Search

This study aims to verify the main factors influencing turnover intention in the Iran hospitality industry. The objective of this study is to construct a fuzzy AHP and fuzzy TOPSIS model to evaluate the dimensions of the hotel employee turnover intention model. The performance evaluation for employee turnover intention includes work itself, supervision, coworkers relationship, salary and benefit, career opportunities, job stress, perceived risk, and job insecurity. These dimensions generate a final evaluation for ranking priority among the employee turnover intention of the proposed model. The importance of dimensions is evaluated by 20 experts, and decision-making is processed through the fuzzy concept and fuzzy environment. From the critical fuzzy AHP and fuzzy TOPSIS analysis results, the study shows that the most important dimensions of employee turnover intention in the hotel industry model are salary and benefits. Moreover, the results indicate that the least important dimensions are the Co-workers Relationship, Supervision, and Career Opportunities. The second group dimensions that impact employee turnover in the context of the COVID-19 epidemic are work itself, job stress perceived risk, and job insecurity. In addition, this study's results show that three-star hotels have the highest value of turnover intention; the second is the Four and Five-star hotels, and the third is the below three-star hotels. The results of the study will help businesses in the field of hospitality have a more comprehensive view of human resource management activities. Especially, this study provides implications for hotel managers in understanding employee behavior and their turnover intention during the context of the COVID-19 epidemic based on the eight proposed dimensions. [ABSTRACT FROM AUTHOR]

Environmental changes resulting from industrial activity have been occurring for many years, and with the increasing production of greenhouse gases and other pollutants, these changes have played a critical role in global warming. Nowadays, all countries have become aware of the great importance of attention to the environment alongside economic growth. Therefore, they are all after solutions that would allow maximum economic growth with minimum harm to the environment. In the present study, the environmental efficiency of a given system is evaluated using data envelopment analysis (DEA). For this purpose, the economic and environmental dimensions are taken into consideration for each decision-making unit (DMU), with the condition of having undesirable outputs in the environmental dimension. Then, using the concept of "order of efficiency", an enhanced DEA method is proposed based on weak and strong disposability axioms, which can be used to compare and rank units with undesirable outputs. Next, the capabilities of the proposed approach are demonstrated through an example involving various industries in Iran. Enhanced DEA not only takes more comprehensive input and output sets into account but also monitors the units based on the principles of sustainability. [ABSTRACT FROM AUTHOR]