The use of electronic means of a public official in carrying out their duties may lead to an instance wherein the person discloses confidential information, which can significantly impact their obligations. After verifying this act as part of electronic misconduct, disciplinary action is enforced upon the concerned party to rectify and ensure proper functioning in delivering public services without any disturbance or infringement. The study presents several significant findings regarding the absence of comparative regulations concerning electronic violations and their judicial evidence. It provides recommendations such as modifying legislative frameworks to enhance public utility disciplinary systems and incorporating rules for electric violations. The fundamental focus revolves around assessing, verifying, and punishing digital misconduct by management or regulatory bodies. Additionally, this research employs descriptive-analytical methods comparing the Jordanian Law with its Egyptian counterpart in exploring these issues.

Conventional intelligent traffic congestion discrimination methods mainly use GPS terminals to collect traffic congestion data, which is vulnerable to the influence of vehicle time distribution, resulting in poor final discrimination effect. Necessary to design a new intelligent traffic congestion discrimination method based on wireless sensor network front-end data collection. That is to use the front-end data acquisition technology of wireless sensor network to generate a front-end data acquisition platform to obtain intelligent traffic congestion data, and then design an intelligent traffic congestion discrimination algorithm based on traffic congestion rules so as to achieve intelligent traffic congestion discrimination. The experimental results show that the intelligent traffic congestion discrimination method designed based on the front-end data collection of wireless sensor network has good discrimination effect, the obtained discrimination data is more accurate, effective and has certain application value, which has made certain contributions to reducing the frequency of urban traffic accidents.

This study inquires about the COVID-19-generated momentum and how it resulted in transformative opportunities for the hard-hit tourism industry in Hawai'i. It also investigates the type of sustainability-based management strategies that were favoured by actors from the industry to help navigate uncertain times and capture transformative opportunities. Findings indicate that actors from the tourism industry in Hawai'i perceived the COVID-19 pandemic as a huliau, or a point of transformation, to reflect and re-evaluate the tourism industry's responsibility and shift toward a recovery focused on sustainability. This research confirms that the pandemic-driven momentum accelerated opportunities for changing and transforming traditional business models and indicators of progress within the tourism industry in Hawai'i. Further research may explore additional Pacific Island countries to gain a deeper understanding of the problem within the region's context.

In India, 70% of the Indian population is dependent on agriculture, yet agriculture generates only 13% of the country's gross domestic product. Several factors contribute to high levels of stress among farmers in India, such as increased input costs, draughts, and reduced revenues. The problem lies in the absence of an integrated farm advisory system. A farmer needs help to bridge this information gap, and they need it early in the crop's lifecycle to prevent it from being destroyed by pests or diseases. This research involves developing deep learning algorithms such as <i>ResNet18</i> and <i>DenseNet121</i> to help farmers diagnose crop diseases earlier and take corrective actions. By using deep learning techniques to detect these crop diseases with images farmers can scan or click with their smartphones, we can fill in the knowledge gap. To facilitate the use of the models by farmers, they are deployed in Android-based smartphones.

In order to overcome the problems of low evaluation accuracy and long evaluation time in traditional teaching quality evaluation methods, a method of English distance information teaching quality evaluation based on decision tree classification algorithm is proposed. Firstly, construct teaching quality evaluation indicators under different roles. Secondly, the information gain theory in decision tree classification algorithm is used to divide the attributes of teaching resources. Finally, the rough set theory is used to calculate the index weight and establish the risk evaluation index factor set. The result of teaching quality evaluation is obtained through fuzzy comprehensive evaluation method. The experimental results show that the accuracy rate of the teaching quality evaluation of this method can reach 99.2%, the recall rate of the English information teaching quality evaluation is 99%, and the time used for the English distance information teaching quality evaluation of this method is only 8.9 seconds.

The patient must follow a good diet to lessen the risk of health conditions. The body needs vitamins, minerals, and nutrients for illness prevention. When the human body does not receive the right amount of nutrients, nutritional disorders can develop, which can cause a number of different health issues. Chronic diseases like diabetes and hypertension can be brought on by dietary deficiencies. The human body receives the nutrients from a balanced diet to function properly. This research has a prototype that enables patients to find nutritious food according to their health preferences. It suggests meals based on their preferences for nutrients such as protein, fibre, high-fibre, low-fat, etc., and diseases such as pregnancy and diabetes. The process implements the recommendation based on the patient's profile (content-relied, K-NN), recommendation relied on patients with similar profiles, and recommendation based on the patient's past or recent activity.

Simulations have been shown to be an effective tool in traditional learning environments; however, as distance learning grows in popularity, the need to examine simulation effectiveness in this environment has become paramount. A casual-comparative design was chosen for this study to determine whether students using a computer-based instructional simulation in hybrid and fully online environments learned better than traditional classroom learners. The study spans a period of 6 years beginning fall 2008 through spring 2014. The population studied was 281 undergraduate business students self-enrolled in a 200-level microcomputer application course. The overall results support previous studies in that computer simulations are most effective when used as a supplement to face-to-face lectures and in hybrid environments.

