Technology has transformed education, perhaps most evidently in course delivery options. However, compelling questions remain about how technology impacts learning. Adaptive learning tools are technology-based artifacts that interact with learners and vary presentation based upon that interaction. This paper compares adaptive learning with a conventional teaching approach implemented in a digital literacy course. Current research explores the hypothesis that adapting instruction to an individual’s learning style results in better learning outcomes. Computer technology has long been seen as an answer to the scalability and cost of individualized instruction. Adaptive learning is touted as a potential game-changer in higher education, a panacea with which institutions may solve the riddle of the iron triangle: quality, cost and access. Though the research is scant, this study and a few others like it indicate that today’s adaptive learning systems have negligible impact on learning outcomes, one aspect of quality. Clearly, more research like this study, some of it from the perspective of adaptive learning systems as informing systems, is needed before the far-reaching promise of advanced learning systems can be realized.

The all-pervasive Internet has created serious problems, such as Internet overuse, which has triggered considerable debate over its relationship with addiction. To further explore its genetic susceptibilities and alternative explanations for Internet overuse, we proposed and evaluated four hypotheses, each based on existing knowledge of the biological bases of addiction, inattention, novelty-seeking, and harm-avoidance. Four genetic loci including DRD4 VNTR, DRD2 Taq1A, COMT Val158Met and 5-HTTLPR length polymorphisms were screened from seventy-three individuals. Our results showed that the DRD4 4R/4R individuals scored significantly higher than the 2R or 7R carriers in Internet Addiction Test (IAT). The 5-HTTLPR short/short males scored significantly higher in IAT than the long variant carriers. Bayesian analysis showed the most compatible hypothesis with the observed genetic results was based on attention (69.8%), whereas hypotheses based harm-avoidance (21.6%), novelty-seeking (7.8%) and addiction (0.9%) received little support. Our study suggests that carriers of alleles (DRD4 2R and 7R, 5-HTTLPR long) associated with inattentiveness are more likely to experience disrupted patterns and reduced durations of Internet use, protecting them from Internet overuse. Furthermore, our study suggests that Internet overuse should be categorized differently from addiction due to the lack of shared genetic contributions.

Group work can provide a valuable learning experience, one that is especially relevant for those preparing to enter the information system workforce. While much has been discussed about effective means of delivering the benefits of collaborative learning in groups, there are some problems that arise due to pragmatic environmental factors such as the part time work commitments of students. This study has identified a range of problems and reports on a longitudinal Action Research study in two universities (in Australia and the USA). Over three semesters problems were identified and methods trialed using collaborative tools. Several promising solutions are presented to the identified problems. These include the use of Google documents and color to ensure team contributions are more even.

Aim/Purpose Information Technology students’ learning outcomes improve when teaching methodology moves away from didactic behaviorist-based pedagogy toward a more heuristic constructivist-based version of andragogy. Background There is a distinctive difference, a notable gap, between the academic community and the business community in their views of the level of preparedness of recent information technology program graduates. Understanding how Information Technology curriculum is developed and taught along with the underpinning learning theory is needed to address the deficient attainment of learning outcomes at the heart of this matter. Methodology The case study research methodology has been selected to conduct the inquiry into this phenomenon. This empirical inquiry facilitates exploration of a contemporary phenomenon in depth within its real-life context using a variety of data sources. The subject of analysis will be two Information Technology classes composed of a combination of second year and third year students; both classes have six students, the same six students. Contribution It is the purpose of this research to show that the use of improved approaches to learning will produce more desirable learning outcomes. Findings The results of this inquiry clearly show that the use of the traditional behaviorist based pedagogic model to achieve college and university IT program learning outcomes is not as effective as a more constructivist based andragogic model. Recommendations Instruction based purely on either of these does a disservice to the typical college and university level learner. The correct approach lies somewhere in between them; the most successful outcome attainment would be the product of incorporating the best of both. Impact on Society Instructional strategies produce learning outcomes; learning outcomes demonstrate what knowledge has been acquired. Acquired knowledge is used by students as they pursue professional careers and other ventures in life. Future Research Learning and teaching approaches are not “one-size-fits-all” propositions; different strategies are appropriate for different circumstances and situations. Additional research should seek to introduce vehicles that will move learners away from one the traditional methodology that has been used throughout much of their educational careers to an approach that is better suited to equip them with the skills necessary to meet the challenges awaiting them in the professional world.

