3 research outputs found
The Influence of Traditional Management Practices on Employee Satisfaction, Turnover Intention, and Perceived Organizational Profitability
No full textThis study explores the influence of traditional management practices on employee satisfaction, turnover intention, and perceived organizational profitability. While modern management approaches often emphasize flexibility and innovation, many organizations, particularly in developing contexts, continue to rely on classical principles such as clear hierarchies, standardized procedures, and centralized decision-making. Using a quantitative research design, data were collected from 230 employees across various sectors in Kabul, Afghanistan. The findings reveal that traditional management practices have a strong positive effect on employee satisfaction and perceived profitability, and a significant negative effect on turnover intention. Regression results showed that these practices explained 71% of the variation in employee satisfaction, 53% in perceived profitability, and 61% in turnover intention. These results suggest that structured management systems still offer practical value, especially in work environments that benefit from predictability and clear direction. The study provides empirical support for the continued relevance of traditional management in modern organizational settings and encourages a balanced approach that blends classical structure with evolving workplace needs
The The Impact of Artificial Intelligence on Human Employment: A Traditionalist’s Perspective
No full textThis study investigates the impact of Artificial Intelligence (AI) on human employment through a traditionalist approach, emphasizing the enduring value of human work amidst rapid technological change. While mainstream discussions often highlight efficiency, innovation, and economic opportunity, this research addresses a critical gap: the emotional, ethical, and existential consequences of AI-driven labor transformations. Using a qualitative methodology, semi-structured interviews were conducted with individuals from diverse professional backgrounds who share a traditional view of work as a source of dignity, identity, and moral purpose. Thematic analysis revealed five central themes: the intrinsic value of human labor beyond economic metrics, fears of alienation and dehumanization, the irreplaceability of human emotional and moral intelligence, ethical and spiritual concerns regarding AI’s unchecked influence, and cautious optimism about ethical human-AI collaboration. The findings suggest that while AI offers significant opportunities, it also poses serious risks to the human soul of work if not carefully managed. The study concludes that innovation must not be pursued at the expense of human dignity and meaning. It calls for businesses, policymakers, and researchers to prioritize human-centered approaches to AI development, ensuring that technology serves humanity, not the other way around. By restoring the traditionalist voice to contemporary debates, this research contributes a vital perspective to the broader conversation about the future of work in an AI-driven world
AI in Service Industries: Effects on Customer Satisfaction, Mediated by Service Quality, and Moderated by Customer Trust
No full textThis study examined the application of artificial intelligence in service industries and its impact on customer satisfaction, focusing on the mediating role of service quality perception and the moderating effect of customer trust in AI. AI-driven technologies have transformed customer service by improving efficiency, personalization, and responsiveness. However, the extent to which these enhancements translated into higher customer satisfaction depended on perceived service quality and trust in AI systems. Using a structured survey across various service industries, particularly in empathy-driven sectors like healthcare and education, the research employed statistical analysis to evaluate AI’s direct and indirect effects on customer satisfaction. The findings indicated that AI significantly enhanced customer satisfaction, with a , substantial direct effect (β = 0.642, p < 0.001) and an additional indirect effect through service quality perception (indirect effect = 0.286, p < 0.001). Service quality perception acted as a crucial mediator (β = 0.305, p < 0.001), confirming its importance in shaping satisfaction outcomes. While customer trust positively influenced satisfaction (β = 0.267, p < 0.001), its moderating effect on AI-driven service interactions was not statistically significant (p = 0.199). These results show that AI adoption aligns with customer expectations and ethical considerations. Future research is recommended to explore the long-term impact of AI on customer trust and examine its effectiveness across various industries that require higher levels of emotional intelligence in service delivery
