درخت حوزه‌های تخصصی

Today, gamification is being used in various areas such as education, health, and business to enhance engagement and increase the system's efficiency. Despite significant scholarly interest, in many cases, undesirable results have been achieved using gamified solutions. This highlights the need for further research to explore these challenges through innovative methodologies and to devise new solutions. Addressing this gap, we conducted a systematic review of the literature on the emerging and growing subject of gamification using the PRISMA methodology and proposed a novel model for the strategic development of future gamification studies. The research led to the identification of 48 qualified empirical studies which have been analyzed to outline the existing views, gaps, and consequently the implications for future research. Through the analysis, we delineate the impact and effectiveness of gamification, highlighting its potential to transform user experience positively when implemented with strategic finesse. Consequently, we propose a novel model for the strategic development of future gamification studies, presenting it in three main dimensions: Contexts, Users, and Elements, and for each dimension, significant and less-paid topics are discussed. In addition, we represent six main suggestions for the design of the entire gamified system: Decision-Making Methods, Success Factors, Validation Methods, Dynamic Design Approach, Timeframe, and Modern Technology. Our proposed model not only facilitates a deeper understanding of gamified systems but also offers actionable insights and guidelines for both academics and practitioners. It is meticulously designed to assist researchers and practitioners in crafting more effective gamified systems that are customized to meet specific user needs and environmental contexts. By doing so, it aims to maximize the sustainable benefits of gamification, ensuring that these systems deliver significant and lasting impacts. This strategic approach integrates the latest advancements in technology and dynamic design principles, establishing a robust framework for the future of gamification research and application.

Objective: A study of the movement of the insurance industry returns shows that the movement of income returns was less than the movement of the insurance industry. The movements of lower income returns than the movements of paid losses of the insurance industry in the economy are due to various variables, including the management of the insurance industry, part of which is due to risk recognition. Which helps managers in the insurance industry, to the extent of tolerable and intolerable risks in the acceptance or non-acceptance of various swimming risks, and by managing them play an important role in marketing and increasing the demand for insurance and in general, In line with the optimal growth of the insurance industry. Methodology: This study, using generalized conditional autoregression models, ARIMA-GARCH / TARCH and beta coefficient, has evaluated the impact of risk on the return of the insurance industry in the Iranian economy during the period 1350-1398. Results: The results showed that there is a significant relationship between the study of the impact of risk on the return of Basma industry. Also in this research, systematic risk has been investigated using the beta coefficient method. Since the result of the impact of risk on the return on income of the insurance industry is 67%. Because the obtained beta coefficient is less than one and greater than zero. This result indicates that the income of the insurance industry is smaller than the general trend of market movement and risk aversion

Predicting bankruptcy risk is one of the most critical issues in corporate financial decision-making. Investors always try to predict the bankruptcy of a firm to reduce the risk of losing their assets, so they are looking for ways by which they can predict the risk of bankruptcy. We predict the position of companies active in the oil and gas industry based on their financial health in the 2020 ranking of S&P global up to three years before 2020. This study uses three data envelopment analysis models (CCR, BCC, and DDEA) and the traditional Altman model for forecasting. We have shown that dynamic data envelopment analysis is a powerful tool for predicting bankruptcy risk.