The Impact of Quality of Big Data Marketing Analytics (BDMA) on the Market and Financial Performance
Bibliographic record
Abstract
Impact of quality of big data marketing analytics (BDMA) was analyzed, with special attention to the BDMA dimensions of technology and information quality, and the level of deployment on perceived market and financial performance. The sample was collected with Canadian and U.S. marketing respondents with experience in big data (BD) deployment (N=236). The model analysis was done with PLS-SEM. The study highlights how technology and information quality are related to the market and financial performance with high predictive validity and strength. Also, the level of deployment had a significant impact on both the technology and information quality in BDMA. The study provides an understanding of how the level of deployment impacts BDMA technology and information quality dimensions; and how they individually contribute to the enhancement of a firm's market and financial performance from the perspective of marketing personnel with experience in deployment of BDMA. It is also evident that the more advanced the firm is in the deployment of BD, the higher the technology and information quality.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".