Success Without Upward Mobility: Evidence from Small Accounting Practices
Bibliographic record
Abstract
How small business success is defined is evaluated by exploring the definitions of and relationships between business and personal success. Survey responses are gathered from those operating sole proprietorship accounting businesses. The data consist of quantitative and qualitative elements. Quantitative responses consist of importance ratings for the nineteen business success items plus the individual respondents' demographic characteristics, such as age and income level. The qualitative data are the written personal success definitions provided by the respondents. Controlling for business and personal characteristics, it is found that client satisfaction and personal success are most important in defining business success, whereas personal success is more closely related to the business success factor of personal success than to client satisfaction. Nineteen success factors are developed based on the financial services sector. Categories are broken down into people-centered, innovation/technology, financial, self-actualization, and networking/social elements. Findings indicate that for this sample of accounting business proprietors, business success is primarily defined by customer satisfaction followed by personal success, which are people-centered dimensions. Further, whether owner-managers with different personal or professional characteristics define business success in the same way, the findings show that they generally do. However, some groups do find financial success to be the most important sign of success. (JSD)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".