Journal of Information Technology Management
Volume:2 Issue: 1, 2010

The success or failure of an organization has a direct relationship with how its human resources are employed and retained. It is the case that organizations keep large amounts of information and data on entrance evaluations and processes. This information, however, is often left unutilized. Data mining is considered a solution for analyzing these data. This paper is investigating educated and objective methods of data analysis. It follows statistical rules, data mining techniques, and the relationship between entrance evaluation scores and personal and professional variables. These factors are studied in order to determine the assignment and rank of potential employees. The database and personnel information of the a Commerce Bank of Iran (in years of 2005 and 2006) is studied and analyzed as a case study in order to identify the labor factors which are considered effective in job performance. The data mining technique that is used in this project serves as the decision-tree. Rules Derivation has been accomplished by the QUEST, CHAID, C5.0 and CART algorithms. The objective and the appropriate algorithms are determined based on seemingly “irrelevant” components, which the Commerce Bank Human Resources management experts described. Results indicated not taking into account the “performance assessment” variable as the objective. Also this project has identified the following from 26 variables have been investigated, five variables as the effective factors in employee promotion: examination score, interview score, degree, years of experience, and job location. The paper's results led in knowledge that can be practical.

Decision trees as one of the data mining techniques, is used in credit scoring of bank customers. The main problem is the construction of decision trees in that they can classify customers optimally. This paper proposes an appropriate model based on genetic algorithm for credit scoring of banks customers in order to offer credit facilities to each class. Genetic algorithm can help in credit scoring of customers by choosing appropriate features and building optimum decision trees. Development process in pattern recognition and CRISP process are used in credit scoring of customers in construction of this model. The proposed classification model is based on clustering, feature selection, decision trees and genetic algorithm techniques. This model select and combine the best decision tree based on the optimality criteria and constructs the final decision tree for credit scoring of customers. Results show that the accuracy of proposed classification model is more than almost the entire decision tree models compared in this paper. Also the number of leaves and the size of decision tree i.e. its complexity is less than the other models.

The overall purpose of this paper is to evaluate the effectiveness of personality attributes as predictors of consumer behavior in contrast to traditional demographic variables in the digital environment. New variables were needed to replace the traditional ones. This research evaluated three groups of consumer behaviors and examined personality and demographic variables. The primary data collected via questionnaire. The results of this research showed significant relationship between personality variables and online consumer behaviors of respondents and suggested that personality attributes were superior at predicting and segmenting online consumer behaviors.

Companies strive to improve market share, grow corporate profit, and gain strategic advantage. In order to achieve these goals, supply chain competency must be placed at the heart of a company’s business model. Using e-Commerce and information and communication technologies (ICT) in today’s changing demands of business has made organizations more responsive and flexible. E-Commerce and Internet have changed the nature of supply chains and re-defined how customers obtain wisdom of products, services, selection, purchasing and using them. Advent of ICT and new business environment has caused emerge of electronic supply chains. This research has proposed a model for presenting factors which affect electronic supply chain’s risk; besides the influence of the risk on financial and non-financial organization’s performance indicators. Studying the influence of the risk on organization’s performance is conducted in a sample of electronic and telecommunication companies. In order to measure these relationships, using correlation and structural equation modeling (SEM) techniques proposed that electronic supply chain risk identification and management have significant impact on organization’s performance improvement.

Enterprise architecture (EA) is a discipline for understanding, engineering, and managing different components of an enterprise and their interrelationships. Moreover, because the risk and impact of EA are pervasive across the enterprise, it is critical to perform an architecture assessment before making any decision about choosing a scenario. This paper provides an approach based on Fuzzy Analytical Hierarchy Process towards EA analysis. It proposes a quantitative method of assessing quality attribute achievement of different scenarios using AHP based on the knowledge and experience of EA experts and domain experts. Due to the vagueness and uncertainty in the judgments of participants, the crisp pair-wise comparison in the conventional AHP is insufficient and imprecise to capture the right judgments. Therefore, the fuzzy logic is introduced in the pair-wise comparison of AHP to make up for this deficiency. The applicability of the proposed approach is demonstrated using a practical case study

By reviewing the literature of KM, it can be understood that there are different views about(to) knowledge management and its applications in public and private sectors which is due to the structural difference between these two economic sectors. In order to be able to exploit the benefits of knowledge management in public sector adequately, it is necessary to identify the best practices of KM. To understand the differences between knowledge management in public and private sector and benchmark capabilities of private sector in public sector, is one of the ways to achieve this goal. In this study, we used survey study and questionnaire tool in order to identify these differences in banks. Results suggest that just in two dimensions of information technology and knowledge management, among five identified dimensions, there is a meaningful difference between public and private banks.