The number of online resources available for teaching and learning in higher education has been growing enormously during the last decade. A recent development is the emergence of Massive Open Online Courses (MOOCs) and of Open Educational Resources (OER). The result is a huge number of videos that are available on line. Can these videos enrich learning? As a pilot study we added sixteen videos to an existing introductory course in Human-Computer Interaction. This course is mandatory in the Bachelor programs Computer Science and Information Science (second year). Watching the videos was optional for the students. The videos originated for the most part from the MOOC Human-Computer Interaction, produced by Stanford University. We offered this course to a pilot group of eight students. The educational context was problem-based learning in distance education. The videos were welcomed by all of the students and were found to be useful in their learning process. The students watched the videos intensively and appreciated them very well. A main reason for the students to be positive about the videos was that they liked to alternate reading texts and watching videos.

Aim/Purpose: This article examines how learning activities draw on resources in the work context to learn. Background The background is that if knowledge no longer is seen mainly as objects, but processes, how then to understand boundary objects? Our field study of learning activities reveals the use of pictures, documents and emotions for learning in the geographically distributed Norwegian Labor Inspection Authority Methodology: The study is a qualitative study consisting of interview data, observation data, and documents. Contribution: Contribute to practice based theorizing. Findings: Three ideal types of representing practices have been identified, i.e., ‘Visualizing’, ‘Documenting’ and ‘Testing’. All three are combined with storytelling, sensing, reflections and sensemaking, which point at the importance of processes in learning. The article also add insights about how emotions can be an important resource for boundary spanning – and sensemaking – by creating the capability of reflecting upon and integrating different knowledge areas in the in- practice context. Recommendations for Practitioners: Look for boundary objects within your field to promote online learning. Recommendation for Researchers: Study boundary objects in work context to understand learning. Impact on Society Role of objects in human learning. Future Research: Focus on how emotions can be used for online learning.

Aim/Purpose: Teachers are being asked to integrate mobile technologies into their content creation and distribution tasks. This research aims to provide an understanding of teachers taking on this process and whether the use of technology has influenced their content creation and distribution in the classroom. Background: Many claim that the use of technology for content creation and distribution can only enhance and improve the educational experience. However, for teachers it is not simply the integration of technology that is of prime concern. As teachers are ultimately responsible for the success of technology integration, it is essential to understand teachers’ viewpoints and lived technology experiences. Methodology: The Task-Technology Fit (TTF) model was used to guide interpretive case study research. Six teachers were purposively sampled and interviewed from a private school where a digital strategy is already in place. Data was then analysed using directed content analysis in relation to TTF. Contribution: This paper provides an understanding of teachers’ mobile technology choices in relation to content creation and distribution tasks. Findings: Findings indicate that teachers fit technology into their tasks if they perceive the technology has a high level of benefit to the teaching task. In addition, the age of learners and the subject being taught are major influencers. Recommendations for Practitioners: Provides a more nuanced and in-depth understanding of teachers’ technology choices, which is necessary for the technology augmented educational experience of the future. Recommendations for Researchers: Provides an unbiased and theoretically guided view of mobile technology use with content creation and distribution tasks. Impact on Society: Teachers do not appear to use technology as a de facto standard, but specifically select technology which will save them time, reduce costs, and improve the educational experiences of their learners. Future Research: A mixed-method approach, including several diverse schools as well as learners would enrich the findings. Furthermore, consideration of hardware limitations and lack of software features are needed.

