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dc.contributor.authorHsieh, Sheng-Fenges-ES
dc.contributor.authorBeretta Custodio, Cleber Henriquees-ES
dc.contributor.authorVasarhelyi, Miklos A.es-ES
dc.date.accessioned2023-11-13T16:59:14Z-
dc.date.available2023-11-13T16:59:14Z-
dc.date.issued2021-09-01es_ES
dc.identifier.issn1577-8517es_ES
dc.identifier.uri10.4192/1577-8517-v21_7es_ES
dc.descriptionArtículos en revistases_ES
dc.description.abstract.es-ES
dc.description.abstractWe investigate and document the textual similarity of key audit matter (KAM) disclosures by using KAM items in auditor’s reports of Spanish companies in fiscal years 2017 and 2018. The main objective is to understand how similar KAMs are disclosed from one year to another. Following prior literature, we use the cosine similarity to measure the textual similarity between KAM items in terms of word usage. We classify and analyze KAM items for two consecutive years based on the following three combinations: (1) KAM topic, (2) KAM topic and auditor, and (3) KAM topic, auditor, and industry of the client being audited. The results indicate that auditors from the same accounting firm tend to have a recurring textual similarity under each KAM topic, and such similarity increases for clients within the same industry. The results add empirical evidence to the understanding of the recurring textual similarity of KAM disclosures.en-GB
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoen-GBes_ES
dc.rightsCreative Commons Reconocimiento-NoComercial-SinObraDerivada Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/es_ES
dc.sourceRevista: International Journal of Digital Accounting Research, Periodo: 1, Volumen: 21, Número: , Página inicial: 183, Página final: 202es_ES
dc.titleThe Textual Similarity of KAM Disclosures for Spanish Companieses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.rights.holderes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.keywords,.es-ES
dc.keywordsKey audit matter, cosine similarity, auditor`s report, text mining.en-GB
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