The rapid expansion of artificial intelligence (AI) in social sciences research introduces both transformative potential and critical ethical dilemmas. This study examines the role of researchers as either ethical gatekeepers or pioneers of AI-driven change. Through a bibliometric analysis of 464 records from the Web of Science Core Collection, we identify key themes in ethical AI discourse using VOSviewer Version 1.6.20. The findings highlight dominant ethical concerns, including governance, bias, transparency, and fairness, emphasizing the need for interdisciplinary collaborations and responsible AI frameworks. While AI offers efficiency and scalability in research, unresolved issues related to algorithmic bias, governance, and public trust persist. The overlay visualization underscores emerging trends such as generative AI, policy-driven governance, and ethical accountability frameworks. This study calls for a shift from passive oversight to proactive ethical stewardship in AI-driven social science research.

The rapid advancement of Big Data and artificial intelligence (AI) has significantly transformed management accounting practices, necessitating a reevaluation of job profiles and skill-sets required for professionals in this field. This study explores managerial accounting practices in Romanian contexts, examining how digital technology aligns with competitive strategy, managerial efficiency, human resources constraints, and limited resources constraints. Grounded in technology determinism and the resource-based view theory, this research identifies factors influencing the successful implementation of and challenges associated with managerial accounting practices. A sequential mediation analysis investigates pathways wherein investments in human resources and constraints related to limited resources influence managerial advancement through digital technology and competitive strategy. This study emphasizes digital technologies’ role in optimizing costs, enhancing operational processes, and facilitating strategic decision-making. This study’s conclusions show that, even in situations with limited resources, digital transformation projects greatly improve managerial effectiveness and competitive strategy. The participants included 406 professional accountants from the Romanian accounting community. Practical implications for companies include the necessity for strategic planning in digital implementations to mitigate constraints and capitalize on opportunities for sustainable growth and competitive advantage. This report provides a path to optimize the potential of digital technology and gives practical recommendations for researchers and business leaders.

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.

This study examines the key drivers behind the adoption of artificial intelligence (AI) in the accounting profession, emphasizing the influence of AI-related knowledge, personality traits, and professional roles. By applying Decision Tree Regression analysis to survey data from accounting professionals, our research identifies AI knowledge as the strongest determinant of AI adoption, underscoring the importance of expertise in technology acceptance. While personality traits play a secondary role, extraversion and openness emerge as significant factors influencing adoption intentions. The study further explores AI applications in financial auditing, tax compliance, and fraud detection, clarifying the specific accounting domains impacted by AI integration. These findings offer valuable guidance for policymakers, educators, and business leaders aiming to equip the accounting workforce with the necessary skills and mindset to navigate the AI-driven transformation of the profession.

This study examines the key factors influencing investment decisions through decision tree regression, grounded in behavioral finance theory. By analyzing a comprehensive dataset incorporating behavioral, demographic, and financial variables—including investment attitudes, decision-making behaviors, financial education, age, income, and education—this study identifies significant predictors of investment outcomes. While the model shows moderate predictive performance (R² = 0.185; MAPE = 172.96%), it identifies hierarchical relationships among behavioral, cognitive, and demographic predictors. These results highlight the complexity of investment decisions and the need for integrative, behavioral-driven approaches in predictive modeling. Investment attitudes (25.88%), decision-making behaviors (19.53%), and financial education (16.68%) emerge as the most influential variables, while traditional demographic factors such as income and age have a lower impact. The hierarchical structure of the decision tree highlights critical decision-making patterns, particularly regarding speculative behaviors and investment attitudes. These findings challenge classical models of rationality by emphasizing the dominant role of behavioral factors in investment decision making. This study contributes to bridging computational modeling with financial economics, demonstrating the utility of decision tree regression in uncovering complex investor behavior. Practical implications include enhancing personalized financial advisory services and designing targeted financial literacy programs to improve decision-making efficiency. These insights, while exploratory, can guide future research and decision-support systems in behavioral finance.

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.