The nexus of digital transformation and innovation: A multilevel framework and research agenda

Saeedikiya, Mehrzad; Salunke, Sandeep; Kowalkiewicz, Marek Article The nexus of digital transformation and innovation: A multilevel framework and research agenda Journal of Innovation & Knowledge (JIK) Provided in Cooperation with: Elsevier Suggested Citation: Saeedikiya, Mehrzad; Salunke, Sandeep; Kowalkiewicz, Marek (2025) : The nexus of digital transformation and innovation: A multilevel framework and research agenda, Journal of Innovation & Knowledge (JIK), ISSN 2444-569X, Elsevier, Amsterdam, Vol. 10, Iss. 1, pp. 1-20, https://doi.org/10.1016/j.jik.2024.100640 This Version is available at: https://hdl.handle.net/10419/327542 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ The nexus of digital transformation and innovation: A multilevel framework and research agenda Mehrzad Saeedikiya a,* , Sandeep Salunke b , Marek Kowalkiewicz a a Queensland University of Technology, School of Management, Centre for Future Enterprise, 02 George Street, Brisbane City, 4000, Queensland, Australia b Queensland University of Technology, School of Management, 02 George Street, Brisbane City, 4000, Queensland, Australia ARTICLE INFO JEL classification: M10 M15 O31 O32 O33 L86 Keywords: Digital transformation Digital technology Innovation Business model innovation Product innovation Process innovation Performance DT ABSTRACT This study addresses the fragmented understanding of the relationship between digital transformation (DT) and innovation by proposing a multi-level framework that integrates diverse disciplinary perspectives. This framework provides deep insight into how DT influences innovation. A systematic literature review was conducted, and based on the findings, a research agenda was outlined as a roadmap to guide future studies. This research contributes to the strategic change and innovation literature by providing a multi-level framework explaining how DT-driven structural change affects innovation, considering various contingencies such as market dynamics, technological advancements, and organizational capacities. Additionally, this study contributes to the innovation and strategic information systems domain by demonstrating that DT’s role as a strategic asset for achieving innovation should align with firm capabilities, structural characteristics, and environmental and external dynamics. Introduction Digital transformation (DT) has been defined as a “fundamental change process enabled by digital technologies that aim to bring radical improvement and innovation to an entity (organization, business network, industry, or society) to create value for its stakeholders by strategically leveraging its key resources and capabilities” (Gong & Ribiere, 2021, p. 10). Recently, DT initiatives have become a significant focus of companies’ investments (Appio et al., 2021; Calderon-Monge & Ribeiro-Soriano, 2023) and have made a substantial impact on the growth of national economies (Taylor, 2022). In 2018, digitally transformed companies contributed approximately 13.5 trillion U.S. dollars to the global GDP (Calderon-Monge & Ribeiro-Soriano, 2023). In 2022, the adoption of digital transformation in firms grew. In the same year, the number of organizations intending to implement data analysis or analytics programs increased significantly compared with the previous year, and 30% of the organizations planned DT investments (Taylor, 2022). Moving forward, DT speed continued to increase. Expenditures on DT are projected to reach 2.15 trillion U.S. dollars by 2023 (Sherif et al., 2024). By 2025, a new milestone will be achieved in DT’s share of digitalization projects. Platform-driven interactions account for approximately two thirds of the 100 trillion U.S. dollars market. In the same year, approximately 90% of the new enterprise applications are predicted to integrate artificial intelligence (AI) into their processes and product offerings (Appio et al., 2021). Looking ahead to 2027, global spending on DT is expected to rise to 3.9 trillion U.S. dollars (Elsersy et al., 2021). This substantial investment highlights organizations’ increasing awareness of and reliance on DT to innovate and grow. Different factors have affected this accelerated pace and increased commitment to DT initiatives. The most significant issues are environmental concerns (Minami et al., 2021; Wang & Su, 2021), efficiency and productivity issues (Müller et al., 2018,; 2019; Sivarajah et al., 2020), and COVID and global health problems (Reuschl et al., 2022; Wade & Shan, 2020; Li et al., 2022b). Whether pushed by external shocks or competitive pressures or by environmental or efficiency desires, such pervasive DT has significantly affected organizational routines, * Corresponding author. E-mail addresses: [email protected] (M. Saeedikiya), [email protected] (S. Salunke), [email protected] (M. Kowalkiewicz). Contents lists available at ScienceDirect Journal of Innovation & Knowledge journal homepage: www.elsevier.com/locate/jik https://doi.org/10.1016/j.jik.2024.100640 Received 17 July 2024; Accepted 5 December 2024 Journal of Innovation & Knowledge 10 (2025) 100640 Available online 12 December 2024 2444-569X/© 2024 The Authors. Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). structures, processes, practices, and outcomes. These changes have considerably affected innovation and related processes, redefining how companies renew their business models to introduce product and process innovation (Bresciani et al., 2021). Along with these disruptions to the industrial landscape, scholarly research has enhanced our understanding of the relationship between DT and innovation. Scattered across various domains and disciplines, literature provides significant insights into this interplay. However, this approach is fragmented and piecemeal. For instance, from a strategic viewpoint, realizing innovation outcomes stemming from DT is imperative, considering organizational, technological, and network capabilities (e.g., He et al., 2023; Yang & Du, 2023; Yao et al., 2023) and its role in driving competitive advantage for firms (e.g., Ferreira et al., 2019; Y. Li et al., 2022; Y. Zhang et al., 2023). Using a service or marketing lens, DT’s role in customer engagement in innovation and its contribution to value co-creation have gained scholarly attention (e.g., Hunke et al., 2022; Kamalaldin et al., 2020; Rohn et al., 2021). Other studies relying on information systems approaches have focused on how emerging digital technologies (such as AI) affect companies’ innovation decisions and outcomes (e.g., Sun et al., 2024) or how the pervasive application of digital technology or its features can affect innovation networks (e.g., Tang et al., 2023; Xing et al., 2023) or improve value creation and capture in regional innovation ecosystems (Wang & He, 2024; Yang & Deng, 2023). In addition to the fragmentation of DT–innovation research in different domains (Appio et al., 2021), existing research tends to adopt different levels of analysis (Nambisan et al., 2019) and varies in terms of its theoretical focus, conceptualization of DT (Vial, 2019), and evaluation of the boundary conditions affecting the benefit of DT for innovation. Most studies tend to accept DT’s positive effect on performance as given (X. C. Guo et al., 2023), discounting the structural and institutional characteristics and contingencies that affect the scope and magnitude of this effect. Therefore, the implications of DT in innovation are yet to be established (Nambisan et al., 2017). To bridge this gap, this study investigates the innovation value of DT through a systematic literature review (SLR). This method is well-suited to the fragmented nature of existing research (Tranfield et al., 2003). It synthesizes previous studies and strengthens the knowledge base, while ensuring transparency and reducing bias (Williams Jr et al., 2021). Our study contributes to the