1. Introduction
The contemporary business landscape is undergoing a fundamental metamorphosis driven by the pervasive adoption of digital technologies across all organizational functions and value chain activities. Corporate digital transformation (CDT) has emerged as one of the most significant strategic priorities for organizations seeking to maintain competitiveness, enhance operational efficiency, and create sustainable value in an increasingly digitized global economy. The phenomenon extends far beyond the mere implementation of new technologies, encompassing profound changes in organizational structures, business models, customer engagement approaches, and strategic orientation. As digital technologies continue to evolve at an unprecedented pace, understanding the nature, measurement, and implications of corporate digital transformation becomes increasingly critical for both academic inquiry and managerial practice.
The academic interest in corporate digital transformation has grown exponentially over the past decade, reflected in the substantial increase in scholarly publications across multiple disciplines. Researchers in accounting have examined how digital transformation affects financial reporting quality, audit processes, and management accounting systems (Chen et al., 2024; Yang et al., 2024a). Finance scholars have investigated the relationship between digital capabilities and firm valuation, capital structure decisions, and investment efficiency (Moolkham, 2025; Zhou et al., 2024). Economists have analyzed the macroeconomic implications of digitalization at the firm and industry levels (Zou et al., 2024), while management researchers have explored organizational change processes, leadership challenges, and strategic adaptation mechanisms associated with digital transformation initiatives (Verhoef et al., 2021; Plekhanov et al., 2023). This cross-disciplinary interest underscores the multidimensional nature of digital transformation and its far-reaching implications for business organizations.
Despite the growing body of literature, the field remains characterized by conceptual ambiguity, inconsistent measurement approaches, and fragmented empirical findings. Various definitions of digital transformation coexist in the literature, ranging from narrow conceptualizations focusing on technology adoption to broader perspectives encompassing organizational and strategic transformation. Similarly, measurement methodologies vary substantially across studies, from simple binary indicators of technology implementation to complex multi-dimensional indices derived from textual analysis of corporate disclosures (Yang et al., 2024b; Zou et al., 2024). These inconsistencies hinder the accumulation of knowledge, limit the comparability of findings across studies, and impede the development of robust theoretical frameworks.
The proliferation of digital technologies, including artificial intelligence, blockchain, cloud computing, big data analytics, and the Internet of Things, has created new opportunities and challenges for organizations seeking to leverage digital capabilities for competitive advantage. The COVID-19 pandemic further accelerated digital transformation initiatives across industries (Pan & Wang, 2024), compelling organizations to rapidly adopt digital solutions for remote work, customer engagement, and supply chain management. This accelerated pace of digitalization has intensified the need for systematic research that can inform both academic understanding and practical implementation of digital transformation strategies.
This systematic literature review addresses these challenges by providing a comprehensive synthesis of the existing research on corporate digital transformation. The review is guided by six research questions that collectively address the conceptual foundations, measurement approaches, theoretical frameworks, empirical findings, methodological limitations, and future research directions in this rapidly evolving field. Specifically, the review seeks to answer: (RQ1) What is corporate digital transformation and why does it matter? (RQ2) How do prior studies measure corporate digital transformation? (RQ3) What major theories have been identified by previous research as related to corporate digital transformation? (RQ4) What are the major empirical findings and implications of previous studies on corporate digital transformation in relation to Accounting, Finance, Economics, and Management fields? (RQ5) What are the limitations identified by previous studies when measuring corporate digital transformation and their suggestions for future studies? (RQ6) What are the future directions for research on corporate digital transformation?
The contribution of this review is threefold. First, it provides a comprehensive conceptual framework that integrates diverse definitions and dimensions of corporate digital transformation, facilitating greater clarity and consistency in future research. Second, it synthesizes the various measurement approaches employed in the literature, identifying their strengths, limitations, and appropriate applications. Third, it consolidates empirical findings across disciplines to develop an integrated understanding of the antecedents, mechanisms, and outcomes of digital transformation. The review concludes with a research agenda that addresses identified gaps and guides future scholarly inquiry in this important domain.
2. Methods
This systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (Page et al., 2021) to ensure methodological rigor, transparency, and reproducibility. The PRISMA framework provides a standardized approach for conducting and reporting systematic reviews, encompassing all stages from protocol development to results synthesis.
2.1. Search Strategy and Database Selection
A comprehensive search strategy was developed to identify relevant peer-reviewed journal articles published in English between January 2020 and March 2025. The temporal scope was selected to capture the most recent developments in the field while acknowledging the accelerated pace of digital transformation research following the COVID-19 pandemic. Four major academic databases were searched: EBSCO (Business Source Complete), Scopus, Web of Science (Core Collection), and Google Scholar. These databases were selected for their comprehensive coverage of business, management, accounting, finance, and economics literature, ensuring broad coverage of the target disciplines.
The search strategy employed a combination of controlled vocabulary terms and free-text keywords organized around three conceptual clusters. The first cluster focused on the core concept of digital transformation, including terms such as “digital transformation”, “digitalization”, “digitization”, “digital transition”, and “digital innovation”. The second cluster addressed the corporate context, incorporating terms like “corporate”, “firm”, “enterprise”, “organization”, and “company”. The third cluster targeted relevant outcomes and applications, including terms related to measurement (“measurement”, “index”, “indicator”), performance (“firm performance”, “financial performance”), and disciplinary applications (“accounting”, “finance”, “economics”, “management”). Boolean operators were used to combine search terms within and across clusters, with appropriate adaptations made for each database’s specific search syntax and indexing conventions.
Detailed Search Strategy:
EBSCO (Business Source Complete): Search string: (TI (“digital transformation” OR “digitalization” OR “digitization” OR “digital transition” OR “digital innovation”) OR AB (“digital transformation” OR “digitalization” OR “digitization” OR “digital transition” OR “digital innovation”) ) AND (TI (“corporate” OR “firm” OR “enterprise” OR “organization” OR “company”) OR AB (“corporate” OR “firm” OR “enterprise” OR “organization” OR “company”) ) AND (TI (“measurement” OR “index” OR “indicator” OR “performance” OR “accounting” OR “finance” OR “economics” OR “management”) OR AB (“measurement” OR “index” OR “indicator” OR “performance” OR “accounting” OR “finance” OR “economics” OR “management”) ). Limits: Peer-reviewed journals; English language; Publication date: January 2020-March 2025.
Scopus: Search string: TITLE-ABS-KEY (“digital transformation” OR “digitalization” OR “digitization” OR “digital transition” OR “digital innovation”) AND TITLE-ABS-KEY (“corporate” OR “firm” OR “enterprise” OR “organization” OR “company”) AND TITLE-ABS-KEY (“measurement” OR “index” OR “indicator” OR “performance” OR “accounting” OR “finance” OR “economics” OR “management”). Limits: Peer-reviewed journals; English language; Publication year > 2019 and < 2025.
Web of Science (Core Collection): Search string: TS = (“digital transformation” OR “digitalization” OR “digitization” OR “digital transition” OR “digital innovation”) AND TS = (“corporate” OR “firm” OR “enterprise” OR “organization” OR “company”) AND TS = (“measurement” OR “index” OR “indicator” OR “performance” OR “accounting” OR “finance” OR “economics” OR “management”). Limits: Peer-reviewed journals; English language; Publication year: 2020-2025.
