The big data analytics to digital marketing path strengthened by knowledge management
Bibliographic record
Abstract
This study investigates the impact of Big Data Analytics (BDA) on digital marketing performance, with a particular focus on the mediating role of Knowledge Management (KM). As organizations increasingly adopt BDA to enhance marketing intelligence, customer engagement, and strategic decision-making, the effectiveness of these initiatives hinges on their ability to manage and translate data into actionable insights. To explore this relationship, a quantitative survey was conducted among marketing managers and digital strategy experts from e-commerce and technology-driven firms. Structural Equation Modeling (SEM) was employed to assess both the direct and indirect effects of BDA on digital marketing performance, with KM serving as a mediating variable. The results demonstrate that KM significantly mediates the relationship between BDA and marketing outcomes, suggesting that the integration of robust KM systems enhances the value derived from data analytics. Firms that align their analytical capabilities with effective knowledge-sharing practices are more likely to achieve agility, foster innovation, and sustain competitive advantage in the digital marketing landscape.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.006 | 0.006 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".