Aim/Purpose: Cyberbullying (CB) is an ongoing phenomenon that affects youth in negative ways. Using online news articles to provide information to schools can help with the development of comprehensive cyberbullying prevention campaigns, and in restoring faith in news reporting. The inclusion of online news also allows for increased awareness of cybersafety issues for youth. Background: CB is an inherent problem of information delivery and security. Textual analysis provides input into prevention and training efforts to combat the issue. Methodology: Text extraction and text analysis methods of term and concept extraction; text link analysis and sentiment analysis are performed on a body of news articles. Contribution: News articles are determined to be a major source of information for comprehensive cyberbullying prevention campaigns. Findings: Online news articles are relatively neutral in their sentiment; terms and topic extraction provide fertile ground for information presentation and context. Recommendation for Researchers: Researchers should seek support for research projects that extract timely information from online news articles. Future Research: Refinement of the terms and topics analytic model, as well as a system development approach for information extraction of online CB news.

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: The focus on human well-being has attracted the attention of consumers, organizations, and marketers to understand the various facets of Front of Pack Nutrition Labelling (FOPNL). This study examines the overall research trends in the FPONL domain and identifies the new research areas. Background: FOPNL is becoming increasingly popular and its influence has been widely examined. Different label schemes have been introduced across different regions in the world. Nevertheless, such interventions are limited in developing economies. Methodology: This study uses bibliometric analysis methods to explore Front of Pack Nutrition Labelling (FOPNL) trends using 602 articles published in selected business journals. Contribution: The paper identifies the new FOPNL research avenues. The study indicates that FOPNL has become a crucial research area, and more research is needed at the organization, managerial, and policy levels. Findings: The study identifies four themes. The first theme identified is the effect of harmful nutrients on health and the role of FOPNL nutrition in changing eating habits. The second theme focused on the government's policy and implementation of FOPNL nutrition labeling regulations. The third theme is dedicated to the work on attention, perception, understanding, and influence of multiple traffic light schemes. The fourth theme relates to the Health Star Rating, Nutri Score, and Healthier Choice FOPNL nutrition labeling schemes. Overall, the paper informs consumers, manufacturers, and regulators about the recent trends in the FOPNL research. Recommendation for Researchers: Though FOPNL has been widely examined in the health and nutrition domain, however, limited research has been done in the marketing domain. Research using neuroscientific methods (e.g. eye tracking) should provide more robust findings. Future Research: There is limited research on FOPNL from emerging economies. Future research can examine how FOPNL may influence people, policy, and private entities.

Aim/Purpose: The aim of the present paper is to present a Noise Decision (ND) scale. First, it reports the development and validation of the instrument aimed at examining organizational factors that have an influence on decision-making and the level of noise. Second, it validates this rating scale by testing its discriminant and convergent validity with other measures to assess decision-making qualities. Background: According to the literature, the concept of noise is the unwanted variability present in judgments. The notion of noise concerns the systematic influence to which individuals are exposed in their environment. The literature in the field has found that noise reduction improves the perception of work performance. Methodology: The first study involves the development of a scale (composed of 36 items) consisting of semi-structured interviews, item development, and principal component analysis. The second study involves validation and convergent validity of this scale. In the first study, there were 43 employees from three medium-sized Italian multinationals. For the second study, a sample of 867 subjects was analysed. Contribution: This paper introduces the first scale aimed at assessing noise within individuals and, in the organizational context, within employees and employers. Findings: Results show that the estimated internal reliability for each of the ND subscales and also the correlations between the subscales were relatively low, suggesting that ND correctly measures the analyzed components. Furthermore, the validation of the psychometric qualities of the ND allowed for the assertion that the influence of noise is present in the decision-making process within the context of work environments, validating the initial hypotheses. Recommendation for Researchers: This paper aims to improve theory and research on decision-making; for example, by providing a possible implementation for scales for evaluating decision-making skills. Furthermore, detecting and limiting noise with a systematic method could improve both the quality of decisions and the quality of thought processes. Future Research: Given the measurement of ND, the study can be a starting point for future research on this topic. Since there is no literature about this construct, it would be necessary to spend more time researching, so that the topic becomes clearer. System noise has been tested by some researchers with a “noise audit,” which means giving the same problem to different people and measuring the differences in their responses. Repeating this kind of audit in conjunction with the ND in a specific work environment could be helpful to detect but also measure the influence of noise.

