mohammad fathian

Cloud Manufacturing (CMfg) enables flexible and customized manufacturing services through dynamic service composition. However, achieving optimal service composition remains challenging due to the need to meet complex Quality of Service (QoS) requirements, including cost, time, quality, and resource workload balance. Notably, previous studies on service composition models have rarely considered workload balancing as part of their QoS criteria, which is critical for maintaining efficient and sustainable resource use. This study addresses this gap by presenting an advanced service composition model that integrates workload balance as an essential QoS metric alongside traditional factors like composite service quality, time, and cost. To further support optimization, the Simulated Annealing (SA) and Tabu Search (TS) algorithms are enhanced with a novel shaking mechanism designed to expand the search space and mitigate premature convergence risks common in metaheuristics. Experimental evaluations conducted on an OR-Library dataset confirm that the enhanced SA algorithm achieves up to a 25% improvement in the fitness function and a 7% reduction in computational time, while the improved TS algorithm achieves a 2% reduction in the fitness function and a 21% decrease in computational time. These findings highlight the model's potential to enhance CMfg service composition efficiency, offering substantial performance benefits over traditional methods. The core contributions of this study include the development of a workload-integrated service composition model and enhancements to SA and TS algorithms for effective problem-solving within this framework.

The objective of this study is to provide a comprehensive classification of methods proposed for measuring the level of job engagement among the workforce, as well as to select the most comprehensive method for examining job engagement across different generations at Iran University of Science and Technology. Additionally, it aims to analyze generational gaps and plan for future policy-making.The research employs an exploratory-descriptive methodology. Initially, a literature review is conducted to categorize existing models comprehensively. Subsequently, Cochran's method is utilized to determine the statistical sample within the university, implementing the Aon Hewitt model through its standard questionnaires using a 5-point Likert scale to assess the current situation.The models identified were categorized into industrial and academic types, totaling eight models, which are detailed in the paper. The most comprehensive model was selected for implementation. In applying the Aon Hewitt model, it was found that the Baby Boomer generation exhibited the highest level of engagement, while Generation Y showed the lowest. Furthermore, the factors and indicators influencing job engagement revealed that Generation Z has a different perspective compared to others; unlike previous generations that considered leadership and management as the highest factors, Generation Z prioritized organizational reputation in their job engagement criteria.

With the emergence of various technologies such as blockchain, internet of things and big data, the approach and perspective to urban service management has changed. These technologies are part of the 4th generation industrial revolution, which are widely used in the creation of electronic services infrastructure, electronic city, and among other things, smart building. Considering the expansion of cities, the increase of urban population, the diversity of urban services and the emergence of new technologies, the development of urban services and its management requires more appropriate plans and models with new technologies in order to eliminate shortcomings. The main goal of this research is to provide a development model of urban services based on blockchain technology. To develop urban services by using the capabilities and effective capacities of blockchain. The type of method is developmental-applicative in terms of the goal, and the research method in this research is a combination of Delphi (qualitative) methods and structural equation modeling (quantitative), so in the first step of the research, data collection is used And information from library studies and surveying were done and in the next step, using the steps of the Delphi method, a semi-structured interview was used to confirm and explore the conceptual model, and in the third step, using the researcher's questionnaire. It is made from a survey of expert community and analyzed by structural equation modeling method using Smart PLS software. The findings of the research indicate that blockchain technology has a positive and significant impact on the development of city services, as well as environmental health services, recreational welfare services, protection and safety services, electronic education services, public transportation services and traffic are influential indicators. are in the development of urban services. The combined reliability of the model is more than 0.740 according to the correlation of the structures with each other

According to the progress of new technologies, changes in the use of technologies in all fields are necessary and important.In this way, there is a need for a transformation in the strategic view and knowledge management with an approach to these technologies. The main goal of this research is to achieve the model of strategic knowledge management with the use of Internet of Things based on block chain in the defense sector, which intends to solve the shortcomings and challenges of knowledge management by using these two technologies. The type of research is applied-developmental in terms of purpose, in order to achieve the results from the research literature, a combined method (quantitative and qualitative) has been used. In the first step, the dimensions and components of the proposed model have been extracted using the Delphi method, in the second step, the relationships between the components, the network of components, and the leveling of the components have been done using the quantitative methods of Dimtel and structural equations.The results of this research show that "Knowledge creation", "Implementation and implementation of strategies", "Evaluation and control of strategies" and "Knowledge sharing" are the most effective components, respectively, and that "Knowledge sharing" has the strongest relationship with other components.Also, the components of "storage (distributed database)", "authentication", "interactivity" and "improving cooperation and transfer" are part of the cause components, among which "storage (distributed database)", It is the most effective component.

