Search Results (253)

The purpose of this study is to characterize, through an exploratory empirical diagnosis, the degree of development and preliminary association between circular economy capabilities and sustainability-oriented digital transformation capabilities in Chamber-affiliated SMEs in Lambayeque, Peru. Guided by three exploratory working hypotheses, the study expected intermediate levels of development, heterogeneous performance across dimensions, and a positive but non-confirmatory coupling between both capability families. A self-administered questionnaire with thirty Likert-type items measured four circular economy dimensions—circular design and eco-design, resource optimization, circular waste management, and circular business models—and four sustainability-oriented digital transformation dimensions—digital technology infrastructure, dynamic digital capabilities, sustainable digital strategy, and digital innovation culture. The initial database contained 111 complete Chamber-affiliated responses; however, seven large Chamber-affiliated firms were retained only as contextual comparators and were excluded from all statistical processing. Consequently, all descriptive, psychometric, and SEM results were calculated using the final analytical sample of 104 micro-, small-, and medium-sized enterprises. The findings show intermediate development in both constructs, higher perceived performance in digital innovation culture and resource optimization, and lower performance in digital technology infrastructure, reverse logistics, platforms enabling circularity, and monetization of circular models. The latent association between the two higher-order constructs was very high (β = 0.985, p < 0.001); however, because global fit indices were below conventional thresholds, this coefficient is interpreted as preliminary evidence of empirical overlap and capability co-occurrence rather than confirmatory evidence of a validated structural model or causal integration. Full article

This article presents a comparative assessment of digital transformation pathways in ASEAN economies, taking into account both the level and the structural composition of digitalization within the region. The study draws on internationally comparable data and advances an original temporal–taxonomic analytical approach based on the composite CMG indicator, which integrates three core dimensions of digital transformation: connectivity, market, and governance–sustainability-trust areas. The focus on Southeast Asian economies is substantively justified by the region’s pronounced heterogeneity in economic development, institutional capacities, and digitalization outcomes, combined with its strategic importance as one of the most dynamic emerging regions where digital transformation plays a pivotal role in shaping long-term growth trajectories and structural convergence prospects. The novelty of the research lies in the construction and application of the research tool: the CMG indicator—as a transparent and replicable measurement framework designed to enable the simultaneous analysis of the intensity of digitalization and its internal structure. Such an approach addresses key limitations of existing digitalization indicators when employed in dynamic and structure-oriented comparative research. Moreover, the integration of synthetic measurement techniques with cluster analysis allows for the identification of distinct digitalization profiles among ASEAN countries and for tracing their differentiated development trajectories under conditions of accelerated digital transformation. The results reveal substantial heterogeneity in digital transformation processes across the region, arising from the combined influence of differences in economic development levels, institutional conditions, market structures, and regulatory frameworks, which jointly shape national digitalization trajectories. The analysis identifies both advanced and relatively stabilized digitalization profiles, as well as catch-up pathways characterized by rapid improvements in digital infrastructure and regulatory environments. The conducted research demonstrates a clear value added resulting from the proposed coherent measurement framework, which enables the analysis of digital transformation from both temporal and taxonomic perspectives and provides new empirical evidence on emerging economies. The findings also have practical relevance, offering insights for the design of context-sensitive digital strategies aimed at reducing structural disparities and supporting inclusive and sustainable digital development in Southeast Asia. Full article

Digital and green technological innovations are increasingly viewed as interdependent components of firm transformation, yet their combined effects are still insufficiently understood from a systems perspective. Rather than treating digitalization and greening as separate innovation trajectories, this study conceptualizes their integration as digital–green collaborative technological innovation and examines whether this socio-technical synergy improves firm-level total factor productivity (TFP). Using panel data for Chinese A-share listed companies from 2008 to 2022, we construct firm-level measures of digital innovation, green innovation, and their interaction based on patent information, and employ multi-dimensional fixed-effects models to assess their productivity effects. The results show that digital–green collaborative technological innovation significantly improves corporate TFP, and this finding remains robust after addressing potential endogeneity, replacing the TFP measure, excluding information-technology-intensive industries, expanding the sample period, and employing invention patents as an alternative proxy for innovation quality. Heterogeneity analysis indicates that the productivity-enhancing effect is more pronounced among non-state-owned enterprises, firms located in central China, and firms operating in regions with stronger intellectual property protection. Further decomposition of TFP reveals that digital–green synergy promotes firm transformation through three channels: improving micro-level production efficiency, optimizing labor allocation efficiency, and enhancing capital allocation efficiency. These findings contribute to systems-oriented research on technological innovation by showing how complementary digital and green capabilities jointly reshape production systems and factor allocation. They also provide practical implications for firms and policymakers seeking to promote sustainable transformation through integrated innovation strategies. Full article

