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Article
Publication date: 16 December 2024

Xinchuang Xu, Wenao Wang, Yuan Zeng, Yujie Dong and Hanzhou Hao

The paper aims to explore the correlation between the agglomeration of regional innovation elements and the attraction of talent.

16

Abstract

Purpose

The paper aims to explore the correlation between the agglomeration of regional innovation elements and the attraction of talent.

Design/methodology/approach

This paper uses the factor analysis method to measure the innovation elements index (IEI). The proportion of the regional resident population and registered population is used to measure the attractiveness of talents. The PVAR model is used to analyze the interaction between innovation element agglomeration and talent attraction.

Findings

(1) According to the annual increase rate of IEI, the order is eastern region > central region > western region. (2) Panel vector autoregressive (PVAR) research shows that the agglomeration of innovation factors has a short-term thrust on the attraction of regional talents. (3) The agglomeration of innovative elements is the Granger cause of talent attraction; talent attraction is not the Granger reason for the agglomeration of innovative elements. (4) Pulse analysis and variance decomposition show that the agglomeration of innovative elements has a one-way positive effect on talent attraction.

Research limitations/implications

This study takes China’s provincial panel data as a sample without considering the differences between cities. There may be significant differences in innovation factor agglomeration and talent attraction in different cities.

Practical implications

The findings of this study provide valuable insights into innovation ecosystem practices. Policymakers should pay close attention to promoting the agglomeration of innovation factors by optimizing the innovation ecosystem in order to increase the attractiveness of talents.

Originality/value

(1) This study uses the proportion of regional resident population and household registration population to measure the attractiveness of talents, which is more realistic. (2) This paper is one of the few that examines the relationship between innovation factor agglomeration and talent attraction.

Details

Management Decision, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0025-1747

Keywords

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Article
Publication date: 16 August 2022

Wanjiao Jia, Shuoshuo Bi and Yingjie Du

This study analyses Chinese data to revisit the relationship between directors’ and officers’ (D&O) insurance and accounting conservatism, aiming to investigate the impact of…

312

Abstract

Purpose

This study analyses Chinese data to revisit the relationship between directors’ and officers’ (D&O) insurance and accounting conservatism, aiming to investigate the impact of investors’ legal protection on the function of D&O insurance.

Design/methodology/approach

The study sample included all A-share firms listed on the Shanghai and Shenzhen Stock Exchanges from 2006 to 2019. Multiple regression was used to investigate the association between D&O insurance and accounting conservatism. The Heckman two-stage model and the propensity score matching method were used to check the robustness of the main results.

Findings

D&O insured companies exhibited greater accounting conservatism. The higher the indemnity limit, the more conservative a firm’s earnings reporting. The positive correlation was stronger when investor protection was relatively weak. The impact of D&O insurance on accounting conservatism was stronger for companies with weaker internal or external supervision mechanisms.

Originality/value

The study findings show that D&O insurance plays a positive role in the governance of listed companies when investors’ legal protection is weak, which supports the effective supervision hypothesis of D&O insurance.

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Article
Publication date: 25 October 2022

Yu Yuan, Jia Liao and Liping Zheng

This study empirically investigates the impact of directors' and officers' liability insurance on corporate environmental investment.

207

Abstract

Purpose

This study empirically investigates the impact of directors' and officers' liability insurance on corporate environmental investment.

Design/methodology/approach

This paper takes A-share listed firms in the most polluting industries from 2013 to 2019 as the research sample. The authors perform multiple regression analysis to examine the research question, and other approaches such as PSM and Heckman two-stage model are applied to test the robustness of the main results.

Findings

The authors find that D&O insurance insured firms significantly decrease the level of corporate environmental investment. The results keep consistent after alleviating potential endogenous concerns. Further analysis shows that the negative association between D&O insurance and environmental investment is more pronounced in firms facing greater environmental pressure and stronger market supervision, and firms located in regions with a rich legal environment.

Research limitations/implications

This research extends the literature on the antecedents of corporate environmental investment and the consequences of D&O insurance.

Practical implications

The study may deepen people's understanding of D&O insurance and inform them of its negative effects. This research sheds light on the potential factor resulting in a relatively low level of corporate environmental investment in China, which has an important policy implication for government to carry out some regulations to make a difference.

Originality/value

Against the backdrop that more importance has been attached to environmental protection globally, this paper is the first study to examine the impact of D&O insurance on corporate environmental investment in the context of the transitional and emerging market-China.

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Article
Publication date: 1 February 2013

Fei Peng, Lili Kang and Jun Jiang

This paper aims to investigate the role that institutional shareholders play in acquisition decisions using micro data in the Chinese stock market during 2003‐2008.

