Open research questions in Digital Transformation in Industry

What the literature leaves open

• However, during this process, the company has encountered practical bottlenecks, including a continuous decline in the marginal returns on digital resource investment, insufficient data interoperability across business processes, and a severe shortage of multi-skilled professionals possessing both digital technical capabilities and cost management expertise.

  Study on the Optimisation of Cost Management in TCL Group’s Value Chain · 2026 · DOI

• Organisations should approach Industry 4.0 and digital transformation as long-term organisational capability-building processes rather than short-term technological projects. Before implementing digital technologies, organisations should assess their readiness, workforce capability, infrastructure, and strategic alignment to support more sustainable transformation outcomes. The study further recommends stronger integration between digital technologies, organisational systems, and supply- chain processes through improved governance, coordination, and collaboration mechanisms. Organisations should also prioritise workforce development, employee engagement, and leadership Discover publication opportunities: www.wr-publishing.org International Journal of Applied Research in Business and Management (ISSN: 2700-8983) Volume: 07 Issue: 08 Year: 2026 https://doi.org/10.51137/wrp.ijarbm.670 support to reduce resistance and improve adaptability during transformation. Finally, policymakers and industry stakeholders should strengthen support mechanisms for SMEs, particularly in areas such as digital skills development, infrastructure support, and technology access. Future research should focus on how organisations translate digital transformation strategies into measurable operational, financial, and sustainability outcomes across different sectors and contexts. More longitudinal and empirical studies are also needed to examine how organisational readiness, leadership capability, and workforce adaptability evolve throughout transformation processes. Further studies could explore emerging areas such as Quality 4.0, Organisation 4.0, digital servitisation, and human-AI collaboration, particularly regarding their implications for organisational structures, workforce roles, and sustainability. Additional research within SMEs and developing economies would also provide important context-specific insights into digital transformation challenges and opportunities.

  Industry 4.0 and Digital Transformation: Enablers, Barriers, and Organisational Pathways to Performance · 2026 · DOI

• The study established a gap in literature with regard to researching some technologies that are deemed critical in the 39 t n e m e g a n a M d n a e c n e l l e c x E s s e n i s u B 6 2 0 2 h c r a M / 1 e u s s I 6 1 e m u o V l t n e m e g a n a M n a b r U n i s e h c r a e s e R l a c i r i p m E d n a l a c i t e r o e h T TSOAI, K. The literature review established gaps in the literature with regard to incorporating advanced technologies and a number of technological aspects when implementing Industry 4. It was also established that an important research theme that is gaining interest is the concept of sustainability and circular economy, although the literature is still sparse in covering this concept.

  IMPACT OF INDUSTRY 4.0 ON THE INTEGRATION OF SMES INTO GVCS: A SYSTEMATIC LITERATURE REVIEW · 2026 · DOI

• 1. 2. 3. 4. 5. 6. 7. Invest in advanced digital technologies. Strengthen cybersecurity measures. Conduct regular employee training programs. Improve data management practices. Encourage collaboration among supply chain partners. Develop long-term digital transformation strategies. Monitor technology performance continuously.

  Impact of Digitalization on Operations and Supply Chain Efficiency · 2026 · DOI

• Building on emerging evidence from the Turkish public sector (Yaşa, 2022), blockchain-based auditing of humanitarian supply chains should be further investigated to institutionalize trust and accountability in aid distribution.

  AI-Enhanced Digital Twin Framework for Humanitarian Logistics: a Decision Support Approach for Crisis Response · 2026 · DOI

• The dominance of data quality and integration issues as implementation barriers, combined with the widespread absence of formal ROI measurement practices, suggests that most organizations remain in the early and transitional stages of AI maturity, where tools have been adopted but the foundational infrastructure and evaluation frameworks necessary to derive strategic value have not yet been established.

  A Study on Digital Transformation in Commerce: AI as a Driver of Innovation in Coimbatore · 2026 · DOI

• jobs Publishing. https://doi.org/10.1787/empl_outlook-2023-en intelligence, 2023: and 15. Tarafdar, M., Beath, C. M., & Ross, J. W. (2019). The technology–work context misalignment: Causes and effects on employee outcomes. In ICIS 2019 Proceedings. 16. Tüfekci, Z. (2020). Networked secrets: Privacy, security and digital transformation. Yale University Press. 17. World Bank. (2024). World development report 2024: Technological transformation and labor markets. World Bank Publications. https://www.worldbank.org 18. World Economic Forum. (2024). The future of jobs report 2024. World Economic Forum. https://www.weforum.org/reports 19. Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.

