Insights into the application of AI-augmented research methods for informing accounting practice: the development – through AI - of accountability-related prescriptions pertaining to seasonal work

Bronwyn Eager, Craig Deegan and Terese Fiedler

Meditari Accountancy Research, 2024, vol. 32, issue 5, 1977-1997

Abstract: Purpose - The purpose of this study is to provide a detailed demonstration of how artificial intelligence (AI) can be used to potentially generate valuable insights and recommendations regarding the role of accounting in addressing key sustainability-related issues. Design/methodology/approach - The study offers a novel method for leveraging AI tools to augment traditional scoping study techniques. The method was used to show how the authors can produce recommendations for potentially enhancing organisational accountability pertaining to seasonal workers. Findings - Through the use of AI and informed by the knowledge base that the authors created, the authors have developed prescriptions that have the potential to advance the interests of seasonal workers. In doing so, the authors have focussed on developing a useful and detailed guide to assist their colleagues to apply AI to various research questions. Originality/value - This study demonstrates the ability of AI to assist researchers in efficiently finding solutions to social problems. By augmenting traditional scoping study techniques with AI tools, the authors present a framework to assist future research in such areas as accounting and accountability.

Keywords: Artificial intelligence; Accountability; Accountability model; Scoping study; Seasonal workers (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eme:medarp:medar-08-2023-2116

DOI: 10.1108/MEDAR-08-2023-2116

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