The quality of big data marketing analytics (BDMA), user satisfaction, value for money and reinvestment intentions of marketing professionals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how the quality of big data marketing analytics (BDMA) impact the satisfaction, perceived value for money and intentions to reinvest as perceived by marketing managers, i.e. the users of BD. Design/methodology/approach Survey data was collected with the help of a marketing research company – mainly among Canadian and US marketing professionals with experience in BDMA deployment ( N = 236). The structural model was analyzed with partial least squares structural equation modeling. Findings Findings indicate that the quality of technology has a significant and positive impact on perceived value for money but not on the satisfaction levels of those who use the data (marketing professionals). Furthermore, information quality is significantly and positively related to satisfaction for marketing professionals – but not the perceived value for money. Both perceived value for money and satisfaction are positively linked to intentions to reinvest in big data. Originality/value This paper examined separately the significance of the technology and information quality of BDMA in assessing its importance on user satisfaction and perceived value for money and, ultimately, on intentions to reinvest among marketing managers. It is noteworthy that the users of the BD (marketing managers) appear to be much more critical of BD than the data generators (BD analysts).
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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 it