MétaCan
Menu
Retour à la cohorte
Enregistrement W168195758

Crime and Small Business: An Exploratory Study of Cost and Prevention Issues in US. Firms [*]

2000· article· en· W168195758 sur OpenAlexaboutno aff
Donald F. Kuratko, Jeffrey S. Hornsby, Douglas W. Naffziger, Richard M. Hodgetts

Notice bibliographique

RevueJournal of Small Business Management · 2000
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueCrime Patterns and Interventions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSmall businessBusinessCashProduct (mathematics)Property crimeWhite-collar crimeGoods and servicesFinanceAccountingEconomicsMarketingLawEconomySociology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This article is an examination of the levels of crime and the methods of crime prevention in US. small business. A survey was taken of 422 small business owners from the Midwest and Southeastern United States to measure the level of occurrences of crime, the methods of prevention employed, and the owners' level of concern about this issue. The results demonstrated a considerable level of activity aimed at controlling crime loss, including various forms of training and other security measures. Differences by industry type were also identified. Business crime is a world-wide issue confronting business owners in every nation. In Russia, for example, where the legal system does not define ownership of assets or transfer of property fights, Coleman (1997) reported that the number of crimes has doubled since 1985, making it 'almost impossible for a Russian entrepreneur to operate within the framework of the (p.74). In Canada, recent statistics place the cost of employee theft (including theft of cash, inventory, and fixed assets) at $20 billion a year. Theft causes 30 percent of all small business failures and comprises 15 percent of the price of goods and services (Holt 1993). Business crime costs the U.S. economy at least $186 billion annually. Estimated at between 2 percent and 5 percent of the gross domestic product, the cost of white-collar offenses may be 100 times that of street crimes, according to FBI statistics (U.S. Small Business Administration 2000). A 1993 fraud survey by the accounting firm KPMG covering 2,000 of the largest Dun and Bradstreet companies in the U.S. found 330 companies reported losses averaging more than $550,000 per company. The total losses reported were in excess of $180 million. This is in companies with strong internal controls and internal audit staffs; what, then, is the risk for small businesses with weak controls and no internal audit staff (Russell 1995)? Crime and its effects are a major issue for small business owners. The United States Chamber of Commerce reported in 1995 that 30 percent of all small business failures resulted from the cost of employee dishonesty--internal crime. In addition, small businesses (under $5 million in sales) are 35 times more likely to suffer from business crime than larger firms (U.S. Department of Commerce 1995). For a fuller picture, consider some of the following figures from national sources. The Federal Bureau of Investigation reports that white-collar crime in the United States has accounted for approximately $41 billion in losses each year during the 1990s. This total includes some startling statistics: $1.1 billion is attributed to credit card and check fraud; $7.0 billion is attributed to embezzlement and internal theft; and $100 million is accounted for by computer fraud (U.S. Small Business Administration 2000). In addition, US News & World Report estimated that crime against business cost companies $128 billion annually in direct losses, litigation, and security expenses (Thompson, Hage, and Black 1992). Finally, a study conducted at the University of Florida in 1994 (Donnelly 1994) attributed 42.1 percent of the shrinkage in retailing inventory to employee theft (32.4 percent was attributed to shoplifting and poor paperwork). Even if these estimates are exaggerated, employee theft is one of the most costly offenses committed by individuals in the United States, and the actual cost of employee crime exceeds the reported quantifiable costs. For instance, increases in sick leave requests, misuse of company materials, vandalism, sabotage, substance abuse, and theft of time all lead to higher prices and increased expenditures made to control these crimes (Kilborn 1992). Recently, computer crimes have posed increasing problems for law enforcement. A survey of 3,500 computer-security professionals by the National Center for Computer Crime Data estimated the annual loss from computer abuse to be more than $555 million nationwide. …

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
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 enseignants0,049
Score d'incertitude au seuil0,098

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,003
Études des sciences et des technologies0,0020,001
Communication savante0,0020,002
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,077
Tête enseignante GPT0,350
Écart entre enseignants0,273 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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

Citations35
Publié2000
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of Small Business ManagementMême sujetCrime Patterns and InterventionsTravaux en français237 207