Personnel capabilities and the quality of big data marketing analytics (BDMA)
Bibliographic record
Abstract
Purpose This paper examines the impact of personnel knowledge on the quality of big data marketing analytics (BDMA) against the backdrop of the knowledge-based theoretical framework. Design/methodology/approach This study employs a cross-sectional survey conducted among marketing professionals in companies that have reached at least the limited deployment level of BDMA. The sample (N = 236) comprised respondents from Canada or the United States. The data were analyzed using PLS-SEM. Findings Business and marketing knowledge emerged as the most crucial factor contributing to the quality of the BDMA, followed by technology management and relational knowledge. Technical knowledge was deemed unrelated to the quality of the BDMA. However, all knowledge constructs were necessary conditions for the quality of marketing analytics to manifest. The research model also indicated that the quality of BDMA was effectively measured through information quality (i.e. completeness, currency, format, and accuracy) and technology quality (i.e. reliability, adaptability, integration, and privacy). Originality/value This paper examines the quality of marketing analytics (MA) as a multi-dimensional higher-order construct and employs PLS-SEM to identify the essential components contributing to a system for high-quality BDMA based on the knowledge constructs of personnel in business/marketing, technical fields, technology management, and relational aspects. Furthermore, the sampling frame included marketing professionals rather than solely IT personnel, highlighting the differences in the provider/user context and potential perceptual variations. All identified knowledge constructs were found to be necessary conditions.
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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 | 0.004 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".