digital transformation

Existing Digital Maturity Models (DMMs) exhibit significant inconsistencies and semantic ambiguities in their dimensional definitions and structural formulations, which hinder effective assessment of digital transformation. This study addresses these limitations through a systematic literature mapping approach, combining automated retrieval with snowballing techniques to conduct a multi-source comparative and content analysis of 76 DMMs. For the first time, it integrates and harmonizes the definitions and constituent elements of ten frequently occurring dimensions—such as organization, strategy, and technology—thereby resolving the prevailing inconsistencies and structural ambiguities across existing models. The work establishes a coherent theoretical foundation and offers practical guidance for developing more consistent, actionable frameworks for evaluating digital maturity.

This study examines how Enterprise Architecture (EA) can be localized within Vietnamese government agencies operating under weak institutional foundations to support digital transformation. Addressing EA’s conceptual ambiguity and poor contextual fit, we propose a dual translation mechanism: “theoretical translation”—abstracting indigenous practices into generalizable concepts—and “contextual translation”—deconstructing EA into actionable, organizationally prioritized interventions. Drawing on a 15-year longitudinal case study and integrating mechanism-based analysis with sensemaking theory, we identify critical diffusion pathways for EA in institutionally immature environments. Our findings extend EA theory’s applicability to digital governance in developing countries and yield a reusable conceptual translation framework. This framework offers methodological guidance for digital capacity building across the Global South, bridging theory-practice gaps in public-sector digital transformation.

The absence of automated, standardized tools for assessing digital transformation (DT) maturity in the public sector hinders evidence-based governance of smart cities. Method: We propose a data-driven DT assessment framework integrating expert surveys with multi-source organizational texts (e.g., official websites) to construct a domain-specific corpus. Leveraging a hybrid architecture—combining lightweight neural networks with fine-tuned Transformer models—we enable automatic, quantitative DT capability evaluation. The framework further incorporates IoT-readiness metrics and an interactive dashboard for real-time monitoring and visualization, adopting a modular, region-agnostic design. Contribution/Results: Validated in Spain’s Valencian Community, the framework achieves high agreement with expert assessments (Cohen’s κ = 0.82), confirming its accuracy and operational utility. It delivers a reusable, scalable, and intelligible assessment paradigm for global smart city DT governance.

Digital Engineering (DE) transformation confronts complex, interdependent socio-technical barriers, yet existing research lacks a systematic understanding of their typologies, root causes, and alignment with U.S. Department of Defense (DoD) policy objectives. To address this gap, this study develops a novel six-dimensional socio-technical barrier taxonomy, uniquely integrating socio-technical systems theory into the DE transformation analytical framework and revealing cross-dimensional cascading effects among barriers. Leveraging a synthesis of literature review, theoretical modeling, and systems engineering principles, the study identifies critical risk nodes impeding policy implementation. The resulting operational risk diagnostic tool enables practitioners to precisely pinpoint bottlenecks, optimize strategic investment priorities, and refine change management pathways—thereby enhancing policy alignment and execution efficacy of DE transformation initiatives.

This study investigates critical barriers and enablers hindering digital transformation and artificial intelligence (AI) adoption among Italian small and medium-sized enterprises (SMEs). Method: Employing a mixed-methods approach—including surveys, in-depth interviews, and multi-case analysis—the research integrates a maturity assessment model with an obstacle classification matrix to develop the first structured, three-dimensional AI adoption evaluation framework tailored to Italian SMEs, encompassing organizational capability, technological fit, and policy support. Contribution/Results: The study proposes a phased AI-driven transformation roadmap and actionable policy recommendations, validated through regional pilots across three Italian territories, yielding a 42% increase in firms’ AI adoption intent. Its core contribution is the first empirically grounded, locally calibrated, and operationally feasible AI adoption assessment and implementation framework for SMEs—offering both theoretical insight and practical guidance for intelligent transformation in European small- and medium-economy contexts.

This study addresses the urgent need for multinational enterprises to align digital efficiency with environmental responsibility amid concurrent green and digital transitions. Integrating Technology Roadmapping (TRM) with the ITU ICT Innovation Ecosystem Toolkit, and complemented by bibliometric analysis and a stakeholder canvas, the authors develop a sociotechnical framework tailored to dual transformation. They propose a novel “sustainable intelligence” paradigm, positioning Global Business Services (GBS) as an operational airlock that bridges macro-level policy pressures and micro-level AI-native workflows. The research further highlights the potential of “intermediate power” hubs—such as Poland, Portugal, and Malaysia—to offer a “third way” within global value chains. The resulting data-driven design approach advances practical pathways for Industry 5.0 in a multipolar digital economy, facilitating coordinated flows of talent and supply chains.

This study addresses the challenges posed by escalating geopolitical, organizational, and technological fragmentation by proposing a multilevel, polycentric digital ecosystem framework spanning individual, organizational, inter-organizational, and global tiers. The framework integrates four key technological clusters—AI and automation, blockchain-based trust mechanisms, federated data spaces, and immersive technologies—to construct a loosely coupled, distributed network that enables cross-border coordination and innovation. By synthesizing these elements, the research extends platform theory and uncovers novel pathways through which AI-driven infrastructures can foster digital integration in an increasingly fragmented world. The proposed approach offers a systematic solution for cross-domain digital collaboration, balancing autonomy with interoperability across diverse institutional and technological contexts.

This study addresses the persistent “pilot purgatory” that hinders the large-scale deployment of industrial extended reality (XR) applications. Through in-depth interviews with 17 industry experts and an ecosystem analysis framework, it reveals that the primary barriers have shifted from technological maturity to organizational readiness and stakeholder coordination. The work proposes a “great reversal” perspective, arguing that systemic factors—such as organizational change resistance, misaligned performance metrics, and internal political dynamics—now constitute the core challenges, rather than technical limitations. Emphasizing a problem-driven, ecosystem-coordinated transition pathway, the study identifies incentive misalignment as a critical friction point, offering both theoretical grounding and practical guidance for scaling industrial XR beyond isolated pilots.

The widespread adoption of artificial intelligence is blurring organizational role boundaries and eroding “invisible work”—such as mentoring and feedback—that underpins professional development and cultural health. Through semi-structured interviews with 24 product professionals in technology firms and subsequent thematic analysis, this study systematically uncovers AI’s dual impact: while enhancing peer-level collaboration, it simultaneously weakens traditional mechanisms of career support. To address these tensions, the research introduces a strategic framework that renders invisible work visible and offers actionable interventions for organizations, leaders, and individuals. These measures aim to preserve cultural sustainability without compromising operational efficiency in AI-integrated workplaces.

This study addresses the persistent challenges impeding the effective integration of Agile and DevOps practices, which are often constrained by cultural, organizational, procedural, and technological barriers that undermine software delivery performance. Through semi-structured interviews with six senior practitioners from Brazil and Germany, the research employs qualitative thematic analysis to systematically identify—within a cross-national context—four core integration challenges and proposes a corresponding solution framework. The findings underscore the pivotal roles of cultural alignment, team autonomy, process coordination, and infrastructure automation, highlighting that organizational and cultural factors are critical enablers of successful technical integration. By elucidating these interdependencies, the study offers actionable, cross-cultural guidance for software organizations seeking to enhance their Agile–DevOps convergence and overall delivery effectiveness.