Core versus Peripheral Innovations: The Effect of Innovation Locus on Consumer Adoption of New Products
Bibliographic record
Abstract
In four experimental studies, the authors investigate the effect of innovation locus—whether the innovation is integrated with the base product (the core locus) or offered as a detachable accessory (the peripheral locus)—on consumers’ adoption intentions. The findings show that offering a really new innovation (RNI) as a detachable peripheral component leads to higher adoption intentions than integrating the same innovation into the core. Innovation locus, however, does not have an effect on incrementally new innovations. The positive effect of peripheral locus (relative to core locus) for RNIs occurs through four mechanisms: (1) reduced schema incongruity, (2) lower risk perceptions, (3) increased benefit understanding, and (4) greater perceived usage flexibility associated with the new product. The authors demonstrate these effects by using stimuli from four product categories and including both attitudinal and behavioral measures of innovation adoption. The findings have implications for product design strategies for RNIs.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".