This research contributes to the cloud computing (CC) literature and information science research by addressing the reality of information sharing and storage behavior (ISSB) of the users' personal information via CC. Gathering information about usage also allows this research to address the paradox between the research and practice. Additionally, this research explores the concept of trust and its role in the behavioral change relative to CC. The findings help reconcile the paradox between the two realms. Essay1 develops and tests cloud computing usage model (CCUM) that assesses ISSB. This model considers the main adoption determinants and the main drawbacks of CC. The study measures the main concerns of users found in the literature, perceived security and perceived privacy. The findings prove surprising on these concerns. Using multiple regression to analyze 129 valid survey responses, the results find that CC users are less concerned about the major issues of security and privacy and will use the technology based on peer usage. Essay 2 examines why users ignore the technology issues and elect to replace the traditional mechanisms for handling their personal information. The results of an interview-based study conducted on 11 normal users and 11 IT professionals clarify their perceptions about CC and examine its readiness to handle their information from an end-user perspective. Essay 3 explores the CC literature to identify the major factors associated with the users' trust beliefs. The research conducted in this essay groups these factors into three categories. The posited and tested model examines the effect of perceived trust on ISSB. A structural equation modeling approach is used to analyze 1228 valid responses and tests the developed cloud computing trust model. The results provide multiple implications for CC researchers, managers, and service providers.
Job centers aid businesses seeking qualified employees and assist job seekers to select and contact employment and training services. Job seekers are also offered the opportunity to assess their skills, abilities, qualifications, and readiness. Furthermore, job centers ensure that job seekers are complying with requirements that they must meet to benefit from job assistance programs such as unemployment insurance. Yet, claimants often procrastinate and/or suspend their job search efforts even though such actions can make them lose their free time and entitlements, and more importantly they may lose the opportunity to take advantage of free information, services, training, and financial assistance for getting a job to which they have already made a claim. The current work looks to Chatman's "small worlds" work, Johnson's comprehensive model of information seeking, and Wilson's "costly ignorance" construct for contributions to understanding such behavior. Identification of a particular trait or set of traits of job seekers during periods of unemployment will inform a new Job Seeking Activities Model (JSAM). This study purposely examines job seeker information behavior and the factors which influence job seekers' behavior, in particular, family tangible support as a social norm effect. A mixed method, using questionnaires for job hunting completers and non-completers and interviews for experts, was employed for data collection. Quantitative data analysis was conducted to provide the Cronbach α coefficient, Pearson's product moment correlation, an independent-sample t-test, effect size, and binary Logit regression. The qualitative data generated from the interview transcript for each section of the themes and subthemes were color coded. Finally, simultaneous triangulation was carried out to confirm or contradict the results from each method. The findings show that social norms, particularly uncontrolled social support provided by their families, are more likely to make job seekers ignore the relevant information about jobs available to them in favor …
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.
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.
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.
In Kuwait, an increase in the use of social media by the Kuwait National Assembly (KNA) has allowed it members to reach out to the public and so advance their political agenda. This study examines social media influences on the decision making process; addresses the lack of academic research in relation to KNA members; and seeks to understand the extent to which public political engagement using social media might affect the outcome of their decision making. The proposed social media influence model (SMIM) was used to explore the relationships and relative importance of variables influencing legislator decision making in a social media environment. The second decade of the twenty-first century saw a number of major issues emerging in Kuwait. A core mixed method design known as explanatory sequential was applied to multiple sets of data generated during KNA members' 14th (2013-2016) and 15th (2016-2018) terms. These data included Twitter messages (tweets), the KNA Information Center Parliamentary Information System legislation documents, and the news media articles. The sample was drawn from KNA membership, some of which used Twitter to comment on major events with specific hashtags and the Kuwaiti news media articles related to the same. Study results confirm and support the proposed SMIM. They also suggest that a single person or a group of individuals (in this case, legislators) can be influenced and motivated to use social media for self-promotion and/or advancing their political agenda. Consequently, they can be used to devise ways for improving the use of social media by KNA members in support of legislative work, which in turn will provide citizens with access to real-time information and enhanced political interaction.
