Yu Chen, Xiaoning Zhu, Xueli Xiong, Cen Zhang and Jiashun Huang
Corporations, as key contributors of greenhouse gas emissions, have been increasingly scrutinized by governments and stakeholders. Corporations have been asked to disclose their…
Abstract
Purpose
Corporations, as key contributors of greenhouse gas emissions, have been increasingly scrutinized by governments and stakeholders. Corporations have been asked to disclose their carbon-related information. This study investigates public corporate carbon disclosure, an imperative communication channel between firms.
Design/methodology/approach
This study uses generalized estimation equation models with a longitudinal panel data of 311 listed firms in the China A-share stock index from 2010 to 2020. This study collected firm-level data from the Carbon Disclosure Project survey, the China Stock Market and Accounting Research, and the National Economic Research Institute of China. Stata was used as the primary statistic software in empirical analyses.
Findings
This study finds that compared to state-owned enterprises (SOEs), private firms are more willing to disclose carbon information under legitimate environmental pressure, and firms in highly distorted factor-markets are reluctant to disclose carbon information. This study finds that factor-distortion markets further moderate ownership and lead private firms in highly distorted factor-markets to behave like SOEs by significantly reducing their carbon disclosures.
Originality/value
This study intends to contribute to the corporate carbon disclosure literature by adding important institutional determinants to the conversation in the context of China.
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Xiaoning Li, Xinbo Liao, Qingwen Zhong, Kai Zheng, Shaoxing Chen, Xiao-Jun Chen, Jin-Xiu Zhu and Hongyuan Yang
The purpose of this paper is to analyze the influencing factors of patients’ financial burden through a case study of hospital on public‒private partnerships (PPP) model (Chaonan…
Abstract
Purpose
The purpose of this paper is to analyze the influencing factors of patients’ financial burden through a case study of hospital on public‒private partnerships (PPP) model (Chaonan Minsheng Hospital of Guangdong Province) and provide some useful information to policymakers for better development of hospitals on PPP model.
Design/methodology/approach
There are total six indicators that are defined as patients’ financial burden, basing on the policy of “indicators of medical quality management and control on the third level large general hospital (2011 edition),” issued by Chinese Government. In total, 23 potentially influencing factors of patients’ financial burden for hospital on PPP model were chosen from the above policy. The five-year (2007‒2011) data for the above 29 indicators come from statistic department of hospital on PPP model. Grey relational analysis (GRA) was applied to analyze the influencing factors of patients’ financial burden for hospital on PPP model.
Findings
A clear rank of influencing factors of patients’ financial burden is obtained and suggestions are provided from results of GRA, which provide reference for policymakers of hospital on PPP model. The five main influencing factors of patients’ financial burden for hospital on PPP model, in sequence, are rescuing critical ill patients on emergency, rescuing critical ill inpatients, inpatient bed occupancy rate, working days per bed and medical building area.
Originality/value
The study on the influencing factors of patients’ financial burden for hospital on PPP model not only provides decision-making for policymaker of hospital and controlling of medical expenditure but also contributes to release patients’ financial burden for hospitals on PPP model.
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Hao Zhang, Qingyue Lin, Chenyue Qi and Xiaoning Liang
This study aims to explore how online reviews and users’ social network centrality interact to influence idea popularity in open innovation communities (OICs).
Abstract
Purpose
This study aims to explore how online reviews and users’ social network centrality interact to influence idea popularity in open innovation communities (OICs).
Design/methodology/approach
This study used Python to obtain data from the LEGO Innovation Community. In total, 285,849 reviews across 4,475 user designs between March 2019 and March 2021 were extracted to test this study’s hypotheses.
Findings
The ordinary least square regression analysis results show that review volume, review valence, review variance and review length all positively influence idea popularity. In addition, users’ in-degree centrality positively interacts with review valence, review variance and review length to influence idea popularity, while their out-degree centrality negatively interacts with such effects.
Research limitations/implications
Drawing on the interactive marketing perspective, this study employs a large sample from the LEGO community and examines user design and idea popularity from a community member’s point of view. Moreover, this study is the first to confirm the role of online reviews and user network centrality in influencing idea popularity in OICs from a social network perspective. Furthermore, by integrating social network analysis and persuasion theories, this study confirms the interaction effects of review characteristics and users’ social network centrality on idea popularity.
Practical implications
This study’s results highlight that users should actively interact and share with reviewers their professional product design knowledge and/or the journey of their design to improve the volume of reviews on their user designs. Moreover, users could also draw more attention from other users by actively responding to heterogeneous reviews. In addition, users should be cautious with the number of people they follow and ensure that they improve their in-degree rather than out-degree centrality in their social networks.
