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The purpose of this study is to evaluate food supply chain stakeholders’ intention to use Industry 5.0 (I5.0) drones for cleaner production in food supply chains.

The authors used a quantitative research design and collected data using an online survey administered to a sample of 264 food supply chain stakeholders in Nigeria. The partial least square structural equation model was conducted to assess the research’s hypothesised relationships.

The authors provide empirical evidence to support the contributions of I5.0 drones for cleaner production. The findings showed that food supply chain stakeholders are more concerned with the use of I5.0 drones in specific operations, such as reducing plant diseases, which invariably enhances cleaner production. However, there is less inclination to drone adoption if the aim was pollution reduction, predicting seasonal output and addressing workers’ health and safety challenges. The findings outline the need for awareness to promote the use of drones for addressing workers’ hazard challenges and knowledge transfer on the potentials of I5.0 in emerging economies.

To the best of the authors’ knowledge, this study is the first to address I5.0 drones’ adoption using a sustainability model. The authors contribute to existing literature by extending the sustainability model to identify the contributions of drone use in promoting cleaner production through addressing specific system operations. This study addresses the gap by augmenting a sustainability model, suggesting that technology adoption for sustainability is motivated by curbing challenges categorised as drivers and mediators.

Against the background of the I50 paradigm, this paper queries in what ways blockchain and blockchain-based applications deployed in the smart city context facilitate the integration of the I50 paradigm in smart urban contexts.

A mixed methods approach is applied. First, by means of desk research and thematic literature review, a conceptual model integrating the I50 paradigm, smart city and blockchain-based solutions is built. Second, science mapping bibliometric analysis (SciMat) based on keywords’ co-occurrence is applied to a sample of 491 research articles to identify key domains of blockchain-based applications’ use in smart city. Third, a semi-systematic literature review complements insights gained through SciMat. Fourth, the findings are interpreted through the precepts of the conceptual model devised earlier.

The key blockchain-based applications in smart cities pertain to two domains, i.e. the foundational, service facilitation-oriented domain, including security (and safety), networks, computing, resource management and the service delivery-oriented domain, including mobility, energy and healthcare. Blockchain serves as the key building block for applications developed to deliver functions specific to each of the thus identified domains. A substantial layering of blockchain-based tools and applications is necessary to advance from the less to the more complex functional domains of the smart city.

At the conceptual level, the intricacies of the (making of the) I50 paradigm are discussed and a case for I50 – smart city – blockchain nexus is made. Easton’s input–output model as well as constructivism is referenced. At the empirical level, the key major domains of blockchain-based applications are discussed; those that bear the prospect of integrating the I50 paradigm in the smart city are highlighted. At the methodological level, a strategic move is made aimed at restoring the literature review’s role as subservient to the key line of exploration, to justify and ultimately support it, rather than to showcase the literature review as the ultimate purpose for itself.

Effective inventory management is crucial for SMEs due to limited resources and higher risks like cash flow, storage space, and stockouts. Hence, the aim is to explore how technology and know-how can be integrated with inventory practices and impact operational performance.

The basis of the analysis was collecting papers from a wide range of databases, which included Scopus, Web of Science, and Google Scholar. In the first phase of the process, a search string with as many as nine related keywords was used to obtain 175 papers. It further filtered them based on their titles and abstracts to retain 95 papers that were included for thorough analysis.

The study introduced innovative methods of measuring inventory practices by exploring the impact of know-how. It is the first of its kind to identify and demonstrate how technical, technological, and behavioral know-how can influence inventory management practices and ultimately impact the performance of emerging SMEs. This study stands out for its comprehensive approach, which covers traditional and modern inventory management technologies in a single study.

The study provides valuable insights into the interplay between technical, technological, and behavioral know-how in inventory management practices and their effects on the performance of emerging SMEs in Industry 5.0 in the light of RBV theory.

The RBV theory and the Industry 5.0 paradigm are used in this study to explore how developing SMEs' inventory management practices influence their performance. This study investigates the effects of traditional and modern inventory management systems on business performance. Incorporating RBV theory with the Industry 5.0 framework investigates firm-specific resources and technological advances in the current industrial revolution. This unique technique advances the literature on inventory management and has industry implications.

With ever-increasing global concerns over environmental degradation and resource scarcity, the need for sustainable manufacturing (SM) practices has become paramount. Industry 5.0 (I5.0), the latest paradigm in the industrial revolution, emphasizes the integration of advanced technologies with human capabilities to achieve sustainable and socially responsible production systems. This paper aims to provide a comprehensive analysis of the role of I5.0 in enabling SM. Furthermore, the review discusses the integration of sustainable practices into the core of I5.0.

