mohammadjavad ershadi

Failure to properly manage limited resources can ultimately lead to failure for these businesses. These businesses play an important role in inventing new methods that lead to technological changes and increased production capacity. It can be said that such units are driven by change and competition, as they alter the overall structure of the market. The creation and development of these businesses is an important policy for creating new jobs, accelerating economic improvement, and fostering the growth of countries. Therefore, special attention should be paid to new businesses. This research aims to identify important factors by examining different models of customer satisfaction as well as theses and papers in this field. By presenting an indigenous model, the research first uses the best-worst fuzzy method to weight the e-business customer satisfaction factors. Then, using a fuzzy TOPSIS technique, it examines one category of e-business, namely online stores, and ranks them in terms of customer satisfaction criteria. The results show that this model, with high computational efficiency and compatibility, highlights the priority of customer satisfaction factors. Additionally, communication and interaction criteria were ranked as the most important, followed by product and service quality, price/time, and sales and after-sales service. Interactions, value/time benchmarking, quality benchmarking, and ultimately sales and after-sales service warranty were identified as the best sub-criteria.

Transportation plays a significant role in the gross domestic product and oil consumption of every nation. In our country, a combination of recent sanctions and underdeveloped rail, air, and sea transportation systems has led to an increased reliance on road transport. Unfortunately, road transport contributes significantly to the emission of greenhouse gases, particularly carbon dioxide. Nevertheless, transportation is a vital aspect of logistics, and addressing pollution in vehicle routing stands as a paramount concern within this realm.This paper introduces a model aimed at optimizing fuel consumption costs, considering various factors such as vehicle load, speed, pollution, as well as parameters like fuel and engine efficiency, incline, traffic density, wind speed and direction, air temperature, asphalt quality, and driver remuneration. Additionally, this mathematical linear mixed-integer model incorporates probabilistic demand and a distribution system involving both delivery and pickup processes, all geared towards cost minimization.By employing this model, organizations can achieve more precise cost estimates, enhanced analysis, and improved planning. Given the NP-hard nature of the problem, its resolution involves the amalgamation of two meta-heuristic algorithms: Extreme Learning Machine (ELM) and Genetic Programming (GP). Experimental results indicate that the developed hybrid algorithm offers highly accurate estimations in a remarkably short time span when compared with similar algorithms.

Today, academic research plays a very influential role in the economic development of countries. These researches are often recorded and disseminated in the form and structure of theses and dissertations in scientific institutes. The better the quality of this data in the systems that collect and distribute it, the more it can be used and exploited by organizations and businesses. Therefore, providing this data requires proper monitoring to put the output of the recording and dissemination process in good condition. This paper offers a framework for evaluating theses and dissertation data quality. In the framework, the data inconsistency coding structure is introduced and presented in Word and PDF files and in the form of metadata (bibliographic information). The approaches presented in data quality methodologies (TDQM and DWQ) are also used to provide solutions to improve data quality in the provisioning phase. At this stage, approaches such as owner attribution to data or process, root cause analysis, process control, and continuous monitoring are considered. The focus group method determines the operational strategies for quality improvement. Finally, process-oriented techniques, such as quality control checklists and image processing, and data-driven approaches, such as data cleansing, are localized and developed in this section to improve the quality of theses/dissertation documents. The provided improvement solutions were categorized into two different groups. Guiding the user in the "Theses/Dissertations" registration process is identified as a process-driven category. On the other hand, introducing a specific format for "Theses/Dissertations" files and resolving the quality issues of PDF files were among the data-driven solutions.

The maturity of business intelligence, which is the main goal of this research, plays an important role in intelligent decision-making, planning, control and monitoring in the field of health care. In order to identify the effective factors, the Delphi method was used and experts' opinions were, and in order to determine the effectiveness and effectiveness of the indicators and finally to prioritize them, used the DANP method. The statistical sample includes 20 targeted academic experts and health care experts. According to the results of the Delphi section, 26 main indicators finalized in the research were identified, which are divided into three main categories including organizational, process and judgment criteria. According to the results of the DANP process, flexible and expandable technical infrastructure criteria, data and system quality and the correct definition of business intelligence problems and processes were prioritized as the three criteria with the highest ranking in the maturity of business intelligence. The business intelligence maturity model proposed by the research can be a road map for the successful implementation of business intelligence in the field of health care.

