مطالب

The transformational leadership is known as one of the most important and effective factors on organizational behaviors of the employee. Creation of the motivation and energy in the employees requires a leader with inspirational qualities. On the other hand, the organizational citizenship behavior is a factor in shaping. The current study was aimed at investigating the effects of transformational leadership on the employees’ organizational citizenship behavior. In addition, some personality traits are revealed in this study to be one of the main factors shaping the behaviors and to be moderating variables. The current study is of applied type in terms of the objective and a descriptive-correlational study in terms of data collection procedures. The data needed for the purpose of the study were collected by the use of questionnaire distributed among The 242 persons of statistical population, and these data have been analyzed by Structural Equations Modelling. The results indicated that all six hypotheses of the study were approved. In other words, the transformational leadership has positive and significant effects on both individual and organizational aspects of the organizational citizenship behavior. Also, the positive moderating role of neuroticism and extroversion in effects of transformational leadership on the individual and organizational aspects of organizational citizenship behavior was approved. The results show that the statistical community can utilize its human resources potential to create competitive advantage and develop the voluntary activities of its employees, provided that it is able to optimally implement transformational leadership style.

In this paper, we focus on improving the performance of recommender systems. To do this, we propose a new algorithm named PBloofI which is a kind of hierarchical bloom filter. Actually, the Bloom filter is an array-based technique for showing the items’ features. Since the feature vectors of items are sparse, the Bloom filter reduces the space usage by using the hashing technique. And also, to reduce the time complexity we used the hierarchical version of bloom filter which is based on B+ tree of order d. Since Bloom filters can make a tradeoff between space and time, proposing a new hierarchical Bloom filter causes a remarkable reduction in space and time complexity of recommender systems. To increase the accuracy of the recommender systems we use Probabilistic version of hierarchical Bloom filter. By measuring the accuracy of the algorithm we show that the proposed algorithms not only decrease the time complexity but also have no significant effect on accuracy.