Aim/Purpose: This brief paper will provide preliminary insight into an institutions effort to help students understand the application of the scientific method as it applies to the business discipline through the creation of a dedicated, required course added to the curriculum of a mid-Atlantic minority-serving institution. In or-der to determine whether the under-consideration course satisfies designated student learning outcomes, an assessment regime was initiated that included examination of rubric data as well as the administration of a student perception survey. This paper summarizes the results of the early examination of the efficacy of the course under consideration. Background: A small, minority-serving, university located in the United States conducted an assessment and determined that students entering a department of business following completion of their general education science requirements had difficulties transferring their understanding of the scientific method to the business discipline. Accordingly, the department decided to create a unique course offered to sophomore standing students titled Principles of Scientific Methods in Business. The course was created by a group of faculty with input from a twenty person department. Methodology: Rubrics used to assess a course term project were collected and analyzed in Microsoft Excel to measure student satisfaction of learning goals and a student satisfaction survey was developed and administered to students enrolled in the course under consideration to measure perceived course value. Contribution: While the scientific method applies across the business and information disciplines, students often struggle to envision this application. This paper explores the implications of a course specifically purposed to engender the development and usage of logical and scientific reasoning skills in the business discipline by students in the lower level of an bachelors degree program. The information conveyed in this paper hopefully makes a contribution in an area where there is still an insufficient body of research and where additional exploration is needed. Findings: For two semesters rubrics were collected and analyzed representing the inclusion of 53 students. The target mean for the rubric was a 2.8 and the overall achieved mean was a 2.97, indicating that student performance met minimal expectations. Nevertheless, student deficiencies in three crucial areas were identified. According to the survey findings, as a result of the class students had a better understanding of the scientific method as it applies to the business discipline, are now better able to critically assess a problem, feel they can formulate a procedure to solve a problem, can test a problem-solving process, have a better understanding of how to formulate potential business solutions, understand how potential solutions are evaluated, and understand how business decisions are evaluated. Conclusion: Following careful consideration and discussion of the preliminary findings, the course under consideration was significantly enhanced. The changes were implemented in the fall of 2020 and initial data collected in the spring of 2021 is indicating measured improvement in student success as exhibited by higher rubric scores. Recommendations for Practitioners: These initial findings are promising and while considering student success, especially as we increasingly face a greater and greater portion of under-prepared students entering higher education, initiatives to build the higher order thinking skills of students via transdisciplinary courses may play an important role in the future of higher education. Recommendations for Researchers: Additional studies of transdisciplinary efforts to improve student outcomes need to be explored through collection and evaluation of rubrics used to assess student learning as well as by measuring student perception of the efficacy of these efforts. Impact on Society: Society needs more graduates who leave universities ready to solve problems critically, strategically, and with scientific reasoning. Future Research: This study was disrupted by the COVID-19 pandemic; however, it is resuming in late 2021 and it is the hope that a robust and detailed paper, with more expansive findings will eventually be generated.

Aim/Purpose: This study examined learning during the COVID-19 crisis, as perceived by preservice teachers at the time of their academic studies and their student teaching experience. Background: The COVID-19 crisis is unexpected. On one hand, it disrupted learning in all learning frameworks, on the other, it may create a change in learning characteristics even after the end of the crisis. This study examined the pro-ductive, challenging, and thwarting factors that preservice teachers encountered during their studies and in the course of their student teaching during the COVID-19 period, from the perspective of preservice teachers. Methodology: The study involved 287 students studying at teacher training institutions in Israel. The preservice teachers were studying online, and in addition experienced online teaching of students in schools, guided by their own teacher. The study used a mixed method. The questionnaire included closed and open questions. The data were collected in 2020. Contribution: Identifying the affecting factors may deepen the understanding of online learning/teaching and assist in the optimal implementation of online learning. Findings: Online learning experience. We found that some of the lessons at institutions of higher learning were delivered in the format of online lectures. Many pre-service teachers had difficulty sitting in front of a computer for many hours—“Zoom fatigue.” Preservice teachers who had difficulty self-regulating and self-mobilizing for study, experienced accumulating loads, which caused them feelings of stress and anxiety. The word count indicated that the words that appeared most often were “load” and “stress.” Some preservice teachers wrote that collaborating in forums with others made it easier for them. Some suggested diversifying by digital means, incorporating asynchronous units and illustrative films, and easing up on online lectures, as a substitute for face-to-face lectures. Online teaching experience in schools. The preservice teachers' descriptions show that in lessons taught in the format of lectures and communication of content, there were discipline problems and non-learning. According to the preservice teachers, discipline problems stemmed from difficulties concentrating, physical distance, load, and failure to address the students' difficulties. Recommendations for Practitioners: In choosing schools for student teaching, it is recommended to reach an understanding with the school about the online learning policy and organization. It is important to hold synchronous sessions in small groups of 5 to 10 students. The sessions should focus on the mental wellbeing of the students, and on the acquisition of knowledge and skills. Students should be prepared for participation in asynchronous digital lessons, which should be produced by professionals. It should be remembered that the change of medium from face-to-face to online learning also changes the familiar learning environment for all parties and requires modifying the ways of teaching. Recommendations for Researchers: A change in the learning medium also requires a change in the definition of objectives and goals expected of each party—students, teachers, and parents. All parties must learn to view online learning as a method that enables empowerment and the application of 21st century skills. Impact on Society: Teachers' ability to deploy 21st century skills in an online environment de-pends largely on their experience, knowledge, skills, and attitude toward these skills. Future Research: This study examined the issue from the perspective of preservice teachers. It is recommended to examine it also from the perspective of teachers and students.