  Artificial Intelligence and Digital Transformation in Labor Market: Implications for Sustainable Development · 2026 · DOI

• It Based on available statistical data and analytical reports, the prospective opportunities for AI implementation in the EECCA markets were evaluated. It was established that the examined macro-region is economically and innovatively heterogeneous. Consequently, practical recommendations for the formation and utilization of the primary imperatives for AI implementation in the operational activities of SMEs vary depending on the existing potential. Poland, the Czech Republic, Slovenia, Slovakia, and Hungary exhibit high potential; Armenia, Bulgaria, Georgia, Serbia, and Romania potential; while Kazakhstan, demonstrate medium Azerbaijan, and Moldova possess below-average potential; and Tajikistan and Kyrgyzstan have low potential. Zuykov: Implementation of AI in Operational Activities: Imperatives for Preparation and Forecasting Consequences Operations and Supply Chain Management 19(3) 455 - 466 © 2026 465 The empirical analysis adopted a parsimonious regression approach. Two model specifications were estimated to evaluate the association between macro-level indicators and technological readiness. In the baseline model, research and development expenditure demonstrates a strong and statistically significant association with technological readiness (β = 0.72, p < 0.001), explaining a substantial proportion of variance (R² = 0.64). In the extended model, research and development expenditure remains highly significant (β = 0.65, p < 0.001), while new business registration shows a positive but more moderate effect (β = 0.18, p < 0.05). The inclusion of the second predictor leads to a modest increase in explanatory power (R² = 0.69), a indicating complementary contribution. The comparison of models indicates increasing model complexity does not substantially alter the central role of innovation investment. Therefore, the findings highlight the importance of national innovation capacity as a key enabling condition for AI adoption in SME operational and supply chain activities. The results should be interpreted as exploratory and indicative rather than predictive.

  Implementation of AI in Operational Activities: Imperatives for Preparation and Forecasting Consequences · 2026 · DOI

• This review has several limitations. First, only English- and Korean-language publications were included, which may have excluded relevant studies in other languages. Second, grey literature, dissertations, conference proceedings, and technical reports were excluded, although DT applications are rapidly evolving in non-journal sources. Third, the included studies were heterogeneous in design, population, technological maturity, and outcome reporting, limiting direct comparison across studies. Fourth, because this was a scoping review, formal quality appraisal was not performed. Finally, several included studies were not nursing-specific but were interpreted according to their potential implications for nursing practice and education.

  Analysis of digital twin applications in nursing practice and education: a scoping review · 2026 · DOI

• The effective implementation of the applied machine learning schemes in the optimization of workflow is inherently an administrative redesign issue and not a technical or an algorithmic one. This study has revealed that the digital transformation is often stifled by a so-called latent access tax structural drag on productivity due to disaggregated data standards, overlapping compliance, and excessive administrative intensity. Just as determined during the discussion of both the enterprise and startup settings; to make the theoretical potential of ML a reality, it is necessary to bridge the so-called capacity wedge between nominal technical supply and effective operational throughput. Gupa (2024) points out that productivity and capacity building of the employees mediate organizational performance in this period; therefore, any framework that does not focus on the human-administrative interface is bound to face a scaling crisis 684 World Journal of Advanced Research and Reviews, 2026, 30(02), 679-687 (Ramesh et al., 2025). Whether to switch isolated automation pilots to industrial-grade digital ecosystems depends on the nullification of the nonlinear access amplifier by neutralizing through unified orchestration layers and human- friendly adoption approaches. In the future, this trend of digital transformation will possibly shift to the direction of autonomous administrative ecosystems. Such systems, which run on agentic ML workflows, will be in a position to navigate internal governance and create indigenous audit trails in real-time, thus avoiding the need to manually document events that occur after the fact. According to (Gupa, 2024), even the high-performing firms of the next generation will be characterized by their Staff Training, Innovation Culture, and Ethics (STICE) models where technical accuracy and administrative simplification can be viewed as mutually dependent variables. In large companies, it involves an ultimate break of the departmental silos of multi-payers in favor of a synthetic single-payer structure (Kabera et al., 2026). In the case of startups, success in the future will be achieved by establishing a scalable administrative base that avoids organizational debt accumulation and traps of complexity when the market is growing fast. The results of this paper indicate that the marginal utility of process friction reduction is convex- that is, the utility of optimization is the largest in the most capacity-bounded environments. Therefore, deployed applied ML must be a structural access policy to reclaim the 30 percent of labor time now lost to friction of the type of billing and manual reconciliation. To further deepen our insight into the relationship between compliance burden and operational throughput, future research must focus on empirically validating the "Administrative Intensity Index" in various industrial fields. Also, as the institutional frameworks such as the 10-Year Health Plan of the UK prove, the transition to value-based measures rewarding throughput and outcomes instead of the volume of activity will be the first driver of sustainable digital development. Finally, efficient operationalization of ML frameworks has to be based on a recursive feedback loop where performance information drives ongoing administrative redesign. Taking the administrative simplification as a structural precondition of system efficiency, organizations can finally get the digital transformation as a positive productivity shock (Badhan et al., 2025). The organizations that excel in the competitive environment of 2026 and beyond will be those that do not see machine learning as an automation tool, but as a system that could release human cognitive capital off the latent tax of systemic complexity, creating a sustainable and fair ecosystem of innovation.