989

Abstract

Purpose

This paper aims to investigate the role that institutional shareholders play in acquisition decisions using micro data in the Chinese stock market during 2003‐2008.

Design/methodology/approach

Acquisition decision is the selection and coordination process of shareholders as strategic alliances, which is determined by corporate acquisition ability, composition of institutional shareholders and concentration of tradable share (TS) in China. The paper uses the Heckman selection model to surmount the selection biases in acquisition decision.

Findings

The paper finds that institutional shareholders, including qualified foreign institutional investors (QFII), social security funds (SSF), security firms (SF) and security investment funds (SIF), as well as TS concentration, affect acquisition probability rather than annual acquisition scale. SSF, SIF and TS concentration can increase acquisition probability while QFII decreases it.

Research limitations/implications

This paper suggests a strategic alliance model in which institutional shareholders choose whether to collaborate with controlling shareholders and management. However, detailed information of the selection and coordination process is unavailable in the authors' data. Future research need provide more evidence of this postulate.

Originality/value

The paper contributes to the published literature in three ways. First, it offers a model to understand the selection and coordination process of acquisition decision. Second, it investigates whether institutional shareholders could effectively monitor annual acquisition scale. Third, it identifies the Heckman selection problem that institutional shareholders could affect PLCs' acquisition decision on whether to acquire rather than how much to acquire.

Details

Management Decision, vol. 51 no. 1
Type: Research Article
ISSN: 0025-1747

Keywords

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Book part
Publication date: 20 January 2021

Yao Lixia

Abstract

Details

Energy Security in Times of Economic Transition: Lessons from China
Type: Book
ISBN: 978-1-83982-465-4

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Article
Publication date: 26 September 2022

Xiongying Wang and Xiang Chen

This paper mainly explores the relationship between digital inclusive finance and financing constraints of technological-based SMEs, and how digital inclusive finance affects the…

803

Abstract

Purpose

This paper mainly explores the relationship between digital inclusive finance and financing constraints of technological-based SMEs, and how digital inclusive finance affects the financing constraints of technology-based SMEs. This paper empirically analyzes the relationship between them through the OLS model, and then further verifies the relationship between them through robust regression and heterogeneity analysis. At the same time, it uses the mechanism test to explore how digital inclusive finance affects the financing constraints of technology-based SMEs. This paper aims to address these issues.

Design/methodology/approach

This paper aims to explain the relationship between digital inclusive finance and financing constraints of technological-based SMEs. Technology-based SMEs always face the difficult problem of “financing difficulty” and “financing expensive” in the development process, which hinders the survival and development of enterprises to some extent. Digital inclusive finance development policy vigorously promoted by the state has alleviated the financing constraints of technology-based SMEs and brought opportunities for their development.

Findings

The results show that the role of digital inclusive finance in alleviating the financing constraints of technology-based SMEs, and incremental supplement and alleviating information asymmetry are the main reasons for digital inclusive finance to alleviate the financing constraints of technology-based SMEs. In view of the availability of digital inclusive financial data, this paper only uses the data from 2014 to 2019.

Originality/value

The authors’ research clearly found that the development of digital inclusive finance alleviates the financing of technology-based SMEs from the two aspects of “incremental supplement” and alleviating information asymmetry, so as to provide corresponding reference basis for the government to formulate a series of plans to support the development of technology-based SMEs.

Details

Kybernetes, vol. 52 no. 2
Type: Research Article
ISSN: 0368-492X

Keywords

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Article
Publication date: 13 February 2017

Jingfu Lu and Min Li

The purpose of this paper is to understand the boundary-spanning behaviors of Party organizations, and the processes and constraints of these behaviors in controlling worker…

402

Abstract

Purpose

The purpose of this paper is to understand the boundary-spanning behaviors of Party organizations, and the processes and constraints of these behaviors in controlling worker unrest in Chinese resource-based state-owned enterprises in the “new work-unit system” using boundary-spanning theory.

Design/methodology/approach

This case study was carried out in a resource-based state-owned enterprise in the “new work-unit system” in China. The research utilized interviews and archival documents, and then coded and analyzed the data using NVivo.

Findings

In China, Party organizations’ boundary-spanning behaviors (PBSBs) in labor relations management are identified, and classified into the behaviors of the ambassador, task coordinator, and scout. Worker unrest can be controlled by these behaviors through the mediation effect of the behaviors of agents in the “new work-unit system” but can also be provoked in the transformation of the “new work-unit system.”

