نیما شریعتی

The present study aims to investigate the impact of organizational virtuousness on social capital with the mediating role of organizational commitment and the moderating role of spiritual intelligence, applying structural equation modeling. The studied population incorporates the employees of the Eghtesad Novin Bank, which is a private bank in Iran. In order to gather data, a standard questionnaire was distributed among the employees. In the end, 560 questionnaires were returned. The data analysis was done through partial least squares structural equation modeling (PLS-SEM). Based on the research findings, organizational virtuousness affects social capital positively. Organizational commitment also mediates the relationship between organizational virtuousness and social capital, and spiritual intelligence plays the role of moderator in the aforementioned relationship. In other words, all hypotheses of the research were confirmed. The findings of the present research show the importance of organizational virtuousness, organizational commitment, and spiritual intelligence in order to strengthen social capital.

Control charts are the most useful tools for controlling the processes statistically. The construction of the control charts requires the estimation of the process parameters using random sample data. Usually the classical estimators of the process parameters are used to construct the control charts. The classical estimators of the parameters of the processes generating autocorrelated data are sensitive to the presence of the outlier observations. Applying classical methods of estimation while outliers are present, introduce biased estimates of the model parameters which result in wrong interpretation of the control chart. In this research a method called Iteratively Robust Filtered Fast Tau (IRFFT) which is insensitive to the presence of the outliers is proposed for estimating the parameters of the autocorrelated models. The newly introduced estimators are used to construct robust control chart for autocorrelated data. The suggested robust control chart is compared with the control chart whose parameters are estimated using LS method. Results of the simulation study for the two methods indicate that the ARL for the suggested robust control chart is much smaller under different scenarios. The findings may be extended to the other time series models.

To obtain reliable model for auto correlated and time series data, robust approach should be considered because outliers and contaminations can have bad effect on parameter estimation of these models. Since most finance data are auto correlated and they are affected by the previous data, they can be modeled by time series regression models. In this paper, the autoregressive (AR) model is investigated and novel robust procedure based on filtered S-estimator is proposed to estimate the parameters of AR model. This model is used to obtain robust forecasting procedure. We present 148 data gathered from a firm which are related to profit as a numerical example and show the efficiency of the proposed estimation approach. The robust model can forecast more accurate than classical model in presence of outlier.