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Project-based temporary organizations, as an efficient organizational form for the execution of complex and innovative tasks, encounter challenges in fostering the effectiveness of inter-organizational cooperation within their temporary, uncertain, and dynamic nature. Although change-oriented organizational citizenship behaviors are recognized for promoting organizational relationships and performance in changing contexts, research in temporary organizational settings remains sparse. This study diverges from the majority concentrating on change-oriented behaviors on intra-organizational leader-employee relations and behaviors, aiming to propose a dynamic adaptive capacity of organizational leaders and explore how leadership capabilities and organizational climate shape their change-oriented organizational citizenship behaviors at inter-organizational level.

Developing a person-organization fit model tailored for complex and dynamic organizational settings, using survey data from 225 leaders with project cooperative experience and structural equation modeling for empirical analysis.

This study reveals the direct positive influences of organizational leaders’ dynamic adaptive capacity on their changed-oriented organizational citizenship behaviors. And, change self-efficacy, as a mediating psychological trait, enhances the positive relation between dynamic adaptive capacity and changed-oriented organizational citizenship behaviors. The findings also highlight person-organization interactions, where organizational justice, acting as a situational and moderating factor, has a positive yet disruptive effect on the relationship between dynamic adaptive capacity, change self-efficacy, and changed-oriented organizational citizenship behaviors.

This research enriches the mechanisms linking dynamic managerial capability in organizational leadership to citizenship behaviors at the micro-level, providing valuable insights for the management and development of temporary cross-organizational cooperation.

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

The COVID-19 pandemic has compelled higher educational institutions to implement alternative educational strategies that rely heavily on internet accessibility and utilisation to monitor and evaluate students. This study aims to find certain indicators for planning and designing future courses of inclusive online education in the domain of disaster risk reduction (DRR).

The study reviews and analyses online teaching and learning experiences of DRR courses. It uses online surveys and interviews to derive the perspectives of selected students and educators in universities in Asia and the Pacific region.

Active engagement is considered to be achieved when students are active in chat boxes, through presentations, through assignments and when the video cameras of students are turned on. On the contrary, students perceive active engagement differently because they face emotional disturbances and health issues due to prolonged screen/digital device use, have inadequate information and communications technology infrastructure or have digital literacy deficiencies among others. The study finds that online courses have many sets of strengths, weaknesses, opportunities and threats, when they are balanced, they can improve DRR courses in the future.

The study is based on the outcome of interviews with 10 experienced educators in DRR courses as well as students from different schools taking courses in DRR education. However, the students are not necessarily taking the courses of the educators interviewed due to the inability of some educators to avail themselves and the challenge of contacting the students. This notwithstanding, the results of this study give a general overview of the situation to be considered in the planning and design of online and distance education.

The results do not reflect the reaction of students and tutors of the same course. Future studies of collecting and analyzing the responses from the students and the educators with the same course could provide tailored solutions.

This study attempts to find solutions to bridging two different perspectives on teaching and learning. The results would be important to strengthening and designing future online courses.

Damage of engineering structures is a nonlinear evolutionary process that spans across both material and structural levels, from mesoscale to macroscale. This paper aims to provide a comprehensive review of damage analysis methods at both the material and structural levels.

This study provides an overview of multiscale damage analysis of engineering structures, including its definition and significance. Current status of damage analysis at both material and structural levels is investigated, by reviewing damage models and prediction methods from single-scale to multiscale perspectives. The discussion of prediction methods includes both model-based simulation approaches and data-driven techniques, emphasizing their roles and applications. Finally, summarize the main findings and discuss potential future research directions in this field.

In the material level, damage research primarily focuses on the degradation of material properties at the macroscale using continuum damage mechanics (CDM). In contrast, at the mesoscale, damage research involves analyzing material behavior in the meso-structural domain, focusing on defects like microcracks and void growth. In structural-level damage analysis, the macroscale is typically divided into component and structural scales. The component scale examines damage progression in individual structural elements, such as beams and columns, often using detailed finite element or mesoscale models. The structural scale evaluates the global behavior of the entire structure, typically using simplified models like beam or shell elements.

To achieve realistic simulations, it is essential to include as many mesoscale details as possible. However, this results in significant computational demands. To balance accuracy and efficiency, multiscale methods are employed. These methods are categorized into hierarchical approaches, where different scales are processed sequentially, and concurrent approaches, where multiple scales are solved simultaneously to capture complex interactions across scales.

Building a smart city is a necessary path to achieve sustainable urban development. Smart city public–private partnership (PPP) project is a necessary measure to build a smart city. Since there are many participants in smart city PPP projects, there are problems such as uneven distribution of risks; therefore, in order to ensure the normal construction and operation of the project, the reasonable sharing of risks among the participants becomes an urgent problem to be solved. In order to make each participant clearly understand the risk sharing of smart city PPP projects, this paper aims to establish a scientific and practical risk sharing model.

This paper uses the literature review method and the Delphi method to construct a risk index system for smart city PPP projects and then calculates the objective and subjective weights of each risk index through the Entropy Weight (EW) and G1 methods, respectively, and uses the combined assignment method to find the comprehensive weights. Considering the nature of the risk sharing problem, this paper constructs a risk sharing model for smart city PPP projects by initially sharing the risks of smart city PPP projects through Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the independently borne risks and the jointly borne risks and then determines the sharing ratio of the jointly borne risks based on utility theory.

Finally, this paper verifies the applicability and feasibility of the risk-sharing model through empirical analysis, using the smart city of Suzhou Industrial Park as a research case. It is hoped that this study can provide a useful reference for the risk sharing of PPP projects in smart cities.

In this paper, the authors calculate the portfolio assignment by EW-G1 and construct a risk-sharing model by TOPSIS-Utility Theory (UT), which is applied for the first time in the study of risk sharing in smart cities.