Accurate funding in order to better manage costs is one of the main concerns of managers. The Health Insurance Organization of Iran, as one of the largest basic insurance organizations, is no exception to this and certainly needs to identify and accurately predict the costs of treatment in order to provide financial resources and obtain the necessary funds in its field of treatment. Using machine learning methods to create a model for predicting treatment costs can be a great help in accurately financing.

This study has provided a model and method for predicting the costs of the organization by using the cost data available in the medical documentation systems of the provinces of the organization during the years 2007 to 2020 and using the SARIMAX and LSTM methods. This method can help to more accurately predict the costs of the organization.

Determining the method with better performance based on the MAPE index alone did not meet the desired model; therefore, by creating a combined method and using the criterion of percentage of realization of the forecast, the optimal model for cost forecasting is presented.

Due to the need for a scientific method to more accurately predict the costs of the organization, the proposed method and model was able to predict the costs of the organization with minimal errors compared to the errors accepted in manual processes.

Cyberspace, as a macro socio-technical system, includes various actors and stakeholders and numerous technical, social, cultural, and economic components. This complexity and diversity inevitably make the use of systematic approaches for structuring the problem inevitable. In this study, we introduced situational analysis as a new edition of grounded theory to structure complex issues. Scientific and philosophical traditions of the last decades of the 20th century, including postmodernism, poststructuralism, social constructivism, actor-network theory, and sociotechnical systems theory are the origins of situational analysis. Therefore, based on situational analysis, we tried providing a clear picture of the national internet concept in Iran. To this end, we first demonstrated the various tools of situational analysis approach and argued why it is helpful in cyberspace policy research programs. Then, we used qualitative data, such as official documents, books, papers, media reports to draw situation maps. Finally, based on the results of using situational analysis, we enumerated the advantages of situational analysis approaches for cyberspace policy.

Social commerce as a new paradigm of commerce has been considered for about a decade. The first attempt to apply this paradigm is to examine its acceptance process. In this research, social commerce acceptance studies have been reviewed with primary and secondary search strategies. The main purpose of this research is to examine the acceptance process from different perspectives and to analyze the research gap that paves the way for future research in this field. Survey perspectives include focus on acceptance steps, basic theories used, focus on value creation, extent of customer engagement, focus on innovation, type of media used, geographical focus, type of business transactions concerned, their influential factors, thematic focus and applied focus area, research methods used, data analysis tools and statistical samples. Accordingly, 203 articles were reviewed with two approaches: quantitative analysis and gap analysis. Quantitative analysis of studies showed that acceptance technology as the highest theories, social networks as most platforms, trust as the most widely used factor, Asia as most continental geographical focus, China and the United States as the most geographical focus of the country. Based on the gap analysis, service-oriented businesses, qualitative methods and modeling, focusing on e-commerce platforms and equipping them with social commerce constructs, and focusing on business-to-business transactions were identified as research gaps.

The emergence of Information and Communication Technology has caused many changes to take place in organizations. Among them, the inter-organizational collaborative networks, in particular, have expanded. This paper aims to review systematically the pertaining research literature and find answers for these questions: First: what is the main subject that the research have been focused? Second what is the main existing research gap in the aforementioned subject? The theory of the paper is relied on “life cycle stages of collaborative networks”. The method of data acquisition covers two techniques of “co-occurrence method of words” in the period 2005 to 2019 and “meta-synthesis method in the period of 2009-2019 for finding the first and second answer, respectively. After conducting the review, the paper first, presents a comprehensive picture of the status of scientific activities in the period 2005 to 2019 and shows that most of the studies have focused on the creation stage of the life cycle of collaboration networks, and at this stage, the issue of partner selection has the most frequency of articles. As to the second question, the paper concludes that carrying out studies on providing a comprehensive approach to the preparation and management of partner profiles, core value mapping and innovation management in order to quickly select partners and reduce the level of risk in long-term collaborative networks are the important subjects to fill the gap.

