Knowledge management and SMEs' digital transformation: A systematic literature review and future research agenda

Hafeez, Shahid; Shahzad, Khuram; Helo, Petri; Mubarak, Muhammad Faraz Article Knowledge management and SMEs' digital transformation: A systematic literature review and future research agenda Journal of Innovation & Knowledge (JIK) Provided in Cooperation with: Elsevier Suggested Citation: Hafeez, Shahid; Shahzad, Khuram; Helo, Petri; Mubarak, Muhammad Faraz (2025) : Knowledge management and SMEs' digital transformation: A systematic literature review and future research agenda, Journal of Innovation & Knowledge (JIK), ISSN 2444-569X, Elsevier, Amsterdam, Vol. 10, Iss. 3, pp. 1-21, https://doi.org/10.1016/j.jik.2025.100728 This Version is available at: https://hdl.handle.net/10419/327623 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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Knowledge management and SMEs’digital transformation: A systematic literature review and future research agenda Shahid Hafeez a,b,* , Khuram Shahzad a,b , Petri Helo a , Muhammad Faraz Mubarak c,d a School of Technology and Innovations, University of Vaasa, Wolffintie 32, 65200 Vaasa, Finland b Innovation and Entrepreneurship InnoLab, University of Vaasa, Yliopistonranta 2, 65200 Vaasa, Finland c Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada d Faculty of Economics and Management, Vytautas Magnus University, Kaunas, Lithuania ARTICLE INFO JEL classification: 032 033 036 Q55 Keywords: SMEs Digital transformation Knowledge exploration Knowledge exploitation Ecosystem Platform Systematic literature review Capabilities Knowledge management ABSTRACT This study aims to identify and explain different collaborative approaches, delineate external actors’roles, and examine the interplay between knowledge exploration and exploitation processes for digital transformation. We conducted a search of academic papers using research terms such as “Digital*, Digital transfor*, industry 4.0 (I4.0), industry 5.0, knowledge exploration, knowledge acquisition, ecosystem collaboration*, knowledge networks, and open innovation”in both the Scopus and Web of Science databases. Altogether, 108 papers met the criteria (e.g., ABS 2 &2+ranking of journals, only journal papers, and focusing on smalland medium-size enterprises (SMEs) digital transformation) for conducting a systematic literature review in this research. The results indicate that external actors play specific roles in supporting SMEs’digital transformation. We found that customers and suppliers push and encourage SMEs in their digital transformation, while coopetition can elicit greater technological benefits for SMEs with close technological and economic proximity. Intermediaries provide knowledge-brokering services, facilitate innovation processes, and enable technology transfer and capacitybuilding for SMEs’digital transformation. Government initiatives, such as favorable policymaking and financial support, are important in promoting and facilitating a collaborative environment for technology development among SMEs. This study’s results present two distinct collaborative mechanisms that SMEs can utilize for digital transformation: (I) core value chain and network actors’collaborations, which provide linear processes for knowledge exploration and exploitation, and (II) ecosystem and innovation platform-based collaborations, in which SMEs adopt the ambidextrous approach as a nonlinear process for knowledge exploration and exploitation for digital transformation. Certain organizational-level factors (organizational capabilities, micro-foundations, operational capabilities, organization strategies, and culture) are important for SMEs’knowledge exploitation in digital transformation. The study also presents an integrated framework and offers directions for future research and important insights for practitioners. Introduction SMEs are responsible for large-scale employment and contribute significantly to most countries’GDP (Kumar et al., 2020). During the last two decades, various exogenous external shocks have caused disruptions in the contemporary business environment. Due to flexible and agile organizational structures, SMEs are better at adapting to such disruptions (Chan et al., 2019). However, they face various challenges in their innovation processes, e.g., limited internal R&D capabilities, resource limitations, and a tendency to focus on short-term economic benefits (Madrid-Guijarro et al., 2009). Recent digital waves have posed new challenges for SMEs to conFig. ways to implement and utilize digital technologies in their business processes, offerings, and business models (Kumar et al., 2020). Digital technologies are changing the competitive landscape at a breakneck pace (Mahmood et al., 2020). Effective utilization of digital technologies leads organizations to have superior organizational performance and outpace their competitors. Thus, implementation and utilization of digital technologies are becoming indispensable for SMEs to achieve process efficiency, reduce operating costs, achieve sustainability targets, develop new products, offer new * Corresponding author. E-mail addresses: [email protected] (S. Hafeez), [email protected] (K. Shahzad), [email protected] (P. Helo), [email protected] (M.F. Mubarak). Contents lists available at ScienceDirect Journal of Innovation &Knowledge journal homepage: www.elsevier.com/locate/jik https://doi.org/10.1016/j.jik.2025.100728 Received 17 October 2024; Accepted 6 May 2025 Journal of Innovation & Knowledge 10 (2025) 100728 2 services (Pfister &Lehman, 2023), and create digitally enabled product-service systems (Gao et al., 2023). Therefore, digital transformation is defined as “SMEs’progression from converting firm processes to digital form to integrating digital technologies into products and services, and finally, transforming entire business models”(Soluk & Kammerlander, 2021). Considering rapid technological advancements, researchers study organizational factors (e.g., culture, innovation strategies, leadership, social capital) (Khin &Kee, 2022), micro-foundations (Christofi et al., 2024), dynamic capabilities (Del Giudice et al., 2021), and knowledge management systems (AL-Khatib et al., 2024) related to the digital transformation of SMEs. Similarly, various theoretical frameworks have been utilized to examine digital transformation, including the resource-based view (Chung &Kim, 2023;Radic et al., 2019), the dynamic capabilities framework (Scuotto et al., 2023), the technology-organization-environment framework (Shukla &Shankar, 2022), and the knowledge-based view (Comacchio et al., 2012;Ricci et al., 2021;Yao et al., 2020). Although these frameworks provide valuable insights, the knowledge-based view (KBV) has recently gained popularity among information systems and innovation management researchers. The primary factor contributing to KBV acceptability is the ability of the framework to elucidate the critical nature of external and internal technological knowledge management for the digital transformation (Haug et al., 2023;Ricci et al., 2021). Indeed, referring to literature on SMEs’innovation processes, studies document that these organizations rely extensively on external stakeholders for new knowledge to overcome their knowledge limitations, develop and enhance their capabilities, envision arising opportunities and challenges, and be better prepared for developing innovation solutions (Hafeez et al., 2025; Shahzad et al., 2025;Tiberius et al., 2021). Knowledge exploration and exploitation processes are key to SMEs’innovations. Recent studies have explored the linkage between SMEs’knowledge depth and breadth for Industry 4.0 adoption (Haug et al., 2023;Ricci et al., 2021). Despite the early efforts in understanding the SMEs’technological knowledge acquisition and its significance for technology implementation, there remains a strong need for further research to explore the interplay between explorative approaches and exploitative mechanisms for technological knowledge exploration and exploitation (Orlandi, 2016). Reflecting upon the existing literature reviews, we acknowledge that studies have highlighted the antecedents and contributing factors of digital transformation in SMEs. For instance, Anshari et al. (2022) highlighted that digital ecosystem readiness and local government support are essential enablers for the adoption of Industry 4.0 technologies among Indonesian SMEs. Slimane et al. (2022) delineate that internal organizational factors such as digital infrastructures, processes, digital managers, and management orientation to organizational change are key enablers in the digital transformation of SMEs. Marino-Romero et al. (2024) explain the interconnection between organizational digital orientation, agility to respond to external disruptions, and digital transformation management. Ramdani et al. (2022) presented a list of environmental antecedents, internal organizational factors, and capabilities to develop digital innovations. Mele et al. (2024) review emphasized the significance of SMEs’internal dynamic capabilities in sensing, seizing, and adapting to digital opportunities and external technological knowledge for effective digital transformation. The referred studies highlight the importance of organizational and individual-level capabilities to contextualize the externally available new technological knowledge. To the best of our knowledge, no prior research has explicitly focused on a systematic synthesis of literature that utilizes a KBV framework that outlines the complex relationships and interplay between SMEs’technological knowledge explorative approaches and exploitative mechanisms for digital transformation. The need for our research is further justified by the call for future research by Ramdani et al. (2022) and Haug et al. (2023) to explore mechanisms and approaches that support SMEs in their quest for the implementation of emerging technologies for developing digital innovations and enabling digital transformation. Table 1 summarizes existing literature reviews and their limitations in the context of knowledge management for digital transformation. In this context, a systematic synthesis of existing literature can contribute significantly to the academic community’s understanding by providing a comprehensive framework explaining the interplay between SMEs’knowledge explorative approaches and internal mechanisms to internalize and utilize the acquired knowledge for digital transformation. Studying the external knowledge exploration and exploitation process is highly interesting, as the field is challenging to analyze and SMEs are less transparent; thus, a systematic literature review helps find new insights. Our findings suggest that SME have two distinct collaborative models: (I) core value chain and network actors’collaborations, and (II) ecosystem and innovation platform-based collaborations. Findings indicate that the nature of collaborations, relationships among actors, exchange of knowledge and resources, and dependencies among actors vary between the two collaborative models, affecting SMEs’capacity to acquire, internalize, and utilize technological knowledge for their digital transformation. Our study contribution lies in presenting two collaborative models of SMEs’technological knowledge exploration and exploitation through the lens of a KBV. Findings also suggest that external actors such as customers, suppliers, intermediary organizations, and competitors play specific roles in SMEs’digital transformation. Our study answers the following three overarching research questions. R.Q-1: How do SMEs explore and acquire external technological knowledge? R.Q-2: How do SMEs internalize and utilize the externally explored technological knowledge for digital transformation? R.Q-3: What are the future research guidelines based on this literature review? The remaining body of the paper is structured into five sections: review planning and methodology, findings, discussion, conclusions, limitations, and future research directions. Review planning and methodology Asystematic literature review (SLR) is “a review of a formulated question that uses systematic and explicit methods to identify, select, and critically appraise relevant research, and to collect and analyze data from the studies included in the review”(Moher et al., 2009, p. 264). This definition asserts that answering a research question through SLR