Knowledge management (KM) is increasingly recognized as a critical enabler of sustainable entrepreneurship (SE), particularly for resource-constrained startups. This study conducts a systematic literature review of 76 articles (1651 cumulative citations) at the intersection of KM and SE, focusing on how KM is deemed to support startups in achieving sustainability. The descriptive analysis delineates publication trends, citation patterns, geographical distributions, and methodological orientations, while the content analysis identifies three dominant thematic clusters: (1) KM’s role in fostering innovation and sustainable competitive advantage, (2) sustainability practices linking green orientation to firm performance, and (3) entrepreneurial orientation driving adaptive KM in startups. Guided by the Antecedent-Mediator-Moderator-Consequence (AMMC) framework, KM emerges as a strategic orchestrator that aligns knowledge sharing, acquisition, creation, and application with sustainable objectives through adaptive resource reconfiguration. KM operates as an antecedent and mediator of sustainability outcomes, suggesting that startups prioritize direct KM implementation over contextual adaptations. These practices simultaneously advance economic, social, and environmental dimensions while catalyzing innovation, strategic governance, and organizational resilience across sustainability-driven, social-purpose, and technology-intensive ventures. However, existing KM frameworks often fail to address the specific challenges faced by startups. This study proposes a research agenda to adapt a KM framework explicitly aligned with the Sustainable Development Goals, integrating digital tools and dynamic capabilities to guide SDG-driven performance metrics within startups’ agile knowledge routines. The study offers practical insights for entrepreneurs, policymakers, and educators, emphasizing the strategic importance of KM in driving sustainable growth and innovation in competitive markets, especially for startups.
In an era where 90 % of startups fail, with a mere 1.5 % attaining substantial success (Startup Genome, 2022), founders navigate the uncertainty exacerbated by fragmented knowledge flows and misaligned strategic positioning. High failure rates primarily arise from stage-strategy dissonance, such as premature scaling without local market validation, and founding team capability gaps. This underscores the significance of knowledge-driven interventions. Ventures that implement structured knowledge codification through cross-domain mentorship and community-based learning hubs exhibit enhanced resilience by accelerating market adaptation (Startup Genome, 2022). Embedding these knowledge management processes enables agile responses to volatility and transforms experiential insights into sustainable growth mechanisms (Cegarra-Navarro et al., 2016; Singh et al., 2023).
Ongoing challenges to resource limitations and market volatility have triggered a paradigm shift, situating knowledge management (KM) and sustainable entrepreneurship (SE) as the twin drivers of startup resilience. This convergence responds to two critical imperatives: the need for KM in resource-constrained environments (Gaimon & Bailey, 2013) and the global demand for businesses to align profitability with the United Nations’ Sustainable Development Goals (SDGs) (Azmat et al., 2023). Crucially, this integration demands concurrent optimization of operational efficiency and impact-oriented innovation, establishing a duality that simultaneously creates strategic imperatives and generates systemic tension for nascent ventures.
The development of SE from traditional triple bottom line (TBL) frameworks (Elkington, 1998) to incorporate circular economies and stakeholder capitalism (Hummels & Argyrou, 2021) underscores the growing need to reconcile economic ambitions with social and environmental responsibilities (Anand et al., 2021; Terán-Yépez et al., 2020). Yet startups face acute tensions: despite 78 % of venture capital firms adopting sustainable investment frameworks (PitchBook, 2022), startups face persistent operational challenges in resolving sustainability promises with investors’ expectations for quick scalability (Horne & Fichter, 2022). Current KM frameworks compound these tensions by assuming resource stability, ignoring the need for frequent strategic pivots, siloing knowledge rather than fostering interdisciplinary synthesis, and relying on static metrics that cannot capture the evolving trade‑offs between growth velocity and sustainability impact (Nonaka et al., 2000; Teece et al., 1997). This critical gap highlights the need for a KM model specifically designed to empower resource‑constrained startups to convert sustainability pressures into engines of innovation and competitive advantage.
The knowledge-based view (Grant, 1996) and dynamic capability theory (Teece et al., 1997) provide complementary perspectives on overcoming this impasse. By conceptualizing KM as a twin strategic practice that converts tacit knowledge into structural capital through the systematic orchestration of knowledge creation, sharing, and application (Nonaka, 1994; Rehman et al., 2022), while enabling dynamic resource reconfiguration in volatile markets (Teece, 2018), startups can transform these knowledge assets into adaptive catalysts for resource constraint mitigation (Baker & Nelson, 2005). This KM-driven approach also enables ventures to leverage intellectual capital (IC), which includes human, structural, and relational assets, to transform sustainability challenges into innovation opportunities (Edvinsson & Malone, 1997). Systematic KM facilitates adaptive capabilities in turbulent markets (Fainshmidt et al., 2016), improves venture survival through risk reduction and efficiency gains (Onyeagam et al., 2020), and empowers startups to mobilize scarce knowledge resources to achieve strategic innovation for sustainability (de Andrade et al., 2023).
Despite the growing recognition of the strategic importance of KM, significant research gaps remain regarding its implementation in startups, especially at the intersection with SE. Most existing studies focus on larger enterprises, leaving an incomplete picture regarding the adaptation of KM to resolve emerging ventures’ unique challenges. Alkathiri et al. (2024) systematically map KM and SE research across 233 publications but overlook the practical demands that startups face when navigating resource volatility and rapid strategic shifts. Similarly, Jiang et al. (2024) chart the interactions among human, structural, and relational capital in green entrepreneurship, yet do not address how emerging ventures can develop the dynamic capabilities and flexible metrics needed to balance scaling speed with sustainability goals. Earlier studies such as Bertello et al. (2022), which examine knowledge‐intensive and SE within entrepreneurial ecosystems, and Paramba et al. (2023), which explore the rise of intellectual capital in sustainable startup performance, emphasize bibliometric trends or ecosystem linkages rather than propose actionable KM frameworks. Furthermore, Al Halbusi et al. (2024) position green KM as a pivotal mechanism mediating between green entrepreneurial orientation (GEO) and sustainability‑oriented innovation, and show that the effect is further strengthened by levels of digital transformation in manufacturing firms. In contrast, many KM practices borrowed from large firms, such as centralized repositories and rigid process standards, hinder the agility and contextual responsiveness that startups require (Koshelieva et al., 2023; Sima et al., 2024). By contrast, startups thrive on lightweight, socialized KM practices, such as real‑time knowledge sharing in cross‑functional teams (de Andrade et al., 2023) and context‑driven knowledge acquisition via customer involvement and absorptive‑capacity routines (Pham & Nguyen, 2024), which collectively enable rapid learning, close customer feedback loops, and iterative innovation. This collective oversight highlights the need for a dynamic, startup-focused KM model that integrates adaptive resource management, cross-functional knowledge flows, and metrics capable of reflecting both growth velocity and sustainability performance.
In an endeavor to bridge these gaps, this study synthesizes existing research and identifies key elements of KM that are considered crucial in cultivating SE in startups, providing a holistic perspective and deeper insights into the relationships between KM, SE, and the unique focus of startups. In addition, it proposes a research agenda for future research to inform further investigations of the nexus between KM and SE in startups, toward an in-depth knowledge base for this field, and to imply possible practical implications. Using a systematic literature review (SLR) methodology, this study addresses the following research questions (RQs):
RQ1: How has the research on the intersection of KM and SE evolved, particularly in startups?
RQ2: How are common KM elements deemed to contribute to shaping SE, particularly in startups?
RQ3: What are the key sustainability outcomes that KM may drive in entrepreneurial ventures?
RQ4: What are the principal entrepreneurial types in which KM is leveraged to achieve sustainability?
RQ5: What should be included in a research agenda for KM in the context of SE for startups?
The rest of this paper is organized as follows. Section 2 outlines the research methodology used for SLR. Section 3 presents both descriptive and content analyses of the identified articles, highlighting key quantitative findings and a qualitative thematic exploration of the role of KM in shaping SE, sustainability outcomes, and various types of entrepreneurship. Section 4 proposes the research agenda derived from the central findings of this study. Finally, Section 5 concludes the paper with the research findings, key implications for future research and practice, and discussion of the limitations of the study.
MethodologyThis study employed SLR to investigate KM within the context of SE, particularly focusing on startups. The SLR approach systematically synthesized existing research, offering a comprehensive overview of the field and identifying key trends, research gaps, and emerging themes (Fisch & Block, 2018; Greenhalgh, 1997; Kassaneh et al., 2021). This method ensured a structured and replicable approach to addressing the study’s RQs by following a clear and transparent methodological protocol (Linnenluecke et al., 2020).
