نشریه علوم و فنون مدیریت اطلاعات
سال نهم شماره 3 (پیاپی 32، پاییز 1402)

When dealing with high-dimensional datasets, dimensionality reduction is a crucial preprocessing step to achieve high accuracy, efficiency, and scalability in classification problems. This research aims to introduce a feature selection method for high-dimensional datasets by employing dimensionality reduction and genetic algorithms. In this study, an innovative algorithm has been developed to determine the mutual information between features and the target class using a new criterion. In this method, new characteristics are generated through the combination or transformation of the original characteristics. In this manner, the multi-dimensional space is transformed into a new space with fewer dimensions. In addition to considering the new criterion of mutual information, a genetic algorithm has been employed to enhance the speed of the proposed method. The performance of this method has been evaluated on datasets of varying dimensions, with the number of features ranging from 13 to 60. The proposed method has been evaluated in comparison to similar methods, focusing on classification accuracy. The results have been promising. The proposed method has been applied using MRMR, DISR, JMI, and NJMIM methods on various datasets. The average accuracies obtained from the proposed method are 65.32%, 74.51%, 70.88%, and 58.2%, indicating the efficiency of the proposed method. According to the results obtained, the proposed method outperformed DISR, JMI, NJMIM, and MRMR on average, except for the sonar data set, where the sonar data set yielded better results than the proposed method.

The application of knowledge generated in universities is crucial for its dissemination outside of academic environments, particularly in industrial settings. Lack of familiarity with these channels and their features can lead to failure in achieving the ultimate goal of this communication. Therefore, the purpose of this research is to investigate the channels of knowledge flow between universities and industries.

This study employs a scientometric approach, utilizing co-word analysis and narrative review techniques. Using the narrative review approach, we conducted searches in the most important international databases, which served as the theoretical framework for this study. In the second stage of this research, a scientometric approach was employed to uncover the connections, key words, and the scientific map formed by the relationships between words. Theco-word technique was utilized for this purpose. In this way, the query "(university AND industry AND (channel OR transfer OR spillovers OR flow OR dissemination))" was searched in the Topic field of the Web of Science database, which is known as one of the most reliable citation databases. The time span was from 1975 to November 27, 2022, and the search was performed in the Web of Science Core Collection, including the Science Citation Index Expanded (SCI-EXPANDED) from 1980 to the present, the Social Sciences Citation Index (SSCI) from 1980 to the present, the Arts & Humanities Citation Index (AHCI) from 1975 to the present, the Conference Proceedings Citation Index - Science (CPCI-S) from 1990 to the present, the Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH) from 1990 to the present, the Book Citation Index - Science (BKCI-S) from 2005 to the present, the Book Citation Index - Social Sciences & Humanities (BKCI-SSH) from 2005 to the present, and the Emerging Sources Citation Index (ESCI) from 2005 to the present. A total of 5178 documents were retrieved using this search strategy. To create a co-word map, the VOSviewer software was utilized. In order to generate an analyzable map, a threshold of at least 50 occurrences was applied, resulting in 86 words being entered for the analysis and creation of a co-occurrence map.

Examining the flow of knowledge channels between universities and industries revealed that these channels may be formal, informal, or a combination of both. Also, the exchange of knowledge between the industry and the university can be intentional or incidental. This knowledge can be explicit or tacit. On the other hand, in channels for transferring knowledge, considerations such as commercialization, cost-benefit analysis, and financial return have also been taken into account. Furthermore, these channels and the transfer of knowledge between universities and industries are considered a form of social capital. Examining the relationship between industry and university from a scientometric perspective, and based on the co-occurrence map of words, it is evident that topics such as the performance and impact of cooperation, knowledge transfer, triple helix, industry, technology transfer, knowledge, entrepreneurship, and research and development are the most important concepts extracted. The scientific map and the formed clusters illustrate the significance of knowledge flow channels between industry and university researchers, highlighting the importance of cooperation in knowledge transfer. In total, four topic clusters have been identified in the co-word map.

The channels between universities and industry have been the focus of various research studies, and the formation of thematic clusters further underscores this significance. The diversity among the channels indicates that a thorough analysis of the knowledge flow between the university and the industry requires the use of multiple indicators.