must be supported by systematic and rigorous methods to identify, select, and evaluate the relevant literature. The SLR is viewed as an appropriate approach to identify key findings in the literature, delineate relationships among different factors, conduct an in-depth evaluation of the literature on the topic, and offer concrete future research directions (Paul &Benito, 2018;Snyder, 2019;Webster &Watson, 2002). Reflecting on our research objectives, which aim to explore the interplay between SMEs’knowledge of exploratory and exploitative approaches for implementing technologies to develop technological solutions, we found the SLR to be the most relevant research approach to help us conduct a rigorous, systematic appraisal of the extant literature to delineate complex relationships embedded in knowledge exploration and exploitation for technological advancements. Paul and Benito (2018),Webster and Watson (2002), and Snyder (2019) suggested four phases for conducting SLRs: designing, conducting, analyzing, structuring &reporting. We followed this four-step procedure to conduct this SLR. Figure 1 presents the phases and components of each phase considered while conducting an SLR. Designing and conducting the review The first step in the design phase is to identify the need and define the SLR’s contribution. SLRs are well-structured approaches to summarizing literature and gaining a deep understanding of the phenomena under S. Hafeez et al. Journal of Innovation & Knowledge 10 (2025) 100728 3 investigation. During the planning phase, it is inevitable for researchers to review extant research critically to ensure that a concrete need exists to conduct an SLR that will help enhance understanding of the relevant academic community, practitioners, or policymakers significantly (Snyder, 2019). Thus, justifying the need for an SLR stems from the limitations of extant research regarding inconclusive evidence, contradictory findings from extant research, and a lack of a framework for academics and practitioners to guide them on specific phenomena. In this context of current research, details on justifications and rationale for conducting this SLR are provided in the introduction section, in which we explicitly explain the limitations of extant research, linking it to a clear literature gap and highlighting the need for further research on the knowledge exploration and exploitation process in SMEs for digital transformation. Furthermore, we identified two models based on different collaborative mechanisms and exploitative strategies that SMEs utilize for knowledge exploration and exploitation. Such contributions are much-needed because the frameworks, on one hand, contribute to the literature on information systems and business management, and on the other hand, provide practical implications for SME management in selecting and prioritizing different collaborative mechanisms, tailored to their innovation goals and internal capabilities. We aimed to achieve these objectives by answering the three research questions (please see the introduction section). The relevant literature search began by defining the conceptual boundaries of key concepts examined in the research. The primary constructs in our research are knowledge exploration, exploitation for digitalization, and digital transformation. The knowledge exploration process has been described as SMEs’new knowledge search, exchange, creation, and acquisition through different collaborations. Knowledge exploitation is the internalization and utilization of acquired knowledge for digital transformation. The definition of digital transformation in this study was adopted from Soluk and Kammerlander (2021):“SMEs’progression from converting firm processes to digital forms, integrating digital technologies into products and services, and transforming entire business models”. As for a definition of SMEs, we found varying definitions based on number of employees and turnover. For example, the European Union (2015) classifies business organizations with 1–250 employees as SMEs, while in the United States, the Small Business Administration classifies organizations with less than 500 employees as SMEs. The same goes for the Middle East and Far East, where SMEs are organizations with fewer than 500 employees. Therefore, it is difficult to find a universal definition of SMEs, so we used 500 employees as a cut-off point for SMEs to widen the study’s scope. Defining the conceptual boundaries is a systematic step in developing the review protocol, as the process helps researchers identify limits and map core concepts of the research. Researchers have contended that reviewing protocol documentation is critical to maintaining conducted studies’validity and reliability (Torracco, 2016). The review protocol helps researchers map out key concepts in the review and limits researcher bias during different stages of conducting the SLR (Ali et al., Table 1 Overview of existing literature review on digital transformation in SMEs. Authors Study Focus Key Themes Limitations & Research Gap Anshari et al. (2022) Open innovation strategies for SMEs’digital transformation - The positive link between digital ecosystem readiness for I4.0 adoption - Relevance of Knowledge management for open innovation implementation - Government as a protector of market regulations - Lack of analysis of technological knowledge exploration and exploitation - Limited to the Indonesian context Slimane et al. (2022) Integrated framework and managerial dimensions for a digital transformation strategy - Digital infrastructure and digital manager relevance for digital transformation - Reconfiguration of organizational and managerial mechanisms - Top Management is responsible for digital change management - Shallow analysis of the relevance of knowledge management for DT Ghobakhloo et al. (2022) Identify influencing factors for I4.0 adoption - Technological determinants - Organizational determinants - Environmental determinants - I4.0 adoption roadmap - Despite explaining environmental determinants, research lacks an in-depth evaluation of SMEs’ various interactions and their relevance for adopting I4.0 technologies. Ramdani et al. (2022) Synthesis of research on digital innovations Overview of: - Digital technologies - Theories for Digital Innovations - Contextual and organizational factors for digital innovations - Lacks in-depth analysis to show how SMEs interact with external actors, and what their role is in digital transformation Mele et al. (2024) Knowledgebased dynamic capabilities for digital transformation - Micro-foundation of dynamic capabilities - Dynamic capabilities for value creation - Dynamic capabilities for - Digital transition - Dynamic capabilities for ‘‘data-driven organizations - Dynamic capabilities for digital transformation in SMEs and family firms - Lack of details on how SMEs leverage their external networks to acquire relevant knowledge and what kind of support they receive from external actors - Missing details on the intricate process of knowledge exploration to exploitation for digital transformation. Marino-Romero et al. (2024) Orientation of digital transformation in the - Digital transformation promotes agility - Lack of analytical depth in leveraging dynamic Table 1 (continued) Authors Study Focus Key Themes Limitations & Research Gap management and organizational processes in organizational processes. - Development of digital capabilities - Strategies for scaling up digital technologies knowledge acquisition and internalization capabilities. - Provide a bibliometric overview of the studies, with a missing systematic literature analysis. S. Hafeez et al. Journal of Innovation & Knowledge 10 (2025) 100728 4 2023). In our research, we focused on concepts related to knowledge exploration and its exploitation in SMEs’digitalization and digital transformation (Figure 2 presents the conceptual framework for keyword selection). The knowledge exploration process includes different types of SME collaboration with external actors, such as supply chain collaborations, interorganizational collaborations, university-industry collaborations, open innovation, network collaborations, ecosystem collaborations, and, most recently, platform-based collaborations. The knowledge exploitation process comprises two stages: first, knowledge internalization, and second, its utilization for different outcomes. Our research focuses on exploring internal factors, capabilities, and organizational strategies that support knowledge internalization and the successful utilization of technological solutions. Thus, based on the review protocol, we started searching for relevant concepts in top journals in information systems management, business, and economics due to the topic’s multidisciplinary nature. After examining 20 research papers on the topic, we compiled an initial list of keyword searches that we conducted in the Scopus and Web of Science databases. Webster and Watson (2002) recommend this technique, as it helps researchers map out the topic and find the most relevant key terms from the leading body of literature on the given topic. During the next step, we consulted one external expert in the field on this keyword combination based on Ali et al. (2023) and Slimane et al. (2022). Using these experts’recommendations, we updated the search query and conducted another literature search. Selecting search terms is an ongoing process, so two research team members conducted separate search queries with lists of search terms. During the final step, both researchers compiled a final list of search terms and conducted literature Fig. 1. Phases of conducting a SLR. Fig. 2. The conceptual framework for keyword selection. S. Hafeez et al. Journal of Innovation & Knowledge 10 (2025) 100728 5 searches in the Scopus and Web of Science databases. Other researchers have argued that selecting a database for a literature search is central to reaching out to relevant research, particularly while conducting review studies (Snyder, 2019;Webster &Watson, 2002). In this regard, one researcher explained that Scopus is a comprehensive database that contains the most significant number of indexed journals (Snyder, 2019). However, other researchers suggested that authors should include at least two databases to broaden coverage of included data (Ramdani et al., 2022;Pfister &Lehmann, 2023). Therefore, we chose Scopus and Web of Science for the literature search to ensure a broader range of data for our review and reduce the risk of omitting important research. The final keyword combination and query are provided in Table 2. During the initial search, we found 848 relevant research papers (485 in Scopus; 363 in Web of Science). Aside from searching for literature on databases, we also backtracked citations from the resulting papers and supplemented data through a manual Google Scholar search. We searched for relevant literature in top-tier and leading business management and information systems journals. Webster and Watson (2002) suggested this search technique, which is beneficial in identifying significant contributions in the research field. The manual search was conducted on Association of Business Schools (ABS)-ranked (Levels 2 and 2+) journals’homepages. Altogether, 50 articles were found in a manual search on Google Scholar and from backtracking citations. However, we were left with 752 peer-reviewed empirical papers after duplicates were removed. We narrowed the review scope by defining inclusion and exclusion criteria. Due to the topic’s interdisciplinary nature, we searched for research papers across different fields of study, i. e., computer science, operations research, decision science, business, and management. A similar approach has been proven beneficial in Robey et al. (2000). We included only papers published in English during the 2004–2024 period. We focused only on research papers published in peer-reviewed journals, excluding conference proceedings and book chapters. This is important because most conference proceedings and book chapters are published without critical evaluations (Sivarajah et al., 2017). Furthermore, we excluded conceptual, editorial, and literature review papers. We focused only on papers with practical research designs because we wanted to focus on backing our findings with empirical evidence. Finally, we only chose papers published in ABS-ranked journals (Levels 2, 3, 4, and 4*) for two reasons: First, we aimed to build this study’s