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The purpose of this paper is to explore how variations in management’s tone within management’s discussion and analysis (MD&A) sections of 10-K reports can serve as an indicator of tax avoidance and highlight the complex relationship between such linguistic shifts and the tax avoidance decisions within firms.

The paper uses a textual analysis approach to identify linguistic cues in MD&A sections of 10-K filings related to tax avoidance, going beyond traditional quantitative measures. The study uses differences in negative word occurrences in MD&A to measure management’s tone change and examines various measures of tax avoidance. The sample covers the period from 1993 to 2017 and comprises all firms with 10-K filings available on EDGAR, totaling over 30,000 firm-year observations.

The findings indicate a complementary relationship between tax avoidance and other drivers of firm performance. When firms have more negative management’s tone, they are less willing to engage in tax avoidance and vice versa. The study’s approach with management’s tone change provides a different and statistically significant improvement in model fit for detecting tax avoidance.

This paper provides actionable insights for detecting tax avoidance through the analysis of management’s tone in corporate disclosures, offering a new tool for researchers, investors and tax authorities. It highlights the importance of linguistic cues as indicators of tax avoidance behavior, complementing traditional financial metrics.

The paper contributes to the literature by using management’s tone change as a time-varying factor to explain tax avoidance behavior. It uncovers a larger set of linguistic cues in MD&A that can be used to detect tax avoidance. This research provides a complementary approach to traditional quantitative tax avoidance measures and offers insights into the overall relationship between tax avoidance and firm performance, going beyond one-dimensional measures typically used in prior literature.

This study aims to propose a new conforming tax measure based on the work of Badertscher et al. (2019).

This study divides total tax avoidance/management (TM) into nonconforming and conforming portions through a regression. The residual of the regression is treated as the conforming tax measure. In addition, the new conforming tax measure is validated via three approaches. Then, this study examines the moderating effect of nonconforming earnings management (EM) on the relationship between conforming TM and firm performance.

The empirical results show that the model has stronger explanatory power than the model proposed by Badertscher et al. (2019). Additionally, the validation results show that the mean value of the conforming tax measure is lower in quasi-private corporations (financially constrained companies) than in matched public corporations (nonfinancially constrained companies), and firms under high market capital pressure are less motivated to engage in conforming tax practices. Furthermore, nonconforming EM positively moderates the conforming tax–ROA association, implying that nonconforming EM can reduce financial reporting costs resulting from conforming tax practices.

This study contributes to conforming tax research in the following ways. First, this study proposes a new conforming tax measure by substituting the cash book tax difference (BTD) for the BTD in the model of Badertscher et al. (2019) (“BKRW”). Second, this study demonstrates theoretically why the cash BTD should outperform the BTD in computing the BKRW conforming tax measure and confirm this empirically. Third, this study presents a three-way conceptual schema that divides corporations into two groups along each of three tax-relevant dimensions. The group of firms that use both conforming and nonconforming tax strategies have different characteristics compared to the other group. This study also validates the conforming tax measure across the two-group dichotomies.

The green innovation behavior of construction enterprises is the key to reducing the construction industry's carbon emissions and realizing the green transformation of the construction industry. The purpose of this study is to reveal the evolutionary mechanism of green innovation behavior in construction enterprises.

This study is based on resource-based theory, Porter's hypothesis and signaling theory. First, a measurement model of the green innovation behavior of construction enterprises was constructed from three aspects: environmental regulation, enterprise resources and public opinion through hierarchical analysis. Then, the state values of the measurement model of green innovation behavior of construction enterprises were calculated through the time series data from 2011–2018. Finally, the Markov chain model was used to predict the evolutionary trend of green innovation behavior of construction enterprises, and the accuracy of the prediction effect of the Markov chain model was verified using the time series data of 2019.

The Markov chain model of green innovation behavior of construction enterprises constructed in this study has high accuracy. This model finds that the transition of the growth state of green innovation behavior in China's construction industry is fluid and predicts the evolution trend of the innovation behavior of construction enterprises. In the future, the green innovation behavior of construction enterprises has a probability of 70.17% to be in a continuous growth state and 40.27% to be in a rapid growth state.

Based on the Markov chain model of green innovation behavior of construction enterprises, this study finds that the transition of the growth state of green innovation behavior of construction enterprises in China has the characteristics of liquidity. In addition, it reveals the development process of the green innovation behavior of construction enterprises from 2011–2018 and predicts the evolution trend of the green innovation behavior of construction enterprises.

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.

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.

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.

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.

The purpose of this paper is to investigate what may influence participation in mobile collaborative consumption (CC). In particular, the authors investigate the effects of individual sociability and psychological antecedents including enjoyment, social connection, altruistic motivation, reputation, trust and embarrassment on the participation intention in this emerging phenomenon, as well as their relationships.

Survey approach was employed to validate the research model.

