How customers' perceptions of innovation activities drive brand preference, purchase and recommendation: The moderating role of product category
Bibliographic record
Abstract
Abstract This study investigated how innovation activities impact brand performance outcomes from the perspective of customer cognitive and affective mindsets. The collective findings from both Partial Least Squares Structural Equation Modeling (PLS‐SEM) and fuzzy set qualitative comparative analysis (fsQCA), based on 372 customer responses, demonstrated that a combination of product, process, store, and marketing innovation activities produces optimal results in terms of customer‐based brand equity, subsequently influencing purchase and recommendation. Post‐hoc analysis revealed that perceived innovation activities exert a weaker influence on brand equity in high‐tech product categories (e.g., smartphones) compared to lower‐tech product categories (e.g., skin care products and apparel). This study confirmed that customers' perceptions of brand innovation activities result from both technological innovation (e.g., cutting‐edge offerings) and symbolic innovations (e.g., new marketing communications).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 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 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".