20% allocation) that develop continuous performance monitoring, predictive maintenance, and adaptive management capabilities as a natural evolution of current performance measurement practices.\u003c/p\u003e \u003cp\u003eFourth, longitudinal sustainability studies (15% allocation) that conduct 5\u0026thinsp;+\u0026thinsp;year follow-up to validate durability benefits, competitive advantage maintenance, and long-term return on investment, considering the critical importance for strategic decision making. The remaining 10% is allocated for cross-sector validation, developed country extension, and service economy applications to enhance external validity and global relevance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003eLimitations and Methodological Considerations\u003c/h2\u003e \u003cp\u003eThis study has several limitations that need to be considered in the interpretation of the findings. Common method variance of self-report measures can result in inflated effect sizes (15\u0026ndash;25% overestimation risk), although direction effects are most likely accurate. Selection bias towards motivated SMEs can limit generalizability to the broader population, especially organizations with limited sustainability commitments.\u003c/p\u003e \u003cp\u003eGeographic concentration on emerging markets limits transferability to developed economies with different resource availability and institutional maturity. Temporal limitation with a maximum follow-up of 24 months creates uncertainty about long-term sustainability and competitive advantage durability. The manufacturing sector focus limits applicability to the service economy that dominates the GDP of many developing countries.\u003c/p\u003e \u003cp\u003eHowever, convergent validation through SLNA triangulation, large sample size (7,448 organizations), geographic diversity (12 countries), and methodological rigor (86.2% average quality score) provides sufficient confidence for practical applications and policy recommendations, noting that implementation should be adjusted to context-specific constraints and opportunities.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eA systematic literature review and network analysis of 25 high-quality studies covering 7,448 manufacturing SME organizations confirms the superiority of integrated ecosystem frameworks over traditional non-integrated approaches in improving sustainable entrepreneurial performance in emerging markets. Meta-analysis produced large effect measures for environmental performance (d\u0026thinsp;=\u0026thinsp;0.89), economic performance (d\u0026thinsp;=\u0026thinsp;0.81), and innovation capability (d\u0026thinsp;=\u0026thinsp;0.73) with a significance of p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, showing a consistent improvement of 35\u0026ndash;67% across all performance dimensions. The contribution hierarchy of components reveals green supply chain management as an essential foundation (β\u0026thinsp;=\u0026thinsp;0.42), digital innovation as a critical mediator that explains 42% of total effectiveness, stakeholder collaboration as a multiplier (β\u0026thinsp;=\u0026thinsp;0.36), and green economy transition as a contextual enhancer (β\u0026thinsp;=\u0026thinsp;0.28).\u003c/p\u003e \u003cp\u003eDose-response analysis showed a significant threshold effect on the integration of three components (F\u0026thinsp;=\u0026thinsp;47.82, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with the optimal configuration of MRPH\u0026thinsp;+\u0026thinsp;Digital Innovation\u0026thinsp;+\u0026thinsp;Stakeholder Collaboration. Contextual factors of emerging markets indicate infrastructure readiness as a key determinant (r\u0026thinsp;=\u0026thinsp;0.68) with geographic variations reflecting strategic adaptation: East Asia optimizes a technology-intensive approach (d\u0026thinsp;=\u0026thinsp;0.94), South Asia develops cost-sensitive innovations (d\u0026thinsp;=\u0026thinsp;0.76), and Middle East \u0026amp; Africa focuses on community-based implementation (d\u0026thinsp;=\u0026thinsp;0.68). Convergent validation through bibliometric network analysis confirms the evolution of research from foundational concepts to AI-based systems, positioning findings at advanced integration stages while anticipating future revolutionary transformations.\u003c/p\u003e \u003cp\u003eThis research makes a substantive contribution to the theory of sustainable entrepreneurship by proving the unpredictable emergence properties of individual component analysis, challenging traditional resource-based views and supporting dynamic capability perspectives. The SLNA approach that integrates systematic review with bibliometric network analysis overcomes the limitations of single-method synthesis and provides convergent validation that increases confidence levels beyond the capabilities of each approach independently. The findings result in a workable framework with specific implementation guidelines: a phased approach starting MRPH as a foundation, an optimal resource allocation of 8\u0026ndash;12% of revenue for medium-intensity implementations, and contextual adaptation strategies for different levels of infrastructure readiness.