Originality/value

The Communist Party plays a key role in labor relations management in China’s SOEs; however, this role has not been explored in any depth. This study builds a model to reveal the “black box” in which the PBSBs influence the agents’ behaviors and how the agents’ behaviors then influence the workers, and in this way control worker unrest.

Details

Employee Relations, vol. 39 no. 2
Type: Research Article
ISSN: 0142-5455

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Article
Publication date: 28 May 2024

Guang-Zhi Zeng, Zheng-Wei Chen, Yi-Qing Ni and En-Ze Rui

Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of…

225

Abstract

Purpose

Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of fields in solving the Navier–Stokes equation and its variants. In view of the strengths of PINN, this study aims to investigate the impact of spatially embedded data distribution on the flow field results around the train in the crosswind environment reconstructed by PINN.

Design/methodology/approach

PINN can integrate data residuals with physical residuals into the loss function to train its parameters, allowing it to approximate the solution of the governing equations. In addition, with the aid of labelled training data, PINN can also incorporate the real site information of the flow field in model training. In light of this, the PINN model is adopted to reconstruct a two-dimensional time-averaged flow field around a train under crosswinds in the spatial domain with the aid of sparse flow field data, and the prediction results are compared with the reference results obtained from numerical modelling.

Findings

The prediction results from PINN results demonstrated a low discrepancy with those obtained from numerical simulations. The results of this study indicate that a threshold of the spatial embedded data density exists, in both the near wall and far wall areas on the train’s leeward side, as well as the near train surface area. In other words, a negative effect on the PINN reconstruction accuracy will emerge if the spatial embedded data density exceeds or slips below the threshold. Also, the optimum arrangement of the spatial embedded data in reconstructing the flow field of the train in crosswinds is obtained in this work.

Originality/value

In this work, a strategy of reconstructing the time-averaged flow field of the train under crosswind conditions is proposed based on the physics-informed data-driven method, which enhances the scope of neural network applications. In addition, for the flow field reconstruction, the effect of spatial embedded data arrangement in PINN is compared to improve its accuracy.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 34 no. 8
Type: Research Article
ISSN: 0961-5539

Keywords

Available. Open Access. Open Access
Article
Publication date: 22 November 2023

En-Ze Rui, Guang-Zhi Zeng, Yi-Qing Ni, Zheng-Wei Chen and Shuo Hao

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural…

1741

Abstract

Purpose

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

Design/methodology/approach

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Findings

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

Originality/value

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 34 no. 1
Type: Research Article
ISSN: 0961-5539

Keywords

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Article
Publication date: 11 June 2024

Cheng Yan, Enzi Kang, Haonan Liu, Han Li, Nianyin Zeng and Yancheng You

This paper delves into the aerodynamic optimization of a single-stage axial turbine employed in aero-engines.

92

Abstract

Purpose

This paper delves into the aerodynamic optimization of a single-stage axial turbine employed in aero-engines.

Design/methodology/approach

An efficient integrated design optimization approach tailored for turbine blade profiles is proposed. The approach combines a novel hierarchical dynamic switching PSO (HDSPSO) algorithm with a parametric modeling technique of turbine blades and high-fidelity Computational Fluid Dynamics (CFD) simulation analysis. The proposed HDSPSO algorithm introduces significant enhancements to the original PSO in three pivotal aspects: adaptive acceleration coefficients, distance-based dynamic neighborhood, and a switchable learning mechanism. The core idea behind these improvements is to incorporate the evolutionary state, strengthen interactions within the swarm, enrich update strategies for particles, and effectively prevent premature convergence while enhancing global search capability.

Findings

Mathematical experiments are conducted to compare the performance of HDSPSO with three other representative PSO variants. The results demonstrate that HDSPSO is a competitive intelligent algorithm with significant global search capabilities and rapid convergence speed. Subsequently, the HDSPSO-based integrated design optimization approach is applied to optimize the turbine blade profiles. The optimized turbine blades have a more uniform thickness distribution, an enhanced loading distribution, and a better flow condition. Importantly, these optimizations lead to a remarkable improvement in aerodynamic performance under both design and non-design working conditions.

Originality/value

These findings highlight the effectiveness and advancement of the HDSPSO-based integrated design optimization approach for turbine blade profiles in enhancing the overall aerodynamic performance. Furthermore, it confirms the great prospects of the innovative HDSPSO algorithm in tackling challenging tasks in practical engineering applications.

Details

Multidiscipline Modeling in Materials and Structures, vol. 20 no. 4
Type: Research Article
ISSN: 1573-6105

Keywords

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