Aim/Purpose: The aim of this qualitative study is to explore social workers’ representations of hate speech (HS), the effects it has on the community, and socio-educational actions aimed at combating it. Background: Hate speech is any form of communication that promotes discrimination, hostility, or violence towards individuals or groups based on their identity. Although its spread is facilitated by particular characteristics of the online environment (such as anonymity and ubiquity), HS has pervasive consequences even in offline reality. In the last year, several community-based projects involving social workers have been implemented to address the problem. Professionals who work with the community play a crucial strategic role in the fight against HS. Therefore, it is imperative to begin by considering their perspective to gain a better understanding of HS and how it can be controlled. Methodology: Following a psycho-sociological perspective, six focus groups were conducted with 42 social workers (19 females and 23 males) belonging to associations or organizations of a different nature, such as NGOs, local social promotion organizations, universities, private social organizations, whose mission included the theme of countering hate speech. Contribution: There are no studies in the literature that consider the views of operators working to counter hate speech within communities. Our study contributes to deepening the knowledge of the phenomenon and identifying the most suitable strategies to combat it, starting from an approach that does not only focus on the online or offline dimension but on an integrated “onlife” approach. The study offers an outline of how hate speech affects the daily lives of the communities in the cities of Torino, Palermo, and Ancona. Additionally, it proposes a grassroots strategy to address hate speech. Findings: The results suggest that strategies effective in countering hate speech in offline contexts may not be effective in online environments. The technological revolution brought about by social media has significantly expanded the potential audience while weakening traditional communities. Addressing hate speech in the present context requires efforts to rebuild fragmented communities, gaining a thorough understanding of how the new virtual public space operates, and prioritizing hate speech as a specific concern only after these initial steps. Recommendation for Researchers: Hate speech represents a violation of human rights and a threat to freedom of expression. The spread of hateful messages has a significant impact on society, as it can negatively influence social cohesion, diversity, and inclusion. Understanding the causes and consequences of hate speech can help develop effective strategies to prevent and counter it, which is a crucial challenge for both research and society as a whole. Studying hate speech should involve the use of interdisciplinary methodologies. Future Research: Future research should focus on comparative analysis at the European Union level to assess the ability of civil society in other countries to develop effective strategies against hate speech.

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: Parasocial relationships are today established on social media between influencers and their followers. While marketing effects are well-researched, little is known about the meaning of such relationships and the psychological mechanisms behind them. This study, therefore, explores the questions: “How do followers on Instagram interpret explicit fiction cues from influencers?” and “What does this reveal about the meaning of parasocial attachment?” Background: With a billion-dollar advertising industry and leading in influencing opinion, Instagram is a significant societal and economic player. One factor for the effective influence of consumers is the relationship between influencer and follower. Research shows that disclosing advertisements surprisingly does not harm credibility, and sometimes even leads to greater trustworthiness and, in turn, willingness to purchase. While such reverse dynamics are measurable, the mechanisms behind them remain largely unexplored. Methodology: The study follows an explorative approach with in-depth interviews, which are analyzed with Mayring’s content analysis under a reconstructive paradigm. The findings are discussed through the lens of critical psychology. Contribution: Firstly, this study contributes to the understanding of the communicative dynamics of influencer-follower communication alongside the reality-fiction-gap model, and, secondly, it contributes empirical insights through the analysis of 22 explorative interviews. Findings: The findings show (a) how followers rationalize fiction cues and justify compulsive decision-making, (b) how followers are vulnerable to influences, and (c) how parasocial attachment formation overshadows rational logic and agency. The findings are discussed with regard to mechanisms, vulnerabilities, rationalizations and cognitive bias, and the social self, as well as the ethics of influencer marketing and politics. Recommendation for Researchers: The contribution is relevant to relationship research, group dynamics and societal organizing, well-being, identity, and health perspectives, within psychology, sociology, media studies, and pedagogy to management. Future Research: Future research might seek to understand more about (a) quantifiable vulnerabilities, such as attachment styles, dispositions, and demographics, (b) usage patterns and possible factors of prevention, (c) cognitive and emotional mechanisms involved with larger samples, (d) the impact on relationships and well-being, and (e) possible conditions for the potential of parasocial attachment.