This research aims to enhance understanding of online retailing through electronic channels (such as mobile devices) and touch points of electronic channels (such as mobile shopping software) from the customer's perspective.
This research is applied for the purpose and descriptive in terms of survey and library data collection methods. The statistical population for this research consists of students at Tehran Azad University. The community consists of more than 4000 people, so according to Morgan's table, the number of samples should be 384. Sampling has been conducted using the available method, and data analysis has been performed using the LSD test. The dependent variable in online shopping is purchase intention, while the independent variables include usefulness, ease of use, pleasure, privacy, and satisfaction.
The research findings indicate that customers currently use eight different devices, including laptops/notebooks, personal computers (PCs), smartphones, tablets, internet-equipped TVs, and in-store kiosks. Additionally, the research findings revealed that purchasing devices can be categorized into four electronic channel categories from the perspective of online retail customers. The floors are categorized as A, B, C, and D, and the coordinates of each are listed in the article.
The research findings indicate that both the technological quality and the situational benefitsof the context influence consumers' use of electronic channels. Also, customers engage inonline shopping through various electronic channels (sets of Internet-enabled devices, such as mobile devices) and multi-channel touchpoints (digital shopping formats, such as mobile shopping apps).
The results indicate that there is no significant difference between the first cluster (A), which involves shopping using personal computers (PCs), laptops, and netbooks, and the second cluster (B), which involves shopping using smartphones and tablets, in terms of usefulness, ease of use, shopping pleasure, privacy, satisfaction, and purchase intention. There is no significant difference between the first cluster (A) and the third cluster (C), i.e., Internet TV (IE TV), in terms of usefulness, ease of use, shopping pleasure, satisfaction, and purchase intention. There is a significant difference between the first cluster (A) and the fourth cluster (D), specifically in-store kiosks, in terms of usefulness, ease of use, shopping pleasure, privacy, satisfaction, and purchase intention. There is a significant difference between the second cluster (B) and the fourth cluster (D) in terms of usefulness, ease of use, shopping enjoyment, privacy, satisfaction, and purchase intention. Also, there is no significant difference between the third cluster (C) and the fourth cluster (D) in terms of usefulness, ease of use, shopping enjoyment, privacy, satisfaction, and purchase intention.

The present research aims to develop a model for automatically inferring the relationships between terms in the Thesaurus of Islamic Sciences using Graph Convolutional Networks (GCN). By employing new algorithms in the field of deep learning, the research seeks to enhance the efficiency of information retrieval in the Thesaurus of Islamic Sciences. To enhance accuracy and comprehensiveness, reduce costs, and improve relationships between terms.
The current research employed used of convolutional networks method, networks, is are one of the crucial techniques methods in the field of learning. This method is capable of leveraging from the relationship patterns in the while also focusing on to the characteristics of each node. The dataset under study comprises all the terms from the thesaurus of Islamic sciences generated between 1994 and the early 2022, which are represented as a graph. The vertices represent the terms, and the edges represent the relationships between the terms in the graph. This graph is provided as input to the convolutional network, which then generates a model for the automatic inference of connections. And in order to analyze the obtained outputs, AP and ROC standards have been used.
The revealed showed the model achieved the average accuracy 75% and a Roc score of 72% obtained for the data. It is noteworthy to accept the results considering that this method was used for the first time in the field of Islamic sciences and thesauruses.
Despite shift in preference opinion thesauri thesauruses to ontologies, the use thesauri remains still of particularly especially in Iran. Compared to previous research, the method used to construct the thesaurus is different, resulting in more reliable outcomes. Consequently, we can expect improved results for various purposes, such as automatic indexing. New advancements in natural language processing and deep learning also give us hope for improvements in information retrieval and automatic indexing.

Process mining connects the disciplines of data mining and machine learning to business process management techniques. A business process is a series of independent and interdependent activities that transform inputs (data, materials, etc.) using one or more resources (such as time, employees, and money). It utilizes the necessary outputs. It is possible to examine the actual behavior of organizations, including the performance of individuals, departments, and resources, using process analysis techniques. The results of the process analysis, which typically includes the organization's business process models, can be compared to the organization's documents and requirements. Thus, processes will be able to be compared, reviewed, monitored, and enhanced. Process mining methods operate based on event logs stored in information systems. Using process mining without high-quality input data will not result in accurate conclusions about an organization's business processes. In recent years, researchers have focused on the evaluation and enhancement of the quality of input data using process mining techniques. The objective of this study is to identify and categorize the most significant data quality issues, as well as recognize the approaches proposed to address this challenge in process mining.

