Crime and Small Business: An Exploratory Study of Cost and Prevention Issues in US. Firms [*]
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it