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: This paper examines the transformative impact of Artificial Intelligence (AI) on professional skills in organizations and explores strategies to address the resulting challenges. Background: The rapid integration of AI across various sectors is automating tasks and reducing cognitive workload, leading to increased productivity but also raising concerns about job displacement. Successfully adapting to this transformation requires organizations to implement new working models and develop strategies for upskilling and reskilling their workforce. Methodology: This review analyzes recent research and practice on AI's impact on human skills in organizations. We identify key trends in how AI is reshaping professional competencies and highlight the crucial role of transversal skills in this evolving landscape. The paper also discusses effective strategies to support organizations and guide workers through upskilling and reskilling processes. Contribution: The paper contributes to the existing body of knowledge by examining recent trends in AI's impact on professional skills and workplaces. It emphasizes the importance of transversal skills and identifies strategies to support organizations and workers in meeting upskilling and reskilling challenges. Our findings suggest that investing in workforce development is crucial for ensuring that the benefits of AI are equitably distributed among all stakeholders. Findings: Our findings indicate that organizations must employ a proactive approach to navigate the AI-driven transformation of the workplace. This approach involves mapping the transversal skills needed to address current skill gaps, helping workers identify and develop skills required for effective AI adoption, and implementing processes to support workers through targeted training and development opportunities. These strategies are essential for ensuring that workers' attitudes and mental models towards AI are adaptable and prepared for the changing labor market. Recommendation for Researchers: We emphasize the need for researchers to adopt a transdisciplinary approach when studying AI's impact on the workplace. Given AI's complexity and its far-reaching implications across various fields including computer science, mathematics, engineering, and behavioral and social sciences, integrating diverse perspectives is crucial for a holistic understanding of AI's applications and consequences. Future Research: Looking ahead, further research is needed to deepen our understanding of AI's impact on human skills, particularly the role of soft skills in AI adoption within organizations. Future studies should also address the challenges posed by Industry 5.0, which is expected to bring about even more extensive integration of new technologies and automation.

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: With a focus on promoting sustainable career paths, this article investigates the intricate relationship between age diversity management and techno-complexity, emphasizing the pivotal role of a supportive work environment. Background: In the modern workplace, the dynamics of age diversity emerge as a crucial element influencing the well-being and productivity of employees, particularly amidst the swiftly evolving digital landscape. This becomes especially pertinent when considering workers’ unique challenges adapting to technological advancements. Methodology: Utilizing a cross-sectional design, data were collected from 160 employees in an Italian multinational company within the metalworking sector. Contribution: This study provides valuable insights into the complex dynamics between the aging climate, colleagues’ support, and techno-complexity. It emphasized the importance of considering the direct effects of organizational factors and their in-direct influences through social dynamics and support structures within the workplace. Findings: The results revealed the mediating role of colleagues’ support in the relationship between the aging climate and techno-complexity. These findings highlight the importance of a supportive work environment in the context of sustainable career development, contributing to a comprehensive understanding of diversity management within the modern digital era. Recommendation for Researchers: Our results open to a series of implications and future directions. First, the unexpected finding regarding the direct relationship between the aging climate and technostress calls for a deeper exploration of the intricacies involved. Future studies could delve into specific organizational contexts, technological demands, and individual differences that may modulate this relationship. Future Research: Future studies could delve into specific organizational contexts, technological demands, and individual differences that may modulate this relationship.

