Search Results

Cyber Addiction and Information Overload and Their Impact on Workplace Productivity
The research examines the relationship between cyber addiction (excessive use of the Internet, CA), information overload (IO), and assesses their impact on workplace productivity. A multi-methods approach is used employing the Internet Addiction Test (IAT) and a quantitative survey design to assess and test the stated hypothesis. The study used random sampling methods targeting 150 respondents from different information technology departments of various organizations. The study hypothesizes that CA and IO have a reciprocal association, which adversely affects workplace productivity and employee performance. From the findings, IAT scores increase with an associated possibility of reporting burnout, fatigue, and other components associated with CA and IO. Also, CA and IO were significantly correlated, as evaluated by the chi-square test, although the same approach did not yield significant associations between CA and perceived internet dependence. Other findings CA components, such as social media use, significantly associated with task postponement and burnout. As well, CA affected IO with substantial impacts on workplace productivity. The paper highlights managerial aspects that organizations should consider in optimizing the output of their staff. Recommendations include wellness programs, altering the leadership and management styles, and sensitizing the employees on the adverse effects caused by the two variables.
Social Exchange Theory in the Context of X (Twitter) and Facebook Social Media Platforms with a Focus on Privacy Concerns among Saudi Students
The current research examines the use of social media and its security settings using the Social Exchange Theory (SET) within a Saudi student environment. This research includes an introduction, literature review, methodology, results, and conclusion with the results section presenting the findings from the three essays. The first essay employs the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology of SET. PRISMA's systematic and exhaustive approach to literature evaluation increases the likelihood of obtaining high-quality, reproducible findings. In the second essay, which focuses on awareness of X's (Twitter) security settings, a quantitative research approach was utilized. A sample of former and current Saudi students (graduate and undergraduate) at the University of North Texas participated in the investigation. This research provides an empirical examination of the use of X (Twitter) and its security features within this community by employing statistical analysis of the data from respondents. Likewise, the same sample of Saudi students from the University of North Texas was used for the third essay in which the use of Facebook's security settings was examined. Having a consistent sample across both studies enables a comparison and a greater understanding of the security awareness and practices of this group across various social media platforms. The findings across the different studies extend our understanding of the role of culture in privacy and security concerns related to social media.
Countering Hate Speech: Modeling User-Generated Web Content Using Natural Language Processing
Social media is considered a particularly conducive arena for hate speech. Counter speech, which is a "direct response that counters hate speech" is a remedy to address hate speech. Unlike content moderation, counter speech does not interfere with the principle of free and open public spaces for debate. This dissertation focuses on the (a) automatic detection and (b) analyses of the effectiveness of counter speech and its fine-grained strategies in user-generated web content. The first goal is to identify counter speech. We create a corpus with 6,846 instances through crowdsourcing. We specifically investigate the role of conversational context in the annotation and detection of counter speech. The second goal is to assess and predict conversational outcomes of counter speech. We propose a new metric to measure conversation incivility based on the number of uncivil and civil comments as well as the unique authors involved in the discourse. We then use the metric to evaluate the outcomes of replies to hate speech. The third goal is to establish a fine-grained taxonomy of counter speech. We present a theoretically grounded taxonomy that differentiates counter speech addressing the author of hate speech from addressing the content. We further compare the conversational outcomes of different types of counter speech and build models to identify each type. We conclude by discussing our contributions and future research directions on using user-generated counter speech to combat online hatred.
An Examination of the Metaverse Technology Acceptance Model in Tourism
The traditional definition of tourism has been transformed by significant advancements in communication and information technology. The concept of Metaverse, derived from the words "meta" (meaning beyond) and "verse" (meaning universe), has redefined how people experience travel. This innovative concept combines virtual reality, augmented reality, and artificial intelligence to create virtually augmented spaces. However, the tourism industry should clarify and narrow down the definition of Metaverse and its intriguing concept for its successful adoption in the future. Thus, it is crucial to define Metaverse tourism and understand how users will accept it in the near future. This study aims to comprehend the technology behind Metaverse tourism, review current research on the topic, and identify the critical factors related to experiential Metaverse tourism. The paper also examines how computer self-efficacy, novelty seeking, subjective norm, job relevance, perceived usefulness, perceived ease of use, and perceived enjoyment can influence expected user satisfaction and behavioral intention, given the context of situational motivation. The findings have significant implications for theory and management, addressing various questions related to users' perceptions, expectations, design considerations, stakeholder preparations, and performance assessment of metaverse technology in tourism applications.
Exposure to Trauma and Its Effect on Information-Seeking Behaviors and Decision-Making Processes
This dissertation examines the impact of trauma on information-seeking behaviors and decision-making processes. Essay 1 includes a qualitative analysis of the transcripts obtained from interviews with four military service members diagnosed with PTSD. The results showed that 75% of this small sample population exhibited addictive behavior that was presented in their information behaviors. All four members indicated that the excessive extent to which they seek information is related to the perceived importance of the information and their level of trust in the sources. Low trust in information sources increases the number of sources searched for validation in this population. Essay 2 involved the collection and analysis of survey data. The results of the stepwise backward regression show that two trauma variables (adult sexual assault, sudden fear) have a significant combined negative effect on decision-making in this population. The analysis and results of a different survey are presented in Essay 3. The stepwise logistic regression analysis results conducted on the summated scales developed showed a strong positive link between trust in scientific experts for scientific information and the dichotomous dependent variable trust in social media for news. The research conducted in this dissertation extends the understanding of how trauma affects people's information-seeking habits and decision-making processes. The findings have implications for how to communicate, policies relevant to information dissemination, and mental health measures. Future research is suggested to understand these behaviors and potential treatments better.
A Factor Analytic Evaluation of the Private Club Members' Benefits Scale
This study's first goal is to investigate whether a 23-item multidimensional scale is a valid and reliable measure of benefits private club members perceive to be important. Seven theoretically plausible model structures are empirically tested: a unidimensional model, a two oblique first-order factors model, a four oblique first-order factors model, a two oblique second-order factors model, a bifactor model with two domain-specific factors, a bifactor model with four domain-specific factors, and two oblique bifactor models. The second goal is to examine the benefits members receive most often from their membership clubs. The multidimensional scale is based on four dimensions: member-to-employee relationship, member-to-member relationship, confidence, and reduced anxiety. Member-to-employee relationship and member-to-member relationship subscales are aligned with social benefits while confidence and reduced anxiety subscales are aligned with psychological benefits. The study participants (N = 114) were recruited through a commercial crowdsourcing platform, Prolific. The results of a Bayesian confirmatory factor analysis (BCFA) provided support for the two oblique bifactor models. Additionally, the social benefits and psychological benefits bifactor scales displayed acceptable reliability. A comparison of the means for each type of benefit revealed that no statistically significant differences existed between the general social benefits factor and the general psychological benefits factor as well as between member-to-employee relationship and member-to-member relationship benefits. However, the mean of reduced anxiety benefits was statistically significantly higher than the mean of confidence benefits. The findings of this study contribute to the theoretical understanding and measurement of private club membership value by examining various dimensions of benefits members perceive to be important. The findings also provide private club managers with a valid and reliable scale for assessing benefits their members perceive to be important.