The aim of the current research is to develop a mathematical programming model to provide a resilience considering the quality of response and non-determinism of covid-19. For this purpose, library studies of a three-objective chain model have been designed with regard to the objectives of sustainability and resilience. The first goal includes resilience and cost, and the next two goals include environmental and social responsibility. Then the model was analyzed using NSGA-III meta-heuristic algorithm. The results show that applying resilience and sustainability leads to better use of materials, purchase of less materials, more recycling, and less disposal of materials, which benefits the supply chain. on the other hand, considering the uncertainty in the model, the results show that uncertainty has an effect on all three objective functions and leads to its deterioration. Regarding the realization of accountability, it is concluded that the realization of accountability requires more cost, intensifying environmental issues and of course improving social responsibility. That is, the more social responsibility increases, the more accountability, but increasing accountability requires increasing costs and worsening environmental issues. Finally, the effective strategies for improving the supply chain during the Covid era were examined, and the capacity increase strategies were determined as the best strategies.

In this research, a hybrid approach based on fuzzy logic and artificial neural network is presented to predict the failure of machines in order to increase productivity. The subject of this research is one of the factories of the automobile industry named Diaco Ide Aria, which operates in the field of automobile parts production. Preventive maintenance requires correct prediction of breakdowns and accidents, equipment and machines so that productivity can be increased by timely and correct maintenance of machines as well as fixing defects and breakdowns. To model the multi-layer perceptron fuzzy-neural network (MLP), first, 100 failures and stops were collected in a period of 15 months and then entered into MATLAB software. The obtained results show that the implementation of fuzzy-neural network and the prediction of machine failure time has reduced the duration and cost of repairs. Therefore, the working time and accessibility of the machines increased and ultimately increased the productivity by 57%, also, the accuracy of the developed neural-fuzzy model was estimated at 94%.

Research information databases and search engines are one of the main resources used by researchers every day. To accurately retrieve information from these databases, data need to be stored correctly. Manual controlling of data quality is costly and time-consuming. Here we suggest data mining methods for controlling the quality of a research database. To this end, common errors that are seen in a database should be collected. Metadata of every record in addition to its error codes is saved in a dataset. Statistics and data mining methods are applied to this dataset and patterns of errors and their relationships are discovered. Here we considered Iran's scientific information database (Ganj) as a case study. Experts defined 59 errors. Intimate features of every record, such as its subject, authors' names and name of the university, with its error codes were saved in a dataset. The dataset containing 41021 records was formed.  Statistics methods and association rules were applied to the dataset and the relationship between errors and their pattern of repetition was discovered. Based on our results, in average by considering 25 % of errors in every subject, up to 80%  of errors of all the records in a subject are covered. All the records were also clustered using K-means clustering. Although there was some similarity between records of different subjects, there was not seen any evident relationship between the pattern of repetition of the errors and the subject of records.

Knowledge is one of the most important competitive and value creating advantages of organizations, and for effective use of knowledge, attention should be paid to its quality. Data driven organizations rely on their data assets. Quality and reliable data in organizations leads to better decision making, and the knowledge quality in data driven organizations leads to growth and increase in profitability and innovation. The present research was conducted in order to investigate and prioritize the dimensions of knowledge quality in data driven organizations.

The statistical population of this research is made up of 10 experts in this field in the Research Iranian Research Institute for Information Science and Technology (IranDoc). In order to collect data, while reviewing the literature on the subject, interviews with experts of Iranian Research Institute for Information Science and Technology and questionnaires were used. For data analysis, the fuzzy DEMATEL method was used to determine the relationships between indicators and the ANP method was used for weighting and prioritizing criteria.

The results of data analysis through questionnaires and solutions using EXCEL software show that among the main criteria, the representational knowledge quality is the highest quality priorities. Also, among the sub-criteria, the degree of Expandability with a weight of (0.15089) was the first priority, Understandability with a weight of (0.14039) was the second priority, and Interpretability was the third priority with a weight of (0.13687).

