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When the covid-19 pandemic started, managers were only able to lead their employees through digital communication. During the subsequent telework a completely new research area, in need of new knowledge, was created. The purpose of this study is to gain an understanding of opportunities and challenges of remote leadership, experienced by managers. This at a time of only digital communication with employees, with no physical meetings or interactions. The study will answer the following two issues, “What factors are important for managers to take into account when applying a leadership style to teleworking employees?” and “Which internal communication functions in the organization have worked better or not according to managers, when remotely managing employees?”. This exploratory study has its theoretical starting point in situational theory, as well as in Richmond, McCroskey and Powell’s six functions in organizational communication. Through semi-structured interviews, empirical data has been collected and then analyzed through coding, thematization and summation. The result of this study indicates that future managers, conducting digital leadership, will need to take each employee's needs and personal situation into account. Managers need to adopt a functioning leadership style, while keeping important factors regarding employees in mind. This in order to successfully lead each teleworking employee through digital communication.

The two key issues of modern Bayesian statistics are: (i) establishing a principled approach for \textit{distilling} a statistical prior distribution that is \textit{consistent} with the given data from an initial believable scientific prior; and (ii) development of a \textit{consolidated} Bayes-frequentist data analysis workflow that is more effective than either of the two separately. In this thesis, we propose generalized empirical Bayes as a new framework for exploring these fundamental questions along with a wide range of applications spanning fields as diverse as clinical trials, metrology, insurance, medicine, and ecology. Our research marks a significant step towards bridging the ``gap'' between Bayesian and frequentist schools of thought that has plagued statisticians for over 250 years. Chapters 1 and 2---based on \cite{mukhopadhyay2018generalized}---introduces the core theory and methods of our proposed generalized empirical Bayes (gEB) framework that solves a long-standing puzzle of modern Bayes, originally posed by Herbert Robbins (1980). One of the main contributions of this research is to introduce and study a new class of nonparametric priors ${\rm DS}(G, m)$ that allows exploratory Bayesian modeling. However, at a practical level, major practical advantages of our proposal are: (i) computational ease (it does not require Markov chain Monte Carlo (MCMC), variational methods, or any other sophisticated computational techniques); (ii) simplicity and interpretability of the underlying theoretical framework which is general enough to include almost all commonly encountered models; and (iii) easy integration with mainframe Bayesian analysis that makes it readily applicable to a wide range of problems. Connections with other Bayesian cultures are also presented in the chapter. Chapter 3 deals with the topic of measurement uncertainty from a new angle by introducing the foundation of nonparametric meta-analysis. We have applied the proposed methodology to real data examples from astronomy, physics, and medical disciplines. Chapter 4 discusses some further extensions and application of our theory to distributed big data modeling and the missing species problem. The dissertation concludes by highlighting two important areas of future work: a full Bayesian implementation workflow and potential applications in cybersecurity. / Statistics