Digital transformation constantly changes the work practices and employee experiences of contemporary work environments. Studies have documented the adverse impact of cutting-edge technologies, such as artificial intelligence, algorithmic systems, and digital platforms on employees’ well-being. However, such findings remain fragmented across technologies, disciplines, and well-being constructs, limiting a coherent understanding of how digitally transformed work conditions affect employees. This study systematically reviews the literature on digital transformation and employee well-being to clarify the conditions, mechanisms, and outcomes that are most consistently identified in prior research. Using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) method, this study identified, screened, and selected 57 peer-reviewed articles (2014–2025) on digital transformation and employee well-being. The Gioia inductive analytical approach was used to synthesise the reviewed studies and to develop higher-order conceptual dimensions. Five interrelated aggregate dimensions were identified: digital transformation conditions, digital resources and demands, mediating processes, contextual factors, and employee well-being outcomes. The findings indicate that employee well-being depends significantly on how digitally intensified demands and available resources are configured, interpreted, and mediated within specific organisational contexts. This review highlights the need for more temporally sensitive, context-specific, and resource-oriented research, and proposes an integrative framework to guide future research and support the design of healthier and sustainable digital workplaces. Full article

Sustainable power systems increasingly require scheduling methods that can coordinate renewable generation, distributed flexibility, and conventional energy-conversion units under forecast uncertainty while directly supporting carbon-emission reduction. However, many data-driven scheduling models still enforce operational constraints through soft penalties or post-processing corrections, which may lead to infeasible schedules during deployment and weaken their reliability in digital-shadow-assisted operation. In addition, conventional cost-oriented scheduling objectives do not explicitly account for the carbon impact of real-time imbalances caused by forecast errors. To address these challenges, this paper proposes a digital-shadowing-enabled deep learning framework for carbon-aware day-ahead scheduling of integrated energy systems. The main methodological contribution is a feasibility-by-design neural decoder that embeds hard physical constraints directly into the network forward pass. By classifying devices into non-memory fast units, non-memory ramp-limited units, and memory-type storage devices, the decoder applies tailored transformations to enforce capacity limits, ramp-rate restrictions, state-of-charge dynamics, and terminal energy consistency by construction. Therefore, the generated schedules are physically feasible without relying on post-hoc repair. In parallel, a carbon-first objective is developed to minimize both scheduled emissions and imbalance-driven emissions, allowing the scheduler to reduce not only planned carbon output but also the carbon impact of real-time corrective actions. Forecast uncertainty is represented through a digital shadow that stores historical forecast-error patterns and generates augmented training scenarios. Case studies based on U.K. data show that the proposed framework produces fully feasible schedules and reduces annual CO₂ emissions by approximately 4.0% compared with a forecast-driven baseline, with larger benefits during high-demand periods. These results demonstrate that combining digital shadowing, constraint-embedded neural decoding, and carbon-aware optimization provides a practical and reliable pathway for low-carbon smart-grid scheduling under uncertainty. Full article

As digital technologies reshape competitive dynamics, firms face increasing pressure to keep pace with the innovation activities of their peers. While digital transformation is often viewed as a driver of sustainable development, less attention has been paid to how peer firms’ digital innovation relates to corporate environmental, social, and governance (ESG) performance within the broader innovation system. Using panel data from Chinese A-share listed firms from 2012 to 2023, this study constructs an industry-level measure of peer digital technology innovation based on the digital patenting activities of other firms in the same narrowly defined industry. The results show that peer digital technology innovation is significantly associated with a decline in ESG performance, revealing trade-offs between digital competitive adaptation and sustainability outcomes. Mechanism analysis further provides suggestive evidence consistent with two potential channels: heightened digital risk perception and stronger managerial short-term orientation. Heterogeneity analysis shows that this negative association is pronounced in small and medium-sized enterprises, tech-intensive industries, and highly competitive markets. In addition, external governance conditions shape the magnitude of this relationship: stronger intellectual property protection amplifies the negative association, whereas greater investor attention attenuates it. Supplemental analysis of ESG rankings further indicates that digital competition may allow specific firms to enhance their relative standing. These findings provide new evidence on the unintended consequences of digital competition and contribute to the literature on corporate digitalization and sustainability. Full article