It is observed that the sub-criterion of expandability and use of knowledge content in the future through knowledge sharing and integration has a much greater impact on the perceived quality of knowledge in data-oriented organizations than other sub-criteria.

Today, quality is one of the most important factors in attracting customer satisfaction and loyalty to service organizations. Therefore, one of the main concerns of managers is to improve the quality of services. With the development of the Internet and the world of communications, a concept called cloud computing has expanded in the world of communications, which provides a new model for the supply, consumption and delivery of computing services. The purpose of this study is to make the optimal decision in choosing the appropriate cloud service according to the conditions of users so that they achieve the highest satisfaction. Fuzzy Delphi method, fuzzy hierarchical analysis method, fuzzy TOPSIS method and finally multi-criteria decision making method are the methods used in this research. The results of the fuzzy Delphi method show that the indicators of transparency, accessibility and reliability should be eliminated. The results of fuzzy hierarchical analysis identified the cost index as the most important index and the support index during demand as the least important index. According to the results of fuzzy TOPSIS based on the weights obtained from fuzzy hierarchical analysis, SAAS, IAAS and PAAS cloud services were ranked first to third, respectively. Using the SAAS service provides numerous benefits to employees and companies, such as reducing time and money spent on time-consuming tasks such as installing, managing, and upgrading software.

In the scientific literature, indexing and quality control are key processes that, if done correctly, can be properly retrieved by search engines by researchers. On the other hand, the use of mechanisms such as infallibility and empowerment of users has made research organizations 100% free from quality control. Also, the restriction on the use of specialized organizational human resources has doubled the importance of paying attention to sampling methods. Although in scientific sources, sampling methods in physical and tangible products have been well and adequately addressed, but in the field of data, especially research data, little work has been done. In this research, a framework for sampling in data quality control processes is provided. Also, an algorithm has been developed for statistical design to minimize type I and II errors. As a case study of research data, the information dissemination database of dissertations / dissertations (pious) of graduates of the whole country (Ganj) has been selected and the research method has been implemented in this database. The results of this study showed that, considering the acceptable quality of many pious information items after registration, sampling is a vital task in improving the efficiency of the information organization and analysis unit. The classification of information items into three categories is critical, main and partial, and determining the number and method of sampling for each category is another result of this research. The framework presented in this research can be localized for various data-driven organizations, especially businesses based on research data. Since any revision of AQL and LTPD values affects type I and II errors, it is necessary to apply the algorithms developed in this research to new AQL and LTPD values as well. Obviously, the results of the algorithm implementation such as number of samples, acceptance number and rejection number will be updated in this process.

Nowadays, business owners and entrepreneurs are constantly looking for ways to increase the flexibility of their employees to meet the goals of their organization to respond the diverse needs of their customers. Optimizing service delivery in production systems and providing appropriate work patterns can ultimately simplify service delivery and increase employee satisfaction. While job rotation is a good way to improve an organization's agility in responding to a variety of demands, addressing this issue without the right attitude will lead to issues such as running costs as well as ergonomics and employee motivation. Hence, in this study, a multi-objective mathematical model of employee scheduling with the goal of minimizing scheduling costs and personnel allocation, enhancing employee satisfaction, and job ergonomics. The Epsilon constraint method is used to solve this multi-objective optimization problem. The results of this study showed that using appropriate multi-objective models can provide optimal results in terms of job rotation tailored to employees' working conditions. Scheduling can also ultimately reduce costs and increase motivation, considering job rotation.

Today, measuring data quality is one of the most important strategies in improving data-driven business processes. Any correct decision to improve systems in this category of organizations depends on an appropriate analysis of data quality. Research data and especially theses / dissertations  of graduates of the whole country based on this principle from various aspects of data quality need to be reviewed and evaluated. In the process of registering, the quality control of metadata is one of the most important parts that examines the information items of documents (such as the name of the researcher, supervisors and advisors, abstract, index, etc.).In the current situation, incompatible documents are identified during the quality control process and after entering in the system (as text), the relevant document is systematically returned to the researcher. Non-standard incompatible identified and the lack of proper classification for incompatible documents leading to informing tastes to researchers, making statistical analysis of metadata quality problems difficult, and also make it impossible to analyze the root of observed errors. Therefore, in this study, the incompatible structure observed after standardization has been used experimentally in two-month periods and the results have been presented. Incompatible in thesis defense date, the title page (Persian and English) and the existence of white pages in the file have been among the most important reasons for returning documents to users.  Also, the analysis of all data showed that 59% of the incompatibles were related to the attached files and 41% to the information recorded in the system. Finally, based on the performed analyzes, guidelines have been provided for the users of the system in order to improve the quality of data, according to specialized areas