The proper location of facilities /service providers is of paramount importance in the business success of several economy sectors for the sake of its effects on the service demand and hence on the market share. A vital problem resulted from modernization, urbanization, and globalization is the reconfiguration of branch locations and service capabilities to match the fast-changing and competitive market, regional economy, and customer distribution. This work introduces a new spatial decision support methodology to restructure branches' network with proposing a mathematical model taken from a real national project in the financial market. It considers establishing the new branches, relocating the current branches, merging the redundant branches, or ones with poor performance into the other branches. Moreover, a credibility-based fuzzy chance-constrained programming model is proposed to consider uncertainty in travel distances and market attractiveness of each node. The data and results are processed using the geographical information system (GIS) for Bank Melli in an urban district of Tehran.

Many real-world networks, including biological networks, internet, information and social networks can be modeled by a complex network consisting of a large number of elements connected to each other. One of the important issues in complex networks is the evaluation of node importance because of its wide usage and great theoretical significance, such as in information diffusion, control of disease spreading, viral marketing and rumor dynamics. A fundamental issue is to identify a set of most influential individuals who would maximize the influence spread of the network. In this paper, we propose a novel algorithm for identifying influential nodes in complex networks with community structure without having to determine the number of seed nodes based on genetic algorithm. The proposed algorithm can identify influential nodes with three methods at each stage (degree centrality, random and structural hole) in each community and measure the spread of influence again at each stage. This process continues until the end of the genetic algorithm, and at the last stage, the most influential nodes are identified with maximum diffusion in each community. Our community-based influencers detection approach enables us to find more influential nodes than those suggested by page-rank and other centrality measures. Furthermore, the proposed algorithm does not require determining the number of k initial active nodes.

Considering the emergence of electronic health records and their related technologies, an increasing attention is paid to data driven approaches like machine learning, data mining, and process mining. The aim of this paper was to identify and classify these approaches to enhance the quality of clinical processes.

In order to determine the knowledge related to the research question, a systematic literature review was conducted. To this end, the related studies were searched in the web of science documentation database, as a comprehensive and authoritative database covering 1536 scientific publications from 2000 to 2019. The studies found from the initial search were investigated and the relevance of their title with the inclusion and exclusion criteria was determined. As a result, 184 articles were selected. Further investigations resulted in 84 studies that remained after reviewing the abstracts and full texts of these articles. These studies were also evaluated with regard to their field of study and the quality of presented evidence. Consequently, the final synthesis was performed on the evidence extracted from these articles.

Examination of the identified evidences resulted in 4 general categories of "event-based approaches", "process intelligence", "clinical knowledge systems", and "data-driven control and monitoring" as data-driven approaches that can be used to manage the quality of clinical processes.

The findings demonstrated that event-bases approaches had more applications as data driven approaches in the context of health care. Furthermore, process mining is a novel approach that can be used by future studies. The results of this study can be used to complement clinical governance procedures regarding emerging data driven opportunities.

The main issue in this study is to investigate the effect of different characteristics of individuals including demographic characteristics and personality traits on the motivational components of gamification design in loyalty club within banking industry. Innovation and the scientific contribution of the present study to gamification systems in the field of banking can be evaluated from two perspectives: determining how the customers with different demographic and personality traits are attracted by the motivational components of gamificatin by conducting a research, and what kind of reward do they expect to receive? This shows the importance of personalizing gamfication-based systems to increase customers’ satisfaction and loyalty. Moreover, based on the results, designing strategies and guidelines for gamification-based systems are provided so that designers can develop appropriate strategies to improve interaction with people. Identifying and applying different demographic and personality traits can increase motivation to participate in customer club activities. 

The present applied research is descriptive-survey in terms of data collection method. In this study, using mixed method approach through interviews with experts and specialists and using questionnaires, the effective components in personalizing gamification were determined and localized in order to biuld efficient interaction with bank customers. In the initial interview phase, 15 experts from related fields, 3 bank deputies, 3 bank managers, 5 faculty members of the Higher Education Institute of Banking and the Central Bank Monetary and Banking Research Institute, 2 banking experts and 2 from Specialists in the field of gamification were selected. It is worth mentioning that Hekmat Iranian Bank was selected as the study case for the present study. To calculate the scores of the five personality types, the standard ten-question version of the BFI questionnaire was used, in which two questions were assigned to identify each attribute, one considering the pros and one the cons of the personality type. It should also be noted that all the analyses and calculations were performed using SPSS v.23. 

