Impact of Business Incubators’ Facilities on Entrepreneurial Ecosystem Creation: A Case of Business Incubator in Jordan
Bibliographic record
Abstract
This study aims at reflecting the impact of business incubator facilities on the creation of an entrepreneurial ecosystem with a focus on the major business incubator in Jordan—King Hussein Business Park—as it expanded new land within a critical large expansion plan through five phases with a full occupancy rate. This example shows more details on how to further develop planned entrepreneurial activities to attract new clients to occupy expanded spaces by focusing on creating a supportive ecosystem for entrepreneurship. This study was conducted on the top and middle management of the companies in the business hub KHBP, the sample consisted of (300) directors. In this context, infrastructure, governmental tax benefits, and services provided are hypothesized to have an effect on the entrepreneurial ecosystem in addition to that the outcomes of this study disclose a favorable influence for the facilities on creation supportive entrepreneurial climate. Infrastructure, Governmental law benefits, and services are found to be significant in the creation of a charming business climate for entrepreneurs. The study’s findings demonstrate that countries with scant exchequers face a lot of troubles in creating an attractive and supportive ecosystem for entrepreneurial activities, and they have to get better their business environment for entrepreneurship development. Governmental policies and reforms also play a vital role in making the projects and businesses much simply.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".