strategy domain by offering a multilevel analytical framework illustrating how structural changes through DT influence a firm’s innovation and performance. It further highlights innovation as a multifaceted, multi-level process that occurs as firms implement digital structural changes alongside other factors operating at the managerial, firm, industry, and broader levels. Additionally, it extends the discussion of information systems (IS) on digital affordances and their role in innovation (Nambisan et al., 2017, 2019) to strategy and innovation disciplines, examining DT as an innovation strategy that drives competitive advantage (e.g., Appio et al., 2021). Research methods Design Following Denyer and Tranfield (2009), Denyer et al. (2008), and Tranfield et al. (2003), this study used an SLR approach to strengthen methodological rigor (Thorpe et al., 2005). SLRs have gained popularity in management and business literature owing to their high procedural analytical objectivity and transparency (Hallinger, 2013). Unlike traditional reviews, SLRs enhance rigor, validity, and generalizability (Denyer & Tranfield, 2009). They synthesized prior research to reinforce the knowledge base of a specific subject while maintaining the principles of openness and minimizing bias (Williams Jr et al., 2021). Moreover, reliable knowledge of the research topic can be gained through well-defined systematic practices and transparently reproducible procedures (Tranfield et al., 2003). Therefore, we conducted an SLR to synthesize fragmented research on the effects of DT on innovation across different disciplines. We adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework (Moher et al., 2016) to ensure transparency and reproducibility, with each step clearly defined. Procedure Following Atif et al. (2021), Tranfield et al. (2003), and Bilbao-Ubillos et al. (2024), a literature review was conducted in three phases: planning and implementation, analysis and synthesis, and reporting. Phase 1.Planning and implementation A planning protocol was designed to guide the overall study. It formulated research questions, established review boundaries, selected keywords and search terms, selected search databases, and defined the inclusion and exclusion criteria. Owing to its extensive coverage of peerreviewed journals in management and business, the Social Science Citation Index (SSCI) was selected as the primary database. A scoping review was performed to identify the keywords. To ensure the inclusiveness and comprehensiveness of the review, the keywords were refined through consultations with two domain experts. Phase 2.Analysis and synthesis Two analyses were performed on the selected articles, including a bibliographic analysis and a content analysis. The bibliographic analysis was used to identify key publications, trends, and methodological and theoretical approaches. Two independent reviewers conducted data extraction to ensure reliability. Discrepancies were resolved by thoroughly discussing the relevance of articles for inclusion. The content analysis was focused on identifying the main themes in the literature and categorizing them into distinct categories. Phase 3.Reporting At this stage, the findings were synthesized to map the role of DT in innovation. A multi-level framework was developed to explain the current literature on the DT–innovation interplay. Review question SLRs offer reliable answers to well-defined and specific research questions (Yuan & Hunt, 2009). They provide a clear, unbiased, and comprehensive summary of the current understanding of a specific research question (Tsafnat et al., 2014). SLRs “bear on a particular question, using organized, transparent, and replicable procedures at each step in the process.” (Littell et al., 2008, pp. 1–2). Therefore, a successful SLR requires clear review questions (Kraus et al., 2021; Lame, 2019; Rother, 2007). The primary research question was, “What role does DT play in innovation in firms?” Following previous studies (Bilbao-Ubillos et al., 2024; Calderon-Monge & Ribeiro-Soriano, 2023; Paschou et al., 2020), two sub-research questions were proposed. -RQ1. What does the current literature reveal about the impact of DT on innovation? -RQ2. Based on the findings of our study, what are the recommended directions for future research? Review boundaries Following recent SLR studies (Bilbao-Ubillos et al., 2024), five criteria were used to exclude/include and refine the articles: (1) publication type, (2) conceptual boundaries, (3) search boundaries, (4) specified timeframes, and (5) keywords. Step 1: The conceptual boundaries of DT were defined. Definitions of DT vary in focus, ranging from technology-driven changes to more M. Saeedikiya et al. Journal of Innovation & Knowledge 10 (2025) 100640 2 holistic views encompassing organizational and strategic transformations. The lack of a parsimonious and universally accepted definition (Vial, 2021) highlights the complexity of DT and the challenges in conceptualizing it as a multifaceted phenomenon. While some scholars emphasize the technological dimension, others focus on DT’s broader organizational, strategic, and societal impacts. The following section provides a critical review of existing conceptualizations to synthesize a definition of DT that is the best fit for investigating its interplay with innovation. The DT literature shows that, although the term has been increasingly discussed and adopted, it has some conceptual clarity issues (Vial, 2021). Moreover, its scope varies significantly across different contexts. Scholars have offered some basic and foundational definitions of DT. (Vial, 2021) defined DT as the improvement of an entity by triggering significant changes through information, computing, communication, and connectivity technologies. This definition establishes the integrative role of digital technology in reshaping business strategies and operations. Gong and Ribiere (2021) conceptualized DT as “a fundamental change process enabled by digital technologies that aim to bring radical improvement and innovation to an entity, such as an organization, business network, industry, or society, to create value for its stakeholders by strategically leveraging its key resources and capabilities” (p.12). By contrast, Li et al. (2018) focused on DT as a transformation driven by information technology that changes business processes, operational routines, and organizational capabilities. This perspective emphasizes DT’s disruptive potential for business models. Similarly, Chanias et al. (2019) emphasized DT’s extra-organizational influence by describing it as a holistic transformation associated with technological and economic changes at the organizational and industry levels. Warner and W¨ ager (2019) tended to rely on technological dimensions. They defined DT as the use of digital technologies (social media, mobile devices, analytics, and embedded devices) to enable significant business improvements. Nambisan et al. (2019) added to this by framing DT in terms of its transformational or disruptive implications. According to these authors, DT leads to new business models, products, and customer experiences. However, some definitions, such as those proposed by Schallmo et al. (2017) and Nadkarni and Prügl (2021), focus primarily on organizational changes triggered by digital technologies. Schallmo et al. (2017) described DT as involving the networking of actors (e.g., businesses and customers) and applying new technologies to enhance company performance. Nadkarni and Prügl (2021) viewed DT as an organizational change shaped by the widespread diffusion of digital technologies. Both definitions emphasize structural changes within organizations that are driven by digital adoption. Hanelt et al. (2021, p. 1160) built on this by defining DT “as organizational change that is triggered and shaped by the widespread diffusion of digital technologies.” Some definitions approached DT