Originality/value
This study integrates social network analysis and persuasion theories to explore the effects of online reviews and users’ centrality on idea popularity in OICs, a vital research issue that has been overlooked.
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Zhihui Men, Chaoqun Hu, Yong-Hua Li and Xiaoning Bai
This paper proposes an intelligent fault diagnosis method, which aims to obtain the outstanding fault diagnosis results of the gearbox.
Abstract
Purpose
This paper proposes an intelligent fault diagnosis method, which aims to obtain the outstanding fault diagnosis results of the gearbox.
Design/methodology/approach
An intelligent fault diagnosis method based on energy entropy-weighted complementary ensemble empirical mode decomposition (EWCEEMD) and support vector machine (SVM) optimized by whale optimization algorithm (WOA) is proposed. The raw signal is first denoised by the wavelet noise reduction method. Then, complementary ensemble empirical mode decomposition (CEEMD) is used to generate several intrinsic mode functions (IMFs). Next, energy entropy is used as an indicator to measure the sensibility of the IMF and converted into a weight coefficient by function. After that, IMFs are linearly weighted to form the reconstruction signal, and several features are extracted from the new signal. Finally, the support vector machine optimized by the whale optimization algorithm (WOA-SVM) model is used for gearbox fault classification using feature vectors.
Findings
The fault features extracted by this method have a better clustering effect and clear boundaries under each fault mode than the unimproved method. At the same time, the accuracy of fault diagnosis is greatly improved.
Originality/value
In most studies of fault diagnosis, the sensitivity of IMF has not been appreciated. In this paper, energy entropy is chosen to quantify sensitivity. In addition, high classification accuracy can be achieved by applying WOA-SVM as the final classification model, improving the efficiency of fault diagnosis as well.
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Xiaoning (Alice) Qian and Eleni Papadonikolaki
Blockchain technology is booming in many industries. Its application in supply chain management is also gradually increasing. Supply chain management (SCM) has long been committed…
Abstract
Purpose
Blockchain technology is booming in many industries. Its application in supply chain management is also gradually increasing. Supply chain management (SCM) has long been committed to reducing costs and increasing efficiency and is trying to optimise resources and reduce the sector's fragmentation. Trust has always been an important factor in managing supply chain relationships, and it also affects the efficiency of supply chain operations. To this end, this study aims to examine how trust is affected by the introduction of blockchain technology in construction supply chain management.
Design/methodology/approach
This study is based on semi-structured interviews and publicly available information from experts in blockchain and construction supply chain management. Through content analysis, the data are analysed thematically to explore how various types of trust, such as system-based, cognition-based and relation-based, are affected by blockchain technology.
Findings
Blockchain technology provides solutions for data tracking, contracting and transferring resources in supply chain management. These applications help enhance the various sources of trust in SCM and provide supply chain partners with protection mechanisms to avoid the risks and costs of opportunistic behaviour in collaboration, shifting trust from relational to system-based and cognition-based.
Research limitations/implications
This study focuses only on inter-organisational rather than interpersonal trust and empirical data from experts whose knowledge and cognition could be subjective.
Practical implications
Leveraging the potential of digitalisation to manage trust requires that leaders and managers actively try to improve contractual arrangements, information sharing and being open to new innovative technologies like blockchain.
Social implications
From a relational view of supply chain management, the extent to which blockchain technology can develop and spread depends on the readiness of the social capital to accept decentralised governance structures.
Originality/value
This study builds upon an original data set and discusses features and applications of blockchain technology, explores the sources and dimensions of trust in supply chain management and explains the impact of blockchain technology on trust.
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Yong-Hua Li, Chi Zhang, Hao Yin, Yang Cao and Xiaoning Bai
This paper proposes an improved fatigue life analysis method for optimal design of electric multiple units (EMU) gear, which aims at defects of traditional Miner fatigue…
Abstract
Purpose
This paper proposes an improved fatigue life analysis method for optimal design of electric multiple units (EMU) gear, which aims at defects of traditional Miner fatigue cumulative damage theory.
Design/methodology/approach
A fatigue life analysis method by modifying S–N curve and considering material difference is presented, which improves the fatigue life of EMU gear based on shape modification optimization. A corrected method for stress amplitude, average stress and S–N curve is proposed, which considers low stress cycle, material difference and other factors. The fatigue life prediction of EMU gear is carried out by corrected S–N curve and transient dynamic analysis. Moreover, the gear modification technology combined with intelligent optimization method is adopted to investigate the approach of fatigue life analysis and improvement.
Findings
The results show that it is more corresponded to engineering practice by using the improved fatigue life analysis method than the traditional method. The function of stress and modification amount established by response surface method meets the requirement of precision. The fatigue life of EMU gear based on the intelligent algorithm for seeking the optimal modification amount is significantly improved compared with that before the modification.