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.
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.
This study analyzes the sociocultural factors that affect Saudi students in the U.S. as they seek information and explores to what extent these factors impact their everyday life information seeking (ELIS) behavior and their information technology behavior (ITB). The factors in this study illustrate the unique sociocultural values that distinguish Saudi students from other international student groups: gender segregation, emphasis on religion, social support, and utilization of the consultation concept. After collecting data from an online survey, the data from linear regression analyses revealed that only one culture factor (the language barrier) showed a significant impact on Saudi student ELIS in the U.S., while the other factors were not statistically significant. Also, the findings indicated that perceived usefulness (PU) and perceived ease of use (PEU) were statistically significant to the ELIS of Saudi students. Furthermore, the study showed that after academic information, food and drink, entertainment, and health were the top student needs, the top ranking sources for everyday life seeking information were social media and the Internet. The findings of the study help to shed light on a sizable user group. As the fourth largest group of international students in the U.S., Saudi students have been underrepresented in research. Also, the study's findings and recommendations provide a more profound understanding of Saudi students for both the hosting American university officials and stakeholders who provide scholarships.
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 …
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.
The purpose of the current study was to confirm or dismiss Saudi students' behavioral intention with regard to using smartphone applications for learning purposes. A quantitative, non-experimental survey research design and descriptive research conducted on the determinants -performance expectancy, effort expectancy, and social influence- that predict Saudi students' intention at University of North Texas to use smartphone applications for learning purposes, based on the unified theory of acceptance and use of technology (UTAUT) as the framework. This study aims at filling gap found in understanding of students' intentions and their behaviors regarding the adoption and use of the Smartphone applications. Data was collected by means of an online questionnaire. The hypothesized model validated empirically using data collected from around 234 Saudi students who enrolled at University of North Texas. The model developed from UTAUT explained 50.1% of the variance of behavioral intention, and behavioral intentions explained 13.6% of the variance of usage behavior. The result of this study support that the determinants of performance expectancy, effort expectancy, and facilitating conditions were the highest predictors of behavioral intentions in using smartphone applications for learning purposes. The results of this study could encourage students, educators, and the Saudi Arabia Ministry of Education to provide educational applications that meet students' needs for information and knowledge.
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.
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.
There is an increasingly number of students taking online classes in lieu of or in addition to the traditional face-to-face format. With this trend, there are questions that naturally come to the surface. The biggest question being "is teaching in the online arena just as effective as the face-to-face arena?" This dissertation aims to pursue that line of questioning by analyzing students' perceptions of the teaching effectiveness for instructors who teach the same course in both an online and face-to-face format in the same semester. The data are analyzed through the lens of the social capital theory. Social capital has never been applied to the classroom before as its focus has traditionally been on community development. However, social capital theory addresses interpersonal relationships and their impact on knowledge sharing behavior. This theory identifies three dimensions, which appear to have a parallel track with the student evaluation components; each is analyzed against each other. These dimensions include structured, cognitive and relational and are compared to the components of the student evaluation tool, which includes organization and explanation of materials, learning environment and self-regulated learning.
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.
This study explored the acceptance of cloud computing (CC) services by small and medium enterprises (SMEs) in Lagos, Nigeria, which has been missing from CC services literature. It aimed to understand the motivations for adoption, the uses of the services, and the benefits they derive from it. The uses and gratification theory was applied as the theoretic framework for this endeavor. An online survey with close-ended and open-ended questions was distributed to 1200 randomly selected participants through email. In total, 392 valid responses were collected and analyzed using descriptive statistics and categories. The results found that SMEs in Lagos, Nigeria had a low level of awareness and appreciation of CC services. The adoption rate was also low. Unlike their counterparts in other regions, SMEs primary concerns were service downtime, stable power supply, and better internet access. The study found that SMEs were not taking full advantage of the capabilities of CC services. Some sections, however, were doing better than others, such as the information and communications sub-sector. This study suggested that targeted interventions should be conducted to raise the awareness of CC services in SMEs, and to improve their efficient and effective use of CC services. The uses and gratification theory was appropriate for guiding this study to understand the acceptance and use of CC services by SMEs in Lagos, Nigeria.