Drawing lessons from the Covid-19 pandemic according to literature, this contribution aims to show that greening the United Nations System with stronger environmental considerations, can help to shift the global economy from fossil energy to renewable energy with public-health resilient systems. This contribution starts with highlighting the fact that past economic crises and the implementation of the Sustainable Development Global Agenda have not been able to generate strong institutional arrangements for sustainable development including climate resilience building and public health resilient systems. This allows us to apprehend the possibility that the Covid-19 pandemic crisis may face the same incapacity. In response to these statements, this contribution shares the opinion that institutional reforms within the United Nations System may lead to perennial normative provisions and institutional arrangements able to make sustainable development happen with resilient public-health systems. This note highlights the fall of GHZ emissions during the Covid-19 pandemic. It shows, however, based on the history of the past crisis, that the huge investment being mobilized to recover from the pandemic can quickly absorb GHZ emissions fall. The way out suggested is that both the Global Economy and the Global Public Health agendas can be revisited to be strengthened by stronger environmental considerations. One of our findings is that multilateralism can adopt suitable institutional arrangements in Global Environmental Governance throughout the current global agenda on International Environmental Governance Reform within the United Nations System.

Mistake proofing is an approach whose main purpose is to make mistakes impossible. This technique can eliminate the possibility of errors by making changes in the process, or by appropriate culture, or by preventing them from making a mistake by sending the appropriate message. Using new tools or technologies to improve the quality expected of the customer. The use of the miscalculation will dramatically increase customer loyalty. Although it has been decades in the making, it has a limited track record in services as well as in information technology and especially in information systems. In this study, based on previous research, the main dimensions of the error-solving strategies are categorized. Then, using the questionnaire, the experts' opinions on prioritization of nonlinearity strategies are discussed. We also select the final criteria based on the nominal batch technique and then prioritize the factors that play an important role in making the information systems unreliable based on the best-worst or BWM weighting method. The results showed that proper planning of quality management programs, skilled staff, advanced facilities and equipment, design capability and finally the use of appropriate controllers as the most important factors of information systems malfunction can be considered as the top priority of service managers. Also, providing appropriate assumptions for information items and improved data model structures can reinforce technological and innovative approaches to information systems.

In developing countries, women are the first hope for family development, growth, and development in their own countries. This research was conducted to evaluate the impact of cultural and social factors on women entrepreneurship in Tehran's municipality's entrepreneurial homes. The statistical population includes 30 municipal entrepreneurial houses in Tehran, each with 100 entrepreneurs. Morgan table was used to determine the sample size, estimated at 265 people. The Cronbach's alpha coefficient test was used to measure the reliability of the research instrument. The findings showed that three variables of social and cultural factors affect women's entrepreneurship, which entered the regression equation over the three stages. In the first step, the individual and personality traits entered the regression equation and its coefficient was 248/0. In the second step, the association with women's associations entered the regression equation, whose coefficient was equal to 9,377; in the third step, the role model entered the equation, which was equal to 339/0. From these relationships, it can be concluded that cultural and social factors have a far more important impact than entrepreneurs on other variables.

Despite abundant research on data quality, no research has so far been conducted which can provide a comprehensive view of data quality methodologies. In the present study, 3909 articles and related researches in the period before 2020 were selected from Web of Science (WOS) and Scopus citation indexes, from among which 27 articles were finally evaluated in line with the research goals, using meta-synthesis method and inclusion criteria. In this regard, while applying the system view and using the open coding method, the related codes to the three main categories of the systemic approach (input, process and output) were extracted. The similar concepts were categorized in sub-codes and then the sub-codes in main codes. The main inputs included the context and status of organization, data and information resources, and data quality dimensions. Steps of data quality methodologies were also classified into three main stages: state reconstruction, measurement/ evaluation, and improvement. In addition, data quality outputs fell into six general categories: list of activities and the related techniques for data quality improvement, controlled or redesigned processes, measured or improved flows and databases, data quality status results, revised data quality policies or rules, and costs and benefits. The results of this study can provide an appropriate instrument for identifying the existing data quality methodologies as well as evaluating the strengths and weaknesses of data quality methodologies.