Aim/Purpose: In the United States, the Centers for Disease Control and Prevention (CDC) tracks the disease activity using data collected from medical practice's on a weekly basis. Collection of data by CDC from medical practices on a weekly basis leads to a lag time of approximately 2 weeks before any viable action can be planned. The 2-week delay problem was addressed in the study by creating machine learning models to predict flu outbreak. Background: The 2-week delay problem was addressed in the study by correlation of the flu trends identified from Twitter data and official flu data from the Centers for Disease Control and Prevention (CDC) in combination with creating a machine learning model using both data sources to predict flu outbreak. Methodology: A quantitative correlational study was performed using a quasi-experimental design. Flu trends from the CDC portal and tweets with mention of flu and influenza from the state of Georgia were used over a period of 22 weeks from December 29, 2019 to May 30, 2020 for this study. Contribution: This research contributed to the body of knowledge by using a simple bag-of-word method for sentiment analysis followed by the combination of CDC and Twitter data to generate a flu prediction model with higher accuracy than using CDC data only. Findings: The study found that (a) there is no correlation between official flu data from CDC and tweets with mention of flu and (b) there is an improvement in the performance of a flu forecasting model based on a machine learning algorithm using both official flu data from CDC and tweets with mention of flu. Recommendations for Practitioners: In this study, it was found that there was no correlation between the official flu data from the CDC and the count of tweets with mention of flu, which is why tweets alone should be used with caution to predict a flu out-break. Based on the findings of this study, social media data can be used as an additional variable to improve the accuracy of flu prediction models. It is also found that fourth order polynomial and support vector regression models offered the best accuracy of flu prediction models. Recommendations for Researchers: Open-source data, such as Twitter feed, can be mined for useful intelligence benefiting society. Machine learning-based prediction models can be improved by adding open-source data to the primary data set. Impact on Society: Key implication of this study for practitioners in the field were to use social media postings to identify neighborhoods and geographic locations affected by seasonal outbreak, such as influenza, which would help reduce the spread of the disease and ultimately lead to containment. Based on the findings of this study, social media data will help health authorities in detecting seasonal outbreaks earlier than just using official CDC channels of disease and illness reporting from physicians and labs thus, empowering health officials to plan their responses swiftly and allocate their resources optimally for the most affected areas. Future Research: A future researcher could use more complex deep learning algorithms, such as Artificial Neural Networks and Recurrent Neural Networks, to evaluate the accuracy of flu outbreak prediction models as compared to the regression models used in this study. A future researcher could apply other sentiment analysis techniques, such as natural language processing and deep learning techniques, to identify context-sensitive emotion, concept extraction, and sarcasm detection for the identification of self-reporting flu tweets. A future researcher could expand the scope by continuously collecting tweets on a public cloud and applying big data applications, such as Hadoop and MapReduce, to perform predictions using several months of historical data or even years for a larger geographical area.

Aim/Purpose This paper focuses on systematizing and rethinking the conformity of modern transdisciplinarity with its prime cause and initial ideas. Background The difficulties of implementing transdisciplinarity into science and education are connected with the fact that its generally accepted definition, identification characteristics, and methodological features are still missing. In order to eliminate these disadvantages of transdisciplinarity, its prime cause and initial ideas had to be detected. It is also important to analyze the correspondence of the existing opinions about transdisciplinarity with the content of these cause and ideas. Methodology The qualitative analysis of the literature reviews on the subject of transdisciplinary was used in order to determine the correspondence of the opinions about the transdisciplinarity with the meaning of its prime cause and initial ideas. These opinions had to be generalized as well. Through this method, it was possible to detect and classify opinions into 11 groups including 39 stereotypes of transdisciplinarity. For substantiation of transdisciplinary approaches that are consistent with the approaches of contemporary science, C.F. Gauss random variables normal distribution was used. The “Gauss curve” helped to show the place of transdisciplinary and systems transdisciplinary approaches in the structure of academic and systems approaches. The “Gauss curve” also demonstrated the step-by-step “broadening of the scientific worldview horizon due to sequential intensification of synthesis, integration, unification, and generalization of the disciplinary knowledge.” Contribution After reconsideration of the results on qualitative analysis of the literature reviews, the generalized definition of transdisciplinarity could be formulated, including the definition for transdisciplinary and systems transdisciplinary approaches. It was proven that transdisciplinarity is a natural stage for the development of contemporary science and education, and the transdisciplinary approaches were able to suggest the methods and tools to solve the complex and poorly structured problems of science and the society. Findings Many existing stereotypes of transdisciplinarity do not meet its prime cause and initial ideas. Such stereotypes do not have deep philosophic and theoretical substantiation. They also do not suggest the transdisciplinary methods and tools. Thus, the authors of such stereotypes often claim them to be transdisciplinary or suggest perceiving them as transdisciplinarity. This circumstance is the reason why many disciplinary scientists, practitioners, and initiators of higher education view transdisciplinarity as a marginal direction of contemporary science. Based on the generalized definition of transdisciplinarity, as well as its prime cause and initial ideas, it was shown that transdisciplinarity is presented in contemporary science in the form of two different approaches, i.e., the transdisciplinary approach and systems transdisciplinary approach. The objective of the transdisciplinary approach is to ensure science development at the stage of synthesis and integration of disciplinary knowledge, while the objective of the systems transdisciplinary approach is to ensure that the problems of modern society are solved through unification and generalization of the disciplinary knowledge. Recommendation for Researchers The researchers should consider that within the limits of the transdisciplinary approach, the disciplinary specialists are managed. Within the limits of the systems transdisciplinary approach, the disciplinary knowledge is managed. Thus, the transdisciplinary approach is efficient for organization and research with participation of the scientists of the complementary disciplines. An example of such research can be a team of researchers of medical disciplines and complementary disciplines from chemistry, physics, and engineering. The systems transdisciplinary approach is efficient for organization and performance of research with participation of the scientists of non-complementary disciplines such as economics, physics, meteorology, chemistry, ecology, geology, and sociology. Future Research In terms of the main initial idea, transdisciplinarity is formed as a global approach. The global approach should have a traditional institutional form: it should be a science discipline (meta-discipline) and have carriers with the transdisciplinary worldview. Training for such carriers can be organized by the universities within the limits of the systems transdisciplinarity departments and Centers of Systems Transdisciplinary Retraining for Disciplinary Specialists. Thus, it is reasonable to initiate discussion for the idea to reform the disciplinary structure of the universities considering creation of such departments and centers.

