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Purpose: To investigate the key technologies facilitating the transition towards Industry 5.0 and analysing the contributions of Nvidia, a prominent leader in this field, to these technological advancements.

Significance of the study: Technology companies such as Nvidia play a critical role in this transformation through their innovative solutions. This study addresses the need to understand this evolving landscape and the significant impact of the Nvidia.

Methodology: This study is a qualitative approach that examines the existing literature and secondary case studies pertaining to Industry 5.0, and Nvidia. This study examines Nvidia’s high-performance graphics processing units (GPUs), the digital twin platform Omniverse, and the humanoid robot technology development platform, Isaac.

Findings: The next generation of GPUs with the Blackwell architecture is expected to further advance the development of large language models. The Nvidia Omniverse platform contributes significantly to the development of digital twins, a crucial technology for Industry 5.0. The Nvidia Isaac platform focuses on the development of humanoid robot technology, which is a key component of Industry 5.0. Utilizing realistic simulations with Isaac Sim, imitating human behavior with GR00T, and leveraging the high-performance processing power of Jetson Thor, the platform facilitates the creation of robots capable of safe and effective human–robot collaboration. Nvidia has emerged as a leader in the artificial intelligence (AI), robotics, and gaming sectors because of its innovative and agile company culture.

Practical implications: Companies can leverage Nvidia’s technological solutions to optimize production processes and enhance both efficiency and sustainability. The human–machine collaboration emphasized by Industry 5.0 will necessitate the reshaping of workforce skillsets and operational approaches.

This study aims to investigate how the supply chain (SC) experts from the UK manufacturing organisations (MO) interpret the drivers and barriers to adopting Industry 4.0 technologies from a technological, organisational and environmental (TOE) point of view. Furthermore, this study evaluates how adoption drivers and barriers influence innovative practices that support Industry 4.0 adoption.

A qualitative narrative inquiry strategy, involving nine semi-structured interviews with leading SC experts including group executives, global directors and vice presidents was adopted.

The people factor across the SC is found critical for successful Industry 4.0 adoption. It was also found that the firm size is an insignificant factor; rather, Industry 4.0 readiness in small and medium enterprises (SMEs) is driven by flexibility and agility, while larger organisations are driven by resource availability and scalability. Evidently, the culture and attitude of the MO tend to facilitate or hinder the Industry 4.0 adoption, therefore, innovative practices in building an expert team, establishing a systematic change management process, actively involving suppliers in the adoption process and continuously monitoring the adoption process were introduced.

By addressing the relationships between the drivers, barriers and innovative practices from a business perspective, a detailed Industry 4.0 adoption framework for the UK Manufacturing Supply Chains (MSC) was developed as a unique theoretical and practical contribution. This study also highlights the lessons learnt from applying Industry 4.0 in the UK context. The findings can also be informed in applying Industry 4.0 technologies in emerging market contexts.

This study aims to explore the transformative potential of digital twins (DTs) in the construction industry by addressing challenges and bridging the technology adoption gap. Focusing on construction offices, on-site environments and their interface, the research identifies key success factors for DT implementation. Emphasizing practical applications, the study foresees a substantial impact on project management, highlighting the pivotal role of DTs in reshaping the construction sector.

We reviewed 30 variables within the DT literature, classifying investigations into three main domains: on-site environments, office environments and site-office interface in construction projects. We employed a questionnaire to examine variables, with collected data analyzed using SmartPLS version 4 software. Confirmatory analysis and structural equation modeling rigorously assessed the model’s reliability, validity and predictive utility.

The study identifies 11 critical factors crucial for successfully implementing DTs in construction projects, emphasizing contributions from office-related (ORF), site-related (SRF) and office-site-related factors (OSRF). Notably, impactful contributions from ORF affirm its pivotal role. The research sheds light on specific aspects within construction offices, on-site environments and their interface, providing valuable insights for industry stakeholders, policymakers and researchers.

Despite discussions on DT’s potential, a research gap exists in addressing essential factors for its effective application in monitoring and optimizing construction projects throughout their lifecycle. This study fills this gap by identifying and categorizing these factors, enhancing the knowledge base for successful DT assimilation and emphasizing the need for practitioners to understand key factors crucial for its integration into construction processes.

The advent of Industry 4.0 (I4.0) and the requirements imposed on companies still need to be clarified. Companies still strive to understand I4.0 requirements and technological, organizational, operational and management challenges. Current literature on I4.0 underlies the importance of a roadmap with structured steps to achieve the benefits of I4.0, mainly focused on augmenting operational performance. Therefore, this paper proposes a roadmap to implement I4.0 focused on operational management concepts, mainly aiming to augment operational performance and bridge the gap between theory and practice regarding roadmaps focused on the operational management dimension.