more comprehensively. They conceptualized it as transforming business activities, processes, competencies, and models to leverage digital technologies. This approach aligns with the perspective of Bughin and Van Zeebroeck (2017), who highlighted that DT is not just about technological integration. Instead, it redefines competitive advantages and value creation. A recurring theme across these definitions was the distinction between digitization, digitalization, and DT. Digitization refers to converting information from analog to digital, whereas digitalization involves transforming digital data into value-creation processes. DT is more substantial. It encompasses a complete rethinking of how organizations create and deliver value. This distinction is critical. Some definitions, such as those of Warner and W¨ ager (2019) and Fitzgerald et al. (2014), may conflate digitalization and DT. While many researchers have concentrated on DT’s technological aspects, others, such as Berghaus and Back (2016), focused on its corporate dimensions. They argued that DT is a continuous process that requires not only the adoption of new technologies but also organizational changes in structure, processes, and governance. This view was further emphasized by Sacolick (2017), who highlighted that DT involves agile organizational developmental processes. The above review shows that, while DT is widely discussed, its conceptual clarity and scope vary significantly across contexts. Synthesizing the previous definitions, we define digital transformation as “an ongoing socio-structural change that leverages digital technologies to create new value toward sustained competitive advantage.” This synthesis has different components that align with the key aspects of DT reported in the literature. Its “ongoing” component refers to the “continuous integration and evolution of digital technologies” (Gupta, 2018; Morakanyane et al., 2017; Warner & W¨ ager, 2019). The “socio-structural change” component is rooted in restructuring business models, processes, and organizational structures (Hanelt et al., 2021; Hess et al., 2016; Li et al., 2018). It also emphasizes that DT is not merely a technological or strategic phenomenon, but also a social phenomenon across the organization dealing with human and human–technology relationships. The phrase “structural” emphasizes that DT is not merely associated with changes in roles or tasks conceptualized by digitalization and digitization. Rather, it includes changes in organizational structures, distinguishing it from digitalization and digitization. The other component, that is, “leverages digital technologies,” highlights the central role of the technology described by Nambisan et al. (2017) and Schallmo et al. (2017). The elements “create new value” and “sustained competitive advantage” are core ideas in Gupta’s (2018) perspective. In their view, DT is a new value proposition that considers organizational strategy. The component “sustained” refers to the emphasis on sustained competitive advantage conceptualized by Morakanyane et al. (2017) and Fitzgerald et al. (2014). This definition further helps to conceptualize its relationship with Schumpeter’s (1934) definition of innovation as a new production function, including new products, methods, markets, and organizational structures. Schumpeter’s focus on renewing the organizational structure and exploring untapped markets resonates with the restructuring and market expansion components of DT, as discussed by Li et al. (2018) and Hanelt et al. (2021). By defining innovation as creating new production functions, Schumpeter’s view supports the idea that DT is not merely about digitizing existing processes but fundamentally reshaping business models, competition, and value creation. This holistic and inclusive approach is obvious in our definition of DT as an ongoing socio-structural change that leverages digital technologies to create new value toward sustained competitive advantage. This definition reflects Schumpeter’s conceptualization of innovation as the introduction of new production and competition methods. Moreover, Schumpeter’s new products, methods, and organizational renewal as innovations reflect the idea that DT drives long-term innovation. As Warner and W¨ ager (2019) and Vial (2021) highlight, integrating digital technologies into organizational processes creates new product and service opportunities by improving production methods and accessing new markets. These ideas are central to Schumpeter’s definition of innovation. Finally, the structural change component in our definition further allows us to understand the structural and technological changes brought about by DT as the creation of new production functions. This synergy justifies the use of our definition of DT as a framework for investigating its impact on innovation. This is particularly relevant when studying how DT boosts organizational renewal, technological advancement, and market expansion (Schumpeter’s innovation types). Step 2. The search boundaries were delimited to encompass journals indexed in the SSCI because of the comprehensive indexing of highquality, peer-reviewed journals in management, business, and social sciences (Calderon-Monge & Ribeiro-Soriano, 2023). Moreover, for quality assurance, and following previous reviews (Baral et al., 2023; Pawar, 2023; Sharma et al., 2023), only articles that appeared in the Australian Business Deans Council (ABDC) list, based on the 2022 edition of the journal ranking guide of the ABDC were included. M. Saeedikiya et al. Journal of Innovation & Knowledge 10 (2025) 100640 3 Regarding publication type, the review focused on journal articles. The language was set to be English. Book chapters, industry articles, editorials, conference proceedings, and book reviews were excluded (Calderon-Monge & Ribeiro-Soriano, 2023; Paschou et al., 2020). Articles discussing the interplay between DT and innovation were also included. Studies focusing solely on either concept, without linking them, were excluded. Moreover, to ensure a focus on high-quality and rigorous academic research, we excluded papers that were not peer reviewed. No timeframe was specified to ensure maximum inclusion of the publications. Step 3. Keyword selection was performed as a critical task (Kraus et al., 2021). We scoped the literature and brainstormed with academics to map relevant keywords (Arksey & O’malley, 2005). After identifying the relevant keywords (Table 1), a combination of keywords was used to identify articles on “Digital Transformation” and “Innovation.” The search strings/formulas were defined based on AND/OR operators. For example, “digita* transfor*” was used to capture variations such as “digital transformation” and “digitalized transformation.” Selection of relevant studies A PRISMA approach (Moher et al., 2016) was used to identify, screen, check eligibility, and select relevant studies based on our exclusion/inclusion criteria (see Fig. 1). - Identification: The identification stage involved setting review boundaries to identify relevant contributions that would be considered for further screening. A literature search was conducted using SSCI, based on the keywords listed in Table 1. First, following Kraus et al. (2022), we used the search string “Digita* transfor*” to identify the papers written on DT and distinguish them from the contributions about associated terms and concepts. We searched for document topics (titles, abstracts, and keywords). This approach identified relevant papers published on this topic. We then refined the results using innovation keywords (Table 1) to identify the contributions of DT to innovation (2675 relevant contributions). Next, we restricted the search to peer-reviewed articles. This reduced the number of contributions to 2398 articles. Finally, the paper was written in English. This step further reduced the number of articles to 2347. - Screening: To maintain the quality