Moving Toward Economic and Digital Sustainability in Marketing Analytics
Bibliographic record
Abstract
Despite analytical advancements, firms have yet to realize the full potential of big data marketing analytics (BDMA) because the poor quality data restricts customer predictions and insightful decisions. Technology and market uncertainty create challenges in understanding business needs, choosing analytical tools, determining customer insights, and market trends. This study aims to assess the quality of marketing analytics, including technology and information quality. Data were collected from 236 North American respondents working in firms with at least limited experience in the deployment of BDMA. The analysis tool was PLS-SEM. The findings supported the hypothesis that technology and market uncertainty negatively influence the quality of analytical outcomes. This study makes a significant theoretical and methodological contribution to BDMA literature by assessing the quality of analytics as an integrated formative construct.
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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 | 0.061 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.025 | 0.034 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".