Aim/Purpose: The year 2020 marks the 50th anniversary of the first official definition of the term “transdisciplinarity.” This paper focuses on a critical analysis of the development of modern transdisciplinarity since its inception. Background: The article presents two main directions for the development of transdisciplinarity. It also shows its identification features, strengths, and weaknesses, as well as the significant role transdisciplinarity plays in science and education. Methodology: The methodology employed in this article is a content analysis of resolutions of international forums as well as articles on transdisciplinarity published from 1970 to 2019. Contribution: For one reason or the other, several of these authors did not quote the opinions of the original authors of transdisciplinarity. The subsequent use of those articles by other authors thus posed some ambiguities about the place and role of transdisciplinarity in science and education. The advent of e-databases has made it possible to access the original forum articles. This further made it possible to refine the original content of the term “transdisciplinarity” and to trace its development without mixing it with vague opinions. Based on these findings, the perception of transdisciplinarity as a marginal trend in science and education could be eliminated. Findings: This paper shows how modern transdisciplinarity is developing into two main directions: transdisciplinarity in science as well as transdisciplinarity in education. These orientations have individual goals and objectives. The transdisciplinarity of scientific research helps to complete the transformation of the potential for interdisciplinary interaction and the integration of disciplines. Whereas, in education, transdisciplinarity (meta-discipline) is about developing an international standard for transdisciplinary education and also describing the content of transdisciplinary competence for students of diverse disciplines at all levels of higher education (bachelor’s, master’s and postgraduate studies). Recommendation for Researchers: Transdisciplinary research involves the interaction of people with disciplinary knowledge plus a degree of scientific outlook. Since disciplinary knowledge domains remain in their disciplinary boxes, it is, therefore, advisable to generalize disciplinary knowledge rather than force them to interact. This is the basis for proposing the systems transdisciplinary approach—which provides a methodology for unifying and generalizing disciplinary knowledge. Future Research: As the research shows, the organizers of modern international forums do not take into account the division of transdisciplinarity development trends. To increase the effectiveness and significance of such forums, it is necessary to return to the practice of organizing special international forums on the transdisciplinarity of science and that of education.

Aim/Purpose: The aim of this paper is to propose an ensemble learners based classification model for classification clickbaits from genuine article headlines. Background: Clickbaits are online articles with deliberately designed misleading titles for luring more and more readers to open the intended web page. Clickbaits are used to tempted visitors to click on a particular link either to monetize the landing page or to spread the false news for sensationalization. The presence of clickbaits on any news aggregator portal may lead to an unpleasant experience for readers. Therefore, it is essential to distinguish clickbaits from authentic headlines to mitigate their impact on readers’ perception. Methodology: A total of one hundred thousand article headlines are collected from news aggregator sites consists of clickbaits and authentic news headlines. The collected data samples are divided into five training sets of balanced and unbalanced data. The natural language processing techniques are used to extract 19 manually selected features from article headlines. Contribution: Three ensemble learning techniques including bagging, boosting, and random forests are used to design a classifier model for classifying a given headline into the clickbait or non-clickbait. The performances of learners are evaluated using accuracy, precision, recall, and F-measures. Findings: It is observed that the random forest classifier detects clickbaits better than the other classifiers with an accuracy of 91.16 %, a total precision, recall, and f-measure of 91 %.

Table of Contents for Volume 22 of Informing Science: the International Journal of an Emerging Transdiscipline, 2019

Aim/Purpose: To discuss the Design Science Research approach by comparing some of its canons with observed practices in projects in which it is applied, in order to understand and structure it better. Background: Recent criticisms of the application of the Design Science Research (DSR) approach have pointed out the need to make it more approachable and less confusing to overcome deficiencies such as the unrealistic evaluation. Methodology: We identified and analyzed 92 articles that presented artifacts developed from DSR projects and another 60 articles with preceding or subsequent actions associated with these 92 projects. We applied the content analysis technique to these 152 articles, enabling the preparation of network diagrams and an analysis of the longitudinal evolution of these projects in terms of activities performed and the types of artifacts involved. Contribution: The content analysis of these 152 articles enabled the preparation of network diagrams and an analysis of the longitudinal evolution of these projects in terms of the activities and types of artifacts involved. Evidence was found of a precedence hierarchy among different types of artifacts, as well as nine new opportunities for entry points for the continuity of DSR studies. Only 14% of the DSR artifacts underwent an evaluation by typical end users, characterizing a tenth type of entry point. Regarding the evaluation process, four aspects were identified, which demonstrated that 86% of DSR artifact evaluations are unrealistic. Findings: We identified and defined a set of attributes that allows a better characterization and structuring of the artifact evaluation process. Analyzing the field data, we inferred a precedence hierarchy for different artifacts types, as well as nine new opportunities for entry points for the continuity of DSR studies. Recommendation for Researchers: The four attributes identified for analyzing evaluation processes serve as guidelines for practitioners and researchers to achieve a realistic evaluation of artifacts. Future Research: The nine new entry points identified serve as an inspiration for researchers to give continuity to DSR projects.

Table of Contents for Volume 23 of Informing Science: The International Journal of an Emerging Transdiscipline, 2020.