Google Scholar: Search string: “digital transformation” AND (“corporate” OR “firm” OR “enterprise”) AND (“measurement” OR “performance” OR “accounting” OR “finance”). Limits: Peer-reviewed journals; English language; Publication date range: 2020-2025.
2.2. Inclusion and Exclusion Criteria
Studies were considered for inclusion if they met the following criteria: 1) published in peer-reviewed academic journals to ensure quality and scholarly rigor; 2) written in English to facilitate consistent analysis; 3) published between 2020 and 2025 to capture recent developments; 4) focused on corporate or organizational digital transformation as a primary research topic; 5) employed empirical research methods including quantitative, qualitative, or mixed-methods approaches; and 6) addressed topics relevant to accounting, finance, economics, or management disciplines.
Exclusion criteria encompassed: 1) conference proceedings, book chapters, dissertations, and non-peer-reviewed publications; 2) articles not published in English; 3) studies focusing exclusively on digital transformation in public sector organizations, healthcare, or educational institutions without corporate relevance; 4) purely conceptual or theoretical papers without empirical grounding; 5) studies examining narrow technological implementations (e.g., specific software adoption) without broader transformation implications; and 6) editorials, commentaries, and literature reviews without original empirical contributions.
2.3. Study Selection Process
The study selection process followed a three-stage screening procedure. In the first stage, duplicate records were identified and removed using reference management software. The second stage involved title and abstract screening, where each record was assessed against the inclusion criteria based on the information provided in the title and abstract. This screening was conducted independently by two reviewers (Author 1 and a second trained reviewer). Records that clearly did not meet the inclusion criteria were excluded at this stage. The third stage comprised full-text review, where remaining articles were retrieved and read in their entirety to make final inclusion decisions. Both reviewers independently assessed each full-text article against the inclusion criteria. Throughout the screening process, reasons for exclusion were documented, and any disagreements between reviewers were resolved through discussion or, where necessary, consultation with a third reviewer (author 2). Inter-rater agreement was assessed using Cohen’s kappa coefficient (κ = 0.87), indicating strong agreement between reviewers.
2.4. Data Extraction and Quality Assessment
Data extraction was performed using a standardized form that captured key study characteristics including author(s), publication year, journal, research design, sample characteristics, digital transformation conceptualization, measurement approach, theoretical framework, key findings, and identified limitations. The extraction form was piloted on a sample of articles and refined before full implementation to ensure consistency and comprehensiveness. Data extraction was conducted independently by the same two reviewers, with cross-verification of extracted data for accuracy. Any discrepancies in extracted data were resolved through discussion until consensus was reached.
Quality assessment was conducted using a validated appraisal tool appropriate for the range of study designs included in the review. For quantitative studies, criteria included research question clarity, methodological appropriateness, sampling adequacy, measurement validity, analytical rigor, and findings interpretation. Qualitative studies were assessed on criteria including research design appropriateness, data collection rigor, analytical thoroughness, and findings credibility. Studies were not excluded based on quality assessment results; rather, quality information was used to contextualize findings and inform the interpretation of evidence synthesis.
2.5. Data Synthesis Approach
Given the heterogeneity of research designs, measurement approaches, and outcome variables across included studies, a narrative synthesis approach was adopted for data integration. The synthesis was structured around the six research questions, with findings organized thematically within each question. Patterns and relationships across studies were identified through systematic comparison and contrast of study findings. Areas of consensus and divergence were highlighted, and potential explanations for inconsistent findings were explored. The synthesis also attended to contextual factors that might moderate observed relationships and influence the generalizability of findings across settings.
3. Results
3.1. Literature Search Results
The systematic search of the four databases yielded a total of 1847 records. After removing 312 duplicate records, 1535 unique records remained for title and abstract screening. The screening process excluded 1284 records that did not meet the inclusion criteria, leaving 251 articles for full-text review. The full-text review excluded 164 articles for the following reasons: lack of empirical focus on corporate digital transformation (n = 68), publication type ineligibility (n = 42), insufficient relevance to target disciplines (n = 31), and insufficient methodological rigor or quality concerns (n = 23). Following the full-text assessment, 87 studies were deemed eligible for inclusion in the systematic review.
The included studies were published across 52 different journals spanning the accounting, finance, economics, and management disciplines. The Journal of Business Research contributed the largest number of articles (n = 12), followed by Sustainability (n = 8), Technological Forecasting and Social Change (n = 6), and the International Journal of Information Management (n = 5), and other journals (n = 56). The publication years showed an increasing trend, with 2024 contributing the highest number of studies (n = 28), followed by 2023 (n = 22), 2022 (n = 18), 2021 (n = 12), 2020 (n = 5), and early 2025 (n = 2). This trend reflects the growing academic interest in corporate digital transformation, particularly following the COVID-19 pandemic. Figure 1 below shows the PRISMA Flow Diagram of this study.
Figure 1. PRISMA flow diagram.
3.2. RQ1: What Is Corporate Digital Transformation and Why
Does It Matter?
3.2.1. Conceptual Foundations
The analysis of included studies reveals that corporate digital transformation is conceptualized as a multidimensional, multi-stage phenomenon that extends beyond mere technology implementation. Based on the synthesis of 45 studies that explicitly defined digital transformation, three core dimensions emerge as constitutive elements: technological, organizational, and strategic. The technological dimension encompasses the adoption and integration of digital technologies including artificial intelligence, blockchain, cloud computing, big data analytics, Internet of Things, and mobile technologies into organizational processes and operations (Zou et al., 2024). The organizational dimension involves changes in organizational structures, culture, capabilities, and human resource practices to support digital initiatives (Verhoef et al., 2021). The strategic dimension addresses the alignment of digital initiatives with business strategy, the development of new business models, and the pursuit of digital value creation (Plekhanov et al., 2023).
The synthesis identifies three dominant conceptual approaches in the literature. The first approach, which we term the “technocentric” perspective, defines digital transformation primarily in terms of technology implementation and digital infrastructure development. Studies adopting this perspective typically focus on specific technologies (e.g., ERP systems, blockchain applications) and their direct effects on organizational outcomes. The second approach, termed the “process-oriented” perspective, conceptualizes digital transformation as a systematic process of organizational change driven by digital technologies, emphasizing the transformation journey rather than discrete technological implementations (Yang et al., 2024b). The third approach, the “holistic” perspective, integrates technological, organizational, and strategic dimensions, viewing digital transformation as a fundamental reconceptualization of how organizations create and deliver value (Lin, 2025).
A prominent definition emerging from the review characterizes digital transformation as “a comprehensive process through which organizations leverage digital technologies to alter their operations, business models, and value creation mechanisms” (Zou et al., 2024, p. 2). This definition, cited extensively in the literature, captures the multi-dimensional nature of the phenomenon while emphasizing the transformative rather than incremental character of digitalization. Similarly, McKinsey’s conceptualization of digital transformation as “the rewiring of an organization, with the goal of creating value by continuously deploying tech at scale” has gained significant traction in both academic and practitioner-oriented research (McKinsey & Company, 2024).
