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Purpose: To examine the hotel industry’s evolutionary journey, charting it from the era of digital manufacturing to the current epoch of Industry 5.0, the emergence of the digital society. Industry 5.0 signifies a fundamental change, where the physical and digital aspects merge to establish a more engaging and integrated hospitality experience.

Study design: Using secondary sources, the transformation’s complex difficulties and possibilities are analyzed. We have designed this chapter based on the importance of trust, ethical data use, and teamwork in successfully navigating the complexity of digital production to the digital society based on the Industry 5.0 era. The industry deals with rising cybersecurity threats, ethical concerns about artificial intelligence (AI), and the critical need for a highly skilled digital workforce. This chapter emphasizes the importance of cross-industry collaboration and knowledge exchange as strategic imperatives.

Findings: The importance of developing a highly skilled workforce capable of effectively navigating the complexities of Industry 5.0. Promoting cross-industry collaborations and knowledge exchange is part of the suggested answer. These collaborative efforts are necessary to create a sustainable and fully connected future for the hospitality sector. This chapter promotes a comprehensive approach to implementing Industry 5.0 in the hotel sector.

Originality: The value of this chapter is that amid a growing dependence on digital connections, this chapter emphasizes the pressing requirement for the hotel industry to confront cybersecurity issues and maintain ethical principles when implementing AI in the hospitality industry.

This study aims to explore the impact of architecture on digital communication mediums, focusing on how social media shapes the public perception and discussion of architectural spaces. It specifically examines the case of the Basilica Cistern/Istanbul, analysing social media interactions post-restoration.

Using newspaper archive scanning and survey technique, this study observed public content on Instagram focusing on the post-restoration period of the Basilica Cistern. 406 (283 valid) people who visited the Cistern and shared their experiences on Instagram between August 2022 and January 2023 participated in a survey. The analysis utilized Python for advanced correlation studies, enabling an in-depth exploration of the interplay between architectural features and social media sharing behaviours.

The analysis revealed that historical significance, lighting elements, role as a photographic backdrop significantly influenced sharing behaviours. Correlations were found between specific spatial features of the cistern and various sharing motivations, such as communication with people, personal gain, and popularity. The study highlights a diverse spectrum of motivations among users, emphasizing the relationship between these motivations and spatial features.

This study underscores the necessity for further inquiry into the intricate dynamics among digital communication, architectural spaces, and user motivations. Limitations include potential challenges in gathering data from social media due to concerns of cyber fraud and the misuse of hashtags.

This research offers novel insights into the interplay between digital communication and architecture. It underscores the potential of digital platforms as valuable data sources for architectural theorizing and practice, particularly in understanding how restorations and architectural changes are perceived and discussed in the digital space.

This study aims to address the challenges faced by healthcare manufacturing SMEs in high-mix, high-volume environments, particularly in light of the COVID-19 pandemic.The authors propose the implementation of the Production Wheel framework as a solution to optimize operational efficiency and cost-effectiveness. Using real-world anonymized data from a high-mix, high-volume manufacturing project, the authors evaluate the feasibility and impact of this framework. The findings indicate significant reductions in changeover and inventory costs, with notable improvements in production efficiency. The study highlights the potential of the Production Wheel in healthcare manufacturing while providing insights based on authentic industry data.

The Production Wheel framework is introduced as an 8-step method that encompasses analyzing historical volumes, identifying key product families, developing current and future state value stream maps, assessing capacity utilization and implementing Kanban-based pull systems with supermarkets between processes. This approach is grounded in Lean manufacturing principles and is tailored to the specific needs of the healthcare manufacturing industry.

The adoption of the Production Wheel framework enables healthcare manufacturers to align their production more closely with actual demand, minimizing waste and reducing overheads. This agile approach facilitates the adaptation to a high mix of products without compromising efficiency, ensuring a resilient and responsive supply chain that can effectively meet the evolving needs of the market.

While this conceptual paper introduces the Production Wheel framework in the context of healthcare manufacturing, future research should focus on the practical implementation and empirical validation of the proposed approach. Case studies and quantitative analyses of the framework’s impact on operational efficiency, inventory management and cost-effectiveness would provide valuable insights for academics and practitioners.

