Emran Ghorbani; HamidReza Mahmoodi; Ebrahim Zarei; Mohammad Hassanzadeh; Majid Bashirzadeh; Nazila Mehrabi
Abstract
ObjectiveThe purpose of this study is to determine the interaction of researchers with online information resources and provide solutions to increase the quality of information retrieval.MethodologyIn terms of purpose, the present study is a fundamental research that falls into the category of content ...
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ObjectiveThe purpose of this study is to determine the interaction of researchers with online information resources and provide solutions to increase the quality of information retrieval.MethodologyIn terms of purpose, the present study is a fundamental research that falls into the category of content analysis research and is based on the underlying theory of Strauss and Corbin (1998) and has been done using Max QDA software. This research is one of the library researches that has used field techniques in terms of data collection method.15 researchers who have the most scientific products and naturally have more interaction with information and search for resources, were selected by judgmental sampling and They have been interviewed.FindingsIn this study, which was interviewed with 15 researchers, it was found that researchers mostly refer to Google search engine and databases of Science Direct, Web of Science and Scopus. They also use advanced search and simple search to find their information. They are also familiar with scientific social networks and have been the most visited and researched by Research Gate, LinkedIn and Academia. They were largely satisfied with the quality and accessibility of online resources, but wanted the information organization to have a single standard and order. ConclusionBased on the results of the interviews, we obtained a model that showed that researchers categorize the retrieved information into four components:1.Useful overt information 2. Useful hidden information 3. Useless overt information and 4. Annoying overt information
HamidReza Mahmoodi; Nazila Mehrabi; Azra Daei
Abstract
IntroductionPerformance evaluation systematically investigates a subject to improve program effectiveness using appropriate, ethical, feasible, and precise methods (Tootanchi et al., 2006). It measures outcomes against indicators to evaluate goal achievement, efficiency, resource effectiveness, process ...
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IntroductionPerformance evaluation systematically investigates a subject to improve program effectiveness using appropriate, ethical, feasible, and precise methods (Tootanchi et al., 2006). It measures outcomes against indicators to evaluate goal achievement, efficiency, resource effectiveness, process quality, and program execution (Parker, 2000; Gholami & Noralizadeh, 2002). Knowledge, a vital organizational asset, enhances competitiveness by facilitating decision-making and performance improvement. As society shifts towards information-driven environments, knowledge technologies become essential, leveraging AI to solve complex issues and enhance decision-making. Effective implementation of these technologies provides competitive advantages by efficiently storing, protecting, processing, and utilizing knowledge, contributing to sustained performance, growth, and innovation. They offer benefits like increased accessibility, cost reduction, time savings, improved communication, innovation, enhanced data storage, reliability, and swift knowledge transfer (Arab-Mazari Zadeh et al., 2007). Thus, evaluating knowledge technology performance is crucial to ensure quality, customer satisfaction, and informed decision-making. Without it, organizations risk inefficiency and resource wastage. This study aims to identify and rank key components for evaluating knowledge technologies to ensure effective assessment and utilization.Literature ReviewHamidizadeh (2016) found a significant positive correlation between expert decision systems and decision-making efficiency, including improved speed, reduced interdepartmental information gaps, and lower organizational costs. Musivand et al. (2015) discovered that knowledge management systems enhance job quality in Iran's Ministry of Sports and Youth by positively impacting knowledge utilization, management, creation, storage, and organization. Naqib et al. (2013) identified the customer aspect as the most influential in knowledge management systems using a balanced scorecard model, with the financial aspect being the most affected. Fazli and Aghshalouei (2008) recommended a hybrid model for assessing decision-making units' performance. Latifi and Mousavi (2008) highlighted four key processes—identification and creation, registration and maintenance, sharing, and internalization—as crucial for effective knowledge management in Iranian software companies. Samimi and Aghaei (2005) proposed a performance evaluation model for knowledge management systems, emphasizing its role in system efficiency enhancement. Internationally, Kumar (2018) emphasized the critical role of knowledge technology in organizational knowledge management, particularly in data accessibility and user services. Mysore et al. (2018) highlighted digital tools like BIM and IoT in the construction industry. Simon and Georgi (2017) developed a framework integrating knowledge search behaviors and tools for asynchronous environments. Kumar et al. (2016) found that organizational culture and leadership, especially democratic styles, significantly influence knowledge absorption, with soft factors outweighing hard factors. Ngugi et al. (2016) demonstrated that knowledge technology positively impacts small enterprises' growth in Nairobi by facilitating skill transfer and process improvement. Milton et al. (1999) highlighted