Aim/Purpose. The most crucial aspects of teaching a foreign language to more advanced learners are building an awareness of discourse modes, how to regulate discourse, and the pragmatic properties of discourse components. However, in different languages, the connections and structure of discourse are ensured by different linguistic means which makes matters complicated for the learner. Background. By uncovering regularities in a foreign language and comparing them with patterns in one’s own tongue, the corpus research method offers the student unique opportunities to acquire linguistic knowledge about discourse markers. This paper reports on an investigation of the functions of multi-word discourse markers. Methodology. In our research, we combine the alignment model of the phrase-based statistical machine translation and manual treatment of the data in order to examine English multi-word discourse markers and their equivalents in Lithuanian and Hebrew translations by researching their changes in translation. After establishing the full list of multi-word discourse markers in our generated parallel corpus, we research how the multi-word discourse markers are treated in translation. Contribution. Creating a parallel research corpus to identify multi-word expressions used as discourse markers, analyzing how they are translated into Lithuanian and Hebrew, and attempting to determine why the translators made the choices add value to corpus-driven research and how to manage discourse. Findings. Our research proves that there is a possible context-based influence guiding the translation to choose a particle or other lexical item integration in Lithuanian or Hebrew translated discourse markers to express the rhetorical domain which could be related to the so-called phenomenon of “over-specification.” Recommendations for Practitioners. The comparative examination of discourse markers provides language instructors and translators with more specific information about the roles of discourse markers. Recommendations for Researchers. Understanding the multifunctionality of discourse markers provides new avenues for discourse marker application in translation research. Impact on Society. The current study may be a useful method to strengthen students’ language awareness and analytic skills and is particularly important for students specializing in English philology or translation. Beyond the empirical research, an extensive parallel data resource has been created to be openly used. Future Research. It should be noted that the observed phenomenon of “over-specification” could be analyzed further in future research.

Aim/Purpose. The goal of this publication is to explore methods for advancing student success in technology related disciplines via improved program classification and selection within higher education. Background. Increased demand for information technology (IT) professionals has been cited as a challenge in many fields including cybersecurity and software development. Many highlight the challenge as not just a numbers gap but a skills gap when comparing industry needs to the curricula in traditional disciplines within higher education. Closing the gap by increasing the number of skilled graduates remains a critical challenge we must address. Methodology. This publication leverages a systematic literature review to identify factors that classify existing higher education programs within the discipline of information technology. Contribution. Research in this area can act as a catalyst to increase relevance of IT related programs as well as graduation rates in technology and engineering. Findings. Authors analyzed forty-four primary studies and found that 56.8% of the publications referenced programs that meet the IT framework definition although they were not classified as IT programs. The findings and further analysis highlight direct challenges between program classification and the potential impact on student success. Recommendations for Practitioners. Research in this area is relevant for academic administrators, private sector executives and others working to increase the technology pipeline. Recommendations for Researchers. Researchers may benefit by exploring thematic analysis as a means of generating relevant classifications and taxonomies that highlight opportunities for improvement in a broad set of subject areas. Impact on Society. Research in this area can serve as a catalyst to increase graduation rates in programs related to technology and engineering. Future Research. This area would benefit from further research by comparing program success rates within varied disciplines. Future research may also produce a classification process.