of the reviews, the articles were screened against the ABDC journal guide rankings. Only articles published in ABDC-listed journals were included in this study. Following this process, the number of articles was further reduced to 1183. - Eligibility: Next, the eligibility for article inclusion was assessed using the fit-for-purpose criterion (Felicetti et al., 2023; Kumar et al., 2022). Two actions were performed at this stage. First, since the focus was to investigate the DT-innovation interplay, the articles exclusively focused on innovation and DT was excluded. Second, we read the abstracts and introductions of the papers to select the final articles. The two authors extracted each article’s theoretical perspectives, methodology, findings, and implications for innovation. Discrepancies were discussed and resolved through a consensus to minimize bias, which led to the inclusion of 118 articles in the study. - Inclusion: The database search was followed by a cross-reference analysis to address the potential limitations of keyword searches and ensure completeness (Paschou et al., 2020). Eight additional articles were retrieved and screened based on the exclusion/inclusion criteria. Following this step, 126 articles were chosen to be included in our study. We analyzed and evaluated this set of articles to identify the bibliographic structure and mechanisms through which DT affects innovation. This analysis yielded a multi-level framework that mapped the research on DT-driven innovations and will guide future research. Analysis and synthesis Following previous studies (Bruton & Lau, 2008; Xu & Meyer, 2013), and to provide a comprehensive view of how DT brings innovation to firms, two complementary analyses were performed on the selected articles: bibliographic and qualitative content analysis. Such an approach enables a researcher to perform analysis and synthesis, that is, to explore the structure and content of existing research. In the analysis part, the researcher examines the structure of the existing research by providing descriptive and statistical analysis of the sample articles. In the context of the current research, the bibliographic analysis identified statistical and descriptive research patterns on the DT–innovation interplay along the spatial and temporal dimensions, while the qualitative content analysis yielded a comprehensive framework that mapped the research domains of DT-based innovations and identified how DT may influence innovation. For the bibliometric analysis, the authors extracted data from the articles, including publication outlets, publication years, overall time trends, theoretical perspectives, methodologies, and key findings. Similar bibliometric data have been reported in high-impact SLRs (Calderon-Monge & Ribeiro-Soriano, 2023; Chintalapati & Pandey, 2022). The qualitative content analysis aimed to identify general patterns in the existing literature. Following Kafetzopoulos (2022) and Shahbaz and Parker (2022), we synthesized our findings using an AMO framework by analyzing antecedents (A), mediators/moderators (M), and outcomes (O). The findings are summarized in the following sections. Part A: Bibliometric results The distribution of the sample by publication outlet and discipline Table 2 presents the distribution of the articles per publication outlet. Overall, technology and innovation management dominated the sample. The next tier was business and management, finance, and knowledge management. This distribution across disciplines suggested that the impact of DT on innovation was multifold and interdisciplinary. The most frequent outlets included Technological Forecasting and Social Change (10.32%), Technology Analysis & Strategic Management (8.73%), and The Journal of Business Research (8.73%). Papers published in Managerial and Decision Economics and Finance Research Letters had the highest frequency (6.35% each) and reflected DT’s economic and financial implications (or investment decisions regarding innovation outcomes). The second most frequent category was innovation journals; the European Journal of Innovation Management (5.56%) and the Journal of Innovation & Knowledge (4.76%) were well represented, with a particular interest in how DT influenced innovation processes and knowledge development within firms. IEEE Transactions on Engineering Management (4.76%) and Technology in Society (4.76%) were the third-most represented categories, with a Table 1 Keywords and search strategies used to identify sample articles. Keyword Search String DT "Digita* transfor*" Innovation Innovation" OR "Innovation Performance" OR "New Market" OR "Research And Development" OR "Novelty" OR "Innovative" OR "Product Innovation" OR "New Service" OR "Enhancement" OR "Radical" OR "Management Innovation" OR "Innovation Efficiency" OR "Technological Innovation" OR "Product Improvement" OR "Diffusion" OR "Research & Development" OR "Process" OR "Exploratory Innovation" OR "Improvement" OR "Process Innovation" OR "Structural change" OR "competitive advantage" OR "Technical Knowledge" OR "Incremental Innovation" OR "Exploitative Innovation" OR "Innovat*" M. Saeedikiya et al. Journal of Innovation & Knowledge 10 (2025) 100640 4 particular focus on the sociotechnical aspects of the DT-innovation interplay. Other journals have investigated DT’s impact on sustainability and green initiatives, in addition to regular innovations. These journals included Business Strategy and the Environment (2.38%) and the Journal of Environmental Planning and Management (1.59%). Journal of innovation & knowledge’s (JIK) contribution to the DT–innovation nexus The JIK has significantly contributed to conversations on DT and its innovation implications. JIK plays an evolving leadership role, and its contributions are summarized and discussed below. JIK covered a wide range of topics, including the impact of DT on innovation performance (L. Li et al., 2022), digital leadership (Chatterjee et al., 2023), and public policy (Peng & Tao, 2022). These include a strategic perspective on risk-taking (M. Y. Liu et al., 2023), an assessment of total factor productivity (Yu et al., 2024; Liu et al., 2023b), and an evaluation of the role of social capital in innovation (Lyu et al., 2022). Additionally, sector-specific insights into asset-intensive organizations (Buck et al., 2023), higher education (R. J. Li et al., 2024), and agribusiness (Xue et al., 2024) were provided, along with environmental innovation (Hung & Nham, 2023) in small and medium (Bashir et al., 2023) and private enterprises (Chen & Yu, 2024). For example, Yu et al. (2024) assessed the impact of DT on innovation investment in Chinese manufacturing firms. The authors reported a significant positive relationship between DT and investment in innovation. In their research, Total Factor Productivity (TFP) due to DT led to internal resource competition between the production and innovation departments. This study also provided policy recommendations for enhancing innovation investments in manufacturing firms. These firms are traditionally known as less innovative sectors. Xue et al. (2024) focused on Chinese agribusinesses. Their findings emphasized DT’s role in facilitating access to essential resources in traditional sectors, such as technology, talent, and capital. Finally, Chen et al. (2024) investigated private enterprises and found that DT significantly promoted innovation, particularly in wealthy regions and larger firms (Zhang et al., 2023b). The sample’s distribution by year Fig. 2 summarizes the publications and citations on the DT–innovation interplay over the years. An increasing trend can be observed in publications and the running sum of citations. We computed a trend model for the number of publications and their citations over Fig. 1. Review boundaries and selection of article search process (PRISMA approach). M. Saeedikiya et al. Journal of Innovation & Knowledge 10 (2025) 100640 5 time. The model