Green innovation in human-centric smart manufacturing (HSM-GI) has emerged as a new paradigm in innovation management for Industry 5.0. The evaluation analysis method is crucial for measuring the development progress and guiding continual improvements of HSM-GI. Since this process of HSM-GI can be regarded as complex and interactive, a holistic picture is often required to describe the interrelations of its antecedents and consequences. In this respect, this study aims to construct a causality network indicator system and proposes a synergy evaluation method for HSM-GI.

Firstly, based on the Driver force-State-Response (DSR) causal-effect framework, this study constructs a holistic indicator system to analyze the interactions between environmental and human concerns of HSM-GI. Secondly, owing to the imprecision of human cognition and synergy interaction in the evaluation process, a flexible hesitant fuzzy (HF) superiority-inferiority synergetic evaluation method is presented. This method quantifies the strengths of causal relationships and expresses the incentives and constraints attitudes of humans. Finally, the proposed framework is applied to six HSMs in the electronic technology industry.

The driving force and state of the HSM-GI system exhibit an upward trend, while the response continues to decline due to changing market demands. The order and synergy degree have shown an increasing trend during 2021–2023, particularly significant for BOE and Haier Smart Home. HSM-GI systems with higher scores mostly have functional coordination and a coherent synergy structure.

This study demonstrates the proposed approach’s applicability and assists policymakers in formulating targeted strategies for green innovation systems.

This study quantitatively investigates the impacts of digital and learning orientations on supply chain resilience (SCR) and firm performance (FP), aiming to fill the gaps in understanding their specific impacts in the context of Industry 4.0 developments and supply chain disruptions.

This study utilized survey techniques and structural equation modelling (SEM) to gather and analyse data through a questionnaire based on a seven-point Likert scale. Hypotheses were formulated based on an extensive literature review and tested using Amos software.

The study confirms SCR’s significant impact on FP, aligning with existing research on resilience’s role in organizational competitiveness. This study uncovers the nuanced impacts of digital and learning orientations on SCR and FP. Internal digital orientation (DOI) positively impacts SCR, while external digital orientation (DOE) does not. Specific dimensions of learning orientation – shared vision (LOS), open-mindedness (LOO) and intraorganizational knowledge sharing (LOI) – enhance SCR, while commitment to learning (LOC) does not. SCR mediates the relationship between DOI and FP but not between DOE and FP.

This research focuses on digital and learning orientations, recommending that future studies investigate other strategic orientations and examine the specific contributions of various digital technologies to SCR across diverse contexts.

The empirical findings emphasize the significance of developing internal digital capabilities and specific learning orientations to enhance SCR and FP, aligning these initiatives with resilience strategies.

This study advances knowledge by distinguishing the impacts of internal and external digital orientations and specific learning dimensions on SCR and FP, offering nuanced insights and empirical validation.

This study aims to explore the factors influencing the evolution of emerging technology innovation network (ETIN) in combination with the key attributes and life cycle of emerging technologies, particularly the impact of multiple knowledge attributes and technology life cycle on the ETIN evolution.

This study collects 5G patent data and their citation information from the Derwent Innovations Index to construct a 5G technology innovation network (5GIN) as a sample network and conducts an empirical analysis of the 5GIN using the temporal exponential random graph model (TERGM).

The results indicate that during the 5GIN evolution, the network scale continues to expand and exhibits increasingly significant core-periphery structure, scale-free characteristic, small-world characteristic and community structure. Furthermore, the findings suggest that the multiple knowledge attributes based on the key attributes of emerging technologies, including knowledge novelty, coherence, growth and impact, have a significant positive influence on the ETIN evolution. Meanwhile, the temporal evolution of ETIN is also found to be correlated with the life cycle of emerging technologies.

This study extends the exploration of emerging technology research from a complex network perspective, providing a more realistic explanatory framework for the factors influencing ETIN evolution. It further highlights the important role that multiple knowledge attributes and the technology life cycle play within this framework.

Since the “Standards + Certification” regional quality brand initiative has become a key component of China’s efforts to promote a quality-driven economy, this paper aims to examine the effect of perception, trust and satisfaction toward the regional quality brand on consumer loyalty.

Data were collected from 401 consumers who have bought “Zhejiang Manufacturing” regional quality brand products. The structural equation modeling (SEM) technique was applied to assess the relationship of the research model.

The results show that brand perception, trust and satisfaction toward regional quality brands all significantly positively impact both consumer behavioral and attitudinal loyalty. Further, the results confirmed that brand trust and satisfaction fully mediate the relationship between brand perception and behavioral loyalty and partially mediate the relationship between brand perception and attitudinal loyalty.

This study presents a conceptual model on the impact of perception toward regional quality brands on consumer loyalty in a field where little research has been done. It offers a consumer-focused analysis that advances the theoretical and empirical knowledge of regional quality brands.

COVID-19 has aggregated the need for a non-contact medical logistic system. A non-contact robot with self-navigation ability has greatly enhanced the efficiency of the medical logistic system. This paper aims to design a new medical logistics robot system for the complex environment of hospitals with dynamic obstacles.

Targeting a medical logistics robot system for a large-scale hospital environment, this study proposed a dynamic obstacle avoidance system to reduce the robot’s delay time as well as frequent route switching. In the algorithm, this study proposed a new loop closure detection with an artificial correction factor. Moreover, this study presented enhanced 3D object detection, improving detection accuracy in hospital environments.

Experimental results confirm that the robot can move along its global path and reach its destination without colliding with stationary or moving obstacles.

The medical logistics robot system has safe and stable performance in real hospital scenarios. The implementation verifies that the robot has effectiveness and reliability in both hardware and software design.