Aim/Purpose. This paper aims to answer the research question, “What are the development phases of the academic discipline of information technology in the United States?” This is important to understand the reason for the growing talent gap in the information technology (IT) industry by reviewing the evolution of information technology across time, how the discipline was formed, evolved, and gained independence from other information and computing disciplines. Background. The COVID-19 pandemic has increased the shortage of IT professionals in the workplace. The root reason for this talent shortage requires understanding from both industry and academic perspectives in order to implement effective initiatives to prepare, recruit, and retain diverse IT professionals at an early stage. Methodology. This paper used a systematic literature review methodology and retrieved 143 primary studies from the ACM and IEEE Xplore digital libraries to review the development phases of the IT discipline as a contributing factor in understanding why, when, and how the population of professionals in IT and other relevant computing disciplines has changed and continues to fluctuate. Thematic analysis was applied to the abstracts of the primary studies, which spanned the period of 1982 to 2021. Contribution. This paper contributes to the understanding of the discipline of IT in the US and contributes foundations to researchers and educators who are working on strategies to fill the talent gap. Findings. Based on the thematic analysis in this paper, the academic discipline of IT has evolved over four phases across a timeline from 1982 to 2021. These phases were: Phase 1 (1982-1991) – Advent of Information Technology; Phase 2 (1992-2001) – Industry IT & DevOps; Phase 3 (2002-2011) – Information Technology and Management in Evolving Industry, Academia, and Research Areas; and Phase 4 (2011-2021) – Information Technology Research & Education. Recommendations for Practitioners. IT occupies an independent disciplinary space from computer science, computer engineering, and information systems. The paper suggests that practitioners seeking to fill the talent gap in IT invest in enabling its academic programs. Recommendations for Researchers. The depth of the IT disciplinary space and its continued evolution over time is ready for exploration. Continued research in this area may yield a better understanding of its role in society, the skills needed to succeed, and how to build programs to empower students with these skills. Impact on Society. Examining the discipline of IT and understanding its independence and interrelated connection with other computing disciplines will help address the shortfalls in academia across the nation by identifying the distinction between each discipline and creating comprehensive programs, degrees, and curricula suitable for various students and professionals across all educational levels. Future Research. Future research will integrate papers’ introductions and conclusions in addition to abstracts, increase the number of databases and reviewers, as well as incorporate papers that focus on other information and computing disciplines such as computer science and information systems to explore the possibility that IT as a discipline was initially practiced in an existing information or computing discipline before it gained independence.

Aim/Purpose. This study captures and describes the discrepancies in the performance matrices of comparable Chinese and American scholars as recorded by Scopus. Background. The contributions of Chinese scholars to the global knowledge enterprise are increasing, whereas indexing bibliometric databases (e.g., Scopus) are not optimally designed to track their names and record their work precisely. Methodology. Coarsened exact matching was employed to construct two samples of comparable Chinese and American scholars in terms of gender, fields of work, educational backgrounds, experience, and workplace. Under 200 scholars, around a third being Chinese and the rest American scholars, were selected through this data construction method. Statistical tests, including logit regressions, Poisson regression, and fractional response models, were applied to both samples to measure and verify the discrepancies stored within their Scopus accounts. Contribution. This study complicates the theory of academic identity development, especially on the intellectual strand, as it shows ethnic scholars may face more errors in how their track records are stored and presented. This study also provides inputs for the discussion of algorithmic discrimination from the academic context and to the scientific community. Findings. This paper finds that Chinese scholars are more prone to imprecise records in Scopus (i.e., more duplicate accounts, a higher gap between the best-statistic accounts, and the total numbers of publications and citations) than their American counterparts. These findings are consistent across two samples and with different statistical tests. Recommendations for Practitioners. This paper suggests practitioners and administrators at research institutions treat scholars’ metrics presented in Scopus or other bibliometric databases with caution while evaluating ethnic scholars’ contributions. Recommendations for Researchers. Scholars and researchers are suggested to dedicate efforts to monitoring their accounts on indexing bibliometric platforms. Impact on Society. This paper raises awareness of the barriers that ethnic scholars face in participating in the scientific community and being recognized for their contributions. Future Research. Future research can be built on this paper by expanding the size of the analytical samples and extending similar analyses on comparable data harvested from other bibliometric platforms.

Helping a company organize and capture the knowledge used by its employees and business processes is a daunting task. In this work we examine several proposed methodologies and synthesize them into a new methodology that we demonstrate through a case study of an electric power distribution company. This is a practical research study. First, the research approach for creating the knowledge map is process-oriented and the processes are considered as the main elements of the model. This research was done in four stages: literature review, model editing, model validation and case study. The Delphi method was used for the research model validation. Some of the important outputs of this research were mapping knowledge flows, determining the level of knowledge assets, expert-area knowledge map, preparing knowledge meta-model, and updating the knowledge map according to the company’s processes. Besides identifying, auditing and visualizing tacit and explicit knowledge, this knowledge mapping enables us to analyze the knowledge areas’ situation and subsequently help us to improve the processes and overall performance. So, a process map does knowledge mapping in a clear and accurate frame. Once the knowledge is used in processes, it creates value.