This research employs a systematic review with the intent of analyzing all valid evidence in order to answer the research questions. This study investigates 102 academic studies published between 2007 and 2021, including conference papers, journal articles, and theses. Towards this end, a systematic three-part research methodology was employed. In the first section, which included the research definition, the research field was defined first, followed by the research objectives and queries. In the concluding step of this section, the research's scope is defined. In the second section, the research methodology and entry criteria for the studies discovered during the search for scientific resources are defined. Finally, the identified studies are evaluated in terms of their citations and classified. In the third section, which is devoted to the evaluation of the research, the concluding research of the study is conducted, and then, based on the investigation of the preceding studies, the findings and conclusions are determined. Important data and evidence were extracted from the collated research, allowing for the creation of the necessary tables and graphs.

In recent years, researchers have paid more attention to data quality challenges in the process mining, according to the findings of recent research. In 2019 and 2020, the greatest number of studies will have been published. It was also discovered that the majority of articles were published in three scientific databases, namely Springer, IEEE, and Elsevier. 51% of the studies examined were presented at prestigious conferences. 36% of the studies were published in prestigious scientific journals, while the remaining 13% were represented in dissertations and university reports. The study of the selected articles revealed that 20 data quality issues that can arise in the input data have been investigated in the literature. These challenges have been categorized into five levels: trace, event, case, activity, and timestamps, and four foundational approaches have been identified that have been used to evaluate and resolve data quality challenges in the mining process. 1) data quality frameworks 2) preprocessing 3) anomaly detection 4) repair. Our findings indicate that preprocessing techniques that seek to remove chaotic and infrequent behaviors from the event log have received more attention than other techniques. In addition, these results demonstrate that, in recent years, the discovery of anomalies and the reconstruction of missing events have become popular research topics within the field of process mining. Examining studies related to the field of data quality in the data mining process reveals an abundance of approaches and methods for addressing data quality challenges. Investigations revealed that the use of colorful Petri nets as a mathematical method has been considered in all selected research projects.

The data needed for process mining methods can be obtained from various sources. One of the major advantages of process mining is that it is not limited to a specific type of system. Any workflow-based system, such as ticketing, resource management, databases, data warehouses, legacy systems, and even manually collected data, can be analyzed as long as it can be separated using case ID, activity, and timestamp attributes. In real-world scenarios, most data is not collected for process mining purposes or is unsuitable for use in process mining analyses. Especially data that is recorded manually or scattered among various isolated systems can contain errors. Despite the efforts made to improve the quality of input data in the mining process, it is still necessary to develop efficient frameworks and methods to identify, evaluate, and address data quality challenges in real business processes, which are often characterized by high volume and complexity. The results of this research can offer a fresh perspective for researchers, data science specialists, and business analysts.