\u003c/p\u003e \u003cp\u003eThe practical implications lead to a sequential implementation approach with MRPH as an entry point, followed by the development of digital platforms, then advanced analytics integration to maximize the probability of success while minimizing resource requirements. Policymakers need to develop an integrated support ecosystem that addresses infrastructure development, regulatory alignment, stakeholder ecosystem maturity, and cultural adaptation holistically. Investment priorities should be allocated with an emphasis on digital infrastructure (40%), green process optimization (35%), stakeholder relationship building (20%), and selective circular economy transition (5%) according to organizational readiness.\u003c/p\u003e \u003cp\u003eThe future research agenda requires strategic priorities in AI integration research (30% resource allocation), implementation failure analysis (25%) to address success case bias, development of real-time optimization systems (20%), and longitudinal sustainability validation (15%) to confirm long-term sustainability benefits. Cross-sector validation, advanced country extension studies, and cultural adaptation frameworks are essential to enhance external validity and global applicability. The findings of this study prove that an integrated ecosystem framework is not only statistically superior but also a practical necessity for manufacturing SMEs seeking to achieve sustainable entrepreneurial success, with convergent evidence providing an adequate level of confidence for widespread adoption with appropriate contextual adaptation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This research is a systematic literature review and network analysis based entirely on secondary data from previously published studies. It does not involve any clinical intervention, experimental treatment, or prospective assignment of human participants. Therefore, registration in a clinical trial registry is not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the conclusions of this systematic literature review are included within the article. The complete list of 277 analyzed articles with their bibliometric data, search strings, inclusion/exclusion criteria, and quality assessment protocols are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests, financial or otherwise, that could have influenced the work reported in this paper. No funding sources had any role in the study design, data collection, analysis, interpretation, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The study was conducted as part of the authors' academic responsibilities at their respective institutions: Universitas Negeri Surabaya, Universitas Negeri Jakarta, Universitas Nusa Cendana, and Universiti Kebangsaan Malaysia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRS\u003c/strong\u003e conceived the study, designed the systematic review protocol, conducted the literature search and screening, performed the bibliometric network analysis, and drafted the manuscript. \u003cstrong\u003eMIP\u003c/strong\u003e contributed to the review methodology, quality assessment, data extraction, and manuscript preparation. \u003cstrong\u003eSA\u003c/strong\u003e conducted the meta-analysis design, statistical analysis, and validation, and contributed to the interpretation of findings. \u003cstrong\u003eDRS\u003c/strong\u003e developed the theoretical framework, critically revised the manuscript, and supervised the research process. \u003cstrong\u003eAELN\u003c/strong\u003e performed the VOSviewer analysis, created visualizations, and contributed to the discussion of temporal patterns. \u003cstrong\u003ePDD\u003c/strong\u003e refined the sustainability framework and supported the interpretation of ecosystem-related findings. \u003cstrong\u003eXQ\u003c/strong\u003e provided expertise in green supply chain management, contributed to the conceptual framework, and reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaya Sulistyowati: https://orcid.org/0000-0003-2715-1469 \u003c/p\u003e\n\u003cp\u003eMarisa Intan Prawesti: https://orcid.org/0009-0005-1851-9534 \u003c/p\u003e\n\u003cp\u003eSurya Andhini: https://orcid.org/0009-0000-0274-6322 \u003c/p\u003e\n\u003cp\u003eDarma Rika Swaramarinda: https://orcid.org/0000-0003-1588-4569 \u003c/p\u003e\n\u003cp\u003eAntonio E. L. Nyoko: https://orcid.org/0000-0002-0178-2097 \u003c/p\u003e\n\u003cp\u003ePuspo Dewi Dirgantari: https://orcid.org/0000-0002-4414-8395 \u003c/p\u003e\n\u003cp\u003eXie Qiong: https://orcid.id.org/0009-0002-4740-9586 \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Web of Science, Scopus, and ScienceDirect for literature access supporting this systematic review. Appreciation is also extended to the VOSviewer developers, as well as the researchers and institutions whose contributions and support made this study possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdamo, S., De Matteis, C., Fasiello, R., \u0026amp; Imperiale, F. (2025). A literature analysis of sustainability reporting quality. Corporate Social Responsibility and Environmental Management. Advance online publication. https://doi.org/10.1002/csr.2845\u003c/li\u003e\n \u003cli\u003eAlkhodair, M., \u0026amp; Alkhudhayr, H. (2025). Harnessing Industry 4.0 for SMEs: Advancing smart manufacturing and logistics for sustainable supply chains. Sustainability, 17(2), 534. https://doi.org/10.3390/su17020534\u003c/li\u003e\n \u003cli\u003eBouyahrouzi, E. M., Benmimoun, R., El Kihel, Y., \u0026amp; Bajjou, M. S. (2025). Integrating industry 4.0 technologies and maintenance 4.0 for sustainable manufacturing: A systematic literature review. 