findings based on leading work in the field. Second, such a criterion helped keep the sample size manageable without omitting relevant work (Calabr` o et al., 2019). After applying ABS criteria, backtracking citations, and manual searches, we had a total of 364 papers that were selected for further criteria checks. During the final step, we checked papers for their relevance to our research goals, i. e., the paper must discuss knowledge exploration or exploitation processes pertinent to technological implementation, upgradation, technological innovations, technological utilization, technological development, technological transfer, or development of technological solutions (products, services, or business models). Table 3 documents the criteria checklist: After applying the quality and eligibility criteria, 108 studies qualified as the study’s final sample. Figure 3 presents a PRISMA flow diagram explaining the identification, screening, eligibility, and selection processes for research papers included in the study’s final sample. Conducting the analyses The data analysis was conducted systematically in a recursive process utilizing NVivo software, VOSviewer software, and manual reading of papers. We conducted keyword and author co-occurrence network mapping during the first phase using VOSviewer, which helped us map the interrelated cluster of studies and find the most influential authors in the research area (Fig.s are provided in the appendix). We then manually read the most cited relevant papers comprising 10% of the total sample. This process enabled us to increase our understanding of concepts, themes, and theoretical frameworks utilized in the context of the researched topic. After manually reading the full papers, we compiled an initial list of factors for the data extraction form. During the next stage, we used NVivo software to conduct automatic code queries on all papers. Such a query is mentioned as a pre-coding process (Bandara et al., Table 2 Final keywords combination and query. Final query 22.04.2024: Scopus: TITLE-ABS-KEY (("Digital inno*" OR "Digital Tech*" OR "Digital*" OR "ICT" OR "technolog*" OR "Industry 4.0" OR "Industry 5.0" OR "smart technolog*" OR "digital integration" OR "business intelligence" OR "Big data" OR "data capabilit*") AND ("knowledge exchange" OR "knowledge search" OR "knowledge sharing" OR "knowledge exploration" OR "knowledge acquisition" OR "knowledge receiving" OR "knowledge exploitation" OR "knowledge utilization" OR "knowledge integration" OR "ecosystem collaborations" OR "open innovation" OR "business ecosystem" OR "knowledge ecosystem" OR "innovation ecosystem" OR "entrepreneurial ecosystem" OR "technological ecosystem" OR "digital ecosystem" OR "service ecosystem" OR "knowledge network*" OR "business network*" OR "co-creat*" OR "co creat*" OR "collaborative innovation" OR "co-innovat*" OR "co innovat*") AND ("SME*" OR "Small medium enterpris*")) AND PUBYEAR >2003 AND PUBYEAR <2025 AND (LIMIT-TO (SUBJAREA, "BUSI") OR LIMIT-TO (SUBJAREA, "COMP") OR LIMIT-TO (SUBJAREA, "ENGI")) AND (LIMIT-TO (DOCTYPE, "ar")) AND (LIMIT-TO (LANGUAGE, "English")) Web of Science: ("Digital inno*" OR "Digital Tech*" OR "Digital*" OR "ICT" OR "technolog*" OR "Industry 4.0" OR "Industry 5.0" OR "smart technolog*" OR "digital integration" OR "business intelligence" OR "Big data" OR "data capabilit*") AND ("knowledge exchange" OR "knowledge search" OR "knowledge sharing" OR "knowledge exploration" OR "knowledge acquisition" OR "knowledge receiving" OR "knowledge exploitation" OR "knowledge utilization" OR "knowledge integration" OR "ecosystem collaborations" OR "open innovation" OR "business ecosystem" OR "knowledge ecosystem" OR "innovation ecosystem" OR "entrepreneurial ecosystem" OR "technological ecosystem" OR "digital ecosystem" OR "service ecosystem" OR "knowledge network*" OR "business network*" OR "co-creat*" OR "co creat*" OR "collaborative innovation" OR "co-innovat*" OR "co innovat*") AND ("SME*" OR "Small medium enterpris*") (All Fields) and Early Access or Review Article or Proceeding Paper or Retracted Publication (Exclude –Document Types) and Editorial Material or Book Review or Correction or Meeting Abstract (Exclude –Document Types) and Management or Business or Computer Science Information Systems or Engineering Manufacturing or Multidisciplinary Sciences (Web of Science Categories) and English (Languages) Table 3 Eligibility criteria. Inclusion criteria Exclusion criteria ABS Rankings of Journal: 2, 3, 4, &4* ABS Rankings of the journal: below 2 Publication Type: Peer-reviewed research articles Publication Type: Book chapters, conference proceedings Years: 2004–2024 Years: Less than 20 years old &articles in the press Methodology: Qualitative, quantitative, mixed methods Methodology: Review articles, editorial, and conceptual papers Content: Articles that primarily focus on technology adoption, implementation, and utilization, and the role of knowledge or collaborations Content: Articles that mention technology outcomes only; articles that mention technology utilization but are not focused on knowledge and collaborations; articles that focus only on internal organizational factors for technology adoption and utilization Language: English Language: Other than English Subject: Business and Economics, Computer Science, Information Systems, Decision Science, Engineering Management Subject: Other than mentioned in the inclusion criteria S. Hafeez et al. Journal of Innovation & Knowledge 10 (2025) 100728 6 2011) that helps understand the most important codes, keywords, and concepts appearing frequently in the given papers. Utilizing NVivo during the review-writing process can reduce the possibility of human error (Chavez et al., 2022), as its features enable researchers to capture, code, retrieve, and analyze literature in a single repository (Bandara et al., 2011). Moreover, the pre-coding process helped us form a broader picture of the retrieved literature and identify dominant codes in our sample papers. By creating a matrix, NVivo also helped us understand co-occurrence between the most frequent terms. This process supplemented compilation of data extraction forms by providing an overview of key emerging terms. Moreover, by forming a word cloud in NVivo, we found that collaboration, knowledge, innovation, technological, and capabilities were the most frequently used terms in the selected papers. Such results validate selected papers’suitability to answer the research questions. Figure 4 presents the word cloud. Based on the following steps, we compiled a list of codes for the data extract form and conducted a manual search for the final coding of the results. Following these steps ensured that the researcher’s biases were mitigated during the selection and analytical process. We compiled an objective list of codes to help us answer the research questions. Figure 5 presents an overview of the steps followed during the data analysis stages of the SLR. Structure and reporting Description of data The annual distribution of published articles indicates that the importance of knowledge exploration and collaborations for digital solutions development has increased over the past decade. As Figure 6 indicates, before 2010, only five research papers on this topic existed, but starting in 2011, an increase in publications can be observed, with the highest number of publications, 18, in 2023, followed by 17 in 2022. Thus, it is evident that there has been a growing interest among researchers concerning the present study’s topic. While the body of literature on this topic has been growing, we observed a dip in 2021, and based on our understanding and extant research, during the COVID-19 pandemic, researchers and companies’focus shifted toward attaining short-term resilience, a strategy known as serving the shocks of disruption. This phenomenon hindered organizations and researchers’ investments in resources to explore, adopt, and develop new Fig. 3. PRISMA flow diagram. Fig. 4. Word cloud for the most frequent terms. S. Hafeez et al. Journal of Innovation & Knowledge 10 (2025) 100728 7 technologies to improve existing processes. The pandemic compelled organizations to utilize technology in their internal organizational practices, such as human resource management (HRM) and communication among teams and employees through online meeting tools (e.g., MS Teams, Zoom, and Skype). Our literature search did not include papers that examined technological development and utilization in the very specific context of communication and HRM practices. Our rationale is that the scope of our research was limited to the collaborative aspect of digital transformation in SMEs, leading to omission of studies that focused merely on organizational internal technology adoption in HRM management and health and biotech sector companies. Moreover, a descriptive data analysis reveals that Technological Forecasting and Social Change was the most popular journal among researchers, publishing 11 papers, followed by Technology Analysis and Strategic Management and Technovation, with eight papers each. Similarly, the Journal of Business Research and Production Planning and Control published six papers each. Figure 7 provides an overview of journals with the highest frequency of published papers on this topic, demonstrating the multi-disciplinary nature of conducted research that expands across journals from different domains, e.g., strategic management, information systems research, innovation management, and engineering management. Findings The data analysis process began with a review of the papers listed in NVivo, in which we compiled and agreed on the list of codes. A content analysis was conducted, and researchers read full papers while analyzing the problem statement, literature gap covered, research question(s), objectives, theories, conceptual framework, methodology, results, and each paper’s contributions. Based on the content analysis, we answered our study research questions: R.Q-1: How do SMEs explore and acquire external technological knowledge? R.Q-2: How do SMEs internalize and utilize externally explored technological knowledge for digital transformation? R.Q-3: What are the future research guidelines based on the literature review? By following the aforementioned research methods, we conducted a detailed analysis of the relevant literature to answer the research questions pertinent to SMEs’external knowledge exploration. This rigorous process ensured that themes and results emerged with acceptable reliability and validity to achieve the study’s objectives. The findings from the literature indicate that in the external business environment, SMEs collaborate with different actors through various mechanisms; therefore, exploring each actor’s role and contribution further is indispensable for understanding SMEs’knowledge exploration process. We also found that these actors collaborate through different mechanisms, which we categorized into (I) core value and network actors’collaborations and (II) ecosystem and innovation platform collaborations. Actors’roles and activities in SMEs’digital transformation journey Customers, suppliers, and competitors Our in-depth exploration of the literature recognized the important role played by customers and suppliers during different phases of digital transformation within SMEs. Review results indicate that customers can be determinants of and catalysts for digital transformation within SMEs. Key players compel SMEs to adopt technological solutions to maintain Fig. 5. Steps followed during the data analysis phase. Fig. 6. Year’s distribution: Literature search conducted in April 2024. S. Hafeez et al. Journal of Innovation & Knowledge 10 (2025) 100728 8 partnerships; however, catalysts motivate and assist SMEs in implementing technological solutions to realize additional benefits and seize opportunities that digital technologies provide. Both determinants and catalysts play essential roles in adoption, implementation, and utilization of digital technologies. Customers’determinant role drives supplier SMEs to explore and adopt technological solutions, leading management to synchronize its digital systems with the broader supply chain. Customer demands increase SMEs’technological, managerial, and operational readiness for implementing I4.0 technologies (Gutierrez et al., 2015;Stentoft et al., 2021). Estensoro et al. (2022) explained that technology non-implementors mainly lacked customers’push