SYNOPSIS Business trends show that more and more employees are creating shadow IT systems—IT systems that are not sanctioned or monitored by the IT department. This paper examines how the use of shadow IT in product costing impacts managers' perceptions of information credibility and managerial decision making. Using two experiments, we find that participants view information from shadow IT systems as less credible and they are less impacted by and less willing to rely on costing reports produced from shadow IT systems versus non-shadow IT systems. We also find that although participants are concerned about the credibility of shadow IT systems, they are not more likely to find simple mathematical errors embedded in shadow IT costing reports relative to non-shadow IT reports. This suggests that although concerned about shadow IT systems, managers still do not exercise sufficient care in evaluating reports created using these systems. The results of our study should prove informative as shadow systems become more prevalent in organizations. Data Availability: Contact the authors.

Previous rankings of accounting literature have largely ignored the subtopic of accounting education research. Given the important role that rankings play in creating incentives and benchmarks, ranking education research may improve both the quality and quantity of research in this subtopic. This paper ranks academic institutions and individual accounting researchers based on their production of accounting education research. We show that the correlation between education research rankings and singular, noneducation research rankings is very low (i.e., ranges from 0.20 to 0.31), emphasizing the importance of considering education rankings separately from other topical areas in accounting research. We also provide evidence of the institutional factors that contribute to producing accounting education research and when professors produce this type of research in their careers. These findings will likely be of interest to current faculty, administrators, and industry leaders as they make decisions based on accounting education research. JEL Classifications: M4; M40; M41; M49. Data Availability: Requests for data may be made to the authors.

ChatGPT, a language-learning model chatbot, has garnered considerable attention for its ability to respond to users’ questions. Using data from 14 countries and 186 institutions, we compare ChatGPT and student performance for 28,085 questions from accounting assessments and textbook test banks. As of January 2023, ChatGPT provides correct answers for 56.5 percent of questions and partially correct answers for an additional 9.4 percent of questions. When considering point values for questions, students significantly outperform ChatGPT with a 76.7 percent average on assessments compared to 47.5 percent for ChatGPT if no partial credit is awarded and 56.5 percent if partial credit is awarded. Still, ChatGPT performs better than the student average for 15.8 percent of assessments when we include partial credit. We provide evidence of how ChatGPT performs on different question types, accounting topics, class levels, open/closed assessments, and test bank questions. We also discuss implications for accounting education and research.

This study creates citation-based rankings for accounting institutions by topical areas (AIS, audit, financial, managerial, tax, and other) and methodologies (archival, analytical, experimental, and other) extending prior count-based ranking studies that disaggregate rankings by topic and methodology. We report separate rankings for different year windows (previous six years, 12 years, and since 1990) and only give institutions credit for authors who currently work for the institution. We show that disaggregated citation-based rankings are important as the correlations for some topic areas and methodologies with an overall ranking are modest. We also show that the correlation for citation-based and count-based rankings can differ significantly in some situations suggesting the importance of considering both types of rankings in decision making. Data Availability: Requests for data may be made to the authors.