The Role of Social Media Influencers in Saudis' Domestic Destination Choice
This study aims to find out the impact of the credibility and content quality of social media influencers on the various stages of the customer's journey and the intention to choose a destination for the Saudi tourist. The target segment was Saudis in general, who are 18 years or above. To conduct this study, 618 usable questionnaires were collected. This study tested twenty-two hypotheses. The result of this study showed that the relationship between content quality, expertise, and similarity was significantly positive with the tourist's desire and information searching. As well, the relationship between the tourist's desire and information searching was significantly positive with the intention of choosing a destination. Conservatism, engagement, and gender were tested as moderators. Conservatism was not significant while the engagement was significant. Gender was only significant in the relationship between content quality and the tourist's desire. This study contributes to information and knowledge in the fields of tourism, hospitality, travel, marketing, tourist behavior, information science, and social media. Researchers and those interested in tourist and customer behavior can benefit from the results of this study. In the industrial field, this study will be very useful to the Saudi government, which has recently begun to rely on tourism and hospitality as a main source of income. As well, the study can be beneficial for tourism, hospitality and travel companies, hotels, restaurants, marketers of tourist destinations, and workers in the field of tourism and hospitality in general to know the best ways to attract tourists through social media influencers.
Antecedents to Reliance on Artificial Intelligence and Predictive Modeling
Artificial intelligence (AI) and predictive modeling are tools used to diagnose a disease, determine how much a home is worth, estimate insurance risks, and detect fraud. AI and predictive modeling are so ubiquitous that they can be why one gets spam and why spam is automatically deleted. Information science integrates interdisciplinary elements of data-driven, behavioral, design, interpretive, and analytical research methodologies to design and understand interactions between digital media, information systems, and humans. This research focuses on the interaction between humans, AI, and predictive models. This research proposes a theoretical framework and a conceptual research model to understand the antecedents to reliance on AI and predictive modeling. The dissertation follows a traditional format that includes three studies. Study 1 employed a deductive quantitative research approach as a survey to model the relationship between trust in science and reliance on formal news sources. Study 2 employed a deductive quantitative research approach as a survey to understand the impact of framing questions and consider an alternative method of measuring society's reliance on science using predictive models. Study 3 employed a deductive quantitative research approach in the form of a survey to posit a new model based on the first two studies. This study benefited from a Toulouse Graduate School grant to fund research using the crowdsourcing platform https://lucidtheorem.com/ to generate a stratified sample of the U.S. population.
Designing Archival Collections to Support Language Revitalization: Case Study of the Boro Language Resource
Indigenous communities around the world are losing their languages at accelerating rates to the effects of the climate crisis and global capitalism. To preserve samples of these languages facing endangerment and extinction, samples of language use (e.g., audio-video recordings, photographs, textual transcriptions, translations, and analyses) are created and stored in language archives: repositories intended to provide long-term preservation of and access to language materials. In recent years, archives of all kinds are considering their origins and audiences. With the emergence of the community paradigm of archiving framework, the roles of archivists, communities, and institutions are under re-examination. Language archives too are reflecting this trend, as it becomes more common for speakers of Indigenous languages (also known as language communities) to document and archive their own languages and histories. As the landscape of language archiving expands, we now see increased emphasis on the re-use of archival material, particularly to support language revitalization—efforts to increase and maintain the use of the language. There are calls for language documentation (and, by extension, language archiving) to prioritize revitalization efforts. This dissertation is a case study of one language archive collection: the Boro Language Resource in the Computational Resource for South Asian Languages (CoRSAL) archive. The Boro Language Resource was created by Boro community members who are both experienced in linguistics and pedagogy and active in language revitalization efforts including research, educational, and cultural initiatives. This case study explores how the collection was designed, and how the material will be used in future language revitalization activities. Because this collection exemplifies the view of language documentation and archiving as revitalization-driven practices, the findings of this case study stand to inform future community archiving efforts aiming to support language revitalization.
Development and Utilization of Big Bridge Data for Predicting Deck Condition Rating Using Machine Learning Algorithms
Accurately predicting the deck condition rating of a bridge is crucial for effective maintenance and repair planning. Despite significant research efforts to develop deterioration models, a nationwide model has not been developed. This study aims to identify an appropriate machine learning (ML) algorithm that can accurately predict the deck condition ratings of the nation's bridges. To achieve this, the study collected big bridge data (BBD), which includes NBI, traffic, climate, and hazard data gathered using geospatial information science (GIS) and remote sensing techniques. Two sets of data were collected: a BBD for a single year of 2020 and a historical BBD covering a five-year period from 2016 to 2020. Three ML algorithms, including random forest, eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), were trained using 319,404 and 1,246,261 bridge decks in the BBD and the historical BBD, respectively. Results showed that the use of historical BBD significantly improved the performance of the models compared to BBD. Additionally, random forest and XGBoost, trained using the historical BBD, demonstrated higher overall accuracies and average F1 scores than the ANN model. Specifically, the random forest and XGBoost models achieved overall accuracies of 83.4% and 79.4%, respectively, and average F1 scores of 79.7% and 77.5%, respectively, while the ANN model achieved an overall accuracy of 58.8% and an average F1 score of 46.1%. The permutation-based variable importance revealed that the hazard data related to earthquakes did not significantly contribute to model development. In conclusion, tree-based ensemble learning algorithms, such as random forest and XGBoost, trained using updated historical bridge data, including NBI, traffic, and climate data, provide a useful tool for accurately predicting the deck condition ratings of bridges in the United States, allowing infrastructure managers to efficiently schedule inspections and allocate maintenance resources.
Diversity, Equity, and Inclusive Behaviors of School Librarians: Perceptions during Times of Crisis
School librarians create an atmosphere where learners feel empowered. Moreover, school library programming should support diversity, equity, and inclusive learning opportunities to facilitate student success. Thus, school librarians are expected to model and advocate for equitable learning spaces while considering the universal design for learning approach to improving accessibility, utilization, and relevance for all library patrons. Although it has been established that school library standards support diversity, equity, and inclusion (DEI), more is needed to know about the impact of a crisis on school library programming and services. In addition, extensive research has not been conducted to determine school librarians' responsiveness and strategies to provide services for their school communities during disruptive times. This study examines school librarians' perceptions of the opportunities and challenges encountered while endeavoring to engage in DEI practices during crises.
Information Receptivity: The Information-Seeking Behavior and Networking Activity of Women in a Rural Texas County Judicial System
This study identified the information seeking behavior and networking practices used by members of a specific marginalized population, that of adult female probationers (AFPs) in a rural county in Texas. The study focused on how they seek out information when faced with a self-identified need in their lives. Beyond the basic "food, shelter and clothing" that comes to mind, the respondents find themselves faced with questions not only about the judicial system but also ones involving health care, employment, transportation, child-care, and other. The study utilized a qualitative research approach to gather data about the AFPs' information behavior and networking activities. The AFPs were asked about their information behavior during their time in the judicial system and after that involvement ended, and about their use of three points of information access: personal social network, physical resources, and electronic resources. Data was also gathered from community members (CMs) who have a role either within the judicial system or external to the judicial system. In its findings, the study determined there is no single point of access to a comprehensive listing of resources for the AFPs to utilize, and that AFPs reported seeking information via two ways as based on the type of question being raised. The study found that a hierarchy of needs should include access to the internet, if not an electronic device itself. The study also found there is a strong relationship between an AFP's personal social network and their support system, and that, beyond access to information, there is an element of information receptivity involved with their success. Based upon the insights provided by the AFPs and CMs, the study provides recommendations to improve information dissemination, especially about available resources, and to facilitate AFPs' access to same. With the study's conclusion, a report will be submitted to members …
Information-Seeking Behaviors of Rural Community-Based Hospice Social Workers during a Pandemic: Challenges and Opportunities
When it comes to hospice care, patients and their families residing in rural counties need competent rural community-based hospice social workers on their team. The study of information seeking behaviors of rural community-based hospice social workers during the global COVID-19 pandemic is significant as there is a need to fill gaps regarding how this specific medical discipline performs their job responsibilities when duties become more complicated due to evolving infection control protocols, decreased direct access to patients, and poor internet services. Community-based rural hospice social workers rely on up-to-date information and resources when providing support services to patients and their families. This has become particularly important during the global pandemic COVID-19. Utilizing Chatman's small world theory, this research lends itself to community-based hospice social workers identifying solutions to the challenges of finding timely and accurate COVID-19 information and resources for hospice patients and their families. Purposive sampling, semi-structured interviews, and qualitative social network analysis (SNA) with ATLAS.ti comprise the methodology for this research. The purpose of this study is to investigate the information seeking behaviors of community-based hospice rural social workers in order to understand how they select their COVID-19 information resources.