knowledge technology's role in supporting key knowledge management activities such as personalization, innovation, and monitoring. These studies collectively underscore the vital role of knowledge technologies and management systems in improving organizational efficiency, decision-making, and overall performance across various sectors.Methodology This applied research utilized a mixed exploratory approach, employing Delphi and Analytic Hierarchy Process (AHP) methods. Data was collected through library-documentary and field methods. The Delphi phase involved an open and closed questionnaire, the latter based on the open questionnaire findings, encompassing 10 components and 39 questions on a Likert scale. The study population included 18 purposively sampled faculty members from Tehran's public universities, with theoretical saturation determining the sample size. The subsequent AHP-designed questionnaire was distributed among 60 academic members and PhD students, achieving a numeric saturation with consistent mean values indicating data adequacy. Validity and reliability were ensured via consistency rates below 0.1 and analyzed using MAX QDA, SPSS 25, and Expert Choice 11 software.ResultsThe dual Delphi rounds in this study reached consensus among panel members, starting with 73 initial codes refined to 38 unique codes across 10 indices: financial costs, system quality, system infrastructure, technology and knowledge service quality, knowledge technology architecture, user interface, user satisfaction, value of results, perceived benefits, and up-to-dateness. A Likert-scale questionnaire in the second round confirmed all 38 components. Table 4 indicates that the financial component, with a weight of 0.178, significantly influences knowledge technology performance evaluation. The consistency rate of 0.09 ensures the reliability and stability of the findings. Other components, ranked by weight, include system quality (0.156), system infrastructure (0.154), user interface (0.125), and others, down to perceived benefits and up-to-dateness (0.038).Discussion Evaluating the performance of knowledge technologies in knowledge-based organizations is vital for identifying learning pathways and creating competitive advantages. Organizations require tools to enhance performance and continuously assess the effectiveness of their knowledge technologies, addressing strengths, weaknesses, opportunities, and threats. This research identified ten key components for performance evaluation: financial, system quality, system infrastructure, knowledge technology service quality, knowledge technology architecture, user interface, user satisfaction, value of results, perceived benefits, and up-to-dateness. The financial component, deemed most critical, includes startup costs, infrastructure and equipment costs, human resources training costs, and AI processing costs. System quality, ranked second, involves flexibility, effectiveness, use of expert systems, ease of access, and support for open access. System infrastructure, third in importance, covers physical and electronic spaces, application modernization, and elimination of outdated infrastructure. User interface, ranked fourth, focuses on usability, accessibility, user-friendliness, and visual appeal. Knowledge technology service quality, sixth in rank, includes information processing quality, metadata management, content volume, and content quality. User satisfaction, seventh, involves automated knowledge management, system efficiency, prediction of user needs, and satisfaction with system effectiveness. Value of results, eighth, includes continuous improvement, enhanced decision-making through AI, alignment of results with needs, and reliability. Perceived benefits and up-to-dateness, both ranked ninth, cover monitoring performance changes, goal achievement assessment, opportunities for new knowledge creation, and performance improvements. These evaluations highlight the operational quality within organizations and the challenges in successful knowledge management implementation. Previous research, such as Hamidizadeh (2016) and Musivand et al. (2015), supports these findings, emphasizing the role of knowledge technologies in decision-making and operational efficiency. International studies also affirm their importance in service delivery, knowledge structuring, and performance enhancement. Thus, evaluating knowledge technologies using these key components is essential for effective utilization and to avoid resource wastage and operational inefficiencies.ConclusionIn the Delphi phase, 10 indicators including the financial component, system quality, system infrastructure, knowledge technology service quality, knowledge technology architecture, user interface, user satisfaction about the system, the value of work results, perceived benefits and benefits from the system, up-to-dateness and 39 items were identified, Each of these indicators also has its own sub-indicators, whose degree of importance has also been examined. Based on the hierarchical analysis method, the financial component is in the first degree, the system quality component is in the second degree, the system infrastructure component is in the third degree, the user interface component is in the fourth degree, the knowledge technology architecture component is in the fifth degree, and the knowledge technology service quality component is in the sixth degree. The user satisfaction component about the system was ranked seventh, the value component of the results obtained from the card was ranked eighth, and the perceived benefits and benefits of the system and the component of being up-to-date were ranked ninth.Acknowledgments The authors consider it necessary to acknowledge and thank all the loved ones who helped us in this research.