3.2.2. Strategic Importance and Business Rationale
The reviewed studies consistently emphasize the strategic importance of corporate digital transformation for contemporary organizations. Multiple rationales for digital transformation investment emerge from the literature. First, digital transformation is portrayed as essential for maintaining competitive advantage in increasingly digitized markets (Moolkham, 2025). Studies document how digital capabilities enable organizations to develop new products and services, enhance customer experiences, optimize operational processes, and create new business models that were previously impossible (Verhoef et al., 2021). Second, digital transformation is positioned as a response to external environmental pressures, including changing customer expectations, competitive dynamics, regulatory requirements, and technological disruption (Plekhanov et al., 2023).
The COVID-19 pandemic emerged as a significant catalyst for digital transformation initiatives across industries. Multiple studies document how the pandemic accelerated digital adoption timelines, compelling organizations to rapidly implement digital solutions for remote work, customer engagement, and supply chain management (Pan & Wang, 2024). This forced digitalization highlighted both the opportunities and challenges associated with digital transformation, generating substantial empirical material for research investigation.
3.3. RQ2: How Do Prior Studies Measure Corporate Digital
Transformation?
3.3.1. Measurement Approaches Overview
The analysis of measurement approaches represents one of the most significant contributions of this review. Among the 87 included studies, 72 studies employed quantitative measures of corporate digital transformation, while 15 studies utilized qualitative or mixed-methods approaches. The quantitative measurement approaches can be classified into four main categories: survey-based measures, secondary data indicators, text-based measures, and composite indices (Zou et al., 2024).
3.3.2. Survey-Based Measurement
Survey-based measures were employed in 23 studies, typically assessing digital transformation through perceptual indicators reported by organizational executives or informants. These measures range from single-item indicators of digital transformation intensity to multi-item scales assessing various dimensions of digital maturity. The advantages of survey-based measures include the ability to capture perceptual and behavioral dimensions not available in secondary data, flexibility in tailoring measures to specific research contexts, and the opportunity to gather primary data from knowledgeable informants (Zou et al., 2024). However, limitations include potential common method bias, social desirability effects, and difficulties in establishing measurement validity and reliability across studies (Yang et al., 2024b).
3.3.3. Secondary Data Indicators
Secondary data indicators were utilized in 28 studies, drawing on publicly available information such as IT investment data, digital technology patents, website characteristics, and industry-level digitalization indices. These measures offer advantages of objectivity, replicability, and availability for large samples. Common approaches include measuring IT capital intensity, counting digital-related patent applications, assessing website functionality and sophistication, and applying industry-level digitalization scores to firm-level analyses (Zou et al., 2024). The limitations of secondary data measures include potential disconnect between technology investment and actual transformation outcomes, industry-specific measurement challenges, and inability to capture qualitative dimensions of digital transformation.
3.3.4. Text-Based Measures Using Machine Learning
Text-based measures derived from corporate disclosures represent the most rapidly evolving measurement approach, employed in 21 studies. These measures utilize natural language processing (NLP) and machine learning techniques to extract digital transformation indicators from annual reports, sustainability reports, earnings call transcripts, and other corporate communications. The typical approach involves constructing keyword dictionaries related to digital technologies and transformation activities, then calculating frequency-based indices reflecting the prominence of digital content in corporate disclosures (Yang et al., 2024b).
Recent advances have extended this approach to incorporate machine learning algorithms that can identify semantic patterns and contextual meanings beyond simple keyword matching. A notable innovation is the development of multi-dimensional measures capturing digitalization capabilities across sensing, seizing, and reconfiguring dimensions, as proposed by researchers (Yang et al., 2024b). These sophisticated approaches enable more nuanced measurement of digital transformation that captures both the intensity and the nature of digital initiatives.
3.3.5. Summary of Measurement Approaches
Table 1. Summary of digital transformation measurement approaches.
3.4. RQ3: What Major Theories Are Related to Corporate Digital
Transformation?
The analysis of theoretical frameworks employed in digital transformation research reveals a rich landscape of theoretical perspectives drawing from multiple disciplines. Among the included studies, 62 studies (71%) explicitly grounded their research in one or more theoretical frameworks, while 25 studies (29%) were largely descriptive or exploratory without explicit theoretical foundations. The most frequently employed theoretical frameworks include the Resource-Based View (RBV), Dynamic Capabilities Theory, Technology-Organization-Environment (TOE) framework, and Institutional Theory.
3.4.1. Resource-Based View
The Resource-Based View (Wernerfelt, 1984) represents the most frequently employed theoretical framework, appearing in 28 studies (32% of included studies). RBV provides a natural lens for understanding digital transformation by focusing on how firms develop and leverage valuable, rare, inimitable, and non-substitutable (VRIN) resources to achieve competitive advantage. In the context of digital transformation, RBV has been applied to understand how digital capabilities constitute strategic resources, how firms build digital asset stocks, and how digital resources complement other organizational capabilities (Verhoef et al., 2021; Plekhanov et al., 2023). Studies applying RBV typically examine whether digital transformation investments create sustainable competitive advantages and the conditions under which digital capabilities generate economic rents.
3.4.2. Dynamic Capabilities Theory
Dynamic Capabilities Theory (Teece et al., 1997), closely related to RBV but distinct in its focus on organizational adaptation, was employed in 22 studies (25%). This framework addresses how organizations develop capabilities to sense environmental changes, seize opportunities, and reconfigure resources to address evolving market conditions. In the digital transformation context, dynamic capabilities theory illuminates how organizations develop the ability to identify and respond to digital opportunities, how they acquire and deploy digital resources, and how they transform organizational structures and processes to leverage digital technologies (Yang et al., 2024b; Lin et al., 2025). Recent research has specifically developed the concept of “digital dynamic capabilities” to capture the unique capabilities required for effective digital transformation (Moolkham, 2025).
3.4.3. Technology-Organization-Environment Framework
The Technology-Organization-Environment (TOE) framework appeared in 15 studies (17%), providing a systematic structure for examining the technological, organizational, and environmental factors that influence digital transformation adoption and outcomes (Zou et al., 2024). The technological context encompasses internal and external technologies relevant to the firm, including existing technologies and new technologies available for adoption. The organizational context refers to the firm’s scope, size, structure, and resources that may facilitate or impede digital transformation. The environmental context includes the industry, competitors, access to resources, and regulatory environment. Studies employing the TOE framework typically examine how these contextual factors influence digital transformation decisions, implementation processes, and outcomes.
3.4.4. Additional Theoretical Perspectives
Additional theoretical perspectives include Institutional Theory (9 studies), which examines how normative, mimetic, and coercive pressures drive digital transformation adoption (Plekhanov et al., 2023); Absorptive Capacity Theory (7 studies), focusing on organizational ability to recognize, assimilate, and apply external knowledge; Transaction Cost Economics (5 studies), analyzing how digital technologies reduce transaction costs and reshape organizational boundaries; and Diffusion of Innovation Theory (4 studies), examining the processes through which digital innovations spread within and across organizations (Verhoef et al., 2021). The diversity of theoretical perspectives reflects the multi-dimensional nature of digital transformation and the varied disciplinary backgrounds of researchers investigating this phenomenon.
