Search Results (710)

With the advent of the Fourth Industrial Revolution, the adoption of digital transformation technologies has accelerated across industries. Digital transformation has become a critical task for businesses, with success depending on strategic responses to rapidly changing environments, where establishing a technology development roadmap is pivotal. This study proposes a framework for a technology roadmap aimed at promoting effective digital transformation and applies it to the Korean water industry. Key technological management areas requiring digital transformation were identified, followed by an evaluation of development trends, preferences, technology levels, and potential through expert surveys. Data were quantitatively analyzed using Euclidean distance and frequency analysis. Results indicate that the most urgent areas for digital transformation in the Korean water industry are natural environment management and prediction, water supply and customer service, water intake and resource facilities, and water purification. Essential technologies to be prioritized in each area were also derived. Unlike previous studies that only compared technological priorities, this research contributes by providing a multi-layered analysis that considers preferences, levels, and potential, constructing a more robust roadmap. 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

The Fourth Industrial Revolution (4IR) has introduced modern, high technologies that are automated, such as precision farming, to enhance agricultural production. However, this comes at the cost of human labor being replaced by machines that are deemed efficient. This study investigated the impact of 4IR automation on agricultural employment in South Africa, spanning from 1990 to 2024. To analyze this, the study employed the Johansen test for cointegration and the vector error correction model to test for long-run and short-run dynamics. Stationarity was achieved, and the Johansen test confirmed cointegration. The vector error correction model results revealed that both long-run and short-run relationships between 4IR automation and agricultural employment exist, indicating that human labor is particularly at risk of being replaced by automation, such as advanced agricultural machinery. The results imply that, although automation improved agricultural productivity, it caused an increase in agricultural unemployment within South Africa. Therefore, to balance the advancement of technology and agricultural employment, the study recommends skills improvement and government intervention for enhancing human labor within the agricultural sector. Full article

This paper analyses the dynamics of connectedness among technology-oriented assets, such as fintech, blockchain, cybersecurity, internet, and disruptive technology indices, on the effect of political signal quality on the transmission of spillovers. Applying the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with frequency-based connectedness, the paper explores dynamic, horizon-dependent spillovers in the interconnection of innovation-based financial markets from January 2015 to April 2025. The findings show consistently high interconnectedness among 4IR assets, but this level increases significantly during the COVID-19 outbreak and the Russia–Ukraine conflict. It is also found that disruptive technology and fintech indices dominate shock transmission among interconnectedness networks. Based on the frequency decomposition approach, it is evident that spillovers arise from short-run dynamics, indicating that 4IR financial systems respond quickly to uncertainty shocks and to synchronized investor behavior. The regression and quantile regression analyses indicate a conditional effect of political signal quality on connectedness, especially during crisis periods marked by higher market uncertainty and stress. Specifically, it is evident that a deterioration in political signal quality increases spillover effects due to information uncertainty and expectation-based investor behavior. This means that, in an innovation-driven financial system, uncertainty is not just transmitted through macroeconomic and financial factors, but also through political communication and information uncertainty. In summary, this paper adds to the existing literature on connectedness by considering political information quality uncertainty in analyzing the 4IR financial system and by identifying how technological integration makes the financial market vulnerable during crises. Full article

Artificial intelligence is one of the key technologies of the Fourth Industrial Revolution and is increasingly significant for companies’ economic performance. Small and medium-sized enterprises (SMEs), the foundation of economic development in most national economies, face numerous challenges and opportunities in applying artificial intelligence in business. This paper aims to examine the economic effects of applying artificial intelligence in SMEs, with a special emphasis on labor productivity, business process efficiency, and reduced operating costs. Empirical research was conducted on a sample of 228 SMEs using a questionnaire, with the data analyzed using multiple linear regression. The research results show that different applications of artificial intelligence have a statistically significant, positive impact on labor productivity and on reducing operating costs. In contrast, their impact on business process efficiency is moderate and partially limited. The operational application of artificial intelligence, such as automation and data analysis, has proven to be the most important factor in economic effects. At the same time, its application in managerial decision-making also has a significant, but somewhat weaker impact. On the other hand, the mere growth of AI applications over time does not necessarily lead to increased efficiency without targeted and concrete implementation. The paper’s results contribute to understanding the role of AI in transforming SMEs and highlight the importance of targeted investments in operational and management applications of AI. The paper provides practical implications for entrepreneurs and economic policymakers in fostering sustainable, competitive development of SMEs. Full article