The systematic literature review (SLR) method is adopted to: explore the understanding of I5.0 and SM; understand the role of I5.0 in addressing sustainability challenges, including resource optimization, waste reduction, energy efficiency and ethical considerations and propose a framework for effective implementation of the I5.0 concept in manufacturing enterprises.

The concept of I5.0 represents a progressive step forward from previous industrial revolutions, emphasizing the integration of advanced technologies with a focus on sustainability. I5.0 offers opportunities to optimize resource usage and minimize environmental impact. Through the integration of automation, artificial intelligence (AI) and big data analytics (BDA), manufacturers can enhance process efficiency, reduce waste and implement proactive sustainability measures. By embracing I5.0 and incorporating SM practices, industries can move towards a more resource-efficient, environmentally friendly and socially responsible manufacturing paradigm.

The findings presented in this article have several implications including the changing role of the workforce, skills requirements and the need for ethical considerations for SM, highlighting the need for interdisciplinary collaborations, policy support and stakeholder engagement to realize its full potential.

This article aims to stand on an unbiased assessment to ascertain the landscape occupied by the role of I5.0 in driving sustainability in the manufacturing sector. In addition, the proposed framework will serve as a basis for the effective implementation of I5.0 for SM.

The main aim of this study is to review different aspects of Industry 5.0 (I5.0) along with Kaizen measures to foster this novel aspect of industrial sustainability. The study makes a comprehensive study to explore the implementation status of I5.0 in industries, key technologies, adoption level in different nations and barriers to I5.0 adoption together with mitigation actions.

To do a systematic study of the literature, the authors have used preferred reporting items for systematic reviews and meta-analysis (PRISMA) methodology to extract articles related to the field of the study.

It has been found that academic literature on the I5.0 is continuously growing as the wheel of time is running. Most of the studies on I5.0 are conceptual-based, and manufacturing and medical industries are the flag bearer in the adoption of this novel aspect. Further, due to I5.0's infancy, many organizations face difficulty to adopt the same due to financial burden, resistive nature, a well-designed standard for cyber-physical systems (CPS) and an effective mechanism for human–robot collaboration. Further studies also provide avenues for future research in terms of the identification of collaborative mechanisms between machines and wells, the establishment of different standards for comparison and the development of I5.0-enabled models for different industrial domains.

The study is the first of its kind that reviews different facets of I5.0in conjunction with Kaizen's measures and application areas and provides avenues for future research to improve an organization's environmental and social sustainability.

The study aims to investigate the integration of human and machine intelligence in Industry 4.0 (I4.0), particularly in the convergence of industrial revolutions 4.0 (IR4.0) and 5.0. It seeks to identify employee competencies aligned with industry 5.0 (I5.0) and propose a framework for deep multi-level cooperation to improve human integration within the intelligence system.

This study uses bibliometric analysis to review 296 research papers retrieved from the Scopus database between 2002 and 2022. The prominence of the research is evaluated by analyzing the publication trend, sample statistics, theoretical foundation, commonly used keywords, thematic evolution, country-based contributions and top-cited documents.

The study observed that research in I5.0 has been limited in the past but has gained momentum since 2015. An analysis of research papers from 2002 to 2022 reveals a gradual shift toward human-centric practices. The literature on I4.0, the internet of things (IoT), artificial intelligence (AI), cloud manufacturing, blockchain and big data analysis has been increasingly highlighting the growing importance of digitalization in the future. An increase in the number of countries contributing to the field of study has also been observed.

This analysis offers valuable insights for managers, policymakers, information technology (IT) developers and stakeholders in understanding and implementing human-centric practices in I5.0. It emphasizes staying current with trends, embracing workforce empowerment through reskilling and upskilling, and prioritizing data privacy and security in adaptable systems. These strategies contribute to developing effective, inclusive and ethically sound approaches aligned with the principles of I5.0.

Industry 5.0 (I5.0) is eventually set to supersede Industry 4.0 (I4.0), despite the fact that I4.0 continues to gain ground in emerging nations like India. Now India is aspiring to be a global manufacturing hub, and I5.0 offers enormous potential to position India as a forerunner in intelligent and collaborative manufacturing systems. Therefore, this research article aims to understand the relationship between I5.0 and sustainable manufacturing (SM) thoroughly; pinpoint its impact and implementation challenges; analyze its impact on Triple-Bottom-Line (TBL) sustainability; and present an inclusive framework for I5.0 implementation for Indian manufacturing enterprises.