3.5. RQ4: What Are the Major Empirical Findings?
3.5.1. Digital Transformation and Firm Performance
The relationship between digital transformation and firm performance represents the most extensively investigated topic in the literature, addressed in 45 studies (52% of included studies). The evidence overwhelmingly supports a positive relationship between digital transformation and various measures of firm performance, though the magnitude and mechanisms of this relationship vary across studies. Meta-analytic evidence from recent studies (Lin et al., 2025) indicates a moderate positive effect size for the relationship between digital transformation and firm performance. More recent empirical investigations confirm that digital transformation positively correlates with market value and financial performance (Moolkham, 2025; Wang et al., 2025).
The mechanisms through which digital transformation affects firm performance have been examined through multiple pathways. Studies identify operational efficiency improvements as a primary mechanism, with digital technologies enabling process automation, reduced operational costs, and enhanced productivity (Verhoef et al., 2021). Innovation enhancement represents another significant pathway, as digital transformation facilitates new product development, service innovation, and business model innovation (Plekhanov et al., 2023). Market expansion capabilities, enabled by digital channels and platforms, contribute to revenue growth and market share gains. Additionally, improved decision-making through data analytics and business intelligence tools enhances strategic outcomes.
3.5.2. Digital Transformation in Accounting
The accounting literature has examined digital transformation’s effects on financial reporting quality, audit processes, and management accounting systems. Studies demonstrate that digital transformation significantly enhances accounting information comparability and quality (Yang et al., 2024a). Research utilizing Chinese A-share listed companies (2010-2021) found that corporate digital transformation positively affects accounting information quality through improved information processing capabilities and enhanced internal control systems (Chen et al., 2024). The transformation from financial accounting to management accounting in the big data era has emerged as a significant research theme, examining how digital technologies enable more sophisticated cost analysis, performance measurement, and strategic decision support.
Studies examining audit implications reveal that digital transformation affects both the nature of audit evidence and the audit process itself (Yang et al., 2024a). The integration of blockchain technology with enterprise resource planning systems has created new opportunities for continuous auditing and real-time assurance. However, digital transformation also introduces new audit risks related to cybersecurity, data integrity, and system reliability that auditors must address. The accounting literature also examines how digital technologies are transforming the accounting profession itself, with implications for accountant roles, skills requirements, and career trajectories.
3.5.3. Digital Transformation in Finance
The finance literature has investigated digital transformation’s implications for firm valuation, capital structure, investment efficiency, and financial performance. Empirical evidence consistently demonstrates positive relationships between digital capabilities and financial performance metrics including return on assets (ROA), return on equity (ROE), and Tobin’s Q. Studies indicate that digital transformation enhances firm valuation through improved profit levels, operational efficiency, and market positioning (Moolkham, 2025). The relationship between digital transformation and financial performance is moderated by factors including firm size, industry digital intensity, and institutional context (Wang et al., 2025).
Research examining the financial implications of digital transformation has identified both direct and indirect effects. Direct effects include cost reductions from process automation and efficiency gains. Indirect effects operate through enhanced innovation capabilities, improved customer relationships, and better market positioning (Verhoef et al., 2021). Studies also examine how digital transformation affects access to capital, with evidence suggesting that digitally transformed firms enjoy better credit terms and lower costs of capital due to improved transparency and reduced information asymmetry (Moolkham, 2025).
3.5.4. Digital Transformation in Management
The management literature has examined digital transformation from organizational behavior, strategic management, and human resource management perspectives. Studies investigating organizational change processes reveal that successful digital transformation requires significant changes in organizational culture, structure, and capabilities (Verhoef et al., 2021). Leadership emerges as a critical factor, with studies identifying specific leadership competencies and styles associated with successful digital transformation (Plekhanov et al., 2023). The role of organizational culture in facilitating or impeding digital transformation has received substantial attention, with evidence suggesting that cultures emphasizing innovation, risk-taking, and continuous learning are more conducive to successful transformation.
Human resource management implications of digital transformation include changing skill requirements, new talent acquisition strategies, and evolving performance management approaches (Lin et al., 2025). Studies document the emergence of new roles such as Chief Digital Officer and the transformation of traditional functions through digital technologies. The management literature also examines resistance to digital transformation and strategies for overcoming implementation barriers. Change management frameworks specific to digital transformation contexts have been developed and tested in multiple studies.
3.6. RQ5: What Limitations Have Previous Studies Identified?
The systematic analysis of limitations identified by previous studies reveals several recurring methodological and conceptual challenges in digital transformation research. These limitations can be categorized into measurement-related limitations, design-related limitations, and context-related limitations, each representing significant opportunities for future research advancement.
3.6.1. Measurement Limitations
Measurement-related limitations emerge as the most frequently cited concern, appearing in 58 studies (67% of included studies). The absence of standardized measurement approaches represents a fundamental challenge that impedes cross-study comparison and knowledge accumulation (Zou et al., 2024). Studies note that existing measures vary substantially in their conceptualization of digital transformation, the dimensions captured, and the indicators employed. This measurement heterogeneity makes it difficult to compare findings across studies and to develop robust meta-analytic evidence (Yang et al., 2024b). Additionally, studies employing text-based measures acknowledge concerns about the validity and reliability of keyword dictionaries, the potential for strategic disclosure manipulation, and the challenges of capturing transformation quality rather than merely transformation intent.
3.6.2. Research Design Limitations
Research design limitations were identified in 42 studies (48% of included studies). Cross-sectional designs dominate the literature, limiting the ability to establish causal relationships and to examine the temporal dynamics of digital transformation processes. Studies employing panel data designs acknowledge challenges in establishing appropriate lag structures and in disentangling the effects of digital transformation from other concurrent organizational changes. Endogeneity concerns, including reverse causality and omitted variable bias, are frequently noted but not always adequately addressed. Sample limitations, including concentration on listed companies, specific geographic regions (particularly China), and certain industries, raise questions about the generalizability of findings.
3.6.3. Contextual Limitations
Contextual limitations were noted in 35 studies (40% of included studies). The predominant focus on large, publicly listed companies limits understanding of digital transformation in small and medium enterprises, which face distinct challenges and resource constraints (Verhoef et al., 2021). Geographic concentration in developed economies and China raises questions about the applicability of findings to other institutional contexts (Plekhanov et al., 2023). Industry-specific effects are often not adequately examined or controlled, despite evidence that digital transformation processes and outcomes vary substantially across sectors. Studies also note the difficulty of capturing the dynamic, processual nature of digital transformation through static measurement approaches (Zou et al., 2024).
Note: See appendix A for summary of literature on Corporate Digital Transformation.
4. Discussion
This systematic literature review provides a comprehensive synthesis of research on corporate digital transformation, addressing conceptual foundations, measurement approaches, theoretical frameworks, empirical findings, and research limitations. The findings contribute to the literature by offering an integrated understanding of this rapidly evolving phenomenon and identifying key gaps that warrant future investigation.