This paper presents a descriptive study which examines the co-alignment between Total Quality Management (TQM) and technology/research and development (R&D) management in predicting organizational performance in terms of quality and innovation. This study improves our understanding of the relationship between TQM and innovation based on the following two major issues. First, this study contributes to the understanding of the co-alignment between TQM and technology management along with R&D management by bridging the gap between the two areas which are often addressed in a separate fashion. Second, this study also examines the impact of the integration between TQM and technology/R&D on quality and innovation performance which have been considered as the primary sources of a competitive advantage. The empirical data were drawn from 148 managers of product companies, located in Markazi province and analyzed using the Structural Equation Modeling technique. The findings indicate that TQM shows a strong predictive power against quality performance but no significant relationship against innovation performance. On the other hand, technology and R&D management shows a significant relationship with quality performance but at a lower level than that of TQM, and shows much stronger relationship with innovation performance. In addition, there is strong and positive correlation between TQM and technology/R&D management. The major implication of this study is that technology/R&D management is an appropriate resource to be used in harmony with TQM to enhance organizational quality performance, particularly innovation.

The development of effective organizations for service provision and responding to the demands of the citizens in a rapid, easy and suitable fashion has been considered by officials of the public sector in recent years in many countries worldwide. The realization of the importance, application and also the role that ITC can play toward promotion of accountability in the public sector, has caused the policy makers in different communities to include it in their agenda and plans and issue the relevant instructions in relation to ITC and laid down the institutional and executive grounds for application of ITC in the governmental sector. This paper has investigated one of basic and challenging problems in connection with ITC application in development of accountability system in governmental sector. The most important aim of the paper is to address the social and infrastructural barriers, digital gap, legal, information security and change management barriers as the most important barriers of ITC implementation; aiming to pave the way for development of the required ground for promotion of accountability to the beneficiaries. The authors have thoroughly reviewed the relevant literature and made several discovery interviews with the professionals of the sector and tried to recognize the main parameters and variables of the research by use of a descriptive and survey method and selected the members of the statistical samples by using random class sampling and, eventually, investigated the hypothesis of the study in governmental companies dealing with mining sector activities. The results showed that the social, infrastructure, digital gap and legal barriers are the most important barriers towards the implementation of ITC towards the accountability in governmental company in mining industries sector and data security and change management are the barriers with the least important

In this paper a simple and effective expert system to predict random data fluctuation in short-term period is established. Evaluation process includes introducing Fourier series, Markov chain model prediction and comparison (Gray) combined with the model prediction Gray- Fourier- Markov that the mixed results, to create an expert system predicted with artificial intelligence, made this model to predict the effectiveness of random fluctuation in most data management programs to increase. The outcome of this study introduced artificial intelligence algorithms that help detect that the computer environment to create a system that experts predict the short-term and unstable situation happens correctly and accurately predict. To test the effectiveness of the algorithm presented studies (Chen Tzay len,2008), and predicted data of tourism demand for Iran model is used. Results for the two countries show output model has high accuracy.

This research has been designed in order to provide an appropriate methodology for Business Model Change and update based on applying new Information and Communication Technologies such as Mobile technology in an organization. In this way, after investigation of all related references in the field of Business Model, ICT change, business model change and applying mobile technology in organizations, the research framework has been derivate. In other phases of research, this framework has been evaluated based on expert judgment and System Dynamic approach. The result of this research includes a well defined methodology suitable for Business Model Change covering five phases and thirty seven detailed steps by relevant deliverable for each step.