The purpose of this research was to analyze how different effects of the COVID pandemic, expressed through pandemic accentuated occupational stress, perceived job insecurity, occupational safety and health perception and perceived organizational effectiveness, may impact turnover intentions of the personnel in the hospitality industry. Our research team designed an online questionnaire which was analyzed with network analysis to depict the relationship between factors, and, then, a confirmatory factor analysis was employed to confirm the distribution of the items to the envisaged five factors. Based on a sample of 324 randomized Romanian hospitality industry staff, the results of our cross-sectional study revealed that occupational safety and health perception, perceived organizational effectiveness and perceived job insecurity in the pandemic accentuated occupational stress to indirectly and significantly impact hospitality industry staff turnover intentions (TI). The results indicated that, while the total effect of PAOS on TI was significant, the direct effect was still significant, while all three mediators remained significant predictors. Overall, mediators partially mediated the relationship between PAOS and TI, indicating that employees with low scores on occupational safety and health perception (OSHP), and perceived organizational effectiveness (POE) and high scores on perceived job insecurity (PJI) were more likely to have higher levels of TI turnover intentions.

The objective of the article is to analyze, based on social exchange theory, the different risk and safety perceptions of employees and customers in the hospitality industry regarding the protections against the SARS-CoV-2 pandemic in an emerging market, namely in Romania. To this end, a questionnaire was administered simultaneously to both categories in Romanian hospitality units obtaining a sample of 561 employees and customers in the sector. While the emergence of SARS-CoV-2 virus has generated a lot of diverse research, studies focused on this particular topic, also regarding both customers and employees, were much less exploited. Therefore, eleven working hypotheses were formulated. It was highlighted that there is a positive perception of safety at work for employees, and jobs are protected from disappearance due to the pandemic through active measures taken by the organization. Employees do not show a significant desire to change jobs due to the new working conditions. The magnitude of safety measures taken had a positive impact on the customers, and customers do not pose a significant threat in hospitality industry units regarding the possibility of infection with the SARS-CoV-2 virus. The paper enlarges the understanding of behavioral effects of the SARS-CoV-2 pandemic, while from a managerial perspective the results are particularly useful for hospitality industry owners in order to attract and retain employees and to communicate and develop better relations with customers.

A bean counter is defined as an accountant or economist who makes financial decisions for a company or government, especially someone who wants to severely limit the amount of money spent. The rise of the bean counter in both public and private companies has motivated us to develop a Bean Counter Profiling Scale in order to further depict this personality typology in real organizational contexts. Since there are no scales to measure such traits in personnel, we have followed the methodological steps for elaborating the scale’s items from the available qualitative literature and further employed a cognitive systems engineering approach based on statistical architecture, employing cluster, factor and items network analysis to statistically depict the best mathematical design of the scale. The statistical architecture will further employ a hierarchical clustering analysis using the unsupervised fuzzy c-means technique, an exploratory factor analysis and items network analysis technique. The network analysis which employs the use of networks and graph theory is used to depict relations among items and to analyze the structures that emerge from the recurrence of these relations. During this preliminary investigation, all statistical techniques employed yielded a six-element structural architecture of the 68 items of the Bean Counter Profiling Scale. This research represents one of the first scale validation studies employing the fuzzy c-means technique along with a factor analysis comparative design.

Sustainable urban development has come to play an essential role in establishing and growing future sustainable cities, or smart cities, which are urban areas that have an optimum carbon footprint, are nature-friendly, and are smart enough to enhance energy efficiency. This study is based on qualitative research in which data were collected from interviews with real estate development specialists. The interviews were addressed to a total of 30 real estate developers from Romania between July and December 2022 and were conducted using the Zoom interface. The aim of this research was to analyze whether familiarity with the smart green concept influences the decision to implement the IoT on a large scale at the organizational level through the perception of specific determining factors in choosing the development of green building projects considering the operational costs. The results revealed a significant indirect effect of green building knowledge on large-scale IoT implementation through the mediator of the perception of operating cost factors, supporting our hypothesis. The direct effect in the presence of the mediator was not found to be significant anymore. Hence, there is full complementary mediation by the perception of operating cost factors on the relationship between green building knowledge and large-scale IoT implementation.

The purpose of this study is to examine the factors that influence vaccination options, including vaccination against COVID-19, in order to develop a management algorithm for decision-makers to reduce vaccination reluctance. This paper’s primary objective is to empirically determine the relationships between different variables that correlate to non-vaccination behavior of the target population, as well as the implications for public health and situational management strategies for future vaccination intentions. We created a questionnaire to investigate the personal approach to disease prevention measures in general and vaccination in particular. Using SmartPLS, load factors for developing an algorithm to manage vaccination reluctance were calculated. The results shows that the vaccination status of an individual is determined by their vaccine knowledge. The evaluation of the vaccine itself influences the choice not to vaccinate. There is a connection between external factors influencing the decision not to vaccinate and the clients’ motives. This plays a substantial part in the decision of individuals not to protect themselves by vaccination. External variables on the decision not to vaccinate correlate with agreement/disagreement on COVID-19 immunization, but there is no correlation between online activity and outside influences on vaccination refusal or on vaccine opinion in general.

This study examines the relationship between apartment prices in Romania and various influencing factors, for which we have constructed a model using multiple linear regression. This type of regression equation's parameters hold practical significance as they aid in establishing the regression equation. The form of the connection, specifically the parameters that define it, greatly influences our understanding of the connection between various quantified variables in the proposed model. By determining the regression function in this manner, we created a model that allows us to make predictions or draw conclusions based on the variables within the model. Microeconomic variables are crucial in influencing real estate price variations at the individual property level. These variables are more specific and relate to factors within the local market of Arad or the property itself. Some key microeconomic variables can influence real estate prices, which are important to ascertain using statistical tools.