Aim/Purpose: As traditional Knowledge Management (KM) struggles to support the personal needs of knowledge workers in a new era of accelerating information abundance, we examine the shortcomings and put forward alternative scenarios and architectures for developing a novel Personal KM System (PKMS). Background: While prior publications focused on the complementing features compared to conventional dynamic KM models, our emphasis shifts to instantiating a flourishing PKMS community supported by a Digital Platform Ecosystem. Methodology: Design science research focusing on conceptual analysis and prototyping. Contribution: The PKMS concept advances the understanding of how digital platform communities may serve members with highly diverse skills and ambitions better to gainfully utilize the platform’s resources and generative potential in their personal and local settings. Findings: We demonstrate how the needs to tackle attention-consuming rising entropy and to benefit from generative innovation potentials can be addressed. Future Research: As this article has iteratively co-evolved with the preparing of a PKMS implementation, business, and roll-out plan, the prototype’s testing, completion, and subsequent migration to a viable system is of primary concern.

Aim/Purpose: There is a need to find a way to utilize narrative storytelling in film to make students more aware of the impacts of global problems and how they are perceived. Background: Two films from the year 2015 from two very different places in the world explore the encroachment and secondary effects of urban civilization upon indigenous cultures. Methodology: An interpretive, qualitative, methodology was used in addressing and discussing the use of these two films as a persuasive communication teaching aid. Contribution: This paper offers an approach to using narratives of films on indigenous issues in education to inform students about real-world issues and the wide impacts of those on various cultures and populations. Findings: Through the discussion of the two films, we suggest that using films with indigenous themes is beneficial to a course curriculum in a variety of subjects from communication to history and politics, to help students visualize the problems at hand. Anecdotally, the authors note that students are more engaged and willing to discuss topics if they have watched films or clips that deal with those topics than if they have simply read about them. Recommendation for Researchers: Technology and use of visuals are used as teaching tools in a variety of fields. Film narratives can be used as a teaching tool in multiple fields and provide insight about a variety of ideas. Identifying films such as those with indigenous themes provides an example of how one film can bring up multiple, real-world, topics and through led discussion student reflection can potentially lead to self-insights and have lasting impacts. Future Research: Additional research and assessment can be done on the impact of teaching with films and their compelling story telling of issues, and what types of questions should be asked to maximize learning and the impact of film narratives.

Aim/Purpose: Although a literature review is the fundamental base for any research, it is often considered tedious and conducted with a lack of methodology and rigor. The paper presents a method for systematically searching and screening literature using modern search technologies. The method focuses on minimizing the amount of manual screening by employing the references among papers. Background: A method to select quality literature effectively using modern search technologies is presented and evaluated. Methodology: The method starts with a keywords search in which the most suitable keywords are identified. In the backward search, promising resources are collected based on the keywords and their reference sections are searched for duplicates to find often cited basic literature. Then, the forward search identifies current literature that cites the basic sources. Contribution: Modern search technologies have the potential to improve the effectiveness of the use of information channels significantly and thus of traditional literature searches. Findings: The selection method was applied to the field of literature review itself and to the field of functional modelling. In both cases, relevant literature was identified within a surprisingly short time. Recommendation for Researchers: Literature reviews should be done systematically by using modern search technologies. Future Research: The presented method may be adapted according to the evolution of search technologies. The tool support for the automated extraction of references should be improved and a quantitative evaluation of the method in comparison to traditional reviews may foster the findings.

Aim/Purpose: Use of the term “rigor” is ubiquitous in the research community. But do we actually know what it means, and how it applies to transdisciplinary research? Background: Too often, rigor is presumed to mean following an established research protocol scrupulously. Unfortunately, that frequently leads to research with little or no impact. Methodology: We identify a sample of 62 articles with “rigor” in the title and analyze their content in order to capture the range of perspectives on rigor. We then analyze how these findings might apply to informing science. Contribution: This paper offers an approach to defining rigor that is theory based and appropriate for transdisciplinary research. Findings: Rigor definitions tend to fall into one of two categories: criteria-based and compliance-based. Which is appropriate depends on the research context. Even more variation was found with respect to relevance, which is often used as a catch-all for research characteristics that aren’t associated with rigor. Recommendations for Practitioners: Recognize that when researchers are referring to rigor and relevance, they of-ten mean these to apply to other researchers rather than to practice. When funding research, it is important to understand who the rigor and relevance are directed towards. Recommendations for Researchers: When using the term “rigor”, think carefully about which meaning is intended and be transparent about that meaning in your writing. Impact on Society: A great deal of public money is invested in achieving research rigor. Society should be aware of what it is buying with that funding. Future Research: Developing a better understanding of research fitness and the factors that contribute to it.

