School of Management Science

The purpose of this study is to explore the social learning theory in terms of connectivism, constructivism, and role modeling approach in a clinical setting. This article also analyze the possible role of connectivism, constructivism, and, role modeling as learning theories in terms of dealing with useful student learning instructional strategies. All these approaches are key concepts to align any level of education (primary, secondary, and particularly higher education). The research is qualitative in design and uses an inductive approach. Using a qualitative content analysis approach enables in-depth study of studies as well as data interpretation, leading to conclusions regarding the meaning of diverse experiences. The authors have given insight into designing teaching practices that adapt to changes in how management, physicians, and medical students observe, learn, communicate, generate and share new knowledge. The current study provides a summary of approaches and theoretical insights about planned and unplanned learning. This research will be valuable in developing role modeling instructional programs. Encouraging clinical instructors to make continual efforts to enhance role modeling and teaching time management and self-control skills can assist clinical teachers to overcome the problems of learning with role modeling. The finding shows that collective learning outperforms individualistic learning in terms of improving engagement, enhancing quality, and producing positive interpersonal outcomes. Encouraging clinical instructors to make continual efforts to enhance role modeling and teaching time management and self-control skills can assist clinical teachers to overcome the problems of role modeling. The building is a theory that affirms that learning is a process for the learner Role modeling is effective for medical students. The implications and future directions would be beneficial for researchers and academicians seeking to empirically extend their study on connectivism, constructivism, and role modeling approaches

Purpose-Human resources transformation is an essential concept that significantly changes how organizations manage their personnel in an ever-changing business environment. This research intends to explore the potential of artificial intelligence (AI) technologies to facilitate human resource (HR) transformation in automobile companies in China. Design/methodology/approach-The research employs qualitative approaches such as interviews and case studies to gain insights and viewpoints from key stakeholders. The interviews were semi-structured and carried out in person, enabling an in-depth investigation of their experiences and perspectives. The duration of each interview ranged from 30 to 40 minutes. Findings-The study explored three main themes: employee acceptability, career growth and performance, using system thinking theory. The findings highlight the various effects of AI technology on HR changes in automobile businesses. First, AI helps increase employee approval by simplifying activities and improving user experience. Second, it greatly impacts professional advancement by providing tailored learning and skillbuilding chances. Third, AI integration has a favorable effect on organizational performance indicators such as efficiency, agility and adaptability. Practical implications-This research's findings significantly contribute to scholars and practitioners engaged in HR reforms. Comprehending the subtle impacts of AI technology on employee acceptability, career advancement and organizational effectiveness helps guide strategic decision-making. Furthermore, these results emphasize the need to connect AI-based HR initiatives with overall corporate goals to enhance impact and efficiency Originality/value-This research provides new insights into how AI technologies interface with HR transformation. The study offers a thorough understanding of intricate dynamics and identifies new possibilities for using AI in HR practices via system thinking theory. The research enhances academic discussion on HR transformation in the age of AI-driven innovation.