  Applied machine learning frameworks for workflow optimization, organizational efficiency and digital transformation · 2026 · DOI

• Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues Enabling technologies and tools for digital twin Source: Scopus, 2026 twin in digital Table 1 shows that the most cited studies technology are dominated by review and conceptual papers, indicating that the field is still strongly shaped by foundational works that define concepts, technologies, and frameworks, enabling research challenges. The highest-cited article by, with 3,661 citations, confirms its central influence in explaining the state of the art of digital twins in industrial contexts. Other highly cited works, such as,, and, further show that scholars frequently refer to studies that clarify the relationship twins, big data, smart between digital manufacturing, Industry 4.0, and modeling approaches. The table also indicates that digital twin research is closely connected with broader transformations, including Industry 5.0, 6G communication, big data, and enabling technologies. The presence of works such as, suggests that digital twin development is increasingly future-oriented digital discussed within infrastructure and industrial systems. technological intelligent 4. CONCLUSION REFERENCES and becoming intelligence, fields of artificial From the bibliometric analysis of the literature concerning digital twin technology, it can be seen that the field of study is rapidly growing increasingly multidisciplinary. The results show that the digital twin is considered to be the main research topic, being highly associated with the IoT, machine learning, smart manufacturing, industry 4.0, and sustainability. The keyword co-occurrence, overlay, and density analysis shows that the recent research trends have become concentrated on decision-making, predictive analytics, energy efficiency, and sustainable development, which means that the focus has shifted from merely using digital twin technology towards more sophisticated applications of its capabilities. The analysis of co-authorship and institutional collaboration networks showed that collaborative work is crucial for the field of digital twins. Finally, from the country collaboration analysis, it becomes clear that China, the US, and several countries contributed significantly to knowledge creation in the field. Lastly, based on the citation analysis, the intellectual underpinning of digital twin research includes the works devoted to its concepts and reviews. in Europe have M. Bimpas, N. Doulamis, A. Doulamis, D. Vamvatsikos, and D.

  Bibliometric Analysis of Digital Twin Technology · 2026 · DOI

• Directions. Cross-sectional limits causality, though IV robustness mitigates this; longitudinal tracking could refine dynamics. Self-reports risk optimism bias, countered by triangulation. Future research might employ data RCTs for strategy causality or AI simulations for to underrepresented sectors like agritech. forecasting, extending barrier In sum, this study delineates a clear imperative: overcoming SME DT barriers demands holistic strategies blending finance, skills, and ecosystems. High-adopters' 28% growth premium (t=4.2, p<0.001) versus laggards signals not just opportunity, but urgency for equitable digital futures. 4.

  Barriers and Strategies in Digital Transformation Adoption Among Small and Medium Enterprises (SMEs) · 2026 · DOI

