Rating scales are one of the most widely used tools in behavioral research. Decisions regarding scale design can have a potentially profound effect on research findings. Despite this importance, an analysis of extant literature in top accounting journals reveals a wide variety of rating scale compositions. The purpose of this paper is to experimentally investigate the impact of scale characteristics on participants' responses. Two experiments are conducted that manipulate the number of scale points and the corresponding labels to study their influence on the statistical properties of the resultant data. Results suggest that scale design impacts the statistical characteristics of response data and emphasize the importance of labeling all scale points. A scale with all points labeled effectively minimizes response bias, maximizes variance, maximizes power, and minimizes error. This analysis also suggests variance may be maximized when the scale length is set at 7 points. Although researchers commonly believe using additional scale points will maximize variance, results indicate increasing scale points beyond 7 does not increase variance. Taken together, a fully labeled 7-point scale may provide the greatest benefits to researchers. The importance of scale labels provides a significant contribution to accounting research as only 5 percent of the accounting studies reviewed have reported scales with all points labeled.

Employees are increasingly monitored through integrated, data analytic-driven continuous (i.e., active) monitoring systems that analyze a wealth of data concerning their behaviors and actions. While use of these active monitoring systems has been advocated for improved performance measurement, increased productivity, and reduced costs, discussion has generally ignored the ethical implications of such monitoring as well as the impact on employees' morale and views of the organization. This study investigates these issues through application of contractarian ethics in the experimental examination of employees' beliefs and intentions based on organizational monitoring practices. In the first experiment, the level of monitoring and pay are varied to understand potential employees' perspectives on organizational ethics, willingness to accept a job with an organization, and their likely job satisfaction. While pay may sway willingness to accept a job and even the level of satisfaction, pay does not affect potential employees' ethical perceptions of the organization. Under high monitoring situations, potential employees consistently rate the ethics of the organization as poor. In a second experiment, four justifications that the literature suggests employers may provide for using employee monitoring are found to have no effect on employees' views about the organization in a high monitoring environment. Data Availability: Please contact 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.

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With the proliferation of data analytics in the field of accounting, educators are in need of resources to enhance their curricula with analytics projects. This paper provides educators with a robust tool that generates large, unique revenue-cycle transaction data with certain realistic properties. The datasets can be used by educators to teach accounting-based data analytic procedures in accounting information systems, auditing, fraud, and data analytics classes. Additionally, multiple potential implementation opportunities for the datasets are proposed and a comprehensive example case is provided.