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.

SYNOPSIS: Robotic process automation (RPA) possesses several features that may give rise to similar but also new adoption effects (both benefits and challenges) relative to other information technology (IT) implementations. Our study adds to the accounting and emerging technologies literature by examining the beginning to end process through which RPA is implemented in the accounting function. We adopt an exploratory and case-centered research methodology based on a mature RPA implementation in an accounting setting. We further corroborate it with interviews with subject matter experts and compare it with another case study of an early-stage RPA implementation. We identify five themes related to RPA adoption in accounting functions: workforce, IT governance, privacy and security, system sustainability, and the measurement of RPA success. We also present key takeaways that may support effective RPA adoption in accounting. Our study provides both academics and practitioners with insights about the effects of RPA implementation.

This paper proposes a model to integrate data analytics into current undergraduate accounting curricula across existing courses rather than offering a stand-alone data analytics course. One of the advantages of curriculum integration is that students are introduced to data analysis in a progressive or sequential way. Furthermore, such an approach typically does not require additional credit hours to reflect the changes made to the accounting curriculum to introduce the emerging technologies used in the accounting profession. The model proposes course learning outcomes (CLOs) related to the data analytics applications linked to specific levels of study and accounting courses. In addition, teaching materials including the main textbook, supplemental reading materials, and case studies are mapped across accounting courses. This model is expected to be beneficial for accounting educators and members of curriculum committees when updating an accounting curriculum to include data analytics.

Constituent demands for improved transparency in governmental reporting have increased since the 2008 financial crisis in the U.S. that impacted the financial well-being of many individuals and entities, including state and local municipalities. Since then, several governmental entities have provided an open data environment. However, these early initiatives do not incorporate robust analytic capabilities that are able to satisfy numerous and varied constituent inquiries. This research proposes development of the ENHanced ANalytic Constituent Environment (ENHANCE) framework in response to the analytic nature of the inquiries from various governmental stakeholders such as citizens, analysts, bond investors, creditors, vendors, auditors, and oversight officials. The ENHANCE framework bridges the gap between constituents' current analytic capabilities and their demands for increased transparency. This research contributes to the literature by highlighting the capabilities of the proposed ENHANCE tool and the underlying series of analytic “apps” that can provide meaningful information to a governmental entity's stakeholders.

Anomaly detection is a concept widely applied to numerous domains. Several techniques of anomaly detection have been developed over the years, in practice as well as research. The application of this concept has extended to diverse areas, from network intrusion detection to novelty detection in robot behavior. In the business world, the application of these techniques to fraud detection is of a special interest, driven by the great losses companies endure because of such fraudulent activities. This paper describes classification-based and clustering-based anomaly detection techniques and their applications, more specifically the application to the problem of certain fraudulent activities. As an illustration, the paper applies K-Means, a clustering-based algorithm, to a refund transactions dataset from a telecommunication company, with the intent of identifying fraudulent refunds.

This paper applies an empirical model of corporate capital structure, optimal debt, and overleveraging to estimate overleveraging measured as the difference between actual and optimal debt. Estimated using a sample of the twenty largest pharmaceutical firms, covering the time span from 2000 to 2018, the model sheds light on an industry-specific default risk. The analysis presented in this paper reveals a concerning trend in the pharmaceutical industry, with corporate excess debt steadily increasing over the past two decades, particularly peaking during the 2008 crisis and after 2013. These findings underscore the critical role of excess debt in exacerbating financial instability and highlight the pharmaceutical sector’s unique challenges, including high R&D intensity and regulatory pressures. By quantifying overleveraging and linking it to financial risk, the paper offers valuable policy implications, emphasizing the need for proactive management of optimal debt levels to mitigate default risks and enhance macroeconomic resilience.