The Evolution of Digital Business and Management Research: A Bibliometric Review of Strategy, Platforms, Analytics, and AI Governance

Digital business and management research has expanded rapidly since 2017, reflecting the growing influence of digital transformation, platform ecosystems, analytics capabilities, and artificial intelligence in organizational settings. This expansion has produced a diverse and increasingly fragmented body of scholarship across strategy, information systems, innovation management, marketing, and organizational theory. Although several reviews have clarified specific subfields, the broader intellectual evolution of digital business research remains insufficiently mapped from a bibliometric perspective. This bibliometric review examines the evolution of digital business and management research. Its objective is to identify publication trends, intellectual foundations, thematic clusters, and emerging research fronts across four focal domains: digital strategy, platform-based business models, data analytics, and AI governance. By integrating performance analysis with co-citation and keyword-oriented interpretation, the article provides a structured view of how the field has developed. The analysis indicates that digital business research has moved from broad discussions of digital transformation toward more specialized debates on strategy formation, ecosystem governance, analytics-driven value creation, and responsible AI-enabled management. It also shows that the field is increasingly shaped by interdisciplinary connections between strategic management, information systems, innovation studies, and organizational governance. Five tables summarise the data source, publication trends, keyword clusters, leading contributors, and emerging research gaps. The review concludes that digital business and management research is becoming more mature, but also more complex. The next stage of scholarship requires stronger integration across platform strategy, analytics capabilities, organizational accountability, and AI governance. Future research should move beyond technological adoption narratives and examine how digital technologies reshape authority, coordination, value capture, and managerial responsibility.

Digital business and management research has developed into a major interdisciplinary field because digital technologies increasingly shape strategy, innovation, organizational design, and competitive advantage. Foundational contributions on digital innovation management and digital transformation positioned digital technologies not merely as operational tools, but as forces that reconfigure products, processes, business models, and institutional arrangements [1, 2]. Subsequent reviews clarified that digital transformation involves strategic renewal, organizational change, and the redefinition of value creation across multiple levels of analysis [3, 4]. This expansion makes the field intellectually rich, but it also creates fragmentation across strategy, information systems, innovation, and management research.

Traditional narrative and systematic reviews have provided important conceptual syntheses of digital transformation, platform competition, and analytics capabilities, but they do not always reveal the field’s citation structure or thematic evolution at scale. Bibliometric analysis complements these reviews by tracing publication growth, influential documents, co-citation relationships, and keyword clusters across a defined corpus [5, 6]. Methodological guidance on bibliometric review design emphasizes that science mapping can identify intellectual bases, research fronts, and structural patterns that remain less visible in purely qualitative reviews [6, 7]. This makes bibliometrics particularly useful for a field whose boundaries are still expanding.

The need for a bibliometric perspective is especially strong because digital business research now spans several interrelated streams. Digital strategy and transformation research examines how firms redesign strategic processes and capabilities in response to digital technologies [8-10]. Platform and ecosystem research investigates how firms coordinate multi-sided interactions, govern complementors, and capture value in digital environments [11-14]. Analytics and AI research further extends the field by examining data-driven capabilities, algorithmic decision-making, and the organizational governance of intelligent systems [15-18].

This article therefore aims to map the evolution of digital business and management research from 2017 to 2025 through a bibliometric review of strategy, platforms, analytics, and AI governance. It uses the 36-article corpus as a focused bibliometric sample that combines methodological references with substantive contributions to digital business scholarship. The review identifies publication trends, key intellectual anchors, thematic clusters, leading outlets, and future research directions. In doing so, it treats digital business research as a connected field rather than as a set of isolated technology-specific literatures.

The bibliometric design follows established guidance for conducting transparent and reproducible science mapping reviews. Bibliometrix provides an R-based environment for performance analysis and science mapping, while general bibliometric guidelines recommend defining the research question, search strategy, inclusion criteria, data cleaning process, and analytical outputs before interpretation [5, 6]. Software-oriented reviews also show that bibliometric studies require careful tool selection because different packages emphasize different forms of citation, co-word, and network analysis [7]. This review therefore combines performance analysis logic with interpretive clustering of the selected literature.

The data source was constructed as a curated corpus of 36 peer-reviewed journal articles published between 2017 and 2025. The corpus includes methodological articles on bibliometric analysis and literature review design, alongside substantive contributions on digital transformation, digital strategy, platforms, analytics, and AI governance [5, 19, 20]. The inclusion criteria required journal publication, direct relevance to digital business or bibliometric mapping, availability of DOI metadata, and publication within the defined time window. Books, reports, theses, websites, and conference papers were excluded to maintain peer-reviewed journal consistency.

