shaban elahi

The most important objectives of this research are to evaluate and illustrate the knowledge map of data mining in covid-19 research, to identify the various components and relationships between data mining concepts in different fields of covid-19 studies. The research method is applied and descriptive. In this research, we utilize the content analysis and text mining techniques. One of the findings of the current research is that a group of researches was related to the "structure and human nature of the disease". Another group of research looked at the set of factors related to adulthood that could be the basis for the absorption of the Covid-19 virus. In the co-authoring section, the highest amount of cooperation was between America and India. The data mining map of Covid-19 is based on thematic modeling including the structure and human nature of the disease, the background and structure of the virus, pandemic prevention, and computer science and artificial intelligence. In this research, according to the use of information technology tools and text mining techniques, a large amount of research has been studied. The presented knowledge map deals with the thematic separation of the researches and information discovery in each collection, so it enables the possibility of comparison between specific thematic groups. In this research, the components of data mining concepts in different fields of Covid-19 studies were examined in six dimensions. In the sixth dimension, the data mining map of Covid-19 was presented based on thematic modeling.

Health insurance, as one of the pillars of the health system and responsible for financially protecting individuals in society against the risk of diseases, also requires an effective monitoring system. This research aims to examine the issues of supervision in health insurance and provide steps to achieve a monitoring and evaluation framework towards Intelligence supervision.

In this study, data were collected from qualitative interviews with health insurance experts and the analysis of secondary data. The proposed PAVA model in health insurance was designed and evaluated based on key indicators extracted from previous studies. Experts were selected using the snowball method until data saturation was reached, resulting in a total of 24 interviews. During the interviews, notes were taken, and the interviews were recorded and analyzed using Atlas.ti8 software. Based on the steps of PAVA and business intelligence, the steps to create the PAVA framework in health insurance were presented.

The health insurance organization has various systems such as medical records, the Omid system, Didban system, eligibility assessment, business partner systems, and electronic prescription. Monitoring is conducted both in-person and remotely. Data analysis from the interviews revealed that monitoring in health insurance faces several challenges. These challenges include the need for data collection, aggregation, and cleansing, the need for data analysis, and the need for an integrated and interactive monitoring system. Specifically, issues such as the lack of comprehensive electronic data, the need for better access to data, and the need for more precise data analysis to identify fraud and misuse were identified.

The results of this study can be used by senior managers of health insurance organizations, managers and experts in the supervision departments, IT managers and experts, organizational knowledge managers, and knowledge workers of the organization, as well as the National Health Insurance Research Center. These findings help improve the efficiency and effectiveness of the supervision system, thereby enhancing the overall performance of the health insurance organization

Blockchain finds application in the public sector, notably in the tax system. The extant literature points to the benefits of employing blockchain in the tax system. A review of studies provides a general view of why and how to implement blockchain technology as well as the identification of existing defects to solve it. In this paper, we investigate how to implement blockchain technology in the value-added tax (VAT) system and answer three questions through a systematic literature review: (1) what are the characteristics of blockchain that can help in the VAT system; (2) what is the appropriate blockchain framework for the VAT system; and (3) what are the appropriate solutions to protect privacy in a blockchain-based VAT system.

We conducted a systematic literature review following the guidelines proposed by Okoli and Prisma flow diagrams. The screening process was carried out in three stages to ensure that each article fits the research questions. The first article in this field was published in 2017, and the number of articles has risen significantly since then.