for technology adoption. Customer demand for technology implementation is one of the major triggers for digital transformation within SMEs (Khin & Kee, 2022), while availability of financial support and funding acts as an explanatory factor for the testing and trial phases of new technology implementation (Ballestar et al., 2020). Even though customer-supplier relationships’determinant nature acts as the trigger for digital transformation, technology implementation in such a context has been met with certain challenges for SMEs, including limited financial resources, lack of organizational innovation and technology implementation strategies (Hansen et al., 2024;Soluk &Kammerlander, 2021), lack of organizational readiness, and lack of needed support from customers (Salvini et al., 2022). Therefore, the customer-supplier relationship’s determinant nature has not been discussed widely in the digital transformation literature. Instead, most researchers have focused on the catalyst’s nature of customer-supplier relationships for digital transformation in SMEs. The catalyst nature of relationships entices customers and suppliers to work closely together and collaborate in implementing and utilizing digital technologies to seize various opportunities arising in their business environments. Notably, digital transformation through such collaborations is not limited to grasping new opportunities. Motivation for collaborations can vary, from increasing supply chain performance (Scuotto et al., 2017) and organizational performance (Radicic et al., 2019), to improving existing organizational processes’effectiveness and efficiency (Ricci et al., 2021). Proximity plays a crucial role in such customer-supplier relations, which can be divided into three types: spatial; technological; and social proximity. Spatial proximity refers to geographical closeness of collaborating actors’locations. Technological proximity encompasses the level of technological knowledge symmetry, alignment between existing technological systems, and organizational technological readiness between customers and suppliers (Benitez et al., 2020;Hwang, 2023;Marullo et al., 2024). Social proximity pertains to the understanding and strengths of social relationships between customers and suppliers (Benitez et al., 2020). Customer and supplier collaborations occur at various phases and levels, directly impacting SMEs’journey toward digital transformation. Estensoro et al. (2022) explained that SMEs during the early stages of digital transformation collaborate with their customers to enhance their technological awareness through discussions. During the second stage, SMEs develop technological awareness and aim to create concrete solutions through collaborations. In this context, they partner with broader networks of customers, which provide them with financial resources and focused workshops on implementing and utilizing new technologies in their organizational processes (Chipika &Wilson, 2006; Ricci et al., 2021). This allows SMEs to enhance technological learning, improve digital readiness, and test various technological solutions through network collaborations. This stage also includes cocreating technological solutions with a network of customers, thereby advancing SMEs’internal technological capabilities for technology adoption and implementation (Benitez et al., 2020;Eikebrokk et al., 2020;Mawson & Brown, 2017;Zhang, 2024). The third stage represents a more sophisticated approach, in which SMEs collaborate with their customers and other participants within ecosystem collaborations. In such collaborations, the facilitating or orchestrating actor establishes the collaboration target, generally with a broader agenda than merely focusing on SME technology development (Li et al., 2023;Sassanelli &Terzi, 2022). Successful collaboration with customers in ecosystem and platform collaborations is evident in the highest technological and relational proximity levels between customers and suppliers, as these collaborations evolve based on their targets. Such proximity is driven by economic, technological, and market factors (Kahle et al., 2020). While the early phases of ecosystem collaborations may be driven by economic factors, the evolutionary perspective suggests that technological and market drivers mutually influence one another regarding digital transformation and the use of technologies to meet current and future market demands. A few studies have analyzed coopetition among SMEs for technological purposes explicitly, and only these studies examined competitors’collaborations, explaining that competitors and organizations with similar business models can stimulate bricolage. SMEs need to be outward-facing and receptive to external stimuli (Haug et al., 2023; AL-Khatib et al., 2024;Hervas-Oliver et al., 2021;Khurana et al., 2022; Radicic et al., 2019). Government organizations The literature evaluation indicated that governments play an Fig. 7. Frequency of papers published in journals. S. Hafeez et al. Journal of Innovation & Knowledge 10 (2025) 100728 15 (Han &Trimi, 2022), and enforce competence development strategies parallel to operational technological implementations to achieve long-term digital transformation goals (Hansen et al., 2024). For example, the availability of emerging technologies and technological convergence between different actors on innovation platforms enable participating SMEs to achieve modularity and test combinations of technological components, thereby achieving operational agility (Han & Trimi, 2022). In the long run, operational agility allows SMEs to develop strategic agility in terms of forming new collaborations (Han &Trimi, 2022), cocreating within collaborative networks I4.0, and integrating new technologies in business offerings (Sassanelli &Terzi, 2022). Similarly, the literature underscores the importance of preconditions for knowledge internalization. SMEs that succeed in these collaborations typically possess strong organizational capabilities, operational capabilities, robust micro-foundations (e.g., leadership skills and employee competencies), and alignment between knowledge management strategies and business goals. This is validated by Estensoro et al. (2022) and Mubarak and Petraite (2020), who argued that the transition to I4.0 requires a combination of internal capabilities and external support from collaborative networks. These collaborations’structured, project-based nature may foster business model innovation in the short term, but limitations in scalability and adaptability prevent SMEs from achieving breakthrough innovations. Thus, in Collaborative Model I, knowledge exploration is a combined activity, in which SMEs collaborate or form interorganizational collaborations to search for new knowledge, and knowledge exploitation is characterized by strong internal capabilities and condition factors. Ecosystem and innovation platform collaborations for digital transformation From the perspective of a KBV, ecosystem and innovation platform collaborations offer a more dynamic and ambidextrous approach to knowledge exploration and exploitation in SMEs. These collaborations are nonlinear, with multiple actors participating in the cocreation of technological solutions. Unlike core value chain collaborations’static nature, ecosystem-based collaborations involve continuous evolution of roles and goals, allowing SMEs to remain adaptable to changing technological demands. This co-evolution process leads SMEs and other actors to adopt an ambidextrous approach toward knowledge management, sharing and acquiring new knowledge in the same collaborations, and exploiting new knowledge through multilayer and multi-actors’ collaborations to develop technological solutions jointly and achieve business model-level configurations and technological maturity (Hafeez et al., 2025). For example, Estensoro et al. (2022) underscored ecosystem collaborations’importance during various stages of digital maturity, enabling SMEs to enhance their learning about I4.0 technologies throughout different stages of digital maturity. This adaptability allows SMEs to engage in exploratory innovation by acquiring new technological knowledge that often is inaccessible through more structured, linear collaborations. Adaptability has been linked further with organizational ambidexterity. Del Giudice et al. (2021) argued that early adopters of technology and innovations share a common approach: ambidextrous exploration and exploitation of new solutions. Organizational ambidexterity is also important for SMEs to overcome their limitations related to technological investments (Scuotto et al., 2017), as well as achieve technological breakthroughs. Ecosystem collaborations challenge SMEs’conventional approaches and encourage them to adapt to continuous changes and adjustments in the interactions’structural and intellectual landscape (Pelletier &Cloutier, 2019;Scuotto et al., 2023). In response to continuous evaluation and external feedback, SMEs develop their innovations and organizational agility (Han &Trimi, 2022). Thus, compared with core value chain actors’collaborations, ecosystem and platform collaborations enhance SMEs’agility by enabling these organizations to sharpen their sensing capabilities, exposing them to wider industry trends and arising opportunities, thereby increasing their knowledge convergence and resource fluidity while receiving continuous support from other actors in pursuing the core value of collaborations. Ecosystem collaborations support this ambidextrous knowledge management approach further, enabling SMEs to leverage complementary skills and resources from other actors within the network, including larger anchor organizations and research institutions. The dynamic cocreation of knowledge within these ecosystems aligns with Mahdiraji et al. (2023) and Mubarak et al. (2021), who highlighted the role of intermediary organizations and government support in facilitating transfer of technological knowledge to SMEs. This boundary-spanning approach is particularly effective in overcoming infrastructural and operational challenges that often hinder SMEs from adopting digital platforms. The iterative nature of learning and relearning within these ecosystems is a significant factor in enabling SMEs to develop technological maturity. SMEs that participate in innovation platforms benefit from continuous adaptation, ensuring that they remain competitive in industries characterized by rapid technological advancement (Corvello et al., 2023). Haug et al. (2023) discussed how SMEs involved in additive manufacturing (AM) networks are better-equipped to build internal capabilities by drawing on knowledge and resources available within the ecosystem. This knowledge internalization process is not limited to absorbing external knowledge, but also involves active participation in its cocreation, thereby strengthening SMEs’position within the ecosystem. Utilization of technological knowledge in ecosystem collaborations is also more robust compared with the core value chain model. By fostering open knowledge exchange and encouraging development of new products and services, these collaborations allow SMEs to achieve high digital transformation levels. Haug et al. (2023) highlighted further that SMEs engaged in knowledge networks to gain AM experience sustained competitive advantages, as they can leverage both internal and external knowledge resources effectively. This ability to adapt and innovate continuously is a key strength of ecosystem-based collaborations, making them a more viable option for SMEs seeking to achieve long-term digital transformation. By comparison, core value chain collaborations’linear nature offers fewer opportunities for sustained innovation. Although these collaborations are effective for incremental technological improvements and short-term business model innovation, they lack the dynamic flexibility required to thrive in an increasingly digital and interconnected business environment. However, ecosystem collaborations provide a more comprehensive framework for fostering organizational learning, innovation, and digital maturity. Figure 10 presents an integrated framework for knowledge exploration and exploitation for SMEs’digital transformation. Conclusion Theoretical implications This research emphasizes the importance of external knowledge exploration and exploitation for SMEs’digital transformation. Through boundary-spanning activities, SMEs can access