Journal of Information Technology Management
Volume:10 Issue: 2, 2018

This study aims to consider the relationship between undergraduate management students’ strategic thinkingskills and their information seeking behaviors. The statistical population for this study was 400 undergraduate students of management faculty in University of Tehran and the sample size was calculated to be 201 students using Cookran formula. In this study, survey methodology was applied and the data were analyzed using structural equation modeling, Pearson correlation, and multiple regression tests. Based on Pisapia’s model of strategic thinking and ACRL model of information seeking behavior , the results indicated that in among the different dimensions of strategic thinking, reframing thinking skill (leading to the formation of new values and getting away from the old clichés) has caused the highest change unit in information seeking behavior.

Community detection is one of the most significant issues in the field of social networks. The main purpose of community detection is to partition the network in such a way that the relations between components of the network are dense. Because of the strong relations among network members in these partitions, you can consider them as a community. Many researchers have developed several algorithms to solve such a problem. Therefore, we present a genetic algorithm based on Topsis which is a multi-criteria decision making method (MCDM). The proposed algorithm uses Topsis to rank solutions based on modularity and modularity density which are two of the most well-known criteria in community detection problem. Thereafter, crossover and mutation operators are only applied on solutions ranked by Topsis. The performance of the proposed algorithm has been evaluated through comparing it against classical genetic algorithm and a greedy one. The results showed that the proposed algorithm outperforms the other two methods. Since the application of MCDM approach has not been reported in the related literature, this paper can be considered as a basis for future studies.

Classification of the cyber texts and comments into two categories of positive and negative sentiment among social media users is of high importance in the research are related to text mining. In this research, we applied supervised classification methods to classify Persian texts based on sentiment in cyber space. The result of this research is in a form of a system that can decide whether a comment which is published in cyber space such as social networks is considered positive or negative. The comments that are published in Persian movie and movie review websites from 1392 to 1395 are considered as the data set for this research. A part of these data are considered as training and others are considered as testing data. Prior to implementing the algorithms, pre-processing activities such as tokenizing, removing stop words, and n-germs process were applied on the texts. Naïve Bayes, Neural Networks and support vector machine were used for text classification in this study. Out of sample tests showed that there is no evidence indicating that the accuracy of SVM approach is statistically higher than Naïve Bayes or that the accuracy of Naïve Bayes is not statistically higher than NN approach. However, the researchers can conclude that the accuracy of the classification using SVM approach is statistically higher than the accuracy of NN approach in 5% confidence level.

Currently, ICT and educational processes are experiencing development and innovation. This new trend will help promote educational technology and enhance innovations regarding educational planning. E-learning is considered as one of the most prominent ICT applications across the world. Advantages of virtual learning have entailed daily usage in various universities. Learning management systems are specific web-based systems to manage, track students, define courses, and evaluate the learners. However, these systems may involve inefficiencies and disadvantages as well. This paper attempts to identify the LMS functional areas in University of Tehran based on a specific conceptual framework and to present the relevant issues and problems for each dimension. The data for the present study were collected using focused group interviews, system observations. The researchers also compared the documents and the university system with that of other universities. The results of the theme analysis indicated that “communication” and “system cooperation” dimensions are involved with more important problems and issues. The researchers believe that the main issues are due to the test modules, evaluations, and systemic and underlying databases.

Knowledge hiding is one of the new concepts in the management of organizational knowledge. Although the nature of the relationship between projects teams members will have a substantial impact on the knowledge hiding behavior, different characteristics of Knowledge can also affect knowledge hiding. Based on the behaviors of the project teams, the aim of this research is to identify what characteristics of transferred knowledge between team members will lead to hiding or sharing it. The research model consisted of 4 variables: knowledge complexity, knowledge uniqueness, knowledge relatedness and knowledge sharing cost determined by the review of the literature and consultation with experts in management. The project team members were randomly selected and the data were gathered through a questionnaire. The results of the factor analysis and regression analysis showed that the uniqueness of knowledge had the greatest impact on knowledge hiding.

The literature regarding Persian text mining indicates that most studies are conducted to detect polarity of opinions on social websites. The aim of this research is presenting an algorithm to identify emotion implemented in the text based on the following six main emotions of happiness, sadness, fear, anger, surprise and disgust. In this research, the emotion will be examined based on unsupervised lexicon method. Identifying emotions conveyed by the texts based on a single emotional word will produce low accuracy because the intervening boosters and negating words can influence the emotion of the text too. Therefore, the algorithm has been implemented in six approaches with different features. In the first approach, the algorithm is capable of detecting only one emotional word in a sentence, and then it improves to detect boosters and negating and stop word list as well. The results of running the algorithm on two domains of data showed that the more features used in the algorithm, the more accurate the algorithm becomes and that the most effective factor is part of speech.