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 …
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.
In this multivariate correlational study, the researcher examined the course culture of an online graduate course whose environment exhibited characteristics of a Community of practice (CoP). An online survey captured data used to explore the relationships among variables shown to describe a CoP in field environments and among student perceptions of their experience in the course culture. A canonical correlation analysis (CCA) and commonality analysis (CA) were conducted using five predictor variables and three criterion variables to evaluate the degree and direction of the relationships. The CCA revealed that the full model was significant, explaining approximately 74% of the variance among the two synthetic variates. Impact, faculty leadership, and connection were the largest contributors to the predictor variate. The criterion variate was primarily explained by value and perceived CoP, with exposure to the profession providing a smaller contribution. The CA confirmed these findings. Results from this study indicate that a CoP could be fostered in an online graduate course. The overall significance of the model indicates teachers can nurture an environment wherein graduate students will take the initiative to work with others to create and acquire knowledge that creates a sense of professional connection with each other and with the profession overall. The results of this study suggest further empirical research in implementing and assessing CoPs in online graduate courses is warranted.
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 …
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.
In their studies on student motivation in th4e 1990s, Gorham & Christophel and Christophel & Gorham found that students perceived their own demotivation to be caused by instructor behaviors. While there are studies that explore the topic of student demotivation and other studies that illustrate the great influence instructors have on student information seeking behaviors, research focusing on the connection between these two concepts is almost nonexistent. Using Gorham & Christophel's concept of instructor-owned student demotivation, this mixed-methods study sought to identify which instructor behaviors doctoral computer science and information science students found demotivating and to what extent their perceptions of these demotivating instructor behaviors influenced their information seeking behaviors in a face-to-face classroom. Demographic and student-perceived demotivating instructor behavior surveys along with semi-structured interviews and follow-up questions were used to collect data. The surveys will be analyzed using descriptive statistics in Excel, and the semi-structured interviews and follow up questions were analyzed using content analysis and Colaizzi's method of phenomenological enquiry in NVivo. The findings showed that instructor demotivating behaviors not only influence student information seeking behaviors in the classroom, but they also can lead to lasting effects on the student. In addition, the participants have expectations of instructor behaviors, which come from their own experiences. These expectations also influence the level of demotivation they feel in a face-to-face classroom.
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.
In the traditional agenda-setting theory, the agenda-setters were the news media and the public has a minimal role in the process of agenda-setting, which makes the public a passive receiver located at the bottom in the top-down agenda-setting dynamics. This study claims that with the development of Information communication technologies, primarily social media, the networked public may be able to set their own agendas through connective actions, outside the influence of the news media agenda. There is little empirical research focused on development and dynamics of public agenda-setting through social media platforms. Understanding the development and dynamics of public agenda-setting may be key to accounting for and overcoming conflicting findings in previous reverse agenda-setting research. This study examined the public agenda-setting dynamics through a case of gun violence prevention activism Twitter network, the #MarchForOurLives Twitter network. This study determined that the agenda setters of the #MarchForOurLives Twitter network are the key Never Again MSD student leaders and the March For Our Lives. The weekly reflected important events and issues and the identified topics were highly co-related with the themes examined in the tweets created by the agenda setters. The amplifiers comprised the vast majority of the tweets. The advocates and the supporters consisted of 0.44% and 4.43% respectively. The tweets made by the agenda setters accounted for 0.03%. The young activists and the like-minded and participatory public could continuously make changes taking advantage of technologies, and they could be the hope in the current and future society.
