Albert Lin, Cindy Kao and Heeju Park
This study aims to develop and evaluate a modular smart garment design framework that simplifies the technical content associated with smart garment design.
Abstract
Purpose
This study aims to develop and evaluate a modular smart garment design framework that simplifies the technical content associated with smart garment design.
Design/methodology/approach
Smart garment design challenges were first identified through literature review and interviews. Then, a modular framework and toolkit was created to address these challenges. Finally, workshops were held to evaluate the modular toolkit.
Findings
Interviews highlighted the need for easier attachment of hard devices to soft textile materials, simpler electrical connection creation and straightforward device selection. A modular framework was proposed and divided into four elements: (1) the Central Computation Module, (2) Peripheral Electrical Modules, (3) Securely Attaching Modules with Substrates and (4) Managing Intra-garment Connections. Workshops showed the modular framework had statistically significant improvements in function and certain ease ratings when compared to non-modular components.
Originality/value
This research identified specific technical challenges faced by smart garment designers and alleviated them through a modular smart garment framework that in workshops outperformed non-modular components in key function and ease ratings.
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Sheng-Qun Chen, Ting You and Jing-Lin Zhang
This study aims to enhance the classification and processing of online appeals by employing a deep-learning-based method. This method is designed to meet the requirements for…
Abstract
Purpose
This study aims to enhance the classification and processing of online appeals by employing a deep-learning-based method. This method is designed to meet the requirements for precise information categorization and decision support across various management departments.
Design/methodology/approach
This study leverages the ALBERT–TextCNN algorithm to determine the appropriate department for managing online appeals. ALBERT is selected for its advanced dynamic word representation capabilities, rooted in a multi-layer bidirectional transformer architecture and enriched text vector representation. TextCNN is integrated to facilitate the development of multi-label classification models.
Findings
Comparative experiments demonstrate the effectiveness of the proposed approach and its significant superiority over traditional classification methods in terms of accuracy.
Originality/value
The original contribution of this study lies in its utilization of the ALBERT–TextCNN algorithm for the classification of online appeals, resulting in a substantial improvement in accuracy. This research offers valuable insights for management departments, enabling enhanced understanding of public appeals and fostering more scientifically grounded and effective decision-making processes.
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Xiaowei Wang, Yang Yang, Albert P.C. Chan, Hung-lin Chi and Esther H.K. Yung
With the increasing use of small unmanned aircrafts (SUAs), many countries have enacted laws and regulations to ensure the safe use of SUAs. However, there is a lack of…
Abstract
Purpose
With the increasing use of small unmanned aircrafts (SUAs), many countries have enacted laws and regulations to ensure the safe use of SUAs. However, there is a lack of industry-specific regulations accounting for the unique features of construction-related SUA operations. Operating SUAs in the construction industry is attributed to specific risks and challenges, which should be regulated to maximize the utility of SUAs in construction. This study, therefore, aims to develop a multi-dimensional regulatory framework for using SUAs in the construction industry.
Design/methodology/approach
A combination of quantitative and qualitative methods was used to compare seven selected national/regional SUA regulations to identify the applicability of implementing the existing regulations in construction. The interview surveys were then conducted to diagnose the challenges of construction-related SUA operations and gather interviewees' suggestions on the regulatory framework for SUA uses in construction.
Findings
The research found that some challenges of construction-related SUAs operations were not addressed in the current regulations. These challenges included the complex and time-consuming SUA operation permit, lack of regulation for special SUA operations in construction, insufficient regulatory compliance monitoring and a lack of construction-related remote pilots' training. A regulatory framework was then developed based on the findings of comparative analysis and interview surveys.
Research limitations/implications
This study mainly compared seven representative countries/regions' regulations, leading to a small sample size. Further research should be carried out to study the SUA regulations in other places, such as South Africa, South America or Middle East countries. Besides, this study's respondents to the interviews were primarily concentrated in Hong Kong, which may cause the interview results to differ from the construction industry in other countries/regions. A large-scale interview survey should be conducted in other places in the future to validate the current findings.
Practical implications
The proposed regulatory framework provides a reference for the policy-makers to formulate appropriate industry-specific SUA regulations and improve the applicability of SUA regulations in the construction industry. It sheds light upon the future of SUA regulations and the development of regulatory practice in this area.
Originality/value
This study is the first to propose a multi-dimensional regulatory framework for operating SUAs in construction by comprehensive policy comparisons and interviews. The regulatory framework offers a fresh insight into the unexplored research area and points out the direction for subsequent studies on SUA regulations in the construction industry.
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The authors examine how the major board reforms recently implemented by countries around the world affect firms' choice of debt.
Abstract
Purpose
The authors examine how the major board reforms recently implemented by countries around the world affect firms' choice of debt.
Design/methodology/approach
Using a quasi-experimental setting of major board reforms around the world that aim to improve board-related governance practices in various areas, this study investigates the impact of effective board monitoring on corporate debt choice. The authors employ difference-in-differences-type quasi-natural experiment method and path analysis for hypotheses testing.