Aim/Purpose: In this paper we propose an ecosystem for translational learning that combines core learning principles with a multilevel construct that embraces the tenets of translational research, namely, teaming, translating, and implementing. The goal of the paper is to argue that knowledge of learning sciences is essential at the individual, team, and organizational levels in the translational science enterprise. Background: The two decades that we can now call the translational era of health and medicine have not been without challenges. Many inroads have been made in navigating how scientific teaming, translating knowledge across the health spectrum, and implementing change to our health systems, policies, and interventions can serve our changing global environment. These changes to the traditional health science enterprise require new ways of understanding knowledge, forging relationships, and managing this new tradition of science. Competency requirements that have become important to the enterprise are dependent on a deep understanding about how people learn as individuals, in teams, and within organizations and systems. Methodology: An individual, team, and organizational conceptual framework for learning in translational ecosystems is developed drawing on the learning science literature, a synthesis of 9 key learning principles and integrated with core competencies for translational science. Contribution: The translational learning ecosystem is a means by which to understand how translational science competencies can be reinforced by core learning principles as teaming, translating, and implementation intersect as part of the translational science enterprise. Findings: This paper connects learning science to tailored principles in a simplified way so that those working translational science with less knowledge of theories of learning and pedagogy may be able to access it in a clear and concise way. Recommendation for Researchers: This paper provides a framework for researchers who engage in the education of translational scientists as well as those who are charged with training new scientists in an emerging field critical to health and medicine. Future Research: The translational ecosystem described can serve to expand how teaching and learning impact scientific advances. In addition, it serves as a means in which to understand the impact of learning on micro, meso, and macro levels.

Aim/Purpose: Labeling a journal as “predatory” can do great damage to the journal and the individuals that have contributed to it. This paper considers whether the predatory classification has outlived its usefulness and what might replace it. Background: With the advent of open access publishing, the term “predatory” has increasingly been used to identify academic journals, conferences, and publishers whose practices are driven by profit or self-interest rather than the advancement of science. Absent clear standards for determining what is predatory and what is not, concerns have been raised about the misuse of the label. Methodology: Mixed methods: A brief review of the literature, some illustrative case studies, and conceptual analysis. Contribution: The paper provides recommendations for reducing the impact of illegitimate journals. Findings: Current predatory classifications are being assigned with little or no systematic research and virtually no accountability. The predatory/not predatory distinction does not accommodate alternative journal missions. Recommendations for Researchers: The distinction between legitimate and illegitimate journals requires consideration of each journal’s mission. To serve as a useful guide, a process akin to that used for accrediting institutions needs to be put in place. Impact on Society: Avoiding unnecessary damage to the careers of researchers starting out. Future Research: Refining the initial classification scheme proposed in the paper.

Table of Contents for Volume 24 of Informing Science: The International Journal of an Emerging Transdiscipline, 2021

Aim/Purpose: This paper aimed to explore the impact of compassion fatigue, compassion satisfaction, and burn-out among the general population during the pandemic. Background: The paper has attempted to explore compassion fatigue, compassion satisfaction, and burn-out among the population at large, especially during the pandemic. This area has not been explored as yet. Methodology: A simple random sample of 98 males and 88 females was collected anonymously through a Google form survey. Part A collected demographic data and Part B comprised of 15 statements with 5 each for compassion fatigue, compassion satisfaction, and burn-out, adapted from a Compassion Fatigue/Satisfaction Self-Test. ANOVA single factor was employed for the three variables of compassion fatigue, compassion satisfaction, and burn-out using a 0.05 significance level. Correlations among the variables were also analyzed. Contribution: The present paper contributes to covering the research gap of investigating the presence of compassion fatigue, compassion satisfaction, and burn-out among the population at large comprising the age group of 18 to 60+ and from different professions. Findings: The findings revealed significant differences in the levels of compassion fatigue, compassion satisfaction, and burn-out in the population at large during the pandemic. Future Research: The findings can be further strengthened by extending it to a larger sample size across different nations and, specifically, studying gender differences during such adverse pandemic situations.

Aim/Purpose: The integration of knowledge through the transdisciplinary method with the three concepts civilization, ideology, and geopolitics (CIG) enables the analysis of international relations in a new perspective and the informing strategists of countries, organizations, analysts, clients, etc. These three concepts express the transdisciplinarity that offers a new theoretical explanation and the informing science approach. Background: The integration of knowledge using the three concepts for the analysis of international relations has found adequate explanations from 1890 until the withdrawal of the United States from Afghanistan. Therefore, the CIG model theoretically and practically finds support for more than a century, as argued in the paper. Methodology: The present paper uses a mixed theory based on transdisciplinary methodology and informing science. The literature was reviewed to find and build the theoretical basis and provide appropriate examples. The theory is also based on the model used by Francis Fukuyama in his books on building and dissolution of states (middle-range theory). Contribution: This paper enables the rethinking of the limitations of research on a theoretical and practical basis that is done in many scientific circles, not to eliminate others but to enrich science even more. Findings: In the paper, the main findings are the following: Integrating the three CIG concepts according to the transdisciplinary method offers a new perspective to explain international relations using the IS method; The integration of the three concepts is worthwhile after 1980, when the model of cabinet governments falls, Bismarck falls, and public opinion starts to emerge; It was after 1980 that theories of civilization and geopolitics began to emerge along with ideologies to apply in practice; These three concepts offer explanations based on a CIG zone and in the periphery of the CIG zone. In the CIG zone the security sphere is more stable and long-term, while in periphery the cooperation is temporary and not long termed; The paper shows that the Cold War period is divided into two periods; The paper also finds that CIG explains with examples the events that happened after the Cold War and until present days; The paper also shows, based on the strategies of the superpowers, how they are extending their influence based on the CIG concepts. The paper also shows new patterns of cooperation and clashes between the superpowers’ security zones, which also provide an explanatory perspective for the USA withdrawal from Afghanistan. (We do not talk in the paper about the Afghanistan issue and USA withdrawal). Recommendation for Researchers: Scientific attributes in the integration of knowledge give researchers a more open and comprehensive perspective to make more accurate and practical analyses of international relations. According to this model, other theories are enriched that use the transdisciplinary method, IS, and the CIG as a model for the integration of knowledge. Future Research: Researchers and practitioners of this CIG model can find answers such as “Why did the USA fail in Afghanistan and why was it successful in Kosovo?” as well as other questions about finding a solution for Iraq, cooperation with China, etc.

