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Unlike previous research that primarily utilized structural equation modelling (SEM) to evaluate safety hazards in subway projects, this research aims to utilize a hybrid approach to investigate and scrutinize the key indicators of safety hazards leading to accidents, thereby hindering the progress of subway projects in China, taking into cognizance the multiple stakeholder’s perspective.

By administering a survey questionnaire to 373 highly involved stakeholders in subway projects spanning Changsha, Beijing and Qingdao, China, our approach incorporated a four-staged composite amalgamation of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), covariance-based structural equation modelling (CB-SEM) and artificial neural network (ANN) to develop an optimized model that determines the causal relationships and interactions among safety hazards in subway construction projects.

The optimized model delineated the influence of individual safety hazards on subway projects. The feasibility and applicability of the model developed was demonstrated on an actual subway project under construction in Changsha city. The outcomes revealed that the progress of subway projects is significantly influenced by risks associated with project management, environmental factors, subterranean conditions and technical hazards. In contrast, risks related to construction and human factors did not exhibit a significant impact on subway construction progress.

While our study provides valuable insights, it is important to acknowledge the limitation of relying on theoretical approaches without empirical validation from experiments or the field. In future research, we plan to address this limitation by assessing the SEM using empirical data. This will involve a comprehensive comparison of outcomes derived from CB-SEM with those obtained through SEM-ANN methods. Such an empirical validation process is crucial for enhancing the overall efficiency and robustness of the proposed methodologies.

The established hybrid model revealed complex non-linear connections among indicators in the intricate project, enabling the recognition of primary hazards and offering direction to improve management of safety in the construction of subways.

Megaprojects provide an ideal context for exploring the dynamic characteristics of stakeholders within a collaborative innovation system. This research aims to examine the changes in stakeholder salience and functional roles during the evolution of such a system.

This study is empirically grounded on the Hong Kong-Zhuhai-Macao Bridge (HZMB) megaproject in China, analyzed with the stakeholder salience, stakeholder functional role, stakeholder dynamics and collaborative innovation system theoretical lens.

The megaproject collaborative innovation system can be divided into four stages: the birth stage, development stage, mature stage and re-innovation stage. Stakeholder salience generally remains unchanged throughout the lifecycle of the collaborative innovation system, except for engineering consulting firms (ECF). ECF transitioned from a definitive stakeholder to an expectant stakeholder upon project completion. The number of definitive stakeholders during the first three stages increases gradually. Besides, stakeholder functional roles shift in eight different directions throughout the lifecycle of the megaproject collaborative innovation system because they possess different core resources necessary for implementing innovations and are positioned differently within the collaborative innovation system.

This study contributes to the theory and practice of collaborative innovation in megaprojects. First, it offers insights into the evolution of megaproject collaborative innovation systems from the perspective of stakeholder interactions. Second, it has significant implications for managing stakeholder relationships based on their salience and functional roles at different stages of the collaborative innovation system.

Radical innovation involves significant technological advances that have a critical impact on both firm growth and society development. Therefore, there is a growing need to understand how to achieve radical innovation. While previous studies have examined the direct effect of digitization on firm innovation, there is still limited knowledge of how digitization affects radical innovation. This paper aims to contribute to further exploring the underlying mechanism of how digitization affects radical innovation by incorporating the serial mediation effect of absorptive capacity.

This paper collected data from Chinese-listed manufacturing firms from 2015 to 2021 and analyzed the data using bootstrap analysis.

The research results show that digitization has a positive impact on radical innovation. Furthermore, digitization can influence radical innovation through potential absorptive capacity and realized absorptive capacity, and the mediation effect of potential absorptive capacity and realized absorptive capacity can occur sequentially. These research results are validated by a range of robustness tests.

This paper contributes to the literature on digitization and radical innovation by validating a model that links digitization through absorptive capacity to radical innovation. This model helps to explain the relationship between digitization and radical innovation, which has previously been a topic of debate. This paper also contributes to the absorptive capacity literature by unpacking the asymmetric roles of the potential absorptive capacity and the realized absorptive capacity. This paper also provides valuable implications for practices.

