Publikation:

Warranty Provisions : Machine-Learning Versus Human Estimates

Lade...
Vorschaubild

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2025

Autor:innen

Schölzel, Simon

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

URI (zitierfähiger Link)
ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Hybrid
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published

Erschienen in

European Accounting Review. Taylor & Francis. ISSN 0963-8180. eISSN 1468-4497. Verfügbar unter: doi: 10.1080/09638180.2024.2444521

Zusammenfassung

This study employs machine learning to shed light on the accuracy of discretionary accounting estimates and the causes of human estimation errors. Using proprietary data from a large European manufacturing firm, we implement a set of prediction models to gauge a pervasive and economically relevant accounting estimate: the warranty provision. We find that machine learning models consistently outperform human experts when compared on the basis of individual warranty obligations. This gap widens when estimates are aggregated across homogeneous classes of products, as the machine makes relatively fewer and less severe overstatements. Applying model interpretability techniques and conducting a series of semi-structured interviews, we identify misspecifications of the managerial estimation model, specifically aggregation bias and anchoring to historical cost, as the primary causes of the larger human errors. Moreover, the interview evidence suggests that various firm-level factors, such as learning frictions, auditors’ preferences for process continuity, and strategic considerations, are important determinants of the design and continued use of misspecified estimation models in practice.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
330 Wirtschaft

Schlagwörter

Accounting estimates, Machine learning, Measurement uncertainty, Managerial errors, Warranty provision

Konferenz

Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690BECKER, Martin, Simon SCHÖLZEL, 2025. Warranty Provisions : Machine-Learning Versus Human Estimates. In: European Accounting Review. Taylor & Francis. ISSN 0963-8180. eISSN 1468-4497. Verfügbar unter: doi: 10.1080/09638180.2024.2444521
BibTex
@article{Becker2025-01Warra-71888,
  year={2025},
  doi={10.1080/09638180.2024.2444521},
  title={Warranty Provisions : Machine-Learning Versus Human Estimates},
  issn={0963-8180},
  journal={European Accounting Review},
  author={Becker, Martin and Schölzel, Simon}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/71888">
    <dc:contributor>Becker, Martin</dc:contributor>
    <dc:creator>Becker, Martin</dc:creator>
    <dcterms:abstract>This study employs machine learning to shed light on the accuracy of discretionary accounting estimates and the causes of human estimation errors. Using proprietary data from a large European manufacturing firm, we implement a set of prediction models to gauge a pervasive and economically relevant accounting estimate: the warranty provision. We find that machine learning models consistently outperform human experts when compared on the basis of individual warranty obligations. This gap widens when estimates are aggregated across homogeneous classes of products, as the machine makes relatively fewer and less severe overstatements. Applying model interpretability techniques and conducting a series of semi-structured interviews, we identify misspecifications of the managerial estimation model, specifically aggregation bias and anchoring to historical cost, as the primary causes of the larger human errors. Moreover, the interview evidence suggests that various firm-level factors, such as learning frictions, auditors’ preferences for process continuity, and strategic considerations, are important determinants of the design and continued use of misspecified estimation models in practice.</dcterms:abstract>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2025-01-15T08:47:41Z</dc:date>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/46"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2025-01-15T08:47:41Z</dcterms:available>
    <dcterms:issued>2025-01</dcterms:issued>
    <dc:language>eng</dc:language>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/46"/>
    <dcterms:title>Warranty Provisions : Machine-Learning Versus Human Estimates</dcterms:title>
    <dc:rights>Attribution-NonCommercial-NoDerivatives 4.0 International</dc:rights>
    <dc:contributor>Schölzel, Simon</dc:contributor>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/71888"/>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by-nc-nd/4.0/"/>
    <dc:creator>Schölzel, Simon</dc:creator>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Ja
Online First: Zeitschriftenartikel, die schon vor ihrer Zuordnung zu einem bestimmten Zeitschriftenheft (= Issue) online gestellt werden. Online First-Artikel werden auf der Homepage des Journals in der Verlagsfassung veröffentlicht.
Diese Publikation teilen