Sales intelligence involves conducting sales operations on a blockchain platform, utilizing smart contracts and artificial intelligence agents to directly monitor all network members. Intelligent sales and the effective use of data in the economic environment of the Metaverse can be crucial steps in gaining the trust and loyalty of customers. Utilizing innovative and intelligent methods for sales in the Metaverse can boost profits, enhance customer interaction, and establish stable relationships with them. The main challenge of selling in the Metaverse is the risk of impersonation and uncertainty surrounding companies and active users in that space. The implementation of intelligent systems, non-fungible tokens, blockchain technology, smart digital marketing, and artificial intelligence sales agents can enhance user trust and confidence, leading to more effective sales and increased profitability. The purpose of the current research is to investigate the factors and consequences of intelligence in the sale of sports appliances and equipment in the Metaverse.
The current research is qualitative. Therefore, two methods, systematic review and Grounded Theory, were used in combination. The statistical community in the Grounded Theory method consists of experts in the fields of business management, information management, and computer science. The sample size of this population, with theoretical saturation, was determined to be 13 people using purposeful and snowball sampling. MaxQDA 20 software was used for data analysis. The data collection tool used in the systematic review of library studies and in the Grounded Theory method was a semi-structured, in-depth interview with experts. Guba and Lincoln's qualitative measure of reliability, along with two quantitative measures, Cohen's kappa and Holstein's coefficient, were utilized to assess the validity and reliability of the research. Considering that the background of the research under study lacked the necessary enrichment to complete the paradigm model, the first step involved using a systematic review method to identify the factors and the central phenomenon of the model. In the continuation of the research, the Grounded Theory method was employed to identify intervening factors, contextual factors, strategies, and consequences, utilizing the expertise of experts. In total, 269 articles were identified on the research topic. After screening, 11 Persian articles from 2020 to 2022 and 20 English articles from 2022 to 2023 were found to be suitable in terms of subject and content, according to the opinion of university professors. After searching for Persian and English articles, the articles were screened. Following a thorough systematic review, the categories of the factors and sales intelligence were identified. The model was completed using the database method and employing open, central, and selective coding. After designing the questions for the qualitative questionnaire and receiving confirmation from supervisors, consultants, and university experts, data collection began. To enhance the validity of the research, a voice recorder was used during the interviews in addition to taking notes. After collecting the qualitative data through open coding, the data was divided into separate parts and analyzed to identify patterns, similarities, and differences. First, during the open coding stage, the categories were identified. Then, in the second stage of analysis, central coding was utilized. The purpose of this step was to establish the relationship between the identified categories in the open coding phase. This coding is called axial because it occurs around the axis of a category. At this stage, the variable of sales intelligence was investigated using the systematic review method with a focus on the phenomenon-oriented approach. An attempt was made to determine the relationship of other categories produced with it.
The results obtained led to the identification of 109 open codes, 33 central codes, and 6 selective codes. Finally, a paradigm model titled "Intelligent Sales of Sports Equipment and Supplies" was presented. Through a systematic review, the following categories were identified as factors influencing sales intelligence with a positive effect: perceived risk, hedonic motivation, engaging interactions, 3D augmented reality catalogue, augmented reality digital content, augmented reality digital advertising, augmented reality applications, augmented reality lead generation, virtual reality branding, virtual reality rebranding, non-exchangeable tokens, and smart sales contracts. By utilizing the Grounded Theory method and experts' opinions, the categories for enhancing digital marketing metrics, digital products, social network promotion, virtual entrepreneurship opportunities, business development, product platform, and commercialization improvement were identified as the outcomes of strategy implementation. The strategy's impact on all events was evaluated positively. Using the Grounded Theory method, the categories of metadata, cloud space, big data, edge computing, artificial intelligence, digital marketing, and the Internet of Things were identified as contextual factors that positively impact the strategy. The categories of digital divide, privacy violation, identity hacking, data and information security, cybercrimes, and the ambiguity of laws and regulations were identified as background factors with a negative impact on the strategy.
The obtained results led to the identification of 109 open codes, 33 central codes and 6 selective codes. Finally, the current research led to the presentation of a paradigm model with the title of intelligent sales of sports equipment.

The aim of this research is to assess the Qom University website using information architecture indicators to determine the website's status and conduct a qualitative review. Regularly reviewing and evaluating websites for their structural and content aspects, and then identifying their strengths and weaknesses, will provide a suitable strategy for policy and decision making.
This research utilized a survey method to investigate the Qom University website, using the evaluation checklist optimized by Sediqi (1400) in June 1402. In this study, foursub-systems of website organization — tagging, navigation, and search — were evaluated based on three fundamental aspects of information architecture: context, user, and content. The evaluation list included two parts: descriptive questions and yes-or-no questions, designed to assess the presence or absence of the investigated characteristics. Finally, the checklist was prepared by the researcher based on the indicators and evaluation criteria, presented in the form of tables, and completed.
The analysis of these lists revealed that Qom University's website achieved 22 out of 37 points in the information organization section, 30 out of 57 points in the labeling section, 42 out of 78 points in the navigation section, and 5 out of 78 points in the search section. He has obtained a total of 46. Therefore, compared to the ideal state, the website under study scored 64% in the organization section, 45% in the tagging section, 81% in the navigation section, and 10% in the search field. Good at organization and navigation, but very poor at search.
Websites are an effective tool for communication between organizations and their audiences. This research aimed to evaluate the Qom University website and to identify its strengths and weaknesses. Based on the research findings, the organization system of the website is in good condition. However, the weaknesses of the university website's organizational plan are primarily related to its structure. Specifically, it lacks an alphabetical organization plan and does not utilize an audience-oriented or social organization plan, as indicated by the organizational chart. While the website navigation system indicates the current page, it does not clearly display all the levels of navigation that the user has traversed. The website's main navigation system must be integrated with the local system. The website search system must be implemented. In addition, integrating a recommender system into the website search system is one method that can be utilized to enhance the performance of the search system.