The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Page et al., 2021), ensuring transparency and rigor throughout each phase of the review. PRISMA was chosen for its well-established framework for conducting systematic reviews, which improved the clarity and reproducibility of the process. Furthermore, this study built upon the systematic review framework proposed by Kitchenham (2004), with the aim of providing a comprehensive synthesis of current knowledge and establishing a foundation for future research. Articles were selected based on predefined inclusion and exclusion criteria, and key findings were extracted to identify research trends, core KM practices, the impact of KM on SE in startups, and potential directions for future research.
Data collectionTo comprehensively identify publications exploring the intersection of KM and SE, particularly in startup contexts, Scopus and Web of Science (WoS) were selected as primary databases. These databases were chosen for their comprehensive coverage of business management, entrepreneurship, sustainability, and knowledge management literature, ensuring broad disciplinary representation essential for this interdisciplinary review. Following research group discussions and expert consultations with two specialists in knowledge management and sustainable entrepreneurship, a systematic search strategy was developed comprising three conceptual blocks using Boolean operators: Block 1 (Entrepreneurship/Startups) included ("entrepreneur*" OR "startup*" OR "start-up*" OR "young venture*" OR "new venture*"); Block 2 (Sustainability) comprised ("sustainab*" OR "triple bottom line"); and Block 3 (Knowledge Management) encompassed ("knowledge management" OR "knowledge process*" OR "knowledge creation" OR "knowledge acquisition" OR "knowledge storage" OR "knowledge sharing" OR "knowledge retrieval" OR "knowledge transfer" OR "knowledge application" OR "knowledge protection" OR "intellectual capital"). The final search combined all blocks using Boolean AND operators: (Block 1) AND (Block 2) AND (Block 3), with complete search strings for both databases provided in Online Appendix A to ensure transparency and reproducibility. The KM search terms were guided by the insights of Heisig (2009), Gaviria-Marin et al. (2018) and Kassaneh et al. (2021), with "intellectual capital" included to acknowledge its importance in organizational performance and sustainable competitive advantage (Bhatti & Zaheer, 2014; Srikalimah et al., 2020).
The literature search covered all articles published from database inception to December 31, 2024, with searches executed on February 13, 2025, capturing the complete evolution of research at the KM-SE intersection. In line with database indexing practices, early-access records available at the time of the search were included, where applicable. Following PRISMA 2020 guidelines (see Fig. 1), the initial search identified 1071 records from Scopus and Web of Science. After automatic filtering at the database level for non-journal and non-English records (n = 324) and removal of 217 duplicates, 530 records remained for title and abstract screening. Inclusion criteria comprised the following: (i) peer-reviewed journal articles and reviews; (ii) published in English; and (iii) substantively addressing the intersection of KM and SE, particularly in startup contexts. Two researchers independently screened these records, excluding 360 irrelevant articles that did not align with the research objectives. The remaining 170 records were sought for full-text retrieval; eight could not be accessed, leaving 162 full-text articles assessed for eligibility. Of these, 88 articles were excluded (24 outside the study’s scope and 64 inadequately addressed the KM-SE intersection). The final sample comprised 76 studies, including two additional papers identified through forward and backward citation tracking beyond the original database searches. A complete list of the included studies, with publication details, study context, methods, and AMMC role assignments, is provided in Online Appendix B to enhance transparency and replicability. All disagreements during screening and coding were resolved through discussion and consensus.
All included articles underwent methodological quality assessment using adapted Critical Appraisal Skills Programme (CASP) checklists for systematic reviews and qualitative studies (Critical Appraisal Skills Programme, 2024), supplemented by Joanna Briggs Institute (JBI) critical appraisal tools where appropriate (Joanna Briggs Institute, 2021). Articles were rated as high, moderate, or low quality based on study design appropriateness, methodological rigor, data analysis quality, and clarity of findings, with disagreements resolved through reviewer discussion. This corpus constitutes the core literature directly addressing our research questions, and provides a foundation for subsequent systematic analysis. In total, 66 studies (86.8 %) were rated as high quality and 10 (13.2 %) as moderate quality, with no studies classified as low quality. A summary table of quality appraisal outcomes is provided in Online Appendix C. A sensitivity check indicated that excluding the 10 moderate-quality studies did not materially alter any AMMC-role patterns, sustainability outcome categories, or the taxonomy of KM practices across entrepreneurship types, confirming the robustness of the synthesis.
Data analysisThe analytical framework adopted a mixed-methods approach (Creswell & Clark, 2017) to systematically address the RQs by combining descriptive and content analyses. To structure and guide qualitative content analysis, this study adopted the Antecedent-Mediator-Moderator-Consequence (AMMC) framework (Baron & Kenny, 1986; Hayes, 2018), which is widely used in management research to analyze causal mechanisms and contextual dependencies. The AMMC framework was selected for its ability to capture the multifaceted roles of KM in driving, shaping, and sustaining SE, especially within dynamic and resource-constrained environments like startups.
To ensure systematic and consistent coding across the dataset, a comprehensive reliability protocol was implemented based on established qualitative research practices (Miles et al., 2014). Two researchers with complementary expertise in KM and SE collaboratively developed a detailed coding framework with explicit definitions and exemplars for each thematic category within the AMMC structure. A pilot coding exercise on 15 articles (20 % of the dataset) tested the framework’s robustness, with both researchers independently coding the same articles followed by systematic comparison and discussion of discrepancies. Cohen’s κ was computed for the AMMC-role coding scheme, yielding an average κ of 0.91 (range: 0.66–1.00), indicating substantial to almost perfect agreement (Landis & Koch, 1977). All disagreements were documented and resolved through collaborative discussion, with the coding framework iteratively refined until substantial consensus was achieved. The remaining articles were then systematically divided between the trained coders, with regular cross-validation meetings every 10–12 articles to maintain alignment and address emerging interpretive challenges, ensuring consistent coding standards throughout the analysis.
The analysis proceeded through two phases:
- 1.
Descriptive analysis: This stage provided a quantitative overview of the 76 selected articles, examining publication trends, citation patterns, geographic distribution, and research methods. This overview established a foundational understanding of the development and focus areas of KM and SE research, particularly in startup contexts.
- 2.
Content analysis: Following coding consistency establishment, thematic analysis was conducted using the AMMC framework to identify KM’s roles as antecedents, mediators, moderators, and consequences in SE. This approach synthesized sustainability outcomes such as triple bottom line alignment, green innovation, and social value creation, emphasizing dynamics unique to startups under resource constraints and rapid growth.
The analysis of 76 selected articles reveals clear trends in KM and SE research over the past two decades. The most notable development is a sharp increase in scholarly interest during the last six years (see Fig. 2). Research activity began to rise significantly from 2019 onward, culminating in a peak in 2024 with 22 publications, accounting for nearly 30 % of the total sample. This surge likely reflects a heightened alignment between KM research and global sustainability agendas, such as the SDGs, and an increasing emphasis on data-driven decision-making within startup ecosystems. However, a sharp dip occurred in 2021, with only two papers published. While this decline coincides with the peak of the COVID-19 pandemic, which disrupted academic collaboration and may have slowed peer-review processes (Behera et al., 2021), it also underscores the field’s vulnerability to external shocks and the need for more resilient research networks. In contrast, the period from 2004 to 2018 saw limited activity, with typically zero to two articles published annually. Despite a modest uptick following SDG adoption in 2015, the gradual growth during this phase suggests that early KM-SE research may have lacked interdisciplinary integration, indicating an opportunity for more cross-sectoral studies (Reinecke et al., 2024).
Citation analysis highlights the growing influence of recent publications in this field. Although earlier studies were limited in number, they gradually accumulated citations over time, demonstrating their lasting relevance. Notably, 2018 marked a peak in citation activity despite a modest number of publications, indicating that key papers from this period had a substantial impact. A prime example is the study by Jiang et al. (2018), which received 311 citations and stands as the most cited work in the dataset. Beyond its empirical findings, this seminal paper established methodological benchmarks that shaped subsequent research trajectories, particularly through the use of dynamic capability theory in sustainable contexts. Moreover, the observed citation lag reflects the enduring influence of earlier contributions. However, one must be cautious of the “Matthew effect,” where prominent authors and high-impact journals disproportionately attract citations, potentially skewing perceived field dynamics (Merton, 1968; Ngatuvai et al., 2021). In recent years, particularly in 2022 and 2023, both publication and citation counts surged, signaling intensified academic interest in the role of KM in promoting sustainable growth and innovation, especially within startup ecosystems. A cumulative citation count of 1651 further reinforces the growing recognition and impact of the field.
Overall, these publication and citation trends underscore the need for a comprehensive, integrative literature review to consolidate fragmented findings, address methodological inconsistencies, and highlight emerging avenues for future investigation, particularly research on crisis resilience and enhanced interdisciplinary collaboration. While rising publication volume and citation counts reflect genuine academic interest in KM’s role within SE, particularly among startups, these metrics are influenced by factors beyond pure scholarly merit, including author prominence, journal visibility, and topical relevance to current societal concerns (Szomszor, 2022). Citation patterns often exhibit temporal lags and are subject to the Matthew effect, where established researchers and prestigious outlets disproportionately accumulate citations regardless of content quality. Despite these interpretative caveats, the overall trajectory confirms the growing strategic importance of KM in enabling SE, particularly for resource-constrained startups navigating volatile markets, and signals significant opportunities for future theoretical and empirical contributions to this emerging field.
