JOURNAL OF INFORMATION TECHNOLOGY MANAGEMENT
Year:2017 | Volume:9 | Issue:3 |Papers Count:14

Outsourcing, a key strategy for Information Technology sourcing due to cost reduction, concentration on core competencies and acquisition of the latest and the most up to date technologies, has been seriously taken into consideration by organizations in the last two decades. Information Technology outsourcing is a key decision in that field which can lead to successful investment management in this area. Despite the popularity and attention to this outsourcing strategy, statistics indicate an increasing failure rate in Information Technology outsourcing projects rooted in the changes occurred due to adapting with this strategy. Moreover, mismanagement of these changes can be considered as a cause for many of these failures which not only led to the failure of outsourcing process but also bring about additional risks and costs. The aim of this paper is to identify and prioritize change management requirements in supplying resources based on Information Technology services outsourcing process. Therefore, following a systematic literature review in preview of Information Technology outsource and change management researches, 12 semi-structured interviews were conducted by experts in the field of outsource and organizational change management. Finally, factors affecting change management success in Information Technology outsourcing were prioritized using Interpretive-Structural Modeling. The present study has contributions both to the developed model and the research methodology in outsource researches domain.

In today's turbulent business environment, organizations have to make changes in their structure such as outsourcing. This change that is a requirement to provide for the needs of that organization and for competitive advantage can be considered as a new trouble for that organization as well. Because of such changes, an organization may lose the workers' (employee) knowledge that is the most important element of the competitive advantage. Thus, the organizations are required to use the most effective knowledge management strategy that is proposed according to the analysis of the key aspects of those organizations. This study examines Sazeh Gostar, the most important suppliers of spare parts for Saipa Automobile Company. At first, the two coding and personalization strategies were applied provided that they were reviewed in the literature. The influential factors were then classified according to McKenzie 7S model. Then, the data were obtained using snowball and targeted sampling from interviews with 9 experts, and through reviewing the company documents. Current status of the company was identified using Theme Analysis. Finally, comparing the influential factors on the company's knowledge management strategy, the appropriate coding strategy was selected.

The aim of this study is to identify and rank the most effective elements to create sustainable competitive advantage in information technology companies. In this study, with the help of a couple of experts from three information technology organizations, the most important intangible assets (intellectual capital, social capital and spiritual capital) were identified as alternatives to the ranking of competitive advantage. Having reviewed the related literature and developed a panel of expertise, 16 suboptions extracted for the intellectual, social and spiritual capital. Five criteria of price, support, design, image and quality were taken into account for competitive advantage and the ranking of alternatives based on these criteria was carried out through the process of network analysis. The results of the network analysis showed that compared to social and spiritual capital, intellectual capital can have a more important role in creating competitive advantage in IT companies. Besides, regarding rating of sub-options, human capital ranked the highest in gaining the competitive advantage. Interface element, vision-orientation/value- orientation and relational/client capital were respectively the next ranks in making sustainable competitive advantage.

Special, privileged or VIP customers are of great significance to the banks since they continuously and broadly invest in deposits and remain loyal to the banks. This loyalty is dependent on the broad and specific services they receive, deposit interests, and the tuned regulatory actions that banks take for according to the grade of special customers and their propensity to risk. In the current research, a dataset of two thousand ordinary and special privileged customers were collected according to their demographics, accounts information, and level of investment in the bank. The grade of special customer and their propensity to taking risks are also determined by the experts of the bank. Afterwards, a range of learning algorithms are applied for designing and validating classification and prediction methods on special customers’ grades and their propensity to risk. Final results are then analyzed and prepared as a set of intelligent and improvable rules that assist the bank managers in formulating interactive and predictive decision making policies from the initiation of the customer relationship with the bank.

Accuracy in estimating the needed effort for software development caused software effort estimation to be a challenging issue. Beside estimation of total effort, determining the effort elapsed in each software development step is very important because any mistakes in enterprise resource planning can lead to project failure. In this paper, a Bayesian belief network was proposed based on effective components and software development process. In this model, the feedback loops are considered between development steps provided that the return rates are different for each project. Different return rates help us determine the percentages of the elapsed effort in each software development step, distinctively. Moreover, the error measurement resulted from optimized effort estimation and the optimal coefficients to modify the model are sought. The results of the comparison between the proposed model and other models showed that the model has the capability to highly accurately estimate the total effort (with the marginal error of about 0.114) and to estimate the effort elapsed in each software development step.

Timing of curriculum planning for students and faculty could be done using diverse methods. The present research concerns with curriculum planning for professors considering the students' opinions. In doing so, the courses and the timing are determined based on the professors' common timetable, the professors' intensive courses timing and the class limitations. To achieve this goal, the genetic algorithm methodology was used in two steps. In the first stage, single-point cutting operator was used and in the second stage of the algorithm, a new intelligent operator called cyclic reverse list (RIL) was used provided that gold, silver and bronze time types were used for different courses. The advantages of this algorithm are using a new appropriate function (hot rolled), as well as new criteria and a new operator (RIL). Unlike conventional methods, in this method the appropriateness is considered in proportion with the whole population and we try to remove the impossible solutions. The optimal solution is chosen from among a multitude of provided responses. Therefore, it was found that we can reach the optimal solutions with regard to a better appropriateness.

