This study explores the impact of artificial intelligence (AI)-based technologies on leadership-based organizational communication and employee performance within contemporary workplaces. While prior research has acknowledged AI’s potential in optimizing communication processes, significant gaps remain in understanding its specific influence on core communication dimensions and organizational outcomes. This study addresses these gaps by examining six key communication elements—informing, message reception, feedback, acceptance, persuasion, and reaction—to assess whether AI technologies significantly enhance employee performance by improving internal communication efficiency and reducing transmission errors, which are crucial for productive interactions. Using a quantitative approach, data were collected via a self-administered questionnaire from 203 employees of a major Romanian food industry company operating globally, including leaders and employees from three Eastern European countries. Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the relationships between communication dimensions and performance. The findings revealed that informing, receiving, and accepting messages, along with reaction-provoking, had strong positive effects on performance, while feedback and persuasion showed moderate impacts. These results emphasize the transformative role of AI in communication processes, optimizing message flow and positively influencing employee behavior, thereby enhancing productivity and organizational efficiency. This research contributes to the growing body of literature by situating AI-driven communication within the broader organizational context, offering actionable insights for managers aiming to integrate AI ethically and effectively. Additionally, it offers a set of recommendations for employees and managers to lead communication process according to the new actual era of digitization, which is offering real benefits for both parts. It also provides a robust foundation for future research, encouraging longitudinal and cross-cultural studies to further investigate AI’s implications for organizational diversity, innovation, and employee well-being.

Approaches to the concept of workforce diversity have developed in recent years. This subject is now under continuous research by specialists in talent management. Simultaneously with the global tightening of COVID-19 restrictions, companies have been imposed upon to support an increasingly higher diversity, thus noticing corporate cultures stimulating communication, innovation, and superior yield results, attracting the best talent. This article analyzes the data obtained through research work targeting the understanding of companies’ orientation toward diversity in the workplace and the assessment of the policies necessary, focusing on four themes: motivation, innovation and creativity, leadership, and social responsibility. Each analyzed variable influenced diversity in the workplace from a significant level (3.33 for motivation) to a high level (4.00 for innovation/creativity and 4.00 for leadership, and 3.71 for social responsibility), showing a genuine interest in Romanian companies supporting diversity in the workplace.

This study explores how visual merchandising and store layout influence customer shopping decisions. This research aims to identify the key factors that shape consumer behavior and provide useful insights for retailers by using interactive marketing factors. Data were collected from 488 respondents in the South Muntenia region of Romania, a key economic hub with significant retail activity. Given its economic diversity and proximity to Bucharest, this region serves as a representative microcosm of consumer behavior trends in Romania. The survey was distributed physically and virtually through social media platforms (Facebook and WhatsApp groups, and email networks) to ensure a diverse and representative sample. To analyze the data, this study utilized SmartPLS, a powerful tool for structural equation modeling (SEM). This approach allowed for an in-depth examination of the relationships between visual merchandising, store layout, and customer shopping behavior, providing a more comprehensive view of the conceptual model. This study’s theoretical contributions stem from its holistic approach, which combines different aspects of visual merchandising and store layout into a unified conceptual model. By analyzing how these elements work together, this study offers a deeper understanding of customer shopping behavior. Retailers are encouraged to prioritize product arrangement, make effective use of exterior lighting, and maintain engaging window displays. These strategies can attract more customers, encourage impulse purchases, and improve the overall store profitability. This study underscores the critical role that visual merchandising and store layout play in shaping customer shopping decisions. By optimizing product layout, enhancing exterior lighting, and creating captivating window displays, retailers can design a more engaging shopping experience that boosts customer satisfaction, increases sales, and builds customer loyalty.

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.