Hayden Wimmer

Associate Professor, Department of Information Technology Georgia Southern University

  • Statesboro GA

Hayden Wimmer is an expert in Information Systems based in data mining and artificial intelligence applied to financial data.

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Georgia Southern University

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Biography

Hayden Wimmer has a Ph.D. from the University of Maryland Baltimore County in Information Systems based in data mining and artificial intelligence applied to financial data. He also holds an M.S. is in Information Systems from UMBC, an M.B.A. from the Pennsylvania State University, and a B.S.in Information Systems from York College of PA.

Prior to academia, he worked in industry for over 10 years in different capacities in Information Technology performing programming, web design and administration, server administration, network configuration, database administration, and of course technical support on all levels. He traveled the world in his professional capacities performing support and integration for a multinational company spending time in various U.S. locations as well as Canada, Mexico, France, Germany, Belgium, and China.

Dr. Wimmer has multiple journal publications related to multi-agent systems, artificial intelligence, data science, and I.S. education; and serves in various editorial capacities including co-editor in chief, board member, and reviewer of various journals and conferences and is a member of the Association of Information Systems. He has taught courses such as programming, database management, project management, I.T. infrastructure, and healthcare informatics. His research is published in top journals such as Decision Support Systems (DSS), Expert Systems with Applications (ESwA), Journal of Computer Information Systems (JCIS), Computers and Geosciences, and Computers in Human Behavior. Dr. Wimmer’s research has been funded for over $300,000 with nearly $200,000 from federal support and $24,000 from industry sources.

Areas of Expertise

Financial Data
Data Mining
Information Systems
Artificial Intelligence

Education

York College of PA

B.S.

Pennsylvania State University

M.B.A.

University of Maryland Baltimore County

M.S.

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Affiliations

  • International Journal of E-Adoption, Editor-in-Chief
  • International Journal of Online Pedagogy and Course Design, Associate Editor
  • Workshop on Information Technology and Systems 2018, Program Committee

Research Grants

Microsoft Azure Research Grant

Microsoft

2018
Microsoft Azure Research Grant

Articles

Problems Associated With Patient Care Reports and Transferring Data Between Ambulance and Hospitals From the Perspective of Emergency Medical Technicians

Issues in Information Systems

Smiljana Cuk, Hayden Wimmer, Loreen M Powell

2017

While many hospitals have converted to electronic medical records, emergency medical services continue to employ paper-based reports. Furthermore, existing research focuses on challenges of information systems from the perspective of nurses, doctors, and hospitals. Little is known about the paper-based challenges facing emergency medical technicians. This study examined emergency medical technician’s paper-based reports for potential problems that may occur if the transferred reports are in an electronic format. Additionally, this study conducted interviews of six emergency medical technicians about perceived benefits from electronic transfer of patient information for transferring patients. Results were positive as the emergency medical technicians liked to see a change from paper to electronic transfer of information. Emergency medical technicians also thought it was difficult to write the report while riding in the back of an ambulance, that information is lost during patient handover, and expressed a desire to follow-up on transferred patients.

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Counterfeit product detection: Bridging the gap between design science and behavioral science in information systems research

Decision Support Systems

Hayden Wimmer, Victoria Y Yoon

2017

In IS research, there is a dichotomy where design science and behavioral science are distinct research paradigms. IS researchers should view these paradigms as complementary with research drawing upon the strengths of both, yet few have done so. This work demonstrates how design science and behavioral science can be united in IS research via counterfeit product detection based on product reviews in an online marketplace. Product authenticity in the online marketplace is a common issue plaguing consumers. The decision process involved in determining product authenticity is lengthy and complex. Despite the pressing need for an automatic authenticity rating system for online shopping, little research has been done to develop such a system and assess its effects on consumer purchase behavior. To respond to this need, our study develops a design artifact, called OnCDS, to automatically calculate the likelihood that a product is counterfeit based on online customer reviews. Drawing upon lexicon-based sentiment analysis approaches and TF-IDF as kernel theories for our design, we employ web scraping, natural language processing, and topic analysis methods to process customer reviews and calculate the counterfeit score of a product. In assessing the effects of OnCDS on consumer behavior, we develop a research model that encompasses trust and perceived risk based on the valence framework. Results show that our design artifact's efficacy is validated and that the counterfeit score affects perceived risk and trust, which in turn influences attitude toward purchase.

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Improving the Efficiency of Big Forensic Data Analysis Using NoSQL

Proceedings of the 10th EAI International Conference on Mobile Multimedia Communications

Md Baitul Al Sadi, Hayden Wimmer, Lei Chen, Kai Wang

2017

The rapid growth of Internet of Things (IoT) makes the task for digital forensic more difficult. At the same time, the data analyzing technology is also developing in a feasible pace. Where traditional Structured Query Language (SQL) is not adequate to analyze the data in an unstructured and semi-structured format, Not only Standard Query Language (NoSQL) unfastens the access to analyzing the data of all format. The large volume of data of IoTs turns into Big Data which just do not enhance the probability of attaining of evidence of an incident but make the investigation process more complex. This paper aims to analyze Big Data for Digital Forensic (DF) investigation using NoSQL. MongoDB has been used to analyze Big Forensic Data in the form of document-oriented database. The proposed solution is capable of analyzing Big Forensic Data in the form of NoSQL more specifically document oriented data in a cost-effective, efficient way as all the tools is being used are open source.

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