There is a significantly positive relationship between age and participation in the lottery, and the results of this study showed that older people are more likely to participate in the lottery. In contrast, such people are not interested in receiving cash. Older people tend to receive large money in a lottery at once, rather than earning cash steadily. It is also noteworthy that there is no significant difference between men and women accordingly.  But in the case of "discounts on the purchase from chain store products" and "discounts on the use of leisure and tourism services", women are reported to be more likely to seek such rewards and the difference is statistically significant. Men, on the other hand, have higher scores in "benefiting from the priority of using banking services", which is why men are often interested in using banking facilities but women mainly welcome discounts and auctions. There is a significantly positive relationship between being an extrovert and the reward of "benefiting from the priority of using banking services". On the other hand, it has a relatively positive relationship with the “discounts of chain stores and recreational and tourism discounts”. It is worth noting that the extroverts in this study did not show any intention to participate in the lottery. In contrast, being compatible is significantly attributed to the lottery bonus. There is a significantly poisitive relationship between being conscientious and "discount on the purchase of chain store products" and there is a relatively positive relationship between being conscientious and the "discount on the use of recreational and tourism services". Interestingly, being psychoanalytic has a negative relationship with all the items except lottery and the use of the priority of using the bank services. It should be noted that most of the coefficients of this personality trait are at the moderate level.

Extroverts are more likely to prefer the motivational components of luck, privilege, and awareness. The results of this study show that people who are compatible are motivated by the motivational component of luck and lottery. On the other hand, due to their tendency to cooperate and being altruistic with others, there was a negative relationship between participants with a compatible personality type and the motivating component of social interaction and communication. Regarding people with conscientious personality trait, motivational components of the badge and ranking table will motivate them, which can be considered as a result of the sense of responsibility and conscientiousness to perform the assigned tasks and receive job completion marks. The results also show that people with psychotropic personality trait have a relatively strong relationship with various motivational components such as epic concepts, feedback, countdown and awareness. Therefore, it can be concluded that there is no correlation between these motivational components and personality traits and these connections are mainly due to the small number of people with this personality type in this study. It should be noted that people with low mental health and high emotional stability have "emotional maturity, self-confidence and stability in their plans and emotions" and most people introduce themselves as a person with high emotional stability in the community because of their desire to show off. Respondents with a high level of imagination and experience were more interested in the motivational components of virtual rewards and competitions due to their sense of invention and curiosity.

This study examines how gamification mechanics could be used in online retailers' loyalty programs. In other words, this article attempts to create a conceptual model for the relationship between gamification mechanics and customer loyalty elements. We used a field study to conduct our research. In order to validate the survey, 450 customers from one of the greatest online stores in Iran were questioned. The results of this survey were used to validate our 11 phrases on the relationship between gamification mechanics and customer loyalty. The results were analyzed using confirmatory factor analysis, path analysis, and model fitness tests in structural equations modeled in the Lisrel software. According to the research findings, the relationship between variables and the proposed conceptual model was confirmed. Based on the performed analysis, all 11 phrases were verified.

Segmentation is one of the most useful tools for a better understanding of customer diversity, and managers can allocate marketing activities efficiently and effectively to homogeneous and prioritized customer groups. Earlier research on the segmentation of financial services customers show that these studies often rely on socio-demographic information and are less paid to psychological and behavioral variables that indicate why customers are selected and preferred. Therefore, the present study aimed to segment the banking industry market based on the attitude and financial behavior of customers.

In order to discover the desired segments, studies were done based on the theme analysis. Then, through a survey of internal and external experts, the indicators were formulated in 56 questions and distributed among the 30 clients as a pre-test. After pre-test confirmation and determining of the sample size, questionnaires were distributed through random cluster sampling and finally 355 full questionnaires were received.

Based on factor analysis, 14 attitudinal factors and financial behavior were identified and analyzed using cluster analysis of participants in four sections: Paid Savers, Financial Experts, Independent Concerned Spenders and Indifferent Followers.

Bank customers have significant differences in terms of demographic characteristics, attitudes and financial behavior. Therefore, in this research, in order to improve the level of customer interaction with the bank, they are divided into four groups and appropriate marketing solutions and recommendations for each of them were presented.