Relevance Criteria when Searching and Evaluating Online Video for Informational Use
Relevance is a core concept in the field of Information Science and a common term in everyday vernacular that generally refers to the usefulness of information. However, relevance has not been sufficiently or consistently defined or explored in the information science literature. Relevance criteria are the factors that information users employ when determining whether information they encounter is relevant. Identifying relevance criteria is a crucial step to understanding relevance. Relevance criteria employed with newer information formats like online video are especially important to study. Online video is now widespread, and people are increasingly likely to rely on video for information. This study identifies relevance criteria employed during relevance assessments of online video through a explanatory sequential mixed-methods study of frequent online video users including students, faculty, librarians, and video professionals. Methods included an online survey and interviews.
Security Aspects of Users' Information Sharing on Social Media
This study aims to investigate college students' security awareness of using social media in sharing information. The two theories that have guided this study are the theory of planned behavior (TPB) and the technology acceptance model (TAM). Data was collected from both undergraduate and graduate students from the University of North Texas (UNT) in Denton. The total responses included 380 students from different majors with 291 valid responses for data analysis; The structural equation model (SEM) Lavaan package was used to find out the best fit of the model. A diagonally weighted least squares (DWLS) was used to model the variables as ordinal in this study's analysis as ordinal data made the model fit substantially. The study found that 6 factors: attitude (AB), subjective norm (SN), perceived behavior control (PBC), perceived usefulness (PU), perceived risks (PR), and security awareness (SA) influenced behavior intention (BI). Also, I found that AB was influenced by PR and SA, as well as SN influenced by SA. Self-efficacy (SE) influenced PBC. On the other hand, the study found that controllability (C) did not influence PBC; perhaps, an individual's skills do not interact with social media security settings. Perceived ease of use (PEOU) did not influence BI; perhaps this occurred because of an individual's inability to prevent his or her information from being disclosed in the future, even if they had taken the right precautions. This study contributed to literature on understanding the nature of information sharing among college students on social media. The results may help college security professionals to evaluate or revise the rules and policies regarding cybersecurity and privacy.
Study of Information Behavior of Opportunistic Insiders with Malicious Intent
Enterprises have focused on mechanisms to track insiders who may intentionally exceed and misuse their authorized access. However, there is an opportunity to understand why a trusted individual would want to exploit the trust and seek information with the intent of a malicious outcome. The detection of insider rogue or nefarious activities with information to which a user is already authorized is extremely difficult. Such insider threats require more deliberation than just considering it to be a problem that can be mitigated only by software or hardware enhancements. This research expects to help gain an early understanding of antecedents to such information behavior and provide an opportunity to develop approaches to address relevant character traits which could lead to a higher propensity of information misuse. This research proposes a theoretical framework and a conceptual research model to understand the antecedent factors to opportunistic information-seeking behavior of individuals. The study follows the three-essay format. Essay 1 explores the scholarly literature published about insider behavior to understand information behavior and proposes the theoretical framework for the study. PRISMA methodology was used for the thematic literature review. Essay 2 is a quantitative study of 424 university students surveyed using an online instrument for their responses to various scenarios in the context of academic dishonesty. Academic dishonesty is proposed as a proxy for information misuse. Essay 3 is a qualitative study engaging senior executives from various industries to understand their perspectives on the behavioral characteristics of individuals as they try to protect their corporate information from being misused and protect their reputation and liability from malicious use of their information.
Transdisciplinary Information Flow and Key Challenges of Effective Knowledge Transfer between Expert and Novice
When the need arises to transmute complex and theoretical ideas into practice or communicate them to the uninitiated, there often exists a gap in the levels of understanding between the parties involved. This research examined knowledge transfer between practitioners within the information technology (IT) domain of interest by asking the following questions: what is the relationship between knowledge spaces for IT experts and novices; and what factors impact knowledge transfer among knowledge spaces for IT experts and novices? This study conducted interviews with a range of IT professionals to identify knowledge states that resulted in knowledge spaces for both experts and novice practitioners. A conceptual model was developed to examine the knowledge transfer process between expert and novice practitioners and identify factors affecting both the knowledge space and the knowledge states. The model also takes in consideration external factors such as organization and culture and organizational environment. The results from the study show that leadership and executive skillsets play a major role in characterizing the expert knowledge state. The results also show that knowledge transfer between and among groups was primarily impacted by interest and awareness factors among experts. Among novices, the largest barriers were cultural in nature (e.g., no management support or requirement) and environmental, such as the lack of aptitude for learning, lack of job/role experience from a new staff member, or existing staff with a hostile attitude.
Understanding Sociotechnical Factors Impacting Cybersecurity Controls on Mobile Devices and Smartphones at the Individual Level
Technological advances such as mobile technology, big data, and machine learning allow businesses to associate advertisements with consumer behaviors to maximize sales. Thus, information about consumer behavior became the central resource of businesses. Recent discussions and concerns about the emerging economic order centered around capturing consumers' data suggest that more research efforts be allocated to address new challenges in different domains, such as health, education, smart cities, and communication. Research on individual cybersecurity behavior is relatively new and requires more attention in academic research. This study has proposed and validated a cybersecurity behavioral model to enrich our understanding of users' behavioral intention (BI) to use cybersecurity controls. An online survey was used to collect information from University of North Texas (UNT) students to explore various technology usage determinants and specific computer security practices. The instrument measured the actual cybersecurity controls behaviors (ACB) by incorporating technical and social factors. Accordingly, the construct of ACB was created and validated to test how it relates to the participants' behavioral intentions. The findings confirm a large number of the proposed relationships. Additionally, the results show that the model explained a significant amount of variance in the proposed dependent variables BI and ACB. Within the context of information control behavior, the relationships between the study's constructs suggest adequate generalizability and robustness of the study's theoretical framework.