The search logic was designed to capture both the methodological and thematic foundations of the field. Keywords combined bibliometric terms such as “bibliometric review,” “science mapping,” “co-citation,” and “keyword co-occurrence” with substantive terms such as “digital transformation,” “digital business,” “platforms,” “analytics,” “artificial intelligence,” and “management research.” The analytical procedures included annual publication counting, outlet classification, thematic grouping, and interpretive mapping of citation relationships across strategy, platform, analytics, and AI governance streams. This approach reflects the broader recommendation that bibliometric reviews should connect quantitative mapping with theoretical interpretation rather than present metrics alone [6, 19].

The final corpus contains articles from journals in business, management, information systems, innovation, and scientometrics-related fields. It includes both highly general reviews of digital transformation and specialized contributions on platforms, big data analytics, and artificial intelligence in organizations [17, 20-22]. Table 1 summarises the bibliometric data source, search protocol, and descriptive statistics of the final corpus. The table is placed here to make the methodological boundaries of the review explicit before the trend and cluster analyses are presented.

The publication trend in the reviewed corpus shows a clear concentration of influential work between 2017 and 2021. Early contributions established the conceptual foundations of digital innovation, platform strategy, big data analytics, and digital transformation [1, 11, 15, 23]. The 2017 publications are especially important because they frame digital innovation management, platform strategy, and analytics-based performance as central components of the emerging digital business research agenda. These works became intellectual anchors for later research on strategy renewal, ecosystem coordination, and data-driven organizational capability.

The 2018 and 2019 publications show the field moving from general recognition of digital change toward more precise theorization of organizational transformation and platform ecosystems. Institutional perspectives on digital innovation, theories of ecosystems, and strategy-making in pre-digital organizations expanded the conceptual vocabulary of the field [2, 10, 12]. Research on digital transformation also became more explicitly linked to business model change, strategic renewal, and innovation management [3, 9, 24]. This period marks a transition from identifying digitalization as a phenomenon to explaining how it reshapes managerial processes and competitive dynamics.

The 2020 and 2021 publications represent the strongest concentration of articles in the corpus, indicating the maturation of digital business research around reviews, frameworks, and governance-oriented debates. Systematic reviews and multidisciplinary reflections consolidated the digital transformation literature, while platform competition and digital platform boundary research clarified ecosystem-level competition [4, 8, 13, 14]. At the same time, AI-focused management research became more visible through work on algorithms at work, trust in AI, artificial intelligence and management, and the automation–augmentation paradox [17, 18, 25, 26]. Table 2 shows the annual publication and citation trends and the most prolific journals.

The later period from 2022 to 2025 shows fewer articles in the focused corpus, but the selected works indicate a shift toward bibliometric consolidation and more specialized domain mapping. Research on digital transformation in business and management provides overview-level synthesis, while newer bibliometric studies address digital transformation management, SMEs, and digital business model innovation [20, 27-29]. This suggests that the field is entering a reflective stage in which scholars increasingly map prior research rather than only proposing new conceptual frameworks. The trend also indicates that digital business research is becoming more differentiated by context, firm type, and governance problem.

The first major theme is digital strategy and transformation, where the literature explains how firms move from technology adoption toward strategic renewal. Digital transformation is treated as a multidimensional process involving organizational redesign, capability development, and business model change rather than isolated IT implementation [3, 8]. Reviews of the field show that strategy, organizational change, and value creation have become central conceptual anchors in digital business research [4, 20, 30]. This stream therefore provides the strategic foundation for interpreting the evolution of the wider field.

The second theme concerns platforms and ecosystems, where digital business research shifts attention from the firm as a standalone actor to the firm as an orchestrator within multi-sided networks. Platform strategy scholarship explains how digital interfaces, complementors, and ecosystem boundaries shape competition and value capture [11-13]. Digital platform ecosystem research further shows that governance, architecture, and participation rules influence how platforms scale and stabilize over time [14, 22]. In bibliometric terms, this theme forms a bridge between strategic management and information systems research.

The third theme is analytics capability and data-driven value creation. Big data analytics research emphasizes that data alone does not produce performance benefits unless firms develop analytical methods, organizational routines, and dynamic capabilities that convert data into managerial action [15, 16, 23]. Systematic reviews of analytics capability show that the literature increasingly links analytics to strategic agility, decision quality, and firm performance [31]. This theme connects digital business research to the measurement of organizational capability and the managerial use of data-intensive technologies.

The fourth theme is AI governance and algorithmic management, which has become increasingly important as organizations delegate tasks and decisions to intelligent systems. AI management research highlights tensions between automation and augmentation, while algorithmic work studies reveal new forms of control, discretion, and accountability in organizational settings [17, 18, 25]. Trust in AI and sociotechnical integration are also central because adoption depends on human interpretation, institutional legitimacy, and responsible design [14, 26]. Together, these four themes show that digital business research has evolved from technology-enabled change toward a broader concern with strategic direction, ecosystem coordination, analytics capability, and governance responsibility.

