Search results

The labor market responds in a differentiated manner during recessions and expansions, and it is of vital importance to know the magnitude asymmetries. The purpose of this paper is to evaluate the effects of the disinflationary monetary policy (2005Q1–2022Q4) through the sacrifice rate measured in terms of unemployment and rate of critical labor conditions (RCLC) with nonlinear auto regressive distributed lag (NLARDL; Shin et al., 2014), which allows to efficiently estimate asymmetric effects in short and long terms in the presence of variables of different integration orders.

The authors estimate an asymmetric accelerationist Phillips curve, augmented with labor precariousness for Mexico (2005Q1–2022Q4) following the NLARDL approach (Shin et al., 2014).

The authors prove that the increase in the unemployment gap has greater disinflationary effects than the RCLC in both the short and the long term; the expansionary phases of the business cycle, which reduce U^Gap, do not have inflationary effects either in the short or in the long run, but improvements in the labor market do, when RCLC is reduced; raising RCLC appears to have been the companies’ main survival strategy since 2015; and these asymmetries can generate a low unemployment trap with high and growing precariousness, with huge dynamic costs for well-being, economic growth, inequality and poverty.

As labor precariousness grows, the implications are several both in the short and long run. In the short run, the most notorious example of the effects on workers has to do with unstable and insecure situations, that disrupt all their life planning options, and health issues. Bohle et al. (2004) found in the Organization for Economic Cooperation and Development countries that casual employees had less desirable and predictable working hours, greater work–life conflict and more associated health complaints than people with permanent jobs.

The approach includes the labor precariousness variable, which describes a new phenomenon in the labor market. Nowadays, workers are facing a new threat since firms are employing a new labor cost reduction strategy in which they do not lay off workers but rather paying them less, working them more hours, or reducing benefits. The asymmetries between the effects of precarity and unemployment can generate a poverty trap in the long run. This problem is, once again, of great relevance in the context of global high inflation.

The asymmetry of the velocity profile caused by geometric deformation, complex turbulent motion and other factors must be considered to effectively use the flowmeter on any section. This study aims to better capture the flow field information and establish a model to predict the profile velocity, we take the classical double elbow as the research object and propose to divide the flow field into three categories with certain common characteristics.

The deep learning method is used to establish the model of multipath linear velocity fitting profile average velocity. A total of 480 groups of data are taken for training and validation, with ten integer velocity flow fields from 1 m/s to 10 m/s. Finally, accuracy research with relative error as standard is carried out.

The numerical experiment yielded the following promising results: the maximum relative error is approximately 1%, and in the majority of cases, the relative error is significantly lower than 1%. These results demonstrate that it surpasses the classical optimization algorithm Equal Tab (5%) and the traditional artificial neural network (3%) in the same scenario. In contrast with the previous research on a fixed profile, we focus on all the velocity profiles of a certain length for the first time, which can expand the application scope of a multipath ultrasonic flowmeter and promote the research on flow measurement in any section.

This work proposes to divide the flow field of double elbow into three categories with certain common characteristics to better capture the flow field information and establish a model to predict the profile velocity.

The purpose of this paper is to explore how effectively leading sustainable hotels have integrated Sustainable Development Goals (SDGs) into their reporting. The main aim is to pinpoint areas for improvement concerning SDG reporting which can help the hospitality industry to achieve a transformation in a more SDG-aligned global tourism system.

For this study, a content analysis technique was used to extract the information regarding strategic consistency of SDG reporting. Both qualitative and quantitative approaches were applied to the analysis of this information. This paper seeks to assess the extent to which the materiality analysis, corporate targets and performance indicators defined by the world’s top sustainable hotels in their sustainability reports are consistent with those SDGs linked to the business. To that end, the authors have selected the most sustainable hotels according to the SAM Corporate Sustainability Assessment in 2020.

The results of this study show that the most sustainable hotel companies did not take a strategic consistency approach when reporting the SDGs. These findings identify four areas for improvement concerning reporting, which may promote the adoption of a strategic and consistent approach in SDG reporting.

This study includes a set of recommendations to provide the market with complete, coherent and comparable information on their contribution to the SDGs and, therefore, foster collective learning to bring about sustainable tourism transformation.

