Search Results for ministry-of-labor-and-social-affairs
Abstract
The research aims to study the correlation and influence of the independent variable organizational Dehumanization in generating the dependent variable cynicism in the workplace This is to reduce cynicism in the workplace, as it is one of the negative phenomena spread in society in general and in the Ministry of Labor and Social Affairs in particular. The research also aims to shed light on one of the modern vocabulary that is dealt with extensively by management writers, which is organizational Dehumanization, and what are the consequences and negative effects that it can have in the Ministry of Labor and Social Affairs. From here, the main problem of the research was identified, which is (What are the reasons that lead to the appearance of cynicism in the Ministry of Labor and Social Affairs? Does the Ministry of Labor and Social Affairs suffer from organizational Dehumanization?). The questionnaire was the main tool for collecting data and information. The research was conducted at the center of the Iraqi Ministry of Labor and Social Affairs, and the research community was represented by all employees at various administrative levels, by choosing a random sample, as (123) questionnaires were distributed, and (104) audited and measurable questionnaires were received. Which represents (85%) of the total distributed questionnaires. In order to process the data, many statistical methods were used, most notably the arithmetic mean, standard deviation, Pearson correlation coefficient, and structural equations model. The most important findings of the research is that there is a significant impact of organizational humanity in generating cynicism in the workplace in the Ministry of Labor and Social Affairs.
Abstract
Artificial intelligence (AI) applications are essential for enhancing payment security and improving customer experience in financial institutions. This research aims to examine the challenges faced by Mastercard in Australia and Visa in Singapore in implementing these technologies. The study focuses on three main areas: challenges related to fraud and privacy, the strategies employed by both companies to address these challenges, and the impact of AI applications on customer satisfaction and trust. The research adopts an analytical methodology that combines both qualitative and quantitative data, relying on diverse sources including academic studies and annual reports. The findings reveal that the effectiveness of AI applications varies across the two markets, reflecting different responses to local challenges. For instance, Mastercard demonstrates a greater reliance on machine learning technologies in Australia to combat fraud, while Visa in Singapore focuses more on enhancing privacy and data protection. The study offers strategic recommendations aimed at improving payment security and customer experience, such as increasing transparency in data usage and strengthening communication with customers. This research contributes to a deeper understanding of the role of AI in financial services, providing valuable insights for companies and practitioners in the sector. By addressing the unique challenges of each market, customer satisfaction can be enhanced and greater trust in digital payment services can be fostered.