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1 – 10 of 406Siyuan Lyu, Shijing Niu, Jing Yuan and Zehui Zhan
Preservice teacher (PST) professional development programs are crucial for cultivating high-quality STEAM teachers of the future, significantly impacting the quality of regional…
Abstract
Purpose
Preservice teacher (PST) professional development programs are crucial for cultivating high-quality STEAM teachers of the future, significantly impacting the quality of regional STEAM education. The Guangdong-Hong Kong-Macao Greater Bay Area, as a region of cross-border cooperation, integrates the resources and advantages of Guangdong, Hong Kong, and Macao, possessing rich cultural heritage and innovative capabilities. Transdisciplinary Education for Cultural Inheritance (C-STEAM) is an effective approach to promoting educational collaboration within the Greater Bay Area, facilitating the integration of both technological and humanities education. This study aims to develop a Technology-Enabled University-School-Enterprise (T-USE) collaborative education model and implement it in the Greater Bay Area, to explore its role as a support mechanism in professional development and its impact on C-STEAM PSTs' professional capital.
Design/methodology/approach
Adopting a qualitative methodology, the study interviewed PSTs who participated in a C-STEAM teacher education course under the T-USE model. Thematic coding is used to analyze their knowledge acquisition, interaction benefits with community members, and autonomous thinking and decision-making in theoretical learning and teaching practice.
Findings
The findings show that the T-USE model significantly enhanced the PSTs' human capital, including teaching beliefs, knowledge, and skills. In terms of social capital, PSTs benefited from collaboration with PST groups, university teaching teams, in-service teachers, and enterprises, though challenges such as varying levels of expertise among in-service teachers and occasional technical instability emerged. For decisional capital, the T-USE model provided opportunities for autonomous thinking and promoted teaching judgment skills through real teaching challenges and scenarios. Reflective practice activities also supported PSTs' professional growth.
Originality/value
This study reveals the effectiveness and internal mechanism of the T-USE model in C-STEAM PST training, offering significant theoretical and practical references for future PST education.
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Geng Huang, Xi Lin and Ling-Yun He
Some existing studies have begun to discuss how trade will change the environment from a country or province perspective. However, so far, only a limited number of studies have…
Abstract
Purpose
Some existing studies have begun to discuss how trade will change the environment from a country or province perspective. However, so far, only a limited number of studies have provided evidence at the product level. This study aims to investigate the environmental impacts of trade at the product level.
Design/methodology/approach
The effects of importing intermediates and capital inputs on energy performance are examined using theoretical analysis. Empirical analyses are conducted using data on product trade, and the effects of importing intermediate inputs and capital inputs on energy efficiency are identified using a Propensity Score Matching-Difference in Difference (PSM-DID) estimation.
Findings
The results demonstrate that importing intermediates and capital inputs effectively enhance energy efficiency. Importing these inputs from foreign markets leads to increased productivity and ultimately improves energy performance.
Originality/value
This research provides new evidence on the relationship between importing and energy use at the product trade level. It offers insights into enterprise behaviors regarding importing intermediates and capital inputs, contributing to a deeper understanding of the environmental effects of trade. Additionally, a micro-theoretical model is developed to examine the impacts of imports on energy efficiency, complementing existing literature with theoretical insights.
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The purpose of this study is to develop an intelligent tutoring system (ITS) for programming learning based on information tutoring feedback (ITF) to provide real-time guidance…
Abstract
Purpose
The purpose of this study is to develop an intelligent tutoring system (ITS) for programming learning based on information tutoring feedback (ITF) to provide real-time guidance and feedback to self-directed learners during programming problem-solving and to improve learners’ computational thinking.
Design/methodology/approach
By analyzing the mechanism of action of ITF on the development of computational thinking, an ITF strategy and corresponding ITS acting on the whole process of programming problem-solving were developed to realize the evaluation of programming problem-solving ideas based on program logic. On the one hand, a lexical and syntactic analysis of the programming problem solutions input by the learners is performed and presented with a tree-like structure. On the other hand, by comparing multiple algorithms, it is implemented to compare the programming problem solutions entered by the learners with the answers and analyze the gaps to give them back to the learners to promote the improvement of their computational thinking.
Findings
This study clarifies the mechanism of the role of ITF-based ITS in the computational thinking development process. Results indicated that the ITS designed in this study is effective in promoting students’ computational thinking, especially for low-level learners. It also helped to improve students’ learning motivation, and reducing cognitive load, while there’s no significant difference among learners of different levels.
Originality/value
This study developed an ITS based on ITF to address the problem of learners’ difficulty in obtaining real-time guidance in the current programming problem-solving-based computational thinking development, providing a good aid for college students’ independent programming learning.
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Irfan Ahmed, Owais Mehmood, Zeshan Ghafoor, Syed Hassan Jamil and Afkar Majeed
This study aims to examine the impact of board characteristics on debt choice.
Abstract
Purpose
This study aims to examine the impact of board characteristics on debt choice.