This study explores the everyday life information seeking (ELIS) behavior of Catholic clergy in Northern Nigeria and describes their information use environment (IUE). It employed a mixed-method case study using survey and episodic interview techniques of data collection. The ELIS of Savolainen, the IUE of Taylor and the small world of Chatman were theoretical frameworks that guided this study. Findings showed that the IUE of these Catholic clergy is shaped by four elements: (1) geographical location and culture, (2) the celibate clergy, (3) their information needs, and (4) the information sources used to resolve these needs. Three types of information needs were identified: essential needs, circumstantial needs and intermittent needs. There was a high interrelatedness between the effects of culture and celibacy on the information seeking of these clergy. They are not likely to cross boundaries of their world to seek particularly essential information about their ministry or private lives. The findings of this study align with Chatman’s proposition that members who live in the round will not cross the boundaries of their world to seek information. The study found problems with access and availability of information, which included lack of familiarity with electronic/online library databases among the clergy, and the lack of archives and documentation of records and historical materials. It recommended the development of an archiving and documentation plan that digitizes paper documents for electronic management, including policies on data curation for the Catholic religious institutions in Nigeria.
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.
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.
Data science employment opportunities of varied complexity and environment are in growing demand across the globe. Data science as a discipline potentially offers a wealth of jobs to prospective employees, while traditional information science-based roles continue to decrease as budgets get cut across the U.S. Since data is related closely to information historically, this research will explore the education of U.S. iSchool professionals and compare it to traditional data science roles being advertised within the job market. Through a combination of latent semantic analysis of over 1600 job postings and iSchool course documentation, it is our aim to explore the intersection of library and information science and data science. Hopefully these research findings will guide future directions for library and information science professionals into data science driven roles, while also examining and highlighting the data science techniques currently driven by the education of iSchool professionals. In addition, it is our aim to understand how data science could benefit from a mutually symbiotic relationship with the field of information science as statistically data scientists spend far too much time working on data preparation and not nearly enough time conducting scientific inquiry. The results of this examination will potentially guide future directions of iSchool students and professionals towards more cooperative data science roles and guide future research into the intersection between iSchools and data science and possibilities for partnership.
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.
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.
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.
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 …
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.
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.
This study applied the impression management theory in the context of social networking to investigate the generalized research question of this dissertation which is "Do active social media presence and various privacy concerns influence online behaviors of students on social media?" The results and conclusions are presented via the conduct of three different studies and the summary provides insights and explain the overall contribution of the research. For each study we developed a research model for which data was collected separately for each of these models. Hypotheses of each model were tested by partial least squares- structural equation modeling techniques using SmartPLS 2.0. Our findings confirm the hypotheses and showed that all the predictors positively influence online social networking behaviors. Active social media presence is operationalized as predictors such as SNS stalking awareness, Selective disclosure, desired differential persona, impression motivation, and information trustworthiness. Privacy concerns have been operationalized as SNS privacy awareness, technology awareness. Online behaviors have been operationalized as responsible image and reckless image.
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.
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.
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.
This dissertation addresses research questions related to defining a smart city and the associated activities. 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 smart city phenomena as it has emerged within the City. Essay 1 assesses building municipal open data capability. The study proposed an Opendata Roadmap Framework to enhance the organization's dynamic capability. The results provide a valuable practical framework to help cities develop open data capability. The results also provide a comparative study or benchmark for similar initiatives with other regional cities and within the nation. Essay 2 measures the residents' understanding and beliefs about smart cities. This portion of the research used a qualitative method that included interviewing residents and city officials to understand their definition of a smart city and what they believe makes a city smart. The interviews focused on understanding resident engagement because it is an important characteristic of a smart city. The gap between the city officials and residents understanding was examined. In addition, the interviews help identify essential factors associated with smart cities like trust in government, perceived security, perceived privacy, trust in technology, and perceived monetary value. Essay 3 examines the acceptance of smart city technologies and factors that affect the adoption of such technologies. This essay uses the insights from the other two essays to propose a smart city Unified Theory of Acceptance and Use of Technology (UTAUT) extension labelled Smart City UTAUT (SC-UTAUT). The new proposed model was tested using a survey method. The 1,786 valid responses were used to test the proposed structural equation model using Smart PLS. Results show a significant relationship between trust in technology, trust in government, perceived …
Makerspaces are a popular, new concept being implemented in public, academic, and school libraries, and as stand-alone spaces. The literature reflects the newness of the topic with a limited number of articles and studies and even less about the users of makerspaces themselves. This study explored information sharing behaviors in the Dallas Makerspace as an informal learning environment and described their preferred method of information transfer from one member to another. It employed a mixed methods methodology using surveys, interviews and observations. The study identified how the rules and policies in place at the makerspace influence the information seeking process and how the Dallas Makerspace exchanges information effectively. Dallas Makerspace is one of the largest non-profit work groups in its size, and this research study answers how information is exchanged in an informal environment.
