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.

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 paper examines the impact of social stratification on trust in recommender systems. Recommender systems have become an essential tool for users to navigate vast amounts of information online, but trust in these systems has become a concern. The focus of this study is to investigate whether social stratification, defined by socioeconomic status, affects trust in recommender systems. We first review the literature on trust in recommender systems and social stratification, highlighting gaps in the current research. We then describe the methodology used in our study, which involves the analysis of valid and consented responses received from 487 participants from different socioeconomic backgrounds, registered in an online survey. This study aimed to investigate the influence of social stratification, specifically income, on trust in recommender systems. Results showed a curvilinear relationship between income and trust in recommender systems, such that moderate income levels were associated with higher levels of trust, while both low- and high-income levels were associated with lower levels of trust. These findings suggest that income plays an important role in shaping users’ trust in recommender systems and highlight the need for future research to examine the complex interplay between social stratification and trust in technology.

This study examines the acceptance of artificial intelligence (AI) among professionals in accounting and auditing by applying the Technology Acceptance Model (TAM). The research addresses the broader question of how AI technologies are integrated into professional decision-making in financial services, with a focus on the determinants that shape user acceptance. A standardized survey, consisting of 21 items designed to measure TAM components, was distributed to accounting and auditing professionals. Data were analyzed through confirmatory factor analysis to validate the construct structure, and Pearson’s correlation and network analyses were used to identify relationships among variables. The results confirmed a robust seven-factor model with significant interrelations, highlighting AI knowledge as a central variable influencing perceived usefulness (PU), self-efficacy, and intention to use AI. These findings demonstrate that professional familiarity and confidence in AI tools are key drivers of technology acceptance. The study concludes that enhancing AI literacy can strengthen organizational readiness for digital transformation. Additionally, the research underscores the contribution of women-led scholarship in promoting responsible and inclusive approaches to AI adoption within the accounting and auditing sectors.

The purpose of this paper is to provide an understanding of corporate risk management practices in Romanian companies, by investigating the risk management approaches Romanian companies take. Our main findings are that Romanian managers are not aware of the magnitude of exposure their companies have to various types of risk – hazard, operational, financial and strategic risks, while they are able to manage rather well all these risks, even the ones that have the lowest impact on the business. At the same time, risk management systems employed by Romanian companies are rather inarticulate and based on traditional approaches towards risk management, which might represent by itself a major source of risk, given the complexity of the business environment they face.