Aim/Purpose: The research problem of this study refers to the manner in which old and new mass media represented the significant social development surrounding two crashes of the Boeing 737 MAX airplane. Methodology: The study follows a qualitative case study methodology based on a sample of newspaper articles, TV programming, specialized technical publications, Twitter posts, and Facebook content. Contribution: The study contributes to understanding specifics and differences in representing extraordinary socio-economic events by different types of media. Findings: Key findings are that these media have constructed different realities surrounding the tragic events and exhibited informing distortions to different degrees. Recommendations for Practitioners: Practical implications of this study are relevant for the institutional and individual clients of informing with regard to selecting appropriate media for use. There are also implications for informers with regard to reducing distortions in informing. Recommendation for Researchers: Social media could be a channel for alternative learning rather than manipulation. Mainstream media were confirmed to be a loudspeaker for authorities as postulated in critical media research, and analytical media provided influential, deeper technical analysis. Future Research: As the Boeing case unfolds, it would be interesting to investigate any evolution in mediated realities.

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.

Aim/Purpose: This article takes ‘tourism decision-making behavior’ as an entry point, and deeply analyzes the factors influencing the travel decision-making of Chinese ‘American Travel’ tourists and their degree of influence, so as to provide a reference for the development of Chinese outbound tourism. Background: With the development of China’s economy and the improvement in people’s level, the outbound tourism market of Chinese residents has developed rapidly. The United States has become an important tourism destination country for Chinese residents’ outbound tourism, and China has also become one of the important tourist source countries of American tourism. However, the rapid development of ‘American tourism’ has also caused competition problems in China’s tourism industry. For example, prices and tourism products have become a means of competition among tourism enterprises. As the main body of consumption, tourists’ decision-making behavior will be affected by various factors. Methodology: Drawing lessons from previous scholars’ research results on tourism decision-making behavior, the influencing factors of tourism decision-making behavior are summarized. A theoretical model and index system of factors influencing tourism decision-making behavior of Chinese residents ‘Travel in the United States’ are established, research hypotheses are put forward, questionnaire data are collected, and SPSS and Amos are used to analyze and verify the theoretical model. Contribution: This research expands the literature on topics related to tourism decision-making in research and practice. It establishes a theoretical model and index system for the factors that influence the decision-making behavior of Chinese residents’ ‘American Travel’ tourism. In addition, we propose countermeasures for tourism products, enterprises, and the government. Findings: Prior knowledge and external information have a positive influence on tourism perception and value perception, and a negative influence on risk perception. Risk perception value perception has a positive and negative influence on tourism decision-making and tourism motivation, respectively. Tourism motivation has a positive influence on tourism decision-making and has a positive impact. Recommendation for Researchers: According to the research conclusions of this article, the following counter-measures and suggestions are put forward from three aspects of tourism: products, enterprises, and governments. On the basis of existing tourism products, relevant operating companies should pay more attention to the upgrading and transformation of tourism, leisure and entertainment products in scenic spots to increase the willingness of tourists to travel. When considering corporate marketing and promotion plans, tourism companies operating related businesses should increase the weight of their marketing budgets in online marketing, increase investment in online marketing, and develop mobile applications that meet the preferences of Chinese residents in the United States. Do a good job in the timely publication of safety reminders and local information. Safety is an important foundation for tourism development and the core concern of many tourists. Future Research: Due to the important research on the impact of tourism activities, the influencing factors are many and complex, and the psychological process of tourism decision-making is carried out directly. There are still unconsidered factors that need to be studied in depth. In the future, it is possible to compare multiple resource-featured themes, and increase the characteristics of potential tourists, and the factors affecting the selection behavior of regional cultural tourists, and so forth, in order to make the research more applicable and practical instructive significance.

Aim/Purpose: There is ample evidence that team processes matter more than the characteristics of individual team members; unfortunately, very few empirical studies have examined communication process variables closely or tied them to team outcomes. Background: The University of Miami Laboratory for Integrated Knowledge (U-LINK) is a pilot funding mechanism that was developed and implemented based on empirically-established best practices established in the literature on the Science of Team Science (SciTS). In addition to addressing grand societal challenges, teams engaged in processes designed to enhance the process of “teaming”. This study uses the Inputs-Mediator-Outputs-Inputs (IMOI) model as a blueprint for an investigation into how team communication processes (shared communication, shared leadership, formal meetings, informal meetings) influence intermediary team processes (goal clarity, role ambiguity, process clarity, trust) and team outcomes (team satisfaction, team productivity). Methodology: Monte Carlo methodologies were used to explore both longitudinal self-report (survey of communication and team outcome variables) data and objective data on scholarly productivity, collected from seventy-eight members of eleven real-world intact interdisciplinary teams to explore how team communication processes affect team outcomes. Contribution: This study is among the few that centers communication practice and processes in the operationalization and measurement of its constructs and which provides a test of hypotheses centered on key questions identified in the literature. Findings: Communication practices are important to team processes and outcomes. Shared communication and informal meetings were associated with increased team satisfaction and increased research productivity. Shared leadership was associated with increased research productivity, as well as improved process and goal clarity. Formal meetings were associated with increased goal clarity and decreased role ambiguity. Recommendation for Researchers: Studying intact interdisciplinary research teams requires innovative methods and clear specification of variables. Challenges associated with access to limited numbers of teams should not preclude engaging in research as each study contributes to our larger body of knowledge of the factors that influence the success of interdisciplinary research teams. Future Research: Future research should examine different team formation and funding mechanisms and extend observation and data collection for longer periods of time.