Electronic Health Record Systems and Cyber Hygiene: Awareness, Knowledge, and Practices among Physicians in Kuwait
This study explored issues related to the adoption and implementation of electronic health record (EHR) systems including building the awareness, knowledge, and experience of physicians toward cyber hygiene. This study used a qualitative research method to assess (a) the barriers to EHR systems adoption and implementation in Kuwait and (b) the level of awareness, knowledge and experiences related to cyber hygiene practices in Kuwait. The findings of the study supported the conceptual framework used to guide the research of the factors impacting the adoption and implementation of EHR systems in Kuwait as well as explore the level of awareness, knowledge, and experience of physicians about both EHR systems and cyber hygiene. The results from the systematic literature review analysis identified seven major barriers. These are financial barriers, time, difficulty of using technology, lack of support, negative attitude, legal and ethical (policies), and cultural barriers. The findings from the semistructured interviews supported the literature findings and provided more in-depth insights into the structural and social issues affecting the adoption and implementation of EHR systems. Given that Kuwait is a member of the Gulf Cooperation Countries (GCC), the results from the literature analysis showed that the problems in Kuwait are similar to those in the GCC. However, the issues confronting the adoption and implementation of EHR seems to be more prevalent in Kuwait compared to other GCC members. The results from the semistructured interviews concerning behavior toward cyber hygiene supported the constructs identified in the conceptual model. The majority of the physicians interviewed lacked the awareness and knowledge needed to practice cyber hygiene. Lack of standards, regulations, and policies impacted the norms and practices of physicians. Most physicians were not aware of regulation or standards pertaining to the use of EHRs. The study contributed significantly to bridging the existing knowledge and literature …
Factors Impacting Physician Patient Interaction Time: Knowledge Transfer and Use of Technology
In this study we explore the factors impacting physician patient interaction time and how these factors can be used to improve health knowledge transfer from physicians to patients. We also investigate how technology tools can be used to improve this interaction time. Physician patient interaction time is important because this is the time when both sides engage with each other, exchange information so that physicians understand their patients' health issues. Given the increasing health care costs and demand for physician time, over the years, this interaction time has been distracted due to many factors. It is important to explore these factors and provide some possible solutions, such as advanced knowledge management systems, such as knowledge portals. To identify the factors impacting this interaction time and the role of technology, we first identified the variables and then developed hypotheses. We used data from the surveys administered by the AHRQ and NCHS to test these hypotheses. We conducted correlation analyses to determine the factors that can be used to improve health knowledge transfer from physicians to patients and how technology tools can be used to improve physician patient interaction time. Our analyses indicate that the factors we identified to improve health knowledge transfer and physician patient interaction time were statistically significant. In this study, we introduced the concept of knowledge management to physician patient interaction time, identified the factors that can be used to improve health knowledge transfer, and paved the way to use technology tools as knowledge tools in medical settings and practices.
Factors Influencing User Experience and Consumer Behavioral Intention to Use Visual Analytics Technology
The purpose of this study was to assess visual analytics technology acceptance and user experience among in vitro fertilization (IVF) consumers. The research aimed to show how visual analytics tools and technologies can be applied in the consumer space to enhance how users interpret healthcare success rate data. This exploratory user evaluation study utilized a quantitative dominant, mixed-methods approach with a convergent parallel design based on the data-validation variant. Survey data were collected from consumers who were currently seeking information about IVF treatment in the United States. The study findings indicated that the extended unified theory of acceptance and use of technology (UTAUT2) constructs of performance expectancy and hedonic motivation influenced consumer behavioral intention to use visual analytics technology, while effort expectancy did not. Further, the findings from the user experience and qualitative analyses indicated that there is strong support for consumer adoption of visual analytics technology for personal healthcare decision-making. These findings may help in the design and development of modern, interactive visualization tools that could be used to visualize public or private healthcare data for analysis by consumers. Stakeholders, including the US Centers for Disease Control and Prevention, the World Health Organization, and medical practitioners, may use the findings to develop improved interactive tools for consumer analysis of large, multi-dimensional data sets.
Personalized Recommendation Using Aspect-Aware Knowledge Graph Learning
This study aims to apply user reviews and numerical ratings toward items to create an aspect-aware high-order representation for a recommendation system. We propose a novel aspect-aware knowledge graph recommendation model (AKGR) with the deep learning method to predict users' ratings on non-interacted items, from which more personalized recommendations can be made. First, we create a sequence-to-sequence encoder and decoder model by exploiting contextual and syntactic information in user reviews to extract aspects critical to items. Then we utilize the principal component analysis (PCA) and the K-means clustering to analyze the extracted aspects for category classification. Based on the aspects, we construct a graph structure to connect users and items which share the same aspect-based opinions for mining user preferences and item attributes. Finally, we combine the user and item latent features from the reviews and the user-item rating matrix to complete the rating prediction task by applying the factorization machine model. We conducted experiments on three aspect extraction datasets and five rating prediction datasets. To verify the effectiveness of the proposed aspect extraction model and rating prediction model, comparison experiments were made with some state-of-the-art baseline models, such as double embeddings convolutional neural network (DE-CNN) and dual graph convolutional network (DualGCN). The experiment results revealed that our proposed aspect extraction model had the best performance for the three datasets with an F1 score of 82.41%, 88.57%, and 73.39%. In the experiments of rating prediction, the proposed AKGR model achieved the best MAE and MSE scores on the five datasets, and there was an average improvement of 4.48% against the best baseline.
A Comparison of Plaintiff and Defense Expert Witness H-Index Scores in Mild Traumatic Brain Injury Civil Litigation
This study examines the background and qualifications of plaintiff and defense experts using the H-Index score as quantification of expert background and qualifications. The goal is to better understand the similarities and differences among the professionals offering paid expert witness testimony in mild traumatic brain injury (mTBI) civil litigation. In this quantitative study, descriptive statistics include the mean and standard deviation scores for the data to support examining measures of central tendency and variance, respectively. The study includes the use of logistic regression and the Wilcoxon signed-rank test, and their statistical assumptions were tested to determine whether they would be used or if it was more appropriate to use a non-parametric test. The study included two research questions: How do the qualifications of plaintiff and defense expert witnesses in mild traumatic brain injury civil litigation compare? and to what extent does a higher h-index correlate with a favorable litigation outcome in a mild traumatic brain injury case? The findings for the hypothesis tests associated with the research questions led to the acceptance of the null hypothesis in each test. There was a lack of asymptotic significance in Hypothesis 1 and a lack of significance in Hypothesis 2. The findings from these tests shall support the discussion of the implications of this research and the direction of future research.
Understanding the Relationship between Critical Literacy, Cultural Literacy, and Religious Literacy for Second-Generation Immigrants
This study explores information seeking behavior of second-generation Muslim immigrants utilizing factors such as critical, cultural, and religious literacy skills. The study examined the second-generation immigrants' ability to balance their parents' and grandparents' native culture and traditions with the culture and traditions of their country. The interview questions were designed using the cognitive authority theory and the figured worlds theory that provides an explanation for the mentality of those who are in environments influenced by culture or religion. An interesting main finding of the study is that participants sought more religious-based rather than culturally-based information. Participants seek information from their parents, communities, and religious leaders, but are particular with who they consider credible and reliable; if the person providing the information follows a similar lifestyle to the participants, they are more likely to hold cognitive authority. Four different themes emerged from the study. The first is "religious focus" where many participants stated that religion is rather static whereas culture can evolve and change with time, location, and events. The second theme emerged is the reliance on family members for religious literacy given the close upbringing of Muslim extended family system. The third theme indicated that although information seeking behavior relied on Google and mobile devices to locate information, in verifying religious content they depended on parents and religious cognitive authorities. The fourth theme emerged is the loss of richness going forward and the concerns about the possible decline in religious information literacy for future generations.