Aim/Purpose: This paper applies informing science principles to analyze the evolution of United States (U.S.) healthcare delivery, exploring how policy shifts, technological advancements, and changing practices have transformed informing processes within this complex system. By examining healthcare delivery through a transdisciplinary lens, we aim to enhance the understanding of intricate informing environments and their dynamics. Background: The U.S. healthcare system epitomizes a complex, evolving transdisciplinary domain intersecting information systems, policy, economics, and public health. Recent transformations in stakeholder information flow necessitate an informing science perspective to comprehend these changes fully. Methodology: We synthesize literature on U.S. healthcare delivery changes, employing informing science frameworks such as Cohen’s “informing environment” concept to analyze the evolution of healthcare informing processes. Contribution: This study expands informing science theory by examining how changes in a complex transdisciplinary system impact information flow, decision-making, and stakeholder interactions. The results provide insights into challenges and opportunities within evolving informing environments. Findings: Our analysis reveals significant alterations in the U.S. healthcare informing landscape due to policy, regulatory, and technological changes. We identify key transformations in client-sender-delivery system relationships, shifts in information asymmetry, and the emergence of novel informing channels and barriers. Recommendation for Researchers: Future studies should develop informing science models capable of capturing the complexity and dynamism of healthcare delivery systems, particularly amidst rapid technological and policy changes. Future Research: Further investigation is needed into how emerging technologies reshape healthcare informing processes and their impact on care quality, accessibility, and cost-effectiveness.

Aim/Purpose: To provide a systematic approach to defining task complexity using a three worlds model previously introduced in informing science research. Background: The task complexity construct presents researchers with a quandary. While it appears useful on the surface, repeated attempts to define it rigorously have failed to gain traction in the broader research community. The level of inconsistency between definitions is shown to have changed little in the past 20 years. Methodology: Using a common framework that treats task complexity as a latent construct residing between sources and outcomes, moderated by both task familiarity and task discretion, separate models for each of the three worlds are developed. Contribution: Our paper proposes a potential path forward by showing how many issues in past task complexity research can be reconciled by framing the construct according to the three worlds model: the world we experience, the world of human artifacts, and the “real world.” Findings: The framework defines experienced complexity as occurring in the mind of the task performer while performing a single task instance, intrinsic complexity as a function of the internal characteristics of the problem space used to perform a bounded set of task instances, and extrinsic complexity as the ruggedness of the fitness landscape in which the task is performed. Recommendation for Researchers: It offers a path to convergence for definitions of task complexity. Future Research: The three worlds of task complexity can potentially be applied to many practical problems.

Aim/Purpose: This study aims to explore students’ digital entrepreneurial intention (DEI) in Kuwait. Specifically, the aim is twofold: (i) to identify and examine the factors influencing and predicting students’ DEI, and (ii) to validate a model of DEI. Background: The advent of modern digital technologies has provided entrepreneurs with many opportunities to establish and expand their firms through online platforms. Although the existing literature on DEI has explored various factors, certain factors that could be linked to DEI have been neglected, and others have not been given sufficient attention. Nonetheless, there has been little research on students’ DEI, particularly in Kuwait. Methodology: To fulfill the research’s aims, a study was conducted using a quantitative method (a survey of 305 students at a non-profit university in Kuwait). Contribution: This study aimed to fill the research gap on the limited DEI research among Kuwait’s students. Several recommendations were suggested to improve the DEI among students in Kuwait. Findings: The study identified five factors that could influence an individual’s intention to engage in digital entrepreneurship. These factors include self-perceived creativity, social media use, risk-taking and opportunity recognition, digital entrepreneurship knowledge, and entrepreneurial self-perceived confidence. Significant solid correlations were between all five identified factors and DEI. However, only self-perceived creativity and entrepreneurial self-perceived confidence were identified as significant positive predictors of DEI among undergraduates in Kuwait. Nevertheless, the main contributor to this intention was the students’ self-perceived confidence as entrepreneurs. Recommendation for Researchers: Researchers should conduct further longitudinal studies to understand better the dynamic nature of DEI and execution. Future Research: Additional research is required to utilize probability sampling approaches and increase the sample size for more generalizable findings.