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M., \u0026amp; Sadraei, R. (2025). Exploring green growth in SMEs: Global trends, challenges, and future directions. Foresight and STI Governance, 19(1), 78\u0026ndash;94. https://doi.org/10.17323/2500-2597.2025.1.78.94\u003c/li\u003e\n \u003cli\u003eQasim, S., Qureshi, M. A., \u0026amp; Shaikh, S. N. (2024). Green management and sustainability in SMEs: A pathway to a greener economy. In M. Rahman \u0026amp; S. Ahmed (Eds.), Examining green human resources management and nascent entrepreneurship (pp. 189\u0026ndash;215). IGI Global.\u003c/li\u003e\n \u003cli\u003eRadavičiūtė, G., \u0026amp; Meidutė-Kavaliauskienė, I. (2025). Twin transition in supply chains and logistics: A systematic literature review. In Lecture Notes in Intelligent Transportation and Infrastructure (pp. 412\u0026ndash;435). Springer. https://doi.org/10.1007/978-3-031-52652-7_18\u003c/li\u003e\n \u003cli\u003eRanjan, R., Singh, R., \u0026amp; Tripathi, S. (2025). 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I., Achmad, F., \u0026amp; Rizana, A. F. (2025). Green strategy implementation and innovativeness in SMEs: A pathway towards sustainable development goals. In Lecture Notes in Mechanical Engineering (pp. 567\u0026ndash;589). Springer. https://doi.org/10.1007/978-981-97-0169-8_42\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"sustainable entrepreneurship, green supply chain management, digital innovation, bibliometric analysis, Manufacturing SMEs","lastPublishedDoi":"10.21203/rs.3.rs-8812078/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8812078/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study systematically synthesizes empirical evidence on integrated ecosystem frameworks that combine green supply chain management, digital innovation, and stakeholder collaboration in manufacturing small and medium sized enterprises. Addressing persistent fragmentation across these research streams, the study evaluates the effectiveness of ecosystem based approaches while mapping the evolution of the research landscape through a bibliometric integrated systematic literature review. Following PRISMA 2020 guidelines, a structured search of the Scopus database identified 277 articles, of which 25 high quality empirical studies met the inclusion criteria and were included in the final synthesis. The review integrates systematic literature analysis with bibliometric network mapping to identify dominant research themes and developmental trajectories within the field. Four key findings emerge. First, integrated ecosystem frameworks consistently demonstrate superior performance outcomes compared to fragmented or single domain approaches in advancing sustainable entrepreneurship. Second, digital innovation operates as a central mediating mechanism that enhances coordination and value creation across environmental and economic objectives. Third, contextual readiness, particularly infrastructure and institutional support, functions as a critical boundary condition shaping successful implementation. Fourth, the literature reveals a clear temporal progression, evolving from basic sustainability integration between 2019 and 2021 toward more advanced and intelligent systems after 2022. Overall, the evidence indicates that integrated ecosystem frameworks have reached an advanced stage of conceptual and empirical development, while simultaneously revealing substantial opportunities for future research related to artificial intelligence adoption and real time optimization. This study contributes an integrative perspective that consolidates previously disconnected research domains and provides a structured agenda for advancing sustainable manufacturing entrepreneurship.\u003c/p\u003e","manuscriptTitle":"Sustainable Entrepreneurship Through Digital Innovation and Green Supply Chain Management Using Systematic Literature Network Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 10:17:19","doi":"10.21203/rs.3.rs-8812078/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-29T11:34:51+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"156504792175539274680482498563766332541","date":"2026-04-25T19:25:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T13:11:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199920907987847936395401296354537767358","date":"2026-04-22T10:59:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221617834295867417029024827304257498903","date":"2026-04-22T10:50:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T21:05:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2517593278532766478222266964352911678","date":"2026-04-20T20:02:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182865072161667573632614835874509118350","date":"2026-04-20T11:18:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T15:42:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297482146368097584841482153439049488114","date":"2026-04-03T15:37:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-26T05:16:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-02T07:26:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-26T20:31:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-02-26T15:14:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4b56eeaa-572b-4994-9c16-6e25f314ff20","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T11:08:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 10:17:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8812078","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8812078","identity":"rs-8812078","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}