Aim/Purpose: Nigeria’s university education goes through incessant strikes by the Academic Staff Union of Universities (ASUU). This strike has led to shared emotion on micro-blogging sites like Twitter. This study analyzed selected historical tweets from the “ASUU” to understand citizens’ opinions. Background: The researchers conducted sentiment analysis and topic modelling to understand Twitter users’ opinions on the strike. Methodology: The researchers used the Valence Aware Dictionary for Sentiment Reasoning (VADER) technique for sentiment analysis, and the Latent Dirichlet allocation (LDA) was used for topic modelling. A total of 10,000 tweets were first extracted for the study. After data cleaning, 1323 tweets were left. Contribution: To the researcher’s best knowledge, no published study has presented a sentiment analysis on the topic of the ASUU strike using the Twitter dataset. This research will fill this gap by providing a sentiment analysis and drawing out subjects by exploring the tweets on the phrase “ASUU.” Findings: The sentiment analysis result using VADER returned 567 tweets as ‘Negative,’ with the remaining 544 and 212 categorized as Positive and Neutral. The result of the LDA returned six topics, all comprising seven keywords. The topics were the solution to the strike, ASUU strike effect, strike Call-off, appeal to ASUU, student protest and student appeal. Recommendation for Researchers: Researchers can use this study’s findings to compare with other contexts of opinion mining. Practitioners may also use the research to understand better the attitudes of their staff and students about the strikes to create actionable solutions before the suspension of the strike. Future Research: Future studies can collect information from other social networking and blogging sites.

Aim/Purpose: Developing Digital Platform Ecosystems (DPE) to transform conventional Knowledge Management Systems (KM/KMS) scenarios promises significant benefits for individuals, institutions, as well as emerging knowledge economies. Background: The academic entrepreneurship project presented is aiming for such a KMS-DPE configuration. Having consolidated this author’s own and external re-search findings, realization is currently commencing with a start-up in a business incubator. Methodology: Design science research applying mixed one-sample case study and illustrative scenario approach focusing on conceptual analysis and entrepreneurship. Contribution: Although (academic) entrepreneurship is a young research area with recently growing interest, publications focusing on this transitional stage between maturing research and projected commercial viability of digital technologies are rare. Findings: A roadmap looking beyond the immediate early-start-up perspective is out-lined by integrating recent development-stage-related DPE-research and by addressing stakeholders diverse informing needs essential for system realization. Recommendations for Practitioners and Researchers: As this transdisciplinary perspective combines KM, informing, design science, and entrepreneurial research spaces, it may assist other researchers and practitioners facing similar circumstances and/or start-up opportunities. Impact on Society: The article advances the understanding of how DPE communities may serve members with highly diverse skills and ambitions better to gainfully utilize the platform’s resources and generative potential in their personal and local settings. Future Research: As the entrepreneurial agenda will complement (not substitute) the academic research, research priorities have been highlighted aligned to three future stages.

