Artificial Inteligence’s (AI) Adoption in Accounting: Managing Large Language Models (LLMs)

<!DOCTYPE html> <html lang="en-US" xml:lang="en-US"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> Artificial Inteligence’s (AI) Adoption in Accounting: Managing Large Language Models (LLMs) | Australian Academy of Business Research </title> <meta name="generator" content="Open Journal Systems 3.3.0.15"> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="Australian Academy of Business Research"/> <meta name="citation_journal_abbrev" content="AABR"/> <meta name="citation_issn" content="2981-9504"/> <meta name="citation_author" content="Giulia Napolitano"/> <meta name="citation_title" content="Artificial Inteligence’s (AI) Adoption in Accounting: Managing Large Language Models (LLMs)"/> <meta name="citation_language" content="en"/> <meta name="citation_date" content="2023/10/09"/> <meta name="citation_volume" content="1"/> <meta name="citation_issue" content="1"/> <meta name="citation_firstpage" content="20"/> <meta name="citation_lastpage" content="31"/> <meta name="citation_abstract_html_url" content="https://aabr.com.au/index.php/aabr/article/view/3"/> <meta name="citation_keywords" xml:lang="en" content="Large language models"/> <meta name="citation_keywords" xml:lang="en" content="artificial intelligence"/> <meta name="citation_keywords" xml:lang="en" content="accounting"/> <meta name="citation_keywords" xml:lang="en" content="trust"/> <meta name="citation_keywords" xml:lang="en" content="ethics"/> <meta name="citation_pdf_url" content="https://aabr.com.au/index.php/aabr/article/download/3/16"/> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Giulia Napolitano"/> <meta name="DC.Date.created" scheme="ISO8601" content="2023-10-09"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2023-10-09"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2024-04-16"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-05-19"/> <meta name="DC.Description" xml:lang="en" content="Introduction: This paper examines the use of artificial intelligence applications to support accounting decisions and it tries to answer the following question: can artificial intelligence assist accounting professionals and what role it has compared to human expertise? Literature review/research gap: This research contributes to the extant literature in a threefold way. First, it expands the debates on how accountants can leverage AI and are influenced by it. Second, it identifies the main trust issues that have so far blocked their extensive adoption. Third, it argues that, in order to advance theories in this area, the accounting field needs empirical studies that allow policymakers and managers to make informed decisions concerning organizational challenges and necessary adaptations. Research method: This study considers the tool of Large Language Models (LLMs), that allow companies to automate tasks and make better decisions given their natural language processing capabilities (Rowdur, 2023), and is informed by recent advances in artificial intelligence-based publications, including quantum information research. Findings: this paper presents how AI technology has influenced the accounting profession so far and in what ways this discipline could be affected in the future, to understand the impact of knowledge-based systems on human users’ knowledge acquisition and retention and considering that, as the development of LLMs continues and despite their impressive abilities, organizations face trust issues when adopting these tools. Theoretical and practical implications: the present study can inform both the theory and the practice and considering that in the near future professional hybrids will emerge, the area of AI in accounting will certainly benefit from interdisciplinary research (Hasan, 2021). Thus, accounting and information science scholars have to collaborate with data scientists to find theoretical frameworks and choose the consequent adequate algorithmic solutions (Kemper and Kolkman, 2019), including information-theoretical concerns with regard to the data needed and how to guarantee a widespread diffusion of AI. Australian Academy of Business Research, Volume 1, issue 1, September 2023, pp 20-31"/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="3"/> <meta name="DC.Identifier.pageNumber" content="20-31"/> <meta name="DC.Identifier.URI" content="https://aabr.com.au/index.php/aabr/article/view/3"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2023 Giulia Napolitano"/> <meta name="DC.Rights" content="https://creativecommons.org/licenses/by/4.0"/> <meta name="DC.Source" content="Australian Academy of Business Research"/> <meta name="DC.Source.ISSN" content="2981-9504"/> <meta name="DC.Source.Issue" content="1"/> <meta name="DC.Source.Volume" content="1"/> <meta name="DC.Source.URI" content="https://aabr.com.au/index.php/aabr"/> <meta name="DC.Subject" xml:lang="en" content="Large language models"/> <meta name="DC.Subject" xml:lang="en" content="artificial intelligence"/> <meta name="DC.Subject" xml:lang="en" content="accounting"/> <meta name="DC.Subject" xml:lang="en" content="trust"/> <meta name="DC.Subject" xml:lang="en" content="ethics"/> <meta name="DC.Title" content="Artificial Inteligence’s (AI) Adoption in Accounting: Managing Large Language Models (LLMs)"/> <meta name="DC.Type" content="Text.Serial.Journal"/> <meta name="DC.Type.articleType" content="Articles"/> <link rel="stylesheet" href="https://aabr.com.au/index.php/aabr/$$$call$$$/page/page/css?name=bootstrapTheme-spacelab" type="text/css" /> </head> <body class="pkp_page_article pkp_op_view has_site_logo"> <div class="pkp_structure_page"> <nav id="accessibility-nav" class="sr-only" role="navigation" aria-label="Quick jump to page content"> <ul> <li><a href="#main-navigation">Main Navigation</a></li> <li><a