This article proposes a method for investigating the patterns of composition and topological structure of the Persian language. The enhanced method analyzes Persian text by representing it as a simultaneous network graph within the framework of complex network theory.

A null model of the same size is generated using the Erdos-Renyi random graph for comparison with the Persian network. The comparison is based on the average path length, clustering coefficient, and hierarchy of both networks. From the analysis of these key features, it can be seen that the Persian network graph differs from the random network. The smaller average path length and high clustering coefficient also confirm the influence of the small-world model in the Persian language.

For the first time, the Persian text was successfully converted into a complex network. An open, unbounded set of over two million words is created using a random forest approach.

The resulting network designed using the Bygram bag model contains 3256 nodes and 79705 edges. In addition, unlike the random network where there is only one community, 12 communities have been identified in the Persian network. Statistical evidence indicates that the Persian network is a scale-free network with a layered composition pattern.

One of the reasons for the failure in developing knowledge-based cities is the incomplete understanding of the dimensions and factors involved in this type of development, as well as the fundamental weakness in determining the strategic priorities of programs and resource allocation decisions. The main achievement of this article is the introduction and explanation of the model of knowledge-based development for cities in a dynamic and integrated manner. The elements of knowledge-based development are effectively explained, relying on the insights of experts in this field. And it has been explained. The application of knowledge is a key factor in sustaining growth and development. In the context of a country's progress, knowledge is considered a competitive advantage in economic, social, political, and cultural matters. Therefore, the development of knowledge-based cities is an approach in which the application of knowledge and information is of great importance. The economy, production, employment, and overall growth are formed based on this approach, and investment in knowledge-related fields has attracted the attention of developed countries. The aim of the study is to identify the components of development in knowledge-based cities.
The study population for this research comprises specialists and experts with knowledge of the research subject. The snowball or chain sampling method, a non-probability sampling technique, will be used when the study units are not easily identifiable. This method was chosen because the units under study were very rare and constituted a small portion of the statistical population. The sample size was determined using saturation or judgmental (targeted) sampling. The researchers achieved theoretical saturation after interviewing 32 people but continued the interviews with 38 people to ensure thoroughness. Data collection was conducted through semi-structured interviews.
The data obtained from the interview, regarding the main research question of identifying the dimensions and components of the development of knowledge-based cities; after categorization and coding, thematic analysis was done Five categories were extracted for the development of knowledge-based cities, which are: 1. Knowledge-based economics with 5 components including: development of advanced industries; incentive and support system; creating a knowledge capital market; smart economy; Smart businesses; 2. intelligent governance with four components including: creating a development-oriented government; freedom of information; development of electronic government; Development of technology infrastructure; 3. Knowledge-based community with four components including civil awareness; intelligent people; knowledge-based education; and Information Society; 4. improving the knowledge of development management with four components including knowledge-based organizations; Development of knowledge cities; Sustainable Development; Development of technical and executive systems and 5. Knowledge and technology policy with five components including commercialization of knowledge; research and development centers; promoting science and technology; National innovation system; and Development of knowledge management. Key success factors in developing knowledge-based cities include: 1. Development of research and development centers and science and technology parks. 2. Attention to single industries. 3. Attention to knowledge management. 4. Strengthening knowledge human capital. 5. Development of technology infrastructure. 6. Creating a national model of innovation. 7. Development of smart and knowledge-based economies. 8. Development of smart governance infrastructure. 9. Network business development. It is suggested that managers create poles of knowledge and technology; develop technological infrastructure; and support systems; Create and development of knowledge-based entrepreneurship and knowledge-based management should take necessary measures.
The key success factors in the creation and development of knowledge-based cities include: 1. Development of research and development centers; 2. Development of science and technology parks; 3. Attention to hi-tech industries (high technology); 4. Expansion of knowledge management in various matters; 5. Strengthening human capital and knowledge;6. Expanding and developing the infrastructure of new communication and information technologies; 7. Creating a national model of innovation; 8. Development of national technical and scientific networks; 9. Paying attention to the development of necessary infrastructure for networked and smart businesses and other cases.