One of the most important issues in Customer Relationship Management is customer segmentation and product offer based on their needs. In practice, Customer’s behavior will change over the time by changes in technology, increase in the number of new customers and new competitors, and product variety. Traditional segmentation models that are static over time cannot predict these changes in customer’s behavior and ignore them. This challenge is especially critical in Telecommunication with high churn rates. In this research, we have used temporal fuzzy clustering to detect significant changes in customers' behavior for a telecom company during a 10-month period. The aim of this study is to find factors that affect structural and gradual changes in clustering model. In addition, we have suggested a method based on Frechet distance to extract similar patterns in customer’s usage behavior. Provided that combining the temporal clustering with trajectory analysis is an effective way to recognize customers’ behavior among the clusters, the results showed that there are seven distinct customer behavior patterns two of which lead to the customer drop or churn. These patterns can be used to reduce the risk and costs of customers churn and to design optimum services.

Since customers react differently to business and marketing on social networks, the researcher is looking for segmenting customers into different categories according to their reaction to marketing in social networks. The present study is a descriptive-exploratory research and the data were collected through a questionnaire. The population of 14, 000 follower of the researcher’s personal page on Instagram were analyzed and a sample 224 members were randomly selected. To analyze the data, a two-step clustering method was applied. As a result, five distinct clusters (the active, the talker, the hesitant, the passive and the averse) were identified. Two segments were reported to be highly influenced by social networks marketing in terms of brand engagement, purchase intention and word of mouth advertisement (WOM). The "Active" are the most influenced group including 18.3% of the population most of whom are single girls or women. The next group that are influenced the most by social networks marketing is the "Talker". This group represents 24.1% of the population, the most populated group. The "Talker" are different from the "Active" in term of their intention to purchase. Totally, 42.2% of the population are reported to be influenced by social networks marketing.

The emergence of social networks is one of the most influential phenomena of the 21st century. Social networking cyberspace creates a broad area of information and a variety of semantic representations. Social networks connect different people with different interests and ideas together. Due to the huge amount of intellectual potential and human thought in social networks, a great number of businessmen or retailors and commerce managers are attracted to such networks. The main objective of this study is to identify the most effective members in the cycle of content propagation on social networks and to propose solutions to improve the propagation of advertising and marketing on social networks to help business owners and managers. The case study of this research is "DigiKala" Instagram social network. At first, using the NodeXL software, the general content of DigiKala Instagram pages were collected. In the next step, applying clustering methods we found effective people using AlSuwaidan framework. Then, the collected data were processed using Matlab software. Finally, the obtained results were evaluated and based on the tests, 9 of the most influential people in accordance with the highest coefficient in publishing content on the social network were introduced.

In the Multi-relational networks, the study of concurrent choices or multiplicity, and exchanging the choices is important. In this paper, we reviewed multiplicity and exchange in the literature and tried to study these effects in the world network of trade and diplomacy. In addition to reporting the results of exploiting other researchers’ contributions in the case of trade and diplomacy relations on our data, we proposed our own solution to study and evaluate social structure in the situation that multiplicity and exchange effects are significant. Due to the significant number of concurrent choices and choice exchange, we used appropriate probable block model of multiplicity and exchange. Specifically, we found that there are four clear patterns in the world trade and diplomacy network: Trade affected by diplomatic hosting, Trade affected by diplomatic activity, Diplomacy affected by export and Diplomacy affected by import.

In this research knowledge management has been studied as an interdisciplinary area. We aim to find an answer for this question that "what are the scientific structure and knowledge map of knowledge management projects regarding these two aspect of subject areas and keywords. For this purpose, nearly 40000 scientific documents including knowledge management as one of their keywords were selected from Scopus database and were studied in various subject areas. In this research, bar charts have been drawn for each index of subject areas and keywords. Besides, using Cooccurrence matrix, adjacency graphs were drawn and then clustered using Average-Link algorithm. Bar charts and graphs were drawn using R and Excel software. The results of this research showed that among the researches on knowledge management in the world, the most relevant scientific fields to knowledge management are Computer Sciences with 32.5%, Business, Management and Accounting with 14.5%, Engineering with 13.7%, Decisive Sciences with 12.6%, Mathematics with 7.07%, and Social Sciences with 6.63%, respectively. The most keywords collocate with knowledge management in the world are Human-Computer Interaction, Information Management, Systems Management, Information Technology, Manufacturing, Acquisition of Knowledge, Semantics, Knowledge Transfer, Ontology and Information Retrieval.