Design/methodology/approach
The sample comprises of unique nonfinancial firms listed in the FTSE 350 over the period 2011–2018. This study uses Tobit and OLS regressions to check the impact of board characteristics on debt choice. The results are robust to the battery of robust checks.
Findings
This study finds that board size and board independence are positively associated with public debt. However, CEO duality and board meetings frequency are inversely associated with public debt. Overall, the findings are consistent with the “financial intermediation theory” that the firms with weak governance rely on bank financing, and firms with better corporate governance go for public debt.
Research limitations/implications
This study offers significant insights for investors and policymakers.
Originality/value
This study offers new insights regarding the role of board characteristics in firms’ debt choice by showing the significant impact of board characteristics on debt choice. The findings indicate that the board’s efficient internal monitoring may substitute external monitoring by the bank.
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Cheng Peng, He Cheng, Tong Zhang, Jing Wu, Fandi Lin and Jinglong Chu
This paper aims to further develop stator permanent magnet (PM) type memory machines by providing generalized design guidelines for double-stator memory machines (DSMMs) with…
Abstract
Purpose
This paper aims to further develop stator permanent magnet (PM) type memory machines by providing generalized design guidelines for double-stator memory machines (DSMMs) with hybrid PMs. This paper discusses the design experience of DSMMs and presents a comparative study of radial magnetization (RM) and circumferential magnetization (CM) types.
Design/methodology/approach
It begins with an introduction to RM and CM operating principles and magnetization mechanisms. Then, a comparative study is conducted for one of the RM-DSMM rotor pole pairs, inner and outer stator clamping angles and low coercive force PMs thickness. Finally, the two machines’ finite element simulation performance is compared. The validity of the proposed machine structure is demonstrated.
Findings
In this paper, the double-stator structure is extended to parallel hybrid PM memory machines, and two novel DSMMs with RM and CM configurations are proposed. Two types of DSMMs have PMs and magnetizing windings on the inner stator and armature windings on the outer stator. The main difference between the two is the arrangement of PMs on the inner stator.
Originality/value
Conventional stator PM memory machines have geometrical space conflicts between the PM and armature windings. The proposed double-stator structure can alleviate these conflicts and increase the torque density accordingly. In addition, this paper contributes to comparing the arrangement of hybrid PMs for DSMMs.
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Qing Wang, Xiaoli Zhang, Jiafu Su and Na Zhang
Platform-based enterprises, as micro-entities in the platform economy, have the potential to effectively promote the low-carbon development of both supply and demand sides in the…
Abstract
Purpose
Platform-based enterprises, as micro-entities in the platform economy, have the potential to effectively promote the low-carbon development of both supply and demand sides in the supply chain. Therefore, this paper aims to provide a multi-criteria decision-making method in a probabilistic hesitant fuzzy environment to assist platform-type companies in selecting cooperative suppliers for carbon reduction in green supply chains.
Design/methodology/approach
This paper combines the advantages of probabilistic hesitant fuzzy sets (PHFS) to address uncertainty issues and proposes an improved multi-criteria decision-making method called PHFS-DNMEREC-MABAC for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Within this decision-making method, we enhance the standardization process of both the DNMEREC and MABAC methods by directly standardizing probabilistic hesitant fuzzy elements. Additionally, a probability splitting algorithm is introduced to handle probabilistic hesitant fuzzy elements of varying lengths, mitigating information bias that traditional approaches tend to introduce when adding values based on risk preferences.
Findings
In this paper, we apply the proposed method to a case study involving the selection of carbon emission reduction collaboration suppliers for Tmall Mart and compare it with the latest existing decision-making methods. The results demonstrate the applicability of the proposed method and the effectiveness of the introduced probability splitting algorithm in avoiding information bias.
Originality/value
Firstly, this paper proposes a new multi-criteria decision making method for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Secondly, in this method, we provided a new standard method to process probability hesitant fuzzy decision making information. Finally, the probability splitting algorithm was introduced to avoid information bias in the process of dealing with inconsistent lengths of probabilistic hesitant fuzzy elements.
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Yi-Cheng Chen and Yen-Liang Chen
In this “Info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activity and e-commerce…
Abstract
Purpose
In this “Info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activity and e-commerce. The purpose of this paper is to model users' preference evolution to recommend potential items which users may be interested in.
Design/methodology/approach
A novel recommendation system, namely evolution-learning recommendation (ELR), is developed to precisely predict user interest for making recommendations. Differing from prior related methods, the authors integrate the matrix factorization (MF) and recurrent neural network (RNN) to effectively describe the variation of user preferences over time.
Findings
A novel cumulative factorization technique is proposed to efficiently decompose a rating matrix for discovering latent user preferences. Compared to traditional MF-based methods, the cumulative MF could reduce the utilization of computation resources. Furthermore, the authors depict the significance of long- and short-term effects in the memory cell of RNN for evolution patterns. With the context awareness, a learning model, V-LSTM, is developed to dynamically capture the evolution pattern of user interests. By using a well-trained learning model, the authors predict future user preferences and recommend related items.
