درخت حوزه‌های تخصصی

Managers are the most valuable asset for every organization. Organizational health is the term given to a business's capacity to adapt in a constantly evolving world. It refers to “the ability of an organization to cope with change and continue to function with a high-performance workplace culture.” And these days, the organizational environment is always shifting. This study aimed to investigate the dimensions and components of managers’ organizational health at the Azad University Branches of Gilan. The results showed that organizational health has 2 dimensions included "structural and managerial". The structural dimension has 5 components including "organizational atmosphere, organization structure, scientific emphasis, monitoring and evaluation, and organizational justice". The managerial dimension has 5 components including "ethics, goal setting, resource support, effective leadership, and consideration". The effect of all dimensions and components on organizational health was confirmed, and the management dimension with a factor loading of (0.960) has the first rank and the structural dimension with a factor load of (0.947) has the last rank. The results of the Kolmogorov-Smirnov test showed that organizational health variables "structural and managerial" have a normal distribution (p> 0.05). The results of the KMO & Bartlett test indicate that these assumptions are confirmed (p> 0.05). The findings of the study also showed that the presented model has a good fit. Organizational health is a vital aspect of many jobs, including educational jobs, in which managers are required to comply with their employees and subordinates. On the other hand, organizational health will create a pleasant environment for the workplace and increase the spirit of creativity to achieve organizational goals.

In healthcare, a diagnosis is reached after a thorough physical assessment and analysis of the patient's medicinal history, as well as the utilization of appropriate diagnostic tests and procedures. 1.7 million People worldwide lose their lives every year due to complications from chronic kidney disease (CKD). Despite the availability of other diagnostic approaches, this investigation relies on machine learning because of its superior accuracy. Patients with chronic kidney disease (CKD) who experience health complications like high blood pressure, anemia, mineral-bone disorder, poor nutrition, acid abnormalities, and neurological-complications may benefit from timely and exact recognition of the disease's levels so that they can begin treatment with the most effective medications as soon as possible. Several works have been investigated on the early recognition of CKD utilizing machine-learning (ML) strategies. The accuracy of stage anticipations was not their primary concern. Both binary and multiclass classification methods have been used for stage anticipation in this investigation. Random-Forest (RF), Support-Vector-Machine (SVM), and Decision-Tree (DT) are the prediction models employed. Feature-selection has been carried out through scrutiny of variation and recursive feature elimination utilizing cross-validation (CV). 10-flod CV was utilized to assess the models. Experiments showed that RF utilizing recursive feature removal with CV outperformed SVM and DT.

The objective of this study is to understand the customer journey in technology product adoption by exploring the experiences and perspectives of individuals who have recently adopted new technology products. This research aims to identify key themes and factors influencing the customer journey from initial awareness to ongoing engagement and retention, providing insights to enhance customer experiences and inform marketing strategies. This qualitative study employed semi-structured interviews with 20 participants selected through purposive sampling to ensure diverse representation. Participants included individuals who had recently adopted technology products within the past six months. Data collection continued until theoretical saturation was reached. The interview transcripts were analyzed using NVivo software, following an inductive approach to identify patterns and themes. The analysis revealed three main themes: Awareness and Decision-Making, First-Time Use and Learning, and Ongoing Engagement and Retention. Initial awareness was driven by advertising, social media, and word-of-mouth, while decision-making was influenced by peer recommendations, brand reputation, and price sensitivity. The first-time use phase highlighted the importance of intuitive design and support resources. Regular updates, community engagement, and product reliability emerged as critical factors in ongoing engagement and retention. Participants valued seamless omnichannel experiences, personalized marketing efforts, and robust support systems. The study underscores the significance of personalized, seamless customer journeys in technology product adoption. Businesses should focus on integrating physical and digital touchpoints, leveraging AI for personalized experiences, and maintaining active user communities to enhance customer satisfaction and loyalty. Addressing cultural nuances in global markets and managing digital signals effectively can further optimize the customer journey. These insights provide valuable guidance for businesses aiming to improve customer experiences and foster long-term engagement in the competitive technology market.