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1 – 5 of 5Jinghan Zhang, Hang Zhou and Xinrui Zhang
This study investigates the role of interlocking director networks (IDN) in driving corporate digital transformation (CDT) and explores the moderating role of agency costs…
Abstract
Purpose
This study investigates the role of interlocking director networks (IDN) in driving corporate digital transformation (CDT) and explores the moderating role of agency costs, diversification and financial distress.
Design/methodology/approach
The analysis uses data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2006 to 2021. A two-way fixed-effects model is employed to assess the impact of IDNs on CDT.
Findings
The results indicate that IDNs positively affect CDT. Furthermore, this effect is enhanced by agency costs and financial distress, while diversification acts as a negative moderator.
Originality/value
Informal institutions such as IDNs play a significant role in corporate governance in China’s relational society. This study focuses on the influence of informal institutions on digital transformation, expanding the understanding of the economic consequences of IDNs and enriching the literature on factors influencing digital transformation.
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Jinghan Du, Haiyan Chen and Weining Zhang
In large-scale monitoring systems, sensors in different locations are deployed to collect massive useful time-series data, which can help in real-time data analytics and its…
Abstract
Purpose
In large-scale monitoring systems, sensors in different locations are deployed to collect massive useful time-series data, which can help in real-time data analytics and its related applications. However, affected by hardware device itself, sensor nodes often fail to work, resulting in a common phenomenon that the collected data are incomplete. The purpose of this study is to predict and recover the missing data in sensor networks.
Design/methodology/approach
Considering the spatio-temporal correlation of large-scale sensor data, this paper proposes a data recover model in sensor networks based on a deep learning method, i.e. deep belief network (DBN). Specifically, when one sensor fails, the historical time-series data of its own and the real-time data from surrounding sensor nodes, which have high similarity with a failure observed using the proposed similarity filter, are collected first. Then, the high-level feature representation of these spatio-temporal correlation data is extracted by DBN. Moreover, to determine the structure of a DBN model, a reconstruction error-based algorithm is proposed. Finally, the missing data are predicted based on these features by a single-layer neural network.
Findings
This paper collects a noise data set from an airport monitoring system for experiments. Various comparative experiments show that the proposed algorithms are effective. The proposed data recovery model is compared with several other classical models, and the experimental results prove that the deep learning-based model can not only get a better prediction accuracy but also get a better performance in training time and model robustness.
Originality/value
A deep learning method is investigated in data recovery task, and it proved to be effective compared with other previous methods. This might provide a practical experience in the application of a deep learning method.
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Jinghan Xu, Shengguo Xia, Lixue Chen, Anbang Gu, Hongdan Yang and Chengxian Li
The purpose of this paper is to investigate the moving boundary conditions on the sliding armature and rail (A/R) interface. As the computational domains involve both moving and…
Abstract
Purpose
The purpose of this paper is to investigate the moving boundary conditions on the sliding armature and rail (A/R) interface. As the computational domains involve both moving and stationary conductors, Lagrangian description and backward difference schemes are adopted for spatial and temporal discretization, arising discontinuities in variables. The proposed formulation can compute the current distribution under high velocities (∼km/s) without numerical oscillations and avoids mesh re-generation, saving computational resources.
Design/methodology/approach
The governing equations in Lagrangian description, backward difference schemes and derivations of moving boundary conditions are shown in detail. The interface matrix is explicitly enforced on the whole domain matrix and pseudocodes are presented for implementation. Moreover, shifted interpolated quantity method is proposed to deal with unevenly sized mesh, which can calculate acceleration scenarios and save computation resources under high velocities. Comparative calculations with previous methods under low velocities are conducted to verify the correctness of computational and physical models.
Findings
The current distributions with constant velocities are consistent with previous two-dimensional and low-velocity studies, further verifying the correctness of the method. The three-dimensional high-velocity results show that the current tends to concentrate near the trailing edge of A/R interface and diffuses into the bulks over time, with higher velocity contributing to less significant current diffusion. The velocity skin effect precedes the magnetic diffusion, conductivity and other factors that influence the current distribution.
Originality/value
The proposed methods can compute the current distributions in railgun under velocity accelerated to over 2,000 m/s, and the results provide more comprehensive understandings of the current evolution process under velocity skin effect in railgun.
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This paper demonstrates that the agency problems within China's stated-owned enterprises (SOE) constitute the characteristics of corporate governance. It argues that the current…
Abstract
This paper demonstrates that the agency problems within China's stated-owned enterprises (SOE) constitute the characteristics of corporate governance. It argues that the current corporatisation of SOEs in China has not improved the performance of the corporatised SOEs because it has failed to address the critical issue of corporate governance. For China, a neo-corporatist approach of corporate governance with a two-tier board structure may have advantages over a neo-liberal approach with a single board. However, the key issue is not to adopt a fixed set of governance models to copy, but to develop its institutional environment that lead to effective corporate governance.
Xin Kang, Danni Zhao and Qiang Liu
The purpose of this paper is to analyse how different strengths of simmelian ties affect knowledge spirals and investigate which major factors affect the influence of simmelian…
Abstract
Purpose
The purpose of this paper is to analyse how different strengths of simmelian ties affect knowledge spirals and investigate which major factors affect the influence of simmelian ties on knowledge spirals.
Design/methodology/approach
The empirical data in this paper were collected through e-mail and interview questionnaires to R&D teams in high-tech manufacturing enterprises in China. The authors obtained 132 teams' valid responses. The interval decision-making trial and evaluation laboratory (interval DEMATEL) method, differential evolution (DE) algorithm and Bayesian structural equation modelling (BSEM) were employed to test the theoretical framework developed for this paper.
Findings
The results show that strong simmelian ties have positive associations with high-performance work practices (HPWPs). Meanwhile, weak simmelian ties have positive associations with HPWPs. Furthermore, HPWPs and knowledge fermentation play a conducive role in the relationship between simmelian ties and knowledge spirals.
Originality/value
This paper contributes in three ways. First, it extends research on the relational antecedents of knowledge spirals. Second, this paper extends the study of social capital related to knowledge spirals. Third, this paper elucidates less familiar factors relating HPWPs to knowledge fermentation by testing the mediating role of HPWPs in knowledge fermentation.
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