Findings
The authors find that the implementation of board reforms is positively associated with firms' preference for public debt financing over bank debt. However, this effect tends to weaken after the fourth year following the implementation of board reforms. In additional analyses, the authors find that “rule-based” reforms have a more pronounced effect on firms' choice of debt than do “comply-or-explain” reforms. Both (1) strengthened firm-level internal governance practices that address concerns about the agency cost of debt and (2) reduced information asymmetries play important roles in facilitating firms' debt choice, but the evidence suggests that the former is the economic mechanism through which country-level reforms affect corporate debt choice.
Research limitations/implications
The study extends the literature examining the heterogeneity of corporate debt choices in a global setting and the literature on the consequences of corporate governance reforms.
Practical implications
The findings demonstrate the effectiveness of the corporate board reforms implemented in countries around the world, addressing concerns from critics about their potential harm or ineffectiveness.
Originality/value
The results indicate that country-level board reforms reduce the extent to which shareholder–creditor conflicts harm shareholders.
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Bowen Zheng, Mudasir Hussain, Yang Yang, Albert P.C. Chan and Hung-Lin Chi
In the last decades, various building information modeling–life cycle assessment (BIM-LCA) integration approaches have been developed to assess the environmental impact of the…
Abstract
Purpose
In the last decades, various building information modeling–life cycle assessment (BIM-LCA) integration approaches have been developed to assess the environmental impact of the built asset. However, there is a lack of consensus on the optimal BIM-LCA integration approach that provides the most accurate and efficient assessment outcomes. To compare and determine their accuracy and efficiency, this study aimed to investigate four typical BIM-LCA integration solutions, namely, conventional, parametric modeling, plug-in and industry foundation classes (IFC)-based integration.
Design/methodology/approach
The four integration approaches were developed and applied using the same building project. A quantitative technique for evaluating the accuracy and efficiency of BIM-LCA integration solutions was used. Four indicators for assessing the performance of BIM-LCA integration were (1) validity of LCA results, (2) accuracy of bill-of-quantity (BOQ) extraction, (3) time for developing life cycle inventories (i.e. developing time) and (4) time for calculating LCA results (i.e. calculation time).
Findings
The results show that the plug-in-based approach outperforms others in developing and calculation time, while the conventional one could derive the most accuracy in BOQ extraction and result validity. The parametric modeling approach outperforms the IFC-based method regarding BOQ extraction, developing time and calculation time. Despite this, the IFC-based approach produces LCA outcomes with approximately 1% error, proving its validity.
Originality/value
This paper forms one of the first studies that employ a quantitative and objective method to determine the performance of four typical BIM-LCA integration solutions and reveal the trade-offs between the accuracy and efficiency of the integration approaches. The findings provide practical references for LCA practitioners to select appropriate BIM-LCA integration approaches for evaluating the environmental impact of the built asset during the design phase.
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Rajasshrie Pillai and Brijesh Sivathanu
Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is…
Abstract
Purpose
Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.
Design/methodology/approach
This study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.
Findings
This research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.
Practical implications
This paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.
Originality/value
This research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.
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Lili Zhang, Jie Ling and Mingwei Lin
The aim of this paper is to present a comprehensive analysis of risk management in East Asia from 1998 to 2021 by using bibliometric methods and tools to explore research trends…
Abstract
Purpose
The aim of this paper is to present a comprehensive analysis of risk management in East Asia from 1998 to 2021 by using bibliometric methods and tools to explore research trends, hotspots, and directions for future research.
Design/methodology/approach
The data source for this paper is the Web of Science Core Collection, and 7,154 publications and related information have been derived. We use recognized bibliometric indicators to evaluate publications and visually analyze them through scientific mapping tools (VOS Viewer and CiteSpace).
Findings
The analysis results show that China is the most productive and influential country/region. East Asia countries have strong cooperation with each other and also have cooperation with other countries. The study shows that risk management has been involved in various fields such as credit, supply chain, health emergency and disaster especially in the background of COVID-19. We also found that machine learning, especially deep learning, has been playing an increasingly important role in risk management due to its excellent performance.
Originality/value
This paper focuses on studying risk management in East Asia, exploring its publication's fundamental information, citation and cooperation networks, hotspots, and research trends. It provides some reference value for scholars who are interested or further research in this field.
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Xuelian Sun, Enmin Feng, Jianguo Liu and Bing Wang
The purpose of this paper is to study some evolving mechanisms for producing weighted networks, as well as to analyze the statistical properties of the networks.
Abstract
Purpose
The purpose of this paper is to study some evolving mechanisms for producing weighted networks, as well as to analyze the statistical properties of the networks.
Design/methodology/approach
A simple one‐parameter evolution model of weighted networks is proposed, in which the topological growth combines with the variation of weights. Based on weight‐driven dynamics, the model can generate scale‐free distributions of the degree, node strength and edge weight, as confirmed in many real networks.
Findings
The exponent of the edge weight can be widely tuned. The unique parameter p controls the edge weight dynamical growth. The authors also obtain the non‐trivial weighted clustering coefficient and the weighted average to the nearest neighbors' degree.
Research limitations/implications
Accessibility and availability of data are the main limitations which apply to the figures.
Practical implications
The new evolving networks method may be beneficial for understanding real networks.
Originality/value
The paper proposes a new approach of explaining the evolving mechanisms of the real networks.