Table of Contents for Volume 25 of Informing Science: The International Journal of an Emerging Transdiscipline, 2022

Aim/Purpose: The present study aims to frame the relationship between job and personal resources (namely, organizational identification and emotional intelligence), burnout, and musculoskeletal disorders (i.e., back pain, upper limb pain, lower limb discomfort), into the theoretical framework provided by the JD-R health model. Background: Empirical research indicates a connection between burnout and the onset of musculoskeletal problems, one of the most important occupational health issues affecting all jobs and organizations. In light of the JD-R health model, we investigated the association between personal and job resources with burnout and musculoskeletal disorders. Methodology: An anonymous online questionnaire was answered by 320 workers (82.4% female, Mage = 42.18; SDage = 12.24) investigating their perceived level of burnout, the presence of musculoskeletal pain (back, neck, and shoulder), and their level of organizational identification and emotional intelligence. Descriptive analysis, correlation, and moderated mediation model were performed using SPSS. Contribution: We confirmed the role of personal and organizational resources in the salutogenic process considered by the JD-R health model. Emotional intelligence, decreasing the perceived level of burnout, limited the development of musculoskeletal disorders. Moreover, when organizational identification presented low and medium levels, the association between emotional intelligence and burnout strengthened. Findings: Our results showed a negative, indirect effect of emotional intelligence on musculoskeletal disorders via burnout. Moreover, we found a moderation of organizational organization, indicating that at low and medium levels of identification, the association between emotional intelligence and burnout is stronger. Recommendation for Researchers: In addition to work factors involved in the link between burnout and musculoskeletal disorders, it is also important to consider personal and emotional factors, which can decrease the occurrence of adverse consequences. Future Research: Future research developments could contribute to a deeper understanding of the mechanisms linking emotional intelligence, burnout, and musculoskeletal problems, as well as consider objective indicators of burnout levels or consider using ecological data collection methodologies (e.g., ecological momentary assessment), to identify patterns and associations between burnout and musculoskeletal disorders.

Aim/Purpose: This study builds upon existing research by investigating the elements contributing to or buffering the onset of burnout symptoms. We examine the relationship between empowering leadership and burnout, considering the concurrent mediation effects of interpersonal workplace conflict, work-home conflict, and support from coworkers. Background: Burnout is a phenomenon that has been widely considered in the scientific literature due to its negative effect on individual and organizational well-being, as well as implications for leadership, coworker support, and conflict resolution. A deeper understanding of burnout prevention strategies across various professional contexts is paramount for enhancing productivity and job satisfaction. Methodology: Using a survey-based cross-sectional design, we employed a combination of Structural Equation Modelling (SEM) and Artificial Neural Network (ANN) to investigate the direct and indirect influences of empowering leadership on four dimensions of employee burnout, mediated by coworker support, interpersonal conflict at work, and work-home conflict. Contribution: This study provides initial insights into the direct and indirect influences of empowering leadership on various dimensions of burnout, highlighting the complex interplay with coworker support, work-home conflict, and workplace interpersonal conflicts. Ultimately, the study provides a comprehensive approach to understanding and mitigating burnout. Findings: Empowering leadership and coworker support can significantly reduce burnout symptoms, while high levels of work-home conflict and interpersonal conflict at work can exacerbate them. Our findings underscore the paramount role of interpersonal conflict in predicting burnout, urging organizations to prioritize resolving such issues for burnout prevention. Recommendation for Researchers: Following our findings, organizations should (a) promote empowering leadership styles, (b) foster coworker support and work-life balance, and (c) address interpersonal conflicts to reduce the likelihood of employee burnout while ensuring that these strategies are tailored to the specific context and culture of the workplace. Future Research: Future research should broaden the exploration of leadership styles’ effects on burnout, identify additional mediators and moderators, expand studies across sectors and cultures, examine differential impacts on burnout dimensions, leverage advanced analytical models, and investigate the nuanced relationship between work contract types and burnout.

