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1 – 10 of 35Xiaojing Zheng and Xiaoxian Wang
This study aims to examine the effect of board gender diversity on corporate litigation in China’s listed firms. The key questions this study addresses are: what are the effect of…
Abstract
Purpose
This study aims to examine the effect of board gender diversity on corporate litigation in China’s listed firms. The key questions this study addresses are: what are the effect of board gender diversity on corporate litigation in terms of both the frequency and severity of consequence, is there any heterogeneous effects of the relationships across firm performance?
Design/methodology/approach
A sample consists of 25,668 firm-year observations from over 3,340 firms is examined using logistic regression analysis and negative binomial regression analysis. The authors also use event study method and ordinary least square (OLS) regression to explore female directors’ effects on reducing the negative consequences of litigation. The logistic regression and OLS regression are reestimated with interaction terms when examining the firm performance heterogeneity.
Findings
The authors document that firms with greater female representation on their boards experience fewer and less severe corporate litigations. Moreover, in high-performing firms, board gender diversity plays a more potent role in reducing the frequency and consequences of corporate litigation than low-performing firms.
Originality/value
This study is among the first to examine the relationship between board gender diversity and the comprehensive corporate litigations under Chinese context. It sheds new light on China’s boardroom dynamics, offering valuable empirical implication to Chinese corporate policymakers on the role of female directors.
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Vicente-Segundo Ruiz-Jacinto, Karina-Silvana Gutiérrez-Valverde, Abrahan-Pablo Aslla-Quispe, José-Manuel Burga-Falla, Aldo Alarcón-Sucasaca and Yersi-Luis Huamán-Romaní
This paper aims to present the novel stacked machine learning approach (SMLA) to estimate low-cycle fatigue (LCF) life of SAC305 solder across structural parts. Using the finite…
Abstract
Purpose
This paper aims to present the novel stacked machine learning approach (SMLA) to estimate low-cycle fatigue (LCF) life of SAC305 solder across structural parts. Using the finite element simulation (FEM) and continuous damage mechanics (CDM) model, a fatigue life database is built. The stacked machine learning (ML) model's iterative optimization during training enables precise fatigue predictions (2.41% root mean square error [RMSE], R2 = 0.975) for diverse structural components. Outliers are found in regression analysis, indicating potential overestimation for thickness transition specimens with extended lifetimes and underestimation for open-hole specimens. Correlations between fatigue life, stress factors, nominal stress and temperature are unveiled, enriching comprehension of LCF, thus enhancing solder behavior predictions.
Design/methodology/approach
This paper introduces stacked ML as a novel approach for estimating LCF life of SAC305 solder in various structural parts. It builds a fatigue life database using FEM and CDM model. The stacked ML model iteratively optimizes its structure, yielding accurate fatigue predictions (2.41% RMSE, R2 = 0.975). Outliers are observed: overestimation for thickness transition specimens and underestimation for open-hole ones. Correlations between fatigue life, stress factors, nominal stress and temperature enhance predictions, deepening understanding of solder behavior.
Findings
The findings of this paper highlight the successful application of the SMLA in accurately estimating the LCF life of SAC305 solder across diverse structural components. The stacked ML model, trained iteratively, demonstrates its effectiveness by producing precise fatigue lifetime predictions with a RMSE of 2.41% and an “R2” value of 0.975. The study also identifies distinct outlier behaviors associated with different structural parts: overestimations for thickness transition specimens with extended fatigue lifetimes and underestimations for open-hole specimens. The research further establishes correlations between fatigue life, stress concentration factors, nominal stress and temperature, enriching the understanding of solder behavior prediction.
Originality/value
The authors confirm the originality of this paper.
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Yongjia Lin, Zhenye Lu, Di Fan and Zhen Zheng
This study aims to investigate the bright and dark sides of environmental, social and governance (ESG) during the COVID-19 pandemic, including both the outbreak and recovery…
Abstract
Purpose
This study aims to investigate the bright and dark sides of environmental, social and governance (ESG) during the COVID-19 pandemic, including both the outbreak and recovery periods, for the Chinese hospitality industry.
Design/methodology/approach
Using panel data of 564 firm-quarter observations from 2018 to 2020, the authors adopt fixed-effects regression estimation with standard errors clustered at the firm level. To address potential endogeneity concerns, the authors also use the two-stage least squares estimator with instrumental variables.