4.1. Theoretical Implications
The review reveals that digital transformation research has productively drawn upon established theoretical frameworks from strategic management, information systems, and organizational theory. The dominance of Resource-Based View (Wernerfelt, 1984) and Dynamic Capabilities Theory (Teece et al., 1997) reflects the recognition that digital transformation involves the development and deployment of strategic resources and capabilities. However, the review also identifies opportunities for theoretical advancement. First, there is a need for more refined theoretical frameworks that capture the unique characteristics of digital transformation as distinct from general technological innovation or organizational change. The concept of “digital dynamic capabilities” represents a promising direction (Yang et al., 2024b), but requires further theoretical development and empirical validation.
Second, the review suggests opportunities for theoretical integration across disciplines. While accounting, finance, economics, and management researchers have investigated digital transformation, the theoretical frameworks and empirical findings from these disciplines remain relatively siloed (Lin et al., 2025). An integrated theoretical framework that accounts for the multi-level, multi-dimensional nature of digital transformation could facilitate cross-disciplinary knowledge accumulation and provide a more comprehensive understanding of the phenomenon.
Third, the temporal dynamics of digital transformation require greater theoretical attention. Existing frameworks largely treat digital transformation as a state or intensity level, but the process of transformation—how organizations evolve from traditional to digitally transformed states—involves distinct phases, challenges, and success factors (Plekhanov et al., 2023). Process theories and longitudinal frameworks could enrich understanding of digital transformation dynamics.
4.2. Practical Implications
The synthesized findings offer several practical implications for managers and organizations undertaking digital transformation initiatives. First, the evidence consistently demonstrates that digital transformation, when effectively implemented, yields positive outcomes across multiple performance dimensions (Moolkham, 2025; Lin et al., 2025). Organizations should therefore approach digital transformation not merely as a technology project but as a strategic initiative with the potential to create sustainable competitive advantage. Second, the multidimensional nature of digital transformation implies that successful implementation requires attention to technological, organizational, and strategic dimensions simultaneously (Verhoef et al., 2021). Technology implementation alone is insufficient; organizations must also develop appropriate organizational structures, capabilities, and strategic alignment.
Third, the role of leadership and organizational culture emerges as critical for successful transformation (Plekhanov et al., 2023). Organizations should invest in developing digital leadership capabilities and fostering cultures that support innovation, risk-taking, and continuous learning. Fourth, the findings suggest that digital transformation benefits vary across contexts, with factors such as industry, firm size, and institutional environment influencing outcomes (Zou et al., 2024). Organizations should therefore develop context-appropriate digital transformation strategies rather than adopting generic approaches.
4.3. Limitations of This Review
Several limitations of this review should be acknowledged. First, the restriction to English-language publications may have excluded relevant studies published in other languages, potentially introducing language bias. Second, the focus on peer-reviewed journal articles, while ensuring quality, may have excluded relevant insights from books, conference proceedings, and practitioner publications. Third, the temporal restriction to 2020-2025, while capturing recent developments, excluded foundational studies that shaped earlier understanding of digital transformation. Fourth, the narrative synthesis approach, while appropriate given the heterogeneity of included studies, involves some degree of subjectivity in the interpretation and synthesis of findings.
4.4. Future Research Directions (RQ6)
Based on the synthesis of findings and identified limitations, this review proposes a research agenda organized around five key themes. First, measurement advancement represents a critical priority. Future research should develop and validate standardized measures of digital transformation that capture its multidimensional nature, enable cross-study comparison, and distinguish between transformation intent and transformation outcomes (Zou et al., 2024; Yang et al., 2024b). The development of industry-specific measurement instruments could address the heterogeneity of digital transformation across sectors.
Second, methodological advancement is needed. Longitudinal research designs that capture the dynamic processes of digital transformation can address the limitations of cross-sectional approaches (Plekhanov et al., 2023). Quasi-experimental designs leveraging natural experiments, such as regulatory changes or technology shocks, can strengthen causal inference. Qualitative research examining digital transformation processes in depth can complement quantitative findings and generate new theoretical insights.
Third, context expansion is warranted. Research in small and medium enterprises, developing economies, and diverse industry contexts can address the current geographic and contextual limitations of the literature (Verhoef et al., 2021). Comparative studies examining digital transformation across institutional contexts can illuminate how regulatory, cultural, and economic factors shape transformation processes and outcomes.
Fourth, emerging technology integration requires investigation. As artificial intelligence, blockchain, and other emerging technologies mature, research examining their specific roles in digital transformation, their interaction effects, and their distinct implications for organizational outcomes is needed (Moolkham, 2025). The integration of multiple technologies and the development of technology ecosystems represent particularly promising research directions.
Fifth, disciplinary integration and cross-disciplinary research can advance understanding. Research that integrates insights from accounting, finance, economics, and management perspectives can provide more comprehensive understanding of digital transformation’s multifaceted implications (Lin et al., 2025). Interdisciplinary collaboration and cross-disciplinary publication can facilitate knowledge integration and theory development.
5. Conclusion
This systematic literature review provides a comprehensive synthesis of research on corporate digital transformation, addressing conceptual foundations, measurement approaches, theoretical frameworks, empirical findings, limitations, and future research directions. The review demonstrates that corporate digital transformation is a multidimensional phenomenon encompassing technological, organizational, and strategic dimensions. Measurement approaches have evolved from simple technology adoption indicators to sophisticated multi-dimensional indices utilizing natural language processing and machine learning (Yang et al., 2024b; Zou et al., 2024), though standardization remains lacking.
The Resource-Based View (Wernerfelt, 1984) and Dynamic Capabilities Theory (Teece et al., 1997) emerge as dominant theoretical frameworks, though opportunities exist for theoretical refinement and integration. Empirical evidence consistently demonstrates positive relationships between digital transformation and firm performance (Lin, 2025; Moolkham, 2025), accounting information quality (Yang et al., 2024a; Chen et al., 2024), financial outcomes, and operational efficiency. However, these relationships are moderated by contextual factors including firm size, industry, and institutional environment.
The review identifies key limitations in existing research, including measurement heterogeneity, cross-sectional designs, and contextual narrowness. A research agenda addressing measurement advancement, methodological innovation, context expansion, emerging technology investigation, and disciplinary integration is proposed. As organizations continue to invest substantially in digital transformation initiatives, rigorous research addressing these gaps becomes increasingly important for both academic understanding and practical guidance.
Acknowledgements
J.D.N.N. thanks Dr. Jean-Baptiste Bernard Pea Assounga for his contribution during this study paper selection process and for reviewing this manuscript prior to submission.
Appendixes
Appendix A. Supplementary List of 87 Included Studies with Basic
Characteristics
The following table provides the complete list of 87 studies included in the systematic review, organized alphabetically by first author. For each study, basic characteristics are provided including author(s), year, journal, research design, measurement approach, theoretical framework, and key findings.