This paper follows a research approach divided into the following stages: a literature review to analyze the I4.0 roadmaps and identify the main components of I4.0; development of the proposed I4.0 roadmap presented; field research to test the roadmap by collecting data from a manufacturing company in the automotive industry; validation of the roadmap through modeling and simulation.

The authors presented a production line design with real-time control, fast response, shop floor coordination and predictive capacity. The results prove that the proposed I4.0 roadmap augments operation performance in the investigated automotive company. The main results were work in process reduction, lead time reduction, output increase, real-time control, shop floor coordination and fast response.

The main novelty of the proposed roadmap is to move toward I4.0 implementation with a focus on the operational management dimension. The roadmap has an innovative combination of the two approaches – lean manufacturing and factory physics – a straightforward roadmap with only three steps: (1) requirements, (2) real-time control and (3) predictive capacity, a structured definition of the approaches and operational management concepts fundamental in each step.

In traditional Bayesian belief networks (BBNs), a large amount of data are required to complete network parameters, which makes it impractical. In addition, no systematic method has been used to create the structure of the BBN. The aims of this study are to: (1) decrease the number of questions and time and effort required for completing the parameters of the BBN and (2) present a simple and apprehensible method for creating the BBN structure based on the expert knowledge.

In this study, by combining the decision-making trial and evaluation laboratory (DEMATEL), interpretive structural modeling (ISM) and BBN, a model is introduced that can form the project risk network and analyze the impact of risk factors on project cost quantitatively based on the expert knowledge. The ranked node method (RNM) is then used to complete the parametric part of the BBN using the same data obtained from the experts to analyze DEMATEL.

Compared to the traditional BBN, the proposed method will significantly reduce the time and effort required to elicit network parameters and makes it easy to create a BBN structure. The results obtained from the implementation of the model on a mass housing project showed that considering the identified risk factors, the cost overruns relating to material, equipment, workforce and overhead cost were 37.6, 39.5, 42 and 40.1%, respectively.

Compared to the traditional BBN, the proposed method will significantly reduce the time and effort required to elicit network parameters and makes it easy to create a BBN structure. The results obtained from the implementation of the model on a mass housing project showed that considering the identified risk factors, the cost overruns relating to material, equipment, workforce and overhead cost were 37.6, 39.5, 42 and 40.1%, respectively. The obtained results are based on a single case study project and may not be readily generalizable.

The presented framework makes the BBN more practical for quantitatively assessing the impact of risk on project costs. This helps to manage financial issues, which is one of the main reasons for project bankruptcy.

This research aims to develop a blockchain smart contract–enabled framework to resolve power imbalance problems in construction payment.

This research adopts a design science research method to develop the blockchain smart contract–enabled framework. The authors then develop a prototype system. Finally, the authors evaluate its performance in solving power imbalance-induced payment problems.

The results show that the prototype system can resolve power imbalance problems in construction payment by allowing project participants to make transparent and decentralized decisions that are self-enforceable by blockchain smart contracts.

This study provides theoretical explanations for how blockchain smart contracts can resolve power imbalances in construction payment; based on that, it proposes a novel blockchain smart contract–enabled method to rebalance the power of stakeholders in construction payment. Thus, it contributes to the body of knowledge on blockchain technology and construction payment.

This study moves beyond a conceptual framework and develops a practical blockchain smart contract system for resolving power imbalances in construction payment, strengthening construction project members' confidence in using blockchain technology.

The proposed blockchain smart contract–enabled solution helps mitigate negative social impacts associated with late payment and non-payment. Furthermore, the research maximizes trust among participants in payment processes to inspire collaborative culture in the construction industry.

This paper introduces a novel blockchain smart contract integrated method, allowing project stakeholders to resolve power imbalance problems in construction payment through decentralized decision-making.

Intestinal mucosa functions as a specialized permeable barrier, facilitating nutrient absorption and safeguarding against external influences. Gut barrier serves as a channel of communication between gastrointestinal and bodily processes. Research investigations have postulated that enhancing gut barrier through microbiota could potentially enhance overall well-being. Hence, this intervention study was designed to assess whether intervention of microbiota modulators delivers intestinal and extra-intestinal benefits.

A randomized, double-blind, placebo-controlled trial was devised to examine the impact of two doses (1.5 and 3 g) of intervention, comprising prebiotics and vitamins on gut barrier indicators (faecal IgA and calprotectin) and microbiota markers (lactobacilli and bifidobacteria) of healthy human subjects. In addition, cholera vaccine challenge test was conducted to assess the influence of the barrier improvement on the mucosal response to a stressor.

After two weeks of intervention, prebiotics and vitamins resulted in a significant (p = 0.04) enhancement of faecal IgA levels compared to placebo. This enhancement coincided with an increase in levels of faecal lactobacilli and bifidobacteria. A significant (p < 0.001) reduction in faecal calprotectin levels was observed in both intervention groups at the end of intervention, compared to placebo. Following cholera vaccine challenge, a markedly (p < 0.03) heightened response was documented in intervention groups.

