• Energy Research

Generation Y (Gen Y) travel spending is set to soar and has enormous implications for hospitality firms’ economic sustainability. However, previous studies have failed to examine the hierarchical interrelationship between e-loyalty attributes in the presence of a hierarchical structure and linguistic preferences. Consequently, the purpose of this paper is to identify a list of e-loyalty attributes and to examine each attribute’s importance to Gen Y consumers’ e-loyalty intentions in the context of tourism. Drawing on an ad hoc survey of 230 graduate students in Taiwan, the study employs exploratory factor analysis to analyze the method’s validity and reliability. Furthermore, 30 Gen Y consumers are invited to evaluate the identified measures using fuzzy linguistic scales. The fuzzy set theory is then employed to translate the qualitative responses into crisp values. The decision-making trial evaluation laboratory is used to understand the interrelationships between the e-loyalty attributes. The empirical findings reveal that trust, utilitarian features, and hedonic features all play essential roles in Gen Y tourists’ e-loyalty. In addition, perceived enjoyment, website competency, emotional connection, candid and authentic site information, and delightful experiences are crucial factors in the formation of customer e-loyalty. Both theoretical and managerial implications are discussed.

GHG emissions growth of Vietnam is highest all over the globe and the carbon intensity of this economy is considered second highest in Asian economies. As the energy intensity level is rising constantly, thereby, the predictions indicate that Vietnam will become highest GHG emission in the region. In order to address the issue, the current article aims to investigate the impact of green finance, technology advancement, energy efficiency, industrialization, and population growth on sustainable development in Vietnamese context. The time chosen for the study is 1991 to 2020. The study has used the Dynamic Auto-regressive Distributed Lags (DARDL) and the Bayesian Auto-regressive Distributed Lags (BARDL) model for data analysis. Findings exposed that green finance, technology advancement, REO, REC, industrialization, and population growth all are positively connected with sustainable development. The study guides the regulators in establishing regulations related to sustainable development through the adoption of green finance, energy, and technology

People try to find the role of government in today’s modern society. Citizens of any country look forward to benefit from government services. Although the government implements laws and policies in all areas of society, people only know about it through government’s services. We describe a good government’s service of organization, department, unit, and division that has an appropriate human strategy. Purpose: Purpose of this study is to investigate which factors have been missing that connects and maintains the sustainability between the leadership style and employees’ satisfaction in the government sector of Mongolia. More specifically, the purpose of the study is to investigate the missing link between leadership style and job satisfaction among Mongolian public sector employees. This study reiterates the mediating role of organizational climate (OC) and work style (WS) in a new proposed model. Methodology: The questionnaire is designed by a synthesis of existing constructs in current relevant literature. The research sample consisted of 143 officers who work in the primary and middle units of the territory and administration of Mongolia. Factor analysis, a reliability test, a collinearity test, and correlation analyses confirm the validity and reliability of the model. Multiple regression analysis, using Structural Equation Modeling (SEM), tests the hypotheses of the study. The sample of this study is chosen from the public organization. Mongolia is a developing country. This country needs good public leaders who can serve citizens. This study will be extended further. In addition, Mongolia really needs sufficient studies. Practical implications: This study has several important implications for studies related to organizational behavior and job satisfaction. Furthermore, the implications of these findings are beneficial to organizations aimed at improving policies and practices related to organizational behavior and human resource management. Regulators and supervisors of private or public organizations aiming to increase the level of their employees’ job satisfaction will also benefit from the findings. Therefore, this study’s new proposed model can be the basis of fundamental research to build a better human resource policy. Although the leadership style is an influential factor for job satisfaction, this study identifies the mediating missing links between the leadership style and employees’ job satisfaction. Findings: The findings of this research indicate that the organizational climate and work style complement and fully mediate the relationship between leadership style and job satisfaction. An appropriate leadership style is most effective when it matches the organizational climate as well as employees’ work style. Furthermore, a suitable organizational climate will increase the level of job satisfaction. If the work style of employees is respected and taken into consideration, the leadership style can find its way into job satisfaction. Originality/value: This study is the first to understand the motivators of job satisfaction in the government sector of Mongolia. This study suggests valuable findings for executive officers who are junior and primary unit’s officers of the register sector of government in Mongolia. The findings of this study help managers and executives in their effort develop and implement successful human resource strategies.

It has been argued in the literature that financial markets with a Confucian background tend to exhibit herding behaviour, or correlated behavioural patterns in individuals. This paper applies the return dispersion model to investigate financial herding behaviour by examining index returns from the stock markets in China and Taiwan. The sample period is from 1 January 1999 to 31 December 2014, and the data were obtained from Thomson Reuters Datastream. Although the sample period finishes in 2014, the data are more than sufficient to test the three hypotheses relating to the stock markets in China and Taiwan, both of which have Confucian cultures. The empirical results demonstrate significant herding behaviour under both general and specified markets conditions, including bull and bear markets, and high-low trading volume states. This paper contributes to the herding literature by examining three different hypotheses regarding the stock markets in China and Taiwan, and showing that there is empirical support for these hypotheses.

Global warming and depletion of the environment are more than a hunch; they are irrefutable facts. While caring for the environment seemed like a luxurious hobby twenty years ago, it has become a crucial necessity these days. However, the sustainable demand for eco-friendly products seems to be less than it should be. In other words, the consumers seem to lack the intent to purchase green products. Therefore, studies on factors affecting sustainable green purchasing intent seem to be crucial now more than ever. This study attempts to shed some light on the matter using structural equation modeling. After receiving the answers of 357 respondents from Thai Generation Y participants, the estimations reveal that attitude towards green packaging and green marketing awareness have the strongest effects on purchasing intention. On the other hand, environmental concern seems to have the weakest effect on purchasing intention. The findings suggest that the producers must pay more attention to packaging and other novel forms of marketing to increase the customers’ intent for purchasing green products.

Estimating wind energy plays an important role in energy science as it can be considered a crucial source of renewable and sustainable energy. In this study, five types of soft computing approaches were implemented to estimate the long-term mean monthly wind speed (W) at 50 weather stations in Iran. The applied models were artificial neural networks (ANN), gene expression programming (GEP), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference system (ANFIS), and random forest (R.F.). In addition, the geographical information (i.e., latitude, longitude, and altitude) and periodicity term (i.e., the number of months in a year) were used to input the models. Results demonstrated that the R.F. technique was the best model for estimating W, utilizing the geographical information and number of the month. Hence, it can be concluded that the applied soft computing techniques can employ the aforementioned inputs for estimating W.