The increasing number of papers reflects the growing interest in implementing blockchain technology. Statistical charts indicate that approximately 60% of the documents are conference papers. Our systematic literature review revealed that blockchain technology can play a crucial role in enhancing the VAT system, particularly in the electronic invoicing process. By identifying existing challenges in electronic invoices, such as transaction forgery, the long process of issuing electronic invoices, and hacking of the central database, blockchain technology, with its features like decentralization, tamper-proofness, transparency, and automation, emerges as a suitable solution. We propose a blockchain-based architecture and a five-step process that includes: authentication and authorization, sending electronic invoice(s), storing the e-invoice(s) in the blockchain, reviewing and verifying the e-invoice(s), and last but not least, payment of VAT. For the security and privacy of taxpayers, we propose the use of a private blockchain, opting for a permissioned blockchain to ensure that only trusted users are part of the network. Network users can include companies, banks, tax authorities, and auditors. In the third and fifth steps, the utilization of smart contracts is recommended. Smart contracts play a role in documenting taxes in the third step. Furthermore, in this stage, leveraging off-chain storage is possible, where the data hash is stored in the blockchain, and the original data is kept separately. In the fifth step, a smart contract can periodically calculate VAT to be deducted from the payers' accounts by banks. Encryption alone cannot guarantee the privacy of electronic invoices, and while zero-knowledge proofs seem to be a suitable solution, they come with considerable complexities.

According to the inherent characteristics of the blockchain, it can be concluded that a blockchain is a suitable option for the VAT system and the right architecture is a permissioned private blockchain. However, further study is required to address privacy concerns.

The research method provides a way to solve or a systematic answer to the research problem. It is a framework for collecting, organizing, analyzing and presenting data to study the research problem. Design science research has emerged as an important approach in information systems research. Thus, this study is a systematic review of theoretical literature with a narrative analysis approach to create a comprehensive understanding of key concepts and specialized attitudes around design science research methods. A road map is designed to express how to use DSR to find the answer to the research question(s) about the design of information technology artifacts. The research is conducted with a systematic review method in two main steps. In the first step, the search for reliable sources was carried out using automatic approaches and snowball forward and backward in reliable databases. After the qualitative evaluation of the obtained sources, 52 articles were approved and selected based on specific criteria. In the second step and during the study, based on the narrative analysis method, four narratives emerged: the philosophical foundations of design science research, the theoretical development approach of research in design science, the methodological choice of design science research, the strategy of design science research. Finally, using the narratives and concepts identified in each narrative, a mapping of the concepts and topics related to the four emerged theme of design science research was done and summarized and drawn in the form of a road map. The compiled roadmap presents the process of using the design science research method in the information technology artifacts design in a structured and accurate way for conducting design science research.

Modern economic growth has been based on mass industrial production and consumption, which have heavily relied on fossil fuels and energy waste since the 18th century. Hence, current socio-technical systems are unsustainable in meeting humans' basic needs, such as energy and mobility. Fossil energy resources are non-renewable and, on the one hand, contribute to emissions that cause unreliable harm to the environment. In this research, the prime theory of Transformational Change illuminates how to use science and technology policy to meet social needs sustainably and inclusively in societies. This article answers questions regarding the essential policies and governance measures that states need to implement for the transition to electric autonomous vehicles (AVs) in the socio-technical system. Using meta-synthesis, followed by a case study and interviews with experts in the electric AV field, the article identifies state policies and governance measures to facilitate the transition of the sociotechnical system into electric AVs. The conceptualization of these roles determines that the state's role is influenced by policy, governance, and legal decisions, which are ultimately implemented through specific combinations of policies.

Within the field of data sharing, discussions surrounding privacy concerns and big data management are extensive. This study aimed to provide a comprehensive framework for health data sharing with the objective of creating value.

This study is a qualitative content analysis, which was conducted using a combination of written sources through a systematic review method, in conjunction with content derived from interviews with experts in information technology and healthcare within hospital and emergency settings. Grounded theory serves as the qualitative methodology, involving three coding phases: open, axial, and selective, facilitated by MAXQDA software.

Qualitative content analysis of the interviews revealed seven main (core) categories and 44 subcategories as driving factors in promoting healthcare data sharing. Simultaneously, inhibiting factors resulted in six main categories and 36 subcategories. The driving factors encompassed technology, education, patient management improvement, data utilization for various purposes, data-related considerations, legal and regulatory aspects, and health-related factors. Conversely, inhibiting factors encompassed security and privacy concerns, legal issues, external organizational influences, monitoring and control activities, financial considerations, and inter-organizational challenges.

