The impact of user-centric measures on the financial performance in the context of big data marketing analytics (BDMA) applications: the influence of a decision-making role
Why this work is in the frame
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Bibliographic record
Abstract
Purpose This paper aims to examine the interrelationships between user-centric measures and their impact on the firm’s perceived financial performance as the respondents’ decision-making role changes. Design/methodology/approach The data was collected jointly with SurveyMonkey, a marketing research company, from marketing professionals working in companies with at least limited experience deploying big data marketing analytics (BDMA) applications. The respondents originated from Canada and the USA, and out of 970 responses in the initial sample, 236 were working in companies with at least limited experience in BDMA deployment. The data analysis used partial least squares structural equation modeling and necessary condition analysis. Findings All hypotheses except one were accepted. Perceived value for money positively and significantly impacted user satisfaction, positively and significantly impacted perceived financial performance. Also, the decision-making role positively and significantly impacted the perceived value for money and user satisfaction but not the perceived financial performance. Originality/value The research contributes to understanding how the decision-maker’s role impacts the perceived user-related performance measures in the BDMA context.
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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 | 0.008 | 0.007 |
| 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 | 0.000 |
| 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