Aim/Purpose: This paper examines the impact of the transformational, servant, and paternalistic leadership styles on employee performance at the middle and senior levels. Background: Transdisciplinary research promotes the integration and development of various sciences. It provides more choices for leaders to adopt ways and practical activities to promote enterprise development. Complexity leadership theory emphasizes that effectively functioning organizations need distinct forms of leadership to work together. Leaders rely on different leadership practices in an emergent collaborative context, and finding an optimal balance is challenging. Many scholars have attempted to explore which leadership styles have a more significant impact on employees by distinguishing and defining types of leadership styles and explaining the process by which they influence employee behavior and performance. Various scholars have further explored and empirically demonstrated the impact of these three types of leadership styles (transformational, servant, paternalistic)on employee performance. While transformational and servant leadership have their roots in the West, paternalistic leadership has roots in China. Few scholars have conducted comparative studies on their positive impact on employee performance. How do these three leadership styles affect employee performance at the middle and senior levels in the Chinese context? Which combination of middle and senior leadership styles performs best? These are the second area that this paper will attempt to explore. Methodology: This study constructs a three-tier model at the senior, middle, and grassroots levels. A questionnaire survey was used to collect data. SPSS 22.0 and Amos were used for data analysis. Contribution: Through its construction of a three-tier model (senior, middle, and grassroots levels), the paper explores the combined effect of three leadership styles (transformational, servant, and paternalistic) on grassroots employees. It explores the impact of senior leaders across levels on grassroots employee performance, which is expected to provide a valuable addition to theories on leadership styles. It is also instructive to examine which leadership style performs better and what middle and senior leadership configurations are more conducive to driving beneficial employee behavior and, ultimately, corporate growth. Findings: The transformational, servant, and paternalistic leadership styles, both at the top and middle levels, have a significant positive relationship with employee performance; the middle leadership style plays a positive mediating role between the top leadership style and employee performance. In terms of impact on employee performance, transformational leadership shows the best results at both the top and middle levels, with paternalistic leadership second and servant leadership at the same level. Regarding which middle and senior leadership style pairing is the best, the sample is relatively small, and the gap between various pairing combinations is not evident from the data. If the sample size is enlarged, the coefficient will likely expand year-on-year. Therefore, we can assume that the pairing effect of top servant leadership and middle transformational leadership is the best, top paternalistic leadership and middle transformational leadership is the second-best, and the combination of top paternalistic leadership and middle-level servant leadership leaders is the weakest. Recommendation for Researchers: This paper extends the study of top and middle leadership’s combined effect on employee performance as a positive response to the call for multi-layer or cross-layer analysis in leadership research. The findings further enrich the literature on leadership style-related theories. The middle leadership style plays a positive mediating role between the top leadership style and employee performance. The trickle-down effect is further verified, i.e., the top leadership will have a permeating influence on employees through the middle leadership, and the top’s influence on the middle is generally more significant than the influence on grassroots employees. However, the difference between the influence of the middle leadership on the grassroots and that of the top on the grassroots is not apparent, which is inconsistent with the trickle-down effect that the middle leadership communicates more with the grassroots and has more influence on the grassroots, and further verification is needed. All three types of leaders positively affected employee performance, with the best being transformational leadership, paternalistic leadership, and servant leadership. This finding is consistent with some scholars and inconsistent with some scholars. The interested scholars can do further research. The better performance of diverse pairings in middle and senior leadership combinations is consistent with previous research suggesting that leadership styles have their own strengths and can be complementary. This paper further provides a comparative study of multiple leadership styles to validate the recognition and adaptability of leadership styles and further explain the complex relationship between leadership styles and employee job performance. Scholars can conduct comparative research on other leadership styles, and there may be different results. Future Research: Because of the cross-sectional data taken, the findings’ generalizability still needs further validation. There are many types of leadership styles, and there are other types of leadership styles that can be explored comparatively, perhaps leading to different findings. From another point of view, various leaders have their strengths, and they are not mutually hindering. More research is needed on team formation in a variety of contexts. Organic organizational structure enables knowledge creation and integration through the process of organizational learning through deep and continuous social interaction or dialogue. So we can further examine the influence process of leaders on employees from how to give full play to their advantages, such as improving shared leadership and shared communication.

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 This paper aims to illustrate, through an exploratory ideographic case study, how a Complex Thinking framework can inform the design of scientific events and the facilitation of scientific Inter and Transdisciplinary groups towards positive emergent outcomes, both at the level of the functioning of the group and the collective complexity of their thinking. Moreover, it aims to show how the choice of facilitation strategies can contribute to positive emergent outcomes in the context of a fully online event, with its inherent constraints. Finally, this study aims to conduct an exploratory qualitative evaluation of the participants’ experiences during School, with a focus on the processes and how they relate to the aims of the School and the goals of the facilitation. Background Science needs to embrace modes of knowing capable of generating more complex (differentiated, integrated, recursively organized, emergent), ecologically fit, and creative responses, to meet the complexity of the world’s challenges. New formats and strategies are required that attend to the facilitation of Inter and Transdisciplinary scientific events and meetings, towards creative and complex outcomes. A Complex Thinking framework provides suggestions for the facilitation of Inter and Transdisciplinary meetings and events through targeting key properties which may lead to the emergence of complex and creative outcomes. Methodology We adopt an ideographic case study approach to illustrate how a complex systems approach, in particular a Complex Thinking framework, grounded in an enactive view of cognition, guided the design choices and the facilitation strategies of an online Inter and Transdisciplinary Advanced Training School (Winter School). We aim to illustrate how the facilitation strategies were selected and used to promote deep and creative interactions within the constraints of an online environment. We adopt an exploratory qualitative approach to investigate the participants’ reports of their experiences of the School, in light of the principles and goals that guided its design and facilitation. Contribution This paper opens a new area of theoretical and applied research, under the scope of a Complex Thinking framework, focused on the facilitation of Inter and Transdisciplinarity at scientific events, meetings, and discussions towards complex and creative outcomes. Findings The results of the exploratory qualitative analysis of the participants’ experiences regarding the event suggest a critical role of its methodology in fostering rich, deep, and constructive interactions, in leading to the emergence of a collective group experience, to the integration of ideas, and in facilitating transformative personal experiences, under the effects of the emergent group processes. It suggests that the strategies employed were successful, anticipating and overcoming the particular constraints of an online event. Recommendations for Practitioners This case study suggests that a Complex Thinking framework can fruitfully guide the design of facilitation strategies and activities for scientific events and meetings, activating a number of key relational processes that contribute to or boost the emergence of positive group experiences and the production and integration of novel ideas. Recommendations for Researchers This study calls for action-oriented and applied research focused on the developmental evaluation of innovations, regarding the facilitation of scientific creativity and integration, within the scope of a Complex Thinking approach. Impact on Society This paper calls for new modes of organization and formats of scientific activities, suggesting that Inter and Transdisciplinary events and meetings may benefit from intentional management and facilitation of interactions between participants to produce transformative impacts. It demonstrates the importance of the organizational principles used to plan and run events that engage multiple and various societal agents, from academics to practitioners and social activists, towards enhancing their richness and relevance to complex real-world challenges. Future Research This study highlights the need for process-focused systematic case study research using complex systems-informed designs to explore how and which facilitation strategies may promote which (interaction of) properties of Complex Thinking and associated processes and how, and under which conditions, these lead to more complex and creative outcomes.