[pt] Em cenários de tomada de decisão, quando um agente, humano ou máquina, necessita de mais conhecimento para decidir devido a uma lacuna de conhecimento, surge uma necessidade de informação. Os usuários podem conscientemente tomar a iniciativa de adquirir conhecimento para preencher essa lacuna através de tarefas de buscas por informação. As consultas do usuário podem ser incompletas, imprecisas e ambíguas. Isso ocorre porque parte da informação necessária está implícita ou porque o usuário não compreende totalmente o domínio ou a tarefa que motiva a busca. Esta condição está prevista nas abordagens de busca exploratória. Embora os Grafos de Conhecimento (KG) sejam reconhecidos como fontes de informação com grande potencial para integração de dados e busca exploratória, eles são incompletos por natureza. Além disso, KGs Crowdsourced, ou KGs construídos pela integração de diversas fontes de informação de qualidade variável, precisam de uma Camada de Confiança para serem eficazes no suporte a processos de tomada de decisão. A avaliação da veracidade do conhecimento depende dos contextos das alegações e das tarefas a serem realizadas ou pretendidas (propósito). Esta pesquisa tem como objetivo preparar e consultar KGs para apoiar a exploração ciente de contexto em cenários de tomada de decisão. As contribuições incluem uma arquitetura para sistemas de apoio à decisão, composta por uma Camada de Decisão, uma Camada de Confiança e uma Camada de Conhecimento que opera sob a hipótese de Mundo Aberto Dual. A Camada de Conhecimento é composta por um Grafo de Conhecimento enriquecido de Contexto (CoaKG) e uma Máquina de Consulta baseada em CoaKG. CoaKG estende um KG padrão com mapeamentos de contexto para identificar o contexto explicitamente representado e regras para inferir o contexto implícito. A máquina de Consulta baseada em CoaKG foi projetada como uma abordagem de resposta a consultas que recupera todas as respostas contextualizadas (possíveis). A Wikidata é objeto de uma Prova de Conceito para avaliar a eficácia da Camada de Conhecimento. / [en] In decision-making scenarios, an information need arises when an agent, human, or machine needs more knowledge to decide due to a knowledge gap. Users can consciously take the initiative to acquire knowledge to fill this gap through information search tasks. User queries can be incomplete, inaccurate, and ambiguous. It occurs because part of the information needed is implicit or because the user does not fully understand the domain or the task that motivates the search. This condition is foreseen within the exploratory search approaches. Although Knowledge Graphs (KG) are recognized as information sources with great potential for data integration and exploratory search, they are incomplete by nature. Besides, Crowdsourced KGs, or KGs constructed by integrating several different information sources of varying quality, need a Trust Layer to be effective. The evaluation of knowledge truthfulness depends upon the contexts of claims and tasks being carried out or intended (purpose). This research aims to prepare and query KGs to support context-aware exploration in decision-making scenarios. The contributions include a framework for Context Augmented Knowledge Graphs-based Decision Support Systems composed of a Decision Layer, a Trust Layer, and a Knowledge Layer that operates under a Dual Open World Assumption. The Knowledge Layer comprises a Context Augmented KG (CoaKG) and a CoaKG Query Engine. CoaKG contains contextual mappings to identify explicit context and rules to infer implicit context. CoaKG Query Engine is designed as a query-answering approach that retrieves all contextualized (possible answers) from the CoaKG. Wikidata is the object of a Proof of Concept to evaluate the effectiveness of the Knowledge Layer.

Predominantly white institutions (PWIs) are experiencing a racial history crisis. This crisis is occurring partially in response to current events including the multiple deaths of Black men and women at the hands of White police officers: Michael Brown in 2014, Breonna Taylor in 2020, and George Floyd also in 2020. Additionally, there has been a resurgence of investigation and national debate around building names and statues on college campuses memorializing people who supported slavery or were members of hate groups. These events coupled with the lack of truth-telling around the roles of Black and African American people and other minoritized communities within the development and prosperity of colleges and universities has centered history as focal point in the diversity, equity, and inclusion spaces. Nearly all colleges and universities include diversity, equity, and inclusion as a center piece of their mission statements, but few address their racialized history. Often, the university's history is told from an ahistorical perspective which places whiteness at the center of the history narrative. With a conceptual framework incorporating both critical race theory and white institutional presence, the study interrogated how universities are telling the stories of their histories regarding African American people. The sample selected for the study included 16 universities designated as leaders in the field of diversity, equity and inclusion. The selected universities received the designation of Diversity Champion from Insight Into Diversity magazine. The history narratives, typically found on the About page of the universities' website, were examined for instances of white institutional presence and counternarratives. Findings were filtered through a critical whiteness framework resulting in four types of categories. Exemplar institutions were countering white institutional presence and whiteness through multiple instances of counternarratives, centering Black and African American history, and confronting racist ideologies within their institutional history narratives. The remaining three categories of institutions were discussed for these instances as well. Overall, the findings concluded that some universities have made progress in the area of historical truth telling, most universities have not fully engaged in this work. The study offered implications for further research in the areas of critical whiteness, white institutional presence, and diverse learning environments. Incorporating the findings of the exemplar university cases, the study also discussed implications for policy and practice for institutions interested in or working on investigating their histories as related to minoritized communities, but specifically histories involving African American people. / Doctor of Philosophy / Predominantly white institutions (PWIs) are experiencing a racial history crisis. This crisis is occurring partially in response to current events including the multiple deaths of Black men and women at the hands of White police officers: Michael Brown in 2014, Breonna Taylor in 2020, and George Floyd also in 2020. Additionally, there has been a resurgence of investigation and national debate around building names and statues on college campuses memorializing people who supported slavery or were members of hate groups. These events coupled with the lack of truth-telling around the roles of Black and African American people and other minoritized communities within the development and prosperity of colleges and universities has centered history as focal point in the diversity, equity, and inclusion spaces. Nearly all colleges and universities include diversity, equity, and inclusion as a center piece of their mission statements, but few address their racialized history. Often, the university's history is told from an ahistorical perspective which places whiteness at the center of the history narrative. With a conceptual framework incorporating both critical race theory and white institutional presence, the study interrogated how universities are telling the stories of their histories regarding African American people. The sample selected for the study included 16 universities designated as leaders in the field of diversity, equity and inclusion.

