Jian Chen, Chunxia Jiang and Yujia Lin
The purpose of this paper is to investigate the determinants of capital structure using a cross-section sample of 1,481 non-financial firms listed on the Chinese stock exchanges…
Abstract
Purpose
The purpose of this paper is to investigate the determinants of capital structure using a cross-section sample of 1,481 non-financial firms listed on the Chinese stock exchanges in 2011.
Design/methodology/approach
Employing four leverage measures (total leverage and long-term leverage in terms of both book value and market value, respectively) this study examines the effects of factors with proven influences on capital structure in literature, along with industry effect and ownership effect.
Findings
The authors find that large firms favour debt financing while profitable firms rely more on internal capital accumulation. Intangibility and business risk increase the level of debt financing but tax has little impact on capital structure. The authors also observe strong industrial effect and ownership effect. Real estate firms borrow considerably more and firms from utility and manufacturing industries use more long-term debt despite compared with commercial firms. On the other hand, firms with state ownership tend to borrow more, while firms with foreign ownership choose more equity financing.
Research limitations/implications
The study uses cross-section data to avoid any potential time effects, which allows the authors to focus on their main research question – to identify the determinants of capital structure for Chinese firms. Future research may gain more insights using panel data and considering other factors such as crisis and financial reforms.
Practical implications
These results may provide important implications to investors in making investment decision and to firms in making financing decisions.
Originality/value
This paper uses by far the largest and latest cross-section sample from the Chinese stock markets, offering a more complete picture of the financing behaviours in the Chinese firms, with known characters and the impact of ownerships.
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Fang Qin, Wei Le, Min Zhang and Yujia Deng
The boom in livestreaming commerce (LSC) has brought significant changes to social interaction methods. Understanding customer engagement in LSC is critical for online sellers who…
Abstract
Purpose
The boom in livestreaming commerce (LSC) has brought significant changes to social interaction methods. Understanding customer engagement in LSC is critical for online sellers who try to enhance the social influence and improve marketing effectiveness of LSC. Based on the stimulus–organism–response (S–O–R) paradigm, this study aims to develop a model to investigate the effects of perceived attributes of LSC (real-time interaction, perceived proximity and perceived authenticity) on social support (informational and emotional support) and subsequent engagement.
Design/methodology/approach
An online survey is conducted to collect data from LSC customers, and data are analyzed using SPSS and SmartPLS.
Findings
The results indicate that informational and emotional support are positively affected by real-time interaction, perceived proximity and perceived authenticity. In turn, informational and emotional support enable and mediate the prediction of customer engagement intention in LSC.
Originality/value
Prior LSC studies tend to focus on the motivation influencing LSC engagement from the perspective of perceived value. This study confirms the importance of perceived attributes of LSC in driving customer engagement from the perspective of social support.
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Under the carbon tax policy, the authors examine the operational decisions of the low-carbon supply chain with the triple bottom line.
Abstract
Purpose
Under the carbon tax policy, the authors examine the operational decisions of the low-carbon supply chain with the triple bottom line.
Design/methodology/approach
This paper uses the Stackelberg game theory to obtain the optimal wholesale prices, retail prices, sales quantities and carbon emissions in different cases, and investigates the effect of the carbon tax policy.
Findings
This study’s main results are as follows: (1) the optimal retail price of the centralized supply chain is the lowest, while that of the decentralized supply chain where the manufacturer undertakes the carbon emission reduction (CER) responsibility and the corporate social responsibility (CSR) is the highest under certain conditions. (2) The sales quantity when the retailer undertakes the CER responsibility and the CSR is the largest. (3) The supply chain obtains the highest profits when the retailer undertakes the CER responsibility and the CSR. (4) The environmental performance impact decreases with the carbon tax.
Practical implications
The results of this study can provide decision-making suggestions for low-carbon supply chains. Besides, this paper provides implications for the government to promote the low-carbon market.
Originality/value
Most of the existing studies only consider economic responsibility and social responsibility or only consider economic responsibility and environmental responsibility. This paper is the first study that examines the operational decisions of low-carbon supply chains with the triple bottom line under the carbon tax policy.
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Shengyu Guo, Yujia Zhao, Yuqiu Luoren, Kongzheng Liang and Bing Tang
Knowledge discovery related to unsafe behaviors promotes the performance of accident prevention in construction. Although numerous studies on accident causation models have…
Abstract
Purpose
Knowledge discovery related to unsafe behaviors promotes the performance of accident prevention in construction. Although numerous studies on accident causation models have discussed the correlations of unsafe behaviors with various factors (e.g., unsafe conditions), limited research explores correlations between unsafe behaviors within accidents. The purpose of this paper is mining strong association rules of unsafe behaviors from historical accidents to clarify this kind of tacit knowledge.
Design/methodology/approach
A case study was adopted as the research approach, in which accident records from building and urban railway construction in China were selected as data resources. The groups of unsafe behaviors extracted from accident records were expressed by the definitions of unsafe behaviors from safety regulations and operating procedures. Frequent Pattern (FP)-Growth algorithm was used for association rule mining, and the critical correlations between unsafe behaviors were represented by the effective strong rules.