Aim/Purpose: A literature review is often criticized for the absence of coherent construction, synthesis of topics, and well-reasoned analysis. A framework is needed for novice researchers to organize and present information obtained from the literature review. Background: Information and communication technologies advancement have yielded overwhelming information. The massive availability of information poses several challenges, including storage, processing, meaningful organization, and presentation for future consumption. Information System Researchers have developed frameworks, guidelines, and tools for gathering, filtering, processing, storing, and organizing information. Interestingly, information system researchers have vast information that needs meaningful organization and presentation to the research fraternity while conducting a literature review on a research topic. Methodology: This paper describes a framework called LACTiC (Location, Author, Continuum, Time, and Category) that we adapted from another framework called LATCH (Location, Alphabetical, Time, Category, and Hierarchy). LATCH was used to organize and present information on e-commerce websites for seamless navigation. We evaluated the LACTiC framework. Contribution: Information System Researchers can use the LACTiC framework to organize information obtained from literature review. Findings: The evaluation reveals that most researchers from information systems organize information obtained from the literature review category-wise, followed by continuum, author, time, and location. Recommendation for Researchers: Overall, the framework works well and can be helpful for researchers for an initial idea for organizing information obtained from the literature review. Future Research: To conceptualize the framework, the study was carried out using Information Systems related literature. To generalize the proposed framework, we may suggest that the study can be extended to other areas of business management, such as marketing, finance, operation, decision sciences, accounting, and economics.

Aim/Purpose: In this article, the subjective perspectives of young people in Ghana on the use of digital media are elaborated. The aim is to make the positions of young people visible in the often adult-dominated discourse on digital media and to overcome adult-centered considerations in academic and public debates. In addition, the focus on young people from the Global South is intended to help make their underrepresented voices present in this discourse. Background: Digital media devices and Internet access are conditional on people’s social, economic, and educational participation. Many people in the Global South in particular are not yet granted such access. For children and young people worldwide, the educational opportunities offered by digital media are associated with potential threats to mental health and well-being. However, young people’s views on digital media are rarely addressed, especially in the Global South. Methodology: Based on a qualitative thematic analysis of responses to open-ended questionnaire questions, young Ghanaians’ views on smartphone use and how it affects academic success are examined. Contribution: By focusing on the subjective perspectives of young people, especially from the Global South, voices that have hardly been heard in the discourse on digital media are made audible. This should help overcome the dominant adult-centered perspectives in this discourse. Findings: For young people in Ghana, digital media are part of their everyday lives and often necessary to succeed at school. At the same time, they are concerned about the dangers, e.g., from overuse or cybercrime, for which they have few strategies to deal with. In their answers, they refer to socio-culturally specific discourses and values as well as to generational hierarchies that they perceive and deal with, which go far beyond the topic of digital media use. This makes clear the social tensions in which the debate about digitalization is embedded. Recommendation for Researchers: Young people’s knowledge of and perspectives on digital media is an important resource for learning to use them in an emancipated way. Future Research: Future research should recognize young people as experts in their own right on the issue, explore ways to include their perspectives in the discourse on digital media use and work with them to harness the future potential of the technology and avoid risks.

