This article provides a literature review on definitions and characteristics of theory development.

This article reviews the information seeking behaviors of clergy.

This presentation explores how different academic disciplines define information quality.

This article explores Dr. Elfreda Chatman's concept of "small world" as applied to the information seeking behaviors of Catholic clergy in Northern Nigeria.

This article provides a literature review regarding mobile technology used for mobile learning in Africa.

This poster describes the planning and implementation process for a walking program for Haslet Public Library patrons in partnership with Texas A&M's Walk Across Texas program.

This poster was present at the DFW Metroplex Linguistics Conference at the University of North Texas main campus in Denton, Texas on November 8,2019. The poster highlights a preliminary linguistic framework for describing kanji.

The research seeks to know if the COVID-19 pandemic has created culture change by measuring the criteria of work outputs and actions of organization members. As a part of this research, a survey was developed to identify how and to what extent workplace culture has been impacted. 111 professional services staff from a North Texas business were surveyed. We identified three factors for criteria of work outputs and behavior that indicates the current level of change of the culture. The three factors are identified as, Expectations (α= 0.786), Accuracy (α= 0.603), and Timeliness (α= 0.552). Factor analysis and descriptive statistics are used to analyze the result from the survey.

Presentation for the dissertation defense of Brenda Reyes Ayala. This presentation builds a theory of information quality for web archives that is grounded in human-centered data.

Poster presented at the 2017 ACM/IEEE-CS Joint Conference on Digital Libraries. explores the possibility of applying Machine Learning for Machine Translation evaluation.

This presentation discusses factors affecting faculty attitudes toward open access institutional repositories uses results from a survey of University of North Texas faculty.

This article reviews literature on the subject of employee motivation to determine whether intrinsic or extrinsic motivation is becoming the driving force of business.

Presentation for the Workshop on Web Archiving and Digital Libraries (WADL) as part of the 2017 ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL). This presentation discusses a paper examining how users perceive the process of web archiving.

Presentation for the 2017 Texas Conference on Digital Libraries. This presentation discusses survey data showing differences in regard to faculty awareness of OA declarations, OA policy, and intent to deposit.

This poster highlights a project undertaken at the University of Houston by the Gerald D. Hines College of Architecture and Design to select and transition records into the University's archives. The relationship between colleges or department and the University archives is examined, and the workflows necessary to ensure consistency through the transfer. The poster was created as part of coursework for INFO 5375 Archival Appraisal in Spring 2020 at the University of North Texas.

Article reporting on how journal articles are presented within the Open Language Archive Community’s (OLAC) OAI-PMH aggregator for language resources. Understanding how secondary journal materials are presented in OLAC records is a first step towards increasing the end-user utility of the OLAC aggregator. It was presented at the 2nd International Workshop on Digital Language Archives held on June 30, 2023 as part of the ACM/IEEE Joint Conference on Digital Libraries 2023.

Article presenting two methods for connecting aggregated records to their source institutional metadata profiles. The use case of the Open Language Archives Community (OLAC) application profile is considered and evaluated. It was presented at the 2nd International Workshop on Digital Language Archives held on June 30, 2023 as part of the ACM/IEEE Joint Conference on Digital Libraries 2023.

Understanding trends is helpful to identify future behaviors in the field, and the roles of people, places, and institutions in setting those trends. Although traditional clustering strategies can group articles into topics, these techniques do not focus on topics over limited timescales; additionally, even when articles are grouped, the generated results are extensive and difficult to navigate. To address these concerns, we create an interactive dashboard that helps an expert in the field to better understand and quantify trends in their area of research. Trend detection is performed using the time-biased document clustering introduced in Behpour et al. (2021) study. The developed and freely available web application enables users to detect well defined trending topics in financial journals by experimenting with various levels of temporal bias - from detecting short-timescale trends to allowing those trends to spread over longer times. Experts can readily drill down into the identified topics to understand their meaning through keywords, example articles, and time range. Overall, the interactive dashboard will allow experts in the field to sift through the vast literature to identify the concepts, people, places, and institutions most critical to the field.

This workshop is presented by members of a University of North Texas research team. First, the team will overview their experience as members of the research team and share experience in areas such as trust formation, team roles, productivity, work-life balance, faculty-students interaction, peer and faculty mentorship, dissertation preparation, and job seeking. Second, the workshop will discuss and brainstorm how this format can be implemented for organizations both with faculty-student teams and with peer-directed teams. Finally, successes and challenges are openly discussed with audience.

Global warming is the term used to describe critical environmental issues and concerns. Social media such as Twitter provides a platform for people to share information, exchange ideas, and express their opinions about current and timely issues. This study utilized contextual analysis to analyze data collected from Twitter for the hashtag "global warming" during the period 2010 & 2011. Using sentiment analysis and topic modeling, the study aimed first at assessing people's perception towards global warming issues, and second study the impact of misinformation from the standpoint of social noise on people's perception of global warming during critical environmental events. The outcome of this study helps create a better understanding of the environmental issues discussed on social media. The sentiment analysis from the data analyzed so far shows that most of the tweets were based on Twitter users' personal opinions and not science. The topic modeling results suggest that Twitter users typically tweeted when a major environmental event occurred due to global warming. Topic modeling also aids in the identification of terms that is associated with social noise. The presence of social noise suggests that misinformation does exist and spreads faster.

Building predictive models for companies often relies on inference using historical data of companies in the same industry sector. However, companies are similar across a variety of dimensions that should be leveraged in relevant prediction problems. This is particularly true for large, complex organizations which may not be well defined by a single industry and have no clear peers. To enable prediction using company information across a variety of dimensions, we create an embedding of company stocks, Stock2Vec, which can be easily added to any prediction model that applies to companies with associated stock prices. We describe the process of creating this rich vector representation from stock price fluctuations and characterize what the dimensions represent. We then conduct comprehensive experiments to evaluate this embedding in applied machine learning problems in various business contexts. Our experiment results demonstrate that the four features in the Stock2Vec embedding can readily augment existing cross-company models and enhance cross-company predictions.