This paper presents a multidimensional analysis of Historic Building Information Modeling (HBIM) segmentation, offering a roadmap towards standardization, a key dimension towards broader adoption within the Cultural Heritage (CH) sector. HBIM faces multiple challenges related to the lack of standardized protocols and varying definitions of Level of Detail (LOD) across applications. Amid the advancements of the fourth industrial revolution, integrating Building Information Modeling (BIM) improves sustainability and digital governance, aligning with the sustainable development agenda. Despite increasing academic interest, the implementation of HBIM remains limited, primarily due to the complexities and heterogeneities inherent in CH artifacts. This study begins with a purely qualitative strategy. Then, it introduces multidimensional and hierarchical clustering analysis to classify the unique characteristics of various HBIM applications such as segmentation, input, and data-capturing media. At the same time, it is a tool for fine-tuning keyword-based selection criteria, which is crucial in systematic or semi-systematic surveys in HBIM segmentation. The thematic analysis output is interrupted just before the conceptualization step, and theme extraction is diverted to correspondence analysis implemented in R, an open-source statistical package. Among the key findings of this paper is the classification of four distinct HBIM application clusters, revealing how specific workflows align with data acquisition methods, input formats, and Level of Detail (LOD) requirements. The analysis exposes critical standardization bottlenecks hindering wider-scale industry adoption, highlighting that challenges are domain-specific. Strong evidence shows that 3D modeling has not reached the required maturity level, with persisting challenges distributed non-uniformly within the applications spectrum. Finally, AI-driven automation relates with poor LOD outcome. Full article

The advent of digital technologies, increasing competition, market globalization, and the fourth industrial revolution compel organizations to rethink their operating models to sustain competitive advantage. At the same time, increasingly informed consumers expect higher levels of personalization, responsiveness, and cost efficiency. In this context, mass customization has emerged as a strategic response enabling firms to deliver tailored products while maintaining acceptable levels of cost, lead time, and operational efficiency. However, operationalizing mass customization remains particularly challenging for small- and medium-sized enterprises (SMEs), especially within normative environments characterized by regulatory and compliance requirements affecting product architectures and manufacturing processes. Although the literature highlights modular product design and product configuration as key enablers, it lacks a structured strategy for their implementation in such contexts. This article aims to develop and validate an operational strategy for mass customization based on these two levers. The methodology adopts an action research approach structured through a hybrid Agile–Stage-Gate framework and validated through its application to a representative portion of the product architecture within a case study. The results highlight the structured integration of variability analysis, modular product design, and configuration logic into an operational process, supporting the management of complexity and the implementation of mass customization in manufacturing SMEs. Full article

With the advent of the Fourth Industrial Revolution and the rise of new forms of productive forces, the ways humans interact with space, objects, and information are being profoundly reshaped, bringing unprecedented possibilities for upgrading interactive spaces—human settlements that integrate physical and digital environments. Against this background, using the literature on interactive space research from the Web of Science (WoS) Core Collection between 1990 and 2025 as the data source, this study employs CiteSpace software to generate scientific knowledge maps, analyzing the historic development, hotspots, and trends in the research of interactive space, providing both theoretical and data support. In terms of results, a total of 458 papers were collected, demonstrating a consistent year-on-year increase. The research spans multiple fields, including computer science, architecture, ecology, physics, design, and behavioristics. Specifically, results indicate that research hotspots in interactive spaces include collaborative governance, social coexistence, and sustainable renewal, all of which are highly relevant to activating human settlements. The vitality of interactive spaces can be constructed across multiple dimensions, (for instance, enhancement based on ecology, environment, culture, and other factors of the space). However, research on interactive spaces still suffers from a lack of interdisciplinary collaboration and multi-domain integration; therefore, it is essential to strengthen cooperation among relevant fields. Current research lacks interdisciplinary integration and dynamic response mechanisms. Based on these findings, this study, through visual analysis, reveals the research hotspots and evolutionary trajectory of interactive spaces and proposes a “technology–humanism–governance” trinity framework. This system should be based on technology as the means, humanism as the guiding principle, and effective governance as the goal. It aims to explore how to leverage the service-oriented and convenient nature of technology in interactive spaces to deepen human-centric design and thereby drive the optimization of systems. Based on these findings, future research on interactive spaces should shift its design philosophy to be more human-centric, establish a multidisciplinary research system, utilize local empirical cases, and develop scalable, applicable theories to construct harmonious, open spaces, enhance human–environment relationships, and provide other countries undergoing urbanization with practical solutions. Full article