Hedonic factor, i.e. enjoyment, and social-related factors including altruistic motivation, reputation and embarrassment emerge as important antecedents of participation in mobile CC. On top of these factors, individual sociability plays an important role, by acting as a direct antecedent of the dependent variable, while also influencing their perceived greater altruistic motivation and reputation from participating in the CC. Yet, sociability has no significant effect on perceived enjoyment and embarrassment associated with the activity, suggesting that addressing these factors may encourage individuals with low sociability to participate.

This study taps into mobile technology to support CC in daily consumption activities, and highlights the factors that influence people’s participation in such activities. More importantly, the findings suggest that while it is more likely for individuals with high sociability to participate in this emerging form of social activities, for consumers with low sociability, addressing the enjoyment aspect and embarrassment issue of the activities may nonetheless promote their participation.

The purpose of this paper is to present a Rao–Blackwellized particle filter (RBPF) approach for the visual simultaneous localization and mapping (SLAM) of small unmanned aerial vehicles (UAVs).

Measurements from inertial measurement unit, barometric altimeter and monocular camera are fused to estimate the state of the vehicle while building a feature map. In this SLAM framework, an extra factorization method is proposed to partition the vehicle model into subspaces as the internal and external states. The internal state is estimated by an extended Kalman filter (EKF). A particle filter is employed for the external state estimation and parallel EKFs are for the map management.

Simulation results indicate that the proposed approach is more stable and accurate than other existing marginalized particle filter-based SLAM algorithms. Experiments are also carried out to verify the effectiveness of this SLAM method by comparing with a referential global positioning system/inertial navigation system.

The main contribution of this paper is the theoretical derivation and experimental application of the Rao–Blackwellized visual SLAM algorithm with vehicle model partition for small UAVs.

The aim of this study is to numerically simulate the density-driven convection in heterogeneous porous media associated with anisotropic permeability field, which is important to the safe and stable long term CO₂ storage in laminar saline aquifers.

The study uses compact finite difference and the pseudospectral method to solve Darcy’s law.

The presence of heterogeneous anisotropy may result in non-monotonic trend of the breakthrough time and quantity of CO₂ dissolved in the porous medium, which are important to the CO₂ underground storage.

The manuscript numerically study the convective phenomena of mixture contained CO₂ and brine. The phenomena are important to the process of CO₂ enhanced oil recovery. Interesting qualitative patterns and quantitative trends are revealed in the manuscript.

This paper aims to propose a noise-robust method to estimate the frequency of the reflective echo to reduce the negative effects of noise and improve the accuracy and resolution of a resonant surface acoustic wave (SAW) sensor.

The proposed approach exploits the singular value decomposition to obtain the frequency information of a SAW response signal and overcome the noise influences.

Compared with the commonly used Fourier transform (FT) method, the accuracy and resolution improvement of the proposed method used in the SAW sensor is validated.

The system using the proposed method delivers lesser standard deviation, that is, delivers higher performance than the conventional system using the fast FT method.

This paper aims to study the mechanism of hydrogen embrittlement in 12Cr2Mo1R(H) steel, which will help to provide valuable information for the subsequent hydrogen embrittlement research of this kind of steel, so as to optimize the processing technology and take more appropriate measures to prevent hydrogen damage.

The hydrogen diffusion coefficient of 12Cr2Mo1R(H) steel was measured by the hydrogen permeation technique of double electrolytic cells. Moreover, the influence of hydrogen traps in the material and experimental temperature on hydrogen diffusion behavior was discussed. The first-principles calculations based on density functional theory were used to study the occupancy of H atoms in the bcc-Fe cell, the diffusion path and the interaction with vacancy defects.

The results revealed that the logarithm of the hydrogen diffusion coefficient of the material has a linear relationship with the reciprocal of temperature and the activation energy of hydrogen atom diffusion in 12Cr2Mo1R(H) steel is 23.47 kJ/mol. H atoms stably exist in the nearly octahedral interstices in the crystal cell with vacancies. In addition, the solution of Cr/Mo alloy atom does not change the lowest energy path of H atom, but increases the diffusion activation energy of hydrogen atom, thus hindering the diffusion of hydrogen atom. Cr/Mo and vacancy have a synergistic effect on inhibiting the diffusion of H atoms in α-Fe.

This article combines experiments with first-principles calculations to explore the diffusion behavior of hydrogen in 12Cr2Mo1R(H) steel from the macroscopic and microscopic perspectives, which will help to establish a calculation model with complex defects in the future.

This paper aims to automatically generate load shedding sequences due to insufficient power supply, to ensure flight safety and complete flight task.

In this paper, a power allocation and load management model, including logical and physical submodels of the distribution system, is first established according to different requirements of the loads in different flight phase and the current total power supply. Then, an optimal load management scheme based on an improved ant colony algorithm is proposed to automatically generate load shedding sequences for both safety-critical and nonsafety critical loads, to achieve a reliable and safe power supply.

To verify the efficiency and feasibility of the algorithm, the proposed method is verified in a virtual simulation platform. Simulation result illustrates that the proposed algorithm is efficient and feasible.

The proposed method can provide guidance on load power supply when the civil aircraft is under abnormal power supply situation.

An optimal load management scheme is proposed by considering different requirements of the loads in different flight phase and the current total power supply.