This paper presents an empirical investigation on user’s satisfaction based on the expectation disconfirmation paradigm theory. The study selects a sample of 332 people from 2449 graduate students who were enrolled at the Birjand University. The study uses a questionnaire developed by Isac and Rusu (2014) [Isac, F. L., & Rusu, S. (2014). Theories of user’s satisfaction and the operationalization of the expectation disconfirmation paradigm. Economic series, university of Targu, 2 (1), 82-88.]. Using path analysis, the study has determined that perceived usefulness and personal experience had the highest impact on users’ satisfaction in electronic library usage. In addition, there was a negative relationship between user interface and users’ satisfaction.

This paper is an attempt to examine the implementation of Lean Manufacturing system in the Indian industries. Predominant elements of Lean Manufacturing, benefits gained after its implementation and obstacles observed by Indian Industry have been recognized. The results of this survey support the opinion that Lean Manufacturing had potential to improve the organizational performance of Indian industries. Nevertheless, Indian industries are required to be passionate to transform their manufacturing by adopting Lean manufacturing to gain the full benefits. A large numbers of literature papers are available on the better side of Lean manufacturing approach and its benefits gained by manufacturing organizations after implementation. But the adverse impacts of Lean manufacturing are not discussed to a great extent. Some drawbacks of Lean manufacturing are also highlighted in this paper.

During the past few years, there have been tremendous efforts on development of Iranian banking industry in an attempt to fight against monopoly of banking system. Bank Pasargad is one of the biggest Iranian banks established for offering retail, commercial as well as investment banking services. The firm was established in 2005 as part of the government & apos; s privatization of the banking system. This paper presents an empirical investigation to determine the most efficient locations for development of this bank. The study uses three main criteria, customer services (C1), Equipment (C2) and Profitability (C3) to compare different alternatives for establishing new branches in city of Tehran, Iran. Using fuzzy data envelopment analysis the study has determined that center of the city could receive the most important attraction followed by north region, west region as well as south west.

Due to more than three million people in Germany with a Turkish migration background country-of-origin (COO)-sensitive, designs of offers directed at this customer segment have been implemented by various corporations and discussed in the management literature for quite a while. Unfortunately, to date most publications have a weak empirical foundation and refrain from simultaneously investigating preference effects of several country-of-origin-sensitive and -neutral offer characteristics among Turkish migrants living in Germany. Therefore, the present paper explores the relative impacts of three COO-sensitive offer characteristics and one COO-neutral attribute of bundled mobile communication offers on preference statements derived from a conjoint-analysis of questionnaire responses of 249 consumers in Germany with Turkish roots. The results suggest that for the offering category in question a COO-neutral feature (cell phone type/brand) shapes the preferences of Turkish migrants almost to the same extent as the three remaining price- and communication-related characteristics investigated. Furthermore, we found that Turkish consumers in Germany encompass four subsegments with distinct preferences with respect to the design of mobile communication offerings. The members of these subsegments in turn differ primarily in terms of their age and gender structures as well as their level of accommodation to the German culture.

Surface roughness and cutting forces are considered as important factors to determine machinability rate and the quality of product. A number of factors like cutting speed, feed rate, depth of cutting and tool noise radius influence the surface roughness and cutting forces in turning process. In this paper, an Artificial Neural Network (ANN) model was used to forecast surface roughness and cutting forces with related inputs, including cutting speed, feed rate, depth of cut and tool noise radius. The machined surface roughness and cutting force parameters related to input parameters are the outputs of the ANN model. In this work, 24 samples of experimental data were used to train the network. Moreover, eight other experimental tests were implemented to test the network. The study concludes that ANN was a reliable and accurate method for predicting machining parameters in CNC turning operation.

Operations research techniques have been used widely in simulating the dynamics of workforce systems. Discrete Event Simulation (DES) and System Dynamics (SD) are among the techniques that have been increasingly used in modelling military workforces. In the last five years, DES has seen more interest in modelling both career management and the training pipeline. Two significant reasons for this are discussed in this paper. This article presents some notes in comparing the two techniques in modelling military workforce. The study found that DES is an appealing method in workforce modelling, especially with a small size population, as it more easily accommodates new personnel attributes and prevents the fractionalisation of personnel through the system.

Web mining has been widely used to discover knowledge from various sources in the web. One of the important tools in web mining is mining of web user’s behavior that is considered as a way to discover the potential knowledge of web user’s interaction. Nowadays, Website personalization is regarded as a popular phenomenon among web users and it plays an important role in facilitating user access and provides information of users’ requirements based on their own interests. Extracting important features about web user behavior plays a significant role in web usage mining. Such features are page visit frequency in each session, visit duration, and dates of visiting a certain pages. This paper presents a method to predict user’s interest and to propose a list of pages based on their interests by identifying user’s behavior based on fuzzy techniques called fuzzy clustering method. Due to the user’s different interests and use of one or more interest at a time, user’s interest may belong to several clusters and fuzzy clustering provide a possible overlap. Using the resulted cluster helps extract fuzzy rules. This helps detecting user’s movement pattern and using neural network a list of suggested pages to the users is provided.

Iran’s 4th development plan has enrolled the federal government to increase energy prices and remove any subsidies paid to energy sector in an attempt to increase productivity and provide better social welfare. It is, therefore, important to investigate the effects of the law on different industries and the subsequent effects on economic growth. This study investigates the impacts of subsidies on the market value of listed companies in Tehran Stock Exchange in Petrochemical and Steel industries. The study selects two firms, Esfahan & apos; s Mobarakeh Steel Company and Fanavaran Petrochemical Co. using Auto Regressive Distributed Lags (ARDL) over the period 2008-2013. The results have indicated that there was a meaningful relationships between removal of subsidies and market value of the firms.

Nowadays, many organizations focus on discovering their customers’ hidden patterns to maintain their competitive position through customer analysis. In fact, more and more organizations are realizing that customers are their most valuable resources. This paper performs a research using data associated with 300 clients of an insurance company in city of Anzali, Iran and they are analyzed using K-Means clustering method. Using demographic variables including gender, age, occupation, education level, marital status, place of residence and clients’ incomes, the study determines the optimal numbers of clusters in order to achieve necessary data for grouping customers. Next, the study uses the method of association rules to find hidden patterns for the insurance industry.