Findings
The results suggest that ESG plays different roles in market- and accounting-based performance during the COVID-19 outbreak and recovery periods. Specifically, ESG practices show a bright side as a reputation builder to mitigate the negative pandemic impact on market-based performance, whereas the dark side of ESG practices consumes firm resources to aggravate the negative pandemic impact on accounting-based performance during the coronavirus outbreak. These results also suggest hospitality companies benefit bountifully from ESG practices during the COVID-19 recovery.
Practical implications
ESG plays a vital role for hospitality firms by providing insurance-like protection during and after the COVID-19 outbreak. Additionally, hospitality firms should evaluate their capability to adapt resource-consuming ESG practices.
Originality/value
Existing hospitality COVID-19 studies have investigated the effect of ESG on firm performance within a short period with mixed results. This study extends the literature by showing the different effects of ESG practices on market- and accounting-based performance during the COVID-19 outbreak and recovery periods.
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Aimin Wang, Sadam Hussain and Jiying Yan
The purpose of this study is to conduct a thorough empirical investigation of the intricate relationship between urban housing sales prices and land supply prices in China, with…
Abstract
Purpose
The purpose of this study is to conduct a thorough empirical investigation of the intricate relationship between urban housing sales prices and land supply prices in China, with the aim of elucidating the underlying economic principles governing this dynamic interplay.
Design/methodology/approach
Using monthly data of China, the authors use the asymmetry nonlinear autoregressive distributed lag (NARDL) model to test for nonlinearity in the relationship between land supply price and urban housing prices.
Findings
The empirical results confirm the existence of an asymmetric relationship between land supply price and urban housing prices. The authors find that land supply price has a positive and statistically significant impact on urban housing prices when land supply is increasing. Policymakers should strive to strike a balance between safeguarding residents’ housing rights and maintaining market stability.
Research limitations/implications
Although the asymmetric effect of land supply price has been identified as a significant contributor in this study, it is important to note that the research primarily relies on time series data and focuses on analysis at the national level. Although time series data offer a macroscopic perspective of overall trends within a country, they fail to adequately showcase the structural variations among different cities.
Practical implications
To ensure a stable housing market and meet residents’ housing needs, policymakers must reexamine current land policies. Solely relying on restricting land supply to control housing prices may yield counterproductive results. Instead, increasing land supply could be a more viable option. By rationally adjusting land supply prices, the government can not only mitigate excessive growth in housing prices but also foster the healthy development of the housing market.
Originality/value
First, the authors have comprehensively evaluated the impact of land supply prices in China on urban housing sales prices, examining whether they play a facilitating or mitigating role in the fluctuation of these prices. Second, departing from traditional linear analytical frameworks, the authors have explored the possibility of a nonlinear relationship existing between land supply prices and urban housing sales prices in China. Finally, using an advanced NARDL model, the authors have delved deeper into the asymmetric effects of land supply prices on urban housing sales prices in China.
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Guijian Xiao, Tangming Zhang, Yi He, Zihan Zheng and Jingzhe Wang
The purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding…
Abstract
Purpose
The purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding and polishing of additive titanium alloy blades to ensure the surface integrity and machining accuracy of the blades.
Design/methodology/approach
At present, robot grinding and polishing are mainstream processing methods in blade automatic processing. This review systematically summarizes the processing characteristics and processing methods of additive manufacturing (AM) titanium alloy blades. On the one hand, the unique manufacturing process and thermal effect of AM have created the unique processing characteristics of additive titanium alloy blades. On the other hand, the robot grinding and polishing process needs to incorporate the material removal model into the traditional processing flow according to the processing characteristics of the additive titanium alloy.
Findings
Robot belt grinding can solve the processing problem of additive titanium alloy blades. The complex surface of the blade generates a robot grinding trajectory through trajectory planning. The trajectory planning of the robot profoundly affects the machining accuracy and surface quality of the blade. Subsequent research is needed to solve the problems of high machining accuracy of blade profiles, complex surface material removal models and uneven distribution of blade machining allowance. In the process parameters of the robot, the grinding parameters, trajectory planning and error compensation affect the surface quality of the blade through the material removal method, grinding force and grinding temperature. The machining accuracy of the blade surface is affected by robot vibration and stiffness.