was significant at p ≤0.05 and shows that the citations per published document increased over the years. The number of citations has grown exponentially over the years. This increase foreshadows interest in this topic in the coming years. Although a timeframe was not specified for the inclusion criteria, the sample papers that met the selection criteria were published between 2018 and 2024. This may be due to the global rise in DT and digital technology spending by firms in 2018–2023, which led to significant investments in DT and innovation activities (Taylor, 2022). In line with these trends in the industry, the academic landscape has experienced exponential growth in publications on the relationship between DT and innovation post 2017 (Appio et al., 2021) and a sharp increase in research evidence on DT published from 2018 onward (Calderon-Monge & Ribeiro-Soriano, 2023). Distribution of methodological approaches Table 3 summarizes the research methodologies used across our sampled articles. The reviewed studies were categorized into quantitative, qualitative, mixed methods, and conceptual papers. Quantitative methods dominated the samples, accounting for 73% of the studies. The most common techniques included panel regression models, PLS-SEM, Heckman two-stage models, and GMM. Qualitative methods represented 14.3% of the studies. Multiple case studies, grounded theory, and single case studies explored how DT affected performance outcomes, specifically innovation. Of the papers, 8.7% used mixed qualitative approaches with quantitative techniques, such as PLS-SEM and fsQCA. Conceptual studies, accounting for 4% of the total, explained the resource-based view (RBV) and dynamic capability view (DCV) views in the context of DT-enabled innovation. Distribution of articles per country and funding status Table 4 summarizes the significant geographical concentration of studies and presents an overview of the geographical distribution of authors and funding sources for research papers on the effect of DT on innovation. Most papers (61.11%) were affiliated with Chinese institutions. This dominance can be attributed to China’s substantial investment in digital technologies and its strategic focus on becoming a global leader in technological advancement. China’s commitment was further evidenced in our sample, as 55.56% of the funded papers received support from Chinese institutions (in contrast to 22.22% of non-Chinesefunded papers). The articles’ focus and share of government-funded papers showed that China specifically targeted innovation through DT to renew its traditional industries and manufacturing sectors. Taiwan, the United States, the United Kingdom, Italy, and other European countries contributed significantly (14.7%, 4.76%, 3.17%, and 3.97% of the papers, respectively). Table 2 The distribution of the sample by publication outlet and discipline. Publication Outlet Record Count % of 126 Discipline Technological Forecasting and Social Change 13 10.32% Technology and Innovation Management Technology Analysis & Strategic Management 11 8.73% Technology and Innovation Management Journal of Business Research 11 8.73% Business and Management Journal of the Knowledge Economy 8 6.35% Knowledge Management Finance Research Letters 8 6.35% Finance Managerial and Decision Economics 8 6.35% Economics European Journal of Innovation Management 7 5.56% Technology and Innovation Management Journal of Innovation & Knowledge 6 4.76% Technology and Innovation Management IEEE Transactions on Engineering Management 6 4.76% Engineering Management Technology in Society 6 4.76% Technology and Society Technovation 3 2.38% Technology and Innovation Management Business Strategy and the Environment 3 2.38% Business and Management Journal of Knowledge Management 3 2.38% Knowledge Management Business Process Management Journal 3 2.38% Business and Management Environment Development and Sustainability 2 1.59% Environmental Sustainability Management Decision 2 1.59% Business and Management Review of Managerial Science 2 1.59% Business and Management Journal of Environmental Planning and Management 2 1.59% Environmental Planning and Management Energy Economics 2 1.59% Economics Journal of Information & Knowledge Management 1 0.79% Knowledge Management Journal of Management & Organization 1 0.79% Business and Management International Review of Economics & Finance 1 0.79% Finance Journal of Environmental Management 1 0.79% Environmental Management Business Horizons 1 0.79% Business and Management Frontiers in Environmental Science 1 0.79% Environmental Science International Journal of Innovation and Technology Management 1 0.79% Technology and Innovation Management European Journal of Finance 1 0.79% Finance Asian Journal of Technology Innovation 1 0.79% Technology and Innovation Management Applied Economics Letters 1 0.79% Economics Corporate Social Responsibility and Environmental Management 1 0.79% Environmental Management International Entrepreneurship and Management Journal 1 0.79% Entrepreneurship Journal of Enterprise Information Management 1 0.79% Information Management and Systems Journal of Global Information Management 1 0.79% Information Management and Systems Long Range Planning 1 0.79% Business and Management Table 2 (continued) Publication Outlet Record Count % of 126 Discipline International Review of Financial Analysis 1 0.79% Finance Academy of Management Discoveries 1 0.79% Business and Management Journal of Product Innovation Management 1 0.79% Technology and Innovation Management R & D Management 1 0.79% Research and Development Management Information and Organization 1 0.79% Information Management and Systems M. Saeedikiya et al. Journal of Innovation & Knowledge 10 (2025) 100640 6 Distribution of articles by theoretical approach The distribution of papers based on theoretical approaches is shown in Table 5. The DCV was the most prevalent at 8.7%, closely followed by the RBV at 7.1%, and the knowledge-based view (KBV) at 6.3%. Together, these studies highlight that many have considered the resource and knowledge integration dynamics to drive DT-enabled innovation. In addition, 5.6% of the studies used open innovation theory, which complemented the RBV, KBV, and DCV by focusing on external resources and knowledge. These knowledge mechanisms were further highlighted, considering that the absorptive capacity (AC) theory and technology-organizationenvironment (TOE) framework appeared in 4.8% of papers. Innovation ambidexterity, represented in 4% of the papers by focusing on internal and external knowledge, complemented the knowledge and resource mechanisms associated with the DT–innovation relationship and complemented the previous KBV, RBV, DCV, AC, and TOE perspectives. Environmental management/circular economy, institutional theory, and organizational learning theory contributed equally (3.2%). Interestingly, 20.6% of the studies did not specify a theoretical framework. This highlights the need for greater theoretical rigor in future studies. Part B: Content analysis results This section synthesizes the findings of the content analysis on the relationship between DT and innovation. Following Khosravi et al. (2019), we employed a systematic content analysis method to translate textual content into distinct categories. Content analysis was conducted in five stages: open coding, coding sheets, grouping, categorization, and abstraction (Elo & Kyng¨ as, 2008). An open code was assigned to each article during this process. These codes were then organized into broader categories such as grouping dynamic and technological capabilities under strategic capability moderators. The categories of moderators, environmental factors, and firm characteristics were integrated into a general “moderators” category. To ensure accuracy, the authors conducted each coding and grouping stage independently. We applied the AMO framework to classify the variables into antecedents, mediators, moderators, and