Aim/Purpose: The paper discusses various factors contributing to disagreements, such as differing experiences, perspectives, and historical narratives, leading to disagreements within families and societies. It explores how beliefs, values, and biases feed into disagreements, with confirmation bias affecting decision-making and the media. Cultural values also play a role, showcasing conflicts between meritocracy and inclusivity in ethical decision-making. Haidt's Moral Foundations Theory highlights differences in value priorities between Western and Eastern societies. The impact of Western values like rationalism, freedom, and tolerance, under threat from Marxist illiberalism on campuses, is dis-cussed. The text also delves into disinformation, emotions in warfare, and the use of fake information and images for propaganda purposes. The need for diligent reporting to avoid spreading disinformation is emphasized, given its potential to create misconceptions and harm diplomatic relations.

Aim/Purpose: The aim of the present study is to investigate how pre-game superstition and anxiety can drive the consumption and purchase of sports products and objects by triathletes. Methodology: We tested our hypotheses via a cross-sectional study on a sample of N=124 triathletes. Contribution: The originality of our work stands in the provision of empirical evidence on the role of superstition and anxiety in characterized consumer decision-making of triathletes. Theoretically and practically, our results can extend our knowledge of the role of cognitive factors in consumer behaviors among athletes. Findings: The results of the Structural Equation Modelling provided evidence of our hypothesized relationship between pre-game anxiety and superstition, and cognitive biases. Pre-game anxiety increases the level of incidence of specific cognitive biases characterized by intuitive and implicit thinking, while superstition leads to more rational and personal cognitive biases, which affect their purchasing of sports products before games and competitions.

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

Labor pain and its relieving methods is one of the anxieties of mothers having a great impact on the quality of care during delivery as well as the patients' satisfaction. The propensity of using non-medicinal pain relief methods is increasing. The present study aimed to compare the effect of Acupressure at two GB-21 and SP06 points on the severity of labor pain. In this quasi-experimental single blind study started on December 2010 and ended on June 2011 in which 150 primiparous women were divided into three groups of Acupressure at GB-21 point, Acupressure at SP-6 point and control group. The intervention was carried out for 20 min at 3-4 and 20 min at 7-8 cm dilatation of Cervix. The pain severity was measured by Visual Analog Scale before and immediately, 30 and 60 min after the intervention. Then, the data were statistically analyzed. No significant difference was found among the 3 groups regarding the pain severity before the intervention. However, the pain severity it was reduced at 3-4 and 7-8 cm dilatation immediately, 30 and 60 min after the intervention in the two intervention groups compared to the control group (p<0.001). Nonetheless, no statistically significant difference was observed between the two intervention groups (p = 0.93). The results of the study showed that application of Acupressure at two GB-21 and SP-6 points was effective in the reduction of the severity of labor pain. Therefore, further studies are recommended to be performed on the application of Acupressure together with non-medicinal methods.

This article is part of the 2024 BCLT-BTLJ-CMTL Symposium.  Angela R. Riley When my tribe, the Citizen Potawatomi Nation of Oklahoma (CPN), established an Eagle Aviary to protect and care for injured eagles that could no longer survive in the wild, it did so with a few goals in mind. ...

The post Tribal Self-Determination and the Protection of Cultural Property appeared first on Berkeley Technology Law Journal.

This article is part of the 2024 BCLT-BTLJ-CMTL Symposium.  Leo Yu The national security concerns surrounding TikTok appear straightforward: it is China. To many policymakers and scholars, the mere connection to China warrants severe measures, including either divestment to an American firm or a complete shutdown. What renders China’s involvement ...

The post TikTok and the Control over the Means of Production in the Fourth Industrial Revolution appeared first on Berkeley Technology Law Journal.

[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.

Mental illnesses are common among college students as well as their non-student peers, and the number and severity of these problems are increasing. It can be difficult to identify people suffering from mental illness and get the help they need early. So in this paper, the SqueezeR_MobileNet method is proposed. It performs feature fusion and early mental health prediction. Initially, outliers in the input data are detected and removed. After that, using missing data imputation and Z-score normalisation the pre-processing phase is executed. Next to this, for feature fusion, a combination of the Soergel metric and deep Kronecker network (DKN) is used. By utilising bootstrapping data augmentation is performed. Finally, early mental health prediction is done using SqueezeR_MobileNet, which is the incorporation of residual SqueezeNet and MobileNet. The devised approach has reached the highest specificity of 0.937, accuracy of 0.911 and sensitivity of 0.907.

