Search results

This study aims to evaluate the impact of green human resource management on cognitive factors, employees' pro-environmental behavior and the environmental performance of hotels in Vietnam.

Drawing upon primary data collected from a sample of 393 hotel employees, this study utilizes covariance-based structural equation modeling (CB-SEM) to test hypotheses and conduct path analysis.

The study’s findings reveal several key insights. Firstly, GHRM positively influences environmental knowledge (EK), environmental attitude (EA), attitude toward pro-environmental behavior (APB), pro-environmental behavior (PB) of employees and environmental performance (EP) of hotels. Additionally, the results also indicate that EK promotes EA, APB and PB, and both EA and EK can predict PB of employees. Surprisingly, the results from CB-SEM analysis reject the direct impact of APB on PB. Moreover, this study also confirmed the positive influence of PB on EP of hotels. Notably, the research delves into the moderating effect of work overload (WO) on the associations between GHRM and PB, as well as APB and PB.

The findings offer valuable recommendations for hotel managers and policymakers, aiming for effective GHRM practices and improvement of employees’ PB and EP of hotels.

This study broadens the understanding of sustainable management practices, specifically in human resource management in the hospitality industry. Notably, it identifies significant avenues to address theoretical gaps left by previous studies.

This study enhances the financial modelling of companies undergoing an Initial Public Offering (IPO) by focusing on internal capability determinants and IPO proceeds.

A hybrid logistic regression and shallow-depth decision tree approach are employed to predict the initial three-year post-IPO performance of companies listed on the Stock Exchange of Thailand (SET) using data from 2002 to 2021.

The results demonstrate that these models not only perform competitively against complex machine learning algorithms but also surpass them in terms of interpretability, an essential feature in financial modelling. The proposed approach effectively captures the effects of each determinant, offering valuable insights into strategic resource allocation and investment decision-making during transition years.

This study introduces a novel application that integrates logistic regression with decision trees to predict multiclass financial performance, filling the gap between complex machine learning techniques and interpretable financial models. It offers practical tools for companies and investors to make informed decisions in challenging post-IPO environments.

This study aims to investigate whether the changes in gender composition of bank board affects Vietnamese bank stability efficiency.

This research covers a panel of 27 commercial banks in Vietnam over a 14-year period from 2007 to 2020. The two-step system generalized method of moments is used to estimate the gender diversity–Vietnamese bank stability efficiency nexus.

The authors find that a greater degree of board gender diversification enhances bank stability efficiency and reduces bank risk-taking in Vietnam. The relationship between gender diversity and the stability efficiency of Vietnamese banks is still valid under the influence of regulatory capital sufficiency and during the financial crisis. These findings are robust to alternative proxies for risk indicators and consistent with the perspectives of stakeholder and behavior theory.

Although this research revisits the relationship between gender diversity and bank risk-taking, it is the first attempt to explore the role of women on board in enhancing the stability efficiency of banks, using the stochastic frontier approach. These findings shed light on the function of gender diversity as a governance instrument for mitigating risk in an emerging market context.