This study has identified key driving and inhibiting factors that influence the sharing of healthcare data. These factors contribute to a more comprehensive understanding of the dynamics surrounding data sharing within the healthcare information system.

Nowadays, customers in any industry are not just looking for a product and expect to receive a personalized service based on their needs and create a different experience from the organization. From another point of view, the design of services according to the customer's needs will require a careful examination of the data related to the customer in different dimensions. Therefore, knowing the customer requires a systematic approach in order to identify the goals, influencing factors, algorithms and methods suitable for this field.

The upcoming research has analyzed the dimensions of customer recognition in the banking industry and its considerations with a data-oriented approach and the application of machine learning. The research method is applied according to the purpose and is meta-Synthesis according to the collection of information. To select the articles, 43 documents published in the period of 2016-2022 were identified as relevant and valid documents by searching in the reliable databases of Web of Science and Scopus, and further, with a meta- Synthesis approach, they were studied and coded.

The results of meta-synthesis led to the identification of three main categories: 1) Customer identification objectives understanding customer insight, identifying customer risk, organizational goals, determining customer lifetime value and product management 2) Customer identification factors: demographic, financial and behavioral and 3) machine learning algorithms: Probabilistic, Neural Networks, Ensemble, Regularization, Regression, Bayesian, Decision Tree, Dimensionality Reduction, Instanced Based and Clustering.

Based on the findings of the current research, according to the purpose of customer identification, available data and selected factors, basic and combined algorithms can be a way forward, but the important point is accurate data pre-processing. Another point is that no distinction has been made between real and legal customers, and most studies have focused on real customers, which can be attributed to the complexity of the chain of financial interactions of legal customers. Also, considering the banks' lack of emphasis on branches regarding the need to complete the information contained in the account opening forms or the lack of designing a suitable service to complete the information on electronic platforms, a more accurate understanding of the customer requires a review of these processes. It is worth mentioning that in none of the studies, customer recognition was done using only demographic factors, and depending on the purpose of the study, the factors were used in combination.

With the introduction of digital crypto currency, the worth of data has been obvious to everybody. Of course, this research is in the sphere of medicine. The goal of this research is to perform a comprehensive evaluation of studies in the area of health information system data sharing and secondary uses of health data, with the goal of generating value in multiple dimensions by sharing these data.

 The researchers used the intelligent Web of science and IEEE search engines to conduct a systematic evaluation of English-language keyword searches. Two tactics have been studied in this respect, one using keywords related to "health data value" and the other using terms linked to "health data sharing."

 Although several studies have suggested solutions for decreasing obstacles to health information system data sharing, they have often simply addressed the problem, according to one criticism of the papers examined. The sharing barrier has no remedy, and its relevance is simply emphasized.

 It was discovered that not all of the successful components of data sharing, particularly data from health information systems, were addressed in the evaluated studies.

With fast development and application, the Internet of Things (IoT) brings more opportunities to business. In the age of the Internet of Things as a disruptive technology, strategies can quickly fall behind, so the rhythm of planning has to keep pace. As well, the IoT concepts have to be added to the strategic agenda. Despite the importance of IoT strategic planning, few studies have been conducted on a suitable model for it. In this paper, to develop such a model using the meta-synthesis method, a systematic review was applied. Finally, the IoT strategic planning model with 4 components and 11 sub-components and their sequence was developed. “Identifying IoT business opportunities”, “evaluating IoT technical capabilities”, “evaluating IoT organizational capabilities”, and “economic feasibility of providing IoT technical and organizational capabilities” have been introduced as the components of the IoT strategic planning model. The model presented in this paper is general, but the implementation of each component, in any organization, industry, or sector (including the public sector) may have operational differences.