HamidReza Mahmoodi; Nazila Mehrabi
Abstract
IntroductionFinding and formulating the problem is the basis of scientific research. Determining the problem requires fundamental questioning and it is not an impromptu thing, but rather a dynamic, regular, systematic, and logical process that takes place before determining the title of the research ...
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IntroductionFinding and formulating the problem is the basis of scientific research. Determining the problem requires fundamental questioning and it is not an impromptu thing, but rather a dynamic, regular, systematic, and logical process that takes place before determining the title of the research by going through the nested and systematic layers of the phenomena. In studies, researches, and scientific productions that take place in universities and educational centers, problem-solving is considered the first and most important step. In fact, it can be said that problem-solving is the recognition and application of knowledge, skills, and abilities that lead to the correct response to the situation or to achieve goals.. When we reach a goal, we are able to solve a challenge or a problem or achieve a better situation, which is problem-solving. By identifying and categorizing obstacles to problem-solving, it is possible to help students identify important and necessary problems. If the problem-finding obstacles are identified and subsequently removed, problems such as lack of progress in scientific productions and repetitive and invalid researches will be avoided. Therefore, the main goal of the current research is to "identify the obstacles of research problem solving".Literature ReviewFotis Kasoulas and Georgia Mega (2007) in a research titled "Creative and critical thinking in the form of problem-finding and problem-solving: a study among elementary school students" found that factors such as relevance and reasoning which are related to critical thinking in problem solving by students play a role. Frank Labanka (2008) in his research entitled "The effect of problem-solving on the quality of scientific research projects arising from an authentic research environment using an open problem" found that factors such as the use of expertise and previous experiences and creative thinking such as flexibility, adaptability, new approach, play a role in the problem-solving process. In Krista Ritchie's (2009) research entitled "Problem-finding Process in Research Education: Focusing on Students' Experiences", it was shown that personality traits such as lack of motivation, lack of interest, anxiety, and other negative emotions affect students' problem-finding performance. In the research of the Guardian (2013) with the title "Research Problem Solving; Undeniable Necessity in Postgraduate Theses", it was shown that obstacles such as collecting information without having a research plan, without examining the background and existing research and not recognizing the limitations of the research, determining the research method before determining the subject, disregarding the appropriate theories and available resources in formulating the research problem, are common mistakes that threaten researchers in setting up the research topic. Problem solving is not a one-dimensional process and the researcher must consider all aspects uniformly to choose the right problem. Mira Begi (2015) in a research entitled "Problem-finding in research; Limitations and solutions focusing on thesis writing and indigenous theorizing in Iranian sociology" stated that problem solving requires critical and creative thinking and in a word, it requires a set of personality traits. In addition to this, the necessary infrastructure for problem solving should also be provided. Baghmirani (2016) in his research entitled "Development of the Conceptual Model of Research Problem-Finding Using Directional Content Analysis Method" found that the concept of problem-finding is in personality characteristics in five dimensions, in behavioral characteristics in four dimensions, in thinking characteristics (Creative) can be developed in six dimensions, (critical) thinking in two dimensions and educational feature in five dimensions.MethodologyThis research is fundamental in terms of its purpose and in terms of gathering information, it is library research that has also benefited from field techniques. In terms of method, this research is a kind of content analysis which was done with the Claysey method. The statistical population of this research was made up of the faculty members of the public universities of Tehran province. 