The co-citation structure of the corpus suggests that the field is organized around several intellectual bases rather than a single dominant theory. Methodological references on bibliometric analysis and science mapping provide the review architecture, while digital transformation reviews supply the conceptual foundation for identifying the field’s boundaries [5-7, 19]. Strategy-oriented transformation articles form one citation base, platform and ecosystem articles form another, and analytics and AI governance articles form more specialized but increasingly connected bases [3, 12, 17]. This structure indicates that digital business research is interdisciplinary by design.

Keyword co-occurrence interpretation points to four recurring term families: digital transformation and strategy, platforms and ecosystems, big data analytics and capability, and artificial intelligence and governance. Digital transformation keywords are typically associated with strategy, organizational change, dynamic capabilities, and business model innovation [8-10]. Platform keywords cluster around ecosystems, platform competition, boundaries, complementors, and value capture [11, 13, 14]. Analytics and AI terms cluster around big data, firm performance, algorithmic control, trust, and human–AI interaction [16, 23, 25, 26].

The temporal shift in clusters shows a movement from early emphasis on digital innovation and analytics toward later attention to platform ecosystems and AI-enabled organizing. Early articles from 2017 and 2018 positioned digital innovation, platform strategy, and big data analytics as central research frontiers [1, 11, 15]. From 2019 onward, digital transformation strategy and organizational renewal became more visible, followed by stronger attention to AI management and algorithmic governance in 2020 and 2021 [3, 9, 17, 18]. Table 3 presents the main keyword clusters and their thematic interpretation.

The cluster analysis shows that the four focal themes are not isolated. Strategy provides the managerial framing for digital transformation, platforms extend the unit of analysis from firms to ecosystems, analytics explains how data becomes a capability, and AI governance addresses the organizational consequences of intelligent systems [8, 12, 17, 31]. This interdependence is important because digital business research increasingly studies how firms combine strategic intent, technological infrastructure, data resources, and governance mechanisms. The field’s maturation can therefore be understood as a shift from separate technology domains toward integrated digital management theory.

Figure 1 synthesises the bibliometric evolution of digital business and management research by showing how strategy, platforms, analytics, and AI governance form interconnected research clusters around a shared methodological core.

The leading journals in the corpus reflect the interdisciplinary character of digital business research. Journal of Business Research is especially prominent because it includes major contributions on bibliometric guidance, digital transformation, big data analytics, AI in marketing, and sociotechnical AI adoption [6, 8, 15, 19, 21, 23, 32]. MIS Quarterly anchors the information systems side of the field through digital innovation management, AI management, and delegation to agentic information systems [1, 17, 35]. Strategic Management Journal, Long Range Planning, and The Journal of Strategic Information Systems provide the strategic and platform-oriented foundations [9-13].

Author influence in the corpus is distributed across several research communities rather than concentrated in a single school. Nambisan appears as a key contributor to digital innovation and digital entrepreneurship research, while Vial, Verhoef, and Hanelt are central to digital transformation synthesis [1, 3, 4, 8, 24]. Gawer, Jacobides, McIntyre, Srinivasan, Rietveld, and Schilling represent the platform and ecosystem stream [11-14]. Berente, Raisch, Krakowski, Kellogg, Valentine, Christin, Glikson, and Woolley anchor the AI governance and algorithmic organizing stream [17, 18, 25, 26].

The dominant research streams can be interpreted through article-level influence. Digital transformation reviews are influential because they define the field’s conceptual boundaries and organize prior work into strategic, organizational, and technological dimensions [3, 4, 8, 20]. Platform studies are influential because they explain how digital competition occurs through multi-sided structures, ecosystem participation, and boundary decisions [11, 13, 14]. AI and analytics articles are influential because they address the managerial consequences of data-intensive and intelligent technologies for performance, trust, decision-making, and control [16, 17, 25, 31].

The distribution of outlets and authors also shows that digital business research is shaped by collaboration across management, information systems, innovation, marketing, and organization studies. Bibliometric studies of digital transformation management and business model innovation indicate that recent work increasingly uses citation and keyword mapping to consolidate this scattered knowledge base [27-29]. Table 4 identifies the most influential journals, authors, and article-level metrics. The table summarizes influence qualitatively because the present corpus is focused and interpretive rather than a full database export.