Artificial intelligence (AI) is transforming the accounting function, but evidence from emerging economies still leaves a central question unresolved: whether the profession’s traditional strengths are sufficient to support this technological transition. This study examines this issue in Peru, where digital reform has advanced rapidly, but professional adaptation remains uneven. Using survey data from 424 accounting professionals and academics, a Covariance-Based Structural Equation Modeling (CB-SEM) approach was employed to test the relationships between Sustainability, Professional Challenges, IFAC Ethics, and AI Orientation. The construct previously described as AI adaptation is now defined more cautiously as AI Orientation, because its indicators capture a broad perceptual orientation toward AI, including perceived challenge, workflow impact, curricular integration, substitution concerns, and routine use, rather than verified adoption behavior. The results confirmed the strength of the traditional professional sequence: Sustainability significantly predicted Professional Challenges (β = 0.637; p < 0.001), and Professional Challenges strongly predicted adherence to IFAC Ethics (β = 0.972; p < 0.001). However, the direct path from Sustainability to IFAC Ethics was not significant (β = 0.200; p = 0.096). Regarding technological integration, the model revealed a structural disconnect. While Sustainability showed a weak direct effect on AI Orientation (β = 0.279; p = 0.045), neither Professional Challenges (β = 0.160; p = 0.510) nor IFAC Ethics (β = 0.037; p = 0.810) significantly predicted AI Orientation. These findings indicate that while the ethical and adaptive core of the Peruvian accounting profession remains highly resilient under pressure, its connection to AI is still fragmented, with algorithmic tools often perceived as external technological systems rather than integrated accounting competencies. Full article

The rapid advancement of laboratory automation, robotics, and digital technologies has significantly transformed laboratory medicine worldwide, improving efficiency, diagnostic accuracy, and quality management. However, the adoption of these technologies in developing countries such as Indonesia remains uneven and is influenced by infrastructural, financial, regulatory, and workforce-related challenges. This structured narrative review aimed to critically examine the current landscape of laboratory automation and robotics in Indonesia, with particular emphasis on implementation challenges, workforce transformation among medical laboratory scientists (Ahli Teknologi Laboratorium Medik, ATLM), and pathways toward equitable integration. Studies published between 2015 and 2025 were identified through PubMed, Scopus, and Google Scholar, complemented by Indonesian regulatory documents, professional guidelines, and relevant grey literature. The review was informed by PRISMA principles and synthesized narratively to explore technological developments, operational impacts, policy contexts, and implementation barriers relevant to Indonesian laboratory systems. The findings indicate that automation and robotics offer substantial benefits, including improved turnaround time, enhanced quality assurance, reduced laboratory errors, and greater operational efficiency. Nevertheless, significant barriers persist, particularly disparities in digital infrastructure, financial constraints, limited workforce readiness, and the absence of comprehensive implementation frameworks. The review further highlights that automation is reshaping rather than replacing the role of ATLM, shifting professional responsibilities toward digital competency, automation oversight, data interpretation, and quality management. Achieving sustainable laboratory automation in Indonesia therefore requires an equity-centered and systems-oriented approach involving regulatory strengthening, workforce development, infrastructure investment, and multi-stakeholder collaboration. With strategic planning and policy alignment, laboratory automation and robotics hold considerable potential to modernize laboratory services and support Indonesia’s broader healthcare transformation agenda. Full article

Requirements management is fundamental to complex projects, especially in areas such as engineering, infrastructure, and defense. This article develops an integrative theoretical framework for requirements management in complex projects, grounded in a PRISMA-guided systematic literature review with a qualitative synthesis of the key dimensions of the field. In this review, 136 studies selected from an initial set of 519 records identified across multiple databases were reviewed. Five pillars were found to underpin the proposal: (i) the definition and traceability of requirements, (ii) the mitigation of uncertainties and risks, (iii) team maturity, (iv) digitalization and organizational transformation, and (v) the application of model-based systems engineering (MBSE). A literature review revealed that high-quality requirements reduce errors, improve predictability, and optimize resources, whereas digital approaches and collaborative practices strengthen the adaptive capacity of projects. Thus, in the proposed framework, these dimensions are organized into a hierarchical structure, with an emphasis on the integration of technical, organizational, and digital processes. One limitation is the lack of empirical validation, necessitating future studies on the practical application of the model in real projects, interviews with experts, and the development of operational metrics. This conceptual model is aimed at contributing to the literature and supporting more resilient, automated, and sustainability-oriented practices in complex environments. Full article