The arrival of the Big Data era has become a major topic of discussion in many sectors because of the premises of big data utilizations and its impact on decision-making. It is an interdisciplinary issue that has captured the attention of scholars and created new research opportunities in information science, business, heath care, and many others fields. The problem is the Big Data is not well defined, so that there exists confusion in IT what jobs and skill sets are required in big data area. The problem stems from the newness of the Big Data profession. Because many aspects of the area are unknown, organizations do not yet possess the IT, human, and business resources necessary to cope with and benefit from big data. These organizations include health care, enterprise, logistics, universities, weather forecasting, oil companies, e-business, recruiting agencies etc., and are challenged to deal with high volume, high variety, and high velocity big data to facilitate better decision- making. This research proposes a new way to look at Big Data and Big Data analysis. It helps and meets the theoretical and methodological foundations of Big Data and addresses an increasing demand for more powerful Big Data analysis from the academic researches prospective. Essay 1 provides a strategic overview of the untapped potential of social media Big Data in the business world and describes its challenges and opportunities for aspiring business organizations. It also aims to offer fresh recommendations on how companies can exploit social media data analysis to make better business decisions—decisions that embrace the relevant social qualities of its customers and their related ecosystem. The goal of this research is to provide insights for businesses to make better, more informed decisions based on effective social media data analysis. Essay 2 provides a better understanding of the influence of …
This research study is conducted to test Workman, Fiszman, Rindflesch and Nahl's information flow-serendipitous knowledge discovery (IF-SKD) model of information behavior, in a clinical care context. To date, there have been few attempts to model the serendipitous knowledge discovery of physicians. Due to the growth and complexity of the biomedical literature, as well as the increasingly specialized nature of medicine, there is a need for advanced systems that can quickly present information and assist physicians to discover new knowledge. The National Library of Medicine's (NLM) Lister Hill Center for Biocommunication's Semantic MEDLINE project is focused on identifying and visualizing semantic relationships in the biomedical literature to support knowledge discovery. This project led to the development of a new information discovery system, Spark. The aim of Spark is to promote serendipitous knowledge discovery by assisting users in maximizing the use of their conceptual short-term memory to iteratively search for, engage, clarify and evaluate information presented from the biomedical literature. Using Spark, this study analyzes the IF- SKD model by capturing and analyzing physician feedback. The McCay-Peet, Toms and Kelloway's Perception of Serendipity and Serendipitous Digital Environment (SDE) questionnaires are used. Results are evaluated to determine whether Spark contributes to physicians' serendipitous knowledge discovery and the ability of the IF-SKD ability to capture physicians' information behavior in a clinical setting.
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.
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.
