Kim Mendoza

Assistant Professor of Accountancy and Grant Thornton LLP Faculty Fellow and Director of the Deloitte Foundation Center for Business Analytics

Biography

Assistant Professor of Accountancy and RC Evans Innovation Fellow Kim Mendoza has always been interested in the intersection of technology and accounting. She brings her expertise to teaching ACCY 570, Data Analytics Foundations, where students develop the skills to bridge the gap between data science and accounting. Kim earned a B.S. in accounting from Loyola Marymount University and an M.S. and PhD in accounting from the University of Washington. She joined Gies Business in 2017. Her research interests include how technology, in the form of computer algorithms and robo advisors, impact investors’ responses to financial advice. She also examines financial and auditing regulation and its impact on key players’ decision-making processes.

Contact

4007 Business Instructional Facility

515 Gregory Dr

Champaign, IL 61820

217-300-5706

kimendoz@illinois.edu

Google Scholar

Listings

Educational Background

Ph.D., Business Administration (Accounting), University of Washington, 2017
Master of Science, Business Administration (Accounting), University of Washington, 2016
Bachelor of Arts, Art History, Loyola Marymount University, 2008
Bachelor of Science, Accounting, Loyola Marymount University, 2008

Positions Held

Associate Academic Director of the Deloitte Foundation Center for Business Analytics, University of Illinois at Urbana-Champaign, 2023-2023
Director of the Deloitte Foundation Center for Business Analytics, Deloitte Foundation Center for Business Analytics, University of Illinois at Urbana-Champaign, 2023 to present
Assistant Professor of Accountancy, University of Illinois at Urbana-Champaign, 2017 to present

Recent Publications

Mendoza, K., & Winn, A. (2022). Improving Performance on Low-Level Audit Tasks: The Interactive Effect of Regulatory Fit and Professional Identity. The Accounting Review.
Hodge, F., Mendoza, K., & Sinha, R. (2021). The Effect of Humanizing Robo-Advisors on Investor Judgments. Contemporary Accounting Research, 38 (1), 770-792.
Mendoza, K. (2020). Reducing Underreporting by Aggregating Budgeted Time. The Accounting Review, 95 (5), 299-319.
Cade, N., Koonce, L., & Mendoza, K. (2020). Using Video to Disclose Forward-Looking Information: The Effect of Nonverbal Cues on Investors' Judgments. Review of Accounting Studies, 25 1444-1474.

Other Publications

Articles

Cade, N., Koonce, L., & Mendoza, K. (2019). Assets and Liabilities: When Do They Exist? Contemporary Accounting Research, 36 (2), 553-587.

Honors and Awards

Grant Thornton LLP Faculty Fellowship, University of Illinois at Urbana-Champaign, 2021 to present
RC Evans Innovation Scholar, University of Illinois at Urbana-Champaign, 2020-2022
Dean's Impact Award, University of Illinois at Urbana-Champaign, 2021
List of Teachers Ranked as Excellent by their Students, University of Illinois at Urbana-Champaign, 2018, 2019, 2020, 2021
Head's Award for Excellence in Accountancy Education, Department of Accountancy, 2019-2020
John Deere Analytics Teaching Fellowship in Accountancy, University of Illinois at Urbana-Champaign, 2019-2020

Research Interests

Applying experimental method and drawing from theories in psychology, to investigate auditing and financial reporting issues. Financial and auditing regulation and its impact on key players' decision-making processes (i.e., auditors, managers, and investors). The influence of accounting information on individual investors' judgements and decisions.

Current Courses

Data Analytics Foundations (ACCY 570) Concepts and foundations underlying data analytics for accounting. Provides fundamental knowledge of how to acquire, organize, synthesize and analyze (possibly 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 Python and Tableau to perform data analysis.