Geographic distributionThe geographic distribution of publications on KM and SE reveals both diversity and regional concentration. Contributions are based on the authors’ institutional affiliations, with coauthored articles credited to each participating region. As illustrated in Fig. 3, the global distribution spans continents, and Fig. 4 delineates the top 18 of the 36 contributing regions (each with at least three publications). China leads with 20 publications, showing a slight predominance of international collaborations over domestic efforts, signaling both robust internal research capacity and strong global partnerships. Italy and India follow closely, each contributing 7 publications with a near-balanced mix of domestic and international collaborations. Indonesia also contributes 7 publications, predominantly through single-country efforts, reflecting a focus on domestic research with limited international engagement.
Other key contributors include the USA and Saudi Arabia (6 publications each), as well as Pakistan (5 publications), all showing a strong emphasis on international collaboration. Regions such as Malaysia and Iran exhibit a near-balanced approach, with a slight inclination toward international collaborations. Similarly, Spain and Poland demonstrate a balanced mix of domestic and international research, whereas France leans slightly toward domestic efforts. The UK stands out for its strong emphasis on multicountry partnerships, likely driven by its cultural diversity and strategic focus on expanding its global academic influence. Smaller research hubs, such as Qatar and Portugal, rely predominantly on international collaborations, integrating into broader global research networks. In contrast, Thailand’s research output is exclusively domestic, highlighting a distinct focus on localized research efforts.
This geographic concentration pattern reflects broader structural inequalities in global academic systems (Amarante et al., 2022). The predominance of international collaborations among smaller research hubs suggests a dependency on established research networks, while the exclusively domestic focus of some countries may indicate either strong local research priorities or limited access to international collaboration opportunities. Furthermore, the near-balanced collaboration patterns observed in several European countries likely reflect the influence of European Union research frameworks, which actively promote cross-border partnerships through funding mechanisms.
Overall, this geographic analysis underscores the global interest in KM and SE, particularly regarding startups. With 13 developed and 23 developing regions contributing, the field indicates higher representation from developing regions. However, the observed distribution may be influenced by structural factors such as regional research funding disparities and the English-language bias of major databases (Scopus, WoS), potentially underrepresenting scholarship published in other languages or local outlets (Arenas-Castro et al., 2024). Moreover, the dominance of certain countries (e.g., China, USA) suggests a “big-country effect,” wherein large research ecosystems can overshadow smaller or resource-constrained regions, limiting the generalizability of findings across diverse cultural and institutional contexts (Kowal et al., 2022). To address these gaps, future research should proactively engage underrepresented regions beyond the current Asia-Europe-North America focus, particularly including African and Latin American contexts, to enrich our understanding of how KM practices translate into sustainable entrepreneurial outcomes in varied cultural and institutional settings. Such efforts will help mitigate geographic bias and foster more comprehensive, globally relevant insights.
Methodological orientationsThe analysis of the methodologies employed in the selected articles reveals a strong preference for quantitative approaches, with qualitative, conceptual, and mixed methods being less common (see Fig. 5). Quantitative methods account for 70 % of all publications (n = 53), with surveys being the most prevalent methodology, representing 65 % of the total. Among the 44 survey-based studies that specified data collection procedures, 18 primarily utilized mixed methods, typically beginning with email or phone consent, followed by data collection through online forms or face-to-face interactions. Online forms were used in 13 studies, mailed questionnaires in 7, face-to-face methods in 5, and only one study relied entirely on postal mail. The frequent use of mixed techniques in survey-based research reflects the need to capture both breadth and depth in understanding KM in SE contexts, especially in startups, where complex issues require more nuanced approaches. Other quantitative methods include the use of secondary data in three studies and mathematical modeling in one study. However, the heavy reliance on cross-sectional surveys may introduce self-reporting and common method biases, thereby limiting the ability to infer causality or capture dynamic KM processes over time (Kock et al., 2021). The scarcity of longitudinal or experimental designs constrains our understanding of how KM practices evolve throughout the lifecycle of sustainable ventures. Consequently, incorporating such designs could strengthen our understanding of how KM practices evolve throughout startup lifecycles, as demonstrated in recent startup resilience studies (Utomo et al., 2025).
Qualitative methodologies are less common, comprising 11 % of the publications, with case studies being the primary approach, including two single-case and four multiple-case studies. While case studies offer rich contextual insights, the limited number of studies and their narrow contextual scope reduce comparative learning across diverse startup settings. Conceptual or theoretical papers represent 5 % of the total, while mixed-methods research accounts for another 9 %, demonstrating the potential of integrating diverse data sources to enhance research insights. The underrepresentation of mixed-methods research is particularly concerning given its potential to provide richer insights into complex phenomena by combining the strengths of both quantitative and qualitative approaches (Molina-Azorin, 2016). The scarcity of conceptual papers indicates a gap in theory development, underscoring the need for stronger theoretical frameworks to guide empirical inquiry in KM and SE. Furthermore, four reviews of the literature contribute to the field by synthesizing previous studies and identifying existing gaps in related areas.
This methodological distribution highlights a prevailing emphasis on empirical, data-driven research but also exposes critical limitations. The relative scarcity of qualitative and mixed-methods studies suggests an opportunity for future research to adopt more integrative and longitudinal approaches, such as action research and multiwave panel designs, to address the complex, context-specific challenges of KM in SE, particularly among startups. Moreover, incorporating experimental or quasi-experimental designs could strengthen causal inferences regarding KM interventions on SE outcomes.
Content analysisA detailed content analysis of the 76 selected articles provides insight into how KM functions in interrelated SE domains. This section explores the intersections among KM, sustainability, and entrepreneurship, with an emphasis on startups. Three key themes emerge as foundational: KM’s role in shaping SE, sustainability outcomes driven by KM practices, and KM practices across entrepreneurship types to achieve sustainability. These themes are interrelated and underscore key areas where early-stage enterprises can leverage knowledge to secure long-term success.
Thematic cluster analysisTo examine conceptual linkages within the KM-SE research landscape, a keyword co-occurrence analysis was conducted using VOSviewer (version 1.6.20). All author keywords with a minimum occurrence threshold of five were retained, ensuring adequate frequency while limiting noise. A full counting method was applied, and network normalization was performed using the association strength technique. Clusters were identified using the VOS clustering algorithm with the default resolution parameter. The resulting network, presented in Fig. 6, consisted of 13 high-frequency keywords grouped into three thematic clusters. To enhance transparency and replicability, Online Appendix D provides the keywords, their occurrence frequencies, and average publication year. The analysis reveals that the interaction among KM, Sustainability, and Entrepreneurship emerges as a dominant thematic structure in the literature. These themes are organized into three coherent and interrelated conceptual groups, each representing a distinct but connected line of inquiry that collectively shapes the KM-SE research domain.
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KM cluster: KM is at the core of innovation and organizational competitiveness, with strong links to IC, innovation, entrepreneurial orientation, and sustainable competitive advantage. This highlights the crucial role of KM, in general, by facilitating knowledge-based interactions, and especially in combination with effective use of IC and entrepreneurial orientation, fostering a firm’s ability to innovate and maintain a sustainable competitive advantage (Alshahrani et al., 2024).
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Sustainability cluster: Sustainability has become an increasingly important focus, closely linked to topics such as sustainable development, GEO, and firm performance, reflecting the focus on balancing economic growth with environmental and social responsibilities. Organizations, especially startups, are adopting green practices to enhance performance and maintain a competitive edge (Almansour, 2024; Wang et al., 2023).
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Entrepreneurship cluster: Entrepreneurship, particularly in startups, is strongly connected to concepts such as innovation, business and economics, KM, GEO, and sustainable development. These connections emphasize the crucial role of entrepreneurship and entrepreneurial orientation in fostering innovation and adaptability, particularly in startups, where business and economic strategies, combined with KM, are essential for achieving sustainable development goals (Alfiero et al., 2025).