Knowledge inertia is the tendency to rely on past procedures, knowledge, or experience to address new issues and problems. In other words, it considers the future as today. In fact, individuals and organizations rely on their previous knowledge and experience to navigate unprecedented and new conditions. Therefore, the present study was conducted to analyze the consequences of knowledge inertia in knowledge-based companies.
The current research utilizes a mixed methods approach, incorporating both qualitative and quantitative methods within the inductive-deductive paradigm. It is also practical in terms of its purpose and exploratory in terms of its nature and method. It is important to note that the statistical population for both the qualitative and quantitative parts of the research consists of experts, including managers of knowledge-based companies in Lorestan province and professors from the management department of Lorestan University. A purposeful sampling method was used to select 14 individuals as sample members. Therefore, according to the principle of theoretical sufficiency (which occurs when the researcher has gathered all available data and information necessary to understand the phenomenon and gain knowledge), the necessary data were collected to the fullest extent. It should be noted that in the qualitative aspect of the thematic analysis approach, apart from reviewing articles, books, and magazines, semi-structured interviews were also conducted. The validity and reliability of these interviews were assessed using the CVR coefficient and the Kappa-Cohen test, respectively. Confirmed. In the quantitative section, the data collection tool is the Delphi questionnaire. Its validity and reliability were confirmed using content validity and inconsistency rate, respectively.
The current research utilizes a mixed approach (qualitative and quantitative). The results obtained in the qualitative part indicate the identification of the consequences of knowledge inertia in knowledge-based companies. The results of this section, following data analysis using the coding approach (open, central, and selective coding) and Atlas.ti software, reveal fifteen factors as consequences of knowledge inertia in knowledge-seeking companies. In the quantitative aspect of the research, the fuzzy Delphi method was employed to prioritize the consequences of knowledge inertia in scientific companies. The results indicate that the disclosure of the organization's strategies, the enhancement of predictability in the organization's operations, and the increase in stagnation and intellectual inertia are significant outcomes. The most significant consequences of knowledge inertia in knowledge-based companies are the loss of creativity and innovation, resistance to change, reduced performance, diminished organizational learning, and decreased agility and flexibility.
Knowledge is considered one of the most fundamental and crucial assets for competing in the new millennium. No matter how many resources an organization has, if it does not leverage modern knowledge and science, its resources will remain stagnant and it will be practically unable to use them optimally. Acquiring knowledge and leveraging it can enhance the organization's growth and excellence, ultimately improving its competitive position. In other words, knowledge determines the efficient and effective utilization of other resources for the organization. In this way, the dominance of knowledge inertia causes the organization to cease acquiring new knowledge and learning, and instead rely on outdated versions and strategies to solve its issues and challenges. In other words, stagnation and intellectual inertia within the organization are increasing day by day. It should be noted that the organization is losing the ability to generate new and original ideas, as well as other methods and techniques to address recurring threats and ultimately predict competitors' actions. On the other hand, knowledge inertia poses a challenge by hindering the organization's ability to respond quickly, effectively, and efficiently to the opportunities created in the environment. This distortion affects the organization's agility and speed of action.