This paper represents a contribution to the discussion on the strategic or symbolic implementation of SDGs at a corporate level. In addition, this paper reflects a deeper understanding of how hotel companies could improve their reporting and management system to contribute to SDGs.

Trust is the glue that holds cooperative relationships together and often exists in an asymmetric manner. The purpose of this study is to explore how to mitigate the issue of losses or increased transaction costs caused by opportunistic behavior in a soft environment where trust asymmetry is quite common and difficult to avoid.

This study focuses on examining asymmetric trust between the government and the private sector in public-private partnership (PPP) projects. Drawing upon both project realities and relevant literature, the primary conditional variables influencing asymmetric trust are identified. These variables encompass power perception asymmetry, information asymmetry, interaction behavior, risk perception differences and government-side control. Subsequently, through the use of a survey questionnaire, binary-matched data from both the government and the private sector are collected. The study employs fuzzy-set qualitative comparative analysis (fsQCA) to conduct a configurational analysis, aiming to investigate the causal pathways that trigger asymmetric trust.

No single conditional variable is a necessary condition for the emergence of trust asymmetry. The pathways leading to a high degree of trust asymmetry can be categorized into two types: those dominated by power perception and those involving a combination of multiple factors. Differences in power perception play a crucial role in the occurrence of high trust asymmetry, yet the influence of other conditional variables in triggering trust asymmetry should not be overlooked.

The findings can contribute to advancing the study of trust relationships in the field of Chinese PPP projects. Furthermore, they hold practical value in facilitating the enhancement of trust relationships between the government and the private sector.

The purpose of this paper is to investigate whether information asymmetry mediates the relationship between disclosure and cost of equity.

The study is based on a sample of 500 companies listed in Bombay Stock Exchange for a period of six years from 2015 to 2021. Panel data regression is applied to analyze the relationship between voluntary disclosure, cost of equity and information asymmetry. Mediation effect of information asymmetry is tested with the help of Barron and Kenny’s (1986) approach.

Findings suggest that in case of Indian companies, disclosure reduces cost of equity directly and indirectly through mediation of information asymmetry. Indian investors value credible information for better estimation of future returns, supporting the validity of estimation risk and stock market liquidity hypothesis, which proposes an inverse relationship between disclosure and cost of equity.

Managers can use the findings to strategize their disclosure policy and secure funds at lower cost. Shareholders can monitor managerial actions by demanding credible disclosures. Government too can encourage voluntary disclosure by providing special incentives to the firms.

This study is a pioneering research that investigates the mediating influence of information asymmetry between disclosure and cost of equity with reference to the Indian corporate landscape.

This paper aims to study the impact of geopolitical uncertainty on corporate cost of debt and the moderating role of information asymmetry between creditors and borrowing firms.

This study uses 5,223 firm-quarter observations on German-listed firms spanning 2010:Q1–2021:Q4. This study regresses the cost of debt financing on the geopolitical risk, accounting quality and other control variables. Information asymmetry is measured using the performance-matched Jones-model discretionary accrual and the stock bid-ask spread. It uses interaction terms to check if information asymmetry moderates the impact of geopolitical uncertainty on the cost of debts and control for the moderating role of business risk. For the sake of robustness check, it uses long-term cost of debt and bond spread as alternative dependent variables. In addition, this study executes instrumental variables regression and propension score matching to control for potential endogeneity problems.

Estimation results show that geopolitical uncertainty exerts a positive impact on the cost of debt. This impact is found to be more important on the cost of long-term debts. Information asymmetry is found to exacerbate the positive impact of geopolitical risk on the cost of debt. These results are robust to the change of the dependent variable and to the mitigation of potential endogeneity. At high levels of information asymmetry, this impact is more important for firms belonging to “Transportation”, “Automobiles and auto parts”, “Chemicals”, “Industrial and commercial services”, “Software and IT services” and “Industrial goods” business sectors.

Geopolitical uncertainty should be seriously considered when setting strategies for corporate financial management in Germany and similar economies that are directly exposed to geopolitical risks. Corporate managers should design a comprehensive set of corporate policies to improve their transparency and accountability during increasing uncertainty. Policymakers are required to implement innovative monetary and fiscal policies that take into consideration the heterogeneous impact of geopolitical uncertainty and information transparency in order to contain their incidence on German business sectors.