Originality/value
This review systematically summarizes the processing characteristics and processing methods of aviation titanium alloy blades manufactured by AM. Combined with the material properties of additive titanium alloy, it provides a new idea for robot grinding and polishing of aviation titanium alloy blades manufactured by AM.
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Mosab I. Tabash, Umar Farooq and Adel Ahmed
Due to an increase in energy demands, it has become vital to devise efficient energy policies. Literature has suggested multiple factors influencing the consumption of specific…
Abstract
Purpose
Due to an increase in energy demands, it has become vital to devise efficient energy policies. Literature has suggested multiple factors influencing the consumption of specific energy types. Among others, institutional quality (INQ) is another factor that can determine energy consumption. Given this, the current study aimed to investigate the impact of INQ on fossil fuel energy (FFE) and renewable energy consumption (REC).
Design/methodology/approach
The empirical analysis was conducted on 20 years (2000–2019) of data from South Asian economies, and regression among variables was established by employing the dynamic ordinary least square and fully modified ordinary least square models. The selection of both techniques is subject to the existence of cointegration identified by the Johansen cointegration test. Other pre-estimation techniques include cross-section dependence and unit root testing validating the estimation of coefficients in the long run.
Findings
The analysis mainly reveals the negative impact of INQ on FFE and the positive impact of INQ on REC. The authors further find the asymmetric impact of control variables including foreign direct investment inflow, economic growth, inflation rate, financial sector development and energy investment on the consumption of both types of energy.
Research limitations/implications
Given the positive influence of INQ on REC, it is recommended to focus on improving the efficiency of institutions specifically those that are directly linked with energy-related policies. A better INQ can ensure environmental sustainability by enhancing the consumption of renewable energy. Therefore, it is advised to exert more efforts to improve the INQ.
Practical implications
In view of the positive influence of INQ on REC, it is recommended to focus on improving the efficiency of institutions specifically that are directly linked with energy-related policies. A better INQ can ensure environmental sustainability by enhancing the consumption of renewable energy. Therefore, it is advised to exert more efforts for improving the INQ.
Originality/value
This study offers robustness to the empirical findings of existing literature on the INQ-REC nexus and complements the underdeveloped literature on the INQ-FFE relationship.
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Fatemeh Binesh, Amanda Mapel Belarmino, Jean-Pierre van der Rest, Ashok K. Singh and Carola Raab
This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.
Abstract
Purpose
This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.
Design/methodology/approach
Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models.
Findings
The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels.
Research limitations/implications
This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR.
Practical implications
This study produced a reliable, accurate forecasting model considering risk and competitor behavior.
Theoretical implications
This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times.
Originality/value
This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.
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XiaoXi Wu, Jinlian Shi and Haitao Xiong
This paper aims to analyze the research highlights, evolutionary process and future research directions in the field of tourism forecasting.
Abstract
Purpose
This paper aims to analyze the research highlights, evolutionary process and future research directions in the field of tourism forecasting.
Design/methodology/approach
This study used CiteSpace to conduct a bibliometric analysis of 1,213 tourism forecasting articles.
Findings
The results show that tourism forecasting research has experienced three stages. The institutional collaboration includes transnational collaboration and domestic institutional collaboration. Collaboration between countries still needs to be strengthened. The authors’ collaboration is mainly based on on-campus collaboration. Articles with high co-citation are primarily published in core tourism journals and other relevant publications. The research content mainly pertains to tourism demand, revenue management, hotel demand and tourist volumes. Ex ante forecasting during the COVID-19 pandemic has broadened existing tourism forecasting research. The future forecasting research focuses on the rational use of big data, improving the accuracy of models and enhancing the credibility of forecasting results.
Originality/value
This paper uses CiteSpace to analyze tourism forecasting articles to obtain future research trends, which supplements existing research and provides directions for future research.