Aim/Purpose: Embitterment comprises a stress-related response to unjust life experiences. Studies have found that it can have a toll on employees’ well-being. However, research on this matter is still in its infancy. Background: Within the scope of the present study, Ι sought to investigate how embitterment relates to burnout – the prolonged consequence of stress. This study further explored whether breaches of psychological contracts can trigger embitterment. Methodology: The study employed a cross-sectional design where two hundred and eight (N = 208) participants from the general population completed an online survey. Contribution: Findings suggest that the toll of embitterment might be much more than what research has suggested so far. Those who experience embitterment can become emotionally exhausted and cynical and these findings can be especially useful when identifying embitterment. Findings: It was found that embitterment related to higher burnout levels and more specifically emotional exhaustion and cynicism. No significant findings were revealed for the relationship between professional inefficacy and embitterment. Also, psychological contract breach was found to be a significant predictor of embitterment, supporting further the notion that perceptions of injustice can trigger feelings of embitterment. Results also showed that embitterment mediated the relationship between psychological contract breach and burnout. Recommendation for Researchers: The study highlights the notion that fairness is a key precursor of embitterment, and this finding is essential when developing interventions to prevent embitterment from arising. Future Research: Future research could use a longitudinal study design to unravel whether burnout represents a precondition or the consequence of embitterment. Future research should also include more objective measures. For example, it would be useful to pair self-report data with more objective measures on embitterment (e.g. clinical interviews).

Aim/Purpose: To explore the incidence of addiction potential within the Iranian public working population, describing how many Iranian public employees fall within the diagnostic categories of low, moderate, and high addiction potential. Also, to investigate the predicting role of occupational variables such as perceived stress, job security, and job satisfaction on addiction potential and belonging to low, moderate, and high addiction potential diagnostic categories. Background: Substance addiction among employees can lead to several negative consequences at the individual and organizational levels. Also, it is the fourth cause of death in Iran. However, few studies have been conducted on the topic among employees, and non among Iranian employees. Methodology: The study participants were 430 employees working in governmental offices of the North Khorasan province, Iran. Descriptive statistical analysis and multiple linear regression analysis were conducted to explore the incidence of addiction potential within the analyzed population and to investigate whether occupational variables such as perceived stress, job security, and job satisfaction predicted low, moderate, or high addiction potential. Contribution: This paper suggests that perceived stress might act as a risk factor for developing addiction, whereas job security and job satisfaction might be protective factors against the likelihood of addiction development. Findings: More than half of the sample showed moderate to high addiction potential. Perceived stress was positively related to addiction potential. Job security and job satisfaction were negatively related to addiction potential. Recommendation for Researchers: When addressing the topic of substance addiction, researchers should focus on the preventative side of investigating it; that is, addiction risk rather than already unfolded addiction. Also, researchers should be mindful of the cultural context in which studies are conducted. Future Research: Future research might investigate other relevant occupational predictors in relation to employee addiction potential, such as leadership style, work-life balance, and worktime schedule, or expand on the relevant causal chain by including personality traits such as neuroticism.

Aim/Purpose: The amount of information published has increased dramatically due to the information explosion. The issue of managing information as it expands at this rate lies in the development of information extraction technology that can turn unstructured data into organized data that is understandable and controllable by computers Background: The primary goal of named entity recognition (NER) is to extract named entities from amorphous materials and place them in pre-defined semantic classes. Methodology: In our work, we analyze various machine learning algorithms and implement K-NN which has been widely used in machine learning and remains one of the most popular methods to classify data. Contribution: To the researchers’ best knowledge, no published study has presented Named entity recognition for the Kikuyu language using a machine learning algorithm. This research will fill this gap by recognizing entities in the Kikuyu language. Findings: An evaluation was done by testing precision, recall, and F-measure. The experiment results demonstrate that using K-NN is effective in classification performance. Recommendation for Researchers: With enough training data, researchers could perform an experiment and check the learning curve with accuracy that compares to state of art NER. Future Research: Future studies may be done using unsupervised and semi-supervised learning algorithms for other resource-scarce languages.

Aim/Purpose: The purpose of this study is to determine the impact of pre-listening activities on Arabic-speaking EFL learners’ comprehension of spoken texts. Background: This study aims to contribute to the current research and to increase our understanding about the effectiveness of pre-listening activities. Specifically, this study seeks to clarify some of the research in this area that seems to be incongruent. Methodology: The study investigates two widely implemented activities in second language (L2) classrooms: vocabulary preteaching and content previewing. Ninety-three native-Arabic speaking EFL learners, whose proficiently levels were beginner, intermediate, or advanced, were randomly assigned to a control group or one of three experimental groups: the vocabulary-only (VO) group, content-only (CO) group, or vocabulary + content (VC) group. Each of the experimental groups received one of the treatments to determine which pre-listening activity was more effective and whether additional pre-listening activities yield additional comprehension. Listening comprehension of the aural text was measured by a test comprising 13 multiple-choice and true-false questions. Contribution: The present study provided additional explanations regarding the long-standing contradicting results about vocabulary preteaching and content previewing. Findings: The results showed that pre-listening activities had a positive impact on Arabic-speaking EFL learners’ listening comprehension, with the VO group significantly increasing their scores on the posttest compared to those of the control or other groups. Vocabulary preteaching was particularly beneficial for more advanced learners. With regard to which pre-listening activity contributed the most to better listening comprehension, vocabulary preteaching was the most effective. Content previewing did not increase comprehension for the CO group and had no additional benefit for the VC group. Recommendation for Researchers: This paper recommends that researchers explore new pre-listening activities that have never studied. Future research should be extended to include other nations and contextual situations to extend our knowledge about the effect of pre-listening activities. As far as listening comprehension can only be achieved when listeners are attentive and engaged, the listening text should be interesting and the lexical coverage of the listening text should be appropriate for all participants. Future Research: The results are to be interpreted carefully because they are limited by the students’ L2 proficiency, demographic, and cultural backgrounds (i.e., first language (L1) proficiency, age, gender, Middle Eastern culture). Results might be quite different if the study was conducted with different populations who have different life and language learning experiences (Vandergrift & Baker, 2015). Therefore, the results of this study indicate there is much room for improvement and a need for further research.