The supply chain for perishable products, especially agricultural goods, has always been one of the most important and challenging management issues at different times. Because, at all stages of the agricultural production process, unsafe and unsanitary factors may endanger the health of agricultural products. In addition, one of the key problems of the agricultural supply chain is the high volume of products wasted throughout the whole supply chain. For example, in Iran, as a developing country, about 30% of all agricultural products are wasted annually. The high volume of agricultural waste is especially important concerning wheat products as a political and strategic product. The results of some studies show that a large amount of wheat waste in developing countries is due to the widespread use of traditional methods in the storage process. Therefore, the application of emerging technologies such as the Internet of Things (IoT) can be an effective solution to this kind of problem. However, there are shortcomings in the IoT deployment in the supply chain, especially in the logistics sector, and researchers need to cover theoretical gaps in this area through modeling and optimization. Therefore, the present study intends to emphasize this important research issue for the first time in Iran using the Action Design Research approach. The most important findings of this research include the conceptual data model, the logical model of the database, and the physical data model for the IoT deployment in the storage section of the wheat supply chain, which has been designed and validated with the participation of industry and software engineering experts. The models designed in this research can be useful for the implementation of IoT technology in wheat storage centers and food factories. The findings of this study can provide a good guideline for officials and decision-makers to deploying IoT in the field of wheat storage.

Actor-Network theory (ANT), in social sense, is an approach to sociological analysis in the field of science and technology studies and focuses on understanding how things get to the situations in which they find themselves. In recent years, this theory has been considered by researchers as a methodological approach to analyze the use of technology in the workplace and describe the various dimensions of socio-technical systems. Despite numerous examples of research based on ANT methodology, no codified framework has been introduced for it and no examples of using this theory in the form of methodology have been found in the country. The present study tries to introduce a framework for data collection and analysis in the context of this theory, while explaining the methodology of this theory in an analytical-descriptive manner. The feasibility and validity of the proposed framework have been assessed in the form of a case study. The results of the research can be effective in the systematic application of ANT methodology, especially in the country.

 Semantic technologies (STs) have made machine reasoning possible by providing intelligent data management methods. This capability has created new forms of interaction between humans and STs, which is called "semantic interaction."  The increasing spread of this form of interaction in daily life reveals the need to identify the factors affecting it and introduce the requirements of a synergistic interaction, which in this study is interpreted as a model of symbiosis.

The main purpose of this study is to investigate what, why, and how human-ST symbiosis occurs in the form of a symbiosis model. Providing such a model could be valuable in developing active strategies in the face of intelligent technologies.

  The study introduces actor-networks of human symbiosis based on the actor-network methodology. Data was collected through in-depth interviews with eight managers, experts, and users in the Computer Research Centers of Islamic Sciences (CRCIS) and examined using the actor network method. All phases of data collection, implementations, coding and analysis were done under NVivo software.

In the human-ST symbiosis, beside human eleven other actors:  Semantic products, context, infrastructure, data, knowledge, social media, Web, scientific centers, organization, AI and ontology are identified. Their interaction establishes seven dynamic actors-networks of symbiosis: Product design and development, use, leadership and management, data, knowledge management, training and contextual conditions.

Semantic products alongside human beings are independent, autonomous, and self-aware actors who are able to go beyond mere mediation of change and govern social change in the Human-ST symbiosis. In such circumstances, man, as the creator and maintainer of the semantic product, in addition to strengthening the technical capabilities in the creation of the product, must entrust to the product the things that the product is able to do.

Given the importance of business intelligence, organizations need tools to evaluate their capabilities in this area that can help them not only in evaluating but also in planning the upgrade path and comparing the level of capabilities at different times. Maturity models are very useful tools to solve this problem. In the path of assessing the level of maturity, a picture of the status of the organization according to the desired indicators is obtained. To this end, several studies have been conducted on the development of business intelligence maturity models and several organizations have taken steps to develop these models experimentally. In the present study, with a systematic literature review in this field, maturity models related to the business intelligence have been extracted and then the theoretical foundations of the model, development methodology and key process areas of the models have been reviewed. This study helps an organization to benefit from one or a combination of the introduced models according to the complexities of their industry and, if they need to develop their own model, it helps them to recognize the essential aspects in developing the business intelligence maturity model.

