You can contact Nohel Zaman at email@example.com.
Nohel Zaman is an Assistant Professor in the Department of Information Systems and Business Analytics at Loyola Marymount University. His research focuses on social media interactions of consumers. Specifically, he investigates defect existence in online reviews across multiple industries, and discovers hospital quality issues from online reviews of patients. He also focuses on identifying product safety concerns across diverse product categories. He received a Ph.D. in Business Information Technology from Virginia Tech in 2019. Additionally, he earned a B.Sc. degree in Business Administration along with a M.Sc. degree in Economics from the University of Texas at Dallas, and a M.Sc. degree in Computational Science and Engineering (concentrated in healthcare information systems) from the North Carolina A&T State University.
Virginia Tech: Ph.D., Business Information Technology 2019
North Carolina A&T State University: M.S., Computational Science and Engineering 2015
University of Texas at Dallas: M.S., Economics 2011
University of Texas at Dallas: B.S., Business Administration 2007
Areas of Expertise (2)
Healthcare Information Systems
Are mortgage loan closing delay risks predictable? A predictive analysis using text mining on discussion threadsJournal of the Association for Information Science and Technology
Loan processors and underwriters at mortgage firms seek to gather substantial supporting documentation to properly understand and model loan risks. In doing so, loan originations become prone to closing delays, risking client dissatisfaction and consequent revenue losses. We collaborate with a large national mortgage firm to examine the extent to which these delays are predictable, using internal discussion threads to prioritize interventions for loans most at risk.
Cross-Category Defect Discovery from Online Reviews: Supplementing Sentiment with Category-Specific SemanticsInformation Systems Frontiers
Online reviews contain many vital insights for quality management, but the volume of content makes identifying defect-related discussion difficult. This paper critically assesses multiple approaches for detecting defect-related discussion, ranging from out-of-the-box sentiment analyses to supervised and unsupervised machine-learned defect terms.
The Relationship between Nurses’ Training and Perceptions of Electronic Documentation SystemsNursing Reports
Electronic documentation systems have been widely implemented in the healthcare field. These systems have become a critical part of the nursing profession. This research examines how nurses’ general computer skills, training, and self-efficacy affect their perceptions of using these systems.
Safeguarding Korean export trade through social media-driven risk identification and characterizationJournal of Korea Trade
In this study, we develop country-of-origin-based product risk analysis methods for social media with a specific focus on Korean-labeled products, for the purpose of safeguarding Korean export trade.
Text Mining Approaches for Postmarket Food Safety Surveillance Using Online MediaRisk Analysis: An International Journal
Food contamination and food poisoning pose enormous risks to consumers across the world. As discussions of consumer experiences have spread through online media, we propose the use of text mining to rapidly screen online media for mentions of food safety hazards. We compile a large data set of labeled consumer posts spanning two major websites. Utilizing text mining and supervised machine learning, we identify unique words and phrases in online posts that identify consumers’ interactions with hazardous food products.
Automated mortgage origination delay detection from textual conversationsDecision Support Systems
For modern mortgage firms, the process of setting up and verifying a new loan, known as origination, is complex and multifaceted. The literature notes that this process is rife with delays that can stunt the firm's business opportunities, but no modern analytical techniques have been developed to address the problem. In this paper, we suggest the use of text analytic and machine learning techniques to predict likely delays.
Facebook Hospital Reviews: Automated Service Quality Detection and Relationships with Patient SatisfactionDecision Sciences
As patient satisfaction is heavily linked to their choice of provider and medical outcomes, hospital administrations routinely consider a bevy of factors to improve patient satisfaction. These considerations are complex, so targeting the most important areas for improvement is challenging. However, consumers’ online reviews of their hospital experience provide a vital lens into the factors associated with their satisfaction. In this study, we use a large dataset of Facebook reviews to construct a taxonomy of potential service attributes that consumers discuss online.