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The authors study how shareholder litigation risk impacts a firm’s decision of real earnings management (REM). This paper aims to shed light on how shareholder litigation risk impacts REM. The authors further explore how the intensifying effect varies systematically conditioning on the degree of information asymmetry and the strength of internal corporate governance.

In this study, the authors use the 1999 Ninth Circuit Court ruling as a quasi-experiment that reduces shareholder litigation risk to address endogeneity and establish a causal inference.

The difference-in-difference tests suggest lower shareholder litigation risk intensifies REM. In other words, higher litigation risk mitigates REM. Cross-sectional test results suggest the negative effect of decreased shareholder litigation is more pronounced when monitoring difficulty is higher, when information environment is more impoverished and when internal corporate governance is weaker. The negative effect is also stronger in firms with higher sensitivity to legal threats.

Protection of investors’ interest is the focus of corporate governance. Designed as an important corporate governance mechanism, shareholder litigation enables investors to pursue legal actions to recover their losses in the event of corporate misbehaviors. However, whether shareholder litigation is an effective corporate governance tool and beneficial to shareholders and firms is not without controversy. The authors contribute to the debate by providing evidence that supports the argument that shareholder litigation threat significantly disciplines REM, a form of costlier earnings management technique and myopic investment behavior.

The aim of the paper is to investigate the effect of labor strength on stock price crash risk and related moderating mechanisms.

To examine the relationship between labor unions and stock price crash risk and, more importantly, whether corporate governance moderates this relationship. Ordinary least squares (OLS), two-stage least squares, cross-sectional analyses, industry-level regressions and firm-level regressions are conducted.

The results suggest a negative impact of labor union strength on stock price crash risk. Further analysis suggests strong corporate governance mechanisms may mitigate the increased stock price crash risk in less-unionized firms.

Labor unions have a long-term horizon in the firm and have strong incentives to monitor managerial opportunism. However, labor unions may also increase financial reporting opacity and collude with managers to gain bargaining power in labor negotiations. The authors’ finding suggests that labor union strength is negatively associated with stock price crash risk. This finding is consistent with the notion that labor unions curb managerial opportunism in information disclosure, resulting in reduced crash risk. More importantly, the authors find corporate governance mitigates the negative impact of reduced unionization on crash risk, providing empirical support for recent regulatory efforts to strengthen corporate governance to prevent stock market crash.

This paper seeks to explore empirically the shareholder reactions to an officer leave of absence (OLOA) announcement.

A standard event study methodology is applied to daily stock holding period returns. The sample studied consists of 104 firms announcing an OLOA and is further delineated by both reasons offered and the title of the officers in the event. A cross‐sectional analysis is used to study the reactions found.

The paper documents a statistically significant negative response from shareholders: a 4.9 percent mean loss across 104 firms in the sample. Cross‐sectional results indicate that board independence and the passage of the Sarbanes‐Oxley Act of 2002 (SOX) influence abnormal returns.

The evidence in this study clearly suggests the value‐relevance of the OLOA announcements. Therefore, a thorough understanding of the impact of OLOA announcements on firm value is important to investors.

Event studies of management turnover primarily focus on the event of formal executive turnover per se and largely ignore events of temporary turnovers. This paper examines market response to OLOA (a corporate event that may precede permanent management turnover). It uncovers varied significant market reactions. Some insignificant results in prior event studies may be partially explained by information released through preceding events of temporary turnovers.

Barton and Simko argue that the balance sheet information would serve as a constraint on accrual‐based earnings management. This paper aims to extend their argument by examining whether the balance sheet constraint increases managers' propensity to use either downward forecast guidance or real earnings management as a substitute mechanism to avoid earnings surprises.

Following Barton and Simko, the paper uses the beginning balance of net operating assets relative to sales as a proxy for the balance sheet constraint. The argument is that because of the articulation between the income statement and the balance sheet, previous accounting choices that increase earnings will also increase net assets and therefore the level of net assets reflects the extent of previous accrual management. Models from Matsumoto and Bartov et al. are used to measure forecast guidance. Following Rochowdhury and Cohen et al., a firm's abnormal level of production costs and discretionary expenditures are used as proxies of real earnings management. The empirical analysis is conducted based on the 1996‐2006 annual data for a sample of nonfinancial, nonregulated firms.

The paper finds that firms with higher level of beginning net operating assets relative to sales are more likely to guide analysts' earnings forecasts downward, and more likely to engage in real earnings management in terms of abnormal increases in production costs and abnormal reductions in discretionary expenditures.

Overall, the paper's evidence suggests that managers turn to real earnings management or downward forecast guidance as a substitute mechanism to avoid negative earnings surprises when their ability to manipulate accruals upward is constrained by the extent to which net assets are already overstated in the balance sheet.

