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.

DetailedSearchStrategy:

EBSCO(BusinessSourceComplete): 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:Searchstring: 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.

WebofScience(CoreCollection): 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.

GoogleScholar: 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.

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.

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.

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.

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.

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 1below shows the PRISMA Flow Diagram of this study.

Figure 1. PRISMA flow diagram.

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 ([15]). The organizational dimension involves changes in organizational structures, culture, capabilities, and human resource practices to support digital initiatives ([11]). 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 ([4]).

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 ([3]). 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 ([7]).

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” ([15], 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 ([6]).

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 ([5]). 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 ([11]). Second, digital transformation is positioned as a response to external environmental pressures, including changing customer expectations, competitive dynamics, regulatory requirements, and technological disruption ([4]).

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 ([9]). This forced digitalization highlighted both the opportunities and challenges associated with digital transformation, generating substantial empirical material for research investigation.

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 ([15]).

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 ([15]). However, limitations include potential common method bias, social desirability effects, and difficulties in establishing measurement validity and reliability across studies ([3]).

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 ([15]). 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 ([3]).

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 ([3]). 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.

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 ([13]) 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 ([11]; [4]). 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 ([10]), 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 ([3]; [7]). Recent research has specifically developed the concept of “digital dynamic capabilities” to capture the unique capabilities required for effective digital transformation ([5]).

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 ([15]). 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 ([4]); 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 ([11]). The diversity of theoretical perspectives reflects the multi-dimensional nature of digital transformation and the varied disciplinary backgrounds of researchers investigating this phenomenon.

3.5.1. 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 ([7]) 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 ([5]; [12]).

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 ([11]). Innovation enhancement represents another significant pathway, as digital transformation facilitates new product development, service innovation, and business model innovation ([4]). 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 ([2]). 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 ([1]). 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 ([2]). 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 ([5]). The relationship between digital transformation and financial performance is moderated by factors including firm size, industry digital intensity, and institutional context ([12]).

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 ([11]). 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 ([5]).

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 ([11]). Leadership emerges as a critical factor, with studies identifying specific leadership competencies and styles associated with successful digital transformation ([4]). 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 ([7]). 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.

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 ([15]). 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 ([3]). 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 ([11]). Geographic concentration in developed economies and China raises questions about the applicability of findings to other institutional contexts ([4]). 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 ([15]).

Note: See appendix A for summary of literature on Corporate Digital Transformation.

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 ([13]) and Dynamic Capabilities Theory ([10]) 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 ([3]), 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 ([7]). 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 ([4]). Process theories and longitudinal frameworks could enrich understanding of digital transformation dynamics.

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 ([5]; [7]). 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 ([11]). 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 ([4]). 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 ([15]). Organizations should therefore develop context-appropriate digital transformation strategies rather than adopting generic approaches.

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.

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 ([15]; [3]). 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 ([4]). 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 ([11]). 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 ([5]). 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 ([7]). Interdisciplinary collaboration and cross-disciplinary publication can facilitate knowledge integration and theory development.

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.

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.

Figure B1.Alternative PRISMA flow diagram (minimalist style).