Unlocking the potential of entrepreneurial ventures through big data analytics and cloud computing: An empirical investigation
Bibliographic record
Abstract
The purpose of the current study is to examine the potentials provided by big data analytics and cloud computing in supporting entrepreneurial ventures. Quantitative approach was adopted through utilizing a questionnaire that was self-administered by (333) operational managers within entrepreneurial ventures operating in Saudi Arabia. SPSS was employed to screen and analyze gathered primary data. Results of study indicated acceptance of study hypotheses and confirmed that big data analytics and cloud computing are able to open many potentials for entrepreneurial ventures through big data analytics and its high potentials of informed decision making, and cloud computing along with its scalability and flexibility. The study suggested that entrepreneurs and their teams must have the necessary skills and knowledge. Investing in education and training programs can help entrepreneurs and their teams to stay up-to-date with the latest technologies and best practices in these areas. Further recommendations were presented in the study. Examining the potentials provided by big data analytics and cloud computing in supporting entrepreneurial ventures is significant because it can help entrepreneurs to make informed decisions, enhance the customer experience, increase efficiency, gain a competitive advantage, and drive innovation.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".