Aim/Purpose: The purpose of this paper is to examine the key research foci, methodologies, and theoretical perspectives adopted by researchers when studying E-government accessibility for persons with disabilities (PWDs), particularly in developing countries. The study aims to develop a conceptual framework for designing accessible E-government for PWDs in developing countries. Background: Studies on E-government accessibility for persons with disabilities in developing countries have been minimal. The few studies conducted until now have failed to integrate PWDs, a population already marginalized, into the digital society. Accessibility has been identified by researchers as a major hindrance to PWDs participating in E-government. It is imperative therefore to examine the manner in which researchers investigate and acquire knowledge about this phenomenon. Methodology : The study synthesizes literature from top IS journals following a systematic literature review approach. The data synthesis focuses on identifying key concepts relating to E-government accessibility for PWDs. Contribution: The study contributes to the field of E-government, with a focus on how E-government services can be made accessible to PWDs. The study calls on researchers to reflect on their epistemological and ontological paradigms when examining accessibility of E-government services in developing countries. Findings: The findings show that most researchers focus on the evaluation of E-government websites and predominantly adopt quantitative methods. The study also reveals that the use of technological determinism as a theoretical lens is high among researchers. Recommendations for Practitioners : The study recommends that E-government web developers and policy makers involve PWDs from design to evaluation in the development of E-government applications. Recommendation for Researchers: The study advocates the need to conduct studies on E-government accessibility by employing more qualitative and mixed approaches to gain in-depth and better understanding of the phenomenon. Impact on Society : This study creates greater awareness and points out inadequacies that society needs to address to make E-government more inclusive of and participatory for PWDs. Future Research: Further empirical work is required in order to refine the relevance and applicability of various constructs in EADM so as to arrive at a framework for addressing E-government accessibility for PWDs in developing countries.

Aim/Purpose: This study investigates the research profile of the papers published in Interdisciplinary Journal of Information, Knowledge, and Management (IJIKM) to provide silhouette information of the journal for the editorial team, researchers, and the audience of the journal. Background: Information and knowledge management is an interdisciplinary subject. IJIKM defines intersections of multiple disciplinary research communities for the interdisciplinary subject. Methodology: A quantitative study of categorical content analysis was used for a thematic analysis of IJIKM. One hundred fifty nine (159) papers published since the inauguration of the journal in 2006 were coded and analyzed. Contribution: The study provides synopsized information about the interdisciplinary research profile of IJIKM, and adds value to the literature of information and knowledge management. Findings: The analysis reveals that IJIKM disseminates research papers with a wide range of research themes. Among the research themes, Organizational issues of knowledge/information management, Knowledge management systems/tools, Information/knowledge sharing, Technology for knowledge/information management, Information/knowledge application represent the five main research streams of IJIKM. The total number of papers on organizational issues of knowledge/information management increased from 16% to 28% during the past 6 years. Statistical method was the most common research methodology, and summarization was the most common research design applied in the papers of IJIKM. The paper also presents other patterns of participant countries, keywords frequencies, and reference citations. Recommendations for Practitioners: Innovation is the key to information and knowledge management. Practitioners of information and knowledge management can share best practices with external sectors. Recommendation for Researchers: Researchers can identify opportunities of cross-disciplinary research projects that involve experts in business, education, government, healthcare, technology, and psychology to advance knowledge in information and knowledge management. Impact on Society: Information and knowledge management is still a developing field, and readers of this paper can gain more understanding of the dissemination of the literature of information and knowledge management involved in all relevant disciplines. Future Research: A longitudinal study could follow up in the future to provide updated and comparative information of the research profile of the journal.