The main purpose of this article is to determine the best plan for improving the current situation of the University of Applied Sciences. The type of goal is applied in this research and combined or qualitative-quantitative methods have been used to achieve this goal. For this purpose, a hybrid model has been designed in which the impact of the quality of information technology services and innovation of the organizational staff of the University of Applied Sciences is examined and evaluated based on user satisfaction. The target population in this study is all staff and students of the University of Applied Sciences and Klein theory and Cochran's formula have been used to determine the appropriate statistical sample in this study. In this study, questionnaires based on field research, expert opinion, and SERVQUAL model were designed to assess the quality of services according to the dimensions of organizational culture, the belief of senior managers, organizational structure, tangible service quality, service reliability, and accountability of officials. The validity of the questionnaires was assessed by the Lavache method and their reliability was assessed based on Cronbach's alpha (0.964). These questionnaires were collected based on the statistical sample and the collected answers were evaluated using SPSS software. Then, using the Pearson hypothesis test, the correlation of research variables was examined and it was found that the most important hypothesis in the quality of IT services is related to the beliefs of senior managers and in the innovation of organizational staff is related to service reliability. Then, the priority of the proposed projects to improve the quality of university services based on the opinions of experts, using the analytic network process (ANP) and the criteria mentioned in the Super Decisions software was examined. Finally, the design evaluation supermatrix was calculated and the best plan was determined.

Vendor performance plays a key role in price, quality, timely delivery and service in achieving the goals of the supply chain. Given this, today's supplier performance has become a critical element in the success or failure of a company. In this study, an idealized planning model with multi-criteria decision-making approach with network analysis tool based on the expansion of fuzzy quality function has been developed. Also, a new way of ranking, selecting, and assigning orders to suppliers is provided. To this end, the concept of expanding fuzzy quality function and network analysis are used to evaluate and rank suppliers and the math planning model for assigning orders to suppliers. The method presented in this paper is a feasible, practical, and useful method for identifying and ranking suppliers as well as assigning orders to them. The advantage of this model compared to similar methods in using QFD and ANP tools is to measure the priorities and needs of the company to rank suppliers, which can listen to the voice of the customer and take their demands, as well as integrate the two tools with planning Ideal for ordering a new approach. Based on the results of this research, technology and timely delivery are the most important features in order to evaluate contractor selection. Selection of contractors in such a way that at the same time returns from the supplier, delays and costs of supply are the least possible, are the most important results of this research.

Key performance indicators are a useful tool in the field of operations management and help senior managers in the organization continually to improve their processes. In research information systems, data is considered as a valuable resource throughout the research process and much of the added value created in this process depends on the quality of the research data. Data quality control, on the other hand, is one of the most important activities to ensure that the quality of an organization's output is optimal, and is nowadays achieved through a powerful tool called control chart. In this paper, indicators such as accuracy, completeness and precision have been developed to monitor the performance of research information systems. The dissertation / thesis data of postgraduate students throughout the country that are disseminated in the GANJ System are monitored using the I-MR control chart. The results of continuous monitoring of data quality in different indices shows that in some of the newly integrated systems, there is a great deal of fluctuation in some indicators such as lack of familiarity with the system and information recording and lack of adequate training in some indices such as accuracy and precision. Lastly, providing timely feedback to students on data quality issues, appropriate notifications when completing fields for graduate students, and providing visual training and formulating necessary guidelines introduced as corrective actions to improve data quality and production process capability.