The Production Wheel framework offers a practical approach for healthcare manufacturers to navigate the challenges posed by the COVID-19 pandemic and the increasing demand for customized products. By integrating this methodology alongside flexible manufacturing systems, manufacturers can enhance their ability to adapt to market disruptions while maintaining optimal operational performance.

This paper contributes to the Lean Manufacturing literature by introducing the Production Wheel concept as a novel framework for healthcare manufacturing. The proposed approach addresses the unique challenges faced by the healthcare manufacturing sector in the wake of the COVID-19 pandemic and provides a strategic roadmap for optimizing operational efficiency and cost-effectiveness.

This paper investigates the influence of blockchain technology on trust and transparency within supply chain management. While existing research suggests blockchain has revolutionary potential, real-world evidence remains limited. This study aims to bridge this gap.

The research relies on transaction cost analysis and principal-agent theory to develop a conceptual model. The model proposes how blockchain fosters trust and transparency, ultimately leading to a market-based governance model within supply chains. Five different blockchain applications were analyzed in a multi-case study through document reviews and expert interviews to test the model’s assumptions.

The study’s findings challenge initial assumptions. The complexity of blockchain networks and a reluctance to share information among participants hinder blockchain technology’s ability to increase trust and transparency. Consequently, the expected reduction in opportunism and uncertainties is not observed, and a market-based governance model fails to materialize. In practice, supply chain partners gravitate toward permissioned blockchains managed by established consortia. Acting as trusted third parties, these consortia assume control over network management, rendering blockchain essentially unnecessary.

This paper sheds light on the practical limitations of blockchain technology in revolutionizing supply chain management. While blockchain promises much, the findings suggest that established consortia currently play a more critical role in fostering trust and transparency within supply chains.

This study aims to identify the role of nature-related features in promoting well-being from the perspective of children as the users of post-disaster schools. It highlights the importance of connectedness to nature in design strategies for post-disaster school reconstruction.

The mixed-method study was conducted in three primary schools reconstructed after the earthquakes in Lombok (Indonesia) and Chiang Rai (Thailand) that integrated some nature-related elements in their design. The questionnaire survey explored the children’s satisfaction with the post-disaster school environment as an indication of well-being.

The findings suggest that priority in post-disaster school design strategies should incorporate nature-related design features that provide opportunities for children to be with friends and to play, to learn in comfortable and safe conditions, to interact with nature and to be in a quiet and relaxing place.

Most post-disaster reconstruction prioritizes the speed of construction rather than the spatial qualities that could help children deal with the disaster. This study provides evidence on the potential of nature-related design features to support the children’s well-being after the disaster.

The global dietary landscape is increasingly witnessing a shift in meat consumption patterns. However, an important question still concerns the direction in which these changes will continue and to what extent, in the near future, most consumers will completely give up meat products in their diet or will only limit their consumption. The second question is who is most susceptible to reducing meat in their diet and who will resist this trend. When looking for an answer to those questions, we employed an agent-based model (ABM) to analyse the data from a nationally representative survey conducted in Poland.

The model takes into account key factors influencing dietary choices through the channel of social influence, including health and ethical concerns, locus of control, food neophobia and meat eating justification.

Our simulations indicate the future dominance of reducetarianism, an upswing in vegan/vegetarianism, and a decline in omnivorism. Additionally, distinct dietary trajectories are emerging among different demographic groups, often echoing initial disparities or unveiling new patterns. The study further confirms the importance of individuals as embedded actors in the transformation of the food system.

The results offer a micro-based understanding of the evolution of dietary patterns, highlighting the value of the ABM in forecasting eating behaviour.

The application of industrial robots in modern production is becoming increasingly widespread. In the context of flexible production lines, quickly and accurately identifying and grasping specified workpieces is particularly important. This study aims to propose a grasping scheme that combines traditional methods with deep learning to improve grasping accuracy and efficiency.

First, a dataset generation method is proposed, which constructs a point cloud dataset close to the real scene without the need for extensive data collection. Then, the 3D object detection algorithm PointPillars is improved based on the features of the scene point cloud, allowing for the analysis of part poses to achieve grasping. Finally, a grasp detection strategy is proposed to match the optimal grasp pose.

Experimental results show that the proposed method can quickly and easily construct high-quality datasets, significantly reducing the time required for preliminary preparation. Additionally, it can effectively grasp specified workpieces, significantly improving grasping accuracy and reducing computation time.

