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Enregistrement W2952454241 · doi:10.1108/aaaj-06-2016-2594

Do sources of occupational community impact corporate internal control? The case of CFOs in the high-tech industry

2019· article· en· W2952454241 sur OpenAlexaff
Junli Yu, Shelagh Campbell, Jing Li, Zhou Zhang

Notice bibliographique

RevueAccounting Auditing & Accountability Journal · 2019
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueCorporate Finance and Governance
Établissements canadiensUniversity of Regina
Organismes subventionnairesnon disponible
Mots-clésCredentialAccountingBusinessControl (management)OfficerQuality (philosophy)AuditHigh techChief executive officerSample (material)MarketingPublic relationsManagementEconomics

Résumé

récupéré en direct d'OpenAlex

Purpose The Chief Financial Officer (CFO), despite being a critical organization member responsible for ensuring quality of financial reporting, audit and compliance, is under-researched. Grouped as a member of top management teams (TMS) in studies, factors influencing decision making in this group rely on static measures of characteristics without regard for dynamic and longitudinal influences of career trajectories and industry occupational group memberships. The relationship between the high-tech industry as a site of notable reported internal control (IC) weakness and influences on CFOs requires closer examination. The paper aims to discuss these issues. Design/methodology/approach The study draws together the upper echelons theory and occupational communities (OCs) to explore the impact of shared values and behavioral norms from different sources on executive decision making. Internal and external sources of OC are proposed and their influence on activities with respect to corporate IC is tested. The sample of 1,573 firm/year observations includes high-tech firms listed on major US exchanges was developed using data from five distinct databases. Executives’ biographic information was manually collected. Findings Results indicate that senior financial executives belong not only to their firm and its culture but also to OCs that extend beyond the firm. Membership in professional credential granting occupational groups has less impact on effective IC than experience in the high-tech industry. In combination, multiple OCs show evidence of compound and counteracting effects on IC. The OC that arises in the high-tech industry makes a measurable positive difference in the quality of IC in sample firms, in contrast with the OC among credentialed accounting and financial professionals. Research limitations/implications This quantitative study of OC reveals the differential impact of different sources of OC and contributes to the literature on TMS a new framework for examining decision making. OC is typically studied through qualitative methods and, thus, potential exists to further explore the specific nature and dynamics of the OCs identified in this study. Practical implications The study highlights the role of broad affiliations and networks among senior financial executives which may have bearing on their ability to effectively manage IC. The role of these networks may also partially explain instances of CFO failure and thus dismissal. Knowledge of the role of OC may help boards of directors in the selection and promotion of senior financial officers of the firm. Originality/value The paper offers a different perspective on professional accounting expertise in one specific industry where incidence of IC weakness is high relative to other industries. Study results expand recent research on TMS to include sociological impacts of cohort groups. Despite generally weaker IC in the high-tech sector, this study demonstrates the value of exploring group membership within the industry as an important predictor of behavior. The result is a new perspective to CFO decision making which illustrates the relevance of OCs among upper echelons. The implications of findings for CFO recruitment and promotion are borne out in recent instances of senior financial executive failure in the sector.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesIntégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,024
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0100,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,002
Science ouverte0,0010,000
Intégrité de la recherche0,0000,003
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,037
Tête enseignante GPT0,282
Écart entre enseignants0,245 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations14
Publié2019
Routes d'admission1
Résumé présentoui

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