In recent years, Blockchain technology has been recognized as one of the most important new technologies affecting businesses and society, with a very bright future ahead. This technology is known as one of the most critical technologies of industry 4. Given that Blockchain technology is a new concept, before implementing it, the organization must understand it and be prepared to accept it; otherwise, the organization will incur irreparable damages. The main purpose of this study is to provide a framework within which organizations can evaluate their readiness to implement Blockchain technology. For this purpose, after a library study and systematic literature review, this study provided the initial research framework using the meta-synthesis method. Finally, the Lawshe model was applied to validate the proposed framework for the organization's readiness to implement Blockchain technology. Based on the findings, the final established framework included seven main dimensions, including Blockchain-based business strategy, operations of a distributed system, culture, people in Blockchain decentralized network, decentralized governance, technology, and Blockchain-based product traceability. Besides, 37 main criteria affected these dimensions, which can help companies increase the level of Blockchain technology readiness. Finally, this framework was used to measure the readiness of an airline company. According to the results of the analysis, Blockchain-based business strategy, culture, and Blockchain-based product traceability showed a medium level of readiness. Moreover, none of the main dimensions of Blockchain technology readiness were sufficiently prepared in this case study. This paper suggests practical recommendations to improve the level of Blockchain technology readiness for the managers

In recent years, the number of articles studying the Internet of Things technology in supply chain management and logistic has grown rapidly. The purpose of this paper is analyzing the studies that have been done in this area by reviewing the relevant literature and while identifying the existing theoretical gaps, introduces future research opportunities. This paper collects and analyzes information using a systematic literature review methodology and paradigm funnel approach. Systematic literature review is a methodology reliable and recommended by a large number of scientific papers. However, selection of certain criteria for inclusion and exclusion of papers introduces publication bias, which should be considered an intrinsic limitation to systematic literature review. Numerous studies have been conducted in the area of Internet of Things and supply chain management, but there is no synthesis of the knowledge that would allow one to draw conclusions regarding the evolving nature Internet of Things in supply chain management and to extend the theories in this area. This paper will address that gap, applying the paradigm funnel technique. For this purpose, Scopus, Science Direct, Emerald and Francis & Taylor databases were searched and a total of 209 articles were retrieved. After initial screening, 105 articles were excluded due to inconsistency with inclusion criteria and after qualitative screening, a total of 36 studies were selected for analysis. The selected 36 articles focus mainly on level 2 and 3 of the paradigm funnel. Only two articles were placed in the first level of the paradigm funnel and no studies in the level of deep hypotheses (fourth level) were observed. Most studies used qualitative methods to collect and analyze data. Also, the findings show that future researches can use empirical observations and data generation to further exploration of internet of things in supply chain management and logistic.

The research is intended to extract repetitive themes in the field of semantic-rich learning and to express the basic opportunities and challenges therein. The method applied was to review the articles published in the WOS database, during the years 2000 to 2020 by using the paradigm funnel technique; moreover the Nvivo software was used for document analysis and theme extraction. In the study, it was found that establishing access to appropriate educational content, proper analysis and representation of knowledge, human capabilities enhancement, personalization of learning, and improving the quality of assessment, are the most important positive effects of using STs in learning; Also, in this study, nine themes and seven major challenges in the field of semantic-rich learning were identified. personalization and adaptation, and the development of various ontologies, are the most cited themes; and access to learning content and concerns about the design and development of learning systems are the most important challenges facing semantic-rich learning environments. We believe that in order to overcome the enumerated challenges, the combination of STs with other emerging cognitive and communication technologies, such as IoT, is necessary and could be the subject of future research in this field.