The coexistence of two industrial revolutions raises questions, which necessitates debates and explanations. Thus, the systematic literature review (SLR) approach is used to address this issue and this study used Web of Science, Scopus, Science Direct and Google Scholar databases. Following a critical SLR, 82 research papers have been cited in this article, and the majority of cited articles were published from 2010 to 2022, to ensure a focused analysis of pertinent and recent scholarly contributions.

I4.0 is considered to be technology-driven, however, I5.0 is perceived to be value-driven. I5.0 is not a replacement or a chronological continuation of the I4.0 paradigm. The notion of I5.0 offers a distinct perspective and emphasizes the necessity of research on SM within the TBL sustainability boundaries. I5.0 introduces a new TBL: resilience in value creation, human well-being and sustainable society. Indeed, I5.0 seems to be economically, socially, and environmentally sustainable while manufacturing products with high productivity.

Theoretical implications pertain to restructuring business models and workforce transformation, whereas practical implications underscore the significance for manufacturing enterprises to embrace I5.0 for their sustainable development. By understanding the nuanced relationship between I5.0 and SM, enterprises can navigate implementation challenges, maximize TBL sustainability and embrace an inclusive I5.0 framework for high productivity and resilience.

The existing literature presents the general notion of I5.0 but lacks in-depth TBL sustainability analysis. This research used a systematic and rigorous SLR approach that evaluates the existing literature, enables an in-depth understanding, identifies research gaps and provides evidence-based recommendations for the decision-making process. Furthermore, this research aims to stand on an unbiased assessment, exploring theoretical and practical implications of I5.0 implementation for manufacturing enterprises and suggesting future research avenues.

This study aims to research the usage of virtual reality as a tool to train employees in a changeover process in a company, and if it guarantees the same level of quality. Moreover, another goal is to understand how it can influence the machines’ availability, reducing the number of hours a machine is stopped to perform training sessions.

To achieve the goals proposed, the action research strategy was used. After defining the context and purpose, two full cycles were applied, composed by five activities: diagnosis, action planning, action taking, action evaluation and specify the learning.

The results are extremely satisfactory. On the one hand, it can be concluded that virtual reality is a reliable training tool, guaranteeing proper training to the employees. On the other hand, based on the previous point, the researchers were able to conclude that it is possible to increase machines’ availability, reducing by 50% the time needed to have the machines stopped to run a changeover training.

To the authors’ knowledge, this is the first study to use the action research strategy to develop a virtual reality simulation as a training tool for machines’ changeover in the automotive industry.

The purpose of the study is twofold. First, it aims to analyse the impact of green human resource management (GHRM) on green employee creativity. Second, it further explores the mediating role of personal moral norms between GHRM and green employee creativity.

Through multi-source and multi-level analysis, data was collected from 46 managers and 315 employees to investigate the role of personal moral norms in GHRM and green employee creativity. For data analysis, multi-level structural equation modelling was applied using Mplus software version 8.6.

Positive and significant effects were observed between GHRM, personal moral norms and green employee creativity. However, personal moral norms partially mediated the relationship between GHRM and green employee creativity.

The study is first in itself to explore the interplay between the variables in a multi-level manner. The present study aims at addressing the urgent call by the United Nations regarding Sustainable Development Goals (SDGs) for three key SDGs, focussing on decent work and economic growth (SDG 8), boosting industry, innovation and infrastructure (SDG 9) and supporting climate action (SDG 12).

This study aims at proposing a hybrid model for early cost prediction of a construction project. Early cost prediction for a construction project is the basic approach to procure a project within a predefined budget. However, most of the projects routinely face the impact of cost overruns. Furthermore, conventional and manual cost computing techniques are hectic, time-consuming and error-prone. To deal with such challenges, soft computing techniques such as artificial neural networks (ANNs), fuzzy logic and genetic algorithms are applied in construction management. Each technique has its own constraints not only in terms of efficiency but also in terms of feasibility, practicability, reliability and environmental impacts. However, appropriate combination of the techniques improves the model owing to their inherent nature.

This paper proposes a hybrid model by combining machine learning (ML) techniques with ANN to accurately predict the cost of pile foundations. The parameters contributing toward the cost of pile foundations were collected from five different projects in India. Out of 180 collected data entries, 176 entries were finally used after data cleaning. About 70% of the final data were used for building the model and the remaining 30% were used for validation.

The proposed model is capable of predicting the pile foundation costs with an accuracy of 97.42%.

Although various cost estimation techniques are available, appropriate use and combination of various ML techniques aid in improving the prediction accuracy. The proposed model will be a value addition to cost estimation of pile foundations.