Findings
The findings identify and distinguish correlations between unsafe behaviors within construction accidents. In building construction, workers and managers should pay attention to preventing unsafe behaviors related to personal protective equipment and machines and equipment. In urban railway construction, workers should especially avoid unsafe behaviors of inadequately dealing with environmental factors.
Practical implications
Tacit knowledge is transferred to explicit knowledge as the critical correlations between unsafe behaviors within accidents are determined by the effective strong rules. Additionally, the findings provide practice guidance for safety management, to collaboratively control unsafe behaviors with strong correlations.
Originality/value
This study contributes to the body of safety knowledge in construction and provides a further understanding of how construction accidents are caused by multiple unsafe behaviors.
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Rare earths are essential materials for many high-tech industries critical to both economic development and national defense. China, the world's dominant supplier of rare earths…
Abstract
Purpose
Rare earths are essential materials for many high-tech industries critical to both economic development and national defense. China, the world's dominant supplier of rare earths, has recently been imposing stricter controls over its production and export. The purpose of this paper is to examine the domestic roots of the changes in China's rare earth industry production and exports in its three-decade rise to the current global monopoly.
Design/methodology/approach
This paper adopts the historical institutionalism approach to analyze the trajectory of industry and trade development. The author analyzes data collected from government whitepapers and reputed scholarly and news sources.
Findings
This paper argues that the Chinese rare earth industry has gone through three periods of development, in which the state attempted to control the market and industry through reformulating rules and institutions to achieve state goals. Domestic state institutions, combined with macroeconomic environment and state governance strategy shaped the three-decade experience of rare earth industry and trade development in China.
Originality/value
This paper builds on existing findings about Chinese state regulations to provide a novel analytical framework to analyze the role of the state in industry and trade development in the rare earth industry. The focus on a single strategic industry seldom studied in the current literature also provides ample empirical value to further scholarly understanding about this industry.
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Yujia Ge, Caiyun Cui, Chunqing Zhang, Yongjian Ke and Yong Liu
To test a social-psychological model of public acceptance of highway infrastructure projects in the Chinese architecture/engineering/construction industry.
Abstract
Purpose
To test a social-psychological model of public acceptance of highway infrastructure projects in the Chinese architecture/engineering/construction industry.
Design/methodology/approach
Through a comprehensive literature review, we established a social-psychological model of public acceptance related to benefit perception, risk perception and public trust. We empirically validated our model by using structural equation model analysis based on a questionnaire survey in the S35 Yongjin Highway Infrastructure Project in Yunnan Province, China.
Findings
Benefit, trust and risk perception had a significant influence on local residents' public acceptance of highway infrastructure projects; benefit perception and trust perception had a greater influence than risk perception. Public acceptance among local male residents over the age of 35 or those with higher education levels was more likely to be determined by the relative dominance of risk and benefit perceived.
Research limitations/implications
This study contributes empirical evidence to the theoretical literature related to locally unwanted land use (LULU) siting and stakeholders in the field of project management from the public perspective. This study also suggests valuable practical implications to authorities, project managers and the public in decision-making and risk communication.
Originality/value
Although previous studies addressed factors affecting public acceptance towards potentially hazardous facilities, understanding of the implications of these social-psychological factors and their effects are still far from sufficient. This study bridges this gap by exploring the determinants of public acceptance towards highway infrastructure projects based on a selected case in China.
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Yujia Han, Nigel David Caldwell and Abhijeet Ghadge
Social network analysis (SNA) seeks to manage the connections between entities through investigating and understanding behaviours and relationships. This study demonstrates the…
Abstract
Purpose
Social network analysis (SNA) seeks to manage the connections between entities through investigating and understanding behaviours and relationships. This study demonstrates the increasing relevance of social network approaches to solving contemporary and looming operations management (OM) and supply chain management (SCM) problems; including the coordination operations challenges raised by increased connectivity.
Design/methodology/approach
The systematic literature review approach adopted here examines 63 papers in OM and SCM published between 2000 and 2019. To-date OM reviews on SNA have focussed on discussing archetypal supply chains, what differentiates this study is the focus on how value was created in other forms of chains and operations.
Findings
This study reveals that current SNA adoption in OM is dominated by a manufacturing style focus on linear, sequential value creation, with a resulting focus only on sequential interdependence. SNA studies on reciprocally co-ordinated value creation (e.g. many service and network operations) are shown to have been neglected and are linked to a new agenda on contemporary management issues.
Research limitations/implications
Beyond encouraging the use of SNA, this study seeks to re-orient SNA approaches towards how contemporary services and networks create value.
Originality/value
Through adopting a unique combination of approaches and frameworks, this study challenges extant work to offer a substantially revised agenda for SNA use in Operations and Supply Chain Management.