The fourth industrial revolution has been discussed generously in literature, as it centers around offering high value and customized products or services to the consumer by harnessing the potential of cutting-edge technologies. It comes as no surprise that it has brought about a paradigm shift in the manufacturing and services sector; however, it is imperative to analyze the variables which influence its adoption. Although there has been an increasing number of studies helping us to understand the adoption of Industry 4.0, there is no structured and process-oriented implementation roadmap that brings together contextual factors for the adoption, nor a step-by-step methodology regarding improvements. Therefore, the authors have conducted a review in which the barriers to Industry 4.0 adoption have been analyzed in a manufacturing context and their corresponding drivers have been discussed. The study reveals that top management commitment, clear strategy, and a skilled workforce play a significant role in the adoption of Industry 4.0 technologies. Afterwards, the authors have developed a conceptual framework for Industry 4.0 adoption by combining DMAIC with a Technology–Organization–Environment (TOE) framework. The recommended framework is designed to facilitate sustainable digital transformation, helping organizations navigate through a structured ability-building process, upskill their workforce, and embrace technologies that align with sustainability objectives. From an academic perspective, the research makes key contributions to technology management literature by utilizing the TOE approach in a proper manner through DMAIC principles. For practitioners, the research work provides an easy four-step process that can assist them in adopting Industry 4.0 technologies in a proper manner. Full article

Frequent introductions and revisions of climate policy instruments constitute a salient exogenous shock to firms’ strategic decisions. From a socio-technical systems perspective, climate policy uncertainty (CPU) represents an external institutional disturbance that reshapes the interaction between firms’ technological upgrading and organizational governance. Using panel data on 1783 Chinese listed firms from 2011–2024, we examined how CPU affects firms’ industrial intelligence. We employed fixed-effects models, mediation, and moderation analyses, supplemented by robustness tests. We found that, first, CPU significantly promotes firms’ industrial intelligence transformation. Second, board governance plays a key moderating role: higher board educational attainment, stronger innovation orientation, and an environmental or risk committee significantly strengthen CPU’s positive effect on industrial intelligence. Third, CPU promotes industrial intelligence mainly through two channels: an opportunity effect via increased R&D investment, and a pressure effect via reduced total factor productivity, pushing firms to adopt intelligent transformation to address productivity pressure. Moreover, this effect is stronger for firms in high-pollution industries, larger firms, and long-established firms. These findings suggest that corporate industrial intelligence is not merely a technological response, but a socio-technical adaptation process shaped by climate policy uncertainty, board governance, and resource reconfiguration. This study provides evidence on firms’ digital, intelligent, and green transformation under climate policy uncertainty and offers implications for board governance and sustainable adaptation. Full article

Digital transformation has become a critical pathway for manufacturing firms seeking to improve responsiveness, resource efficiency, and long-term sustainability. However, existing studies have paid limited attention to how digital transformation strategies generate organizational agility across different stages of sustainable manufacturing transformation. Drawing on dynamic capability theory, this study develops a stage-contingent Strategy–Ambidexterity–Agility framework and conducts a longitudinal single-case study of Contemporary Amperex Technology Co., Limited (CATL) from 2011 to 2023. The findings show that organizational agility develops cumulatively through three transformation stages. In the initial stage, a lean-oriented strategy supports balanced ambidexterity and cultivates customer agility through production optimization. In the development stage, an enhancement-oriented strategy enables exploitation-dominant combined ambidexterity and builds market agility through cross-functional integration and closed-loop business logic. In the industry-leading stage, a leap-oriented strategy supports exploration-dominant combined ambidexterity and fosters value chain agility through ecosystem orchestration, intelligent operations, and circular value creation. This study contributes to the literature on digital transformation and sustainable manufacturing by showing how stage-contingent digital strategies shape ambidexterity configurations, generate layered agility capabilities, and support sustainability-oriented manufacturing outcomes. Full article

