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INTRODUCTION The phenomenon of organizational insentience has been one of the main challenges in recent years and refers to a situation in which the employees of an organization have become indifferent to their organizational environment. The current research was conducted to investigate the causes and contexts of the formation of organizational insentience in the Red Crescent Society of Tehran province, Iran. METHODS: This applied study was conducted based on the descriptive-correlation method of data collection. The statistical population of this research included all the managers of Red Crescent Society in Tehran province in 2021. The samples (n=90) were selected using Cochran's statistical formula and stratified random sampling. The required data were collected using a researcher made organizational insentience questionnaire. The validity and reliability of the questionnaire were checked and confirmed. The collected data were analyzed in Smart PLS software using structural equation modeling with partial least squares approach. FINDINGS: The results of the research showed that causal conditions had a direct, positive, and significant effect on the central category with a standard beta coefficient of 0.725, t=17.625, and P=0.001 at the level of one percent error. Moreover, contextual conditions with a beta coefficient of 0.410, t=3.107, and P=0.002 had a direct, positive, and significant effect on the category of strategies at the level of one percent error. Intervening conditions had a direct, positive, and significant effect on strategies at the level of one percent error (standard beta coefficient=0.221, t=3.008, and P=0.003). The central category had a direct, positive, and significant effect on the strategy category with a beta coefficient of 0.334, t=2.282, and P=0.023 at the five percent error level. Based on the results, at one percent error level, the strategies presented in the research had a direct, positive, and significant effect on the outcomes (standard beta coefficient=0.347, t=3.769, and P=0.000). In total, the results showed the existence of organizational insentience in the Red Crescent Society of Tehran province, which can be reduced by applying appropriate methods. CONCLUSION: According to the findings, by reducing the sense of meaninglessness, double standards, eliminating organizational discrimination, applying appropriate procedures in strategies and their implementation, paying attention to the structural dimensions of the Red Crescent Society in Tehran province, and implementing meritocracy management, it would be possible to increase organizational social capital, the level of interest, commitment, and responsibility of employees towards the Society and work, organizational independence, organizational justice, the atmosphere of trust, and supportive behaviors from the Society and employees. By adopting these measures, a step would be taken toward preventing the formation of organizational insentience in the Red Crescent Society of Tehran province.

Net Asset Value (NAV) has long been a key performance metric for mutual fund investors. Due to the considerable fluctuation in the NAV value, it is risky for investors to make investment decisions. As a result, accurate and reliable NAV forecasts can help investors make better decisions and profit. In this research, we have analysed and compared the NAV prediction performance of our proposed deep learning models, such as N-BEATS and NBSL, with the FLANN model in both univariate and multivariate settings for five Indian mutual funds for forecast periods of 15, 20, 45, 63, 126, and 252 days using RMSE, MAPE, and R2 as evaluation metrics. A large forecast horizon was chosen to assess the model's consistency, reliability, and accuracy. The result reveals that the N-BEATS model outperforms the FLANN and NBSL models in the univariate setting for all datasets and all prediction horizons. In a multivariate setting, the outcome demonstrates that the N-BEATS model outperforms the FLANN model across all datasets and prediction horizons. The result also shows that, as the number of forecast days grew, our suggested models, notably N-BEATS, maintained consistency and attained the highest R2 value throughout the longest forecast duration.