The purpose of this paper is to address the opposing views of the relationship between directors’ and officers’ liability insurance (D&O insurance) and stock price crash risk in a…
Abstract
Purpose
The purpose of this paper is to address the opposing views of the relationship between directors’ and officers’ liability insurance (D&O insurance) and stock price crash risk in a major Asian emerging stock market.
Design/methodology/approach
This paper finds an endogenous relationship between D&O insurance and stock price crash risk. Hence, the two-stage least squares regression analysis is used to address the endogeneity issue when the relationship is examined. Moreover, this paper further controls the quality of other corporate governance mechanisms to investigate whether D&O insurance still has an effect on stock price crash risk.
Findings
The effect of D&O insurance coverage is significantly negatively related to firm-specific stock price crash risk in Taiwan. More importantly, even when the quality of other corporate governance mechanisms is controlled, the negative relationship between D&O insurance coverage and firm-specific stock price crash risk remains significant. The evidence supports that D&O insurance serves as an effective external monitoring mechanism, strengthens corporate governance, and thus reduces stock price crash risk.
Originality/value
Emerging Asian markets suffer a dearth of research on the relationship of D&O insurance coverage and the firm-specific stock price crash risk. Investigating the relationship in Taiwan, the present study fills the research void. The findings show that D&O insurance plays an important role in reducing stock price crash risk of Taiwanese firms even when other corporate governance mechanisms are in place.
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Seock-Jin Hong and François Domergue
The Korean airline industry continues to change in 20-year cycles structurally. The major changes are in their market through deregulation and liberalization resulting in adding…
Abstract
The Korean airline industry continues to change in 20-year cycles structurally. The major changes are in their market through deregulation and liberalization resulting in adding more carriers, especially low-cost carriers (LCCs) from 2006. The authors categorize three types of LCCs in Korea: (1) independent LCCs, (2) LCCs subsidized by existing airlines as airlines-within-airlines (AwAs), and (3) LCCs supported by conglomerates and local governments. Independent LCCs have suffered financially during the research period from 2009 to 2013, especially from the impaired capital, even though these LCCs are growing rapidly and expanding their markets in domestic and international routes. AwAs’ efficiency is higher than that of independent LCCs, the roles in the market are limited because of cannibalization by their mother company.
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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…
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.
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This research studies the behavior choice of key actors in the construction supply chain and constructs a model which includes the benefit function of the government, contractors…
Abstract
Purpose
This research studies the behavior choice of key actors in the construction supply chain and constructs a model which includes the benefit function of the government, contractors and owners, aiming at improving the coverage of green buildings.
Design/methodology/approach
In this paper, tripartite dynamic game is studied and simulated based on duplicate dynamic equation. The tripartite game under government intervention is rarely considered, and government punishment measures are seldom introduced into the research.
Findings
According to the simulation results, the practical insights in line with the development of green supply chain are put forward. Rewards and punishments affect the development of the supply chain. New technologies and new materials accelerate the development of green supply chain and then improve the coverage of green buildings.
Research limitations/implications
This paper constructs a dynamic model based on complete information rationality, which is difficult to realize in practice, for information is incomplete and human rationality is limited.
Originality/value
In fact, the government has not issued a punishment document to introduce a new variable adjustment model.
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Julian Marx, Beatriz Blanco, Adriana Amaral, Stefan Stieglitz and Maria Clara Aquino
This study investigates the communication behavior of public health organizations on Twitter during the COVID-19 vaccination campaign in Brazil. It contributes to the…
Abstract
Purpose
This study investigates the communication behavior of public health organizations on Twitter during the COVID-19 vaccination campaign in Brazil. It contributes to the understanding of the organizational framing of health communication by showcasing several instances of framing devices that borrow from (Brazilian) internet culture. The investigation of this case extends the knowledge by providing a rich description of the organizational framing of health communication to combat misinformation in a politically charged environment.
Design/methodology/approach
The authors collected a Twitter dataset of 77,527 tweets and analyzed a purposeful subsample of 536 tweets that contained information provided by Brazilian public health organizations about COVID-19 vaccination campaigns. The data analysis was carried out quantitatively and qualitatively by combining social media analytics techniques and frame analysis.
Findings
The analysis showed that Brazilian health organizations used several framing devices that have been identified by previous literature such as hashtags, links, emojis or images. However, the analysis also unearthed hitherto unknown visual framing devices for misinformation prevention and debunking that borrow from internet culture such as “infographics,” “pop culture references” and “internet-native symbolism.”
Research limitations/implications
First, the identification of framing devices relating to internet culture add to our understanding of the so far little addressed framing of misinformation combat messages. The case of Brazilian health organizations provides a novel perspective to knowledge by offering a notion of internet-native symbols (e.g. humor, memes) and popular culture references for misinformation combat, including misinformation prevention. Second, this study introduces a frontier of political contextualization to misinformation research that does not relate to the partisanship of the spreaders but that relates to the political dilemmas of public organizations with a commitment to provide accurate information to citizens.