new technological knowledge and complement resources to improve their internal digital infrastructures, technology maturity, and technological learning pertinent to the digital transformation of processes, offerings, and business models (Crupi et al., 2020;Haug et al., 2023;Ricci et al., 2021). Boundary-spanning activities enhance SMEs’collaborations with various external stakeholders, such as customers and suppliers’networks, competitors, intermediary organizations, and government-representing organizations (Hafeez et al., 2025). For example, customer and supplier networks are central to pushing and encouraging SMEs to utilize the latest technologies for organizational innovations (Del Giudice et al., 2021;Estensoro et al., 2022). SMEs can learn from competitors in well-structured and properly managed coopetition-based collaborations. A successfully managed coopetition S. Hafeez et al. Journal of Innovation & Knowledge 10 (2025) 100728 16 can earn higher technological benefits for SMEs due to close technological and economic proximity. Similarly, intermediary organizations are keystone actors in SMEs’external environment. Intermediary organizations have dynamic and varying roles for setting up, executing, managing, and governing collaborations between business and nonbusiness actors. Intermediary organizations provide knowledge-brokering services, facilitate innovation processes, enable knowledge and technology transfer, and assume a greater role by enabling sociotechnical transitions’larger role. This advanced role is executed by systematic intermediary organizations, which indirectly act as capacity-building actors for SMEs in the digital transformation of their offerings and business models (França et al., 2022;Kahle et al., 2020). We also posit that government organizations and representative bodies’initiatives, such as favorable policymaking and financial support, are important in promoting and articulating a collaborative environment for technology testing and implementation among resource-constrained SMEs. It has been demonstrated that SMEs utilize two distinct modes of collaboration for their digital transformation: (I) core value chain network actors’collaborations and (II) ecosystem and innovation platform-based collaborations. The nature of collaborations, relationships among actors, exchanges of knowledge and resources, and dependencies among actors vary between the two collaborative models. This variation affects SMEs’capacity to acquire, internalize, and utilize new knowledge for digital transformation of their processes, offerings, and business models. For example, in Collaborative Model I, knowledge exploration and exploitation for digital transformation are viewed as linear models, having certain organizational capabilities, microfoundations, operational capabilities, and organizational strategies as preconditioning factors for knowledge exploitation. In Collaborative Model II, SMEs adopt an ambidextrous approach as a nonlinear process for knowledge exploration and exploitation for digital transformation. In such a model, organizational capabilities, micro-foundations, and operational capabilities are moderating and mediating factors, rather than preconditioning. Finally, the integrated framework (Figure 10) synthesizes literature on external collaborations for knowledge exploration and exploitation (Ricci et al., 2021;Mahdiraji et al., 2023;Haug et al., 2023;Suh &Sohn, 2015) for SMEs’digital transformation (Scuotto et al., 2017;Mubarik et al., 2022;Del Giudice et al., 2021; Estensoro et al., 2022;Hafeez et al., 2025). Theoretical Contributions This research also contributes to the literature by providing novel insights on the relevance of the KBV of SMEs’digital transformation. The present study’s findings warrant explicit and detailed insights into the interplay between exploratory approaches and exploitative mechanisms for knowledge management in the context of SMEs’digital transformation. We contributed to the literature by demonstrating that external knowledge acquisition and internalization are integral to boundary-spanning activities (Anshari et al., 2022); digital infrastructure and internal capabilities are important for knowledge exploitation (Ben Slimane et al., 2022); customers, suppliers, intermediaries, and government organizations are key external determinants in unique technological knowledge exploration (Ghobakhloo et al., 2022;Ramdani et al., 2022); knowledge exploration aligned with the exploitation process can develop unique dynamic capabilities for digital transformation (Mele et al., 2024); and new knowledge utilization contributes to operational and strategic agility for SMEs’digital transformation (Marino-Romero et al., 2024). Table 6 provides an overview of our contribution to the literature gap. Practical implications Our research presents practical implications for SME management, experts from intermediary organizations, and policymakers from government institutes. SME Management ○SMEs that aim to leap forward to the digital transformation of offerings and business models can shape creation of their necessary preconditions in advance actively. They should consider proactively scanning their external environments to find possible support and complementary skills and resources for digital transformation. Fig. 10. Integrated framework on knowledge exploration and exploitation for SMEs’digital transformation. S. Hafeez et al. Journal of Innovation & Knowledge 10 (2025) 100728 17 ○Digital transformation requires infrastructural, operational, and managerial readiness for successful technology implementation. SME management must align these functions with technology and innovation strategies to maximize output from technology implementation and utilization. ○SME management, particularly top management, should place greater emphasis on developing technical and digital competencies. These competencies will support development of dynamic capabilities within business organizations, enabling them to thrive in current and future markets, as well as navigate utilization of technologies for business purposes. ○SME management should be open to collaborations with external networks to acquire new knowledge on implementing emerging technologies. Such collaborations can compensate for low internal technological maturity and help SMEs achieve a competitive advantage. ○To overcome internal resource limitations, SMEs that enter into ecosystem collaborations to acquire access to shared resources, such as machinery, and promote shared technological infrastructure can increase interoperability, thereby alleviating financial constraints on advanced technology utilization. ○SME management is encouraged to collaborate with universities and research institutions to utilize digital innovation labs and dedicated expertise to accelerate digital transformation. ○SME managers need to articulate a formalized strategy to make capacities of their human capital, associated learning processes, and assumption of risks more effective. Managers should be encouraged to conduct boundary-spanning activities that stimulate new and successful business models within their organizations. ○Designing and communicating a digital strategy provides a success story that redirects employees toward digital transformation; thus, having a strategy roadmap for digital transformation is suggested for SME management. Table 6 The present study’s theoretical contributions. Authors Key Findings Limitations & Research Gap Our contributions Anshari et al. (2022) •The positive link between digital ecosystem readiness for I4.0 adoption •Relevance of Knowledge management for open innovation implementation •Government as a protector of market regulations Lack of analysis of technological knowledge exploration and exploitation. Limited to the Indonesian context. The ambidextrous nature of knowledge exploration and exploitation is a unique feature of ecosystem collaborations promoting SMEs’ digital readiness. In some contexts, government organizations act beyond the protection of market regulators as innovative facilitators through policies and funds. Slimane et al. (2022) •Digital infrastructure and digital manager relevance for digital transformation •Reconfiguration of organizational and managerial mechanisms •Top Management is responsible for digital change management Shallow analysis of the relevance of knowledge management for Digital transformation. Digital infrastructures, digital leadership, strategic reconfigurations, and top management social capital are influenced by the SME’s capacity to collaborate, and these factors act as preconditions for technological knowledge exploitation. Ghobakhloo et al. (2022) •Technological determinants •Organizational determinants •Environmental determinants •I4.0 adoption roadmap Despite explaining environmental determinants, research lacks an in-depth evaluation of SMEs’various interactions and their relevance for the adoption of I4.0 technologies. Customers, suppliers, and competitors are core actors in environmental determinants for developing solutions related to I4.0 adoption. Intermediary organizations of various types are increasingly emerging as capacity-building actors in the boundaryspanning activities of SMEs. Ramdani et al. (2022) Overview on: •Digital technologies •Theories for Digital Innovations •Contextual and organizational factors for digital innovations Lacks in-depth analysis to show how SMEs interact with external actors and their role in digital transformation. Collaboration with external actors provides SMEs with knowledge and resources to test and trial new technologies. Co-creation and co-development of technological solutions lead these organizations to develop digital innovations. Mele et al. (2024) •Micro-foundation of dynamic capabilities There is a lack of details on how SMEs leverage A combination of knowledge exploration, Table 6 (continued) Authors Key Findings Limitations & Research Gap Our contributions •Dynamic capabilities for value creation •Dynamic capabilities for •Digital transition •Dynamic capabilities for ‘‘data-driven organizations •Dynamic capabilities for digital transformation in SMEs and family firms their external networks to acquire relevant knowledge and what kind of support they receive from external actors. Missing details on the intricate process of knowledge exploration to exploitation for digital transformation. aligned with the exploitation process, can develop unique dynamic capabilities for digital transformation. Marino-Romero et al. (2024) •Digital transformation promotes agility in organizational processes. •Development of digital capabilities •Strategies for scaling up digital technologies Lack of analytical depth in leveraging dynamic capabilities for knowledge acquisition and internalization. Provide a bibliometric overview of the studies, with a missing systematic literature analysis. New knowledge exploration and its utilization led SMEs to develop operational and strategic agility for digital transformation. S. Hafeez et al. Journal of Innovation & Knowledge 10 (2025) 100728 18 ○Resource-constrained SMEs are encouraged to collaborate with different ecosystem actors and participate in online communities to acquire external expertise that is essential for digital transformation. Policymakers ○The government should provide tailored support programs and enhanced access to education and training for SMEs. The aim should be to prioritize long-term economic development and capacity building for effective networking among SMEs. ○For disruptive technologies, the government shall provide incentives for investment in new technologies, as well as make technical support and training programs available. This support can increase SMEs’willingness to adopt disruptive technologies and leverage their potential benefits. ○Policymakers shall consider the importance of social, behavioral, and relational factors alongside technical aspects in digital transformation efforts. Policymakers can leverage these insights to develop and strengthen IT-related support agendas tailored to SMEs’needs, particularly smaller ones, through customized training programs, coaching initiatives, and other support mechanisms. ○Public support programs should be demand-led, catering to traditional SMEs’diverse needs. Subsidies and financial support for SMEs can smooth out implementation of disruptive technologies in SMEs’manufacturing processes; the government should provide tailored