Search Results (492)

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

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

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 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

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

Measuring technology is used in various ways in the logistics industry for defect inspection and loading optimization. Recently, in the context of the fourth industrial revolution, research has focused on measurement automation combining AI, IoT technologies, and measuring equipment. The 3D scanner used for field logistics measurements offers high performance and can handle large volumes quickly; however, its high unit price limits adoption across all lines. Entry-level sensors are challenging to use due to measurement reliability issues: their performance varies with changes in object location, shape, and logistics environment. To bridge this gap, this study proposes a systematic framework for geometry measurement that enables reliable length and width estimation using only a single entry-level distance sensor. We design and build a conveyor-belt-based data acquisition setup that emulates realistic logistics transfer scenarios and systematically varies transfer conditions to capture representative measurement disturbances. Based on the collected data, we perform robust feature extraction tailored to noisy, condition-dependent signals and train an artificial neural network to map sensor observations to geometric dimensions. We then verified the model’s performance in measuring object length and width using test data. The experimental results show that the proposed method provides reliable measurement results even under varying transfer conditions. Full article

Artificial intelligence (AI) is a core technology driving the Fourth Industrial Revolution and serves as a foundation for sustainable technological competitiveness. Despite the rapid growth of AI-related patent filings in Korea, the overall quality of these patents remains relatively low. This study examines the determinants of patent quality in university–industry (UI) collaboration and investigates how firms’ R&D capability moderates this relationship. Using 90,782 AI patents filed with the Korean Intellectual Property Office (KIPO) between 2013 and 2023, the Patent Quality Index (PQI) was constructed by integrating forward citations, patent-family size, and the number of claims through min–max normalization. Regression analyses reveal that UI collaboration per se has no significant average effect on PQI, but firms with stronger R&D capability achieve higher patent quality through collaboration. In addition, greater collaboration depth and accumulated prior experience significantly enhance PQI, while the negative effect of technological cognitive distance is mitigated by absorptive capacity. These findings demonstrate that sustainable innovation outcomes depend not merely on the quantity of collaboration but on the synergy between qualitative collaboration structures and internal R&D capabilities. By linking open innovation theory with absorptive capacity, this study provides empirical evidence for fostering sustainable innovation ecosystems in which universities and firms co-create technological value. Full article

In this epoch of the Fourth Industrial Revolution (4IR), digital advancement, technological initiatives, and advancements, as well as their imperative role, have improved universities’ performance and transformed business models, practices, and processes. This study aimed to systematically review the existing digital transformation discourses as the main axis for the higher education (HE) sector. It elucidates the required skill sets, benefits, barriers, and challenges brought about by digital transformation. The article commences by identifying the relevant literature on digital transformation in general, which is not confined to one methodology. It also includes the case studies that were conducted across the globe, the skills needed to drive the transformation agenda, the benefits, the barriers, and the challenges that impede digital transformation in the HE sector. This article found that the sector has been exploiting digital tools to improve performance, business processes, restructuring systems, structures, and practices. The myriad of digital transformation impacts, benefits, and skills to drive the digital transformation have been overshadowed by diverse barriers and challenges in the HE sector. Constraints such as inadequate funding, employee resistance, limited digital literacy, and insufficient infrastructure stand in opposition to the principles of the connectivity theory, which emphasises access, interaction, and the knowledge flow as prerequisites for effective digital integration. Full article

This entry presents the history, geography, business, regulations, and the roles of gig workers, platform/algorithms, and employers, focusing primarily on the USA and the EU. The gig economy is informally referred to also as the fourth industrial revolution or the 1099 economy, emphasising sharing, freelance, or platform work; it is a complex and changing business model and regulatory environment. In practice, the gig economy refers to a tripartite relation between workers, platforms/apps, and employers, leading to a two-sided market, where algorithms match supply and demand for paid labour and clients. It is only recently that the gig economy has started to be conceptualised, and its implications, challenges, and impacts are captured in economic law and society, including the power dynamics related to the interplay between economics, technology, regulation, and communities. Conceptually, the gig economy is important, as small paid work has always been present in society for all types of workers and beneficiaries. This new business model of on-demand work has some perceived advantages, such as freedom of work, under-regulation, efficient use of capital, driving down costs, and improving services. However, there is a dualisation of anti-power between workers and non-employers that may lead to precarious work, less free workers, and shadow corporations that distort the market using game changers like digital management algorithms. Currently, the size of the gig economy comprises 154–435 million gig workers out of the world’s 3.63 bn workers, with a market size of USD 557 bn, and is still expanding. Full article

Geopolitical disruptions such as trade wars, sanctions, and political instability threaten global supply chain (SC) resilience. As a result, multinational corporations face financial losses, operational delays, and strategic uncertainties, creating an urgent demand for innovative risk management and scenario-planning strategies. Traditional risk management methods struggle to keep pace with the complexity of these events. This study explores the metaverse, combining VR, AR, digital twins, AI, and blockchain, as a tool for enhancing SC risk management. By enabling immersive scenario planning, real-time risk visualization, and collaborative decision-making, the metaverse supports agile and resilient supply chains. This research proposes a conceptual framework integrating key fourth industrial revolution (4IR) technologies to address geopolitical SC disruptions systematically. This model fosters digital preparedness, simulation-based learning, and adaptive coordination. While technological, organizational, and regulatory challenges persist, the study demonstrates that metaverse-enabled systems can support future-ready SC resilience strategies. Full article

The recent synergistic explosion of artificial intelligence and the world of machines, in a bid to make them smarter entities as a result of the fourth industrial revolution, has resulted in the concept of chatbots, which have evolved over the years and gained heightened attention for the sustainability of most human corporations. Organisations are increasingly utilising chatbots to enhance customer engagement through the process of agent-based autonomous sensing, interaction, and enhanced service delivery. The current state of the art in chatbot technology is such that the system lacks the ability to conduct text-sensing in a bid to acquire new information or learn from the external world autonomously. This has limited the current chatbot systems to being system-controlled interactive agents, hence, strongly limiting their functionalities and posing a question on the purported intelligence. In this research, an integrated framework that combines the functionalities and capabilities of a chatbot and machine learning was developed. The integrated system was designed to accept new text queries from the external world and import them into the knowledge base using the SQL (Structured Query Language) syntax and MySQL workbench (version 8.0.44). The search engine and decision-making cluster was built in the Python (version 3.12.7) coding environment with the learning process, solution adaptation, and inference, anchored using a reinforcement machine learning approach. This mode of chatbot operation, with an interactive capacity, is known as the mixed controlled system mode, with a viable human–machine system interaction. The smart chatbot was assessed for efficacy using performance metrics (response time, accuracy) and user experience (usability, satisfaction). The analysis further revealed that several self-governed chatbots deployed in most corporate organisations are system-controlled and significantly constrained, hence lacking the ability to adapt or filter queries beyond their predefined databases when users employ diverse phrasing or alternative terms in their interactions. Full article