Aim/Purpose: The study aims to examine the mediating role of job motivation and affective and normative commitment on the relationship between perceived organizational support (POS) and job turnover intention. Background: POS refers to employees’ beliefs and perceptions concerning the extent to which the organization values their contributions, cares about their well-being, and fulfils their socio-emotional needs. To date, research has shown that employee turnover is a complex construct resulting from the interplay of both individual and organizational variables, such as motivation and climate. Methodology: Cross-sectional data were collected from 143 employees of an Italian industrial company. Paper-and-pencil questionnaires were used to assess respondents’ POS, job motivation, affective and normative organizational commitment, and turnover intentions. Contribution: Specifically, in this research, we aim at examining (i) the indirect effect of POS on turnover intention via (ii) job motivation and (iii) normative and affective commitment. Findings: Results show that high POS is associated with high levels of job motivation and affective and normative commitment, which in turn are negatively linked to turnover intentions. Recommendation for Researchers: Researchers should not lose sight of the importance of studying and delving into the concept of turnover intention given that, from an organizational point of view, losing personnel means losing competencies, which need to be replaced through assessment, selection, training, and development, processes that are often challenging and expensive. Future Research: Future research should further investigate the role of motivation and commitment, other than additional variables, for POS and turnover intention. Longitudinal studies and further testing are required to verify the causal processes stemming from our model. Future research could consider linking employees’ self-reported measures with objective data concerning turnover rates.

Table of Contents for Volume 26 of Informing Science: The International Journal of an Emerging Transdiscipline, 2023

Aim/Purpose: The aim of this paper is to examine Gen Z students located in a representative region of the United States when it comes to technology use, news and information gathering behaviors, civic engagement, and social concerns and whether differences exist based on institutional type. The purpose is to report this information so that academics can better understand the behaviors, priorities, and interests of current American students. Background: This paper investigates the mindset of Generation Z students living in the United States during a period of heightened civic unrest. Through the lens of the Theory of Generations, Uses and Gratifications Theory, and Intersectional Theory, this study aims to examine the Gen Z group and compare findings across populations. Methodology: An electronic survey was administered to students from 2019 through 2022. The survey included a combination of multiple responses, Likert scaled, dichotomous, open-ended, and ordinal questions. It was developed in the Survey Monkey system and reviewed by content and methodological experts to examine bias, vagueness, or potential semantic problems. The survey was pilot-tested in 2018 before implementation in order to explore the efficacy of the research methodology. It was then modified accordingly before widespread distribution to potential participants. The surveys were administered to students enrolled in classes taught by the authors, all of whom are educators. Participation was voluntary, optional, and anonymous. Contribution: This paper provides insight into the mindset of Generation Z students living in the United States, which is helpful to members of academia who should be informed about the current generation of students in higher education. Studying Generation Z helps us understand the future and can provide insight into the shifting needs and expectations of society. Findings: According to the findings, Gen Z are heavy users of digital technologies who use social media as their primary source for gathering news about current events as well as information for schoolwork. The majority of respondents considered themselves to be social activists. When institutional type was considered, there were notable differences with the students at the Historically Black College or University (HBCU), noting the greatest concern with a number of pressing issues, including racial justice/Black Lives Matter, women’s rights, gun violence, immigration reform, and human trafficking. Less significance across groups was found when LGBTQIA+ rights and climate change were considered. Recommendation for Researchers: As social media continues to proliferate in daily life and become a vital means of news and information gathering, additional studies such as the one presented here are needed. In other countries facing similarly turbulent times, measuring student interest, awareness, and engagement is highly informative. Future Research: Future research will explore the role that influencers have in opinion formation and the information-gathering habits of Gen Z.