programs. ○Policymakers should be aware of technology transfer centers and their activities, identify centers that can fulfill the boundaryspanning role, and support initiatives that aim to increase their effectiveness by providing incentives to technology transfer centers and enhancing their human capital and social capabilities, enabling them to orchestrate numerous interfaces with crossdisciplinary teams and stakeholders. ○Government policymakers are encouraged to provide customized sector-specific support strategies to strengthen internal financing, such as tax benefits and subsidies, and enhance external collaboration using intellectual properties, such as licensing. ○Policymakers should revise regulations to facilitate digital transformation, encourage investments in infrastructure and technology, and develop programs to improve digital platforms’ operational management. The study’s limitations This study provides valuable insights into SMEs’digital transformation; however, several limitations should be considered when interpreting the nuances of this research. Methodologically, the reliance on an SLR inherently depends on the quality and scope of the selected literature. The decision to focus on ABS-ranked journals with a minimum ranking of Level 2 may have excluded relevant insights from lower-ranked journals, conference proceedings, and published theses, i. e., this choice might have narrowed our findings’scope, yet our outcomes’validity is justifiable with the large sample size of papers used. Xu et al. (2018) suggested that researchers retrieve a substantial number of papers when analyzing a mature topic, thereby maintaining stringent quality criteria for robust and rigorous findings. However, we encourage future researchers to include gray literature to yield more comprehensive results. Similarly, the use of Scopus and Web of Science databases, while comprehensive, excluded studies from other repositories or regional and specialized journals that could have provided broader perspectives. Furthermore, inclusion of only English-language publications introduces a Western-centric bias, overlooking valuable contributions from non-English-speaking regions. The study also predominantly employed an established theoretical framework, i.e., the KBV, which, although robust, may not have fully captured emerging paradigms or interdisciplinary perspectives on digital transformation. Another potential limitation of a KBV is the overemphasis on knowledge as a key strategic asset, which may overshadow internal organizational dynamics, such as employee training, change management, and resistance to technological adoption. Furthermore, cross-cultural variations in SMEs’digital transformation practices remain underexplored, and the findings may not fully represent diverse socioeconomic contexts. However, KBV captures comprehensive details on the various factors and actors related to the knowledge exploration and exploitation process in digital transformation. It enables researchers to zero in on details of mechanisms of organizational boundary-spanning activities and their linkage with knowledge exploration. Similarly, it provides a powerful lens through which to understand knowledge internalization and utilization for organizational capabilities and innovation development, thereby offering an opportunity to develop a comprehensive understanding of SMEs’digital transformation processes. Moreover, while the findings address sectoral practices, they are generalized across SMEs, potentially overlooking industry-specific nuances. Given digital transformation’s dynamic nature, the reliance on published studies from 2004 to 2024 may not have captured the most recent technological advancements or disruptions adequately. Finally, the study lacks empirical validation, relying solely on secondary data, which limits the ability to test the proposed frameworks in real-world scenarios or specific SME contexts. These limitations underscore the need for future research to expand the scope of analysis, incorporate primary data, and explore underrepresented aspects of SMEs’digital transformation journey. Future research directions While the extant literature provides valuable insights into the collaborative frameworks and digital transformation strategies employed by SMEs, several avenues for further exploration remain. Addressing these literature gaps would deepen understanding of how SMEs can manage knowledge, foster innovation, and achieve long-term digital transformation more effectively. The following directions are proposed to guide future research: •Future research aveues for external stakeholders’relevance in digital transformation ○Future research should focus on understanding the mechanisms that enable SMEs to overcome digital transformation barriers related to scalability within digital ecosystems. This could involve investigating the roles of intermediary organizations, government interventions, and innovation platforms in facilitating resourcesharing and providing support structures for SMEs. Studies that examine how digital platforms and collaborative networks foster scalability would provide actionable insights for both policymakers and practitioners. ○Given the diverse economic, social, and cultural landscapes in which SMEs operate, future studies should undertake crosscultural and regional comparisons to explore how knowledge management and digital transformation differ across geographical contexts. Comparative studies could examine how intermediaries and government organizations have different support and resource structures in developed economies vs. those in emerging markets, and how they support SMEs in their digital transformation differently. ○Understanding local cultural factors’role in shaping SMEs’responses to digitalization could help develop more culturally sensitive and effective strategies. ○While much extant research has focused on exploring SMEs’capabilities, more research is needed to explore the key capabilities, learning mechanisms, and micro-foundations of intermediary actors that lead SMEs to digital transformation. S. Hafeez et al. Journal of Innovation & Knowledge 10 (2025) 100728 19 ○Government support enables SMEs to embark on digital transformation journeys, but extant research on specific policies and incentives that are most effective in fostering SMEs’digital transformation remain scarce. Future research should evaluate the impact from various government interventions––such as tax incentives, grants, subsidies, and digital infrastructure investments––on SMEs’digital growth. Studies that assess how these policies can be tailored to address SMEs’unique needs in different sectors and regions would provide valuable insights for policymakers. •Future research avenues on internal factors in digital transformation ○Much of the literature emphasizes the importance of organizational capabilities in internalizing external knowledge, and future studies should consider leadership and micro-foundations’role in this process. Further investigation into how microfoundations––such as individual competencies, routines, and organizational culture––contribute to effective knowledge internalization could yield practical insights for enhancing SME performance in knowledge-intensive environments. ○Future research is needed to develop comprehensive metrics that assess digital transformation’s impact on SME competitiveness. Such metrics could evaluate changes in market share, operational efficiency, innovation output, and customer satisfaction due to digitalization. By establishing clear benchmarks for digital transformation success, future studies can provide SMEs with actionable insights on measuring and optimizing their digital initiatives for sustained competitive advantage. •Future research avenues on methods and contexts ○To gain a more comprehensive understanding of long-term impacts from digital transformation, future research should employ longitudinal studies that track SMEs’progression through different stages of digital maturity. Such studies could identify critical inflection points at which SMEs either advance or stagnate during their digital transformation journeys. Moreover, they could explore the factors that enable SMEs to transition from early-stage digital adoption to full-fledged digital integration, providing a clearer roadmap for practitioners aiming to sustain digital growth. ○While much current extant research on knowledge management within SMEs is generalizable, future studies should investigate sector-specific practices, particularly in industries in which digital transformation poses unique implications. For example, industries such as manufacturing, healthcare, and finance face distinct challenges and opportunities in their digitalization efforts. Tailoring knowledge management frameworks to specific sectors would provide more nuanced and actionable recommendations for firms operating in diverse industrial contexts. •Future research avenues on sustainability and digital transformation’s ethical aspects ○With the rapid adoption of AI, big data, and automation across SMEs, these technologies’ethical dimensions need to be addressed. Future research should explore technological integration’s ethical implications, including issues related to data privacy, algorithmic transparency, and the potential for job displacement. By investigating how SMEs can balance technological innovation with ethical responsibility, scholars can contribute to the development of more sustainable and socially responsible digital transformation strategies. ○With growing global attention being paid to sustainability, future research should explore how SMEs can integrate sustainable practices into their digital transformation strategies. This could involve examining the intersection of sustainability objectives and digital transformation, particularly how SMEs leverage digital technologies to reduce their environmental impact, optimize resource use, and contribute to broader sustainability goals. Investigating how sustainability-oriented digital transformation can enhance SMEs’competitive positioning while aligning with the United Nations’Sustainable Development Goals (SDGs) would add a valuable dimension to current discourse. ○Future research also can explore strategies and approaches that lead SMEs in their twin transformation. CRediT authorship contribution statement Shahid Hafeez: Writing –review &editing, Writing –original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Khuram Shahzad: Writing –review &editing, Validation, Supervision, Resources, Project administration, Funding acquisition, Conceptualization. Petri Helo: Writing –review &editing, Validation, Supervision, Resources, Conceptualization. Muhammad Faraz Mubarak: Writing – review &editing, Writing –original draft, Validation, Conceptualization. Acknowledgments We sincerely acknowledge the financial support of the FOUNDATION FOR ECONOMIC EDUCATION (Liikesivistysrahasto) under grant numbers 190283 and 230398, the Kaute Foundation and the Evald and Hilda Nissi Foundation. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jik.2025.100728. References Albors-Garrig´ os, J., Etxebarria, N. Z., Hervas-Oliver, J. L., & Epelde, J. G. (2011). Outsourced innovation in SMEs: A field study of R&D units in Spain. International Journal of Technology Management, 55(1/2), 138–155. Ali, O., Abdelbaki, W., Shrestha, A., Elbasi, E., Alryalat, M. A., & Dwivedi, Y. K. (2023). A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. Journal of Innovation & Knowledge, 8(1), Article 100333. AL-Khatib, A. W., Shuhaiber, A., Mashal, I., & Al-Okaily, M. (2024). Antecedents of Industry 4.0 capabilities and technological innovation: A dynamic capabilities perspective. European Business Review, 36(4), 566–587. Anshari, M., & Almunawar, M. N. (2022). Adopting open innovation for SMEs and Industrial Revolution 4.0. Journal of Science and Technology Policy Management, 13 (2), 405–427. Ates, A., & Acur, N. (2022). Making obsolescence obsolete: Execution of digital transformation in a high-tech manufacturing SME. Journal of Business Research, 152, 336–348. Ballestar, M. T., Díaz-Chao, ´ A., Sainz, J., & Torrent-Sellens, J. (2020). Knowledge, robots, and productivity in SMEs: Explaining the second digital wave. Journal of Business Research, 108, 119–131. Bandara, W., Miskon, S., & Fielt, E. (2011). A systematic, tool-supported method for conducting literature reviews in information systems. In ECIS 2011 Proceedings [19th European Conference on Information Systems] (pp. 1–13). AIS Electronic Library (AISeL)/Association for Information Systems. Battistella, C., Ferraro, G., & Pessot, E. (2023). Technology transfer services impact on open innovation capabilities of SMEs. Technological Forecasting and Social Change, 196, Article 122875. Ben Slimane, S., Coeurderoy, R., & Mhenni, H. (2022). Digital transformation of small and medium enterprises: A systematic literature review and an integrative framework. International Studies of Management &Organization, 52(2), 96–120. Benhayoun, L., Le Dain, M. A., Dominguez-P´ ery, C., & Lyons, A. C. (2020). SMEs embedded in collaborative innovation networks: How to measure their absorptive capacity? Technological Forecasting and Social Change, 159, Article 120196. Benitez, G. B., Ayala, N. F., & Frank, A. G. (2020). Industry 4.0 innovation ecosystems: An evolutionary perspective on value co-creation. International Journal of Production Economics, 228, Article 107735. Berkowitz, H., & Souchaud, A. (2024). Filling successive technologically induced governance gaps: Meta-organizations as regulatory innovation intermediaries. Technovation, 129, Article 102890. S. Hafeez et al. Journal of Innovation & Knowledge 10 (2025) 100728 20 Bharati, P., & Chaudhury, A. (2010). Impact of knowledge acquisition on technology assimilation. Journal of Computer Information Systems, 51(2), 97–106. Bojica, A. M., Estrada, I., & Mar fuentes-fuentes, M. D. (2018). In good company: when small and medium-sized enterprises acquire multiplex knowledge from key commercial partners. Journal of Small Business Management, 56(2), 294–311. Bonesso, S., Comacchio, A., & Pizzi, C. (2011). Technology sourcing decisions in exploratory projects. Technovation, 31(10–11), 573–585. Brown, R., & Mason, C. (2014). Inside the high-tech black box: A critique of technology entrepreneurship policy. Technovation, 34(12), 773–784. Cadden, T., Weerawardena, J., Cao, G., Duan, Y., & McIvor, R. (2023). Examining the role of big data and marketing analytics in SMEs innovation and competitive advantage: A knowledge integration perspective. Journal of Business Research, 168, Article 114225. Caetano, M., & Amaral, D. C. (2011). Roadmapping for technology push and partnership: A contribution for open innovation environments. Technovation, 31(7), 320–335. Calabr` o, A., Vecchiarini, M., Gast, J., Campopiano, G., De Massis, A., & Kraus, S. (2019). Innovation in family firms: A systematic literature review and guidance for future research. International Journal of Management Reviews, 21(3), 317–355. Cenamor, J., Parida, V., & Wincent, J. (2019). How entrepreneurial SMEs compete through digital platforms: The roles of digital platform capability, network capability, and ambidexterity. Journal of Business Research, 100, 196–206. Chakravarty, S. (2022). Resource-constrained innovation in a technology-intensive sector: Frugal medical devices from manufacturing firms in South Africa. Technovation, 112, Article 102397. Chan, C. M., Teoh, S. Y., Yeow, A., & Pan, G. (2019). Agility in responding to disruptive digital innovation: A case study of an SME. Information Systems Journal, 29(2), 436–455. Chavez, Z., Hauge, J. B., & Bellgran, M. (2022). Industry 4.0, transition or addition in SMEs? A systematic literature review on digitalization for deviation management. The International Journal of Advanced Manufacturing Technology, 119(1–2), 57–76. Chipika, S., & Wilson, G. (2006). Enabling technological learning among light engineering SMEs in Zimbabwe through networking. Technovation, 26(8), 969–979. Christofi, M., Khan, H., Zahoor, N., Hadjielias, E., & Tarba, S. (2024). Digital transformation of SMEs: The role of entrepreneurial persistence and market sensing dynamic capability. IEEE Transactions on Engineering Management, 1–18. Chung, H., & Kim, K. (2023). Can open innovation improve technological outcomes for digital transformation? Structural approach to strategic decisions of Korean ICT SMEs. Managerial and Decision Economics, 44(8), 4404–4421. Clegg, B., Little, P., Govette, S., & Logue, J. (2017). Transformation of a small-tomedium-sized enterprise to a multi-organisation product-service solution provider. International Journal of Production Economics, 192, 81–91. Comacchio, A., Bonesso, S., & Pizzi, C. (2012). Boundary spanning between industry and university: The role of Technology Transfer Centres. The Journal of Technology Transfer, 37(6), 943–966. Corvello, V., Felicetti, A. M., Steiber, A., & Al¨ ange, S. (2023). Start-up collaboration units as knowledge brokers in Corporate Innovation Ecosystems: A study in the automotive industry. Journal of Innovation &Knowledge, 8(1), Article 100303. Crupi, A., Del Sarto, N., Di Minin, A., Gregori, G. L., Lepore, D., Marinelli, L., & Spigarelli, F. (2020). The digital transformation of SMEs –a new knowledge broker called the digital innovation hub. Journal of Knowledge Management, 24(6), 1263–1288. Del Giudice, M., Scuotto, V., Papa, A., Tarba, S. Y., Bresciani, S., & Warkentin, M. (2021). A self-tuning model for smart manufacturing SMEs: Effects on digital innovation. Journal of Product Innovation Management, 38(1), 68–89. Dezi, L., Ferraris, A., Papa, A., & Vrontis, D. (2021). The role of external embeddedness and knowledge management as antecedents of ambidexterity and performances in Italian SMEs. IEEE Transactions on Engineering Management, 68(2), 360–369. Dodourova, M., & Bevis, K. (2014). Networking innovation in the European car industry: Does the Open Innovation model fit? Transportation Research Part A: Policy and Practice, 69, 252–271. Doh, S., & Kim, B. (2014). Government support for SME innovations in the regional industries: The case of government financial support program in South Korea. Research Policy, 43(9), 1557–1569. Doloreux, D., Shearmur, R., Suire, R., & Berthinier-Poncet, A. (2023). Which types of firm use collaborative innovative spaces? Creativity and Innovation Management, 32(1), 141–157. Eikebrokk, T. R., & Olsen, D. H. (2020). Towards a process theory of IS business value cocreation: Insights from enterprise systems adoption in an SME cluster. International Journal of Information Management, 32, 1–32. Estensoro, M., Larrea, M., Müller, J. M., & Sisti, E. (2022). A resource-based view on SMEs regarding the transition to more sophisticated stages of Industry 4.0. European Management Journal, 40(5), 778–792. Ferneley, E., & Bell, F. (2006). Using bricolage to integrate business and information technology innovation in SMEs. Technovation, 26(2), 232–241. França, J. A., Lakemond, N., & Holmberg, G. (2022). The coordination of technology development for complex products and systems innovations. Journal of Business & Industrial Marketing, 37(13), 106–123. Fukugawa, N. (2018). Division of labor between innovation intermediaries for SMEs: Productivity effects of interfirm organizations in Japan. Journal of Small Business Management, 56, 297–322. Gao, J., Zhang, W., Guan, T., Feng, Q., & Mardani, A. (2023). Influence of digital transformation on the servitization level of manufacturing SMEs from static and dynamic perspectives. International Journal of Information Management, 73, Article 102645. Garrigos, J. A., Etxebarria, N. Z., Hervas Oliver, J. L., & Ganzarain, J. E. (2011). Outsourced innovation in SMEs: A field study of R&D units in Spain. International Journal of Technology Management, 55(1/2), 138–156. Ghobakhloo, M., Iranmanesh, M., Vilkas, M., Grybauskas, A., & Amran, A. (2022). Drivers and barriers of industry 4.0 technology adoption among manufacturing SMEs: a systematic review and transformation roadmap. Journal of Manufacturing Technology Management, 33(6), 1029–1058. Goduscheit, R. C., & Knudsen, M. P. (2015). How barriers to collaboration prevent progress in demand for knowledge: A dyadic study of small and medium-sized firms, research and technology organizations and universities. Creativity and Innovation Management, 24(1), 29–54. Gupta, H., & Barua, M. K. (2016). Identifying enablers of technological innovation for Indian MSMEs using best–worst multi-criteria decision making method. Technological Forecasting and Social Change, 107, 69–79. Gutierrez, A., Boukrami, E., & Lumsden, R. (2015). Technological, organisational and environmental factors influencing managers’decision to adopt cloud computing in the UK. Journal of Enterprise Information Management, 28(6), 788–807. Hafeez, S., Juszczyk, O., & Takala, J. (2021). A Roadmap for successful IoT implementation: empirical evidence from the energy industry. Issues in Information Systems, 22(1), 92–113. Hafeez, S., Shahzad, K., & De Silva, M. (2025). Enhancing digital transformation in SMEs: The dynamic capabilities of innovation intermediaries within ecosystems. Long Range Planning, Article 102525. Han, H., & Trimi, S. (2022). Towards a data science platform for improving SME collaboration through Industry 4.0 technologies. Technological Forecasting and Social Change, 174, Article 121242. Hansen, A. K., Christiansen, L., & Lassen, A. H. (2024). Technology isn’t enough for Industry 4.0: On SMEs and hindrances to digital transformation. International Journal of Production Research, 1–21. Haug, A., Wickstrøm, K. A., Stentoft, J., & Philipsen, K. (2023). Adoption of additive manufacturing: A survey of the role of knowledge networks and maturity in small and medium-sized Danish production firms. International Journal of Production Economics, 255, Article 108714. Heger, T., & Boman, M. (2015). Networked foresight—The case of EIT ICT labs. Technological Forecasting and Social Change, 101, 147–164. Hervas-Oliver, J. L., Sempere-Ripoll, F., & Boronat-Moll, C. (2021). Technological innovation typologies and open innovation in SMEs: Beyond internal and external sources of knowledge. Technological Forecasting and Social Change, 162, Article 120338. Ho, Y. P., Ruan, Y., Hang, C. C., & Wong, P. K. (2016). Technology upgrading of smalland-medium-sized enterprises (SMEs) through a manpower secondment strategy –A mixed-methods study of Singapore’s T-Up program. Technovation, 21–29. 57–58. Hwang, I. (2023). Evolution of the collaborative innovation network in the Korean ICT industry: A patent-based analysis. Technology Analysis &Strategic Management, 35(2), 221–236. Ietto, B., Ancillai, C., Sabatini, A., Carayannis, E. G., & Gregori, G. L. (2022). The role of external actors in SMEs’human-centered industry 4.0 adoption: An empirical perspective on Italian competence centers. IEEE Transactions on Engineering Management, 71, 1057–1072. Isensee, C., Teuteberg, F., Griese, K. M., & Topi, C. (2020). The relationship between organizational culture, sustainability, and digitalization in SMEs: A systematic review. Journal of Cleaner Production, 275, Article 122944. Kahle, J. H., Marcon, E., Ghezzi, A., & Frank, A. G. (2020). Smart products value creation in SMEs innovation ecosystems. Technological Forecasting and Social Change, 156, Article 120024. Khin, S., & Hung Kee, D. M (2022). Identifying the driving and moderating factors of Malaysian SMEs’readiness for Industry 4.0. International Journal of Computer Integrated Manufacturing, 35. Khurana, I., Dutta, D. K., & Ghura, A. S. (2022). SMEs and digital transformation during a crisis: The emergence of resilience as a second-order dynamic capability in an entrepreneurial ecosystem. Journal of Business Research, 150, 623–641. Kiani, A., Yang, D., Ghani, U., & Hughes, M. (2022). Entrepreneurial passion and technological innovation: The mediating effect of entrepreneurial orientation. Technology Analysis &Strategic Management, 34(10), 1139–1152. Kim, S., Kim, H., & Kim, E. (2016). How knowledge flow affects Korean ICT manufacturing firm performance: A focus on open innovation strategy. Technology Analysis &Strategic Management, 28(10), 1167–1181. Kiron, K. R., & Kannan, K. (2018). Application of fuzzy analytical network process for the selection of best technological innovation strategy in steel manufacturing SMEs. International Journal of Services and Operations Management, 31(3), 325–348. Kolade, O., Obembe, D., & Salia, S. (2019). Technological constraints to firm performance: The moderating effects of firm linkages and cooperation. Journal of Small Business and Enterprise Development, 26(1), 85–104. Koo, B. S., & Lee, C. Y. (2019). The moderating role of competence specialization in the effect of external R&D on innovative performance. R&D Management, 49(4), 574–594. Kumar, R., Singh, R. K., & Dwivedi, Y. K. (2020). Application