JOURNAL OF BUSINESS INTELLIGENCE MANAGEMENT STUDIES
Year:2019 | Volume:8 | Issue:29 |Papers Count:8

Rapid developments of the internet and the increasing growth of social media have provided new opportunities for today's businesses. Using these media, organizations can build up deep and lasting relationships with their customers. Moreover by engaging customers with their brand, they can create positive outcomes for themselves, customers and society. Accordingly, this study seeks to identify Antecedents, dimensions, and consequences of customers brand engagement in social media in the banking industry. In order to achieve the goal, In-depth interviews were conducted with customers who following one or more banks on social media, and data analysis was performed by using content analysis method. The results of the research were presented in a research model consisting of three divisions of Antecedents (individual, organizational and media), consequences (individual, organizational and social) and dimensions of customers brand engagement. Finally this study provided suggestions to use these media to engage customers.

vStock market has been one of the most influential economic phenomena in the world for many years. The main players in the stock market are investors that are always looking to make the most profit. Since prices of stock market transactions is Impressionable from political, economic, social problems and the high volatility of prices, the prediction of stock market is very difficult. The main solution for more profits in the market is making the right decisions about buying and selling appropriate stocks in appropriate time. Therefore, prediction is the most important requirements for traders. I this research, a new hybrid algorithm is proposed that uses imperialist competitive algorithm as a feature selection method and fuzzy adaptive neural inference system as a prediction function. This approach uses 63 features that affect the stock market, including economic features, Iran and other countries stock market indexes, technical analysis indexes and Japanese Candlestick on a daily basis in the period from 2010-2016. The Exchange Stock Index for the next day is considered as the target variable. The results show that the hybrid model includes Adaptive Neural Fuzzy Inference System (ANFIS) and Imperialist Competitive Algorithm, is much appropriate. This model is compared with a single ANFIS model has better approximation speed and the ability to predict the stock.

The present study seeks to evaluate the effect of social media on people's entrepreneurial intention. In this study, the components of entrepreneurial intention are derived from the Linan’ s Entrepreneurial Intention Model. These components include attitudes towards entrepreneurial behaviors, social norms, and self-belief. The present study is quantitative and the research method is descriptive. In this research, questionnaire was used for data collection. Students of Allameh Tabatabaei University in Tehran are samples of the study. Gathered data was analyzed by using t-test, factor analysis, and structural equation model with SPSS and LISREL software. Results showed that there was a significant relationship between social media and entrepreneurial intention and its components. Furthermore, social media strongly influences entrepreneurial intention and its components. Moreover, the most significant impacts were related to entrepreneurial self-belief, social norms, and attitudes towards entrepreneurial behavior, respectively. Finally, all hypotheses were confirmed by structural analysis and structural equation modeling.

Detecting existing communities in social networks is a significant process in analyzing these networks. In recent years, the community detection problem has become popular for detecting structures of social networks. Due to high importance of this problem, various algorithms have been developed in the literature to find communities of complex networks. In this research, a hybrid meta-heuristic consisting of the genetic algorithm (GA) and the invasive weed optimization (IWO) method have been proposed which aims to find appropriate and high quality solutions for the community detection problem. In this hybrid method, the initial solutions are generated via the IWO algorithm, and thereafter the optimization process is continued by means of the genetic algorithm. The proposed algorithm is known as the GAIWO. Fitness of solutions is determined in terms of the modularity density criterion. Modularity density has a maximization essence and determines the quality of detected communities. To evaluate the efficiency of the GAIWO, four other methods have been employed and their results have been compared. Comparisons have been made on several networks with different sizes. Input parameters of all algorithms have been tuned by a design of experiments approach. The outputs indicate appropriate efficiency of the proposed algorithm. Validation of the results have been investigated by means of the Normalized Mutual Information (NMI) metric.

Nowadays the growth in the use of social networks among different classes of world community is increasingly undeniable. Social networks database include Rich and valuable resources whose release and analysis with the purpose of marketing, publicity, National Security, Health and etc. can benefit researchers of public and private institutions. But respect the privacy of the entities whose information is available to data miner analysis is essential as a legal protocol. In this Paper, through qualitative methodology Meta synthesis, all related dimensions, indicators and codes were identified and then the importance and priority of each of the factors was determined. Subsequently, the improved model of anonymity was presented by an optimizing firefly algorithm and fuzzy clustering. The result of simulation and assessment of the proposed model on the data of four social networks such as Facebook, YouTube, Twitter and Google+ depicts that privacy preserving of data with the lowest distortion ratio and the more usefulness of data.

The main objective of this research is to provide a customer loyalty model for e-commerce recommender systems. The proposed model is developed using Delone and McLean Information System success model and a set of factors which are identified from the literature. To test the research hypotheses of the developed model, a questionnaire survey is conducted and the data is collected from the 384 customers in a B2C website. We used SPSS and SmartPLS software for descriptive statistics and path analyses and to verify the proposed model. The result of the Structural Equations Modeling showed that trust has a significant relationship with the customers’ satisfaction in the e-commerce recommendation systems. In addition, the results revealed that satisfaction with the recommended products can improve the customers’ loyalty in the B2C recommendation systems. The proposed model will help the e-commerce managers to improve their website recommendation systems and increase the sale of the products by achieving the customers’ loyalty in the online shopping websites.

In this study, social dimensions of obstacles to achieve alignment in IT and business have been identified through a case study in the Post Bank of Iran. The data have been collected through conducting 10 interviews with experts in both fields of IT and business as well as reviewing some internal documents of the bank. In this study, 20 of the main obstacles and problems of achieving alignment were identified. The findings of the study showed that “ lack of joint strategic planning” , “ organizational units’ inappropriate understanding about each other” , “ unsuitable administrative structure of the bank” , “ lack of a strong Organization and Methods office” , “ lack of precise definition of tasks and roles in projects” , and “ limited cooperation and lack of mutual support in achieving alignment between IT and business” are among the main obstacles to achieve alignment. Understanding these obstacles, while providing background for future studies on achieving alignment considering its social dimensions for researchers, helps organizations make effective efforts to bring about an alignment between IT and business by identifying these obstacles.