Benedikt J. Plate

Experts tend to produce complex texts but mistakenly consider them an easy read for their audience. Accounting professors likely exhibit such expert bias in formulating exams. Consequently, students struggle with comprehension, giving rise to high failure rates and a shortage of talent for the profession. This study explores the potential of ChatGPT for overcoming expert bias and improving financial accounting exam texts to increase comprehension and performance and reduce frustration and confusion.

We ask three key questions: (1) Does text modification with ChatGPT lead to increased perceived comprehension? (2) Does text modification with ChatGPT result in better performance? and (3) Does perceived comprehension mediate the relationship between ChatGPT-based text modification and performance? We find that students benefit from ChatGPT-based modification through increased perceived comprehension and reduced confusion and frustration. Causal mediation analysis suggests that improved perceived comprehension attributes significantly to exam performance, but we cannot document an overall positive effect.

This study evaluates the impact of students' usage of generative artificial intelligence (GenAI) tools such as ChatGPT on their exam performance. We analyse student essays using GenAI detection systems to identify GenAI users among the cohort.

Employing multivariate regression analysis, we find that students using GenAI tools score on average 6.71 (out of 100) points lower than non-users. While GenAI may offer benefits for learning and engagement, the way students actually use it correlates with diminished exam outcomes. Exploring the underlying mechanism, additional analyses show that the effect is particularly detrimental to students with high learning potential, suggesting an effect whereby GenAI tool usage hinders learning. Our findings provide important empirical evidence for the ongoing debate on the integration of GenAI in higher education.

Trust between firms and investors ensures the functioning of the capital market. In addition to financial statement audits, internal corporate decisions regarding accounting and communication policies also contribute to building trust.

This study examines the influence of high reporting frequencies and benchmarks on the dimensions of accounting policy decisions, as well as companies’ assessments of alternative information channels and narrative reporting elements. Based on a survey of Prime Standard companies, it highlights the challenges and opportunities that arise for financial auditing and consulting in this context.

This study examines the impact of generative artificial intelligence (GenAI) usage on investor reactions by focussing on the use of GenAI to create or modify 'Management's Discussion and Analysis of Financial Condition and Results of Operations' (MD&A) sections of 10-K filings.

Analyzing 6,977 firm-year observations from filing years 2021 to 2023, we find that using GenAI for MD&A texts is associated with adverse short-term investor reactions measured by signed and absolute cumulative abnormal returns (CAR). Using causal mediation analysis, we show that GenAI enhances financial disclosures' readability, leading to greater CAR. Tone, in contrast, remains unaffected, suggesting firms prioritize simplifying over tone manipulation. However, the mediation effect is outweighed by the negative direct effect, leading to a significantly negative impact of GenAI usage in narrative disclosures on investor reactions.

This study examines the effect of accounting reporting complexity (ARC) on real economic investment decisions. Prior literature documents adverse capital market effects of ARC, suggesting that complexity in financial disclosures induces information frictions between managers and external debt or equity capital providers and, thereby, increases firms' cost of external capital.

Based on the pecking order of internal and external financing, we analytically predict that organizational investments in firms with higher ARC oscillate more strongly with fluctuations in internally available funds, translating into a higher sensitivity of organizational investment. Consistently, our empirical results document that firms with higher ARC expand organizational investment more aggressively when internal funds rise and cut them more decisively when funds fall.

We account for "non-standard errors" in our evidence-generating process by computing our OLS regression across 512 specifications with different research design choice combinations. Our empirical results remain statistically significant in 93.36 percent, indicating high robustness and validity of our empirical findings.

The Journal of Information Systems (JIS) is a leading peer-reviewed journal published by the American Accounting Association. JIS publishes research on the intersection of accounting and information systems, covering topics such as data analytics, artificial intelligence, cybersecurity, and the design and use of accounting information systems. The journal is listed in the ABS Academic Journal Guide and the VHB Rating 2024.