The main contribution of this paper is the integration of a novel dataset generation method, improvements to the PointPillars algorithm for 3D object detection and the development of an optimal grasp detection strategy. These advancements enable the grasping system to handle real-world scenarios efficiently and accurately, demonstrating significant improvements over traditional methods.

This study investigated leaders’ decision-making during crises. Using grounded theory research, we explored whether leaders’ use of either individualistic or collectivistic decision-making styles is based on the context of a crisis. In addition, the authors looked into the components of building leaders’ efficacy during a crisis event and gauged their attitudinal and behavioral reactions to the crisis.

The authors conducted interviews with 30 C-suite executives during the first wave of the COVID-19 pandemic to determine their responses to crises. The authors then used open, axial, and selective coding to examine their data, which was then extracted for representative themes and overarching dimensions. In total, The authors received 12 major themes largely summarizing into 6 overarching dimensions – context of decision-making, leaders’ efficacy, individualistic approach to decision-making, collectivistic approach to decision-making, process of decision-making, and outcomes.

The research presents a decision-making framework clearly bifurcating situations with the need for individualistic and collectivistic decision-making. While, a high level of urgency and scarce resources encouraged leaders to adopt an individualistic approach in which most of the decisions were intuition-based and only high-level stakeholders were involved, the less time urgent and uncertain situation allowed leaders to put significant effort into building their own knowledge systems and involving others. In addition, leaders’ efficacy is understood in terms of their attitudes, readiness and reactions, which further elaborated upon their knowledge systems to allow them to excel in decision-making irrespective of the approaches they chose.

The research has implications for leaders in the decision-making roles to be effective during both individualistic and collectivistic decision-making.

With the development of Web information systems, steel e-commerce platforms have accumulated a large number of quality objection texts. These texts reflect consumer dissatisfaction with the dimensions, appearance and performance of steel products, providing valuable insights for product improvement and consumer decision-making. Currently, mainstream solutions rely on pre-trained models, but their performance on domain-specific data sets and few-shot data sets is not satisfactory. This paper aims to address these challenges by proposing more effective methods for improving model performance on these specialized data sets.

This paper presents a method on the basis of in-domain pre-training, bidirectional encoder representation from Transformers (BERT) and prompt learning. Specifically, a domain-specific unsupervised data set is introduced into the BERT model for in-domain pre-training, enabling the model to better understand specific language patterns in the steel e-commerce industry, enhancing the model’s generalization capability; the incorporation of prompt learning into the BERT model enhances attention to sentence context, improving classification performance on few-shot data sets.

Through experimental evaluation, this method demonstrates superior performance on the quality objection data set, achieving a Macro-F1 score of 93.32%. Additionally, ablation experiments further validate the significant advantages of in-domain pre-training and prompt learning in enhancing model performance.

This study clearly demonstrates the value of the new method in improving the classification of quality objection texts for steel products. The findings of this study offer practical insights for product improvement in the steel industry and provide new directions for future research on few-shot learning and domain-specific models, with potential applications in other fields.

Given that the current sustainability assessment in higher education institutions primarily relies on qualitative methods with relatively limited quantitative tools, the purpose of this study is to design a tool that could be used to comprehensively assess the overall state of higher education institutions’ sustainability.

The authors based the “Model to Assess the Sustainability of Higher Education Institutions” on the Triple Bottom Line (TBL) framework of economic, environmental and social factors, and established its primary dimensions as educational level, research capacity, community outreach, campus operations, campus experience and assessment reports. They designed the College Organisational Sustainability Scale (CO-SS) based on this research model, drawing their inspiration from the qualitative research tool, the Sustainability Assessment Questionnaire, and taking the following validation steps: expert review (n = 10), pilot testing (n = 150) and formal experiments (n = 1108). These steps were taken to optimise the scale items, test the model’s validity and assess its reliability.

After undergoing rigorous scientific validation, CO-SS was unequivocally confirmed as an effective and reliable tool, demonstrating its accurate reflection of the level of sustainability in higher education institutions.

The authors took an industry-specific approach by relying on the TBL and the Sustainability Assessment Questionnaire to construct and validate the CO-SS. Furthermore, the CO-SS has the potential to evolve into a self-assessment tool for higher education institutions, and a reliable foundation for data-driven decision-making in the realm of organisational sustainability at universities.