Weight Initialization for Convolutional Neural Networks Using Unsupervised Machine Learning
The goal of this work is to improve the robustness and generalization of deep learning models, using a similar approach to the unsupervised "innate learning" strategy in visual development. A series of research studies are presented to demonstrate how an unsupervised machine learning efficient coding approach can create filters similar to the receptive fields of the primary visual cortex (V1) in the brain, and these filters are capable of pretraining convolutional neural networks (CNNs) to enable faster training times and higher accuracy with less dependency on the source data. Independent component analysis (ICA) is used for unsupervised feature extraction as it has shown success in both applied machine learning and modeling biological neural receptive fields. This pretraining applies equally well to various forms of visual input, including natural color images, black and white, binocular, and video to drive the V1-like Gabor filters in the brain. For efficient processing of typical visual scenes, the filters that ICA produces are developed by encoding natural images. These filters are then used to initialize the kernels in the first layer of a CNN to train on the CIFAR-10 dataset to perform image classification. Results show that the ICA initialization for a custom made CNN produces models with a test accuracy up to 12% better than the standard model in the first 10 epochs, which for specific accuracy thresholds reduces the number of training epochs by approximately 40% (to reach 60% accuracy) and 50% (to reach 70% accuracy). Additionally, this pre-training results in marginally higher accuracy even after extensive training over 50 epochs. This proposed method of unsupervised machine learning to pre-train weights in deep learning improves both training time and accuracy, which is why it is observed in biological networks and is finding increased application in applied deep learning.
Adoption of Wearable Devices by Older Adults
This dissertation is organized in a traditional format while including three essays that address specific research questions. Essay 1 examined the relationship between physical activity and community engagement and their effect on mental well-being among older men and women. Data from National Health and Aging Trends Study (NHATS) from 2018 to 2020 were explored and the posited relationships were tested. This essay provides empirical support that older adults who are reasonably active and involved in the community have greater mental well-being than those who isolate themselves. Essay 2 provides insight into older adults' motivation to improve their physical activity through the use of a fitness tracker. The key finding from this study is that wearables, especially fitness trackers, can significantly facilitate increased physical activity. Essay 3 is a mixed-methods study to understand older adults' perception of the usefulness of fitness trackers and interaction with such devices. Findings suggest that to increase the adoption of fitness trackers among older adults, makers could improve the esthetics and quality of the wristband in addition to the battery life of the tracker.
Data Quality Evaluation and Improvement for Machine Learning
In this research the focus is on data-centric AI with a specific concentration on data quality evaluation and improvement for machine learning. We first present a practical framework for data quality evaluation and improvement, using a legal domain as a case study and build a corpus for legal argument mining. We first created an initial corpus with 4,937 instances that were manually labeled. We define five data quality evaluation dimensions: comprehensiveness, correctness, variety, class imbalance, and duplication, and conducted a quantitative evaluation on these dimensions for the legal dataset and two existing datasets in the medical domain for medical concept normalization. The first group of experiments showed that class imbalance and insufficient training data are the two major data quality issues that negatively impacted the quality of the system that was built on the legal corpus. The second group of experiments showed that the overlap between the test datasets and the training datasets, which we defined as "duplication," is the major data quality issue for the two medical corpora. We explore several widely used machine learning methods for data quality improvement. Compared to pseudo-labeling, co-training, and expectation-maximization (EM), generative adversarial network (GAN) is more effective for automated data augmentation, especially when a small portion of labeled data and a large amount of unlabeled data is available. The data validation process, the performance improvement strategy, and the machine learning framework for data evaluation and improvement discussed in this dissertation can be used by machine learning researchers and practitioners to build high-performance machine learning systems. All the materials including the data, code, and results will be released at: https://github.com/haihua0913/dissertation-dqei.
Digital Equity in K-12 Education: Conceptualization and Analysis of Students' Digital Opportunity
Although digital equity is a recognized challenge in our K-12 school system, there is little research in using a holistic framework to investigate pre-conditions necessary for K-12 students to participate in digital learning and online processes. A conceptual framework of students' digital opportunity (SDO) is developed to represent the essential components of digital connectivity. The four key components are broadband internet availability, broadband usage, digital device ownership, and speed quality. A composite measure of SDO was created to quantitatively represent and measure the differences across 3,138 counties in the United States. Furthermore, spatial autocorrelation was applied to evaluate if the distribution of the SDO score is associated with geographical characteristics at the county level. The result showed the presence of significant county-level clusters with concentrations of high or low SDO scores. While the spatial analysis provided evidence of where the gaps in digital opportunities are located, there are underlying factors at the micro level that would need further investigation. This study suggests a collective approach between private and public entities to address the K-12 digital equity issue. The necessary conditions presented in the SDO model must be addressed first in order to bring change to K-12 students and schools in terms of obtaining high quality and reliable broadband internet and digital devices for learning with technology. Two research outputs are available from this research to allow others to further evaluate digital equity among K-12 schools and students.
Life Coaches, Communities of Practice, and Everyday Life Information Seeking and Practices: An Exploratory Case Study
Life coaching is a rapidly expanding industry that focuses on client development, enhancement of life experience, and goal attainment often when clients are experiencing personal, professional, and social change. Online communities of practice (CoPs) provide opportunity for individuals to connect, share experiences, and learn from each other under the auspices of a unifying theme or subject. Since the 1990s, CoPs have spread from education to other areas of business and industry and continue to shape participant professional development. However, the everyday life information seeking and practices of life coaches remains unexplored within information science literature from the perspectives of life coach engagement in seeking information, life coach engagement in CoPs, and life coach interactions with other coaches. The purpose of this research study was to explore life coach perspectives of coaching, the diverse information needs of life coaches, the types and strength of relationships between life coaches and CoPs, the role of coaching certification and/or licensing as contributing to the professionalization of life coaching, and the means of communication exchange by life coaches through information communication technologies. This mixed method study focused on life coaches who self-identify as belonging to a CoP and those that do not. Theoretical frameworks for this study included Savolainen's everyday life information seeking (ELIS) and Wegner and Lave's characterization of CoPs. Methods utilized for this study included an online survey, semi-structured interviews, and social network analysis.
Social Networking Sites Usage Behavior: Trust and Risk Perceptions
This dissertation addresses research questions related to defining user's trust and risk perceptions associated with social networking usage behavior in relation to the repeated privacy and security breaches. The general research question is explored in the dissertation via the conduct of three related studies. The finding from these three investigations are presented in the results section as 3 essays that collectively examine the social networking sites usage behavior. Essay 1 proposes a conceptual model based on the review of multiple breaches. The review provides a conceptual model which is further analyzed using a quantitative survey in the second essay. Essay 2 measures the trust and risk perceptions associated with different sources of information when presented with multiple breaches. This portion of the research used a quantitative method that included surveying of college students from University of North Texas (UNT) to understand the relation between user's trust and risk perceptions. Essay 3 examines the social networking usage behavior on account of repeated privacy and security breaches. This essay uses the insights from the other two essays to identify the usage behavior and how it is affected. The proposed model was tested using a survey questionnaire method. Results show a significant relationship between the positives, negatives, technology usage, repeated breaches that impacts usage behavior. The dissertation concludes with a summary of how the three essays make a cumulative contribution to the literature as well as providing practical guidance that identifies social networking usage behavior.