Short-form video advertisements have recently gained popularity and are widely used. However, creating attractive short video advertisements remains a challenge for sellers. Based on the visual-audio perspective and signaling theory, this study investigated the impacts of three visual features (number of shots, pixel-level image complexity and vertical versus horizontal formats) and two audio features (speech rate and average spectral centroid) on user engagement behavior.

We conducted a field study on TikTok. To test our various hypotheses, we used regression analysis on 2,511 videos containing product promotion information posted by 60 sellers between January 1, 2020 and November 20, 2021.

For visual variables, the number of shots and pixel-level image complexity were found to have nonlinear (inverted U-shaped) relationships with user engagement behavior. The vertical video form was found to have a positive effect on comments and shares. In the case of audio variables, speech rate was found to have a significant positive effect on shares but not on likes and comments. The average spectral centroid was found to have significant negative influences on likes and comments.

This study provides specific suggestions for sellers who create short-form videos to improve user engagement behavior.

This study contributes to the literature on short-form video advertising by extending the potential drivers of user engagement behavior. Additionally, from a methodological perspective, it contributes to the literature by using computer vision and speech-processing techniques to analyze user behavior in a video-related context, effectively overcoming the limitations of the widely adopted survey method.

This study explores how medium and large manufacturing firms integrate environmental innovations to foster sustainability practices. It seeks to uncover the specific challenges these firms face and the strategies they employ to effectively implement sustainable initiatives in response to growing environmental pressures.

Using a qualitative phenomenological approach, the study analyzes twelve detailed case studies, engaging in conversational interviews to capture the lived experiences of key stakeholders. Through direct quotations and thematic analysis, the research offers in-depth insights into the practical implementation of sustainability practices driven by environmental innovations.

The study identifies those environmental innovations – both in products and processes – are critical drivers for addressing specific sustainability challenges. Firms that successfully implement these innovations do so by leveraging internal resources and external collaborations, which enable them to navigate environmental regulations, reduce waste and enhance operational efficiency. However, the findings also highlight significant barriers such as financial constraints and resistance to change within traditional manufacturing systems.

The study is limited by the use of a convenience sampling method and potential biases in interview responses. Future research should adopt more robust sampling techniques and explore longitudinal impacts to mitigate subjectivity and enhance generalizability.

The findings offer practical insights for manufacturing firms seeking to implement sustainability practices. By understanding the mechanisms through which environmental innovations can be integrated into their operations, firms can develop more effective strategies for enhancing environmental performance.

This study contributes original insights into the strategic application of environmental innovations within the manufacturing sub-sector. It highlights the role of innovations in overcoming sustainability challenges, offering a nuanced understanding of how firms can align operational goals with environmental imperatives.

The purpose of this study is to prepare a state-of-the-art review on advanced ceramic materials including their fabrication techniques, characteristics, applications and wettability.

This review paper presents the various types of advanced ceramic materials according to their compounding elements, fabrication techniques of advanced ceramic powders as well as their consolidation, their characteristics, applications and wetting properties. Hydrophobic/hydrophilic properties of advanced ceramic materials are described in the paper with their state-of-the-art application areas. Optical properties of fine ceramics with their intrinsic characteristics are also presented within. Special focus is given to the brief description of application-based manipulation of wetting properties of advanced ceramics in the paper.

The study of wetting/hydrophobicity/hydrophilicity of ceramic materials is important by which it can be further modified to achieve the required applications. It also makes some sense that the material should be tested for its wetting properties when it is going to be used in some important applications like biomedical and dental. Also, these advanced ceramics are now often used in the fabrication of filters and membranes to purify liquid/water so the study of wetting characteristics of these materials becomes essential. The optical properties of advanced ceramics are equally making them suitable for many state-of-the-art applications. Dental, medical, imaging and electronics are the few sectors that use advanced ceramics for their optical properties.