Sensemaking tasks, such as reading many news articles to determine the truthfulness of a given claim, are difficult. These tasks require a series of iterative steps to first forage for relevant information and then synthesize this information into a final hypothesis. To assist with such tasks, visual analytics systems provide interactive visualizations of data to enable faster, more accurate, or more thorough analyses. For example, semantic interaction techniques leverage natural or intuitive interactions, like highlighting text, to automatically update the visualization parameters using machine learning. However, this process of using machine learning based on user interaction is not yet well defined. We begin our research efforts by developing a computational pipeline that models and captures how a system processes semantic interactions. We then expanded this model to denote specifically how each component of the pipeline supports steps of the Sensemaking Process. Additionally, we recognized a cognitive symmetry in how analysts consider data items (like news articles) and their attributes (such as terms that appear within the articles). To support this symmetry, we also modeled how to visualize and interact with data items and their attributes simultaneously. We built a testbed system and conducted a user study to determine which analytic tasks are best supported by such symmetry. Then, we augmented the testbed system to scale up to large data using semantic interaction foraging, a method for automated foraging based on user interaction. This experience enabled our development of design challenges and a corresponding future research agenda centered on semantic interaction foraging. We began investigating this research agenda by conducting a second user study on when to apply semantic interaction foraging to better match the analyst's Sensemaking Process. / Doctor of Philosophy / Sensemaking tasks such as determining the truthfulness of a claim using news articles are complex, requiring a series of steps in which the relevance of each piece of information within the articles is first determined. Relevant pieces of information are then combined together until a conclusion may be reached regarding the truthfulness of the claim. To help with these tasks, interactive visualizations of data can make it easier or faster to find or combine information together. In this research, we focus on leveraging natural or intuitive interactions, such organizing documents in a 2-D space, which the system uses to perform machine learning to automatically adjust the visualization to better support the given task. We first model how systems perform such machine learning based on interaction as well as model how each component of the system supports the user's sensemaking task. Additionally, we developed a model and accompanying testbed system for simultaneously evaluating both data items (like news articles) and their attributes (such as terms within the articles) through symmetrical visualization and interaction methods. With this testbed system, we devised and conducted a user study to determine which types of tasks are supported or hindered by such symmetry. We then combined these models to build an additional testbed system that implemented a searching technique to automatically add previously unseen, relevant pieces of information to the visualization. Using our experience in implementing this automated searching technique, we defined design challenges to guide future implementations, along with a research agenda to refine the technique. We also devised and conducted another user study to determine when such automated searching should be triggered to best support the user's sensemaking task.

