Comparing Mediation Effect of Functional and Emotional Value in the Relationship between Pros of Applying Big Data Analytics and Consumers’ Responses
Bibliographic record
Abstract
Applying Big Data analytics application brings many benefits for e-vendors and customers. Exploring the effect of consumer perceived value to consumers’ responses under applying Big Data analytics is lacking. And, what kind of perceived values do customers have more concerns under Big Data era. Therefore, the aims of this study are to analyze relationship between pros of applying Big Data analytics and Consumers’ responses under multiple mediators of perceived values as functional value and emotional value. Data analysis was done in a sample of 349 respondents. The results show that applying Big Data analytics have significant positive effect on customers’ responses. Functional and emotional values act as important mediators on the relationship between applying Big Data analytics and consumers’ responses. There are no significant different between mediator effect of functional value and emotional value. The findings of this study will have implications for e-vendors to understand the important mediator of perceived value on customers’ responses under Big Data analytics era.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".