Aim/Purpose: This review paper aims to unveil some underlying machine-learning classification algorithms used for political election predictions and how stack ensembles have been explored. Additionally, it examines the types of datasets available to researchers and presents the results they have achieved. Background: Predicting the outcomes of presidential elections has always been a significant aspect of political systems in numerous countries. Analysts and researchers examining political elections rely on existing datasets from various sources, including tweets, Facebook posts, and so forth to forecast future elections. However, these data sources often struggle to establish a direct correlation between voters and their voting patterns, primarily due to the manual nature of the voting process. Numerous factors influence election outcomes, including ethnicity, voter incentives, and campaign messages. The voting patterns of successors in regions of countries remain uncertain, and the reasons behind such patterns remain ambiguous. Methodology: The study examined a collection of articles obtained from Google Scholar, through search, focusing on the use of ensemble classifiers and machine learning classifiers and their application in predicting political elections through machine learning algorithms. Some specific keywords for the search include “ensemble classifier,” “political election prediction,” and “machine learning”, “stack ensemble”. Contribution: The study provides a broad and deep review of political election predictions through the use of machine learning algorithms and summarizes the major source of the dataset in the said analysis. Findings: Single classifiers have featured greatly in political election predictions, though ensemble classifiers have been used and have proven potent use in the said field is rather low. Recommendation for Researchers: The efficacy of stack classification algorithms can play a significant role in machine learning classification when modelled tactfully and is efficient in handling labelled datasets. however, runtime becomes a hindrance when the dataset grows larger with the increased number of base classifiers forming the stack. Future Research: There is the need to ensure a more comprehensive analysis, alternative data sources rather than depending largely on tweets, and explore ensemble machine learning classifiers in predicting political elections. Also, ensemble classification algorithms have indeed demonstrated superior performance when carefully chosen and combined.

Table of Contents for Volume 27 of Informing Science: The International Journal of an Emerging Transdiscipline, 2024

[Meg O’Neill] 00:08 Hello and welcome to the Berkeley Technology Law Journal podcast. My name is Meg O’Neill and I am one of the editors of the podcast. Today we are excited to share with you a conversation between Berkeley Law LLM student Franco Dellafiori, and Professor Bertrall Ross. Professor ...

The post Berkeley Technology Law Journal Podcast: Will ChatGPT Tell Me How to Vote? Democracy & AI with Professor Bertrall Ross appeared first on Berkeley Technology Law Journal.

This work demonstrates a hybrid deep learning (DL) model for lung cancer (LC) detection using CT images. Firstly, the input image is passed to the pre-processing stage, where the input image is filtered using a BF and the obtained filtered image is subjected to lung lobe segmentation, where segmentation is done using squeeze U-SegNet. Feature extraction is performed, where features including entropy with fuzzy local binary patterns (EFLBP), local optimal oriented pattern (LOOP), and grey level co-occurrence matrix (GLCM) features are mined. After completing the extracting of features, LC is detected utilising the hybrid efficient-ShuffleNet (HES-Net) method, wherein the HES-Net is established by the incorporation of EfficientNet and ShuffleNet. The presented HES-Net for LC detection is investigated for its performance concerning TNR, and TPR, and accuracy is established to have acquired values of 92.1%, 93.1%, and 91.3%.

Takahito YOSHIDA,Takaharu YAGUCHI,Takashi MATSUBARA, Vol.E107-D, No.11, pp.1458-1462
Accurately simulating physical systems is essential in various fields. In recent years, deep learning has been used to automatically build models of such systems by learning from data. One such method is the neural ordinary differential equation (neural ODE), which treats the output of a neural network as the time derivative of the system states. However, while this and related methods have shown promise, their training strategies still require further development. Inspired by error analysis techniques in numerical analysis while replacing numerical errors with modeling errors, we propose the error-analytic strategy to address this issue. Therefore, our strategy can capture long-term errors and thus improve the accuracy of long-term predictions.
Publication Date: 2024/11/01

Myung-Hyun KIM,Seungkwang LEE, Vol.E107-D, No.11, pp.1449-1452
White-box cryptographic implementations often use masking and shuffling as countermeasures against key extraction attacks. To counter these defenses, higher-order Differential Computation Analysis (HO-DCA) and its variants have been developed. These methods aim to breach these countermeasures without needing reverse engineering. However, these non-invasive attacks are expensive and can be thwarted by updating the masking and shuffling techniques. This paper introduces a simple binary injection attack, aptly named clear & return, designed to bypass advanced masking and shuffling defenses employed in white-box cryptography. The attack involves injecting a small amount of assembly code, which effectively disables run-time random sources. This loss of randomness exposes the unprotected lookup value within white-box implementations, making them vulnerable to simple statistical analysis. In experiments targeting open-source white-box cryptographic implementations, the attack strategy of hijacking entries in the Global Offset Table (GOT) or function calls shows effectiveness in circumventing run-time countermeasures.
Publication Date: 2024/11/01

Qinghua WU,Weitong LI, Vol.E107-D, No.11, pp.1422-1432
Multi-focus image fusion involves combining partially focused images of the same scene to create an all-in-focus image. Aiming at the problems of existing multi-focus image fusion algorithms that the benchmark image is difficult to obtain and the convolutional neural network focuses too much on the local region, a fusion algorithm that combines local and global feature encoding is proposed. Initially, we devise two self-supervised image reconstruction tasks and train an encoder-decoder network through multi-task learning. Subsequently, within the encoder, we merge the dense connection module with the PS-ViT module, enabling the network to utilize local and global information during feature extraction. Finally, to enhance the overall efficiency of the model, distinct loss functions are applied to each task. To preserve the more robust features from the original images, spatial frequency is employed during the fusion stage to obtain the feature map of the fused image. Experimental results demonstrate that, in comparison to twelve other prominent algorithms, our method exhibits good fusion performance in objective evaluation. Ten of the selected twelve evaluation metrics show an improvement of more than 0.28%. Additionally, it presents superior visual effects subjectively.
Publication Date: 2024/11/01

