<p> Internal controls have been and continue to be an important concern for companies, shareholders, and accounting professionals. Strong internal controls enable managers and upper-level accountants to ensure the integrity of the financial data and assist in making strategic decisions within the company to maintain a competitive advantage. The present study sought to answer the research question: To what extent does Chief Financial Officer (CFO) turnover and auditor turnover relate to internal control weaknesses for public companies that have reported weaknesses from 2010 to 2013, controlling for company size? To answer the research question, secondary data obtained through Audit Analytics were analyzed using multiple linear regressions. Data were collected for companies that reported one or more internal control weaknesses each year between 2010 and 2013. Companies were categorized as large or small based on their accelerated or non-accelerated filing status. Only companies that maintained the same filing status for all 4 years and experienced an auditor or CFO change during the selected time period were included in the sample. A statistically significant correlation was found between CFO turnover and internal control weaknesses when factoring in company size. Specifically, company size was linked to the number of internal control weaknesses with larger companies reporting significantly fewer internal control weaknesses than smaller companies. More research is necessary to understand which characteristics of small and large companies may relate to internal control weaknesses. However, regardless of company size, the present research suggests that organizations can use agency theory to strengthen their internal controls and reduce vulnerabilities by improving governance over individual roles and responsibilities.</p>

Artificial Intelligence (AI) is the way forward in accounting and auditing. The purpose of this study was to examine the relationship between accounting students’ level of technology readiness (TR) and AI technology adoption (TA). This quantitative study examined the independent variables of TR, perceived ease of use (PEOU), and perceived usefulness (PU) and the dependent variable of TA. Moreover, the present study examined the mediating effect of PEOU and PU on the relationship between TR and TA. The present study was related to individual accounting students’ perceptions of TR and TA. Student participants (n = 101) recruited for this study were randomly sampled from 2 universities in Southern California, the United States. An online questionnaire consisting of 30 items regarding perceptions of TR, PEOU, PU, and TA was administered. The bivariate correlation and regression between variables showed that TR, PEOU, and PU positively influence TA; TR positively influences PEOU and PU; and PEOU positively influences PU. Mediation analysis showed that both PEOU and PU mediate the relationship between TR and TA. Because of the significant relationships among variables, the model met the criteria for technology readiness and acceptance model (TRAM) and Model 6 of process mediation. This study adds to the empirical research regarding the relationships between the constructs of TR and TA of AI within higher education, in which there is a gap in the literature. The study contributed by applying the TRAM construct to the use and adoption of AI. TR, PEOU, and PU are important constructs within higher education and predict AI TA by accounting students. Additionally, TR is a precursor to PEOU and PU of AI for this population. For practice, universities should enhance use perceptions by creating opportunities for accounting students to interact with AI. Effective adoption of AI in accounting curricula aimed at enhancing students’ perceptions is essential to increase their adoption of AI and overall career readiness. For research, replicating the study at other universities, examining other factors that influence students’ adoption of AI, and exploring other AI topics in higher education could expand the literature on technology readiness and TA of AI.