International Journal of Information Science and Management
Volume:12 Issue: 1, Apr 2014

Recently, internet is widely used in our daily life, caused online customer growing rapidly, then the amount of online store rising quickly. Therefore, it is an important issue for online stores to stand out from the crowd, even boost sales and profit. Many enterprises had invested resource on online store, but great amount of stores were closedown. The major reason is operator spend many efforts on valueless marketing campaign, caused popularity and repeat purchase intention raising ineffectively, finally folded up online store. Consequently, this research summarizing the critical factors for online store operating, and using structural equation model (SEM) to establish “Online Store Popularity Effect Model”. Through the model, this research can understand (1) relationship between online store popularity and marketing campaign, service quality, product power, website design; (2) effect between purchase intention and customer value, brand image; (3) moderating impact of consumption ability. This result not only indicates the critical point that enterprise should call more attention, but draws a whole new perspective for academic researcher.

These days, due to growing the e-commerce sites, access to information about items is easier than past. But because of huge amount of information, we need new filtering techniques to find interested information faster and more accurate. Therefore Recommender Systems (RS) introduced for solving this problem. Although several recommender approaches have proposed, Collaborative Filtering (CF) approaches are the most successful ones. These approaches use historical behaviors of users for making recommendation. Next generation of CF, called Trust-based CF, use social relations and activities for measuring trust between users. One important step in these approaches is measuring the similarity between users, which affect recommendation results. Therefore variety methods for this reason have been proposed. In this paper, we will review and categorize the measurement methods. We will also analyze the methods to identify their characteristics, benefits and drawbacks.

The purpose of this paper is to identify the factors of customer satisfaction in Export Development bank of Iran (EDBI). In this study, purposive sampling technique was employed to recruit 50 customers classified as government service holder, private service holder and businessmen from all EDBI branches. We have done Kaiser-Meyer-Olkin Measure of Sampling Adequacy, Bartlett's Test of Sphericity, Cronbach's Alpha Reliability Statistics, inter-correlation, factor analysis and factor ranking. This research showed that management information service, service factor, time management and behavioral factor are the key factors which are representing customer satisfaction in EDBI. Management information services is the combination of 7 variables such as computerized service, innovative service, smooth and hassle free, staff availability, skilled and knowledgeable employees, service charge, transparency in services. In addition, service factor amalgamation of 3 variables which are service infrastructure, systematic, accurate service and cordial, customer friendly; helpful employee more over time management factor is also the combination of 3 variables such as waiting time, timely information and effective handling of complaint. Finally, commitment of employees and timely services are constitute of behavioral factor. Among major four factors behavioral factor obtain the highest rank comparing with other factor. The paper contributes to previous research by adding to existing knowledge regarding what constitutes e-banking service. The paper makes key recommendations towards enhancing current online financial services delivery.

Supplier evaluation and selection is complex which caused by the dynamic and fuzzy environment. Current MCDM methods do not consider the nature of the environment that can affect the process of evaluation and ranking. The aim of this study is to propose a dynamic fuzzy hybrid MCDM method for evaluation, ranking and selection. The proposed method employs Fuzzy Analytic Hierarchy Process (FAHP) for weighting of criteria, and Fuzzy Inference System (FIS). The FIS determines the effectiveness ratio for FAHP method and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS). The proposed method has been applied for supplier selection in a steel company to illustrate its applicability, flexibility and accuracy in different decision making situations.

Nowadays, the Internet has had a rapid expansion and presence in all areas of the individuals, institutions, companies and organizations lives. This has made large extent of data available so that the users need to access their desired information or services through using search engines. To continue and promote providing services, search engines require a source of income. Sponsored search advertising is the main source of revenue for search engines. In sponsored search, limited areas of the search results page are dedicated to display advertising so that the advertiser pays the advertisement cost only per click. To assign advertising areas, an auction is run among advertisers. Several models have been proposed to hold a sponsored search auction. In this study, a new model is proposed to increase the revenue of the search engine. Evaluated using synthetic data, the results of the ad-supported implementation of the search engine model indicate improved revenue in comparison to existing models.

Governments around the worlds especially in developing countries face with tremendous challenges. High or moderate rate of corruption can be named as one of the most important of these challenges. Corruption and its inefficiency can deviate resources allocation to unproductive sectors of economy. Trust in social networks or within the society has two antithetic effects in deployment of corruption in government and bureaucratic systems. Governments may want to use public trust for eradicating corruption in one hand and by the other hand particularistic trust may cause corruption. Generalized and particularistic trusts have two effects on corruption. E- Government a solution can decrease the harmful effects of particularistic trust extension. This paper wants to introduce and use these two phenomena in the field of public economics by concentrate on corruption and e- Government as a solution.

Nowadays, marketing managers are more concerned with identifying and understanding customer behavior in the online space. Since the customers in online space are not visible, it is much essential to have more information about them to provide better services. Customer segmentation is one way to improve the customer problems in an online space. Identifying characteristics of customers and optimal resource allocation to them according to their value to the company is one of the major concerns in the field of ​​customer relationship management and determining factors in E-business success. The purpose of this study is clustering customers online of a mobile sales website based on their lifetime value and RFM model. At the proposed framework in this study after determining the values ​​of RFM model include recently, frequency and monetary of purchase and weighting them using Shannon entropy, a self-organizing map is applied to the segmentation of customers. The customers are categorized into four main segments and characteristics of customers online in each of the segments are identified. Mobile sales website customers are identified by segmenting customers in terms of the pyramid of customer lifetime value. Finally, suggestions are proposed to improve customer relationship management system.

Nowadays many businesses have realized the importance of customer relationship systems to help the potential of it to gain new customers, keep old customers, adding value to their products and services by a more effective communication. With further development of information technology, customer relationships is also very important in the field of financial since many of the finance and banking transactions done via internet and physically presence of customers are not required. This situation provides an opportunity for banks to use the implementation of efficient systems of customer relationship to attract customers and gain a competitive advantage over competitors. On the other way, having anti-fraud and anti-money laundering systems, is an important issue for banks and governments that cause normal and secure economic – financial cycle as well as a wide range of trusted clients (large or small, domestic or foreign, etc). For an optimal performance of these systems, we need to have some basic information that are properly collected and analyzed. In this article we have tried to evaluate a number of ECRM&CRM criteria in our banking system and further be investigate which of these criteria might be more effective for use in anti-fraud and anti-money laundering systems to be used later in these systems.