An Information Theoretic Analysis of Multimodal Readability
Educators often inquire about the readability of books and other documents used in the classroom, with the idea that readability supports students' reading comprehension and growth. Documents used in classrooms tend to be language-based, so readability metrics have long focused on the complexity of language. However, such metrics are unsuitable for multimodal documents because these types of documents also use non-language modes of communication. This is problematic because multimodal reading is increasingly recognized as a 21st-century skill. One information theoretic solution is transinformation analysis, an approach that measures readability as the difference between the objective entropy of a document and the subjective entropy of its reader. Higher transinformation indicates more information complexity. This study explored the viability of transinformation analysis as a measure of multimodal readability. Think aloud screen recordings from 15 eighth grade "advanced readers" of Episode 2 of the born-digital novel, Inanimate Alice served as the dataset. Findings showed that 14 of the readers attended to less than half the information in the story. Mean readability was .57, indicating a complex reading experience. Readers attended to and recalled information primarily from the linguistic mode, which may have been a strategy for reducing cognitive load, or it may have reflected beliefs that reading is a language-based activity. The strong traditional readers in this study appeared to be weak at multimodal reading. In addition to its theoretical and methodological implications, the study's findings have implications for the practical need to create more opportunities for multimodal reading experiences in contemporary classrooms and libraries.
Modeling Email Phishing Attacks
Cheating, beguiling, and misleading information exist all around us; understanding deception and its consequences is crucial in our information environment. This study investigates deception in phishing emails that successfully bypassed Microsoft 365 filtering system. We devised a model that explains why some people are deceived and how targeted individuals and organizations can prevent or counter attacks. The theoretical framework used in this study is Anderson's functional ontology construction (FOC). The methodology involves quantitative and qualitative descriptive design, where the data source is the set of phishing emails archived from a Tier 1 University. We looked for term frequency-inverse document frequency (Tf-idf) and the distribution of words over documents (topic modeling) and found the subjects of phishing emails that targeted educational organizations are related to finances, jobs, and technologies. Also, our analysis shows the phishing emails in the dataset come under six categories; reward, urgency, curiosity, fear, job, and entertainment. Results indicate that staff and students were primarily targeted, and a list of the most used verbs for deception was compiled. We uncovered the stimuli being used by scammers and types of reinforcements used to misinform the target to ensure successful trapping via phishing emails. We identified how scammers pick their targets and how they tailor and systematically orchestrate individual attack on targets. The limitations of this study pertain to the sample size and the collection method. Future work will focus on implementing the derived model into software that can perform deception identification, target alerting and protection against advanced email phishing.
PubMed Commons: What Happened on the Way to the Forum? Retrospective Explanatory Case Study Research and Lessons Learned from the U.S. National Library of Medicine's Online Forum for Open Science
The U.S. National Library of Medicine brought the intensifying interest in open science to national attention when it joined enthusiastic scientists to introduce and host an Amazon-like rating forum on PubMed—the world's largest database of indexed biomedical and life sciences literature. The result was PubMed Commons. In June 2013, the commenting forum was introduced for open discussion about published scientific literature as part of a three-pronged approach to improve research rigor, reproducibility, and transparency. In Feb. 2018, the forum was unexpectedly discontinued. This retrospective explanatory case study research asked the question, "What happened on the way to the forum?" Answers came from a variety of resources using multiple methodologies for data collection and analysis. Historical data from PubMed Commons' 7,629 comments and 1,551 commenters; key informant interviews with PubMed Commons editors; and a systematized search for published articles, gray literature; and social media content about PubMed Commons were analyzed using computer-mediated discourse analysis and a social network analysis. Results from the quantitative content analysis described a forum with little participation, and the qualitative content analysis demonstrated that active forum members were focused primarily on providing links to other information resources and discussing aspects of post-publication peer review. The social network analysis revealed a disconnected network, which was supported by a sociogram showing a community of independents with only seven small clusters. Findings pointed to 11 factors that affected the forum's adoption and use. Rogers' diffusion of innovation theory scaffolds a forum innovation agility model developed from this work to offer a better understanding of organizational processes and to aid organizations interested in introducing and managing a similar forum. PubMed Commons was a missed opportunity. No comparable alternative is available to promote open science and serve as a tool for the expected paradigm shift in the way we do scholarly communication …
Take the Trouble to Compile a Whole New World: The Role of Event-Based Participatory Projects in Institutional Archives
Event-based mediated participatory archives, in which communities of ordinary people contribute their records to archives during collection day events represent a paradigm shift within the archival field. Applying a qualitative approach, this study investigates event-based mediated participatory archives using Bastian's communities of records and memory as a guiding framework. Using the Mass. Memories Road Show as a case study, data collection and analysis took place over three phases. In Phase I, archive supporting documents were collected and analyzed using "against the grain" historical analysis methods. In Phase II, data from the Mass. Memories Road Show digital collections were collected and analyzed using grounded theory analysis methods. In Phase III, ethnographic research data, including a direct observation and semi-structured interviews, was collected and analyzed using ethnographic analysis methods. The results of this study suggest that community participants' motivations to contribute to participatory archives are rooted in self-fulfillment while institutional archives personnel members' intentions are based in inclusive community-building. Furthermore, the contribution of records to the archives allows community participants to share personal stories that serve as evidence of their historical legacies and as affirmation of their roles in their communities. Throughout the findings, moments of connection which enable the sharing of stories are highlighted—speaking to the importance of the collection day event.
Understanding the Knowledge, Skills, and Abilities (KSAs) of Data Professionals in United States Academic Libraries
This study applies the knowledge, skills, and abilities (KSA) framework for eScience professionals to data service positions in academic libraries. Understanding the KSAs needed to provide data services is of crucial concern. The current study looks at KSAs of data professionals working in the United States academic libraries. An exploratory sequential mixed method design was adopted to discover the KSAs. The study was divided into two phases, a qualitative content analysis of 260 job advertisements for data professionals for Phase 1, and distribution of a self-administered online survey to data professionals working in academic libraries research data services (RDS) for Phase 2. The discovery of the KSAs from the content analysis of 260 job ads and the survey results from 167 data professionals were analyzed separately, and then Spearman rank order correlation was conducted in order to triangulate the data and compare results. The results from the study provide evidence on what hiring managers seek through job advertisements in terms of KSAs and which KSAs data professionals find to be important for working in RDS. The Spearman rank order correlation found strong agreement between job advertisement KSAs and data professionals perceptions of the KSAs.
Artificial Intelligence Teammates in a Collaborative Information Seeking Environment from the Perspective of Women Engineers in the United States
The purpose of this study was to collect design requirements from women engineers on artificial intelligence teammates such as Microsoft Teams, Slack, and Trello. A mixed methods research design was used for this study with an online survey and semi-structure interviews. The study results revealed design requirements from women engineers including solutions to sociotechnical issues that could arise from artificial intelligence teammates in the workplace. The results showed various ways women engineers collaborate in the workplace with and without artificial intelligence. Additionally, women engineers' attitude towards artificial intelligence was examined to identify if there was a correlation to self-efficacy. This research study fills a previous study gap that solicited design requirements from research scientists, by soliciting practitioners. Practitioners such as women engineers are underrepresented in the workplace, and they could benefit from an artificial intelligence teammate with their design requirements. Finally, this study contributes to the information science literature on collaborative information seeking, artificial intelligence design, and engineers' information seeking behaviors.
A Quantitative Approach to Understand Cyberbullying
After more than two decades, bullying and cyberbullying is still negatively impacting the lives of many of our youth and their families. The prevalence of the phenomenon is widespread and part of the everyday life activities. The impact of cyber aggression and violation can have severe consequences, up to the destruction of lives. While cyberbullying prevention programs exist, not much progress seems to have been made in the effort to combat the phenomenon. This research provides new insights into how to extract information by using existing research and online news articles, with the aim to create new or improve existing cyberbullying prevention efforts. The intent is to inform prevention programs.