This review paper includes various advanced ceramic materials according to their compounding elements, different fabrication techniques of powders and their consolidation, their characteristics, various application area and hydrophobic/hydrophilic properties.

The purpose of this study was to validate the feasibility of the proposed microstructure-based model by comparing the simulation results with experimental data. The study also aimed to investigate the relationship between the orientation of graphite flakes and the failure behavior of the material under compressive loads as well as the effect of image size on the accuracy of stress–strain behavior predictions.

This paper presents a microstructure-based model that utilizes the finite element method (FEM) combined with representative volume elements (RVE) to simulate the hardening and failure behavior of ferrite-pearlite matrix gray cast iron under uniaxial loading conditions. The material was first analyzed using optical microscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) to identify the different phases and their characteristics. High-resolution SEM images of the undeformed material microstructure were then converted into finite element meshes using OOF2 software. The Johnson–Cook (J–C) model, along with a damage model, was employed in Abaqus FEA software to estimate the elastic and elastoplastic behavior under assumed plane stress conditions.

The findings indicate that crack initiation and propagation in gray cast iron begin at the interface between graphite particles and the pearlitic matrix, with microcrack networks extending into the metal matrix, eventually coalescing to cause material failure. The ferritic phase within the material contributes some ductility, thereby delaying crack initiation.

This study introduces a novel approach by integrating microstructural analysis with FEM and RVE techniques to accurately model the hardening and failure behavior of gray cast iron under uniaxial loading. The incorporation of high-resolution SEM images into finite element meshes, combined with the J–C model and damage assessment in Abaqus, provides a comprehensive method for predicting material performance. This approach enhances the understanding of the microstructural influences on crack initiation and propagation, offering valuable insights for improving the design and durability of gray cast iron components.

This study aims to examine the relationship between metaverse usage motivations and metaverse behavioral intention through Self-Determination Theory.

This study analyzed a sample of 501 South Korean individuals aged 18 and older who had prior experience using the metaverse, using a moderated mediation model.

The results indicate a significant direct effect between metaverse usage motivation and tourist behavioral intention, with tourist satisfaction significantly mediating this relationship. In addition, the authenticity and flow of metaverse tourism also have significant moderating effects on the model.

These insights can inform targeted marketing strategies, guiding the future development of metaverse tourism and hospitality.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

For over 25 years, the United States Green Building Council (USGBC) has significantly influenced the US sustainable construction through its leadership in energy and environmental design (LEED) certification program. This study aims to delve into how Baton Rouge, Louisiana, fares in green building adoption relative to other US capital cities and regions.

The study leverages statistical and geospatial analyses of data sourced from the USGBC, among other databases. It scrutinizes Baton Rouge’s LEED criteria performance using the mean percent weighted criteria to pinpoint the LEED criteria most readily achieved. Moreover, unique metrics, such as the certified green building per capita (CGBC), were formulated to facilitate a comparative analysis of green building adoption across various regions.

Baton Rouge’s CGBC stands at 0.31% (C+), markedly trailing behind the frontrunner, Santa Fe, New Mexico, leading at 3.89% (A+) and in LEED building per capita too. Despite the notable concentration of certified green buildings (CGBs) within Baton Rouge, the city’s green building development appears to be in its infancy. Innovation and design was identified as the most attainable LEED benchmark in Baton Rouge. Additionally, socioeconomic factors, including education and income per capita, were associated with a mild to moderate positive correlation (0.25 = r = 0.36) with the adoption of green building practices across the capitals, while sociocultural infrastructure exhibited a strong positive correlation (r = 0.99).

This study is beneficial to policymakers, urban planners and developers for sustainable urban development and a reference point for subsequent postoccupancy evaluations of CGBs in Baton Rouge and beyond.

This study pioneers the comprehensive analysis of green building adoption rates and probable influencing factors in capital cities in the contiguous US using distinct metrics.