Originality/value
The traditional fatigue life analysis method does not consider the influence of working condition and material. The life prediction results by using the method proposed in this paper are more accurate and ensure the safety of the people in the EMU. At the same time, the combination of intelligent algorithm and gear modification can improve the fatigue life of gear on the basis of accurate prediction, which is of great significance to the portability of EMU maintenance.
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Kan Jiang, Dailan Zhou, Xiaoning Bao and Silan Mo
Considering that when endorsing the same product, virtual influencers with different identity types (self-created, collaborative) can have different impacts on consumers'…
Abstract
Purpose
Considering that when endorsing the same product, virtual influencers with different identity types (self-created, collaborative) can have different impacts on consumers' purchasing behaviors, this paper aims to explore how to maximize the impact effects of the VIs' respective identities. It provides companies with new perspectives on endorsement strategies.
Design/methodology/approach
The interaction between VI identity type and post type (informational, storytelling) on purchase intention was analyzed in four experiments (N = 1,007), considering informational and normative social influence as intermediate mechanisms and consumer self-construal as moderators.
Findings
The findings show that self-created VI is suited to informational posts and collaborative VI to storytelling posts. This identity-content match effectively triggers the social influence mechanism: informational posts of self-created VI significantly enhance informational social influence. In contrast, storytelling posts of collaborative VI primarily stimulate normative social influence. Consumer self-construal also moderates the process of influencing mechanisms.
Originality/value
Based on social influence theory and matching theory, this paper confirms the existence of an interaction between VI identity types, which influences consumers' purchase intention through informational and normative social influence. This finding fills the research gap in the field of VI endorsement strategy. It also emphasizes the importance of consumer self-construal and contributes new insights into the related field.
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Xiaodong Zhang, Ping Li, Xiaoning Ma and Yanjun Liu
The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was…
Abstract
Purpose
The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm.
Design/methodology/approach
Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized.
Findings
This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm.
Practical implications
Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast.
Originality/value
As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization.
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Guangjian Xu and Yan Wu
The purpose of this paper is to examine the financing and provision of basic public services in China. The main issue addressed is how to reform the public finance system to…
Abstract
Purpose
The purpose of this paper is to examine the financing and provision of basic public services in China. The main issue addressed is how to reform the public finance system to achieve quality and fairness in the provision of basic public services.
Design/methodology/approach
Based on an historical analysis of the functional transformation of the public finance system in China and on an empirical analysis of the current public finance system and the public service provision system, a comprehensive understanding was gained about the relationship between the financing and provision of basic public services.
Findings
The paper argues that there is a close relationship between the provision of basic public services and the functional changes made to the public finance system. Based on a systematic retrospective study of the Chinese Government’s efforts to improve basic public services over the last three decades, this paper offers policy suggestions on further public finance restructuring that would support better service provision.
Originality/value
By analyzing issues in the public service provision system, this paper contributes to the debate about the efficiency improvement made to governmental functions in China.
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Heng-yang Lu, Jun Yang, Wei Fang, Xiaoning Song and Chongjun Wang
The COVID-19 has become a global pandemic, which has caused large number of deaths and huge economic losses. These losses are not only caused by the virus but also by the related…
Abstract
Purpose
The COVID-19 has become a global pandemic, which has caused large number of deaths and huge economic losses. These losses are not only caused by the virus but also by the related rumors. Nowadays, online social media are quite popular, where billions of people express their opinions and propagate information. Rumors about COVID-19 posted on online social media usually spread rapidly; it is hard to analyze and detect rumors only by artificial processing. The purpose of this paper is to propose a novel model called the Topic-Comment-based Rumor Detection model (TopCom) to detect rumors as soon as possible.
Design/methodology/approach
The authors conducted COVID-19 rumor detection from Sina Weibo, one of the most widely used Chinese online social media. The authors constructed a dataset about COVID-19 from January 1 to June 30, 2020 with a web crawler, including both rumor and non-rumors. The rumor detection task is regarded as a binary classification problem. The proposed TopCom model exploits the topical memory networks to fuse latent topic information with original microblogs, which solves the sparsity problems brought by short-text microblogs. In addition, TopCom fuses comments with corresponding microblogs to further improve the performance.
Findings
Experimental results on a publicly available dataset and the proposed COVID dataset have shown superiority and efficiency compared with baselines. The authors further randomly selected microblogs posted from July 1–31, 2020 for the case study, which also shows the effectiveness and application prospects for detecting rumors about COVID-19 automatically.
Originality/value
The originality of TopCom lies in the fusion of latent topic information of original microblogs and corresponding comments with DNNs-based models for the COVID-19 rumor detection task, whose value is to help detect rumors automatically in a short time.