Corporate sustainability has become a crucial aspect of business strategy, particularly for multinational corporations (MNCs) aligning their operations with global sustainability goals. The successful implementation of sustainability strategies is often influenced by organizational factors, with organizational culture playing a significant role.  This thesis investigates the influence of organizational culture on the implementation of corporate sustainability strategies within an MNC subsidiary. It seeks to understand how different aspects of organizational culture facilitate or hinder the adoption and execution of sustainability initiatives. As a result, an exploratory research approach is employed, utilizing qualitative methods such as interviews. Through in-depth analysis of data collected from a selected MNC subsidiary, the study identifies key drivers and barriers within the organizational culture influencing sustainability strategy adoption and execution. Findings highlight the significance of leadership support, employee engagement, communication channels, and individual beliefs in fostering a conducive environment for sustainability initiatives. This research provides empirical insights into how organizational culture affects the implementation of corporate sustainability strategies, offering practical guidance for organizations to facilitate the effective implementation of sustainability initiatives.

Thesis advisor: Christopher P. Salas-Wright / Around the world, attacks against humanitarian aid workers pose a pervasive and intransigent threat to health and human rights, but evidence about the complex factors that predict perpetrators' behavior as well as attack outcomes remains quite limited. While previous studies have addressed several aspects of local and global trends of attacks against humanitarian assistance, more evidence is also needed to understand the dynamics of recurrent incidents, small-scale attacks, as well as patterns of events across time and space; and how the observed trends are driven by conflict-related and contextual factors. In this dissertation, we investigate the predictors and spatiotemporal dynamics of attacks against humanitarian assistance from 1997 to 2022 using publicly-available datasets from the Aid Worker Security Database (AWSD, Humanitarian Outcomes, 2022) and the Armed Conflict Dataset version 22.0 (ACD; Glaeditsch et al., 2002; Davies et al., 2022). / Thesis (PhD) — Boston College, 2022. / Submitted to: Boston College. Graduate School of Social Work. / Discipline: Social Work.

This study examines the technology and appointment scheduling habits of older adults over the age of 55 through an exploratory sequential three phase mixed methods study. Phase One of this study examined features of ten existing mHealth applications through a qualitative content analysis, then a mHealth wireframe was developed from the app to replicate in addition to a redesigned version. Phase Two of the study was a thirty-four questions survey with 40 participants that inquired about their background with appointment scheduling, prior experience with technology, and demographics. After that, the mHealth applications were revised into two mHealth application prototypes. Lastly, Phase Three conducted a user test with the two mHealth prototypes through A/B testing with 15 participants. Findings from the survey showed the preferred method of scheduling an appointment among participants was primarily in-person or by phone. The user test revealed that some participants were willing to use a mHealth application to schedule an appointment if it was deemed easy to use. Recommendations for future research suggests that the iterative design process of a prototype with an underserved population would garner feedback inclusive of those older adults who are less tech savvy. The major contribution of this research was the development of the mHapps Framework which will be tested in a future study.

Yes / Entrepreneurs need to be more resilient. As economic disruptions are inevitable, organizations need to have resilient leaders. Resilient organizations possess the capacity to sustain favorable transformations and successfully navigate numerous hurdles during periods of crisis or adversity. The purpose of this research is to develop a new measurement tool for resilient leadership within the context of Small and Medium-Sized Enterprises (SMEs) in Malaysia. Despite vast empirical evidence on the topic of leadership toward sustainable performance, validated assessments of resilient leadership are still underexplored. Therefore, the researchers initiated a quantitative research approach by gathering data from 100 SME leaders across various industries. The newly developed resilient leadership questionnaires were electronically distributed to the respondents. The data was analyzed using SPSS 26.0. The data analysis comprised descriptive statistics, reliability analysis, and exploratory factor analysis. The results revealed and verified six- dimensions of resilient leadership, which include adaptability, emotional intelligence, visionary, spiritual intelligence, growth mindset, and internal ecosystem. The measurement scale developed, and the factor structure produced can be used to assess and develop more resilient leaders that could help to sustain business performance among SMEs. The inclusion of a spiritual element into the model adds value and provides a holistic view of a resilient leadership model that is not only applicable to the context of SMEs in Malaysia but also across borders.