Studies show that there are shortcomings in the deployment of the Internet of Things (IoT) in the supply chain of agricultural products, especially in the field of quality control in the logistics sector, and researchers can model the existing theoretical gaps through modeling and optimization. Therefore, the purpose of this paper is to identify the most important categories affecting the deployment of the Internet of Things in the wheat supply chain storage sector and explain and mapping the relationship between these categories. For this purpose, the present article uses systematic review methodology and meta-synthesis method by searching Web of Science and Scopus citation databases. Then, the grounded theory coding procedures were used to determine categories and themes. Finally, the results of meta-synthesis lead to the identification and extraction of 3 macro categories; Internet of Things technology, the main category (IoT-based storage), and the results and consequences of Internet of Things deployment.

Despite existing studies mostly analyzing successful cases of catching-up, this paper examines a fairly unsuccessful case of catching-up by a latecomer automobile firm. To this end, using the strategy of case study and thematic analysis, themes and concepts related to the conceptual framework are elicited and categorized as well. The results of this study are operationalized conceptual framework, as the key contribution of the paper, introduces new elements attributed to the regimes of technology and market of automobile industry. The elements include technology transfer, diversity of technological areas, tacit knowledge, backward linkages, economy of scale, local supply chain, capital-goods attitude towards a consumer goods, oligopolistic market, and homogeneous market. In addition, this paper adds two new factors (i.e. industrial strategy and structural issues) to the existing catch-up models. And finally, adopting coherent policies aligned with industry’s regimes and industrial strategy could provide appropriate environment for indigenous firms to catch-up with forerunners.

The wheat supply chain has always been one of the most critical management issues at different times. What distinguishes the wheat supply chain is the importance of factors such as the possibility of endangering the wheat safety and quality and its high volume of waste, especially in the storage sector. Some authors believe that the Internet of Things (IoT) can consider as one of the best solutions to solve agricultural supply chain problems. Therefore, this study aimed to evaluate the impact of IoT deployment on wheat supply chain storage. This research's data collected through distributing questionnaires between 54 companies from the private and public sector in Khorasan Razavi province in 2020. Confirmatory Factor Analysis, One-sample Wilcoxon Test, and Friedman Test was used to analyze of hypothesis. The results show that IoT deployment in the wheat supply chain's storage sector can have several positive consequences in the economy, performance, and marketing area and cost reduction, increase revenue, maintaining quality, improve monitoring, and increase customer satisfaction positively affected. Moreover, the findings of this study indicated that increasing revenue with an average of 3.52 is the most critical impact of IoT deployment. Then, maintaining the wheat quality and customer satisfaction (3.06), improving monitoring (2.71), and cost reductions (2.70) is in the next ranks of IoT impact on the wheat storage sector.

Challenges associated with changes in the last ten years confirmed the importance of the use of the software by businesses. Today, organizations are viewed differently the cost and quality of software. While the IS organization formally reduce redundant development and engineering costs, the basic nature of the software in a product or service on the evaluation is development process. Thus, organizations are looking for effective software development.

In this study, Six Sigma methodology is used which focuses on improving the process and reducing variability. Six Sigma projects use one of two DMAIC and DFSS methodologies.

Although the two dominant approaches are available, But if they are participating. One of the solutions containing is CMMI model that is as a framework for improvement in the methods and service delivery. Another option is to use the Six Sigma program to articulate the issues of quality and customer satisfaction. It is necessary to understand whether these attitudes are compatible to use with integrated systems or should be used independently of each other.

Originality/Value:

This paper addresses the issues of coherence and consistency of these two methodologies and checks their coherence strategies.