Additional nodes further illustrate how themes related to KM drive innovation, competitive advantage, and agility in dynamic markets. Entrepreneurial Orientation reflects a firm’s strategic commitment to environmental sustainability in terms of green innovation, proactiveness, and risk-taking (Shehzad et al., 2023) and is particularly crucial for startups to reduce costs, build resilience, and garner sustainability-based legitimacy in the marketplace (Al Halbusi et al., 2024; Yan & Hu, 2024). Sustainable Entrepreneurship also serves as a key node, bridging sustainability and entrepreneurship by matching knowledge domains with sustainable practices to foster innovation. SMEs have also emerged as a notable node in the literature, with researchers increasingly focusing on their role, emphasizing their relative sparsity and the urgent need for more concentrated studies on startups. By integrating these concepts, especially for emerging and small enterprises, organizations can effectively exploit knowledge, adopt sustainability, and maximize entrepreneurial performance. These interconnected themes offer a roadmap for understanding how KM, entrepreneurial orientation, and sustainability converge in the business landscape today, while emphasizing the need to explore how dynamic capabilities can be developed and utilized to address the unique challenges faced by startups.
KM and its role in shaping SEFollowing the methodological introduction of the AMMC framework, this section presents empirical findings on how KM assumes various strategic roles in shaping SE. The synthesis of 68 empirical studies provides strong empirical support for theoretical predictions from the knowledge-based view (Grant, 1996), which posits that knowledge-based resources serve as primary antecedents for competitive advantage. This evidence demonstrates how these foundational theoretical principles apply to SE contexts. Additionally, the findings align with resource orchestration theory (Sirmon et al., 2011), revealing that KM functions as a strategic orchestrator that, through adaptive resource reconfiguration, aligns knowledge with entrepreneurial objectives to embed sustainability within resource-constrained ventures (Loon, 2019). Drawing on a knowledge-based view (Grant, 1996), dynamic capability theory (Teece et al., 1997), and resource orchestration theory (Sirmon et al., 2011), this study conceptualizes KM as: “A strategic orchestration of knowledge practices, including but not limited to acquisition, sharing, integration, and application, that reconfigure organizational assets, mitigate resource constraints, and institutionalize sustainability logics within entrepreneurial ventures.” This formulation is not proposed as a new theory, but rather as an integrative extension of established KM perspectives, particularly the process-oriented view articulated by Dalkir (2013) and the conceptualizations of knowledge practices proposed by Hislop et al. (2018), adapted to the resource-constrained and sustainability-driven realities of entrepreneurial ventures. This extended framing consolidates these foundational approaches while tailoring their application to the SE context.
The empirical analysis strongly supports theoretical expectations regarding KM’s multifaceted roles within the AMMC framework, with the operational definitions and theoretical mechanisms detailed in Table 1. As Table 2 demonstrates, both general KM constructs and specific KM practices exhibit polyfunctional roles consistent with predictions from dynamic capability theory (Teece et al., 1997), though with distinct distributional characteristics. These findings reflect the contextual plasticity of knowledge constructs in bridging entrepreneurial intent and sustainability outcomes, integrating both general topics (KM and IC) and specific KM practices. Notably, while general KM demonstrates versatility across all four AMMC roles, specific KM practices show stronger clustering in antecedent and mediating process functions, aligning with the resource orchestration theory of Sirmon et al. (2011), which positions KM as a primary mechanism for resource structuring and deployment. The analysis reveals fewer instances of moderating or consequential roles, particularly among specific KM practices, consistent with the theoretical expectations from the knowledge-based view, which emphasizes the foundational rather than conditional nature of KM. This configuration indicates areas for further research on the long-term and context-dependent impacts of KM in dynamic, resource-constrained startup environments. The specific empirical relationships documented in Table 2, including direct effects (β), indirect effects (θ), and moderation effects (γ) across multiple studies, provide robust empirical grounding for these theoretical assertions.
Antecedent–Mediator–Moderator–Consequence (AMMC) roles of knowledge management (KM) in sustainable entrepreneurship (SE).
| AMMC role | Operational definition | Theoretical mechanism |
|---|---|---|
| Antecedent (A) | KM initiates SE processes through proactive practices | Exogenous enablers structuring sustainability trajectories (Sirmon et al., 2011) |
| Mediator (M) | KM bridges inputs to SE outcomes | Transmission channels converting resources into sustainable actions (Hayes, 2018) |
| Moderator (M) | KM governs SE efficacy under boundary conditions | Contingent factors altering pathway strength/direction (Baron & Kenny, 1986) |
| Consequence (C) | KM emerges as a byproduct of SE maturity | Endogenous capabilities institutionalizing sustainability logics (DiMaggio & Powell, 1983) |
AMMC roles of KM constructs in SE.
| No. | Construct | Frequency | Role | Mechanism | Example study |
|---|---|---|---|---|---|
| 1 | Knowledge management (KM) | 28 | Antecedent | KM processes → Green entrepreneurial behavior (β=0.321**)KM processes → Transformative innovation (β=0.32***) → Green entrepreneurial behavior (θ=0.129***) | Wang et al. (2024) |
| Mediator | Entrepreneurial leadership → KM practices → Sustainable growth (θ=0.105***)Management innovation → KM practices → Sustainable growth (θ=0.095***) | Shaik et al. (2024) | |||
| Moderator | Green social behaviors → Business/Social/Technology innovation × KM→ Entrepreneurial success↑ Moderated by KM (γ=−0.269***)/ (Social: γ=0.108*)/ (Technological: ns) | Tu (2024) | |||
| Consequence | Entrepreneurial orientation → KM capacity (β=0.117*)Absorptive capacity → KM capacity (β=0.566***)Entrepreneurial orientation → Absorptive capacity → KM capacity (θ=0.227***) | Jallad and Karadas (2024) | |||
| 2 | Intellectual capital (IC) | 14 | Antecedent | IC → SMEs’ sustainability (β=0.46***)IC → Competitive advantage (β=0.26***) → SMEs’ sustainability (θ*) | Srikalimah et al. (2020) |
| Mediator | GEO → Green IC (β=0.445***) → Sustainable performance (β=0.567***) (θ=0.144***)GEO → Green IC → Sustainable business model innovation → Sustainable performance (θ=0.024*) | Zhang et al. (2024) | |||
| 3 | Knowledge sharing (KS) | 17 | Antecedent | KS behavior → Dynamic capability (β=0.201***)KS behavior → SE performance (β=0.153*)KS behavior → Dynamic capability → SE performance (θ=0.029*) | Tshiaba et al. (2021) |
| Mediator | Social innovation → KS culture → Entrepreneurial success (θ=0.088**)Environmental innovation → KS culture → Entrepreneurial success (θ=0.151**) | Lin et al. (2024) | |||
| Moderator | Entrepreneurship → KI × KS atmosphere → Sustainable innovation capability↑ Moderated by KS atmosphere (γ=0.217**) | Sun et al. (2022) | |||
| 4 | Knowledge acquisition (KA) | 13 | Antecedent | KA→ Corporate sustainable performance (β=0.103*)KA→ Frugal innovation (ns) | Kun (2022) |
| Mediator | GEO → Green KA→ Environment/ Economic/ Social performance (θ=0.218***)/ (θ=0.221***)/ (θ=0.219***) | Baquero (2024) | |||
| 5 | Knowledge creation (KC) | 11 | Antecedent | KC → Successful green ventures (β=0.015, ns)KC → Innovative practices → Successful green ventures (θ=0.049*)KC → KS → Successful green ventures (θ=0.094**) | He et al. (2024) |
| Mediator | Digital capabilities → Green KC (β=0.598***) → Sustainable development of new venture (θ=0.280***) | Zhuge et al. (2023) | |||
| 6 | Knowledge application (KAP) | 8 | Antecedent | KAP → Corporate sustainable performance (β=0.223***)KAP → Frugal innovation (β=0.364***)KAP → Frugal innovation→ Corporate sustainable performance (θ=0.086**) | Kun (2022) |
| Mediator | Green KA→ Green KAP (β=0.282***) → Green innovation (β=0.599***)Generative AI adoption → Green KAP (β=0.258***) → Green innovation (β=0.599***) → Green entrepreneurship success (β=0.274***)Generative AI adoption → Green KAP (β=0.258***) → Green entrepreneurship success (β=0.130*) | Wang and Zhang (2024) | |||
| 7 | Knowledge integration (KI) | 5 | Mediator | GEO → KI (β=0.250***) → Green innovation performance (β=0.168***) (θ=0.042**) | Idrees et al. (2025) |
| Moderator | GEO × KI and transfer → Environmental/Financial performance↑ Moderated by KI and transfer (γ=0.122*)/ (γ=0.135*) | Jiang et al. (2018) | |||
| 8 | Knowledge transfer (KT) | 5 | Moderator | GEO × KT and integration → Environmental/Financial performance↑ Moderated by KT and integration (γ=0.394*)/ (γ=0.417*) | Asad et al. (2023) |
Note: Some articles can be included in more than one area. β= Direct effect (Antecedent/Consequence); θ = Indirect effect (Mediator); γ = Moderation effect (Moderator). Significance levels: ***p < 0.001, **p < 0.01, *p < 0.05, ns: not significant.
