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1 – 3 of 3Yang Lou, Brian Wright, Yicheng Wang and Chenhui Zhao
This study aims to test the effect of conforming tax avoidance on earnings persistence.
Abstract
Purpose
This study aims to test the effect of conforming tax avoidance on earnings persistence.
Design/methodology/approach
The authors use multiple linear regression incorporating firm and year-fixed effects to examine the association between conforming tax avoidance and earnings persistence. Additionally, the authors explore the moderating effect of conforming tax avoidance on the association between current earnings and future (one-year) operating cash flow (OCF). To guarantee the strength and reliability of the results, the authors perform several additional tests.
Findings
The empirical findings reveal a statistically significantly negative association between conforming tax avoidance and earnings persistence. Additionally, the authors observe that conforming tax avoidance significantly and negatively moderates the relationship between current earnings and future OCF. Furthermore, the principal regression outcomes exhibit resilience in a range of robustness checks.
Originality/value
This research contributes to the accounting literature by offering a novel perspective on understanding one of the potential reasons behind firms exhibiting low earnings quality or persistence. Specifically, the authors attribute this phenomenon to the downward impact of conforming tax management. Furthermore, the results propose that the tax authority should be vigilant toward companies exhibiting low earnings persistence, particularly quasi-private firms and those with low financial constraints, as they may have implemented or be motivated to engage in conforming tax strategies that preceded the observed reduction in earnings persistence.
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Xingwei Li, Jingru Li, Jinrong He, Yicheng Huang, Xiang Liu, Jiachi Dai and Qiong Shen
The surging market demand for green construction materials has brought opportunities for construction materials enterprises' greenwashing behavior (GWB). This study aims to…
Abstract
Purpose
The surging market demand for green construction materials has brought opportunities for construction materials enterprises' greenwashing behavior (GWB). This study aims to establish the causal relationship among the influencing factors of GWB and reveal the key influencing factors from the perspective of Chinese construction materials enterprises under multi-agent interactions.
Design/methodology/approach
This study is based on stakeholder theory, resource-based theory and the green development behavior and performance of industrial enterprises (GDBP-IE). First, with the literature analysis, an index framework of the influencing factors of enterprises' GWB was constructed from five dimensions (including 15 factors): environmental regulation, public scrutiny, market environment, corporate resources and corporate green development (GD) performance. Second, the interactive relationship among influencing factors was obtained by a questionnaire survey. Finally, the data are processed and analyzed with the grey-DEMATEL (Decision-making Trial and Evaluation Laboratory) method.
Findings
Among the factors, corporate information transparency has the greatest impact on the other factors, and consumer green preferences are most influenced by others. The most critical and important factor is the corporate social performance factor. In China, corporate social performance, corporate information transparency, corporate size and media supervision are the key factors influencing the GWB of construction materials enterprises.
Originality/value
This study provides a new perspective on the literature related to GWB by considering multi-agent interactions and extends the evidence from the construction materials industry for research on the drivers or influencing factors of enterprises' bad environmental behavior. Furthermore, it adds insights from China for further research on the governance strategies of GWB in other countries.
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Zhen Li, Zhao Lei, Hengyang Sun, Bin Li and Zhizhong Qiao
The purpose of this study was to validate the feasibility of the proposed microstructure-based model by comparing the simulation results with experimental data. The study also…
Abstract
Purpose
The purpose of this study was to validate the feasibility of the proposed microstructure-based model by comparing the simulation results with experimental data. The study also aimed to investigate the relationship between the orientation of graphite flakes and the failure behavior of the material under compressive loads as well as the effect of image size on the accuracy of stress–strain behavior predictions.
Design/methodology/approach
This paper presents a microstructure-based model that utilizes the finite element method (FEM) combined with representative volume elements (RVE) to simulate the hardening and failure behavior of ferrite-pearlite matrix gray cast iron under uniaxial loading conditions. The material was first analyzed using optical microscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) to identify the different phases and their characteristics. High-resolution SEM images of the undeformed material microstructure were then converted into finite element meshes using OOF2 software. The Johnson–Cook (J–C) model, along with a damage model, was employed in Abaqus FEA software to estimate the elastic and elastoplastic behavior under assumed plane stress conditions.
Findings
The findings indicate that crack initiation and propagation in gray cast iron begin at the interface between graphite particles and the pearlitic matrix, with microcrack networks extending into the metal matrix, eventually coalescing to cause material failure. The ferritic phase within the material contributes some ductility, thereby delaying crack initiation.
Originality/value
This study introduces a novel approach by integrating microstructural analysis with FEM and RVE techniques to accurately model the hardening and failure behavior of gray cast iron under uniaxial loading. The incorporation of high-resolution SEM images into finite element meshes, combined with the J–C model and damage assessment in Abaqus, provides a comprehensive method for predicting material performance. This approach enhances the understanding of the microstructural influences on crack initiation and propagation, offering valuable insights for improving the design and durability of gray cast iron components.
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