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: 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: This is an introductory paper for the developing special series on applied psychology and informing science. It takes into account the spirit of informing science to launch the first of three articles in the series on applied psychology. The paper concludes by raising questions for future investigations.

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: 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: 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.

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. ...

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This article is part of the 2024 BCLT-BTLJ-CMTL Symposium.  Blake E. Reid I am grateful to the BTLJ and BCLT for the opportunity to participate in this symposium’s panel on race, Internet platforms and Section 230. It’s a fortunate and timely opportunity to discuss Spencer Overton’s and Catherine Powell’s critical ...

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This paper proposes an efficient solution for personalised web directories recommendation using fast FCM+DQN. At first, web directory usage file obtained from given dataset is fed into the accretion matrix computation module, where visitor chain matrix, visitor chain binary matrix, directory chain matrix and directory chain binary matrix are formulated. In this, directory grouping is accomplished based on fast FCM and matching among query and group is conducted based on Kumar Hassebrook and Kulczynski similarity. The user preferred directory is restored at this stage and at last, personalised web directories are recommended to the visitors by means of DQN. The proposed approach has received superior results with respect to maximum accuracy of 0.910, minimum mean squared error (MSE) of 0.0206 and root mean squared error (RMSE) of 0.144. Although the system offered magnificent outcomes, it failed to order web directories in the form of highly, medium and low interested directories.

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%.

This paper presents a novel deep learning technique called quantum dilated convolutional neural network-DenseNet (Q-DenseNet) for prediction of heart disease in spark framework. At first, the input data taken from the database is allowed for data partitioning using fast fuzzy C-means clustering (FFCM). The partitioned data is fed into spark framework, where pre-processed by missing data imputation and quantile normalisation. The pre-processed data is further allowed for selection of suitable features. Then, the selected features from the slave nodes are merged and fed into master node. The Q-DenseNet is used in master node for the prediction of heart disease. The performance improvement of the designed Q-DenseNet model is validated by comparing with traditional prediction models. Here, the Q-DenseNet method achieved superior performance with maximum of 92.65% specificity, 91.74% sensitivity, and 90.15% accuracy.

This paper studies a dual-channel supply chain consisting of one risk-averse manufacturer and one risk-averse retailer with stochastic demand. Herein, the manufacturer provides value-added services to enhance channel demand. First, the optimal pricing and service decisions of the channel members are investigated under different settings, i.e., the cooperative game, Bertrand game, and manufacturer Stackelberg (MS) game models. Second, the effects of channel members' risk aversion on optimal channel prices and expected utilities are analysed under the assumption that the manufacturer service is a decision variable and an exogenous variable, respectively. Third, sensitivity analysis and numerical simulation are performed to verify our propositions consistently and seek more managerial implications. The findings suggest that the manufacturer's value-added services in their direct channel will improve the direct price while decreasing the retail price. Consumers' channel loyalty degree has a great influence on the optimal price decisions and the performance of the channel members. The direct price increases while the retail price decreases in the manufacturer's value-added services. The retailer's risk aversion has a greater influence on price decisions than that of the manufacturer.

Lihan TONG,Weijia LI,Qingxia YANG,Liyuan CHEN,Peng CHEN, Vol.E107-D, No.11, pp.1472-1475
We present Ksformer, utilizing Multi-scale Key-select Routing Attention (MKRA) for intelligent selection of key areas through multi-channel, multi-scale windows with a top-k operator, and Lightweight Frequency Processing Module (LFPM) to enhance high-frequency features, outperforming other dehazing methods in tests.
Publication Date: 2024/11/01

Congcong FANG,Yun JIN,Guanlin CHEN,Yunfan ZHANG,Shidang LI,Yong MA,Yue XIE, Vol.E107-D, No.11, pp.1463-1467
Currently, an increasing number of tasks in speech emotion recognition rely on the analysis of both speech and text features. However, there remains a paucity of research exploring the potential of leveraging large language models like GPT-3 to enhance emotion recognition. In this investigation, we harness the power of the GPT-3 model to extract semantic information from transcribed texts, generating text modal features with a dimensionality of 1536. Subsequently, we perform feature fusion, combining the 1536-dimensional text features with 1188-dimensional acoustic features to yield comprehensive multi-modal recognition outcomes. Our findings reveal that the proposed method achieves a weighted accuracy of 79.62% across the four emotion categories in IEMOCAP, underscoring the considerable enhancement in emotion recognition accuracy facilitated by integrating large language models.
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