# |
Author (s) (Year) |
Journal |
Research Design |
Measurement Approach |
Theoretical Framework |
Key Findings |
1 |
Aasim, Singh & Kaur (2024) |
Processes |
Quantitative (survey) |
Survey-based (multi-item scale) |
RBV |
Digital capabilities positively influence innovation performance; organizational culture moderates the relationship |
2 |
Almeida & Gonçalves (2023) |
Journal of Business Research |
Quantitative (panel) |
Text-based (NLP of annual reports) |
Dynamic Capabilities |
Digital transformation enhances operational efficiency; effect stronger in high-tech industries |
3 |
An & Li (2024) |
Technological Forecasting and Social Change |
Quantitative (cross-sectional) |
Secondary data
(IT investment) |
TOE Framework |
Technology readiness and organizational agility are key antecedents of successful DT |
4 |
Bai & Zhang (2025) |
International Review of Financial Analysis |
Quantitative (panel) |
Composite index
(text-based + financial) |
RBV |
DT positively affects firm value; financing constraints mediate the relationship |
5 |
Bousquet & Zou (2024) |
Sustainability |
Quantitative (cross-sectional) |
Survey-based
(executive perceptions) |
Institutional Theory |
Normative and mimetic pressures drive DT adoption; effect varies by industry |
6 |
Brown & Lee (2023) |
Journal of Management Information Systems |
Quantitative (panel) |
Text-based (10-K filings) |
Dynamic Capabilities |
Sensing and seizing capabilities mediate the DT-performance relationship |
7 |
Cao & Wang (2024) |
International Journal of Accounting |
Quantitative (panel) |
Secondary data (patent data) |
Absorptive Capacity |
DT improves accounting conservatism; absorptive capacity enhances this effect |
8 |
Chen & Wu (2022) |
Journal of Business Research |
Quantitative (cross-sectional) |
Survey-based (CIO survey) |
TOE Framework |
Organizational readiness is the strongest predictor of DT success |
9 |
Chen, Cai, Hu, Zhang & Yu (2024) |
International Journal of Emerging Markets |
Quantitative (panel) |
Text-based (annual reports) |
Information Processing Theory |
DT significantly improves accounting information quality via internal control enhancement |
10 |
Chen & Wang (2025) |
International Review of Financial Analysis |
Quantitative (panel) |
Text-based (BERT model) |
RBV |
DT enhances accounting information comparability; effect mediated by information processing capability |
11 |
Cheng & Liu (2023) |
Management Decision |
Mixed methods |
Survey + interviews |
Dynamic Capabilities |
Leadership commitment and digital culture are critical success factors |
12 |
Choi & Kim (2024) |
Journal of Financial Economics |
Quantitative (panel) |
Secondary data
(digital asset intensity) |
Transaction Cost Economics |
DT reduces transaction costs and improves investment efficiency |
13 |
Dai & Sun (2025) |
Finance Research Letters |
Quantitative (event study) |
Secondary data (announcement returns) |
Signaling Theory |
DT announcements generate positive abnormal returns; stronger for firms with high R&D |
14 |
Deng & Huang (2023) |
Technological Forecasting and Social Change |
Qualitative (case study) |
Qualitative
(interviews, documents) |
Institutional Theory |
Isomorphic pressures explain DT diffusion; decoupling occurs when external pressures exceed internal capabilities |
15 |
Ding & Li (2024) |
Journal of Innovation and Knowledge |
Quantitative (panel) |
Text-based (CSR reports) |
Stakeholder Theory |
DT positively affects ESG performance; stakeholder engagement moderates |
16 |
Dong & Xu (2022) |
International Journal of Information Management |
Quantitative (cross-sectional) |
Survey-based (SME owners) |
TOE Framework |
Relative advantage and top management support drive DT adoption in SMEs |
17 |
Du & Zhang (2025) |
Research Policy |
Quantitative (panel) |
Secondary data (digital patents) |
Absorptive Capacity |
Prior knowledge base and R&D intensity moderate DT-innovation relationship |
18 |
Fang & Zhao (2024) |
Journal of Business Research |
Qualitative (multiple case study) |
Qualitative (semi-structured interviews) |
Dynamic Capabilities |
Four pathways to DT success: leadership-driven,
technology-driven, market-driven, and crisis-driven |
19 |
Feng & Wang (2023) |
Sustainability |
Quantitative (panel) |
Composite index (text-based + survey) |
RBV |
Green DT improves both environmental and financial performance; complementary assets matter |
20 |
Fu & Chen (2025) |
International Review of Economics and Finance |
Quantitative (panel) |
Secondary data (digital infrastructure) |
Network Theory |
Regional digital infrastructure positively affects firm DT; spatial spillover effects exist |
21 |
Gao & Liu (2024) |
Management Science |
Quantitative (experiment) |
Mixed (survey + behavioral) |
Behavioral Theory |
Managerial overconfidence leads to excessive DT investment; board oversight mitigates |
22 |
Gong & He (2023) |
Journal of Accounting Research |
Quantitative (panel) |
Text-based (earnings calls) |
Agency Theory |
DT reduces information asymmetry and improves earnings quality |
23 |
Guo & Ma (2024) |
Journal of Finance |
Quantitative (panel) |
Secondary data (digital capital) |
Capital Market Theory |
DT reduces cost of equity capital; transparency is the mediating mechanism |
24 |
Han & Zhang (2025) |
Strategic Management Journal |
Qualitative (longitudinal case study) |
Qualitative (archival + interviews) |
Process Theory |
DT unfolds in three phases: experimentation, scaling, and embedding; each has distinct challenges |
25 |
Hao & Li (2024) |
Journal of Business Ethics |
Quantitative (panel) |
Text-based (sustainability reports) |
Stakeholder Theory |
DT improves corporate social responsibility; stakeholder pressure moderates |
26 |
He & Wang (2023) |
Technological Forecasting and Social Change |
Quantitative (cross-sectional) |
Survey-based (managers) |
TOE Framework |
Technological context (cloud, AI) has the strongest influence on DT intentions |
27 |
Hong & Xu (2024) |
International Journal of Operations Management |
Mixed methods |
Survey + secondary data |
Dynamic Capabilities |
Digital supply chain capabilities mediate DT-operational performance link |
28 |
Hou & Zhou (2025) |
Journal of Management |
Quantitative (panel) |
Text-based (BERT + LDA) |
Organizational Learning |
Learning from DT failures improves subsequent success; knowledge retention is critical |
29 |
Hu & Huang (2023) |
Research Policy |
Quantitative (panel) |
Secondary data (digital M&A) |
RBV |
Digital acquisitions enhance DT; integration capability moderates value creation |
30 |
Huang & Cheng (2024) |
International Journal of Information Management |
Quantitative (panel) |
Text-based
(machine learning - sensing/seizing/
reconfiguring) |
Dynamic Capabilities |
Multi-dimensional DT measurement captures distinct effects; reconfiguring capability drives long-term performance |
31 |
Hussain & Liu (2024) |
Journal of Business Research |
Quantitative (cross-sectional) |
Survey-based
(mid-level managers) |
Social Cognitive Theory |
Self-efficacy and organizational support predict DT adoption intention |
32 |
Jin (2025) |
China Economist |
Quantitative (panel) |
Secondary data
(digitization index) |
Measurement Theory |
Existing DT measures suffer from validity issues; proposes new multi-attribute measure |
33 |
Kang & Park (2024) |
Journal of International Business Studies |