Practical implications
The findings inform decision-makers and public health organizations about framing devices that are tailored to internet-native audiences and can guide strategies to carry out information campaigns in misinformation-laden social media environments.
Social implications
The findings of this case study expose the often-overlooked cultural peculiarities of framing information campaigns on social media. The report of this study from a country in the Global South helps to contrast several assumptions and strategies that are prevalent in (health) discourses in Western societies and scholarship.
Originality/value
This study uncovers unconventional and barely addressed framing devices of health organizations operating in Brazil, which provides a novel perspective to the body of research on misinformation. It contributes to existing knowledge about frame analysis and broadens the understanding of frame devices borrowing from internet culture. It is a call for a frontier in misinformation research that deals with internet culture as part of organizational strategies for successful misinformation combat.
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The proportional distribution of social labor is a general law governing human social and economic activities, also a law discovered by Marxist political economy that governs…
Abstract
Purpose
The proportional distribution of social labor is a general law governing human social and economic activities, also a law discovered by Marxist political economy that governs socialist economic operations and development based on public ownership.
Design/methodology/approach
This law draws on Marx's vision of future society, but how it is adopted is not only subject to the way a country's economy interacts but also to the influence of a country's historical and cultural traditions. Generations of the CPC and state leaders since Mao Zedong have made unremitting explorations for its application.
Findings
As socialism with Chinese characteristics enters a new era, the Party Central Committee with Comrade Xi Jinping at the core adheres to the standpoints, viewpoints and methods of Marxist political economy, draws from the splendid Chinese traditional culture that values integrity, peace and harmony of all, builds on the reality of China's socialist market economy development, has summed up the features of socialist economy development with Chinese characteristics, and has proposed the five-sphere integrated plan, the four-pronged comprehensive strategy.
Originality/value
The new development concept of “innovation, coordination, green development, openness, and sharing” for socialism with Chinese characteristics, all reflecting the Party's deepening understanding of coordinated development, the gradual formation of the general thought and policy methods of the country's economic regulations based on the coordination and balance of economic structure, the continuous explorations to open a new chapter of contemporary Marxist political economy, China's experience and wisdom, and the Party's confidence in the theories it applies, the road it takes, its system and its culture. The coordination and balance of economic structure are a major theoretical innovation of socialist political economy with Chinese characteristics in the new era.
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Mei-Hsin Wang and Hui-Chung Che
This research explores support vector machine (SVM) with Gaussian radial basis function kernel (RBF) as the model and Analysis of Variance (ANOVA) for forecasting the invalidation…
Abstract
Purpose
This research explores support vector machine (SVM) with Gaussian radial basis function kernel (RBF) as the model and Analysis of Variance (ANOVA) for forecasting the invalidation re-examination decisions of China invention patents, it is beneficial to support patent monetization for corporate intellectual capital.
Design/methodology/approach
There were 8,666 China invention patents with their existing invalidation re-examination decisions during 2000∼2021 chosen to conduct classification model training and prediction for the accuracy of invalidation re-examination decisions through SVM with RBF. Statistical significance was performed by ANOVA to identify indicators for these invention patents selected in this research. These selected 8,666 China invention patents were divided into two groups based on their invalidation re-examination decisions during 2000∼2021 in Table 1, which Group 1 included 5,974 invention patents with all valid or partially valid claims, and Group 0 included 2,692 invention patents with all invalid claims. Thereafter, each group was further divided into sub-groups based on 13 major regions where the applicants filed invalidation re-examination. The training sets for Group 1, Group 0 and the sub-groups were selected based on the patent issued in January, February, April, May, July, August, October and November; while the prediction sets were selected from the invention patents issued in March, June, September and December.
Findings
The training and prediction accuracies were compared to the existing invalidation re-examination decisions. Accuracies of training sets were ranged from 100% in region 7 (Beijing) and region 9 (Shanghai) to 95.95% in region 1 (US), and the average accuracy of invalidation re-examination decisions was 98.95%. While the accuracies of prediction sets for Group 1 were ranged from 100.00% in region 7 (Beijing) to 90.78% in region 13 (Overseas-others), and the average accuracy of classification was 95.96%, this research’s outcomes confirmed the purpose of applying SVM with RBF to predict the patentability sustainability.
Originality/value
This research developed an empirical method through SVM with RBF to predict patentability sustainability which is crucial for corporate intellectual capital on patents. In particular, the investments on patents are huge, including the patent cultivation and maintenance, developments into products or services, patent litigations and dispute managements. Therefore, this research is beneficial not only for corporation, but also for research organisations to perform cost-effective and profitable patent strategies on intellectual capital.