Aim/Purpose: Vis-à-vis management of crisis and disaster situations, this paper focuses on important use cases of social media functions, such as information collection & dissemination, disaster event identification & monitoring, collaborative problem-solving mechanism, and decision-making process. With the prolific utilization of disaster-based ontological framework, a strong disambiguation system is realized, which further enhances the searching capabilities of the user request and provides a solution of unambiguous in nature. Background: Even though social media is information-rich, it has created a challenge for deriving a decision in critical crisis-related cases. In order to make the whole process effective and avail quality decision making, sufficiently clear semantics of such information is necessary, which can be supplemented through employing semantic web technologies. Methodology: This paper evolves a disaster ontology-based system availing a framework model for monitoring uses of social media during risk and crisis-related events. The proposed system monitors a discussion thread discovering whether it has reached its peak or decline after its root in the social forum like Twitter. The content in social media can be accessed through two typical ways: Search Application Program Interfaces (APIs) and Streaming APIs. These two kinds of API processes can be used interchangeably. News content may be filtered by time, geographical region, keyword occurrence and availability ratio. With the support of disaster ontology, domain knowledge extraction and comparison against all possible concepts are availed. Besides, the proposed method makes use of SPARQL to disambiguate the query and yield the results which produce high precision. Contribution: The model provides for the collection of crisis-related temporal data and decision making through semantic mapping of entities over concepts in a disaster ontology we developed, thereby disambiguating potential named entities. Results of empirical testing and analysis indicate that the proposed model outperforms similar other models. Findings: Crucial findings of this research lie in three aspects: (1) Twitter streams and conventional news media tend to offer almost similar types of news coverage for a specified event, but the rate of distribution among topics/categories differs. (2) On specific events such as disaster, crisis or any emergency situations, the volume of information that has been accumulated between the two news media stands divergent and filtering the most potential information poses a challenging task. (3) Relational mapping/co-occurrence of terms has been well designed for conventional news media, but due to shortness and sparseness of tweets, there remains a bottleneck for researchers. Recommendations for Practitioners: Though metadata avails collaborative details of news content and it has been conventionally used in many areas like information retrieval, natural language processing, and pattern recognition, there is still a lack of fulfillment in semantic aspects of data. Hence, the pervasive use of ontology is highly suggested that build semantic-oriented metadata for concept-based modeling, information flow searching and knowledge exchange. Recommendation for Researchers: The strong recommendation for researchers is that instead of heavily relying on conventional Information Retrieval (IR) systems, one can focus more on ontology for improving the accuracy rate and thereby reducing ambiguous terms persisting in the result sets. In order to harness the potential information to derive the hidden facts, this research recommends clustering the information from diverse sources rather than pruning a single news source. It is advisable to use a domain ontology to segregate the entities which pose ambiguity over other candidate sets thus strengthening the outcome. Impact on Society: The objective of this research is to provide informative summarization of happenings such as crisis, disaster, emergency and havoc-based situations in the real world. A system is proposed which provides the summarized views of such happenings and corroborates the news by interrelating with one another. Its major task is to monitor the events which are very booming and deemed important from a crowd’s perspective. Future Research: In the future, one shall strive to help to summarize and to visualize the potential information which is ranked high by the model.

Aim/Purpose: The understanding of disease spread dynamics in the context of air travel is crucial for effective disease detection and epidemic intelligence. The Susceptible-Exposed-Infectious-Recovered-Hospitalized-Critical-Deaths (SEIR-HCD) model proposed in this research work is identified as a valuable tool for capturing the complex dynamics of disease transmission, healthcare demands, and mortality rates during epidemics. Background: The spread of viral diseases is a major problem for public health services all over the world. Understanding how diseases spread is important in order to take the right steps to stop them. In epidemiology, the SIS, SIR, and SEIR models have been used to mimic and study how diseases spread in groups of people. Methodology: This research focuses on the integration of air traffic network data into the SEIR-HCD model to enhance the understanding of disease spread in air travel settings. By incorporating air traffic data, the model considers the role of travel patterns and connectivity in disease dissemination, enabling the identification of high-risk routes, airports, and regions. Contribution: This research contributes to the field of epidemiology by enhancing our understanding of disease spread dynamics through the application of the SIS, SIR, and SEIR-HCD models. The findings provide insights into the factors influencing disease transmission, allowing for the development of effective strategies for disease control and prevention. Findings: The interplay between local outbreaks and global disease dissemination through air travel is empirically explored. The model can be further used for the evaluation of the effectiveness of surveillance and early detection measures at airports and transportation hubs. The proposed research contributes to proactive and evidence-based strategies for disease prevention and control, offering insights into the impact of air travel on disease transmission and supporting public health interventions in air traffic networks. Recommendations for Practitioners: Government intervention can be studied during difficult times which plays as a moderating variable that can enhance or hinder the efficacy of epidemic intelligence efforts within air traffic networks. Expert collaboration from various fields, including epidemiology, aviation, data science, and public health with an interdisciplinary approach can provide a more comprehensive understanding of the disease spread dynamics in air traffic networks. Recommendation for Researchers: Researchers can collaborate with international health organizations and authorities to share their research findings and contribute to a global understanding of disease spread in air traffic networks. Impact on Society: This research has significant implications for society. By providing a deeper understanding of disease spread dynamics, it enables policymakers, public health officials, and practitioners to make informed decisions to mitigate disease outbreaks. The recommendations derived from this research can aid in the development of effective strategies to control and prevent the spread of infectious diseases, ultimately leading to improved public health outcomes and reduced societal disruptions. Future Research: Practitioners of the research can contribute more effectively to disease outbreaks within the context of air traffic networks, ultimately helping to protect public health and global travel. By considering air traffic patterns, the SEIR-HCD model contributes to more accurate modeling and prediction of disease outbreaks, aiding in the development of proactive and evidence-based strategies to manage and mitigate the impact of infectious diseases in the context of air travel.