Product innovation has become a crucial part of the sustainability of the coconut agro-industry in Indonesia, covering upstream and downstream sides. To overcome this challenge, it is necessary to create several model stages using a hybrid method that combines machine learning based on multiple criteria decision making and mass balance analysis. The research case study was conducted in Tembilahan district, Riau province, Indonesia, one of the primary coconut producers in Indonesia. The analysis results showed that potential products for domestic customers included coconut milk, coconut cooking oil, coconut chips, coconut jelly, coconut sugar, and virgin coconut oil. Furthermore, considering the experts, the most potential product to be developed was coconut sugar with a weight of 0.26. Prediction of coconut sugar demand reached 13,996,607 tons/year, requiring coconut sap as a raw material up to 97,976,249.

Reinforcement Quantum Annealing: A Quantum-Assisted Learning Automata Approach We introduce the reinforcement quantum annealing (RQA) scheme in which an intelligent agent interacts with a quantum annealer that plays the stochastic environment role of learning automata and tries to iteratively find better Ising Hamiltonians for the given problem of interest. As a proof-of-concept, we propose a […]

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Results using the reinforcement learning technique on two SAT benchmarks using a D-Wave 2000Q quantum processor showed significantly better solutions with fewer samples compared to the best-known quantum annealing techniques.

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Social exchange theory is a broad theory that has been used to explain trust as an outcome of various exchange relationships, and research commonly presumes trust exists between exchange partners that have prior relationships. In this paper, we contribute to social exchange theory by isolating the trust outcomes of interorganizational exchanges from other outcomes emphasized by learning and knowledge-based perspectives, and by specifying important boundary conditions for the emergence of trust in interorganizational exchanges. We make such a theoretical contribution within the domain of strategic alliances by investigating the effects of previous alliance agreements, or prior ties, between the partnering firms. We find that prior ties generally lead to learning about a partner's anticipated behavioral patterns, which helps a firm predict when self-interested behavior may occur and know how to interact with the partner during the coordination and execution of the alliance tasks. By contrast, it is evident that the kind of trust emphasized in social exchange theory is not generally rooted in prior ties and only emerges from prior relationships under certain conditions. We discuss the implications of these findings for research on social exchange theory and for delineating the theory's domain of applicability.

'What's going on?' Within the context of our critically-informed teaching practice, we see moments of deep learning and reflexivity in classroom discussions and assessments. Yet, these moments of criticality are interspersed with surface learning and reflection. We draw on dichotomous, linear developmental, and messy explanations of learning processes to empirically explore the learning journeys of 20 international Chinese and 42 domestic New Zealand students. We find contradictions within our own data, and between our findings and the extant literature. We conclude that expressions of surface learning and reflection are considerably more complex than they first appear. Moreover, developing critical reflexivity is a far more subtle, messy, and emotional experience than previously understood. We present the theoretical and pedagogical significance of these findings when we consider the implications for the learning process and the practice of management education.

Do firms learn from their failed innovation attempts? Answering this question is important because failure is an integral part of exploratory learning. In this study, we explore whether and under what circumstances firms learn from their small failures in experimentation. Building on organizational learning literature, we examine the conditions under which prior failures influence firms' R&D output amount and quality. An empirical analysis of voluntary patent expirations (i.e., patents that firms give up by not paying renewal fees) in 97 pharmaceutical firms between 1980 and 2002 shows that the number, importance, and timing of small failures are associated with a decrease in R&D output (patent count) but an increase in the quality of the R&D output (forward citations to patents). Exploratory interviews suggest that the results are driven by a multi-level learning process from failures in pharmaceutical R&D. The findings contribute to the organizational learning literature by providing a nuanced view of learning from failures in experimentation.

We consider how social and psychological connections among CEOs explain the propensity for corporate leaders to distance themselves socially from journalists who engage in negative reporting about firm leadership at other companies, and we examine the consequences for the valence of journalists' subsequent coverage. Our theoretical framework suggests that journalists who have engaged in negative coverage of a firm's leadership and strategy are especially likely to experience distancing from other leaders who (i) have friendship ties to the firm's CEO, (ii) are demographically similar to the CEO on salient dimensions, or (iii) are socially identified with the CEO as a fellow member of the corporate elite. Our theory and findings ultimately suggest that, due to the multiple sources of social identification between CEOs, journalists who engage in negative coverage of firm leadership tend to experience social distancing from multiple CEOs, and such distancing has a powerful influence on the valence of journalists' subsequent reporting about firm leadership and strategy across all the firms that they cover. We also extend our theoretical framework to suggest how the effect of social distancing on the valence of journalists' coverage is moderated by the early and late stages of a journalist's career.