Statistically constrained economic design for profiles usually refers to the selection of some parameters such as the sample size, sampling interval, smoothing constant, and control limit for minimizing the total implementation cost while the designed profiles demonstrate a proper statistical performance. In this paper, the Lorenzen-Vance function is first used to model the implementation costs. Then, this function is extended by the Taguchi loss function to involve intangible costs. Next, a multi-objective particle swarm optimization (MOPSO) method is employed to optimize the extended model. The parameters of the MOPSO are tuned using response surface methodology (RSM). In addition, data envelopment analysis (DEA) is employed to find efficient solutions among all near-optimum solutions found by MOPSO. Finally, a sensitivity analysis based on the principal parameters of the cost function is applied to evaluate the impacts of changes on the main parameters. The results show that the proposed model is robust on some parameters such as the cost of detecting and repairing an assignable cause, variable cost of sampling, and fixed cost of sampling.

The increasing growth of web usage has made the importance of search engines and their quality assessment more responsive to users' needs. One of the approaches to quality assessment is user satisfaction. The use of a search engine depends on user satisfaction, which is a measure of the quality of the search engine. Many search engines have been built in Iran, as well. Some of these engines are widely used to search for information on the web, and some are found in a database Iran scientific information database (GANJ) is one of the databases that have its search engine. It contains nearly 530,000 dissertations and thesis and is one of the key scientific databases in the country with many users. Therefore, assessing the satisfaction of users of this system with its broad audience is crucial. In this research, indicators based on the EQOAL model were developed to evaluate users' satisfaction with the GANJ System. Indices developed based on the Delphi method were screened and localized to assess users' satisfaction with GANJ System. User satisfaction was also assessed using an EQual questionnaire. The results of the data analysis of the 156 questionnaires collected show that usability, information, and service interaction are the significant determinants of users' satisfaction with the treasure system, respectively. In terms of usability, the ease of working with "GANJ" is a top priority. Likewise, the ease of interaction with the system and the apparent attractiveness of the system were introduced as the main factors.

Today, the quality of electronic services provided through the Web site of companies and institutions is an area where organizations can gain competitive advantage. Quality of service, customer satisfaction, and value are three elements that managers in any organization should give serious consideration to when delivering the desired service to their customers. Web service quality is one of the key missions in the organizations that provide services through the website. In this regard, using the EQual model as one of the most up-to-date web quality assessment models, we will identify the factors affecting web quality. The main features of this model will be weighted by the network analysis process from the users' perspective. In this study, firstly a review of current models in service quality assessment has been done and EQAL method has been selected for determining effective indicators on website quality. Finally, after identifying the importance of key indicators of web quality assessment, improvement strategies are presented in a case study. One of the most important websites providing services to researchers, named dissertation dissertation database / country alumni (treasure), was selected as the study. Usability, service interaction, and information quality metrics have the highest importance, respectively, in the quality of the website studied.

Today, the e-learning system has become an integral part of a learning system at the cutting edge. The staff of the organization of registration of documents and real estate in order to increase productivity and especially reduce the waste of time and energy, as well as the use of high user levels and flexibility in receiving educational services are keen to use e-learning services, and this has led to the expansion of this type of training in the organization. Is. The present study has developed various aspects of the evaluation of the e-learning system based on the ECVAL model. The organization of registration of documents in view of the geographical dispersion of satellite organizations and the necessity of using this system have been studied in this study. The developed model consists of various indexes such as apparent standards, content, security, compatibility with virtual instruments, etc. To this end, a 3D questionnaire has been designed and distributed. In this questionnaire, questions were asked to evaluate employee satisfaction and distributed among 80 employees of this organization. The results of the evaluation and statistical analysis show that the apparent character is of the highest importance to the users, and the security issue is of low importance to them. Finally, executive suggestions were made to improve the quality of the e-learning system.

One of the tools for improving and managing teachers' knowledge is using training and holding training courses. Understanding the importance and necessity of training and professional development of human resources becomes clear when we believe in the fact that the development of a country depends on the training of quality, qualified and up-to-date human resources with scientific information and scientific capabilities. In this paper, tried to study relationship among different dimensions of knowledge management and the role of this dimensions in improvement of teachers’ performance participating in educational courses. Dimensions of knowledge management such as the composition, distribution, maintenance and development of knowledge will help teachers prepare for the dynamics of macro-societal conditions in various cultural, economic, political and other field and have greater efficiency and effectiveness in the long time. The general purpose of this study is, investigate the relationship between different dimensions of knowledge management and the role of these dimensions in improving the performance of teachers participating in training courses. The specific objectives of this study were to determine the relationship between knowledge combinations and the efficiency, relationship between knowledge distribution and the efficiency, relationship between knowledge retention and the efficiency, and relationship between knowledge development and the efficiency of teachers participating in training courses.