Corebanking is a comprehensive and integrated system for responding timely and accurately to customer's demands and reporting their information. This task is done through various communication channels between customers and banks and a common database between all branches in order to staying in competition. For the optimum and strategical usage and utilizing IT in our goals path, we have to use a roadmap to implement new technologies. So far for different fields and usage and control of technologies this strategic roadmaps have been provided. COBIT as an IT governance framework is one of these frameworks which is used for both guide and control of IT in organizations. In this study, we provide a core banking IT governanvce model based on COBIT framework. For this reason we determine the activities required for operation of corebanking by using structural equation modeling and significance of relationship between COBIT processes and activities with the activities needed to achieve core banking. To allocate the sub-process of the COBIT for each of the activities of corebanking we used questionaries and Delphi technic.

Emissions resulted from transportation activities may lead to dangerous effects on the whole environment and human health. According to sustainability principles, in recent years researchers attempt to consider the environmental burden of logistics activities in traditional logistics problems such as vehicle routing problems (VRPs). The pollution-routing problem (PRP) is an extension of the VRP which consists of routing a number of vehicles to serve a set of customers and determining their speed on each route segment so as to minimize a function of comprising fuel, emissions and driver costs. This paper proposes an adaptive large neighborhood search for the robust PRP (RPRP) under demand uncertainty. The achieved results indicate a premium performance of the solutions obtained by the proposed robust models.

Nowadays, web-based services like E-Commerce and E-Banking make fundamental changes to the ways of using internet and human's life. Web shares direct media with low costs between services of businesses and their customers. Businesses need to record, study and analyze their user's behavior and interests in order to adapt content and interface of their web site with user's interest for targeted marketing and advertising and then complete the process of personalization. For this purpose and for analysis of users’ behavior and making recommendations based on the users’ behavior, web mining approaches can be used. In this paper, a model was developed which can be applied for analyzing and predicting user's behaviors of a specific web site. First, users were clustered with affinity propagation algorithm and then, their behaviors were analyzed using sequential pattern mining algorithm called CM-SPADE. In the next step, for each cluster, User's profile was created. Then by using these profiles, recommendations can be made for new users. At last, the represented model was evaluated and the final results was acceptable.

Experts, as immaterial reserves, always are noteworthy in developed countries and the importance of this issue, leads us to better utilization of these valuable resources as one of the indicators of communities’ development. This research, by extracting and studying the effective factors in knowledge transfer process which is created by Iranian graduates from universities of the developed countries, classified and measured the effectiveness of this factors. For this purpose, considered factors in knowledge transfer process at all levels of knowledge transfer were extracted and categorized by studying the results of previous research. These factors were extracted through literature review and then interviews with relevant experts. The resultant factors were analyzed through 115 completed questionnaires by confirmatory factor analysis method. As a result of this study, factors such as "the ability to understand the results of research and the acceptance of new ideas by the recipient of knowledge", "the need for employment of specialist manpower", "sanctions imposed by international institutions", "mutual respect" and "enactment of incentive laws" are the most important factors that affecting knowledge transfer process

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 customer's 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.

Identification and tracking scientific changes is critical for scientific policy makers. This research proposed a model for identification and tracking changes in Data Quality research area. We used cumulative citation network in order to find research communities and their changes during the time. The proposed model can be applied in other scientific disciplines. It can also shows all types of scientific changes including birth, growth, merging and death. In order to verify the model in Data Quality area, we selected all papers that is published between 1970 and 2009 that covers more than 7000 papers. It is shown that Data Quality research area is studied in different disciplines. According to the results, there is 82 percent correlation between number of citations and the growth of Data Quality communities that shows the importance of citation for community survival and growth.

