Big Data Analytics: Driving Project Success, Continuity, and Sustainability
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 study examines the impact of Big Data applications on various facets of organizational performance, specifically focusing on Business Success, Business Continuity, and Organization Sustainability. Data was collected through self-reported questionnaires and analyzed using SPSS AMOS, incorporating Pearson correlation tests and regression analyses. The results demonstrate that Big Data has a positive but generally weak correlation with these organizational aspects. Notably, mitigating risks and identifying hidden market trends stand out as the most significant factors for Business Success. Business continuity during unexpected disruptions and efficient resource allocation are crucial for Business Continuity. For Organization Sustainability, the direct impact of retrieved data on sustainable decisions and Big Data analytics for planning eco-sustainable futures are key. These findings underscore the potential of Big Data in enhancing organizational performance, suggesting areas where organizations can harness data for strategic advantages. Further research and broader datasets may offer deeper insights into these relationships.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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