The Status of the Organization of Knowledge in Cultural Heritage Institutions in Arabian Gulf Countries
No published studies to date examined the practices in creation or adoption of metadata in cultural heritage institutions or evaluated metadata in bibliographic databases in the Arabian Gulf counties and assessed its potential interoperability in the aggregation that would provide a central point of access to bibliographic databases of cultural heritage institutions. This exploratory study aimed to address this gap with the goal of: (1) developing understanding of the current state of information representation and knowledge organization in cultural heritage collections in Arabian Gulf countries, and (2) exploring perspectives for future developments such as creating regional large-scale portals similar to Digital Public Library of America, Europeana etc. that facilitate discovery by aggregating metadata and possible barriers to these developments.. The study is focused on a Kuwaiti, Qatari, and Omani libraries, museums, and archives. The mixed-methods research combined semi-structured interviews of the bibliographic database managers at 15 cultural heritage institutions and in-depth content analysis of a sample of 412 metadata records that represent items in these bibliographic databases for accuracy, completeness, consistency, use of knowledge organization systems, etc. This study findings make a research contribution important for evaluating the feasibility and planning of future aggregations of cultural heritage bibliographic databases. Results provided insights into possible ways to achieve interoperability in metadata for such digital portals in the Arabian Gulf region.
Sustaining Multilinguality: Case Studies of Two Multilingual Digital Libraries
Digital libraries have become valuable learning resources for information users. However, language barriers have greatly limited information access for many digital libraries, as users do not understand those languages. This study explored technical and operational challenges digital libraries faced in sustaining multilinguality. Using the multiple-case method, the study investigated two digital libraries that have sustained multilinguality for over a decade: the World Digital Library and the Digital Library of the Caribbean. On-site interviews were conducted at both digital libraries and the related documents were analyzed. The findings of the study showed that the two multilingual digital libraries faced many technical and operational challenges and employed various approaches to find solutions. A model of challenges and approaches in sustaining multilinguality was presented. As the first such case study, this research enriches the existing literature, and has theoretical, practical, and methodological implications for the research of multilingual digital libraries. The findings of the study provide useful guidelines and insights for the digital library community in sustaining multilingual services.
Understanding the Diffusion of COVID-19-Related Information on Social Media
Very few studies have examined information sentiment and explored other factors that contribute to health information dissemination and sharing. In particular, there is a lack of studies that performed these combined analyses in the modern social network environment during the disease outbreaks, such as with zika, ebola, SARS, or COVID-19. This study aimed to fill the gap in the literature by investigating what drives social media users to widely disseminate health-related information during a pandemic. Diffusion of innovation theory and theory of planned behavior were the basis of the theoretical approach utilized to answer the research questions. The two theories identified antecedents of sharing online health information. Data was collected through an online survey distributed to students in a higher education institution in the United States of America. The study revealed the extent of the relationships between the four major factors derived from the previous literature—attitudes toward sharing, beliefs toward source, peer influence, and information sentiment—and the behavioral intention to share information. The results would support the information science literature by offering and testing a new model that identifies the factors that affect users' intentions to share health information in the social network environment. This study will further the understanding and application of health information behavior research.
At the Junction of Dissemination and Implementation: Facilitating Access to Behavior Analytic Research
Research in scholarly communication is usually limited to the use and dissemination of scientific material by scholars. This excludes the transfer of knowledge from research producers to service providers. Some may argue the primary function of science is to investigate the conditions in the lab so everyday interactions with the environment are more effective and efficient. This is the underlying philosophy of the science of behavior analysis. Comprised of a basic science, an applied science and a philosophy the field of behavior analysis relies on research developments to inform effective practice. Guided by dissemination processes studied in information science, this investigation revealed the content layer in behavior analysis is primarily comprised of journal articles. Ninety four percent of the research artifacts cited in the current content layer are from journal articles. Other dissemination channels used to develop the behavior analytic content layer included scientific magazine articles, oral reports, dissertations and theses, and unpublished manuscripts. The information use environment for professionals in this field is very different than that of the scholars; most professionals do not have access to a university library. Therefore, the research producers are disseminating developments via communication channels some service providers cannot access. This investigation reveals the only dissemination channel that provides continuous access to the content layer is reaching out via informal communication; All other dissemination channels do not provide access to the entire content layer, do not provide the entire scholarly work, and/or include a barrier to access (often an associated cost). This is a concern for the field of behavior analysis as professional recommendations cannot be based on the best available evidence if the evidence is not accessible. This is a concern for the field of information science as the study of scholarly communication should not be limited to scholars alone. The process of …
Factors that Affect HIPAA Compliance: A Bibliometrics Study
According to the U.S. Department of Health and Human Services (HHS), patients and providers do not understand the Health Information Privacy and Accountability Act (HIPAA). Non-compliance with HIPAA is primarily due to confusion, along with insufficient understanding. HSS has taken measures to simplify the language they use to communicate HIPAA, however, they have not taken steps that consider if one's culture, religious and social perspectives, institutional training, credentials, and comprehension of legal terminology affects medical providers and non-clinical administrative personnel's abilities to understand HIPAA. This research uses bibliometrics to examine the literature from January 2010 – September 2020 that addresses HIPAA's use of legal terminology, literacy level, and institutional training, along with religious and social perspectives, and credentials of medical providers and non-clinical administrative personnel. A total of 107 articles were examined, 42 were assigned article influence scores with values that were less than 1.00, which is a below-average influence score for the article. There were 29 articles with values equal to or above 1.00, which translates to an equal or above-average influence score. The remaining 36 articles did not have article influence scores and were assigned values as not available. Results of the review of the literature indicate that legal terminology, literacy level, training, credentialing and religious and social perspective had no or little effect in understanding HIPAA.
The Scope and Value of Healthcare Data Science Applications
Health disparities are a recognized public health concern and the need to address these disparities remains worthy of bringing new methods that assist in closing the gap. This research examined the effectiveness of data science to highlight health disparities, and to convey the value of data science applications in related health care applications. The goal of this research was accomplished by undertaking a multi-phased and multi-method approach, best represented in three individual essays. In essay one, a systematic literature review assessed the state in current academic literature of data science applications used to explore health disparities and to determine its applicability. The systematic review was guided by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. Essay two assessed the capacity of data science software to address the effectiveness of these data science technologies in examining health disparities data. This was conducted using KDnuggets data pertaining to analytics, data science, and machine-learning software. The research in this essay demonstrated the potential utility of leading software to perform the kinds of data science operations that can achieve improved care in healthcare networks by addressing health disparities. Essay three provided an appropriate case study to showcase the value data science brings to the healthcare space. This study used a geographic information system to create and analyze choropleth maps to determine the distribution of prostate cancer in Texas. SPSS software was used to assess the social determinants of health that may explain prostate cancer mortality.
Document and Information Experience in Virtual Zenanas: An Exploration of a Diaspora Small World
The word diaspora is currently understood as the large scale voluntary movement of people, along with capital and goods due to the mechanisms of globalization. Adopting a diaspora, gender and leisure perspective, this dissertation looked at the information and document experiences of a particular fan community of women belonging to the Indian diaspora and the online spaces created and occupied by them (fan fiction blogs which can be viewed as book clubs). The study also looked at memory making and documenting of the same as a part of document experience, resulting in what can be termed as "serendipitous memory archives." The blogs hosting fan fiction and the mediated practices they support were viewed as documents for the study. The online spaces were conceptualized as small worlds and the theoretical framework used for the study consisted of a preliminary model of a small world (based on literature review and my understanding of the world under study), information experience as a concept as well as document experience models. The results show that social ties play a big role in the information and document experience, while memory making and documenting of the same are also seen to happen as part of the document experience. The results also show that adopting a document perspective enables us to see the myriad ways in which information is experienced, freeing us from considering as information only that which helps us in meeting a purpose or which fills a gap. Implications and suggestions for future research are discussed.