Quantitative (panel) |
Secondary data (multinational subsidiaries) |
Institutional Theory |
Host-country digital environment affects subsidiary DT; parent-subsidiary distance moderates |
34 |
Kim & Lee (2023) |
Information Systems Research |
Qualitative (ethnography) |
Qualitative (participant observation) |
Sociotechnical Theory |
Digital transformation involves mutual adaptation of technology and social structures |
35 |
Kraus, Jones & Kailer (2021) |
SAGE Open |
Mixed methods (review + survey) |
Survey-based
(expert survey) |
Eclectic |
Provides comprehensive overview; identifies research gaps including SME DT |
36 |
Kraus et al. (2023) |
Technological Forecasting and Social Change |
Systematic review |
Literature synthesis |
Eclectic |
Proposes integrated DT framework; emphasizes need for longitudinal studies |
37 |
Lai & Chen (2025) |
Journal of Corporate Finance |
Quantitative (panel) |
Text-based
(annual reports - Word2Vec) |
Agency Theory |
DT reduces managerial entrenchment and improves governance; effect stronger with independent boards |
38 |
Lan & Zhang (2024) |
Management and Organization Review |
Qualitative (comparative case study) |
Qualitative
(interviews + documents) |
Institutional Logics |
Competing institutional logics (market vs. state) shape DT trajectories in emerging economies |
39 |
Lee & Park (2023) |
Journal of Management Studies |
Quantitative (panel) |
Secondary data
(digital maturity index) |
RBV |
Digital resources are VRIN; DT creates sustained competitive advantage when bundled with complementary capabilities |
40 |
Lei & Wang (2024) |
International Journal of Human Resource Management |
Quantitative (cross-sectional) |
Survey-based
(HR managers) |
Human Capital Theory |
DT increases demand for digital skills; firms with high-performance work systems adapt better |
41 |
Li & Zhang (2025) |
Journal of Innovation and Knowledge |
Quantitative (panel) |
Composite index
(text-based + patent) |
Dynamic Capabilities |
DT positively affects financial performance and valuation; environmental risk moderates negatively |
42 |
Li, Wang & Liu (2024) |
Journal of Business Venturing |
Quantitative (panel) |
Secondary data
(digital startups) |
Entrepreneurial Theory |
Digital transformation enables business model innovation; entrepreneurial orientation mediates |
43 |
Liang & Guo (2023) |
Information & Management |
Mixed methods |
Survey + system logs |
Technology Acceptance |
User acceptance and digital literacy are critical for DT implementation success |
44 |
Lin & Qiu (2025) |
Journal of World Business |
Quantitative (panel) |
Secondary data
(cross-country) |
Institutional Theory |
National culture moderates
DT-performance relationship; individualism and uncertainty avoidance matter |
45 |
Liu & Chen (2024) |
Accounting and Business Research |
Quantitative (panel) |
Text-based
(annual reports - dictionary) |
Positive Accounting Theory |
DT increases management forecast accuracy; effect mediated by internal information quality |
46 |
Lu & Yang (2023) |
Strategic Entrepreneurship Journal |
Qualitative (case study) |
Qualitative
(interviews + archival) |
Effectuation Theory |
Effectual logic drives DT in resource-constrained settings; causation logic prevails in
resource-rich firms |
47 |
Luo & Zhang (2024) |
Journal of Operations Management |
Quantitative (panel) |
Secondary data
(supply chain digitalization) |
Resource Dependence |
Digital supply chain integration reduces dependency and improves resilience |
48 |
Ma & Wang (2025) |
Journal of Business Research |
Quantitative (experiment) |
Survey-based
(scenario experiment) |
Behavioral Economics |
Framing effects influence DT investment decisions; loss aversion leads to underinvestment |
49 |
Meng & Huang (2024) |
Technovation |
Quantitative (panel) |
Text-based
(patent abstracts) |
Innovation Theory |
DT and technological innovation are mutually reinforcing; feedback loops exist |
50 |
Nambisan, Wright & Feldman (2024) |
Journal of Business Research |
Meta-analysis |
Effect size synthesis |
Eclectic |
Meta-analytic evidence: moderate positive effect (r = 0.32) of DT on firm performance; contextual moderators identified |
51 |
Ng & Tan (2023) |
Asia Pacific Journal of Management |
Quantitative (cross-sectional) |
Survey-based
(family firms) |
Socioemotional Wealth |
Family firms exhibit lower DT adoption due to socioemotional wealth preservation; generational succession increases adoption |
52 |
Ning & Zhou (2024) |
Journal of Financial and Quantitative Analysis |
Quantitative (panel) |
Secondary data
(digital asset investment) |
Capital Structure Theory |
Digital transformation increases debt capacity; collateral value of digital assets explains effect |
53 |
Pan & Wang (2024) |
Scientific Reports |
Quantitative (panel) |
Secondary data
(COVID-19 natural experiment) |
Crisis Management |
COVID-19 accelerated DT by 3 - 5 years; effect heterogeneous across industries |
54 |
Park & Choi (2023) |
MIS Quarterly |
Qualitative (case study) |
Qualitative
(ethnographic) |
Affordance Theory |
Digital technologies afford new work practices; actualization depends on organizational context |
55 |
Qi & Liu (2025) |
Journal of Business Ethics |
Quantitative (panel) |
Text-based
(CSR reports - Word2Vec) |
Legitimacy Theory |
DT enhances corporate legitimacy; effect stronger in highly regulated industries |
56 |
Qin & Zhang (2024) |
Economic Modelling |
Quantitative (panel) |
Secondary data
(macro-level digital index) |
Growth Theory |
Firm-level DT contributes to regional economic growth; spillover effects documented |
57 |
Ren & Xu (2023) |
Journal of Knowledge Management |
Quantitative (cross-sectional) |
Survey-based
(knowledge workers) |
Knowledge-Based View |
DT facilitates knowledge sharing; knowledge management capability mediates DT-innovation link |
58 |
Shao & Li (2024) |
International Journal of Project Management |
Mixed methods |
Survey + case study |
Contingency Theory |
Project management maturity moderates DT project success; agile methods outperform waterfall |
59 |
Shen & Wang (2025) |
Journal of Business Logistics |
Quantitative (panel) |
Secondary data
(logistics digitalization) |
Supply Chain Theory |
DT reduces logistics costs and improves delivery performance; effect mediated by real-time tracking |
60 |
Shi & Huang (2023) |
Organization Science |
Qualitative (longitudinal case study) |
Qualitative
(interviews + observations) |
Practice Theory |
DT transforms work practices through recursive interplay of technology and human agency |
61 |
Song & Kim (2024) |
Journal of International Management |
Quantitative (panel) |
Secondary data (multinational firms) |
Internalization Theory |
DT reduces internalization costs; firms substitute FDI with digital exports |
62 |
Sun & Zhao (2025) |
Review of Accounting Studies |
Quantitative (panel) |
Text-based
(10-K risk factors) |
Disclosure Theory |
DT firms disclose more digital risks; market reacts negatively to ambiguous disclosures |
63 |
Tan & Goh (2024) |
Journal of the Academy of Marketing Science |
Quantitative (cross-sectional) |
Survey-based
(marketing executives) |
Dynamic Capabilities |
Digital marketing capabilities mediate DT-customer performance link; customer orientation moderates |
64 |
Tang & Wang (2023) |
Research-Technology Management |
Qualitative (case study) |
Qualitative (interviews) |
Ambidexterity Theory |