Nan WU,Xiaocong LAI,Mei CHEN,Ying PAN, Vol.E107-D, No.11, pp.1433-1443
With the development of the Semantic Web, an increasing number of researchers are utilizing ontology technology to construct domain ontology. Since there is no unified construction standard, ontology heterogeneity occurs. The ontology matching method can fuse heterogeneous ontologies, which realizes the interoperability between knowledge and associates to more relevant semantic information. In the case of differences between ontologies, how to reduce false matching and unsuccessful matching is a critical problem to be solved. Moreover, as the number of ontologies increases, the semantic relationship between ontologies becomes increasingly complex. Nevertheless, the current methods that solely find the similarity of names between concepts are no longer sufficient. Consequently, this paper proposes an ontology matching method based on semantic association. Accurate matching pairs are discovered by existing semantic knowledge, and then the potential semantic associations between concepts are mined according to the characteristics of the contextual structure. The matching method can better carry out matching work based on reliable knowledge. In addition, this paper introduces a probabilistic logic repair method, which can detect and repair the conflict of matching results, to enhance the availability and reliability of matching results. The experimental results show that the proposed method effectively improves the quality of matching between ontologies and saves time on repairing incorrect matching pairs. Besides, compared with the existing ontology matching systems, the proposed method has better stability.
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

Yuan LI,Tingting HU,Ryuji FUCHIKAMI,Takeshi IKENAGA, Vol.E107-D, No.11, pp.1396-1407
1 millisecond (1-ms) vision systems are gaining increasing attention in diverse fields like factory automation and robotics, as the ultra-low delay ensures seamless and timely responses. Superpixel segmentation is a pivotal preprocessing to reduce the number of image primitives for subsequent processing. Recently, there has been a growing emphasis on leveraging deep network-based algorithms to pursue superior performance and better integration into other deep network tasks. Superpixel Sampling Network (SSN) employs a deep network for feature generation and employs differentiable SLIC for superpixel generation. SSN achieves high performance with a small number of parameters. However, implementing SSN on FPGAs for ultra-low delay faces challenges due to the final layer’s aggregation of intermediate results. To address this limitation, this paper proposes an aggregated to pipelined structure for FPGA implementation. The final layer is decomposed into individual final layers for each intermediate result. This architectural adjustment eliminates the need for memory to store intermediate results. Concurrently, the proposed structure leverages decomposed layers to facilitate a pipelined structure with pixel streaming input to achieve ultra-low latency. To cooperate with the pipelined structure, layer-partitioned memory architecture is proposed. Each final layer has dedicated memory for storing superpixel center information, allowing values to be read and calculated from memory without conflicts. Calculation results of each final layer are accumulated, and the result of each pixel is obtained as the stream reaches the last layer. Evaluation results demonstrate that boundary recall and under-segmentation error remain comparable to SSN, with an average label consistency improvement of 0.035 over SSN. From a hardware performance perspective, the proposed system processes 1000 FPS images with a delay of 0.947 ms/frame.
Publication Date: 2024/11/01

Zhigang WU,Yaohui ZHU, Vol.E107-D, No.11, pp.1385-1395
This article focuses on improving the BiSeNet v2 bilateral branch image segmentation network structure, enhancing its learning ability for spatial details and overall image segmentation accuracy. A modified network called “BiconvNet” is proposed. Firstly, to extract shallow spatial details more effectively, a parallel concatenated strip and dilated (PCSD) convolution module is proposed and used to extract local features and surrounding contextual features in the detail branch. Continuing on, the semantic branch is reconstructed using the lightweight capability of depth separable convolution and high performance of ConvNet, in order to enable more efficient learning of deep advanced semantic features. Finally, fine-tuning is performed on the bilateral guidance aggregation layer of BiSeNet v2, enabling better fusion of the feature maps output by the detail branch and semantic branch. The experimental part discusses the contribution of stripe convolution and different sizes of empty convolution to image segmentation accuracy, and compares them with common convolutions such as Conv2d convolution, CG convolution and CCA convolution. The experiment proves that the PCSD convolution module proposed in this paper has the highest segmentation accuracy in all categories of the Cityscapes dataset compared with common convolutions. BiConvNet achieved a 9.39% accuracy improvement over the BiSeNet v2 network, with only a slight increase of 1.18M in model parameters. A mIoU accuracy of 68.75% was achieved on the validation set. Furthermore, through comparative experiments with commonly used autonomous driving image segmentation algorithms in recent years, BiConvNet demonstrates strong competitive advantages in segmentation accuracy on the Cityscapes and BDD100K datasets.
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.

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.

Underpinned by stakeholder and agency theories, this study adopts a qualitative multiple-case study approach to explore and analyse various stakeholder interests and how they affect digitalisation in the health sector of a developing country (DC). The study's findings revealed that four key stakeholder interests - political, regulatory, leadership, and operational - affect digitalisation in the health sector of DCs. Further, the study found that operational and leadership interests were emergent and were triggered by some digitalisation initiatives, which included, inter alia, the use of new eHealth software and the COVID-19 vaccination exercise, which established new structures and worked better through digitalisation. Conversely, political and regulatory interests were found to be relatively enduring since they existed throughout the pre- and post-digitalisation eras. The study also unearthed principal-agent conflicts arising from technological, organisational and regulatory factors that contribute to the paradoxical outcomes of digitalisation in the health sector.