Examination of Online Health Information Seeking Effectiveness: Case Studies of Online Health Communities in COPD Patients
When people access online health information, unfortunately, they have access to both clinically accurate and inaccurate information that they may then utilize to make informed personal health decisions. This research fills a gap in the literature of online health communities as they relate to chronic obstructive pulmonary disease (COPD). The conduct of this research required a multi-phased and multi-method approach, best presented in three distinct essays. In Essays 1 and 2, data gathering within two online health communities specific to COPD allowed this study to address three research questions: (1) what are the information needs of COPD patients that result in their participation in online health communities; (2) what are the information sources offered to the participants in these online communities; and (3) is the information obtained via those communities credible. Essay 1 harvested data from a moderated website hosted by a non-profit organization for patients with COPD and Essay 2 harvested data from a non-moderated Facebook group also serving this unique group. Data Miner, a Chrome extension designed to extract data, was used to collect data, key words and themes which brought an understanding of the health information needs of participants and identified what health information sources were preferred. Using NIH guidelines, the credibility of sources exchanged were evaluated for both groups. The research presented in Essay 1 showed that COPD patients have health information needs and that a clinically monitored social health online community, that is available 24/7 to answer questions that arise at the time of need, provides much needed support. The research in Essay 2 illustrates the need for healthcare workers to be aware of unmoderated sites and promote these sites for the purpose of socialization only, and not for medical information. Building on the knowledge gained through the data analysis in Essays 1 and 2 …
Factors Impacting Performance Measurement and Knowledge Transfer in a Training Environment
Most training performance measurement tools rely heavily on quantitative metrics that do not consider factors impacting knowledge transfer and behavioral change such as social relationships and company culture. This study observed a training performance measurement tool for a major U.S.-based airline. Analysis of the measurement tool consists of: a pilot study providing a baseline for the current gaps in training performance measurement, a survey of flight attendants to understand how company cultural and social factors impact learning and knowledge transfer, and focus groups to provide an in-depth analysis of what the underlying company cultural and social factors are. Both quantitative and qualitative analysis were utilized to identify the impact of cultural factors and social relationships on performance measurement to provide in-depth understanding of the role of tacit knowledge transfer in the training environment. Results show that cultural factors such as empathy, coaching, and on-the-job training, negatively impact the accuracy of flight attendants' ability to measure learning and knowledge transfer. A second finding shows social factors, personality, and agreement, show a strong trend towards negatively affecting the ability to accurately measure learning and knowledge transfer. The proposed knowledge transfer measurement model was further modified to reflect the findings and the results of this study. Further recommendations include altering the measurement tool, prioritization of skills, and communicating purpose.
Information Seeking Behaviors of Transitioning Veterans When Job Hunting in North Texas
This study explored a part of our population that can be misunderstood, marginalized, and underserved: veterans who are seeking to transition from the military to employment in North Texas. At the time of this research (before the COVID-19 pandemic), overall unemployment in North Texas was only 3.9%.Veteran unemployment was calculated at approximately the same before considering the underemployed veterans or those who have given up finding employment (and before the COVID-19 global pandemic), and that calculation likely exceeds 16-18% according to the Texas Workforce Commission. By understanding the information-seeking behaviors of the veteran population targeting North Texas for future employment, their ability to find useful information for successful relocation, attainment of employment, and the resources that enables their sense-making processes, the services provided to veterans seeking employment can be improved. Further understanding can be gained by using a qualitative approach that references Dervin's sense making model (SMM) as the framework. The revelations and conclusions can be used to improve existing programs, inspire new programs, and provide answers that are useful to the Veteran's Administration (VA), other veteran-focused organizations, policymakers, non-profits who serve veterans, veterans themselves, and future employers who enable successful transitions by providing satisfying and inclusive employment opportunities for veterans.
User Privacy Perception and Concerns Regarding the Use of Cloud-Based Assistants
Cloud-based assistants like the Google Home and the Amazon Alexa have become ubiquitous in our homes. Users can simply communicate with the devices using a smartphone application. There are privacy concerns associated with cloud-based assistants. For example, users do not know what type of information is being sent to the device manufacturer, if the device is stealthily listening to conversations, data retention, or who else has access to the data. Privacy is about perception. The goal of this study is to determine user privacy concerns regarding cloud-based assistants by adopting a quantitative research method. The study used a privacy decision framework that lists three core components, which are technology controls, understanding user privacy preference, and government regulations. The research used Dervin's sensemaking model to describe users' privacy perception using the privacy decision framework and improved on a privacy perception survey instrument from previous dissertations. An understanding of user privacy concerns with cloud-based assistants is required to provide a comprehensive privacy guidance to stakeholders. The significance of this study is in the identification of the privacy perception of users of cloud-based assistants and the extent to which the components of the theoretical framework can impact user privacy perception. The results of this study serve as a guide for device manufacturers and other stakeholders in prioritizing privacy design decisions.
Examining Human Information Behavior on Social Media: Introducing the Concept of Social Noise
Social media information behavior is increasingly critical, impacting not only individuals and groups but the beliefs, values, and direction of society and culture. The purpose of this study was to investigate how persistent observation by members of the online network influences social media users' information behavior, resulting in the phenomenon of social noise. Data analytics, including LDA, LSA, and clustering methodologies, were performed but could not provide information about the users' motivations. Using an ethnographic approach, participant observations and interviews were conducted with Facebook users as they interacted with informational posts, and the data collected was coded using a recursive method. Four key constructs of social noise were identified, and sub-codes were assigned within each construct as patterns emerged, providing insight into the different facets of social noise. Additionally, in most instances more than one of the four constructs were present, layering their influence on the information behavior. Based on these findings, social media users are not always interacting with information based on true personal beliefs or desires; instead, concerns surrounding their personal image, relationships with others, core beliefs, and online conflict are influencing their observable information behavior. The results of this exploratory study provide a basis to further develop the social noise model. Qualitative data provides insight into the thinking and motivations behind social media users' observable information behavior, specifically in the areas of cultural agency, relationship management, image curation, and conflict engagement.
Exploration of RDA-Based MARC21 Subject Metadata in Worldcat Database and Its Readiness to Support Linked Data Functionality
Subject of information entity is one of the fundamental concepts in the field of information science. Subject of any document represents its intellectual potential -- 'aboutness' of the document. Traditionally, subject (along with title and author) is the one of three major ways to access information, so subject metadata plays a central role in this process and the role is constantly growing. Previous research concluded that the larger bibliographic database is, the richer subject vocabularies and classification schemes are needed to support information discovery. Further, a high proportion of information objects are unretrievable without subject headings in metadata records. This exploratory study provides the analysis of the subject metadata in MARC 21 bibliographic records created in 2020; and develops understanding of the level and patterns of 'aboutness' representation in the MARC 21 bibliographic records. Study also examines how these records apply the recent RDA and MARC21 guidelines and features intended to support functionality in a Linked Data environment. Methods of Social Network Analysis were applied along with content analysis, to answer research questions of this study. Suggestions for future research, implications for education, and practical recommendations for library metadata creation and management are discussed.
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