Nat PAVASANT,Takashi MORITA,Masayuki NUMAO,Ken-ichi FUKUI, Vol.E107-D, No.11, pp.1453-1457
We proposed a procedure to pre-process data used in a vector autoregressive (VAR) modeling of a temporal point process by using kernel density estimation. Vector autoregressive modeling of point-process data, for example, is being used for causality inference. The VAR model discretizes the timeline into small windows, and creates a time series by the presence of events in each window, and then models the presence of an event at the next time step by its history. The problem is that to get a longer history with high temporal resolution required a large number of windows, and thus, model parameters. We proposed the local density estimation procedure, which, instead of using the binary presence as the input to the model, performed kernel density estimation of the event history, and discretized the estimation to be used as the input. This allowed us to reduce the number of model parameters, especially in sparse data. Our experiment on a sparse Poisson process showed that this procedure vastly increases model prediction performance.
Publication Date: 2024/11/01

Keitaro NAKASAI,Shin KOMEDA,Masateru TSUNODA,Masayuki KASHIMA, Vol.E107-D, No.11, pp.1444-1448
To automatically measure the mental workload of developers, existing studies have used biometric measures such as brain waves and the heart rate. However, developers are often required to equip certain devices when measuring them, and can therefore be physically burdened. In this study, we evaluated the feasibility of non-contact biometric measures based on the nasal skin temperature (NST). In the experiment, the proposed biometric measures were more accurate than non-biometric measures.
Publication Date: 2024/11/01

Naoya NEZU,Hiroshi YAMADA, Vol.E107-D, No.11, pp.1408-1421
Modern memory devices such as DRAM are prone to errors that occur because of unintended bit flips during their operation. Since memory errors severely impact in-memory key-value stores (KVSes), software mechanisms for hardening them against memory errors are being explored. However, it is hard to efficiently test the memory error handling code due to its characteristics: the code is event-driven, the handlers depend on the memory object, and in-memory KVSes manage various objects in huge memory space. This paper presents MemFI that supports runtime tests for the memory error handlers of in-memory KVSes. Our approach performs the software fault injection of memory errors at the memory object level to trigger the target handler while smoothly carrying out tests on the same running state. To show the effectiveness of MemFI, we integrate error handling mechanisms into a real-world in-memory KVS, memcached 1.6.9 and Redis 6.2.7, and check their behavior using the MemFI prototypes. The results show that the MemFI-based runtime test allows us to check the behavior of the error handling mechanisms. We also show its efficiency by comparing it to other fault injection approaches based on a trial model.
Publication Date: 2024/11/01

One of the main tasks in tax administration is debt management. The main goal of this function is tax due collection. Statements are processed in order to select strategies to use in the debt management process to optimise the debt collection process. This work proposes to carry out a data mining process to predict debts of taxpayers with high probability of non-payment. The data mining process identifies high-risk debts using a survival analysis on a dataset from a tax administration. Three groups of tax debtors with similar payment behaviour were identified and a success rate of up to 90% was reached in estimating the payment time of taxpayers. The concordance index (C-index) was used to determine the performance of the constructed model. The highest prediction rate reached was 90.37% corresponding to the third group.

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.

The Republic of India has witnessed an enormous leap in financial transactions after a sudden demonetisation in 2016. The study represents an in-depth analysis of the factors influencing e-wallets usage post-COVID situation covering the National Capital Region. The scientifically collected data were subjected to Pearson's correlation to recognise the correlation amongst the selected e-wallets. The usage of e-wallets is observed mainly during recharge, UPI payments, and utility payments. Through psychometric response and interaction analysis, six factors were selected and examined for data distribution and stable observation using standard deviation and variance coefficient. The coefficient of variance for six factors was observed ≤ 1. The weight of the factors noted to be secured way (0.184), to take advantage of cashback (0.182), low risk of theft (0.169), fast service (0.1689), ease to use (0.156), and saves time (0.139) using principal component eigenvectors analysis. Freecharge and Tez wallets reveal a maximum 99.2% correlation.