Despite its relevance to corporate financing conditions, little is known about the impact of geopolitical uncertainty on the cost of debt financing. To the best of the authors’ knowledge, there is still no empirical evidence on how information asymmetry between creditors and borrowing firms shapes the impact of geopolitical uncertainty on the cost of debt. This paper tries to fill this gap by interacting two measures of information asymmetry with geopolitical uncertainty. In contrast with previous studies, this study shows that the impact of geopolitical uncertainty on the cost of debt is non-linear and heterogeneous. The results show that the impact of geopolitical uncertainty does not exert the same impact on the cost of debt instruments with different maturities. This impact is found to be heterogeneous across business sectors and to depend on the level of information asymmetry.

The gift-giving literature has documented several cases in which givers and recipients do not see eye-to-eye in gift-giving decisions. To help integrate this considerable segment of the gifting literature, this paper aims to develop a social norms-based framework for understanding and predicting giver-recipient asymmetries in gift selection.

Five experimental studies test the hypotheses. Participants in these studies evaluate gifts used in previous research, choose between gifts as either gift-givers or gift-recipients, and/or indicate their level of discomfort with choosing different kinds of gifts. The gifts vary in ways that allow the authors to test the social norms-based framework.

Gift-giving asymmetries tend to occur when one of the gifts under consideration is less descriptively, but not less injunctively, normative than the other. This theme holds for both asymmetries recorded in the gift-giving literature and novel ones. Indeed, the authors document new asymmetries in cases where the framework would expect asymmetries to occur and, providing critical support for the framework, the absence of asymmetries in cases where the framework would not expect asymmetries to emerge. Moreover, the authors explain these asymmetries, and lack thereof, using a mechanism that is novel to the literature on gift-giving mismatches: feelings of discomfort.

This research has multiple theoretical implications for the literatures studying gift-giving and social norms. A limitation of this work is that it left some (secondary) predictions of its model untested. Future research could test some of these predictions.

Billions of dollars are spent on gifts each year, making gift-giving a research topic of great practical importance. In addition, the research offers suggestions to consumers giving gifts, consumers receiving gifts, as well as marketers.

The research is original in that it creates a novel framework that predicts both the presence and absence of gift-giving asymmetries, introduces a psychological mechanism to the literature on giver-recipient gift choice asymmetries, and unifies many of the mismatches previously documented in this literature.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

This paper aims to investigate the relationship between voluntary disclosure and the cost of capital as a direct relationship and as an indirect relationship mediated by information asymmetry. It provides evidence from Jordan as a developing economy.

The sample was selected from the companies listed in the first market of the Amman Stock Exchange during the period 2010–2019. Four exclusion criteria were used in selecting the companies for analysis.

The findings show that the cost of capital and information asymmetry are negatively affected by voluntary disclosure, as well as that the cost of capital is positively affected by information asymmetry. In addition, information asymmetry does not mediate the relationship between voluntary disclosure and the cost of capital.

This research looks at the mediating effect of information asymmetry in the relationship between voluntary disclosure and the cost of capital; thus, it provides new explanations about it using empirical evidence from a developing economy. As a necessary consequence, this research has the potential to significantly contribute to the existing body of knowledge and literature in this field.

Previous literature posits that corporate governance and information asymmetry are the main factors in making efficient investments. Meanwhile, a growing body of studies is of the opinion that corporate governance can also mitigate the problem of information asymmetry and consequently exerts significant impacts on the association between information asymmetry and investment efficiency. This study aims to analyze the impact of corporate governance and information asymmetry on investment efficiency. It also tests the moderating role of corporate governance in the relationship between information asymmetry and investment efficiency.

The sample consists of 4,082 firms domiciled in 20 developed countries over the years from 2003 to 2019, including 33,812 firm-year observations. The bid–ask spread is used as a proxy for information asymmetry. To measure corporate governance performance, a proxy provided by ASSET4 is employed, and to determine the optimal levels of investments, we relied on the growth opportunity. To estimate the models, ordinary least squares and generalized method of moment are used.

The results reveal that information asymmetry is inversely related to investment efficiency, and, corporate governance mitigates this negative association.

This paper sheds light on the role of corporate governance in firms as a lever for mitigating information asymmetry and tries out information asymmetry and agency theories in relation to the impact of information asymmetry on investment efficiency. It also confirms the theory stating that corporate governance can be considered as a determinant of investment efficiency.