outcomes (Shahbaz & Parker, 2022). The antecedent in our framework was DT, which influences firm innovation. Mediators represented the mechanisms that channel DT’s effects on innovation, whereas moderators were boundary conditions that either strengthened or weakened the relationships between DT, mediators, and innovation outcomes. Finally, we synthesized the results into a multi-level framework visually representing the linkages between these categories (Fig. 3). The following sections discuss our findings regarding these linkages. Outcomes The results indicated that DT positively affected a firm’s innovativeness (Chen & Yu, 2024; M. Y. Liu et al., 2023; Romero & Mammadov, 2024; Yu et al., 2024) and overall performance (X. C. Orero-Blat et al., 2024; Guo et al., 2023; Wang, 2023; Zhai & Liu, 2023; Guo et al., 2023b). DT could drive business models, products, and process innovations (Bresciani et al., 2021) and boost enterprise vitality by Fig. 2. Trend analysis of publications and citations over the years. Table 3 Distribution of articles by methodological approaches. Category Number of Papers Percentage Example Methodologies Used Quantitative 92 73.0% Panel regression, Fixed-effect, and random-effect models, PLS-SEM, Fixed-effects Poisson model, Heckman two-stage model, Hierarchical regression, Serial mediation, Spatial Durbin model Qualitative 18 14.3% Multiple case studies, Grounded theory, Single case study Mixed Methods 11 8.7% Mixing qualitative methods with Questionnaire survey, PLS-SEM, and fsQCA Conceptual 5 4.0% Conceptual frameworks based on theories such as resource-based view, diffusion of innovation theory, dynamic capabilities view, organization learning theory M. Saeedikiya et al. Journal of Innovation & Knowledge 10 (2025) 100640 7 enhancing productivity by reducing asymmetric information and optimizing resource allocation (Yang & Deng, 2023; Yu et al., 2024). Sample articles suggested that DT’s effects on innovation could be shaped within and across an organization’s borders. Internally, DT influenced labor inputs and promoted intrapreneurship (Cheng et al., 2023). It strengthens organizational innovation by establishing team cohesion, trust, and information exchange. These were the enablers of structural, strategic, and systemic innovation within the firm (Zhang & Fan, 2024). Moreover, DT could restructure management control systems (Pizzi et al., 2021; Wang & He, 2024) to make innovation practices more efficient. Externally, DT improved a firm’s capacity to absorb and transform knowledge and resources and enhance customer value creation through dynamic capabilities in small and medium enterprises. This supported the creation of new distribution channels, business models, and mechanisms of value delivery (Matarazzo et al., 2021). DT also facilitated knowledge recombination processes (layering, grafting, or integration) toward innovation (Lanzolla et al., 2021). Moreover, DT promoted open innovation by boosting collaboration and knowledge sharing within and beyond the firm (Kim & Park, 2024; Luan et al., 2024; Urbinati et al., 2020). These collaborations improved innovation quality and encouraged the disclosure of valuable information (Bereczki & Füller, 2024; Che et al., 2023; Pang & Wang, 2023). Furthermore, DT saved firm resources to support investment in patent applications and invention activities (Y. Zhang et al., 2023). DT also extended its influence across supply chains by improving network layouts. In doing so, DT facilitated knowledge sharing, enhanced resource integration, and enhanced customer innovation capabilities (Q. H. Liu et al., 2024). In addition, DT reduced innovation risk by alleviating financing constraints and reducing production costs (Q. H. Liu et al., 2024). Regarding the types of innovation, DT drove both commercial and green/sustainable innovations (R. J. Lian & Zhang, 2024; Li et al., 2024; Ribeiro-Navarrete et al., 2023). For instance, for commercial innovations, cognitive computing capabilities enhanced firms’ entrepreneurial qualities such as risk-taking and proactiveness (Gupta et al., 2023; Korherr et al., 2023). Similarly, adopting AR/VR solutions in DT enabled various innovations, including product/service offerings, business processes, and business model innovations (Pessot et al., 2023). Additionally, smart technologies fostered digital process innovations by facilitating organizational unlearning and reconfiguring established processes (Wang et al., 2023). DT also affected green and sustainable innovation (Zhao and Fang, 2023). It positively influenced green technology innovation (Du et al., 2023) by optimizing human capital, easing financial constraints, and increasing media attention (Lin & Xie, 2023; Lu et al., 2023; Qiong Xu et al., 2023). Moreover, DT enhanced the quality and quantity of green technological innovation by increasing R&D intensity and reducing risk (Y. Xu et al., 2023). Through this process, firms could strengthen their collaborative networks of innovation and access to financing (Tang et al., 2023). DT’s impact on innovation could be channeled through innovation ambidexterity, that is, simultaneous radical and incremental innovations (R. J. Li et al., 2024; Wang & He, 2024; Zhu & Li, 2023; Li et al., 2024b). DT affected a firm’s breadth and depth of knowledge. This helped deepen the exploration and exploitation of new opportunities (Zhou, Yang et al., 2023). It was specifically associated with innovation radicality for firms with a high technological orientation in their top management teams (Pessot et al., 2023; Yang et al., 2023). Social media platforms utilized DT-enhanced ambidexterity through knowledge transfer practices (Scuotto et al., 2020). DT shaped radical and incremental innovations differently. The shape was an inverted U for incremental innovation and it had a direct linear effect (Duan et al., 2023). Finally, although many of the above studies showed a positive link between DT and innovation, in line with Usai et al. (2021), we argue that Table 4 Distribution of articles based on country and funding status. Country Number of Authors Percentage (%) Funding Source Number of Papers Percentage (%) China 77 61.11% Chinese-Funded 70 55.56% Taiwan 9 7.14% Non-Chinese-Funded 28 22.22% South Korea 2 1.59% Total Funded Papers 98 77.78% Norway 1 0.79% Unfunded Papers 28 22.22% Spain 4 3.17% Total Papers 126 100% Italy 5 3.97% Brazil 2 1.59% Saudi Arabia 3 2.38% United Kingdom 4 3.17% Germany 2 1.59% Portugal 3 2.38% United States 6 4.76% South Africa 1 0.79% India 2 1.59% France 1 0.79% Finland 1 0.79% Total 126 100% Table 5 The distribution of articles based on their theoretical approaches. Theoretical Approach Record Count Percentage Dynamic Capabilities View 11 8.7% Resource-Based View (RBV) 9 7.1% Knowledge-Based View (KBV) 8 6.3% Open Innovation Theory 7 5.6% Absorptive Capacity Theory 6 4.8% Technology-Organization-Environment (TOE) Framework 6 4.8% Innovation Ambidexterity 5 4.0% Environmental Management/Circular Economy 4 3.2% Institutional Theory 4 3.2% Organizational Learning Theory 4 3.2% Corporate Social Responsibility (CSR) 3 2.4% Human Capital Theory 3 2.4% Organizational Change Theory 3 2.4% Search and Recombination Theory 2 1.6% Organizational Inertia Theory 2 1.6% Total Factor Productivity (TFP) 2 1.6% Organizational Unlearning Theory 2 1.6% Innovation Diffusion Theory 2 1.6% Path Dependency Theory 1 0.8% Low-Carbon Knowledge Search (LCKS) 1 0.8% Green Knowledge Management (GKM) 1 0.8% Herd Behavior Theory 1 0.8% R&D Strategy and Flexibility 1 0.8% Competitive Strategy Theory 1 0.8% Unspecified Theory 26 20.6% Total 126 100% M. Saeedikiya et al. Journal of Innovation & Knowledge 10 (2025) 100640 8 Another point related to participation dynamics is that, as noted earlier, these interactions are dynamic, feedback-driven, and real-time. Therefore, a resource-based conceptualization of these dynamics cannot provide a sound understanding of the nature of the