Information literacy is defined as a set of skills required to navigate effectively in the information society. Public libraries, as one of the most important and influential institutions for providing information, play a significant collaborative role in training and enhancing information literacy skills. Today, in the age of post-literacy and with the spread of media based on new digital technologies, traditional stages of literacy are being left behind, and new literacies are emerging. Information literacy studies have also begun to make new strides in providing frameworks that surpass models and standards. This has led to significant advancements in the study of this field, considering the flexibility and broader inclusion of frameworks compared to standards. In the third millennium, information literacy is considered a crucial skill for all members of society, and the advancement of any society depends on its progress towards establishing an effective information society. In this regard, the role of public libraries as a fundamental component of the information society and a system that fosters and sustains information literacy skills is more significant than in the past. The research aims to establish a framework for enhancing information literacy skills training for the patrons of public libraries within the country's public library system.
The research is of a practical nature and falls under mixed methods studies. The research community included four information literacy frameworks and 18 experts in information literacy from the country. Research data were collected and analyzed by combining three content analysis approaches of information literacy frameworks, conducting three round-trips of the Delphi panel of information literacy experts, and using the DEMATEL Technique. The findings were presented using Invivo, Excel, MATLAB, and XMind version 8 software.
The proposed framework consists of 7 main components and 52 indicators. The components "information dissemination" (10.482) and "information evaluation" (9.712) had the highest relative weight and degree of importance, while the component "combination of information" (9.434) was found to have the lowest degree of importance compared to the other components. The components have been obtained.
The resulting framework can be used to address the lack of coherent planning and continuous implementation of information literacy skills training in public libraries, thereby supporting the achievement of the institution's training goals and missions. The resulting framework of this research can be used at more detailed levels in formulating and designing context-oriented micro-models of information literacy in various types of public libraries, such as regional, urban, rural, and mobile libraries. It can also be effective to address the research gaps in the field of information literacy within the context of public libraries in Iran by following proper scientific flow. Given the goals and responsibilities of public libraries as a public university and one of the most prominent and trusted centers for education, it is crucial to make a serious effort to empower and enhance the information literacy skills of the audience. Utilizing information literacy frameworks as a practical strategy for teaching and improving information literacy skills in public libraries can be considered as a key approach for fostering sustainable information development in societies and contributing to the overall information sustainable development of the community.

One of the distinctive features of successful universities today is their entrepreneurial characteristics, and the absence of this in Iran's higher education system is quite noticeable and tangible. Currently, the universities in the country are facing numerous challenges, including graduate unemployment, brain drain, inadequate student skill training, student demotivation, a quantitative increase in students without a corresponding increase in quality, and limited interaction between the industry and the university. The root of these issues can be attributed to the low level of investment and insufficient focus on various dimensions of entrepreneurship. There is also a tendency to prioritize theoretical knowledge over scientific and practical knowledge, and a lack of emphasis on fostering creativity and innovative abilities in learners. A review of the country's macro policies also reveals that the incorporation of entrepreneurship into educational systems has long been a concern. However, in practice, we have observed a lack of emphasis on this issue by universities. Therefore, the present research was conducted to identify the elements of the educational entrepreneurship model in universities and higher education institutions in Iran and to present a conceptual model.
The current research is applied in terms of its purpose. In terms of data, it is qualitative and based on systematic database theory. In terms of implementation, it is exploratory and based on an inductive approach. The statistical population consists of educational entrepreneurs, educational entrepreneurship policy makers, and entrepreneurship professors from 8 top universities in the country: Tehran University, Tarbiat Modares University, Sharif University, Amir Kabir University of Technology, Shahid Chamran University of Ahvaz, Isfahan University of Technology, Shahid Beheshti University, and Kerman University of Art. In order to select the sample, a purposeful and conscious approach was used, and sampling was done using the snowball method. The interview was conducted in accordance with the guidelines for 30 to 60 minutes. Sampling was conducted until theoretical saturation was reached, and ultimately, 15 people were interviewed. To collect data, the library method (scanning) and the field method (interview) were used. To assess the validity of the findings, three pluralistic methods were employed to test the reliability of the model. These methods included retesting the work process, conducting a reliability test among the coders, and interviewing new participants. Finally, for data analysis, the theoretical coding technique was used in three stages: open coding, central coding, and selective coding.
The results obtained indicate that a macro view is necessary for educational entrepreneurship in universities, considering the entire model. It cannot be expected that student entrepreneurship will increase if not all aspects are studied. Several factors contribute to the development of an effective model of educational entrepreneurship in universities. Some factors serve as the foundation for educational entrepreneurship, while others act as catalysts for its emergence. Additionally, there are factors that impede its progress. In order to address this issue, it is essential to identify and implement basic strategies. By considering various factors and implementing these strategies, positive outcomes and results in educational entrepreneurship can be achieved.