Research growth in organisational behaviour research, has increased the importance of paying attention to anti-citizenship behaviours. The current research with the aim of quantitative combination, has examined the results of research in effect of underlying factors of organisational anti-citizenship behaviours using meta-analysis method and CMA2 software and 55 articles during the time period of 2000-2020. The results showed a positive significant link between underlying factors of organisational anti-citizenship behaviours and occurrence of these behaviours and this influence was 0.389, 0.338, 0514 and 0.498 (structural, organisational, managerial, employment and professional and socio-economic and cultural factors). The level of connection found relating to each four occurrences is '68 links, 49 links, 93 links and 71 links'. Findings indicate that minute attention has been paid to organisational anti-citizenship behaviours, especially to job and professional factors in research works. Research should be conducted to control and manage these behaviours more purposefully in organisations.

In Italy, the reimbursement provided to healthcare organisations for medical and surgical procedures is based on the diagnosis related group weight (DRGW), which is an increasing function of the complexity of the procedures. This makes the reimbursement an upper unlimited function. This model does not include the relation of the volume with the complexity. The paper proposes a mathematical model for the optimisation of the reimbursement by determining the optimal mix of volume/procedure, considering the relation volume/complexity and DRGW/complexity. The decreasing, linear, and increasing returns to scale have been defined, and the optimal solution found. The comparison of the model with the traditional approach shows that the proposed model helps the healthcare system to discern the quantity of the reimbursement to provide to health organisations, while the traditional approach, neglecting the relation between the volume and the complexity, can result in an overestimation of the reimbursement.

Background and Objective: In response to the challenges of poor mapping outcomes and susceptibility to obstacles encountered by indoor mobile vehicles relying solely on pure cameras or pure LiDAR during their movements, this paper proposes an obstacle avoidance method for indoor mobile vehicles that integrates image and LiDAR data, thus achieving obstacle avoidance for mobile vehicles. Materials and Methods: This method combines data from a depth camera and LiDAR, employing the Gmapping SLAM algorithm for environmental mapping, along with the A* algorithm and TEB algorithm for local path planning. In addition, this approach incorporates gesture functionality, which can be used to control the vehicle in certain special scenarios where “pseudo-obstacles” exist. The method utilizes the YOLO V3 algorithm for gesture recognition. Results: This paper merges the maps generated by the depth camera and LiDAR, resulting in a three-dimensional map that is more enriched and better aligned with real-world conditions. Combined with the A* algorithm and TEB algorithm, an optimal route is planned, enabling the mobile vehicles to effectively obtain obstacle information and thus achieve obstacle avoidance. Additionally, the introduced gesture recognition feature, which has been validated, also effectively controls the forward and backward movements of the mobile vehicles, facilitating obstacle avoidance. Conclusion: The experimental platform for the mobile vehicles, which integrates depth camera and LiDAR, built in this study has been validated for real-time obstacle avoidance through path planning in indoor environments. The introduced gesture recognition also effectively enables obstacle avoidance for the mobile vehicles.

This paper how to apply dynamic topic models to a set of cybersecurity documents to understand how the concepts found in them are changing over time.

The post paper: Temporal Understanding of Cybersecurity Threats appeared first on UMBC ebiquity.

Basics of the Adtech Ecosystem This guide delves into the intricacies of the adtech ecosystem, an elaborate mesh of platforms and technologies designed to facilitate and enhance the purchase and sale of digital advertising. Within this system, crucial elements such as ad servers, DSPs (Demand Side Platforms), SSPs (Supply Side Platforms), and ad [...]

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.