This study illustrated that combination of prebiotics and vitamins could effectively modulate gut barrier and microbiota markers in healthy individuals, which contribute to a good gut health. These findings establish a foundation for delivering optimal bodily functions dwelling from a healthy gut.

This is one of its kind study which has probed into the physiological response with improvement in gut health markers.

This paper aims to provide empirical evidence regarding Lean Six Sigma (LSS) practices supported by Industry 4.0 (I4.0) technologies in heavy vehicle manufacturing processes.

A two-case study was performed involving LSS specialists, leaders and managers of two heavy vehicle manufacturers in Brazil. The data analysis procedure combined content analysis techniques, conceptual maps and network analysis.

The results provide consistent evidence of synergies between LSS and I4.0, including digital mistake-proofing, digital andon, e-kanban, statistical monitoring as well as process mapping aided by cyber-physical systems (CPS) and big data analytics (BDA). To enable such interactions, companies need to invest in automation architectures, system integration, human–machine interfaces and analytical skills.

This study relies on data from a two-case study carried out in two companies from a single manufacturing sector in Brazil. For this reason, the findings cannot be generalized to the entire automotive industry.

There is still a lack of comprehensive research on the application of digital technologies in LSS practices. This is the first study which provides empirical evidence regarding the LSS practices supported by I4.0 technologies used by heavy vehicle manufacturers.

This study aims to explore key questions within the context of Asian countries: How do artificial intelligence (AI) and blockchain adoption in accounting influence enterprise risk management and environmental, social and governance (ESG) performance? What role does enterprise risk management have as a mediator in this relationship? In addition, how does environmental uncertainty shape the interplay between AI and blockchain adoption in accounting, enterprise risk management and ESG performance?

The authors collected data from Thomson Reuters Eikon Datastream, initially targeting the 20 Asian countries with the highest gross domestic product (GDP) per capita. Using stringent selection criteria, the research sample included 22,212 firms from these countries: Bahrain, China, Hong Kong, Indonesia, Israel, Japan, Jordan, Kazakhstan, South Korea, Kuwait, Lebanon, Malaysia, Oman, Qatar, Saudi Arabia, Singapore, Sri Lanka, Thailand, the United Arab Emirates and Vietnam. After a rigorous screening process, the final sample comprised 1,742 firms, representing 17,420 firm-year observations over the 2014–2023 period. This paper applied maximum likelihood structural equation modeling to analyze the data.

The findings reveal that both AI and blockchain adoption in accounting, along with enterprise risk management, positively impact ESG performance in the Asian context. Enterprise risk management serves as a mediating factor between AI and blockchain adoption in accounting and ESG performance. In addition, environmental uncertainty significantly moderates the relationships between AI and blockchain adoption in accounting and enterprise risk management, as well as between enterprise risk management and ESG performance.

This study uncovers the interplay between internal factors – such as AI and blockchain adoption in accounting and enterprise risk management – and external factors, notably environmental uncertainty, in fostering sustainable value for Asian firms. Internal factors enable firms to integrate ESG considerations into their operations, facilitating risk mitigation and enhancing ESG performance. Meanwhile, heightened environmental uncertainty drives the adoption of sustainable practices. Consequently, Asian Governments should prioritize the development of regions characterized by high environmental uncertainty to advance national sustainable development goals and encourage responsible business practices.

This study contributes to the existing literature by uncovering the combined effects of internal and external factors on ESG performance, offering empirical evidence from Asian countries with high GDP per capita. Specifically, it underscores the efficacy of AI and blockchain adoption in accounting and enterprise risk management, as well as the moderating role of environmental uncertainty, within the Asian context.

Purpose: Industry 5.0 is characterized by a revolution in the industrial field where humans collaborate with machines. This study aims to highlight the role of the concept of the “Digital Twin” (DT) within Industry 5.0, aiming to predict the effects of natural disaster scenarios in advance and to take preventive measures more effectively.

Need for the study: The innovations brought by Industry 5.0 demonstrate the possibility of creating DTs of cities to predict and minimize the effects of natural disasters. This is of great importance in terms of preparation for future natural disasters and risk management.

Methodology: This study was conducted by analyzing the fundamental principles of Industry 5.0 and the concept of DTs. Scientific literature and industry reports were examined to explore how DTs can be used in the field of risk management related to natural disasters.

Findings: The use of DTs has significant potential in simulating natural disaster scenarios in advance and predicting potential damages. For example, through DTs of cities, the effects of disaster scenarios such as earthquakes, tsunamis, and floods can be analyzed in advance, and necessary measures can be taken accordingly.

Practical implications: These findings offer important practical implications for decision-makers working in areas such as urban planning and infrastructure management. The use of DTs can assist in the development of preparation and risk management strategies for natural disasters, thereby minimizing the impact of disasters and ensuring the safety of individuals.