10 people were selected as a sample using the sequential pooled sampling method. A semi-structured interview method was used to collect data. Four criteria of validity or acceptability, certainty of stability, confirmability, and transferability or fit were used to evaluate the validity and accuracy and robustness of the data.ResultsThe result of the analysis of 10 interviews was 16 general themes or categories and 77 primary concepts. Of course, 386 primary concepts were obtained at the beginning, and after careful examination and removal of synonyms, the number of concepts was reduced to 77 codes. Identified classes include self-deprecation, passive linearity, lack of motivation, lack of work, lack of skills, ignorance, negative consensus, inferential analysis, superficiality, weak intelligence related to individual obstacles, dry educational management, inefficient human resources, weak content related to educational obstacles, family scientism, individualism related to cultural barriers; structural instrumental gap related to structural barriers; and institutional instability is related to institutional barriers to problem solving.DiscussionEvidences and surveys showed that a set of obstacles prevent a person from choosing an important and necessary issue. The analysis showed that there are problems and obstacles in the five personal, educational, cultural, structural, and institutional dimensions that fuel the individual's inability to solve problems. In the context of individual obstacles, it can be pointed out that novice researchers have characteristics of self-deprecation, passive linearity, lack of creativity, sufficient purpose and motivation, unfamiliarity with domestic and international databases, and relative lack of proficiency in English, ignorance and knowledge gaps, lack of analytical power and inference power, as well as the negative correlation of inference analysis that can make a person face problems in problem solving. Along with personal obstacles, a set of non-personal obstacles can be seen; some of these obstacles are related to educational obstacles. The inappropriateness of university research regulations and guidelines and the managers' approach to problem solving and research is a linear and binary approach; an approach that destroys creative thinking. The weakness of human resources (inexperience of professors and faculty members) of the university can also be a problem. Familiarity with the research method can be considered as the alphabet of problem solving. Until the course units of the research seminar and research method are not taught correctly, we cannot hope for the success of the students in problem solving. The weakness of the course content, which refers to the provision of inappropriate teaching resources, non-native and not up-to-date resources, is one of the educational obstacles. In the field of cultural barriers, we can mention the avoidance of science and insufficient education of parents, which fuel the individual's weakness in problem solving. Facilities and infrastructures are needed for problem solving. Failure to provide equipment indicates the existence of a structural tool gap, which in turn is considered a potential risk for the problem-solving process. The lack of problem-solving institutions is another obstacle to research problem-solving. If these institutions are created in the heart of the library, information science specialists serve the scientific communities through a new channel by identifying and classifying the important and necessary issues of society.ConclusionIt can be concluded that problem-solving is a conscious, creative, and meticulous process of searching, identifying, refining, finding, and choosing a research problem among several problems; a set of individual and non-individual factors (educational, cultural, structural, and institutional) make this process difficult. This research helped to identify and categorize research problem-finding obstacles with an interpretive approach. Considering the limited literature of scientific communities in the field of problem-solving obstacles, it can be claimed with a high confidence factor that a significant part of the scientific community's knowledge gap in the field of problem-solving obstacles was filled with the new information of this research. Also, this research has shown the interdisciplinary nature of information science and epistemology.AcknowledgmentsThe authors consider it necessary to acknowledge and thank all the loved ones who helped us in this research.
