Determinants of Big Data Analytics (BDA) Adoption among Small and Medium Enterprises (SMEs)
Bibliographic record
Abstract
Businesses must be innovative in the current "knowledge-based economy" if they intend to create and maintain a competitive advantage over rivals. The adoption of big data analytics (BDA) is thus revolutionary for precise decision-making and top performance in the present industrial environment. BDA application has attracted interest particularly due to the prospects and benefits that may be realized from its utilization in both academic and practical circles. Nevertheless, its adoption in small and medium-sized (SME) businesses is unknown thus far, prompting this study. To determine the determinants influencing SME preparedness to adopt BDA, the current study used the Technology Organization Environment (TOE) framework. The results supported the positive effects of BDA adoption on organizational marketing and financial performance in SMEs. Hence, understanding the factors influencing BDA acceptance enables managers to undertake the right initiatives, which are essential for its successful implementation. The outcomes also render it possible for BDA service providers to draw in and spread its prevalence among SME businesses.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".