Originality/value
Based on the relations among users and items for recommendation, the authors introduce a novel concept, virtual communication, to effectively learn and estimate the correlation among users and items. By incorporating the discovered latent features of users and items in an evolved manner, the proposed ELR model could promote “right” things to “right” users at the “right” time. In addition, several extensive experiments are performed on real datasets and are discussed. Empirical results show that ELR significantly outperforms the prior recommendation models. The proposed ELR exhibits great generalization and robustness in real datasets, including e-commerce, industrial retail and streaming service, with all discussed metrics.
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Rongrong Shi, Qiaoyi Yin, Yang Yuan, Fujun Lai and Xin (Robert) Luo
Based on signaling theory, this paper aims to explore the impact of supply chain transparency (SCT) on firms' bank loan (BL) and supply chain financing (SCF) in the context of…
Abstract
Purpose
Based on signaling theory, this paper aims to explore the impact of supply chain transparency (SCT) on firms' bank loan (BL) and supply chain financing (SCF) in the context of voluntary disclosure of supplier and customer lists.
Design/methodology/approach
Based on panel data collected from Chinese-listed firms between 2012 and 2021, fixed-effect models and a series of robustness checks are used to test the predictions.
Findings
First, improving SCT by disclosing major suppliers and customers promotes BL but inhibits SCF. Specifically, customer transparency (CT) is more influential in SCF than supplier transparency (ST). Second, supplier concentration (SC) weakens SCT’s positive impact on BL while reducing its negative impact on SCF. Third, customer concentration (CC) strengthens the positive impact of SCT on BL but intensifies its negative impact on SCF. Last, these findings are basically more pronounced in highly competitive industries.
Originality/value
This study contributes to the SCT literature by investigating the under-explored practice of supply chain list disclosure and revealing its dual impact on firms' access to financing offerings (i.e. BL and SCF) based on signaling theory. Additionally, it expands the understanding of the boundary conditions affecting the relationship between SCT and firm financing, focusing on supply chain concentration. Moreover, it advances signaling theory by exploring how financing providers interpret the SCT signal and enriches the understanding of BL and SCF antecedents from a supply chain perspective.
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Kwang-Jing Yii, Zi-Han Soh, Lin-Hui Chia, Khoo Shiang-Lin Jaslyn, Lok-Yew Chong and Zi-Chong Fu
In the stock market, herding behavior occurs when investors mimic the actions of others in their investment decisions. As a result, the market becomes inefficient and speculative…
Abstract
In the stock market, herding behavior occurs when investors mimic the actions of others in their investment decisions. As a result, the market becomes inefficient and speculative bubbles form. This study aims to investigate the relationship between information, overconfidence, market sentiment, experience and national culture, and herding behavior among Malaysian investors. A total of 400 questionnaires are distributed to bank institutions' investors. The survey design based on cross-sectional data is analyzed using the Partial Least Squares Structural Equation Model. The results indicate that information, market sentiment, experience, and national culture are positively related to herding behavior, while overconfidence has no effect. With this, the government should strengthen regulations to prevent the dissemination of misleading information. Moreover, investors are encouraged to overcome narrow thinking by expanding their understanding of different cultures when making investment decisions.
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Shirley Jin Lin Chua, Shiuan Ping Beh, Nik Elyna Myeda and Azlan Shah Ali
This study aims to improve the use of digitalization in facilities management (FM) for shopping complex facilities in the post-COVID-19 era. The resumption of economic activities…
Abstract
Purpose
This study aims to improve the use of digitalization in facilities management (FM) for shopping complex facilities in the post-COVID-19 era. The resumption of economic activities, especially in shopping complexes, poses challenges for FM with throngs of shoppers. To tackle these challenges, enhanced and innovative FM practices are necessary.
Design/methodology/approach
The study used a qualitative research approach, incorporating case studies, interviews, observations and documentation. It focused on super-regional shopping complexes in the Klang Valley, Malaysia, selecting two complexes for qualitative data collection. Supplementary data were gathered from various sources, including government policy publications, websites, books, journal papers and archival records.
Findings
The research provides valuable insights into FM innovations and the application of FM digitalization in shopping complexes after the COVID-19 pandemic. It also addresses challenges faced by FM teams during this period. Recommendations for implementing FM digitalization in super-regional shopping complexes post-COVID-19 include developing skilled personnel, defining appropriate work scopes, strategies and policies, using cost-effective software, and increasing occupant awareness. The involvement of outsourced service providers is advised, emphasizing their understanding of the organization’s business model and innovative approaches.
Originality/value
The findings offer new perspectives on the characteristics of FM digitalization in the commercial sector during business disruptions caused by the pandemic. The proposed strategies are grounded in real industry implementations, aiming to enhance the FM digitalization approach for improved business performance.
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