• 7.1. Conclusion This paper is based on institutional theory, discusses the influence of coercive, normative, and mimetic pressures on the digital transformation of enterprises. By conducting empirical research on 286 Chinese enterprises, it is found that institutional pressures have significantly facilitated the digital transformation of enterprises, among which the effect of imitative pressure is the most obvious. The driving mechanism of the external institutional environment on the digital transformation of enterprises is revealed, proving that institutional pressure not only acts as a constraining force but also serves as an important driving force in promoting enterprise innovation and capacity reconstruction. Meanwhile, this study verifies that the relationship between institution and performance takes its mediating path through digital transformation, hence providing new empirical evidence for understanding how institutional factors achieve improvement of performance through technological and organizational change. The research result enhances the explanatory power of institutional theory within digitalized business settings and offers conceptual guidance for firms to build competitive advantages in complex policy and market environments. 7.2. Limitations of the Study This study is innovative in both theoretical and empirical aspects; there are still some limitations. Although this study has achieved relatively systematic results both in the theoretical and empirical aspects, there are some limitations that provide direction for future research. First, the sample of this paper mainly comes from some enterprises in China, with relatively limited sample scope. The research conclusions may be affected by the industry characteristics and regional institutional environment differences. In addition, this study is based on cross-sectional data analysis and cannot fully reflect the dynamic relationship between institutional pressure and digital transformation. The causal direction still needs to be further verified in longitudinal or longitudinal studies. 7.3. Future Research Future research can expand on this basis by expanding sample sources and comparing the differentiated effects of institutional pressures across different countries, industries, and organizational types. The second is to use longitudinal data or mixed research methods (such as case studies, in-depth interviews, or fsQCA analysis) to reveal the long-term mechanisms between institutional environment, digital transformation, and performance.

  How External Institutional Forces Shape Digital Transformation: Uncovering the Mediated Pathways toward Performance Improvement · 2026 · DOI

• Despite its contributions, the study has several limitations: 1. reliance on secondary data sources rather than primary empirical data 2. absence of quantitative validation of the proposed model 3. potential bias in case selection and interpretation 4. limited analysis of security and environmental impacts These limitations are consistent with challenges identified in previous studies on IoT-enabled logistics systems (Taj et al., 2023; DHL, 2023).

  Enterprise Architecture and IOT Integration in Logistics Optimization · 2026 · DOI

• Future research should focus on: 1. quantitative evaluation of EA-IoT integration using real-world data; 2. development of standardized frameworks and protocols; 3. investigation of cybersecurity challenges in IoT-enabled logistics (Gubbi et al., 2013); 4. exploration of sustainability and environmental impacts; 5. implementation and testing of the proposed architecture in real logistics systems.

  Enterprise Architecture and IOT Integration in Logistics Optimization · 2026 · DOI

• Future research on AI in SCM should focus on the following key directions: JBMS 8(7): 15-40 1) Theorizing Data Ecosystems and Governance Future studies should explore how organizations design and manage data ecosystems, particularly in inter- organizational settings. Future research should examine how organizations redesign governance structures to accommodate human–AI collaboration, particularly in complex supply chain networks.

  Artificial Intelligence Applications in Supply Chain Management: A Systematic Review of Empirical Evidence and Future Research Directions · 2026 · DOI

• This study examined the emergence of Management 4.0 and its transformative role in reshaping smart manufacturing organizations under accelerating digital transformation and Industry 4.0 adoption. Management 4.0 is conceptualized as a managerial transformation framework that integrates advanced digital technologies with organizational structures, human capabilities, and governance systems to enable adaptive, intelligent, and data-driven decision-making. While often framed as a linear progression toward managerial enhancement, the findings indicate that its realization is inherently socio-technical, requiring deep structural reconfiguration rather than incremental technological adoption. The systematic literature synthesis demonstrates that effective implementation depends on a tightly coupled socio-technical and dynamic capability configuration in which technological infrastructure, organizational architecture, managerial capabilities, and workforce transformation coevolve. However, the literature remains fragmented, with limited theoretical convergence across technological, organizational, and governance perspectives. Importantly, this review establishes that Management 4.0 is not value-neutral. It embodies a structural tension between technological augmentation and human agency that remains under-theorized. Alongside performance and efficiency gains, it generates systemic risks inherent to AI-enabled organizational environments. These include managerial deskilling, driven by increasing reliance on algorithmic recommendations that may erode experiential judgment; algorithmic bias, arising from opaque models and biased data foundations; surveillance intensification, which reshapes workplace autonomy and power relations through pervasive monitoring; and over-automation, which can reduce organizational reflexivity and increase dependency on algorithmic systems. Collectively, these dynamics signal a transition toward algorithmically mediated governance structures characterized by cognitive dependency and diluted accountability. Accordingly, governance and ethics must be reconceptualized as embedded design dimensions of socio-technical systems, rather than ex-post compliance mechanisms. This requires integrating transparency, explainability, auditability, and human-in-the-loop control into the architecture of Management 4.0 systems. Similarly, ESG principles remain insufficiently operationalized and should be embedded as intrinsic system design constraints rather than external reporting obligations. Theoretical Implications: This study advances Management 4.0 as a socio-technical and dynamic capability-based transformation architecture for intelligent manufacturing systems.