The objective of this study was to explore through the Model of Theory of Planned Behaviour the most important variables that influence the practice of physical and sports activity at home supported by virtual training in the context of the COVID-19 pandemic. A cross-study was proposed between countries from three continents, distributing the questionnaire in Spain (Europe), Pakistan (Asia), and Colombia (South America) to ensure a comprehensive study. The methodology of structural equations using partial least squares was used. The empirical exploratory study supported the hypotheses proposed, with the most important result that confinement due to the COVID-19 pandemic has been a factor causing the practice of physical and sports activity at home. This is one of the first studies to examine sports practice at home and the new context of sports practice that has generated disruptive technologies and the global crisis of the COVID-19 pandemic.

A semantically rich framework to enable real-time knowledge extraction from short length semi-structured documents Lavana Elluri 10:30-11:30 Monday, 4 November 2019, ITE346 Knowledge is currently maintained as a large volume of unstructured text data in books, laws, regulations and policies, news and social media, academic and scientific reports, conversation and correspondence, etc. Most of these […]

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Ph.D. Dissertation Defense Modeling and Extracting Information about Cybersecurity Events from Text Taneeya Satyapanich 9:30-11:30 Monday, 18 November, 2019, ITE346? People now rely on the Internet to carry out much of their daily activities such as banking, ordering food, and socializing with their family and friends. The technology facilitates our lives, but also comes with […]

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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.

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The PALS system captures physical context from sensed data, reasons about the context and associated context-driven policies to make access-control decisions and detect intrusions into smart home systems based on both network and behavioral data

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CEO severance has captured the attention of a wide array of audiences, yet it remains largely unexplored by management scholars. This paper offers a rigorous theoretical examination of CEO severance with the goal of developing a foundation for a systematic research agenda. In particular, we consider if, and how, severance agreements can be effective in serving the interests of both CEOs and shareholders. We argue that severance agreements have potential value as both an executive recruitment and governance tool, but that the way they are conventionally structured undermines the value that shareholders realize from them. The implications of structure have been almost entirely overlooked by scholars, perhaps because the influence of compensation consultants has left little variance in how severance agreements are implemented across firms. We address this gap by theorizing about how severance agreements could be structured to effectively generate value for executives and shareholders. To do this, we introduce a categorization of key dimensions of CEO severance agreements, and consider how each of these dimensions can be structured to facilitate CEO recruiting, while simultaneously mitigating future governance problems. Our propositions offer new opportunities for governance and compensation scholars to link CEO severance agreements to important organizational outcomes.

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.

'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.

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.

Drawing on expectancy violation theory, we explore the effects of anticipatory impression management in the context of acquisitions. We introduce impression offsetting, an anticipatory impression management technique organizational leaders employ when they expect a focal event will negatively violate the expectations of external stakeholders. Accordingly, in these situations, organizational leaders will announce the focal event contemporaneously with positive, but unrelated information. We predict impression offsetting will generally occur in the context of acquisitions, but also more frequently for specific acquiring firms and acquisitions that are more likely to lead to an expectancy violation. We also posit that offsetting will effectively inhibit observers' perceptions of events as negative expectancy violations by positively influencing shareholder reactions to acquisition announcements. Consistent with our hypotheses, in a sample of publicly traded acquisition targets, we find evidence for impression offsetting, in which characteristics of both acquirers and their announced acquisitions predict its frequency of use. We also find evidence that impression offsetting is efficacious; on average, it reduces the negative market reaction to acquisition announcements by over 40 percent, which translates into approximately $246 million in market capitalization.

Management educators often assume that research-based arguments ought to be convincing to students. However, college students do not always accept even well-documented research findings. Among the reasons this might happen, we focus on the potential role of psychological mechanisms triggered by scholarly arguments that affect students' self-concepts, leading them to engage in self-enhancing or self-protective responses. We investigated such processes by examining students' reactions to a research argument emphasizing the importance of intelligence to job performance, in comparison to their reactions to research arguments emphasizing the importance of emotional intelligence and/or fit. Consistent with our predictions, students were less likely to accept the argument for the importance of intelligence compared to the alternative, less threatening, arguments (i.e., the importance of emotional intelligence or fit). Further, acceptance of the argument about the importance of intelligence was affected by students' grade point average (GPA) and moderated by their emotional stability. Specifically, consistent with self-enhancement theory, students with lower GPAs were more likely to reject the argument for intelligence and give self-protective reasons for their responses, whereas students with higher GPAs were more likely to accept the argument and give self-enhancing reasons. Implications for future research and for management teaching are discussed.