The impact of <scp>Big Data Analytics</scp> on firm sustainable performance
Bibliographic record
Abstract
Abstract This study evaluates the impact of Big Data Analytics (BDA) on firm sustainable performance (FSP). BDA is conceptualized as a dual construct comprising predictive and prescriptive analytics, while FSP is considered from a triple bottom line (TBL) perspective comprising the economic, social, and environmental lines of firm performance. The study relies exclusively on independent third‐party BDA and FSP data pertaining to 522 firms from the US S&P500 Index and the Canadian S&P500/TSX60 Index. The data is analyzed with ordinary least squares (OLS) regression, and the findings reveal, on aggregate, that BDA has a direct, positive, and significant effect on overall FSP. The results of the piecemeal analysis show that BDA is positively related to the economic, social, and environmental dimensions. Furthermore, our distinction between predictive and prescriptive analytics suggests that prescriptive analytics outperforms the FSP results obtained with predictive analytics moderately. The study insights provide strategic knowledge for firms seeking to leverage digitalization for enhanced corporate citizenship while boosting their digital capabilities. The impact of technology, especially Big Data, on sustainability, has gained traction in the literature, yet this is the first study to delve deeper into the detailed relationships between both constructs by deciphering and quantifying the impact of BDA components on the TBL.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".