  Management 4.0 in Smart Manufacturing: A Systematic Review, Gap Analysis, and Future Roadmap · 2026 · DOI

• At the time of writing this article, the KaizenAI methodology has been fully implemented up to the Predictive Model Development phase (DO Phase). The predictive system is technically functional and has been validated using standard ML metrics on historical data, meeting all high-priority technical requirements defined during the design phase. The remaining phases to be executed correspond to the operational implementation of the system in the actual production environment (continuation of the DO Phase), the verification of results through comparison of operational indicators before and after implementation (CHECK Phase), and the standardization and scaling of the solution to other plant processes (ACT Phase). The results presented in this article are limited to the technical validation of the predictive model on historical data and do not yet include measurements of real operational impact, as the system has not been deployed in production. Full validation of the KaizenAI methodology will require the effective implementation of the system, the inclusion of additional performance indicators, and the measurement of their variation over an operational period of at least three months—work that will constitute the next stage of the research project.

  KaizenAI: Methodology for the integration of machine learning in manufacturing processes based on Kaizen principles. Case study: Bottling industry · 2026 · DOI

• This model needs to be replicated in other geographical areas in future studies to prove external validity. Lastly, the industry-specific dynamics, especially in high-tech industries or government-based organizations, could be investigated in future research to gain a better insight into the contextual variations in the outcome of AI adoption.

  AI-Augmented Decision Intuition and SME Strategic Adaptability among SMEs: The Mediating Role of Human–AI Collaboration Quality and the Moderating Role of Technological Anxiety · 2026 · DOI

• Limitations Single-author methodology and single-author validation — the primary constraint. All 39 corpus papers share a single authoring perspective. The theoretical concepts introduced in this paper — including artefactual absorptive capacity (Section 3.4) and the NUDEDA Digital DNA and Autonomous extensions (Section 9.3) — have not been independently evaluated by researchers examining the same deployment evidence. This is the primary methodological constraint of this paper. The validation presented in Section 8 is design-science validation in the DSR sense (Hevner et al., 2004) — evidence of internal consistency and cross-domain applicability — not independent empirical validation of the causal claims. The distinction matters and is not hedged. Multi-site, multi-author validation is the highest-priority research agenda item. Pending DOIs. Six of the eight domain applications cited in Section 8 have DOIs listed as pending. These applications are design contributions in the DSR sense; they are not confirmed empirical findings. Readers should treat them as they would pre-registered studies that have not yet reported results: the design is documented and the claims are stated, but the evidence is not yet publicly archivable and peer-reviewable. No controlled comparison study. SMILE's claims rest on a corpus of case applications, not on controlled comparative studies. Observed outcomes in SMILE deployments cannot be causally attributed to SMILE rather than to other factors — organisational capability, stakeholder quality, resource availability — that may correlate with the decision to adopt a structured methodology. Phase timelines are domain-specific. SMILE specifies phase logic but not phase duration. Practitioners who interpret SMILE's phase descriptions as implying specific timelines will miscalibrate their planning. Edge technology maturity. Phases 5 and 6 depend on edge-native AI capabilities — LQM reduction to SQMs, federated learning, on-device inference — that are at early maturity stages in several domains. The Phase 5 claims about autonomous operation within governance boundaries assume technical capabilities that are available in some contexts but not yet validated in others. 6G and holographic society are anticipatory. The 6G capabilities referenced in Section 7.6 face documented terahertz propagation constraints (Tataria et al., 2021). The holographic society endpoint describes an anticipated state that is dependent on a 10–15 year standardisation trajectory, not a currently operational capability. Cultural and political context. SMILE was developed primarily in European and Australian deployment contexts. The methodology's assumptions about stakeholder governance, data sovereignty, and regulatory frameworks reflect these contexts and may require modification for other environments. 13.2 Future Research Multi-site comparative validation.

  SMILE v4.2: The Reality Fabric — Universal Methodology for Impact-Driven Digital Twin Implementation · 2026 · DOI

• CDT adoption faces organizational barriers including workforce resistance, skill gaps in AI and data engineering, and uncertain return on investment, particularly for small and medium-sized enterprises. Quantifiable metrics demonstrating operational value and cost-benefit analysis for CDT deployment across different industrial sectors and organizational scales are lacking.