This study examines the transformation of environmental governance processes in recreational tourism in Turkey and Lithuania through artificial intelligence (AI)-supported waste management applications. The research focuses on the contributions of AI-based applications to sustainable destination management, environmental sustainability, and data-driven governance processes. A case study design was used within the framework of qualitative research methods. The dataset was obtained through semi-structured interviews with a total of 40 experts from Turkey and Lithuania. The data were analyzed using content analysis with the NVivo 14 program. The research findings reveal significant differences between the two countries in terms of digital infrastructure, institutional coordination, governance structures, and AI integration capacity. In Turkey, AI-supported waste management applications are still in their development phase; processes are largely shaped by managerial initiative, project-based approaches, financial constraints, and lack of institutional coordination. In contrast, Lithuania exhibits a more systematic and institutionalized digital governance structure thanks to EU-supported environmental and digitalization policies. However, data security, system sustainability, and high technology costs in small-scale recreation areas stand out as significant problem areas for Lithuania. This study addresses an underexplored intersection between artificial intelligence applications and environmental governance within recreational tourism contexts, contributing to the emerging literature on digital transformation in sustainable destination management. The findings reveal that AI-supported environmental management systems have significant potential to strengthen sustainable tourism management, increase operational efficiency, and support data-driven sustainable destination strategies. These findings offer practical implications for destination managers and policy makers by highlighting how AI-enabled environmental governance systems can enhance sustainability-oriented decision-making and improve operational efficiency in recreational tourism areas. Full article

This study examines how the strategic integration of digital transformation and national innovation capacity contributes to accelerating sustainable development in Saudi Arabia by focusing on six Sustainable Development Goals (SDGs): SDG 4—Quality Education, SDG 7—Affordable and Clean Energy, SDG 8—Decent Work and Economic Growth, SDG 9—Industry, Innovation and Infrastructure, SDG 12—Responsible Consumption and Production, and SDG 13—Climate Action. Using annual data on ICT use, ICT access, R&D expenditure, and patent applications, the analysis evaluates both the direct and joint relationships between these indicators and SDG performance. Digital transformation is captured through ICT use and ICT access, while national innovation capacity is represented by R&D expenditure and patent applications, reflecting the input and output dimensions of formal innovation activity. The findings indicate that the direct long-run effects of digital transformation and national innovation capacity on the six SDGs are not statistically significant, suggesting that these domains have not yet become standalone drivers of educational advancement, clean-energy adoption, economic performance, industrial upgrading, sustainable resource management, or emissions reduction. In contrast, their interaction produces substantial positive effects on SDG 4, SDG 7, SDG 8, and SDG 9, highlighting improvements in educational quality, renewable energy transition, productivity, and industrial innovation. The interaction also has significant negative effects on SDG 12 and SDG 13, as reflected by reductions in CO₂ intensity and environmental pressures. These results indicate that meaningful progress toward the SDGs emerges when digital capabilities and national innovation capacity evolve jointly, rather than through isolated improvements in ICT infrastructure or innovation inputs. Robustness checks using a composite SDG index confirm the stability of these complementary effects. These findings suggest that Saudi Arabia can accelerate progress toward the SDGs by adopting integrated policies that link ICT expansion with stronger R&D systems, patent commercialization, technological innovation, and sustainability-oriented industrial transformation across education, energy, industry, resource efficiency, and climate action. Full article

Nanotechnology offers transformative potential for wastewater treatment, yet its full-scale implementation remains bottlenecked by the “lab–reality gap”. While bench-scale studies using idealized matrices report outstanding pollutant removal efficiencies, performance routinely deteriorates in authentic wastewater due to complex matrix interferences, natural organic matter (NOM) competitive binding, fouling dynamics, and unpredictable nano–bio transformations. Moving beyond traditional reviews that focus heavily on material synthesis and theoretical capacities, this review provides a novel, systems-oriented, and function-driven perspective on environmental nanotechnology. We critically evaluate the operational stability and behavior of nano-enabled systems under realistic conditions, categorizing nanomaterial roles into reactive interfaces, selective barriers, signal generators, and biological modulators. Crucially, this work examines the synergistic integration of nanotechnology with advanced oxidation processes (AOPs), membrane bioreactors, and digital intelligence—including artificial intelligence (AI) and real-time nanosensing—to achieve smart fouling management and circular resource recovery. Finally, we propose a comprehensive, multidimensional evaluation framework that simultaneously assesses technical efficiency, stability, scalability, economic feasibility, environmental safety, and system compatibility. This review delivers a pragmatic roadmap to bridge the chasm between isolated laboratory discovery and robust, sustainable, field-scale wastewater engineering. Full article