This is an applied research in research categories based on goals. Survey data from questionnaire gathered from teachers in two, four, six & twelve restricts of Tehran province including different points based on geographical restrictions. Research variable comprises knowledge management including four components of knowledge development, knowledge preservation, knowledge distribution and knowledge combination; and teachers’ performance variables includes capabilities and skills of the teachers. Pearson and multivariate & step by step regression are the most important techniques of analysis in this research. The questionnaire had 33 questions that measure seven questions of knowledge composition, six questions of knowledge distribution, seven questions of knowledge retention, and thirteen questions of knowledge development. The questions measured on a five-point Likert scale from strongly disagree to strongly agree.

The findings of study showed that there were significant relationship among dimensions of knowledge flow and barriers to knowledge flow and ranking with the maturity of knowledge management at the development level. Furthermore, relationship between dimension of managerial and individual factors at standardization level, as well as dimension of knowledge features and communication channels, optimization level, knowledge source dimension and at the level of innovation were significant. It should note that there were obstacles in the dimension of organizational factors and time factors. In addition, libraries profited from sustainable measures in the early stages of knowledge management maturity, but had more difficulties in the innovation stage than in the other stages. In other the words, findings showed that the relationship among knowledge combination, knowledge distribution, knowledge preservation and knowledge development dimensions and teachers’ development & improvement were all significant. The results depicted that knowledge preservation, knowledge distribution, knowledge combination and knowledge development had the highest importance in teachers’ performance. Totally, these variables are able to predict 49.5 percent of the changes in teachers’ capabilities. In addition, the regression’s results indicated that knowledge preservation, knowledge improvement, knowledge distribution and knowledge combination were the most important factors in the teachers’ skills. These variables can predict 48.8 percent of overall changes in teachers’ skills. According to Analytic Network Process (ANP) structure, and among knowledge management dimensions, knowledge combination allocated the highest weight in the effectiveness of teachers’ educational courses, and knowledge distribution, knowledge preservation and knowledge development followed respectively.

In the present study, researchers tried to investigate the relationship between the dimensions of knowledge management and teacher training courses. Providing appropriate facilities based on knowledge management in order to share knowledge among different teachers have significant impact on the final performance of teachers participating in different courses. Knowledge development, as the fourth dimension of knowledge management affects the performance of teachers participating in training courses. In the other words, providing empowerment to teachers in a way that allows them to generalize their training courses in different areas greatly increase teacher productivity. Assigning the task of knowledge acquisition from foreign sources to one of the teachers in different educational areas in order to register knowledge and experience, developing new knowledge base for educational courses, storing, selecting and organizing experiences from trainings and using encouragement and motivation systems to develop knowledge considered as the most important results of this research.

Recent researches suggest that data is known as one of the most valuable resources of any organization. On the other hand, today science and technology data have become a key to the growth and development of each country. Treasure system plays a key role in advancing science and technology in the country and providing researchers with the services of effective and efficient research. Accordingly, the performance of this system is one of the factors influencing this mission. On the other hand, the major part of the observed quality problems in the area of information dissemination by the system is rooted radically in the output of another system called the registration system, which is at the very beginning of the process of recording, organizing and disseminating information. Determining the desirable status (or level of quality) in each field of the metadata from a variety of quality aspects such as accuracy, inconsistency, completeness, etc. is one of the most vital steps in the field of quality of the registration system. In this study, after reviewing the standard
data quality management models, a comprehensive framework was developed to determine the quality dimensions of the registration system The TDQM model was selected as one of the most widely used data quality model to evaluate the components of the system from its main four-dimensional core, accessibility, terrain and display. Then, in the form of a Quality Management System, key factors will be determined in the quality control system of the registration system. The data quality plan presented after the validation by the team of experts as the reference point for determining the non-conformities of the data. At the end of the present study, practical steps were taken to improve the quality of this system.