  A Survey on Cognitive Digital Twins: AI Integration, Cross-Platform Knowledge Services, and Intelligent Automation · 2026 · DOI

• The predictive robustness of CDTs remains constrained by data quality, model interpretability, and cross-system integration challenges. Empirical validation of how data quality degradation, incomplete cross-platform integration, and model interpretability tradeoffs affect CDT decision accuracy across different operational domains has not been comprehensively studied.

  A Survey on Cognitive Digital Twins: AI Integration, Cross-Platform Knowledge Services, and Intelligent Automation · 2026 · DOI

• Most existing CDT systems process primarily structured and textual data with limited ability to interpret speech, gestures, emotional states, and behavioral context. Multimodal perception integrating computer vision, speech processing, physiological sensing, and emotion-aware reasoning specifically for CDT-based human-AI collaboration in Industry 5.0 environments remains incomplete.

  A Survey on Cognitive Digital Twins: AI Integration, Cross-Platform Knowledge Services, and Intelligent Automation · 2026 · DOI

• CDTs must process high-volume, high-velocity heterogeneous data streams with constraints on computational inference costs, simulation latency, long-term contextual memory requirements, and energy consumption in resource-constrained industrial environments. Real-time processing optimization techniques for CDTs requiring millisecond-level reliability have not been adequately addressed.

  A Survey on Cognitive Digital Twins: AI Integration, Cross-Platform Knowledge Services, and Intelligent Automation · 2026 · DOI

• Comprehensive privacy-preserving frameworks for CDTs that continuously collect behavioral, operational, and contextual data remain underdeveloped. While federated learning, homomorphic encryption, and zero-trust architectures show promise, their specific integration into CDT architectures for protecting sensitive operational intelligence has not been systematically evaluated.

  A Survey on Cognitive Digital Twins: AI Integration, Cross-Platform Knowledge Services, and Intelligent Automation · 2026 · DOI

Most-cited papers in Digital Transformation in Industry

• Digital Transformation: An Overview of the Current State of the Art of Research · SAGE Open · 2021 · 911 citations
• Digital Twin: Generalization, characterization and implementation · Decision Support Systems · 2021 · 761 citations
• The smart circular economy: A digital-enabled circular strategies framework for manufacturing companies · Journal of Business Research · 2020 · 671 citations
• Linking circular economy and digitalisation technologies: A systematic literature review of past achievements and future promises · Technological Forecasting and Social Change · 2022 · 597 citations
• Implementing a Digital Strategy: Learning from the Experience of Three Digital Transformation Projects · California Management Review · 2020 · 592 citations
• Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions · Journal of Cleaner Production · 2024 · 529 citations
• Industry 4.0 integration with socio-technical systems theory: A systematic review and proposed theoretical model · Technology in Society · 2020 · 521 citations
• Impact of digital transformation on the automotive industry · Technological Forecasting and Social Change · 2020 · 472 citations
• Strategies for Digitalization in Manufacturing Firms · California Management Review · 2020 · 470 citations
• Digital transformation: Five recommendations for the digitally conscious firm · Business Horizons · 2020 · 439 citations

Most recent work

• Smart reconfigurable manufacturing towards Industry 5.0 · Journal of Manufacturing Systems · 2026
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• Enhancing viability for dairy products manufacturing through explicit and tacit knowledge management: the interacting role of artificial and human intelligence · Journal of Knowledge Management · 2026
• Navigating the digital frontier: challenges and opportunities in sustainable manufacturing through digitalization · foresight · 2026
• Pioneering digital transformation: unveiling the blockchain and digital twin technologies’ synergy · Journal of Engineering, Design and Technology · 2026
• Digital transformation of SMEs: the nexus of entrepreneur orientation, artificial intelligence adoption and digital literacy · Digital Policy, Regulation and Governance · 2026
• A conceptual framework for machine vision integration in manufacturing SMEs · Discover Artificial Intelligence · 2026
• Analyzing the critical factors of digital readiness for construction enterprises · Engineering Construction & Architectural Management · 2026
• Digital Transformation and its Impact on Business Efficiency and Growth · International Journal of Emerging Research in Science Engineering and Management · 2026
• Digital twins for energy management: bridging virtual intelligence and physical systems · Applied Energy · 2026

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