The purposes of this study are: a) to understand the treatments compliance behavior of the patient with chronic disease at the behavioral level, particularly, the relationship between treatments compliance behavior and normative beliefs; b) develop a behavioral model of patient's treatments compliance behavior that could be used for predicting, combating, treating, tracking and controlling the treatments compliance behavior of the patients with chronic disease. Seventy-two patients from senior daycare centers in the Dallas area, who suffer or had suffered from at least, one chronic disease, participated in the study. Data gathering was conducted using paper-based questionnaire. The most significant finding of this study is the relationship between normative beliefs and the treatments compliance behavior of the patient with chronic disease. Normative beliefs were found to have significant impact on the treatments compliance intent and behavior of the patients with chronic disease. Another important finding showed that side-effects of prescribed treatments have little or no influence on the treatments compliance behavior of the patient with chronic disease. A relationship between the effectiveness of medicine, particularly, predictive medicine, and treatments compliance behavior was established. The design of the study was intended to provide coverages for a set of constructs that may be the interacting units in the environment of any chronic disease treatments decision. It depicts relational, information communications links between the constructs. The Imhonde model of treatments compliance behavior was designed to include cultural norms and other beliefs that are significant for real-time human ailments decisions behaviors. It is recommended that further studies may include the use of a larger population of participants from diverse cultures and localities in multiple states and countries, with the object of finding the differences that culture and local environments may have on the normative leaning for treatments compliance behavioral decisions in chronic disease cases.
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.
Information-seeking behavior is a mixture of activities and attitudes, oftentimes motivated by an individual's need to make a decision. One underlying element of this mixture is cognitive authority - which sources (e.g., individuals, institutions, texts, etc.) can be trusted to fulfil the information needs? In order to gain insight into the dynamics of cognitive authority selection behavior which is an information seeking behavior, this study explored primary source text data (316 text records) that reflected selection in the mundaneness of life (advice column submissions and responses). Linguistic analysis was performed on the data using the Linguistic Inquiry Word Count (LIWC2015) software package. Pearson correlation and 1-sample T tests revealed the same 45 statistically significant relationships (SSRs) in the word usage behavior of all subgroups. As a result of the study, the gap in research formed from the lack of quantitative models of cognitive authority relationships was addressed via the development of the Wordprint Classification System which was used to generate a cognitive authority relationship model in the form of a cognitive authority intra-segment wordprint. The findings and implications of this study may provide a contribution to the body of work in the area of information literacy and information seeker behavior by revealing factors that information scientists can address to help meet information seekers' needs. Additionally, the Wordprint Classification System may be used in such disciplines as psychology, marketing, and forensic linguistics to create to create models of various relationships or individuals through the use of written or spoken word usage patterns.
The purpose of this study is to investigate the use of decisions support systems (DSS) by critical care physicians and to address the following questions: Does the use of a decision support system during diagnosis reduce diagnostic error and how are decision support systems used by critical care physicians? There are no studies that address these research questions in a clinical setting. The information assessment method (IAM) was used to guide the development of the survey questions. Critical care physicians from the University of Oklahoma Health Sciences Center were surveyed. Chi squared test for independence was used to determine the relationship between DSS use and diagnostic error rates. There were three main findings of the study: (1) use of a DSS by a critical care physician can decrease diagnostic error by up to 60%; (2) 56% of critical care physicians are using a DSS during diagnosis to learn something new, confirm something they already knew, and/or to reassure themselves; and (3) the increased use of a DSS by critical care physicians can lead to a decrease in the belief of the ability of a DSS to reduce diagnostic error.
Using a case study approach this study investigated how college students in Vlore, Albania seek and use information resources for academic and personal needs and whether they follow a pattern similar to Brenda Dervin's sense-making, or Marcia Bates' berry-picking information seeking models. Influencing factors studied were economic factors, information communication technologies and information culture/policy. A literature review showed that no previous published research has studied information seeking behavior of college age students and faculty in Albania. Thirty-four college students and two full time faculty completed a survey and a smaller group were interviewed. The results of the study indicate that Google is the main source for seeking information for both academic and personal purposes. College students are not introduced or taught on how to evaluate information sources. The information communication technology needs improvement to support information needs. The library as a major information resource was not apparent to most students. College students utilize berry-picking as the information seeking model and faculty use sense-making, as a model of information seeking. This study adds to the knowledge of the information seeking behavior of college students in a developing country, the need for information literacy courses at the university level, and the identification of additional areas of research regarding information communication technologies, information policy, and literacy for developing countries.
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