This study adds to prior literature that examines how managers trade off different mechanisms used to meet or beat analysts' earnings expectations. It also contributes to the extant literature by providing further insights on the role of balance sheet information in the process of managing earnings and/or earnings surprises.

This study aims to investigate how high-speed railway (HSR) development affects urban construction investment (UCI) bond yield spreads based on China’s background.

This study constructs a quasi-natural experiment and adopts regression analyses to empirically examine the relation between HSR development and UCI bond yield spreads. The empirical analysis is based on a Chinese sample of 15,109 bond offering observations from 2008 to 2019.

The results show that HSR development reduces UCI bond yield spreads. Mechanistic analysis shows that HSR development increases land prices and the level of urbanization, which in turn lowers the UCI bond yield spreads. In addition, the impact of HSR development on UCI bond yield spreads is more significant at higher marketization levels and lower degrees of dependence on land finance cities where UCI corporations are located.

The results imply that transportation infrastructure improvement, such as HSR development, helps to enhance the credit of local governments and the solvency of UCI corporations and ultimately reduces the financing cost of UCI bonds.

This paper provides theoretical support and empirical evidence for the impact of transportation infrastructure construction on the implicit debt risks of local governments in China, which enriches the research on the “HSR economy” from a micro perspective and expands the research on the influencing factors of local governments’ debt risk.

The environment of international business (IB) and the capabilities of emerging market multinational enterprises (EMNEs) as well as their home countries have changed significantly, leading to some new features of liability of origin (LOR). This paper aims to extend the LOR literature by particularly focusing on the LOR of Chinese multinational enterprises (MNEs) and by taking into account the heterogeneity among industries and across individual MNEs.

Based on the stereotype content model and organizational legitimacy perspective, this study explores how LOR influences Chinese MNEs’ cross-border acquisition completions. Several hypotheses were tested by using a binary logistic regression model with panel data techniques based on data of 780 Chinese MNEs’ acquisition deals between 2008 and 2018.

The results of this study show that when the competence dimension of China’s LOR is perceived as high in the host country, Chinese MNEs are less likely to complete cross-border acquisitions. Moreover, deals are less likely to be completed when the warmth dimension of China’s LOR is perceived to be low. Global experience and the foreign-listed status of individual Chinese MNEs can alter the relationship between the LOR and deal completions.

This study advances and enriches the LOR research. It shows that a high level of competence in the home country has led to LOR for Chinese MNEs rather than the low level of competence proposed by existing LOR studies; and the LOR for Chinese MNEs is also determined by the perceived low level of warmth in the home country resulting from the geopolitical conflicts between two countries. In addition, the LOR suffered by EMNEs could vary based on certain industry- and firm-level characteristics. The findings of this study provide important practical implications for emerging economy governments and for firms intending to go abroad.

The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.

The purpose of this paper is to develop a new inverse controller for servo‐system position tracking control based on neural network (NN) and model reference adaptive control (MRAC).

First, the model of general servo‐systems is analyzed. Then, a MRAC based on neural network control (NNC) is proposed with mathematical prove of stability. In addition, several simulation cases and experiments are listed to verify the usability of the control scheme.

This scheme consists of an MRAC, an online NN controller and a robust controller in velocity‐loop. For reducing influence which arose from modeling error, unknown model dynamics, parameter variation, and load changes, the NN controller is introduced to counteract the various influence mentioned above dynamically. MRAC, NNC, and robust controller adjust system to track the approximate velocity‐loop reference model. In this way, the position‐loop is not sensitive to the disturbance on velocity‐loop, and the whole velocity‐loop can be treated as a simple linear model when designing the other parts of the system. In addition, a novel inverse control method based on linear velocity signal filter is introduced to this scheme. In this case, the MRAC, NNC, and robust controller perform as an adaptive inverse controller, which keeps the velocity signal tracking the position loop controller output.

The paper presents a new inverse controller with NNC and MRAC which is practical and flexible.

The aim is to apply pseudo‐random correlation method to detect very weak electrical signals because of various natural and artificial electron‐magnetic interferences in electrical prospecting.

Electrical prospecting is an important method of geophysical exploration and the electrical prospecting instruments are required to detect very weak electrical signals against strong interferences. Recently, pseudo‐random correlation coding has been widely applied in telecommunications and measurement and test systems to improve the signal noise ratio with great success, but has not been used in electric prospecting. This paper theoretically investigated the application model of pseudo‐random correlation techniques in electrical prospecting.

The model of pseudo‐random correlation techniques in electrical prospecting, including its principle, detailed protocol and parameter selection, is established.