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Yujia Liu, Changyong Liang, Jian Wu, Hemant Jain and Dongxiao Gu
Complex cost structures and multiple conflicting objectives make selecting an appropriate cloud service difficult. The purpose of this study is to propose a novel group consensus…
Abstract
Purpose
Complex cost structures and multiple conflicting objectives make selecting an appropriate cloud service difficult. The purpose of this study is to propose a novel group consensus decision making method for cloud services selection with knowledge deficit by trust functions.
Design/methodology/approach
This article proposes a knowledge deficit-based multi-criteria group decision-making (MCGDM) method for cloud-service selection based on trust functions. Firstly, the concept of trust functions and a ranking method is developed to express the decision-making opinions. Secondly, a novel 3D normalized trust degree (NTD) is defined to measure the consensus levels. Thirdly, a knowledge deficit-based interactive consensus model is proposed for the inconsistent experts to modify their decision opinions. Finally, a real case study has been carried out to illustrate the framework and compare it with other methods.
Findings
The proposed method is practical and effective which is verified by the real case study. Knowledge deficit is an important concept in cloud service selection which is verified by the comparison of the proposed recommended mechanism based on KDD with the conventional recommended mechanism based on average value. A 3D NTD which considers three values (trust, not trust and knowledge deficit) is defined to measure the consensus levels. A knowledge deficit-based interactive consensus model is proposed to help decision-makers reach group consensus. The proposed group consensus model enables the inconsistent decision-makers to accept the revised opinions of those with less knowledge deficit, rather than accepting the recommended opinions averagely.
Originality/value
The proposed a knowledge deficit-based MCGDM cloud service selection method considers group consensus in cloud service selection. The concept of knowledge deficit is considered in modeling the group consensus measuring and reaching method.
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Jingjing Xiong, Zhen He, Yujia Deng, Min Zhang and Zehong Zhang
To face profound changes from decreasing funding, growing patient expectations and increasing competition in the health-care market, public hospitals began to implement effective…
Abstract
Purpose
To face profound changes from decreasing funding, growing patient expectations and increasing competition in the health-care market, public hospitals began to implement effective quality management (QM) practices following manufacturing and other service industries. However, there was little knowledge relevant to the impact of QM practices on the performance of public hospitals. The study aims to shed some further light on this issue.
Design/methodology/approach
The paper extends the previous empirical research to the health-care sectors and suggests the research framework of QM practice-performance relationships in public hospitals. For validation purposes, a cross-sectional survey involving 204 quality managers and directors of large public hospitals was carried out between April and October 2013 in Zhejiang Province, China. Structural equation modeling was used to test the hypothesized relationship between QM practices and hospital performance.
Findings
Empirical results support that the implementation of QM practices can bring many benefits to sample hospitals. The dimensions of employee relations and process management are directly related to the health-care and non-health-care performance of public hospitals.
Originality/value
It explores the relationship between QM practices and hospital performance based on empirical results from Chinese public hospitals, whereas few studies have been conducted within the context of public health-care sectors in developing countries. The empirical results could enhance hospital managers’ understanding of the nature of QM practice-performance relationship and help mangers re-allocate more resources to those elements of the QM systems that have the most significant impact on hospital performance.
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Atanu Roy, Sabyasachi Pramanik, Kalyan Mitra and Manashi Chakraborty
Emissions have significant environmental impacts. Hence, minimizing emissions is essential. This study aims to use a hybrid neural network model to predict carbon monoxide (CO…
Abstract
Purpose
Emissions have significant environmental impacts. Hence, minimizing emissions is essential. This study aims to use a hybrid neural network model to predict carbon monoxide (CO) and nitrogen oxide (NOx) emissions from gas turbines (GTs) to enhance emission prediction for GTs in predictive emissions monitoring systems (PEMS).
Design/methodology/approach
The hybrid model architecture combines convolutional neural networks (CNN) and bidirectional long-short-term memory (Bi-LSTM) networks called CNN-BiLSTM with modified extrinsic attention regression. Over five years, data from a GT power plant was uploaded to Google Colab, split into training and testing sets (80:20), and evaluated using test matrices. The model’s performance was benchmarked against state-of-the-art emissions prediction methodologies.
Findings
The model showed promising results for GT CO and NOx emissions. CO predictions had a slight underestimation bias of −0.01, with root mean-squared error (RMSE) of 0.064, mean absolute error (MAE) of 0.04 and R2 of 0.82. NOx predictions had an RMSE of 0.051, MAE of 0.036, R2 of 0.887 and a slight overestimation bias of +0.01.
Research limitations/implications
While the model demonstrates relative accuracy in CO emission predictions, there is potential for further improvement in future research.
Practical implications
Implementing the model in real-time PEMS and establishing a continuous feedback loop will ensure accuracy in real-world applications, enhance GT functioning and reduce emissions, fuel consumption and running costs.
Social implications
Accurate GT emissions predictions support stricter emission standards, promote sustainable development goals and ensure a healthier societal environment.
Originality/value
This paper presents a novel approach that integrates CNN and Bi-LSTM networks. It considers both spatial and temporal data to mitigate previous prediction shortcomings.