From Data to Sustainability: Exploring the role of Big Data Analytics
Bibliographic record
Abstract
This study investigates the impact of Big Data Analytics on Green Innovation in fostering sustainable business practices. The objective is to explore how companies can improve their environmental performance, optimize resource usage, and reinforce their sustainability commitments. A qualitative case study was conducted in a food processing company, integrating semi-structured interviews and document analysis. To enhance the depth of the analysis, VOSviewer software was utilized to map the relationships between key concepts, detect co-occurrences of sustainability-related terms, and visualize emerging trends in the dataset. A thematic analysis revealed that Big Data Analytics plays a key role in driving green innovation, leading to measurable improvements in sustainability indicators. Moreover, advanced data-driven technologies such as predictive analytics, AI modeling, automation, and business intelligence dashboards play a pivotal role across five production lines and waste management operations. Hence, this research offers significant implications for both the literature and businesses regarding the application and understanding of big data analytics and the adoption of advanced technologies to enhance sustainable business practices.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".