Joshua Herbold
Teaching Associate Professor of Accountancy
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Educational Background
- Doctorate of Philosophy, Accountancy, University of Illinois at Urbana-Champaign, 2005
- Bachelor of Science, Accountancy, University of Illinois at Urbana-Champaign, 1995
Positions Held
- Teaching Associate Professor in Accountancy, University of Illinois at Urbana-Champaign, 2019 to present
- Chair, Accounting & Finance, University of Montana, 2017-2019
- Associate Professor of Accounting, Accounting & Finance, University of Montana, 2012-2019
- Assistant Professor of Accounting, Accounting & Finance, University of Montana, 2005-2012
- Visiting Assistant Professor of Accounting, Accounting & Finance, University of Montana, 2004-2005
Recent Publications
- Herbold, J., & Manuel, T. Forthcoming. Are Online Roth versus Traditional IRA Calculators Useful. CPA Journal, November 2019 30-37.
Other Publications
Case Studies
- Herbold, J., McNellis, C., & Barone, G. Forthcoming. Larson Industries: A Case on Identifying and Researching Revenue Recognition Issues. Issues in Accounting Education.
Presentations
- Herbold, J. (2019). Torrefied Tonewoods: Modern Manufacturing with Vintage Values. North American Case Research Association (NACRA) 2019 Start-up Case Session.
Current Courses
- Decision Making for Atg (ACCY 302) Decision making implications of information provided to organization managers and to external stakeholders such as investors, creditors, customers, and regulators. Concepts from economics, statistics, and psychology emphasize the use of quantitative techniques to comprehend uncertainty and risk. Projects, together with a series of practical workshops, facilitate self-discovery of knowledge and development of a variety of professional skills and attitudes.
- Data Analytics Foundations (ACCY 570) Concepts and foundations underlying data analytics for accountancy. Provides fundamental knowledge of how to acquire, organize, synthesize and analyze large volumes of data to address questions and problems. After completing this course, students should (1) have a foundational understanding of the techniques underlying data analytics, (2) recognize scenarios and identify appropriate tools for various types of data analysis and (3) use common computer-based tools to perform data analysis.
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