As Table 2 shows, General KM emerges as the most prevalent construct (n = 28), with its multifunctional roles in SE operating as an antecedent, mediator, moderator, and consequence that systematically align entrepreneurial actions with sustainability imperatives, as depicted in Fig. 6. As an antecedent, extensive empirical evidence demonstrates that KM establishes foundational conditions for SE through multiple mechanisms: structured KM processes precede strategic decisions and foster resource optimization, with Cardoni et al. (2020) showing that SMEs with robust KM frameworks achieve superior economic sustainability through systematic knowledge capture; KM processes serve as drivers of green entrepreneurial behavior by providing cognitive frameworks for environmental opportunity recognition (Wang et al., 2024); and KM orientation cultivates organizational mindsets that spur sustainable practices, as de Guimarães et al. (2018) find that firms with strong KM orientations gain competitive advantages through earlier adoption of cleaner production initiatives. Building on these foundational roles, KM’s mediating function transforms entrepreneurial inputs into sustainable outcomes through adaptive learning mechanisms, with substantial research showing that KM serves as a critical pathway between entrepreneurial competencies and sustainable performance (Al Koliby et al., 2024), facilitates organizational integration processes that enhance SE practices (Akinlotu & Cavlan, 2023), and most significantly, nurtures entrepreneurial leadership as a pathway to sustainable growth through enhanced organizational resilience and strategic agility (Shaik et al., 2024). KM also functions as a moderator, dynamically shaping innovation-performance relationships across contexts, as Tu (2024) demonstrates that KM significantly moderates the relationship between green social behavior and entrepreneurial success, strengthening positive effects for social and business innovation while having limited impact on technological innovation processes. Finally, as a consequence, KM evolves into institutionalized capabilities that sustain long-term viability, with Jallad and Karadas (2024) revealing that entrepreneurial orientation generates KM capacity as a performance outcome that becomes a self-reinforcing capability supporting ongoing innovation and competitive positioning. This multifunctional nature positions KM as both the foundation and the culmination of sustainable entrepreneurial processes. It establishes initial conditions, facilitates transformation, moderates outcomes, and ultimately crystallizes into enduring organizational assets that enable sustained competitive advantage.
Similarly, IC emerges in 14 articles. IC refers to the collective intangible assets of an organization, including human capital (skills and knowledge of employees), structural capital (organizational processes and systems), and relational capital (networks and relationships with stakeholders). This interpretation is fully consistent with findings from the IC and sustainability literature, where recent systematic reviews (Alvino et al., 2021) highlight IC as a core knowledge-based asset that underpins sustainable value creation. These assets are developed and utilized to strengthen competitive advantage, stimulate innovation, and promote sustainability through the creation of long-term value (Alvino et al., 2021; Bourdieu, 1986; Ngah et al., 2015). As an antecedent, IC establishes foundational conditions for SE, with Srikalimah et al. (2020) demonstrating that intellectual capital positively affects both competitive advantage and SMEs’ sustainability, establishing IC as a robust predictor of sustainable performance in resource-constrained environments. This is further supported by Rustiarini et al. (2022), who show that IC positively drives green innovation, sustainability performance, and financial performance in Indonesian SMEs. Building on these foundational roles, IC’s mediating function transforms entrepreneurial orientations into sustainable outcomes through knowledge-based resource orchestration, with Zhang et al. (2024) demonstrating that green IC serves as a crucial mediator between GEO and sustainable performance in Chinese agribusinesses, facilitating the transformation of entrepreneurial vision into tangible sustainability outcomes through sustainable business model innovation. This dual role places IC as both conduit and catalyst, complementing dynamic capabilities in KM to collectively optimize resource orchestration and institutionalize circular value creation.
Zooming in on specific KM practices, knowledge sharing is revealed to be most adaptable, playing all three roles as an antecedent, mediator, and moderator. Its role in enhancing dynamic capabilities (Tshiaba et al., 2021) and mediating the impact of social innovation on entrepreneurial success (Lin et al., 2024) underscores its central position in the KM-SE nexus (Sun et al., 2022). Similarly, knowledge acquisition modestly enhances corporate sustainability, whereas frugal innovation and sustainability performance are driven directly by knowledge application (Kun, 2022). These practices, while differing from one another, play synergistic roles: knowledge creation indirectly supports green ventures by means of innovative practices and knowledge sharing (He et al., 2024), while knowledge integration and knowledge transfer amplify the impact of entrepreneurial orientation on environmental and financial performance, institutionalizing sustainability collectively in recursive knowledge flows (Asad et al., 2023; Jiang et al., 2018). Furthermore, practices such as knowledge dissemination, knowledge exploitation, and knowledge protection, although not included in Table 2 owing to their lower frequency, still contribute to long-term sustainability and competitiveness. Together, these practices enable startups and other enterprises to align their strategies with sustainability, while maintaining a competitive edge.
In conclusion, KM serves as the engine of SE, bridging entrepreneurial intent with sustainability outcomes through its multi-theoretical and polyfunctional nature. By orchestrating knowledge practices, enterprises, especially startups, can foster dynamic capabilities, mitigate resource constraints, and institutionalize sustainability logic within ventures. Future research should prioritize the interplay between KM’s evolving roles and the unique challenges of entrepreneurial ecosystems to ensure that theoretical rigor aligns with practical relevance in advancing sustainable venturing.
Sustainability outcomes driven by KM in entrepreneurial venturesThis section explores sustainability outcomes in entrepreneurial ventures driven by effective KM practices, building on the AMMC framework. The analysis of 72 studies (excluding 4 reviews) reveals four dominant sustainability outcomes catalyzed by KM practices in these enterprises, as shown in Table 3. Percentages indicate the share of AMMC role-specific coded occurrences assigned to each outcome category. Collectively, these outcomes reflect KM’s capacity to bridge strategic intent with measurable socio-ecological performance while maintaining economic viability. To contextualize these percentages, the AMMC coding process yielded 64 total role assignments across 68 empirical studies (30 antecedent, 25 mediator, 4 moderator, and 5 consequence). Multi-role coding was permitted, and several studies contributed to more than one role, while a small number of studies did not receive any AMMC-role assignment. Consequently, the average density of coding is approximately one role per study. This distribution indicates that the observed concentration of Antecedent and Mediator roles, and the relative scarcity of Moderator and Consequence roles, reflect the empirical evidence rather than a coding artefact.
1. Core sustainability dimensions (n = 63)
Taxonomy of sustainability outcomes and associated KM roles.
Note: Percentages in each column indicate the proportion of all role-specific codes falling into each subcategory (e.g., 28 antecedent-role occurrences out of 30 total). Because one article may contribute multiple codes, counts reflect coded occurrences rather than article frequencies. Row-level percentages for each sustainability-outcome family are reported separately in Online Appendix E.
KM systematically enables ventures to achieve integrated sustainability (n = 37), where economic, social, and environmental objectives are synergistically aligned. For instance, SMEs leveraging KM-driven dynamic capabilities often institutionalize circular resource flows, reducing waste (environmental), lowering costs (economic), and fostering community partnerships (social) through shared stakeholder value (Le et al., 2024; Qader et al., 2022). However, partial sustainability remains common: 41 % of the studies exhibit an isolated focus on single dimensions, such as economic sustainability (n = 16) through knowledge exploitation and performance measurement systems in SMEs (Cardoni et al., 2020), social sustainability (n = 8) through co-creational customer capital and green intellectual capital (Verma et al., 2023), and environmental sustainability (n = 2) through green core competence and innovation climate in entrepreneurial SMEs (Khan et al., 2023). The empirical evidence strongly supports this assertion: more than 93 % of all antecedent-role assignments (28 out of 30) fall within core sustainability dimensions (e.g., structured knowledge application for TBL alignment), demonstrating that ventures prioritize direct KM implementation over contextual adaptation when pursuing sustainability outcomes. This preference is further evidenced by the limited use of the Moderator KM (only 4 coded instances), indicating that startups tend to favor immediate, direct knowledge deployment rather than contextually adaptive KM strategies. This predominant focus on direct implementation potentially explains the persistence of unbalanced environmental outcomes observed across the sample.