Artificial immune system (AIS) is one of the most meta-heuristic algorithms to solve complex problems. With a large number of data, creating a rapid decision and stable results are the most challenging tasks due to the rapid variation in real world. Clustering technique is a possible solution for overcoming these problems. The goal of clustering analysis is to group similar objects.
AIS algorithm can be used in data clustering analysis. Although AIS is able to good display configure of the search space, but determination of clusters of data set directly using the AIS output will be very difficult and costly. Accordingly, in this paper a two-step algorithm is proposed based on AIS algorithm and hierarchical clustering technique. High execution speed and no need to specify the number of clusters are the benefits of the hierarchical clustering technique. But this technique is sensitive to outlier data.
So, in the first stage of introduced algorithm using the proposed AIS algorithm, search space was investigated and the configuration space and therefore outlier data are determined. Then in second phase, using hierarchical clustering technique, clusters and their number are determined. Consequently, the first stage of proposed algorithm eliminates the disadvantages of the hierarchical clustering technique, and AIS problems will be resolved in the second stage of the proposed algorithm.
In this paper, the proposed algorithm is evaluated and assessed through two metrics that were identified as (i) execution time (ii) Sum of Squared Error (SSE): the average total distance between the center of a cluster with cluster members used to measure the goodness of a clustering structure. Finally, the proposed algorithm has been implemented on a real sample data composed of the earthquake in Iran and has been compared with the similar algorithm titled Improved Ant System-based Clustering algorithm (IASC). IASC is based on Ant Colony System (ACS) as the meta-heuristics clustering algorithm. It is a fast algorithm and is suitable for dynamic environments. Table 1 shows the results of evaluation.
The results showed that the proposed algorithm is able to cover the drawbacks in AIS and hierarchical clustering techniques and the other hand has high precision and acceptable run speed.

Under the critical thermal conditions, the human body cannot adapt itself to the environment by using physiological thermoregulatory mechanisms. Under these conditions, using the protective clothing is one of the effective ways to protect the human body against the thermal injuries. Therefore, in the present study, the effect of using Phase Change Materials (PCMs) on the performance of firefighters’ protective clothing has been numerically investigated under the critical scorching conditions. The main contribution of this study is the simultaneous modeling of a PCM based protective clothing with physical and physiological mechanisms of the human body. For this purpose, a multi-layer protective clothing with a PCM layer has been considered and its thermal performance has been investigated under scorching conditions for three different arrangements of the layers. The results show that the middle layer of protective clothing is the best position for implementing the PCM. Also, and is the best melting temperature for the mentioned PCM is about 40˚C. Moreover, the results indicate that using the PCMs in protective clothing can increase the thermal tolerating time from 300 seconds (for non-PCM protective clothing) up to 900 seconds, under the scorching conditions.

Performance evaluation models of information and communication technology (ICT) sectors have been already considered with many perspectives of ICT management. Since ICT has two distinct aspects of usefulness and harmfulness for the environment, therefore, this research with regard to the new dimension named environment, is going to present a new performance evaluation model with the approach of environment. To achieve such a model, the integration of criteria of the I.R.Iran ICT National Quality Award (ICTINQA), Green IT Balanced Scorecard, Iranian green management model, standards of the International Telecommunication Union (ITU) and other models are used. Afterwards, the most comprehensive suggested criteria are presented with redefinition under the title of fundamental concepts of green communications framework. For this purpose, by using the method of exploratory and confirmatory factor analysis, the final factors were determined and weighted using hierarchical analysis technique.

In recent years، virtual communities have been significantly popular and almost all of them are targeted for business profitability. In our study، the profitability of these communities for businesses has been analyzed from a commercial perspective. The study considers virtual community as a business model which can have a great impact on consumer purchase decision process. With respect to reference groups and word-of-mouth recommendations in virtual communities، contributing factors have been detected and a model of impacts and relationships between these factors has been presented. Hypotheses of the mentioned model are evaluated by making use of a questionnaire completed by 110 members of virtual communities. Using AMOS program and path analysis method، statistical analysis of received responses showed that factors such as virtual community''s information quality، trust between people، sense of belonging، virtual community members’ perceived benefit and members’ permeability are effective to different degrees، and there is no significant relationship between social identity of members and their permeability.

Information Technology Governance is considered within the responsibilities of the Board of directors and executive management organization and is an inseparable part of governance and the recipient organizational leadership structures and processes that will ensure that organizational information technology supports strategies and organizational goals and develops them as well. Due to its comprehensive nature and dependence of organizations on information technology, the importance of unity among the information technology strategies and business strategies has increased the said unity is the initial aim of information technology governance. this study, compares the maturity in information technology governance between two selected organizations in financial and manufacturing services sectors in Iran using Cobit framework. The results indicate that there are some differences in maturity of information technology governance within two sectors and financial services sector compared with manufacturing one is more matured regarding information technology governance. The average maturity of information technology governance in financial services sector is (1.99) and the average maturity in information technology governance for manufacturing sector is (1.57).