Changes in the business environment over recent years have led many large companies to seek asymmetric technological collaboration with small firms. However, because of the complexity and multidimensionality of collaboration process, their failure rate is high. Therefore, it is necessary to identify the success factors of asymmetric technological collaboration. However, most previous studies focused on the study of one specific factor or have been conducted in the context of developed countries. Therefore, the present article attempts to identify the essential factors for the success of collaboration by studying 8 cases (Multi-case study) of asymmetric technological collaboration in Iran's nanotechnology sector.The results of the analysis led to the identification of 16 essential success factors for asymmetric technological collaborations, including transparency of goals and tasks, alignment of technology with the requirements of large firms and information asymmetry

Introduction:

Health insurance supervision and control has important role in achieving the health insurance and health system mission and goals, but it has not received much attention. This study aimed to review the researches related to health insurance supervision and control, and categorize the used topics and related data sources.

Methods:

The study was conducted using systematic mapping method. A total of 316 research papers (published until the end of 2018) were investigated in related databases. Atlas.ti8 software and keywording technique were used for analysis and data extraction.

Results:

Seven health insurance supervision (health insurance program/policy monitoring, cost control, coverage and financial protection, equity in health, health insurance data and claims assessment, and people/insured and health service supplier and control topics) as well as nine data sources were identified.

Conclusion:

Health insurance program/policy monitoring and cost control topics had the most research number, and people/insured and health service supplier topics were the least related researches.

Extended Abstract Abstract Over the past decade, e-government domain has attracted a lot of attention throughout the world. to get Desirable level of satisfaction, Governments seek to provide high quality services to citizens. However, to provide high quality services, it is essential to study the evaluation method of e-government services quality. In this study, firstly, by systematic review of previous studies, models and frameworks for assessing the quality of traditional, electronic and e-government services have been identified and a list of dimensions has been developed to assess the quality of provided services. Therefore, in this research, 14 service quality evaluation framework/models (traditional, e-government, and e-government) have been systematically reviewed and extracted according to research questions analyzed and a framework for assessing the quality of e-government services has been provided. The goal is to cover the weaknesses of different models with the strengths of other framework/models and to improve the defects in the new framework. Then, considering the situation in Iran, the dimensions and components of the proposed framework were presented by a questionnaire consisting of a semi-structured questionnaire (a combination of open and closed questions) with a survey of e-government experts, an analysis and model for Iran.

Knowledge management (KM) makes noticeable changes in different levels of new organizations. Human components related to knowledge sharing and workers motivation are two important components in successful implementation of knowledge management systems. The aim of this study is to determine the situation of knowledge sharing and workers motivation, before and after of implementing knowledge management system and to present a framework for companies active in chilling industry. In this study, we extracted and examined 9 components consist of social interactions, culture, subjective norms, social media, efficient knowledge, helping enjoyment, trust, feedback, and perceiving the usefulness of social media. Also, a framework was presented for knowledge sharing and workers motivation and according to this framework the situation of these two components was indicated before and after implementation of knowledge management system in 2 companies active in chilling industry. As a result, this framework can indicate the situation of knowledge sharing and employees’ motivation before and after implementation of knowledge management in an organization and can help private organizations to improve their current situation. The results showed that employee motivation and knowledge sharing after implementation of KM in comparison with the previous one has a significant difference and improved. With the help of this framework, the chilling industry can examine the impacts of KM implementation on employees’ motivation and knowledge sharing.

The importance of technology creating and investments requires tangible results in the area of the dissemination of applied knowledge created with the commercialization capability and transferring it to the market in the form of patent. However, there is no studies suggested the integrated framework for patent creation affecting factors. The purpose of this research is to identify the factors affecting the creation of patents and to explain how these factors are interconnected. This qualitative research, attempts to explore and extract the most important factors influencing the creation of patents using systematic review and meta-synthesis method. Finally, by extracting and encoding factors in the form of semantic codes and conceptual codes try to mapping the relation between them. Based on the results, the knowledge management, technology management services, patenting processes management, research and development activities and international interactions in research and development have the greatest role in balancing the patent creation affecting the factors. Such a study, will not only help recognizing the most important factors in this area by introducing the patent creation factors in a systematic method, but also, help policymakers to identify the most effective policies to promote innovation, Which improve the context for patent creating.