2. Innovation-driven outcomes (n = 28)
KM’s role as an innovation accelerator is most pronounced in green technology development (n = 15), where AI-driven knowledge recombination (e.g., material design prediction) and green knowledge sharing enhance R&D efficiency in cleantech startups (Wang & Zhang, 2024; Zhang et al., 2022). However, despite the alignment of policy-driven innovation (n = 7) with the SDGs (notably SDGs 9 and 17), only a limited fraction of ventures institutionalizes KM systems to align with policy frameworks. Marulanda-Grisales et al. (2024) demonstrate this challenge in Medellín’s social enterprises, where knowledge cocreation practices, such as regular stakeholder design workshops for joint service prototype development, directly support SDG target 9.5 (enhance scientific research and encourage innovation). However, a few ventures have systematized these KM approaches within formal policy-aligned frameworks. This highlights the need for public policy interventions (e.g., legal recognition) to bridge SDG-oriented innovation and actionable knowledge governance. Circular economy solution (n = 3) relies on GEO systematically enabled by digital tools (e.g., IoT for resource tracking) and knowledge sharing mechanisms to operationalize waste-to-resource models (Al Halbusi et al., 2024). Similarly, frugal innovation system (n = 3) integrates external knowledge sharing under resource constraints, driven by sustainability-oriented leadership and entrepreneurial bricolage, to achieve frugal functionality, cost efficiency, and socio-environmental ecosystems (Iqbal et al., 2025). Mediator KM is the common role in this category, accounting for 48 % of all mediator-role assignments (12 out of 25). It typically links practices such as knowledge sharing and knowledge integration to advances in green technologies and circular innovation. Moderator KM, although proportionally high within this outcome family (3 out of 4 moderator-role assignments; 75 %), appears infrequently overall, limiting its broader influence. This indicates that ventures seldom use contextual KM mechanisms to shape innovation pathways, underscoring the need for policy and institutional support to better align innovation efforts with regulatory ecosystems.
3. Strategic governance outcomes (n = 20)
KM drives sustainability outcomes through governance mechanisms that institutionalize multistakeholder collaboration. Institutionalized culture (n = 17) embeds sustainability into organizational norms, as evidenced by Shaik et al. (2024), where entrepreneurial leadership mediates the alignment of KM practices, intellectual property protection, and management innovation with SME sustainable growth, emphasizing absorptive capacity as a critical moderating factor. Stakeholder governance models (n = 3), as analyzed in Lamperti et al. (2023), highlight incubators’ role in transferring explicit and tacit knowledge (e.g., through training, coaching, and networking) to foster sustainability impact awareness and assessment in startups, thereby bridging ecosystem actors. Mediator KM plays the most prominent role within this outcome, representing 28 % of all mediator-role assignments (7 out of 25). These mechanisms often support the internalization of sustainability norms and leadership-driven cultural change. Antecedent KM (6 out of 30; 20 %) and Moderator KM (1 out of 4; 20 %) appear less frequently, indicating a relatively limited use of KM to proactively shape or condition governance processes. This imbalance suggests that ventures seldom leverage KM to create stronger alignment with policies or institutional landscapes, which may restrict the scalability and institutional robustness of their governance practices.
4. Organizational resilience outcomes (n = 11)
KM practices foster resilience through adaptive resilience (n = 6) and digital resilience (n = 5). Adaptive resilience emphasizes non-digital agility, exemplified by Palestinian SMEs in conflict zones leveraging absorptive capacity and entrepreneurial orientation to navigate crises via strategic pivots (e.g., diversifying supply chains) and decentralized decision-making (Jallad & Karadas, 2024). Digital resilience integrates AI and blockchain tools for predictive recovery, as observed in Brazilian startups that use cloud-based knowledge repositories and AI-driven analytics to preempt market shifts and secure intellectual property (de Andrade et al., 2023). Together, these approaches balance reactive agility and proactive technological integration, fortifying ventures against socio-economic and environmental shocks. Consequence KM is relatively prominent in this category, accounting for 40 % of all Consequence-role assignments (2 out of 5). These mechanisms often involve post-crisis knowledge codification and learning processes that support adaptive resilience. Mediator KM appears with the lowest frequency (3 out of 25 Mediator assignments), and Moderator KM is not represented at all, suggesting minimal use of KM to build resilience proactively or adaptively. The limited presence of digital resilience mechanisms and absence of structured feedback loops further indicate gaps in linking tactical agility with more systematic, technology-enabled forms of organizational adaptation.
KM practices for sustainability across entrepreneurship typesThis section analyzes how entrepreneurial types (especially startups and SMEs) leverage KM practices to achieve sustainability, drawing on a systematic review of 55 studies. The findings are synthesized into three archetypes (summarized in Table 4), each demonstrating unique KM-sustainability linkages.
1. Sustainability-driven ventures: institutionalizing green logics
Taxonomy of KM practices for SE.
These ventures embed environmental and social values in economic development through KM that is aligned with their purposes. Circular economy integrators (n = 11) maximize resource flows by integrating KM-based industrial symbiosis networks (Le et al., 2024) with entrepreneurial bricolage, making waste value streams while balancing priorities between markets and society (Iqbal et al., 2025). Transition-driven innovators (n = 11) promote cleaner production systems by utilizing frugal innovation models, where formal knowledge dissemination enhances sustainability performance (Kun, 2022), but structural deficiencies in process innovation restrain scalability (Ahmad et al., 2020). Green tech pioneers (n = 6) formalize sustainability through AI-boosted R&D acceleration, leveraging green structural capital and customer co-production in renewable energy innovations (Almansour, 2024; Khan et al., 2023). Despite these diverse KM approaches across sustainability-driven ventures, most studies fail to explicitly map specific KM practices (e.g., knowledge sharing or collaborative learning) onto concrete SDG targets, revealing a clear gap in systematically linking KM activities to measurable sustainable development outcomes. Together, these subgroups highlight the duality in KM between being both an eco-innovation enabler and social-ecological mediator, enhancing eco-innovation in conjunction with embedding sustainability in organizational practices, stakeholder relations, and long-term value creation.
2. Social-purpose ventures: justice-centered knowledge praxis
These enterprises, primarily startups and SMEs, shift KM toward equity and cultural resilience. Community-embedded enterprises (n = 6) embed indigenous knowledge in sustainable models (e.g., biodiversity-based tourism) (Dias et al., 2023), but face attrition in intergenerational tacit knowledge in artisanal industries (Gupta & Bhattacharya, 2016). Inclusive development architects (n = 5) co-design with marginalized groups to develop solar technologies (Verma et al., 2023) and deconstruct gender barriers through intersectional training (Epezagne Assamala et al., 2022), although imbalances in power pose risks to paternalism. Social value-centered enterprises (n = 3) align profit with social well-being through self-imposed ethical constraints (Siqueira & Honig, 2019), illustrating that creativity and community happiness foster sustainable growth (Usai et al., 2020). These ventures embody a paradigm shift in KM, in which value creation is not only economic but is firmly intertwined with social justice, ecological stewardship, and recovery of cultural legacies.
3. Technology-intensive ventures: orchestration of digital knowledge
This group applies KM to manage tensions between innovation protection versus recombination. Architectural knowledge reconfigurers (n = 6) are beset by an innovation-implementation paradox: entrepreneurial orientation converts intellectual capital into innovation (Alshahrani et al., 2024), but weak systems for measuring performance erect barriers to economic sustainability (Cardoni et al., 2020). Inflexible knowledge protection regimes also inhibit recombination processes (Heenkenda et al., 2022). Digital ecosystem innovators (n = 5) address these limitations through blockchain-based openness (Singh et al., 2020) and cloud-based repositories that balance knowledge accessibility with intellectual property controls (Ardi et al., 2022). Deep tech synthesizers (n = 2) combine the defensive management of intellectual property (Wang & Zhang, 2024) with open innovation absorption, cultivating high-tech entrepreneurship, as seen in NASA/IBM’s strategic knowledge processes (Bhardwaj, 2019). In combination, these ventures demonstrate strategic knowledge configuration reconciling control with openness to achieve dynamic capabilities to drive both technological advancement as well as value creation in complex innovation ecosystems.
Discussion and research agendaFollowing a systematic synthesis of the literature, this study identifies critical gaps in understanding how KM enables SE, particularly within startups. By integrating the findings of descriptive and content analysis, we propose a targeted research agenda to advance both theoretical understanding and practical insights.
Customizing KM frameworks for startupsExisting studies have provided insights into how KM enhances organizational performance, particularly through processes such as knowledge acquisition, sharing, and application (Akinlotu & Cavlan, 2023; Al Koliby et al., 2024; Kun, 2022). However, the analyses indicate that traditional KM frameworks, which are primarily designed for established enterprises, may not adequately address the resource constraints and dynamic operating environments characteristic of startups (Oliva & Kotabe, 2019). This finding contrasts with Almansour (2024), who demonstrates that digital technologies, particularly artificial intelligence, can effectively bridge this gap by enabling startups to leverage green intellectual capital and co-creational capital for innovation despite resource constraints. While Almansour (2024) shows promising outcomes for AI-enhanced KM in startups, it primarily focuses on technology-intensive ventures, whereas our analysis reveals that customization needs extend across diverse startup types, including sustainability-driven and social-purpose ventures. Our results further suggest that startups require more adaptive and context-sensitive KM models that not only facilitate integrated sustainability outcomes but also overcome the limitations of conventional approaches. Similarly, Shaik et al. (2024) provide empirical evidence that KM practices, when combined with intellectual property protection and management innovation, significantly nurture entrepreneurial leadership and drive sustainable growth in SMEs, but their model presumes organizational stability rarely found in early-stage ventures. These contrasts underscore the need for adaptive, context-sensitive KM models rather than one-size-fits-all solutions, tailored to different startup types (e.g., low-tech, social-purpose, sustainability-driven). Future research should develop modular KM architectures that startups can assemble and adapt, such as peer learning networks, just-in-time micro-learning, and minimal viable documentation, to fit their unique resource profiles and sustainability objectives.