With the continuing improvement in the capacity of electrical prospecting transmitters, the pseudo‐random correlation method will be widely used in electrical prospecting.

The pseudo‐random correlation techniques is originally investigated for its application in electrical prospecting. This paper is aimed at researchers and engineers in geophysical exploration.

This paper aims to analyze how smart city construction affects destination competitiveness and elucidates the potential mechanisms of digital economy. Also, the regional heterogeneity of smart city construction’s influence on destination competitiveness is discussed.

Given the quasi-natural experiment characteristics of China's smart city construction pilot, this study applies a time-varying difference-in-differences approach using a panel dataset of 272 Chinese prefectural-level cities to examine the causal effects of smart city construction on destination competitiveness.

Results indicate substantial enhancement of urban destination competitiveness from smart city construction, with this effect escalating annually. Digital infrastructure and digital finance serve as influence mechanisms. The positive impacts of smart city construction on urban tourism competitiveness do not differ by geographic location, rather there are significant differences between cities of different administrative levels. The impact of smart city construction on destination competitiveness is more significant in low administrative level cities. The improvement of economic development level and innovation ability helps to exert the positive impact of smart cities on tourism competitiveness.

This study constructs a new panel data set for smart city construction and destination competitiveness based on multi-source data and posits a theoretical linkage among smart city construction, digital economy and destination competitiveness. This paper provides invaluable insights on how to boost destination competitiveness by creating smart cities and leveraging the digital economy. Tourism sectors should proactively engage in smart city construction and foster the digital transformation of tourism.

本文旨在分析智慧城市建设如何影响目的地竞争力, 并阐明数字经济的潜在机制。此外, 还讨论了智慧城市建设对目的地竞争力影响的区域异质性。

鉴于中国智慧城市建设试点的准自然实验特征, 本研究基于272个中国地级城市的面板数据集, 采用双重差分方法检验了智慧城市建设对目的地竞争力的因果影响。

研究结果表明, 智慧城市建设显著增强了城市目的地竞争力, 这种影响每年都在增加。数字基础设施和数字金融是影响机制。智慧城市建设对城市旅游竞争力的积极影响不因地理位置而异, 不同行政级别的城市之间存在显著差异。智慧城市建设对低行政级别城市目的地竞争力的影响更为显著。经济发展水平和创新能力的提高有助于发挥智慧城市对旅游竞争力的积极影响。

本研究基于多源数据构建了一个新的智慧城市建设和目的地竞争力面板数据集, 并在智慧城市建设、数字经济和目的地竞争之间建立了理论联系。本文就如何通过创建智慧城市和利用数字经济来提高目的地竞争力提供了宝贵的见解。旅游部门应积极参与智慧城市建设, 促进旅游业的数字化转型。

El objetivo de este artículo es analizar cómo afecta la construcción de ciudades inteligentes a la competitividad de los destinos y dilucidar los posibles mecanismos de la economía digital. Se aborda también la heterogeneidad regional de la influencia de la construcción de ciudades inteligentes en la competitividad de los destinos.

Dadas las características de experimento casi natural del proyecto piloto de construcción de ciudades inteligentes en China, este estudio aplica un enfoque de diferencias en diferencias temporales utilizando un conjunto de datos de panel de 272 ciudades chinas de nivel de prefectura para examinar los efectos causales de la construcción de ciudades inteligentes sobre la competitividad de los destinos.

Los resultados indican una mejora sustancial de la competitividad de los destinos urbanos gracias a la construcción de ciudades inteligentes, efecto que aumenta cada año. La infraestructura digital y las finanzas digitales actúan como mecanismos de influencia. Los efectos positivos de la construcción de ciudades inteligentes sobre la competitividad Del turismo urbano no difieren en función de la ubicación geográfica, sino que las diferencias significativas se producen entre ciudades de diferentes niveles administrativos. El impacto de la construcción de ciudades inteligentes en la competitividad de los destinos es más significativo en las ciudades de bajo nivel administrativo. La mejora del nivel de desarrollo económico y la capacidad de innovación contribuyen al impacto positivo de las ciudades inteligentes en la competitividad turística.

Este estudio construye un nuevo conjunto de datos de panel para la construcción de ciudades inteligentes y la competitividad de los destinos basado en datos de múltiples fuentes y plantea un vínculo teórico entre la construcción de ciudades inteligentes, la economía digital y la competitividad de los destinos. Este artículo ofrece un valioso conocimiento sobre cómo impulsar la competitividad de los destinos mediante la creación de ciudades inteligentes y el aprovechamiento de la economía digital. Los sectores turísticos deberían participar de forma proactiva en la construcción de ciudades inteligentes y fomentar la transformación digital del turismo.