The Importance of Entrepreneurship in Micro and Small Enterprises in Uncertainty and Barrier Scenarios
Bibliographic record
Abstract
Economic, political, and technological uncertainties require organizations to prepare adequately for new events that occur each day. Economic crisis, changes in the policies of each country and technological developments are classic examples of these changes in organizations. Seasonality is one of the economic phenomena that occur, and different forms of management are required. The seasonality that is described as a systematic and temporal imbalance of the tourist phenomenon that does not need to be regular, caused by the climate or by the vacation periods, and is based on the consumer behavior that can be expressed in terms of the number of visitors, traffic, highways, jobs, and tickets at attractions. Therefore, through a careful analysis of seasonality, it is possible that the losses are predicted, and the negative impact is not so grotesque on the company. For this, it is good that the manager uses the cost assessment methods to assume the seasonality of sales and thus be able to guard against the negative effects of the same. The objective of this research is to understand how owners of micro and small businesses in coastal cities consider themselves entrepreneurs or not and how they deal with obstacles such as seasonality through cost management. The methodology used was based on an exploratory and bibliographic research with a qualitative approach, seeking to analyze the triad: Entrepreneurship, Seasonality and Cost Management. The field research was applied through a semi-structured questionnaire to the sample of 100 Microentrepreneurs in the trade of goods and services in Caraguatatuba and São Sebastião, central cities of the North Coast of São Paulo. The main results show that most of the interviewed entrepreneurs perceive a certain impact caused by seasonality, understand the use of Cost Management to improve their processes and are adapted to one of the main costing methods highlighted by the bibliography.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".