Role of KM in different enterprise typesAmong the studies analyzed, distinct patterns emerge regarding the role of KM across various types of enterprises. Of the 72 articles (excluding literature reviews), 36 focus on SMEs, 19 on startups (either directly or indirectly), and 17 on general enterprises or unspecified organizations. Content analysis reveals that while KM and IC are instrumental in promoting integrated sustainability across economic, social, and environmental dimensions in larger enterprises, startups require more adaptive KM practices to balance growth and sustainability amidst resource constraints. This aligns with Durst and Edvardsson (2012), who emphasize that SMEs face unique KM constraints owing to competing activities and fewer human resources to manage multiple functions compared to large companies. Our analysis extends this insight by showing that startups encounter even more acute challenges, necessitating not only resource-efficient KM but also methods that rapidly adapt to volatile markets and uncertain business models. For example, sustainability-driven ventures leverage KM to institutionalize green logics, while social-purpose and technology-intensive ventures utilize tailored KM practices to address unique challenges such as indigenous knowledge integration, participatory innovation, and the reconciliation of innovation protection with recombinative processes. In contrast, Xu et al. (2005) found that large organizations benefit from more structured, hierarchical KM systems with formal documentation processes, while SMEs rely more heavily on informal knowledge sharing and personal relationships. Our findings suggest that startups operate at an even more informal level, often depending entirely on tacit knowledge and ad-hoc learning mechanisms that must be quickly formalized as they scale. These differences underscore the need for empirical research on customized, context-sensitive KM models that address the diverse requirements of different enterprise types and advance both theory and practice in SE.
Cross-national comparisons of KM practicesGeographic analysis indicates that a substantial portion of research on KM for SE involves international collaboration, yet only a few studies conduct empirical cross-national comparisons. Although 66 authors from 31 countries participated in such collaborations, the literature rarely examines how KM practices vary across cultural, market, and regulatory contexts. This oversight contrasts with the systematic review by Stoian et al. (2024), who reveal that national culture shapes knowledge sharing behaviors and enablers across multinational subsidiaries, underscoring the importance of contextual cultural factors in KM implementation. They find studies concentrated in China, the USA, and Europe, while emerging regions such as Latin America and Africa are underrepresented, which aligns with our findings. This variation is especially pronounced when comparing developed and developing nations, where enterprise roles and resource endowments differ markedly (Ronaghi & Mosakhani, 2022). Lee and Choi (2003) further warn that empirical models tested solely within a single country risk limited generalizability, and advocate for cross-cultural replication to validate relationships among KM enablers, processes, and outcomes across varied institutional environments. While some scholars have called for cross-national validation of findings (Al Halbusi et al., 2024; Zhang et al., 2022), comprehensive empirical studies remain scarce. Future research should therefore prioritize comparative investigations to determine how region-specific factors such as cultural norms, digital maturity, and institutional frameworks influence startups’ ability to leverage KM for sustainable outcomes across diverse national settings.
Aligning KM with SDGsDespite the recognized role of KM in supporting organizational sustainability by harmonizing economic, social, and environmental objectives, the literature reveals a critical gap in aligning KM practices with the broader agenda of the SDGs. This alignment is not only beneficial but also essential, as evidenced by Pais et al. (2023), whose systematic literature review reveals that KM is indispensable for implementing the SDGs and achieving the 2030 Agenda, with knowledge sharing and dissemination being the most crucial processes for SDG achievement. Their findings demonstrate that developing countries, in particular, must invest in KM to make meaningful progress toward the SDGs, yet only six studies in their comprehensive review specifically focus on using KM for SDG targets, indicating a substantial research and practice gap. Similarly, Upadhyay and Patel (2023) underscore the symbiotic relationship between KM and SDGs, demonstrating how knowledge sharing, organizational learning, and information dissemination can be harnessed to address complex sustainable development challenges and create tangible impacts on poverty eradication, quality health, climate action, and sustainable consumption patterns. However, our analysis reveals that ventures tend to embrace a more limited approach that focuses on individual sustainability facets instead of adopting the full complexity of multidimensional challenges required for comprehensive SDG alignment. Consequently, existing KM practices may not be sufficiently tailored to stimulate comprehensive sustainability strategies or to directly impact particular targets in the SDGs, such as gender equality (SDG 5), responsible consumption and production (SDG 12), and climate action (SDG 13). Although some promising examples exist, they are isolated cases rather than systematic implementations. For instance, knowledge sharing mechanisms where collaborative learning systems optimize material flows and minimize production waste have shown potential to support SDG 12.5 through waste reduction (Jayasinghe et al., 2021), but such approaches have not been widely adopted. Future research should explore how integrative and tailored KM frameworks, particularly within emerging enterprises, can be effectively aligned with the SDGs. In particular, investigations should focus on the mechanisms through which KM practices translate into measurable contributions to individual SDG targets. This would enable scholars and practitioners to develop balanced sustainability strategies that concurrently advance economic, social, and environmental goals.
Enhancing dynamic capabilities through KM in startupsDynamic capabilities, which enable firms to integrate, build, and reconfigure competencies in response to environmental changes, are critical for entrepreneurial success (Teece et al., 1997). Although the importance of dynamic capabilities is well-documented in general enterprises and SMEs (Jiang et al., 2018; Qader et al., 2022), our analysis reveals that the specific mechanisms by which KM enhances these capabilities in startups remain underexplored. Effective KM practices, such as knowledge acquisition and integration, help organizations absorb market information and build dynamic capabilities (Zahra & George, 2002); yet, the particular pathways in startup contexts require further elucidation. Bergshem and Gustafsson (2023) demonstrate that in knowledge-intensive startups, absorptive capacity, knowledge transfer, and knowledge application respectively underpin the micro-foundations of sensing, seizing, and reconfiguring capabilities, providing empirical evidence of KM’s direct contribution to dynamic capabilities in a single startup case. Ambrosini and Bowman (2009) further clarify the conceptual linkages between KM routines and dynamic capabilities, arguing that deliberate KM strategies form the basis of sensing and seizing activities in new venture. Zahra et al. (2006) show that startups also rely on external resource orchestration, that is leveraging alliances, investors, and institutional support, to develop dynamic capabilities in ways distinct from those of established firms. This gap is particularly pressing given that startups often operate under conditions of extreme uncertainty and limited resources, making the efficient use of knowledge a key lever for adaptability and growth. Future research should employ longitudinal and industry-specific case studies to examine how KM supports the development of dynamic capabilities in startups, thereby ensuring rapid innovation and market adaptability.
Digital tools for enhancing KM in startupsWhile KM frameworks are foundational for sustainable organizational growth, the potential of digital tools in enhancing KM for startups remains underexplored. Big data text analytics can transform unstructured information into actionable knowledge. Khan and Vorley (2017) demonstrate that applying text analytics to KM literature yields insights into emerging themes and supports data-driven decision making, illustrating its capacity to improve both knowledge acquisition and sharing in organizations. Oliva (2014) identifies barriers to effective KM, including limited technological infrastructure and lack of in-house expertise, and proposes a maturity model in which digital tools progressively strengthen KM practices at each stage of organizational development. Our review confirms these benefits but also uncovers a significant implementation gap: most startups lack the infrastructure, budget, or technical skills required to adopt advanced analytics or mature KM systems. Consequently, lightweight, cost-effective solutions are imperative. These could include cloud-based repositories with intuitive interfaces (Khan & Vorley, 2017), mobile knowledge-sharing applications that facilitate real-time collaboration (Oliva, 2014), and basic analytics dashboards that require minimal technical training. User-friendly design and embedded guidance features can help overcome skill shortages and encourage adoption among nontechnical staff. Future research should therefore co-design such tools with startup practitioners, integrating their feedback on usability, data privacy, and scalability, and rigorously evaluate their impact on KM efficiency, knowledge retention, and sustainability performance. Scholars should also examine how emerging technologies, particularly AI-mediated green innovation and KM (Almansour, 2024), machine learning algorithms for pattern recognition and scalable cloud platforms, can be tailored to the dynamic, resource-limited environments of new ventures. Focusing on ease of implementation, cost containment, and measurable outcomes will ensure that digital KM innovations deliver practical benefits without imposing excessive complexity or expense.