Calls for greater diversity in management research, education and practice have increased in recent years, driven by a sense of fairness and ethical responsibility, but also because research shows that greater diversity of inputs into management processes can lead to greater innovation. But how can greater diversity of thought be encouraged when educating management students, beyond the advocacy of affirmative action and relating the research on the link between multiplicity and creativity? One way is to think again about how we introduce the subject. Introductory textbooks often begin by relaying the history of management. What is presented is a very limited mono-cultural and linear view of how management emerged. This article highlights the limits this view outlines for initiates in contrast to the histories of other comparable fields (medicine and architecture), and discusses how a wider, deeper and more engaged understanding of history can foster thinking differently.

Drawing on human capital theory, strategy scholars have emphasized firm-specific human capital as a source of sustained competitive advantage. In this study, we begin to unpack the micro-foundations of firm-specific human capital by theoretically and empirically exploring when employees perceive their skills to be firm-specific. We first develop theoretical arguments and hypotheses based on the extant strategy literature, which implicitly assumes information efficiency and unbiased perceptions of firm-specificity. We then relax these assumptions and develop alternative hypotheses rooted in the cognitive psychology literature, which highlights biases in human judgment. We test our hypotheses using two data sources from Korea and the United States. Surprisingly, our results support the hypotheses based on cognitive bias - a stark contrast to the expectations embedded within the strategy literature. Specifically, we find organizational commitment and, to some extent, tenure are negatively related to employee perceptions of the firm-specificity. We also find that employer provided on-the-job training was unrelated to perceived firm-specificity. These findings suggest that firm-specific human capital, as perceived by employees, may drive behavior in ways not anticipated by existing theory - for example, with respect to investments in skills or turnover decisions. This, in turn, may challenge the assumed relationship between firm-specific human capital and sustained competitive advantage. More broadly, our findings may suggest a need to reconsider other theories, such as transaction cost economics, that draw heavily on the notion of firm-specificity and implicitly assume widely shared and unbiased perceptions.

In this inductive study, we shift the focus of stigma research inside organizational boundaries by examining its relationship with organizational identity. To do so, we draw on the case of Keystone, a social enterprise in the East of England that became stigmatized after it initiated a program of support for a group of migrants in its community. Keystone's stigmatization precipitated a crisis of organizational identity. We examine how the identity crisis unfolded, focusing on the forms of identity work that Keystone's leaders enacted in response. Interestingly, we show not only that the internal effects of stigmatization on identity can be managed, but also that they may facilitate unexpected positive outcomes for organizations.

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.

Power in business schools is ubiquitous. We develop individuals for powerfull positions. Yet, the way we deal with power is limited by our utilitarian focus, avoiding the visceral nature of power. In relation to this we address two questions business schools don't ask: what is the experiential nature of power? How are we teaching power? We use experiential, aesthetic developments on power in the social sciences to critique the rational-utilitarian stance on power found in business schools, drawing on the work of Dewey and French philosopher Levinas to treat power as a lived phenomenon. We overview and critique approaches to teaching power in business curricula informed by our own research on Executive MBA students learning through choral conducting. Taking an appreciative-positive stance, this research showed students developing new, non-rational, non-utilitarian understandings of power. They developed nuanced learning on the feeling, relationality and responsibility of exercising power. Coming out of this we argue for more experiential and reflexive learning methods to be applied to the phenomena of power. Finally we shine a reflexive light on ourselves and our 'power to profess', suggesting ways we can change our own practice to better prepare our students for the power they wield.

Beside making organizations look like their peers through the adoption of similar attributes (which we call alignment), this paper highlights the fact that conformity also enables organizations to stand out by exhibiting highly salient attributes key to their field or industry (which we call conventionality). Building on the conformity and status literatures, and using the case of major U.S. symphony orchestras and the changes in their concert programing between 1879 and 1969, we hypothesize and find that middle-status organizations are more aligned, and middle-status individual leaders make more conventional choices than their low- and high-status peers. In addition, the extent to which middle-status leaders adopt conventional programming is moderated by the status of the organization and by its level of alignment. This paper offers a novel theory and operationalization of organizational conformity, and contributes to the literature on status effects, and more broadly to the understanding of the key issues of distinctiveness and conformity.