phenomenon. Rather, we need to focus on theories that conceptualize DT-driven innovation as a co-creative, collective, collaborative, and dynamic phenomenon that shapes the interactions, commitments, and intentions of its stakeholders. Therefore, it is necessary to reconceptualize these mechanisms and focus on dynamism. In this regard, some sample articles relied on perspectives such as the dynamic capability perspective (e. g., Pang & Wang, 2023; Zhang et al., 2022) or open innovation (e.g., Wu et al., 2022) theories. However, the value co-creation perspective and the social exchange theory have not yet been explored in this context. Future research should develop new explanations of DT-driven innovation and performance using dynamic lenses. For example, the user participation phenomenon on DT platforms using a dynamic capability lens should answer the following question: How can firms streamline their innovation process considering extended market reach and enhanced user participation thanks to DT? Similarly, future research should answer the following question through a social exchange lens: How do social exchange contracts shape and act in DT-enabled collaborative networks for innovation? Moderators: Deeper investigation of boundary conditions Research on the effects of DT on innovation has included a variety of boundary conditions that may put some contingencies on this effect. Although these boundary conditions are diverse, further investigation of the contextual conditions related to DT and innovation could generate valuable insights into this interplay. For example, a country’s digital economy or infrastructure development level can boost its entrepreneurial outcomes (Orlandi et al., 2021). Simultaneously, digital infrastructure can signal the availability of technological or technical capital for the success of DT initiatives. Therefore, gaining cross-country or comparative insights into existing research based on the digital infrastructure development level is valuable. In addition to the intense need for cross-country insights, industrylevel insights should be incorporated into existing research. While there are some insights into the technical and technological aspects of industry competition (e.g., Niu et al., 2023; Zhou, Xu et al., 2023), marketization (Wang & He, 2024), technological environment (Wang et al., 2023), and technological upgrading (Yang & Deng, 2023), insights into the sociological and cultural aspects of industries are rare. For example, DT adoption varies according to industry standards and technological capabilities. Thus, the benefits of DT for innovation may differ across firms with different capabilities and in different industries. Industries experiencing high customer expectations should establish co-creation mechanisms through DT to better meet marketing demands. While the boundary conditions for the direct effect of DT on innovation are diverse, the moderating factors between the antecedent–mediators and those between mediators–outcomes are underexplored. Therefore, a more detailed understanding of the moderators that influence antecedent–mediators and mediator–outcomes is needed. In the first category, firm capabilities, including dynamic capabilities, innovation capabilities, decision-making styles, and change management practices, were studied. However, many other factors enable firms to activate or translate DT into these mediating mechanisms. Surprisingly, there is a lack of studies investigating the environmental and contextual factors. For example, while DT enables firms to access innovation financing or reduce the financial risks of innovation (Liu et al., 2024; Q. H. Liu et al., 2024; Yong Xu et al., 2023), the ease of access to financing and the strength of financial institutions at the regional or national level can modify this relationship. DT also provides a knowledge-accelerating mechanism (Gong et al., 2023; Lanzolla et al., 2021; Scuotto et al., 2020; Urbinati et al., 2020; van Meeteren et al., 2022). However, this acceleration of knowledge sharing and accumulation can depend on industry norms, the level of technological sophistication, and the performance of innovation ecosystems and networks in regions or industries. Therefore, industrial, regional, and national institutions can activate or enhance DT-enabled innovation mechanisms. The same lines of reasoning can be applied to further investigate the moderators between the mediators and outcomes. For example, once innovation mechanisms are activated, firm characteristics should enable them to maximize their use of these mechanisms toward innovation. In our sample articles, a firm’s absorptive capacity, agility, environmental dynamism, technological capability, and ambidextrous innovation strategies were found to accelerate its benefits from these mechanisms toward innovation. For example, the market structure and size can define a firm’s incentive to innovate. In terms of market structure, small fragmented markets in which players have limited power can incentivize firms to innovate as a means of differentiation. In the presence of large players, innovation can focus more on quality to help small players maintain their relative power. Markets with higher levels of technology infrastructure or those with a high presence of incubators can provide firms with tools to maximize their benefits from the innovation that DT enables. They can also help firms improve their time-to-market to meet emerging user demands. The regulatory environment can also affect a firm’s innovation standards and quality (Q. Xu et al., 2023). Table 7 Research agenda based on bibliographic results. Research Agenda Gaps Future Research Recommendations Addressing Fragmented Research and Interdisciplinary Integration Fragmented research landscape across disciplines, underrepresentation in strategy and entrepreneurship Integrate strategic and entrepreneurial perspectives to explore how DT strategies impact entrepreneurial activities and business model innovations. Increasing Geographical and Funding Diversity Geographical concentration in China, heavy reliance on Chinese funding sources Include perspectives from underrepresented regions to capture a comprehensive picture of DT’s influence on innovation across different cultural and economic contexts. Diversifying Methodological Approaches Limited adoption of longitudinal approaches, lack of methodological diversity, and need for multi-level studies. Increase the adoption of longitudinal approaches to understand how DT’s performance outcomes, such as innovation, emerge over time. Diversify methodological approaches, e.g., using configurational approaches (Lin et al., 2022; Cortese et al., 2024). Include more multi-level studies. Investigate team-level dynamics, norms, and practices enabling innovation in digitally transformed firms. Integration of Theoretical Perspectives Lack of integration of theoretical perspectives at different levels, limited consideration of DT’s dynamic nature Integrate theoretical perspectives at macro, meso, and micro levels to better conceptualize and explain firms’ innovation outcomes. Consider the dynamic nature of the exchanges between firms and their wider environment beyond viewing DT as merely an innovation optimization function. M. Saeedikiya et al. Journal of Innovation & Knowledge 10 (2025) 100640 15 Finally, while many studies investigate how DT brings about successful outcomes for a firm, there is a paucity of research on the organizational roles that amplify DT’s performance effects (Zoppelletto et al., 2023). For example, the sample articles show that DT can increase a firm’s knowledge depth, breadth, and resource sharing. However, it is important to know how different roles and the structure of their relationships can facilitate the exchange of knowledge, resources, and support to fertilize DT’s innovation outcomes. Research agenda based on bibliometric results Addressing fragmented research and