Clarifying the causal relationship between GEO and KMA conceptual ambiguity in the reviewed studies concerns the causal sequencing between GEO and KM. One stream positions GEO as the initiating force that motivates firms to develop green knowledge routines, with empirical evidence showing that environmentally proactive strategic intent stimulates knowledge creation, sharing, and intellectual capital formation that subsequently enhance green innovation and sustainability performance (Le et al., 2024; Zhang et al., 2024; Idrees et al., 2025). Conversely, a second stream conceptualizes KM as the enabling foundation from which GEO emerges, demonstrating that intelligence generation, knowledge diversification, and knowledge application strengthen entrepreneurial ecosystems and innovation-oriented cultures, thereby cultivating environmentally oriented intentions and behaviors, particularly in early-stage or resource-constrained ventures (Bhardwaj, 2019; Wang et al., 2024). Taken together, these perspectives suggest that GEO and KM may operate as a reciprocal, mutually reinforcing system rather than a unidirectional sequence, with the more prevalent GEO→KM pathway in current studies reflecting an applied focus on green innovation outcomes rather than theoretical dominance. The dominant causal direction is likely contingent on venture maturity and context: KM-led GEO may be more salient for young or knowledge-intensive startups establishing foundational capabilities, whereas GEO-led KM may prevail in growth-oriented ventures with established environmental intent. Future research should employ longitudinal or cross-lagged designs to compare these competing models and determine when each pathway is most relevant, thereby providing clearer guidance on whether practitioners should prioritize cultivating KM capabilities to foster GEO or leveraging GEO to activate sustainability-oriented KM processes.
Building on these insights, this study proposes a KM framework (see Fig. 7) that connects dynamic capabilities and entrepreneurial orientation, both critical for startups, to tailored KM practices that support sustainability. This framework bridges core KM constructs, such as knowledge sharing and intellectual capital, to specific sustainability outcomes, including integrated sustainability and sustainable innovation. It explicitly integrates digital tools, such as artificial intelligence and big data analytics, allowing startups to sense market shifts, take advantage of opportunities, and align growth with sustainability objectives, particularly targeting SDGs such as responsible consumption and production (SDG 12). By addressing diverse entrepreneurship types, such as sustainability-driven and technology-intensive ventures, the framework demonstrates how digitally enhanced KM practices foster adaptability across contexts such as regional economic disparities and digital maturity. To operationalize these agenda items and fulfill their research objectives, Table 5 outlines the preferred methods for empirical testing. This integration reconciles traditional KM with digital innovation, laying the groundwork for scalable, evidence-based interventions that help startups balance rapid growth with sustainability while advancing the SDG agenda.
Agenda items, research objectives, and preferred methods.
This study critically examined the intersection of KM and SE, with a particular focus on startups, through a systematic review of 76 articles retrieved from extensive Scopus and WoS scientific databases. The analysis unfolded in two key phases. First, the descriptive analysis demonstrated a growing academic interest in this field, especially during 2019–2024, while also highlighting trends in geographic distribution (with notable contributions from developing countries) and a predominance of quantitative methodologies, particularly survey-based studies. Second, the content analysis reveals distinct thematic clusters that illustrate how KM, sustainability, and entrepreneurship coalesce into unique pathways to leverage knowledge practices across different venture types. Drawing on the AMMC framework, this study shows that KM acts as a strategic orchestrator that embeds sustainability by aligning knowledge practices with entrepreneurial objectives through adaptive resource reconfiguration. In this process, key KM practices, such as knowledge sharing, acquisition, creation, and application, emerge as crucial levers for fostering dynamic capabilities, mitigating resource constraints, and institutionalizing sustainability logics within entrepreneurial ventures. KM primarily functions as an antecedent or mediator driving sustainability outcomes, suggesting that ventures tend to prioritize direct KM implementation over contextual adaptations required to address complex challenges, particularly in resource-constrained startups. Furthermore, the review reveals complementary insights from the classification of sustainability outcomes and entrepreneurial types. The analysis shows that KM enables integrated sustainability as well as innovation, strategic governance, and organizational resilience, while its application varies across sustainability-driven, social-purpose, and technology-intensive ventures, with each category demonstrating unique pathways for leveraging KM practices to foster sustainability. These findings underscore the need for tailored KM frameworks that address unique challenges and opportunities in diverse entrepreneurial contexts.
Despite the expanding body of literature, the review uncovers several critical areas for further investigation. There is a pronounced dearth of research focusing on the customization of KM frameworks specifically for startups, which are characterized by limited resources and rapidly changing market conditions. Furthermore, the emerging role of digital tools, such as AI, in enhancing KM practices for startups is underexplored, as is the relationship between KM and the development of dynamic capabilities in these early-stage ventures. Additionally, empirical comparisons of KM practices across different types of enterprises and regional contexts are scarce, and alignment of KM practices with the SDGs is not adequately addressed. Future research should target these gaps by developing modular, context-sensitive KM architectures, conducting cross-national and cross-sectoral empirical studies, exploring SDG-driven performance metrics, and examining the reciprocal dynamics between GEO and KM to advance both theoretical understanding and practical application of KM in driving SE. Based on these insights, the study develops a KM framework that integrates dynamic capabilities with entrepreneurial orientation, focusing on startups to promote sustainability.
This study makes several important contributions to the existing literature. First, it expands the scope of previous systematic literature reviews by integrating the dimensions of KM, IC, and SE, thus offering a comprehensive analysis of how specific KM practices, such as knowledge sharing, acquisition, creation, and application, enable sustainability, particularly within startups. Second, it develops a structured framework that classifies KM practices alongside sustainability outcomes and different types of entrepreneurship. This framework demonstrates how various KM can facilitate integrated sustainability outcomes, innovation-driven sustainability, strategic governance, and organizational resilience across diverse entrepreneurial contexts, including sustainability-driven, social-purpose, and technology-intensive ventures, thereby enabling startups to effectively balance growth with sustainable practices. Third, the research agenda outlines critical research directions, advocating KM frameworks tailored for startups, incorporation of digital tools, enhancement of dynamic capabilities, and alignment of KM practices with SDGs. These contributions offer a solid foundation for future research and provide practical insights for startups seeking to leverage KM as a strategic tool for sustainability.
In terms of practical contributions, the study offers several recommendations. For entrepreneurs, the results emphasize making KM practices part of core business processes through contextual adaptations. Though knowledge sharing and application remain fundamental, startups should prioritize light-weight mechanisms compatible with their budget constraints. Examples include embedding knowledge integration into agile processes as an alternative to formal processes or from open innovation platforms for cost-effective knowledge acquisition. In addition, knowledge co-creation with women’s cooperatives could directly realize SDG 5 (equality) by formalizing marginalized voices in product design, while machine learning-augmented knowledge analytics could measure climate action effects by monitoring emissions impact by way of cross-functional knowledge integration. Policymakers can support these efforts through synchronized metrics for KM-sustainability, one that rewards startups for reporting both epistemic results (e.g., cycles of process innovation) and sustainability gains (e.g., SDG 12-aligned reductions in material waste through supply chain knowledge optimization). Educators, for their part, should revisit entrepreneurship curricula to educate on phase-based KM adoption, illustrating how early-stage startups could first emphasize tacit knowledge retention through mentorship initiatives before systematically integrating digital knowledge repositories as they expand. Such context-specific approaches allow startups to transform generic KM practices into measurable sustainability drivers while maintaining operational agility.
Despite these contributions, several limitations of this review should be more critically acknowledged. First, as with many systematic literature reviews, this study is subject to inherent limitations including publication bias, primarily owing to the restriction to English-language journal articles. This exclusion may overlook relevant evidence published in other languages or in grey literature such as conference papers, theses, industry reports, and working papers, which can offer valuable insights, especially in emerging or interdisciplinary fields. Second, the analysis was limited to specific databases, potentially omitting relevant studies available in other sources. Third, the current classification of KM practices, such as knowledge acquisition, sharing, creation, and application, may not fully capture the complexity and evolving nature of KM, particularly in dynamic and resource-constrained entrepreneurial ecosystems like startups. Finally, while this review highlights the role of KM in promoting sustainability and introduces an initial framework, further investigation is required to gather both qualitative and quantitative empirical data to refine KM frameworks specifically tailored for startups. Future studies should expand the classification of KM practices by integrating emerging knowledge processes (e.g., knowledge co-creation, knowledge integration, and digital knowledge flows) and examine their application in diverse industries, organizational cultures, and sectors to improve our understanding of the role of KM in driving sustainable growth.
Ethics statementThis study is a systematic literature review and does not involve any human participants, animals, or identifiable personal data. Therefore, ethical approval was not required.
CRediT authorship contribution statementFurong Cai: Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Ettore Bolisani: Writing – review & editing, Supervision, Project administration, Conceptualization. Tomas Cherkos Kassaneh: Writing – review & editing, Supervision, Methodology. Behrooz Moradi: Writing – review & editing.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
This work was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no 101034319 and from the European Union - NextGenerationEU.
