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This study examines the association between geopolitical risk (GPR) and corporate tax, which is a major source of revenue for the government and a significant explicit cost for firms. The authors use a comprehensive measure of GPR to study its effects on corporate taxes by using an international sample.

The authors adopt the geopolitical measure constructed by Caldara and Iacoviello (2022) as a proxy for GPR and cash-effective tax rate benchmarked with statutory tax rate to measure corporate tax avoidance. The authors employ panel regression with fixed effects (FEs) to investigate the impact of GPR on corporate tax avoidance. The authors also conduct a battery of robustness tests to ensure the strength of the study’s results.

This study’s empirical results indicate that sample firms increase their tax avoidance amid increasing GPR. Further analyses show that financial constraints incentivize firms to avoid taxes during rising geopolitical tensions. The authors also provide evidence on the role of firm-level and country-level governance in weakening the association between GPR and tax avoidance.

Policymakers and governments may strengthen the enforcement rule to limit aggressive tax practices of corporates during GPR to balance fiscal deficit. In addition, this study sheds light on the debate among administrators and politicians over the efficacy of current tax laws and governance structures in the presence of heightened GPR.

The authors extend the literature on GPR by analyzing its effect on corporate tax avoidance. Unlike existing single-country studies, the authors use a cross-country setup to investigate the impact of GPR on tax avoidance, making this study’s results more generalizable as the authors control for a host of country, industry, and time factors. Apart from political uncertainty, terrorism, and climatic issues, the authors document GPR as a strong macroeconomic driver of corporate tax avoidance. The authors make a new contribution to the literature on the moderating role of governance and institutional factors on the association between tax avoidance and GPR in an international context. The authors also contribute to the literature on macroeconomic determinants of tax avoidance.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

This study aims to investigate the effect of the dynamics and potential of the board of directors on investment efficiency and the comparability of financial information in companies listed on the Tehran Stock Exchange.

The number of observations for this study includes 1,218 observations from companies listed on the Tehran Stock Exchange during 2014–2020. The authors used econometric statistical methods such as multiple linear regression, the Chow and Hausman test and the Kendall correlation coefficient using Eviews software to conduct the research. To measure the board’s effectiveness, two variables are used, including board dynamics and potential.

The results showed a positive and significant relationship between dynamics, board potential and investment efficiency. Also, no significant relationship was observed between the board dynamics and the comparability of financial information. Finally, a positive and significant relationship exists between the board’s potential and the comparability of financial information.

The importance of this research is the use of board proxies, including the dynamics and potential of the board. In addition, other variables of board characteristics, such as size, independence, ownership and gender, and the relationship between these variables with investment efficiency and comparability of financial information, have been examined in this study.

Ethical concerns in the field of trade and business and research efforts in the area of management and implementation of ethical standards in the organizational chain in the current situation, mainly due to the impact on the performance and procedures of organizations, and, finally, the effect on the views and attitudes of stakeholders and users of corporate information have become critical. Therefore, this study aims to investigate the impact of business ethics on stress, anxiety and depression and the success of auditors in Iran and Iraq.

The study’s statistical population includes all partners, managers and auditors working in auditing firms. Three hundred sixty-five questionnaires were completed by Iranian respondents and 250 questionnaires by Iraqi respondents in 2021. Also, the analysis of variance and regression of ordinary least squares have been used for data analysis and testing of hypotheses.

The research results show a negative and significant relationship between the results of business ethics and stress, anxiety and depression of auditors in Iran and Iraq and a positive relationship between business ethics and auditors’ success in Iran and Iraq.

As the current research was conducted in emerging financial markets such as Iran and Iraq, which are highly competitive, along with special economic conditions, and as the occupation of the ISIS terrorist group, the civil wars in Iraq, the world’s severe economic sanctions against Iran and following the global crisis of COVID-19, both countries have had special conditions. Therefore, the current research can bring helpful information to the readers and help everyone develop science and knowledge in this field.

This study aims to address the relationship between economic complexities (EC) and the green economy (GE) with fraud in the listed companies on the Tehran stock exchange. The authors study whether EC and GE increase the detection of financial statement fraud.

The authors used a multiple regression model based on the panel data method and fixed effect model to test hypotheses. The sample includes 1,351 companies listed on the Iranian stock exchange from 2014 to 2021.

The results show a negative and significant relationship between EC and GE with financial statement fraud.

Since this research is the first to address the mentioned topic in emerging markets, it provides helpful insights for financial statement users, analysts and legal entities. The study fills the literature gap and promotes knowledge regarding its relevant literature.

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

Bayesian cubature (BC) has emerged to be one of most competitive approach for estimating the multi-dimensional integral especially when the integrand is expensive to evaluate, and alternative acquisition functions, such as the Posterior Variance Contribution (PVC) function, have been developed for adaptive experiment design of the integration points. However, those sequential design strategies also prevent BC from being implemented in a parallel scheme. Therefore, this paper aims at developing a parallelized adaptive BC method to further improve the computational efficiency.

By theoretically examining the multimodal behavior of the PVC function, it is concluded that the multiple local maxima all have important contribution to the integration accuracy as can be selected as design points, providing a practical way for parallelization of the adaptive BC. Inspired by the above finding, four multimodal optimization algorithms, including one newly developed in this work, are then introduced for finding multiple local maxima of the PVC function in one run, and further for parallel implementation of the adaptive BC.

The superiority of the parallel schemes and the performance of the four multimodal optimization algorithms are then demonstrated and compared with the k-means clustering method by using two numerical benchmarks and two engineering examples.

Multimodal behavior of acquisition function for BC is comprehensively investigated. All the local maxima of the acquisition function contribute to adaptive BC accuracy. Parallelization of adaptive BC is realized with four multimodal optimization methods.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

This study explores the link between ISO 9001 certification, personal data protection and firm performance using financial balance sheet and survey data. The security aspect of data protection is analyzed based on the major requirements of the General Data Protection Regulation and mapped to the relevant controls of the ISO/IEC 27001/27002 standards.

The research analysis is based on 96 ISO 9001–certified and non-certified publicly traded manufacturing and service firms that responded to a structured questionnaire. The authors develop and empirically test their theoretical model using the structural equation modeling technique and follow a difference-in-differences econometric modeling approach to estimate financial performance differences between certified and non-certified firms accounting for the level of data protection.

The estimates indicate three core dimensions in the areas of “policies, procedures and responsibilities,” “access control management” and “risk-reduction techniques” as desirable components in establishing the concept of data security. The estimates also suggest that the data protection level has significantly impacted the performance of certified firms relative to the non-certified. Controlling for the effect of industry-level factors reveals a positive relationship between data security and high-technological intensity.

The results imply that improving the level of compliance to data protection enhances the link between certification and firm performance.

This study fills a gap in the literature by empirically testing the influence of data protection on the relationship between quality certification and firm performance.