Danielle D. Booker , Josette R.E. Pelzer , Jeremy R. Richardson
{"title":"Integrating data analytics into the auditing curriculum: Insights and perceptions from early-career auditors","authors":"Danielle D. Booker , Josette R.E. Pelzer , Jeremy R. Richardson","doi":"10.1016/j.jaccedu.2023.100856","DOIUrl":null,"url":null,"abstract":"<div><p>This study aims to help audit educators determine how, where, and to what extent audit data analytics (ADA) should be integrated into existing auditing curriculum to better prepare students for the auditing profession. We interview early-career auditors to understand their experience with ADA and add their perspective to the literature on audit curriculum changes. This group performs many of the tasks where ADA techniques would be applied and can provide valuable insight to audit educators based on their work experience and recent completion of auditing and/or data analytics courses. Responses indicate that early-career auditors rely more routinely on traditional ADA tools (e.g., Excel) and that the use of advanced ADA tools (e.g., Power BI and Alteryx) is relatively limited. Furthermore, firm-provided training on advanced ADA tools focuses on navigating tools rather than interpretation of ADA output. We also find that, regardless of the ADA tool used, the skillset used most frequently is interpretation of ADA output to identify anomalies and determine the impact on audit testing, which could be emphasized in existing audit courses. Most participants expressed that rather than removing content from the audit course to make room for data analytics, faculty should integrate ADA within existing content, encouraging audit educators to change <em>how</em> they teach, rather than <em>what</em> they teach. Specifically, faculty can provide hands-on examples of ADA to supplement lectures on existing topics or assign case studies that use ADA tools. These findings have implications for immediate changes to integrate ADA into auditing courses that are more manageable than a complete overhaul of the audit curriculum.</p></div>","PeriodicalId":35578,"journal":{"name":"Journal of Accounting Education","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Accounting Education","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0748575123000283","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 0
Abstract
This study aims to help audit educators determine how, where, and to what extent audit data analytics (ADA) should be integrated into existing auditing curriculum to better prepare students for the auditing profession. We interview early-career auditors to understand their experience with ADA and add their perspective to the literature on audit curriculum changes. This group performs many of the tasks where ADA techniques would be applied and can provide valuable insight to audit educators based on their work experience and recent completion of auditing and/or data analytics courses. Responses indicate that early-career auditors rely more routinely on traditional ADA tools (e.g., Excel) and that the use of advanced ADA tools (e.g., Power BI and Alteryx) is relatively limited. Furthermore, firm-provided training on advanced ADA tools focuses on navigating tools rather than interpretation of ADA output. We also find that, regardless of the ADA tool used, the skillset used most frequently is interpretation of ADA output to identify anomalies and determine the impact on audit testing, which could be emphasized in existing audit courses. Most participants expressed that rather than removing content from the audit course to make room for data analytics, faculty should integrate ADA within existing content, encouraging audit educators to change how they teach, rather than what they teach. Specifically, faculty can provide hands-on examples of ADA to supplement lectures on existing topics or assign case studies that use ADA tools. These findings have implications for immediate changes to integrate ADA into auditing courses that are more manageable than a complete overhaul of the audit curriculum.
期刊介绍:
The Journal of Accounting Education (JAEd) is a refereed journal dedicated to promoting and publishing research on accounting education issues and to improving the quality of accounting education worldwide. The Journal provides a vehicle for making results of empirical studies available to educators and for exchanging ideas, instructional resources, and best practices that help improve accounting education. The Journal includes four sections: a Main Articles Section, a Teaching and Educational Notes Section, an Educational Case Section, and a Best Practices Section. Manuscripts published in the Main Articles Section generally present results of empirical studies, although non-empirical papers (such as policy-related or essay papers) are sometimes published in this section. Papers published in the Teaching and Educational Notes Section include short empirical pieces (e.g., replications) as well as instructional resources that are not properly categorized as cases, which are published in a separate Case Section. Note: as part of the Teaching Note accompany educational cases, authors must include implementation guidance (based on actual case usage) and evidence regarding the efficacy of the case vis-a-vis a listing of educational objectives associated with the case. To meet the efficacy requirement, authors must include direct assessment (e.g grades by case requirement/objective or pre-post tests). Although interesting and encouraged, student perceptions (surveys) are considered indirect assessment and do not meet the efficacy requirement. The case must have been used more than once in a course to avoid potential anomalies and to vet the case before submission. Authors may be asked to collect additional data, depending on course size/circumstances.