interdisciplinary integration Bibliometric analysis revealed a fragmented research landscape across various disciplines, including business and management, economics, technology and innovation, knowledge management, environmental management, information systems, and finance. Journals such as the Journal of Business Research, Technological Forecasting, and Social Change publish the maximum number of articles on DT and innovation. However, entrepreneurship and strategy journals were underrepresented. Future research should bridge this gap by integrating strategic and entrepreneurial perspectives in the study of DT and innovation. Further methodological diversification and theoretical integration Most articles in this review were based on a single theory (e.g., Holopainen et al., 2022), and 26 did not have distinctive theoretical perspectives. Surprisingly, many studies have used single theoretical lenses to study a multifaceted, interdisciplinary phenomenon and its performance effects, and most studies have approached DT’s performance effects in cross-sectional designs (e.g., Barragan et al., 2024; Lozada et al., 2023) or short periods after implementation (e.g., Yong Y. Zhao et al., 2023; Xu et al., 2023). Combining these two tendencies in the sample articles, we argue that future research could rely on adopting more longitudinal approaches and integrating more theoretical lenses (multi-level designs). Specifically, longitudinal studies provide an opportunity to understand how DT performance outcomes, such as innovation, emerge over time. In terms of the need for multi-level studies or multi-perspective ones (Urbinati et al., 2020), the sample articles suggest that future research can better conceptualize and explicate the innovation outcomes of firms using a combination of macro-, meso‑, micro levels, and theoretical perspectives. Considering theoretical integration, multi-level methodological approaches for understanding DT and its relationship with innovation can be a fruitful study area. For example, while transaction cost economics can provide some insights into information asymmetry and transaction costs, it can be integrated with innovation diffusion theories or other firm-level theories, such as a knowledge-based view of the firm, to better conceptualize the risk perception or incentive structures of knowledge sharing for DT-enabled innovation. These insights can be further enhanced using game-theory approaches to understand users’ knowledge-sharing behaviors. Similarly, the social exchange theory can be coupled with the knowledge integration perspective to better explain the nature of relational contracts that affect users’ knowledge-sharing behaviors and mechanisms for DT-enabled interactions and value cocreation efforts. The same reasoning can be applied to shared resources and governance mechanisms beyond the firm level (e.g., ecosystem) and how these exchanges can eliminate opportunistic behaviors when using shared resources and enhance the viability of the ecosystem or digital platforms on which firms play a role (Zoppelletto et al., 2023). Practical and policy implications This systematic review revealed that DT’s potential to boost innovation was most effective when aligned with an organization’s strategic orientations, environmental contingencies, firm characteristics, and context. DT must be viewed not merely as a technological upgrade or renewal but also as a strategic asset for boosting innovation in products, services, and processes. An interactive and synergetic relationship among technology, creativity, and the external environment is required to benefit from DT toward innovation and firm performance. Another key insight from this review is that DT deals with the knowledge and resource dynamics around firms and facilitates innovation mechanisms by connecting various stakeholders such as customers, suppliers, and partners through digital ecosystems. However, the assumption that DT automatically enhances innovation performance may be misleading. To leverage the full potential of DT toward innovation, firms must communicate a culture of openness, knowledge sharing, support, and trust within themselves and the broader ecosystem and ensure that DT is aligned with the internal strategies and the resource and knowledge dynamics in the broader ecosystem and environment. Conclusion The current study aimed to investigate the state of research on DT’s effect on innovation and provide a research agenda that acts as a roadmap for future research. Based on a bibliographic and content analysis of 126 articles, this study presents a multi-level framework for understanding the effects of DT on innovation, mediating mechanisms, and boundary conditions. Given the contingencies shaped by the structural, environmental, and firm characteristics that affect the magnitude and scope of this effect, the framework provides a fundamental understanding of how the effects of DT on firm innovation and performance are actualized. CRediT authorship contribution statement Mehrzad Saeedikiya: Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Sandeep Salunke: Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology, Investigation, Data curation, Conceptualization. Marek Kowalkiewicz: Writing – review & editing, Writing – original draft, Visualization, Supervision, Methodology, Conceptualization. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments We would like to thank the editorial team of JIK, and the reviewers for their invaluable feedback throughout the review process. 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Organizational roles in the context of digital transformation: A micro-level perspective. Journal of business research, 157, Article 113563. Author Biographies: Mehrzad Saeedikiya is a Ph.D. Scholar at Queensland University of Technology Business School, where he is the recipient of two prestigious scholarships from QUT and the Centre for Future Enterprise. Mehrzad has contributed his expertise through teaching and research roles at Tsinghua University, Bologna University, UAB Barcelona, and the University of Milan. His current research at QUT is centered on exploring the intersection of digital transformation and innovation. Guided by QUT’s leadership in the information system and entrepreneurship domains, Mehrzad is interested in how dynamic capabilities emerge and interact with other firm capabilities, ultimately influencing companies’ innovative performance. Mehrzad’s work on the interplay of digital technology, digital transformation, and innovation has appeared in the Journal of Cleaner Production, International Journal of Entrepreneurship Behaviour and Research and Small Business Economics. Sandeep Salunke: Associate Professor Sandeep Salunke earned his PhD at the University of Queensland. His doctoral thesis investigates the competitive strategies of entrepreneurial project-oriented firms through the lens of dynamic capabilities. Sandeep’s research is being developed into papers for leading academic journals, including Industrial Marketing Management and Journal of Business Research, and Journal of Product Innovation Management, to name a few. He is involved in three large ARC Discovery projects at QUT Business School and UQ Business School. Sandeep’s research is centered around dynamic capabilities, service innovation, competitive strategy, and technology entrepreneurship. Marek Kowalkiewicz: Marek Kowalkiewicz is a Professor and Chair of Digital Economy at QUT Business School. Recognized as one of the Top 100 Global Thought Leaders in Artificial Intelligence by thinkers360, he has extensive experience leading global innovation teams in Silicon Valley and holding research positions at SAP and Microsoft Research Asia. His upcoming book, titled "The Economy of Algorithms: AI and the Rise of the Digital Minions," delves into the impact of AI on the digital economy. M. Saeedikiya et al. Journal of Innovation & Knowledge 10 (2025) 100640 20