of industry 4.0 technologies in SMEs for ethical and sustainable operations: Analysis of challenges. Journal of Cleaner Production, 275, Article 124063. Lee, J., Kim, C., & Choi, G. (2019). Exploring data envelopment analysis for measuring collaborated innovation efficiency of small and medium-sized enterprises in Korea. European Journal of Operational Research, 278(2), 533–545. Lepore, D., Vecciolini, C., Micozzi, A., & Spigarelli, F. (2023). Developing technological capabilities for Industry 4.0 adoption: An analysis of the role of inbound open innovation in small and medium-sized enterprises. Creativity and Innovation Management, 32(2), 249–265. S. Hafeez et al. Journal of Innovation & Knowledge 10 (2025) 100728 21 Li, H., Yang, Z., Jin, C., & Wang, J. (2023). How an industrial internet platform empowers the digital transformation of SMEs: Theoretical mechanism and business model. Journal of Knowledge Management, 27(1), 105–120. Madrid-Guijarro, A., Garcia, D., & Van Auken, H. (2009). Barriers to innovation among Spanish manufacturing SMEs. Journal of Small Business Management, 47(4), 465–488. Mahdiraji, H. A., Yaftiyan, F., Abbasi-Kamardi, A., Jafari-Sadeghi, V., Sahut, J. M., & Dana, L. P. (2023). A synthesis of boundary conditions with adopting digital platforms in SMEs: An intuitionistic multi-layer decision-making framework. The Journal of Technology Transfer, 48(5), 1723–1751. Mahmood, T., & Mubarik, M. S. (2020). Balancing innovation and exploitation in the fourth industrial revolution: Role of intellectual capital and technology absorptive capacity. Technological Forecasting and Social Change, 160, Article 120248. Marino-Romero, J. A., Palos-S´ anchez, P. R., & Velicia-Martín, F. (2024). Evolution of digital transformation in SMEs management through a bibliometric analysis. Technological Forecasting and Social Change, 199, Article 123014. Maroufkhani, P., Iranmanesh, M., & Ghobakhloo, M. (2023). Determinants of big data analytics adoption in small and medium-sized enterprises (SMEs). Industrial Management &Data Systems, 123(1), 278–301. Marullo, C., Shapira, P., & Di Minin, A. (2024). Enhancing SME innovation across European regions: Success factors in EU-funded open innovation networks. Technological Forecasting and Social Change, 201, Article 123207. Mawson, S., & Brown, R. (2017). Entrepreneurial acquisitions, open innovation and UK high growth SMEs. Industry and Innovation, 24(4), 382–402. Mele, G., Capaldo, G., Secundo, G., & Corvello, V. (2024). Revisiting the idea of knowledge-based dynamic capabilities for digital transformation. Journal of Knowledge Management, 28(2), 532–563. Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). The PRISMA Group Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. BMJ, 339, b2535. Mubarak, M. F., & Petraite, M. (2020). Industry 4.0 technologies, digital trust and technological orientation: What matters in open innovation? Technological Forecasting and Social Change, 161, Article 120332. Mubarak, M. F., Petraite, M., & Kebure, K. (2021). Managing intellectual capital for open innovation: Components and processes?. The dynamics of intellectual capital in current era (pp. 149–169). Singapore: Springer Singapore. Mubarik, M. S., Bontis, N., Mubarik, M., & Mahmood, T. (2022). Intellectual capital and supply chain resilience. Journal of Intellectual Capital, 23(3), 713–738. Muscio, A. (2007). The impact of absorptive capacity on SMEs’collaboration. Economics of Innovation and New Technology, 16(8), 653–668. Orlandi, L. B. (2016). Organizational capabilities in the digital era: Reframing strategic orientation. Journal of Innovation &Knowledge, 1(3), 156–161. Park, B. I., & Ghauri, P. N. (2011). Key factors affecting acquisition of technological capabilities from foreign acquiring firms by small and medium-sized local firms. Journal of World Business, 46(1), 116–125. Park, J., Kim, J., Woo, H., & Yang, J. S. (2022). Opposite effects of R&D cooperation on financial and technological performance in SMEs. Journal of Small Business Management, 60(4), 892–925. Paul, J., & Benito, G. R. (2018). A review of research on outward foreign direct investment from emerging countries, including China: What do we know, how do we know, and where should we be heading? Asia Pacific Business Review, 24(1), 90–115. Pelletier, C., & Cloutier, L. M. (2019). Conceptualising digital transformation in SMEs: An ecosystemic perspective. Journal of Small Business and Enterprise Development, 26(6/ 7), 855–876. Petruzzelli, M. A., Murgia, G., & Parmentola, A. (2022). How can open innovation support SMEs in the adoption of I4.0 technologies? An empirical analysis. R&D Management, 52(4), 615–632. Pfister, P., & Lehmann, C. (2023). Returns on digitisation in SMEs—a systematic literature review. Journal of Small Business &Entrepreneurship, 35(4), 574–598. Prodi, E., Tassinari, M., Ferrannini, A., & Rubini, L. (2022). Industry 4.0 policy from a sociotechnical perspective: The case of German competence centres. Technological Forecasting and Social Change, 175, Article 121341. Pundziene, A., & Geryba, L. (2023). Managing technological innovation: Dynamic capabilities, collaborative innovation, and born-digital SMEs’performance. IEEE Transactions on Engineering Management, 71, 6968–6981. Radicic, D., Douglas, D., Pugh, G., & Jackson, I. (2019). Cooperation for innovation and its impact on technological and non-technological innovations: Empirical evidence for European SMEs in traditional manufacturing industries. International Journal of Innovation Management, 23(05), Article 1950046. Ramdani, B., Raja, S., & Kayumova, M. (2022). Digital innovation in SMEs: A systematic review, synthesis and research agenda. Information Technology for Development, 28(1), 56–80. Ricci, R., Battaglia, D., & Neirotti, P. (2021). External knowledge search, opportunity recognition, and Industry 4.0 adoption in SMEs. International Journal of Production Economics, 240, Article 108234. Robey, D., Boudreau, M. C., & Rose, G. M. (2000). Information technology and organizational learning: A review and assessment of research. Accounting, Management and Information Technologies, 10(2), 125–155. Salvini, G., Hofstede, G. J., Verdouw, C. N., Rijswijk, K., & Klerkx, L. (2022). Enhancing digital transformation towards virtual supply chains: A simulation game for Dutch floriculture. Production Planning &Control, 33(13), 1252–1269. Sassanelli, C., & Terzi, S. (2022). The D-BEST reference model: A flexible and sustainable support for the digital transformation of small and medium enterprises. Global Journal of Flexible Systems Management, 23(3), 345–370. Saunila, M., Ukko, J., & Rantala, T. (2019). Value co-creation through digital service capabilities: The role of human factors. Information Technology &People, 32(3), 627–645. Scuotto, V., Caputo, F., Villasalero, M., & Del Giudice, M. (2017). A multiple buyer–supplier relationship in the context of SMEs’digital supply chain management. Production Planning &Control, 28(16), 1378–1388. Scuotto, V., Crammond, R. J., Murray, A., & Del Giudice, M. (2023). Achieving global convergence? Integrating disruptive technologies within evolving SME business models: A micro-level lens. Journal of International Management, 29(6), Article 101095. Scuotto, V., Nicotra, M., Del Giudice, M., Krueger, N., & Gregori, G. L. (2021). A microfoundational perspective on SMEs’growth in the digital transformation era. Journal of Business Research, 129, 382–392. Shahzad, K., & Hafeez, S. (2022). Digital trust in business ecosystem collaboration: Leveraging digital technologies to develop a framework. Trust, Digital Business and Technology (pp. 242–254). Routledge. Shahzad, K., & Hafeez, S. (2023). Creating trust through communication within a knowledge ecosystem: An empirical perspective of SMEs. Communication, Leadership and Trust in Organizations (pp. 62–77). Routledge. Shahzad, K., Hafeez, S., Heimo, T., M¨ aenp¨ a¨ a, A., Mubarak, M. F., & Evans, R. (2025). Developing the Innovation Capabilities of SMEs: The Role of Intermediary Firms in Knowledge Ecosystems. IEEE Transactions on Engineering Management, 72, 604–618. Shukla, M., & Shankar, R. (2022). An extended technology-organization-environment framework to investigate smart manufacturing system implementation in small and medium enterprises. Computers &Industrial Engineering, 163, Article 107865. Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263–286. Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. Soluk, J., & Kammerlander, N. (2021). Digital transformation in family-owned Mittelstand firms: A dynamic capabilities perspective. European Journal of Information Systems, 30(6), 676–711. Soluk, J., Decker-Lange, C., & Hack, A. (2023). Small steps for the big hit: A dynamic capabilities perspective on business networks and non-disruptive digital technologies in SMEs. Technological Forecasting and Social Change, 191, Article 122490. Son, S. C., & Zo, H. (2023). Do R&D resources affect open innovation strategies in SMEs? The mediating effect of R&D openness on the relationship between R&D resources and firm performance in South Korea’s innovation clusters. Technology Analysis & Strategic Management, 35(11), 1385–1397. Sony, M., Antony, J., Tortorella, G., McDermott, O., & Gutierrez, L. (2024). Determining the critical failure factors for Industry 4.0: An exploratory sequential mixed method study. IEEE Transactions on Engineering Management, 71, 1862–1876. Stentoft, J., Wickstrøm, K. A., Philipsen, K., & Haug, A. (2021). Drivers and barriers for Industry 4.0 readiness and practice: Empirical evidence from small and mediumsized manufacturers. Production Planning &Control, 32(10), 811–828. Suh, J., & Sohn, S. Y. (2015). Analyzing technological convergence trends in a business ecosystem. Industrial Management &Data Systems, 115(4), 718–739. Tiberius, V., Schwarzer, H., & Roig-Dob´ on, S. (2021). Radical innovations: Between established knowledge and future research opportunities. Journal of Innovation & Knowledge, 6(3), 145–153. Torraco, R. J. (2016). Writing integrative literature reviews: Using the past and present to explore the future. Human Resource Development Review, 15(4), 404–428. Tranekjer, T. L., & Knudsen, M. P. (2012). The (unknown) providers to other firms’new product development: What’s in it for them? Journal of Product Innovation Management, 29(6), 986–999. Tremblay, D. G., & Yovo, A. D. (2015). Territory, innovation processes in SMEs, and intermediary actors: The case of the ICT sector in the Greater Montreal Area. International Journal of Technology Management, 69(1), 1. Troise, C., Jones, P., Candelo, E., & Sorrentino, M. (2023). The role of entrepreneurial alertness, digital platform capability, organisational agility and business model innovation on young innovative companies’performance. Technology Analysis & Strategic Management, 1–14. Wadhwa, P., McCormick, M., & Musteen, M. (2017). Technological innovation among internationally active SMEs in the Czech economy: Role of human and social capital of CEO. European Business Review, 29(2), 164–180. Wang, J., & Bai, T. (2024). How digitalization affects the effectiveness of turnaround actions for firms in decline. Long Range Planning, 57(1), Article 102140. Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, xiii–xxiii. Wei, J., Zhang, X., & Tamamine, T. (2024). Digital transformation in supply chains: Assessing the spillover effects on midstream firm innovation. Journal of Innovation & Knowledge, 9(2), Article 100483. Wynarczyk, P. (2013). Open innovation in SMEs: A dynamic approach to modern entrepreneurship in the twenty-first century. Journal of Small Business and Enterprise Development, 20(2), 258–278. Xu, X., Chen, X., Jia, F., Brown, S., Gong, Y., & Xu, Y. (2018). Supply chain finance: A systematic literature review and bibliometric analysis. International Journal of Production Economics, 204, 160–173. Yao, J., Crupi, A., Di Minin, A., & Zhang, X. (2020). Knowledge sharing and technological innovation capabilities of Chinese software SMEs. Journal of Knowledge Management, 24(3), 607–634. Zangiacomi, A., Pessot, E., Fornasiero, R., Bertetti, M., & Sacco, M. (2020). Moving towards digitalization: A multiple case study in manufacturing. Production Planning &Control, 31(2–3), 143–157. Zhang, H. (2024). Non-R&D innovation in SMEs: Is there complementarity or substitutability between internal and external innovation sourcing strategies? Technology Analysis &Strategic Management, 36(5), 916–930. S. Hafeez et al.