意图
本文旨在分析旅游预测领域的研究重点、演化过程和未来的研究方向。
设计/理论/方法
本研究使用 CiteSpace 软件对 1213 篇旅游预测文章进行了文 献计量学分析。
结果
结果表明, 旅游预测研究经历三个阶段。机构合作包含国际机构合作和 国内机构合作, 需要持续加强国家之间的合作, 作者之间的合作多以校内合作为 主。高引用文章不仅发表在旅游领域的核心期刊还发表在其他专业的核心期刊上。 旅游预测研究的主要内容为旅游需求、收入管理、酒店需求和游客量。新冠疫情 期间的事前预测拓宽了现有的旅游预测研究。未来预测的研究重点在于合理利用 大数据, 提高模型的准确定以及提高预测结果的可信度。
创意/价值
本文使用 CiteSpace 分析旅游预测文章得到未来研究趋势, 既是对 现有研究的补充, 又为今后的研究提供方向。
Objetivo
Este artículo pretende analizar los aspectos más destacados de la investigación, el proceso evolutivo y las futuras orientaciones de la investigación en el campo de la previsión turística.
Diseño/metodología/enfoque
Este estudio utilizó CiteSpace para realizar un análisis bibliométrico de 1213 artículos sobre previsión turística.
Resultados
Los resultados muestran que la investigación sobre previsión turística ha experimentado tres etapas. La colaboración institucional incluye la colaboración transnacional y la colaboración institucional nacional. La colaboración entre países aún debe reforzarse. La colaboración entre autores se basa principalmente en la colaboración dentro del campus. Los artículos con una alta cocitación se publican principalmente en las principales revistas de turismo y en otras publicaciones relevantes. El contenido de la investigación se refiere principalmente a la demanda turística, el revenue management, la demanda hotelera y los volúmenes turísticos. La previsión previa y durante la pandemia de la COVID-19 ha ampliado la investigación existente sobre previsión turística. La futura investigación sobre previsiones se centra en el uso racional de los big data, la mejora de la precisión de los modelos y el aumento de la credibilidad de los resultados de las previsiones.
Originalidad/valor
Este artículo utiliza CiteSpace para analizar artículos de previsión turística con el fin de obtener futuras tendencias de investigación, lo que complementa la investigación existente y proporciona orientaciones para futuras investigaciones.
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Shreya Sangal, Gaurav Duggal and Achint Nigam
The purpose of this research paper is to review and synthesize the role of blockchain technology (BCT) in various types of illegal activities, including but not limited to fraud…
Abstract
Purpose
The purpose of this research paper is to review and synthesize the role of blockchain technology (BCT) in various types of illegal activities, including but not limited to fraud, money laundering, ransomware attacks, firearms, drug tracking, cyberattacks, identity theft and scams.
Design/methodology/approach
The authors conducted a review of studies related to illegal activities using blockchain from 2015 to 2023. Next, a thematic review of the literature was performed to see how these illegal activities were conducted using BCT.
Findings
Through this study, the authors identify the relevant themes that highlight the major illegal activities performed using BCT, its possible steps for prevention and the opportunities for future developments. Finally, the authors provide suggestions for future research using the theory, context and method framework.
Originality/value
No other research has synthesized the illegal activities using BCT through a thematic approach to the best of the authors’ knowledge. Hence, this study will act as a starting point for future research for academic and technical practitioners in this area.
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Osman M. Karatepe, Ülker Çolakoğlu, Gülseren Yurcu and Şule Kaya
This paper aims to explore financial anxiety and generalized anxiety as the serial mediators linking perceived organizational support (POS) to career commitment.
Abstract
Purpose
This paper aims to explore financial anxiety and generalized anxiety as the serial mediators linking perceived organizational support (POS) to career commitment.
Design/methodology/approach
Data were collected from 388 managerial and nonmanagerial employees in diverse service areas, such as restaurants, airlines and hotels in Turkey. The direct and mediating effects were tested via the PROCESS macro.
Findings
Financial anxiety partly mediates the impact of POS on career commitment. The findings further reveal that financial anxiety and generalized anxiety serially mediate the effect of POS on career commitment.
Practical implications
Management should work with mentors to provide employees with psychosocial support during the COVID-19 pandemic. When employees perceive that the firm really cares about them and values their contribution during these challenging days, they display lower anxiety and higher career commitment. Management should also retain employees who are high on career commitment because such employees possess a sense of calling and are unlikely to quit. These implications may not be considered new. However, management would need such employees concerning the firm’s performance recovery after COVID-19.
Originality/value
Workers in the service industries suffer from financial and generalized anxieties and display reduced career commitment during COVID-19. However, little is known about the antecedents and outcomes of financial anxiety among hospitality and tourism workers. More importantly, no empirical piece has tested these anxiety variables as the mediators linking POS to career commitment in the pertinent literature so far.
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