The impact of the decision-making role on perceived satisfaction, value for money, and reinvest intentions at varying levels of perceived financial performance in the context of Big Data Marketing Analytics
Bibliographic record
Abstract
Business resources and processes such as Big Data Marketing Analytics (BDMA) are becoming increasingly focused on meeting the needs and objectives of customers. Consequently, this research aims to use PLS-SEM to explain the interaction and relationships between the core user-centric performance measures of BDMA, such as user satisfaction, value for money and reinvestment intention. Also, the significance of the decision-making role was explored in this context. Finally, the impact of perceived financial performance was investigated to see its impact on the examined relationships. The impact of value for money on user satisfaction, the impact of the decision-making role on user satisfaction, and finally, the impact of the decision-making role on the reinvestment intentions were found to be significant for individuals who scored either low or high perceived financial performance. Furthermore, all the observed relationships in the dataset were positive, whereas only three were positive and significant for individuals who scored low on perceived financial performance. Overall, it is clear that perceived financial performance has a vital role in BDMA deployment, where understanding the influence of user authority in decision-making enables managers to design better organizational plans by integrating inputs from multiple organizational cross-layers. Also, the results indicate that a user’s decision-making role influences user-centric measures in BDMA deployment, which reveals how user perceptions and authority play a vital role in the context of BDMA in firms.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".