href="#main-content">Main Content</a></li> <li><a href="#sidebar">Sidebar</a></li> </ul> </nav> <header class="navbar navbar-default" id="headerNavigationContainer" role="banner"> <div 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Query" placeholder=""> </div> <button type="submit" class="btn btn-default">Search</button> </form> </div> </nav> </div> </header> <div class="pkp_structure_content container"> <main class="pkp_structure_main col-xs-12 col-sm-10 col-md-8" role="main"> <div class="page page_article"> <nav class="cmp_breadcrumbs" role="navigation" aria-label="You are here:"> <ol class="breadcrumb"> <li> <a href="https://aabr.com.au/index.php/aabr/index"> Home </a> </li> <li> <a href="https://aabr.com.au/index.php/aabr/issue/archive"> Archives </a> </li> <li> <a href="https://aabr.com.au/index.php/aabr/issue/view/1"> Vol. 1 No. 1 (2023): Australian Academy of Business Research </a> </li> <li class="active"> Articles </li> </ol> </nav> <article class="article-details"> <header> <h1 class="page-header"> Artificial Inteligence’s (AI) Adoption in Accounting: Managing Large Language Models (LLMs) </h1> </header> <div class="row"> <section class="article-sidebar col-md-4"> <h2 class="sr-only">Article Sidebar</h2> <div class="download"> <a class="galley-link btn btn-primary pdf" role="button" href="https://aabr.com.au/index.php/aabr/article/view/3/16"> PDF </a> </div> <div class="list-group"> <div class="list-group-item date-published"> <strong>Published:</strong> Oct 9, 2023 </div> <div class="list-group-item keywords"> <strong> Keywords:</strong> <div class=""> <span class="value"> Large language models, artificial intelligence, accounting, trust, ethics </span> </div> </div> </div> </section> <div class="col-md-8"> <section class="article-main"> <h2 class="sr-only">Main Article Content</h2> <div class="authors"> <div class="author"> <strong>Giulia Napolitano</strong> </div> </div> <div class="article-summary" id="summary"> <h2>Abstract</h2> <div class="article-abstract"> <p><strong>Introduction: </strong>This paper examines the use of artificial intelligence applications to support accounting decisions and it tries to answer the following question: can artificial intelligence assist accounting professionals and what role it has compared to human expertise?</p><br /> <p><strong>Literature review/research gap: </strong>This research contributes to the extant literature in a threefold way. First, it expands the debates on how accountants can leverage AI and are influenced by it. Second, it identifies the main trust issues that have so far blocked their extensive adoption. Third, it argues that, in order to advance theories in this area, the accounting field needs empirical studies that allow policymakers and managers to make informed decisions concerning organizational challenges and necessary adaptations.</p><br /> <p><strong>Research method: </strong>This study considers the tool of Large Language Models (LLMs), that allow companies to automate tasks and make better decisions given their natural language processing capabilities (Rowdur, 2023), and is informed by recent advances in artificial intelligence-based publications, including quantum information research.</p><br /> <p><strong>Findings: </strong>this paper presents how AI technology has influenced the accounting profession so far and in what ways this discipline could be affected in the future, to understand the impact of knowledge-based systems on human users’ knowledge acquisition and retention and considering that, as the development of LLMs continues and despite their impressive abilities, organizations face trust issues when adopting these tools.</p><br /> <p><strong>Theoretical and practical implications: </strong>the present study can inform both the theory and the practice and considering that in the near future professional hybrids will emerge, the area of AI in accounting will certainly benefit from interdisciplinary research (Hasan, 2021). Thus, accounting and information science scholars have to collaborate with data scientists to find theoretical frameworks and choose the consequent adequate algorithmic solutions (Kemper and Kolkman, 2019), including information-theoretical concerns with regard to the data needed and how to guarantee a widespread diffusion of AI.</p><br /> <p><strong>Australian Academy of Business Research, Volume 1, issue 1, September 2023, pp 20-31</strong></p> </div> </div> </section> <section class="article-more-details"> <h2 class="sr-only">Article Details</h2> <div class="panel panel-default issue"> <div class="panel-heading"> Issue </div> <div class="panel-body"> <a class="title" href="https://aabr.com.au/index.php/aabr/issue/view/1"> Vol. 1 No. 1 (2023): Australian Academy of Business Research </a> </div> </div> <div class="panel panel-default section"> <div class="panel-heading"> Section </div> <div class="panel-body"> Articles </div> </div> <div class="panel panel-default copyright"> <div class="panel-body"> <a rel="license" href="https://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" src="//i.creativecommons.org/l/by/4.0/88x31.png" /></a><p>This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> </div> </div> </section> </div> </div> </article> </div> </main> <aside id="sidebar" class="pkp_structure_sidebar left col-xs-12 col-sm-2 col-md-4" role="complementary" aria-label="Sidebar"> <div class="pkp_block block_custom" id="customblock-aabr-submission-site"> <h2 class="title">AABR Submission Site</h2> <div class="content"> <p>Please make a submission by using the "<strong>MAKE A SUBMISSION</strong>" link below. <u>You may not receive any notification of your submission in your email</u>. 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Artificial Inteligence’s (AI) Adoption in Accounting: Managing Large Language Models (LLMs) | Australian Academy of Business Research