The accelerated expansion of artificial intelligence (AI) is reshaping economic systems, labour markets and democratic life, giving rise to hybrid human–AI societies. In this context, education becomes a strategic arena for enabling sustainable and socially just transitions within the Fourth Industrial Revolution. This article examines how digital competence can be reconceptualized to prepare future citizens and educators for these emerging societal configurations, with particular attention to the Ibero-American context. A conceptual framework is proposed that integrates algorithmic literacy, critical data awareness, AI ethics, human–AI collaboration skills, and civic and socio-emotional capacities as core dimensions of “next-decade” digital competence. Methodologically, the study combines three complementary approaches: (a) a structured review of interdisciplinary literature on AI, digital competence and sustainability; (b) an analysis of international and regional policy documents and competence frameworks relevant to Ibero-America; and (c) selected empirical insights drawn from the first author’s doctoral research on digital competence and AI use in teacher education. The findings reveal significant tensions between rapid AI adoption and persistent structural inequalities in the Global South, while identifying key leverage points for aligning teacher education, public policy and institutional strategies with the Sustainable Development Goals. The proposed framework aims to support policymakers, universities and international organizations in fostering inclusive and sustainable AI-driven social change while mitigating new forms of exclusion and dependency. Full article

The Fourth Industrial Revolution has accelerated the adoption of advanced digital technologies in construction, with Digital Twin (DT) emerging as a data-driven framework for enhancing project performance, efficiency, and sustainability. Despite these advantages, DT adoption in construction remains limited due to high implementation costs, data integration challenges, and a lack of standardized practices, especially in real-time data utilization and lifecycle management. This study presents a PRISMA-guided systematic literature review of DT applications across the construction lifecycle. The study addresses three main objectives: (1) to analyze DT’s adoption across construction lifecycle phases, (2) to identify barriers and benefits to DT adoption, and (3) to explore research gaps and potential advancements. Peer-reviewed journal articles published between 2003 and 2024 were retrieved from the Scopus and Web of Science databases using structured keyword combinations related to Digital Twin and the built environment. From an initial pool of 3109 records, 53 studies met predefined inclusion criteria. They were analyzed using a lifecycle-oriented thematic coding framework examining application domains, enabling technologies, reported benefits, and implementation constraints. Unlike prior reviews that focus on specific technologies or lifecycle segments, this study provides a lifecycle-wide synthesis of DT maturity across design, construction, operation, and demolition phases. The findings indicate that DT applications are most developed in the design and operation phases, particularly through integration with Building Information Modeling (BIM) and Internet of Things (IoT) systems for simulation, monitoring, and predictive maintenance. In contrast, construction-phase adoption is constrained by challenges in real-time data integration, while demolition and end-of-life applications remain largely conceptual. Overall, current DT implementations are predominantly phase-specific rather than lifecycle-integrated, therefore emphasizing the need for standardized data frameworks, scalable architectures, and cross-phase governance strategies to enable end-to-end lifecycle digitalization in construction. Full article

The global construction industry is a primary driver of environmental degradation, resource depletion, and carbon emissions, necessitating an urgent transition toward sustainable practices. Biomimicry, the emulation of nature’s time-tested strategies, offers a transformative pathway for this shift, yet its systematic adoption remains inconsistent. This study utilises the Push–Pull–Mooring (PPM) framework to identify the critical drivers and contextual influencers of biomimicry adoption within the South African construction sector. A quantitative research approach was employed, involving a structured questionnaire survey of 104 diverse built environment professionals and subsequent analysis through Descriptive Statistics and Exploratory Factor Analysis (EFA). Descriptive results indicate that providing biomimicry education and training, increasing stakeholder awareness, and improving the availability of biomimetic technology are the highest-ranked drivers for sustainable transformation. EFA revealed a singular, dominant component termed the “systematic driver of biomimicry thinking”, which accounts for 54.2% of the total variance. The result emphasises the necessity of legal frameworks, policy monitoring, and government support. The findings conclude that while the Fourth Industrial Revolution provides the technological tools for bio-inspired innovation, a multi-layered approach combining institutional policy reforms with interdisciplinary education is essential to overcome traditional industry moorings. These insights offer a roadmap for stakeholders to leverage biomimicry as a cornerstone of resilient, regenerative and sustainable construction. Full article