The first major gap is the limited integration between digital strategy and AI governance. Digital transformation studies explain strategic renewal and organizational change, but they often give less attention to the governance of algorithmic decision systems and the distribution of accountability inside firms [3, 9, 17]. Conversely, AI governance research examines automation, augmentation, trust, and algorithmic control, but it is not always embedded in broader strategy and business model debates [18, 25, 26]. Future research should connect digital strategy with AI accountability so that governance becomes part of transformation design rather than a late-stage compliance issue.

The second gap concerns platformization and its organizational consequences. Platform research has developed strong explanations of ecosystems, competition, boundaries, and complementor coordination [11-14, 22]. However, less is known about how platform dependence affects managerial autonomy, labor control, data ownership, and value distribution across ecosystem participants. Research on algorithmic organizing and learning algorithms suggests that these questions are increasingly important because digital platforms often govern through data, ranking, recommendation, and automated coordination mechanisms [25, 33].

The third gap concerns measurement and performance attribution in analytics-driven digital business. Big data analytics studies link analytics capability to firm performance, but the causal pathways between data infrastructure, organizational routines, decision quality, and financial outcomes remain difficult to isolate [16, 23, 31]. Similar measurement challenges appear in digital transformation research, where the outcomes of transformation may include strategic flexibility, customer experience, innovation speed, and organizational learning rather than only short-term financial performance [4, 8]. A stronger future research agenda should therefore combine bibliometric mapping with theory-driven measurement models.

The fourth gap concerns the uneven development of bibliometric evidence across subfields. Recent bibliometric studies have begun to map digital transformation management, SME transformation, business model innovation, and AI in marketing, but several important areas remain underdeveloped [21, 27-29]. Table 5 maps the identified research gaps to emerging directions and proposed research questions. The table is intended to translate the bibliometric synthesis into a forward-looking research agenda.

A first limitation concerns corpus coverage and database bias. Bibliometric studies are highly sensitive to search strings, database selection, indexing rules, and document inclusion criteria, which can influence the apparent structure of a field [5, 6]. Although software tools such as Bibliometrix, VOSviewer-oriented workflows, and other bibliometric packages support transparency, they cannot eliminate the risk of missing relevant articles or overrepresenting highly indexed journals [5, 7]. For digital business research, this limitation is important because relevant work is distributed across management, information systems, innovation, marketing, and organization studies.

A second limitation is that citation metrics do not fully capture theoretical depth, methodological quality, or practical significance. Citation counts can be affected by publication age, journal visibility, review status, and the cumulative advantage of already prominent articles [6, 19]. This is especially relevant in a fast-moving field where recent work on AI governance, digital transformation management, and business model innovation may not yet have had enough time to accumulate citations [17, 27, 29]. As a result, bibliometric prominence should be interpreted as one indicator of influence rather than as a direct measure of scholarly quality.

A third limitation is that keyword and co-citation maps require interpretation and may simplify complex theoretical relationships. Articles on digital transformation, platforms, analytics, and AI governance may share terms but differ substantially in assumptions, levels of analysis, and normative implications [8, 12, 16, 18]. Bibliometric clustering is therefore most useful when combined with close reading and conceptual synthesis, as recommended by review methodology scholarship [6, 19]. The present review should be read as a structured quantitative mapping of a focused corpus, not as a substitute for detailed theory evaluation.

This bibliometric review has mapped the evolution of digital business and management research from 2017 to 2025 across the four central themes of strategy, platforms, analytics, and AI governance. The analysis shows that the field has moved from broad digital transformation narratives toward a more differentiated research landscape concerned with strategic renewal, platform ecosystems, data-driven capabilities, and algorithmic accountability. It also shows that digital business research is increasingly interdisciplinary, drawing from strategic management, information systems, innovation studies, marketing, and organization theory.

The review identifies a field that is intellectually mature enough to possess recognizable clusters, but still fragmented enough to require stronger integration. Strategy research explains why and how firms transform, platform research explains ecosystem-level coordination and competition, analytics research explains data-driven capability formation, and AI governance research explains the organizational consequences of intelligent systems. The next challenge is to study these themes together rather than treating them as separate streams.

Future digital business research should move beyond general claims that digital technologies create value and should instead examine the conditions under which value is produced, captured, governed, and contested. More integrative work is needed on AI accountability, platform power, analytics measurement, human–AI organizing, and context-sensitive transformation. The field’s future contribution will depend on its ability to connect technological change with strategic judgment, organizational responsibility, and societal impact.

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Aisha Bello, Ibrahim Musa & Zainab Sule contributed to this work.

Authors and affiliations

Department of Digital Enterprise Studies, Faculty of Management Sciences, University of Ibadan, Ibadan, Nigeria
Aisha Bello & Zainab Sule

Department of Business Analytics and Systems, Faculty of Commerce, Ahmadu Bello University, Zaria, Nigeria
Ibrahim Musa

Corresponding author

Correspondence to Aisha Bello

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