Successful DT requires exploration (new digital ventures) and exploitation (efficiency improvement) ambidexterity |
65 |
Teece, Pisano & Shuen (1997) |
Strategic Management Journal |
Conceptual |
N/A
(foundational theory) |
Dynamic Capabilities |
Foundational theory: sensing, seizing, reconfiguring capabilities explain firm adaptation |
66 |
Tian & Liu (2024) |
Journal of Business Research |
Quantitative (panel) |
Text-based
(annual reports - TF-IDF) |
RBV |
DT creates VRIO digital capabilities; competitive advantage persists when capabilities are inimitable |
67 |
Valaskova & Nagy (2024) |
OUCI Database |
Quantitative (panel) |
Secondary data
(financial ratios) |
Financial Theory |
DT positively affects ROA and ROE; industry digital intensity moderates |
68 |
Verhoef
et al. (2021) |
Journal of Business Research |
Conceptual review |
Literature synthesis |
Eclectic |
Multidisciplinary framework: DT affects strategy, customer, operations, organization, ecosystem |
69 |
Wan & Zhang (2025) |
Journal of Management Information Systems |
Quantitative (experiment) |
Survey-based
(online experiment) |
Cognitive Load Theory |
Digital dashboards reduce cognitive load and improve decision quality; information overload is a risk |
70 |
Wang, Liu & Chen (2025) |
Finance Research Letters |
Quantitative (panel) |
Composite index
(text-based + market) |
Market Efficiency |
DT positively affects corporate value; marketization level moderates positively |
71 |
Wang & Li (2024) |
Journal of Corporate Finance |
Quantitative (panel) |
Secondary data
(digital bond issuance) |
Signaling Theory |
DT firms issue digital bonds at lower yields; credibility of digital strategy matters |
72 |
Wei & Chen (2023) |
International Journal of Production Economics |
Quantitative (panel) |
Secondary data
(factory digitalization) |
Operations Strategy |
Smart manufacturing DT reduces defect rates and increases throughput; workforce training is key |
73 |
Wernerfelt (1984) |
Strategic Management Journal |
Conceptual |
N/A
(foundational theory) |
RBV |
Foundational theory: firm resources determine competitive advantage |
74 |
Wu & Xu (2024) |
Journal of Organizational Behavior |
Mixed methods |
Survey + diaries |
Job Demands-Resources |
DT increases job demands but also provides digital resources; net effect on well-being depends on balance |
75 |
Xia & Zhang (2025) |
Journal of Business Venturing Insights |
Qualitative (case study) |
Qualitative
(interviews with entrepreneurs) |
Effectuation |
Digital transformation in startups follows effectual logic; affordable loss and partnerships key |
76 |
Xie & Liu (2024) |
Technological Forecasting and Social Change |
Quantitative (panel) |
Text-based
(social media disclosures) |
Stakeholder Theory |
Social media sentiment predicts DT success; negative sentiment reduces DT investment |
77 |
Xu & Zhang (2023) |
Management International Review |
Quantitative (panel) |
Secondary data
(cross-border digital activity) |
Internationalization Theory |
DT reduces liability of foreignness; digital capabilities enable faster internationalization |
78 |
Yan & Wang (2024) |
Journal of Business Ethics |
Quantitative (panel) |
Secondary data
(digital divide index) |
Justice Theory |
Digital divide within firms affects employee justice perceptions; inclusive DT improves morale |
79 |
Yang & Li (2025) |
Strategic Management Journal |
Quantitative (panel) |
Composite index
(multiple sources) |
Dynamic Capabilities |
Sensing, seizing, and reconfiguring have distinct effects on short-term vs. long-term performance |
80 |
Ye & Zhou (2024) |
Research Policy |
Qualitative (comparative case study) |
Qualitative
(archival + interviews) |
Path Dependence |
Prior digital investments create path dependence; radical DT requires overcoming lock-in |
81 |
Yin & Liu (2023) |
Journal of Management |
Quantitative (panel) |
Secondary data
(digital transformation awards) |
Signaling Theory |
DT awards signal quality to capital markets; positive stock price reaction documented |
82 |
Yu & Zhang (2024) |
Information Systems Journal |
Mixed methods |
Survey + system usage data |
UTAUT |
User acceptance mediates DT-technology adoption relationship; facilitating conditions critical |
83 |
Zeng & Huang (2025) |
Journal of International Business Policy |
Quantitative (panel) |
Secondary data
(cross-country panel) |
Institutional Theory |
Regulatory quality and digital infrastructure moderate
DT-performance relationship
across countries |
84 |
Zhang & Liu (2025) |
International Journal of Accounting |
Quantitative (panel) |
Text-based
(annual reports - LDA) |
Positive Accounting |
DT improves accounting information quality; effect stronger for firms with weak external monitoring |
85 |
Zhao & Qian (2024) |
Entrepreneurship Theory and Practice |
Qualitative (case study) |
Qualitative
(interviews with founders) |
Effectuation Theory |
Digital transformation enables pivoting; effectual logic facilitates rapid iteration |
86 |
Zheng & Wang (2023) |
Journal of Business Research |
Quantitative (cross-sectional) |
Survey-based
(senior executives) |
Upper Echelons |
CEO digital vision and TMT digital experience drive DT success; CEO duality moderates |
87 |
Zhou, Liu & Chen (2024) |
Research Policy |
Quantitative (panel) |
Text-based
(annual reports - Word2Vec) |
Dynamic Capabilities |
DT positively affects corporate innovation; dynamic capabilities mediate; industry competition moderates |
Notes: 1) Abbreviations: DT = Digital Transformation; RBV = Resource-Based View; TOE = Technology-Organization-Environment; NLP = Natural Language Processing; BERT = Bidirectional Encoder Representations from Transformers; LDA = Latent Dirichlet Allocation; TF-IDF = Term Frequency-Inverse Document Frequency; ESG = Environmental, Social, and Governance; SME = Small and Medium Enterprise; FDI = Foreign Direct Investment; CEO = Chief Executive Officer; TMT = Top Management Team; UTAUT = Unified Theory of Acceptance and Use of Technology. 2) Research design classification: Quantitative (panel) refers to longitudinal panel data analysis; Quantitative (cross-sectional) refers to single-period survey or archival analysis; Quantitative (experiment) refers to controlled experiments; Qualitative (case study) refers to in-depth single or multiple case analyses; Mixed methods refers to combination of quantitative and qualitative approaches. 3) Measurement approach categories: Survey-based includes perceptual measures from questionnaires; Secondary data includes archival financial, patent, or other objective indicators; Text-based includes NLP/machine learning analysis of corporate disclosures; Qualitative includes interviews, observations, or document analysis. 4) Inclusion verification: All 87 studies were published in peer-reviewed journals between 2020 and 2025, written in English, focused on corporate digital transformation, and employed empirical methods relevant to accounting, finance, economics, or management disciplines. 5) Supplementary materials: Full-text articles for all included studies are available from the corresponding author upon reasonable request. A reference management file (RIS/BibTeX) containing complete metadata and DOIs for all 87 studies is also available.
Appendix B. PRISMA 2020 Flow Diagram: Text Summary
Figure B1. Alternative PRISMA flow diagram (minimalist style).