There has been an increasing need to manage the water, energy, and food (WEF) Nexus in an integrated and sustainable way using Industry 4.0 (4IR) technologies. While 4IR technologies can significantly improve resource management and sustainability in the Nexus, their uptake across the WEF Nexus has been uneven due to institutional fragmentation and limited digital capability. This study assessed a multi-level human capital framework developed to facilitate the structured integration of 4IR technologies into the WEF Nexus. The study leveraged human capital theory and Strategic Human Resource Management to frame capability development as a multi-level process. The study employed a mixed-methods approach, initially surveying 262 professionals in the WEF Nexus to assess essential skill capabilities. This was followed by a two-round Delphi procedure involving 12 experts to refine and validate the multi-level framework. The findings reveal that digital transformation in the WEF Nexus is dependent on the synchronisation of national policy frameworks, organisational learning cultures, and individual skill sets. These levels do not work as separate enablers; instead, they work together as an integrated capacity ecosystem in which misalignment at any point will hinder the effective integration of 4IR technologies. The validated framework provides a systematic approach to understanding the interplay of human capital processes within socio-technical systems and provides a structured approach for designing comprehensive strategies that strengthen digital readiness across sectors. The study advocates for a system-embedded human capital (SEHC) approach and contributes to ongoing discussions on innovation in the public sector by highlighting the systemic nature of human capital development in sustainability- and resource-critical sectors. Full article

The Fourth Industrial Revolution reshapes manufacturing and workforce demands, yet a persistent gap remains between industry needs and engineering education. While proficiency in industrial robotics, adaptive control, and automation becomes critical, traditional education struggles to bridge the theory–practice divide. This systematic review examines technology transfer from factory to classroom to develop authentic Industry 4.0 competencies. Following PRISMA 2020 guidelines, we synthesized 52 empirical studies (2019–2025) focusing on technology complexity, pedagogical approaches, and learning outcomes. Random-effects meta-analysis of 12 representative studies reveals large positive effects: Hedges’ g of 0.786 (95% CI: 0.726–0.846, p < 0.001) with homogeneous effects ($I^2$ = 0.00%, p = 0.464), indicating robust generalizability. However, critical gaps emerged: only 7.7% employ actual industrial manipulators versus educational kits, adaptive control pedagogy remains limited, and fault-tolerant systems teaching receives minimal attention. Technology complexity analysis reveals clear progression from educational kits through semi-industrial platforms to industrial systems, with significant differential effects on transferable skills (r = 0.68, p < 0.001). This study proposes the ARC Framework integrating technology taxonomy, competency progression, pedagogical strategies, and assessment rubrics. Cost–effectiveness analysis demonstrates remote labs optimize impact-per-investment ratios ($45 vs. $280 per student), providing an evidence-based framework for technology transfer in engineering education. Full article

The dynamic development of Industry 4.0 technologies, referred to as smart manufacturing technologies (SMTs), is significantly changing both production systems and quality management practices. The aim of this article is to analyse the impact of smart manufacturing technologies on the seven principles of quality management (QMP). The research is based on a narrative, semi-systematic review of the literature from the Web of Science and Scopus databases from the last seven years, using thematic analysis. Traditional interpretations of QMP principles were compared with new conditions resulting from the implementation of technologies such as the Internet of Things, big data, artificial intelligence, cloud computing, vision systems, virtual and augmented reality, and additive manufacturing. The results indicate that SMTs do not eliminate quality management principles, but significantly change the way they are implemented. There is a shift towards product personalisation, shorter product life cycles, decentralised decision-making, flexible and autonomous processes, digital surveillance, and intensive use of real-time data. The article argues that SMT and QMP are complementary approaches—technologies increase the effectiveness and efficiency of quality management, but do not replace it. The considerations presented here are a starting point for further empirical research on the new ‘Quality 4.0’ model in the intelligent manufacturing environment. Full article

Low-voltage grids are undergoing rapid change as rooftop photovoltaics, electric vehicles and other distributed energy resources increase their share of demand. Without new local control, these trends risk more frequent voltage problems and costly reinforcement, which can slow affordable and just energy transitions. This article proposes a MATLAB/Simulink methodology for designing and comparing PID and Mamdani fuzzy volt–var controllers implemented at a single PV inverter in a radial low-voltage feeder. The feeder model aggregates residential demand, two PV units, a small wind unit, battery storage and an EV charging event; controller performance is assessed using time-domain simulations and scalar indices of overshoot, undershoot, settling time, time outside a ±5% voltage band, and reactive power usage. In the studied high-PV scenario, both controllers maintain acceptable voltage quality with limited overshoot and short settling times, while the fuzzy controller yields smoother transients at the expense of slightly higher but still modest reactive power adjustments. The results illustrate how accessible digital tools can help system operators and regulators explore local volt–var strategies that increase renewable hosting capacity and power quality compliance without immediate grid reinforcement, thereby supporting sustainable electrification in the context of the fourth industrial revolution. Full article