Bibliographic record
Abstract
Convergence in the electronics sector has enabled the addition of disparate new functionalities to existing base products (e.g., adding mobile television to a cell phone or Internet access to a personal digital assistant). This research investigates the role of two factors—(1) the goal congruence between the added functionality and the base and (2) the nature of the base product (utilitarian versus hedonic)—on the evaluation of such convergent products (CPs). The author proposes that the evaluation of CPs with a utilitarian versus hedonic base is subject to an asymmetric additivity effect. Specifically, whereas CPs with a utilitarian base gain more from adding an incongruent, hedonic functionality than a congruent, utilitarian one, CPs with a hedonic base gain less from an incongruent, utilitarian addition than a congruent, hedonic one. This asymmetry is because hedonic additions enhance the pleasure of using a utilitarian base, whereas utilitarian additions may dilute the existing hedonic image of a hedonic base. The moderating role of prior ownership of the base of a CP is also explored. The author proposes that the effects of goal congruence are stronger for owners than for nonowners, but only for CPs with a hedonic base, not for those with a utilitarian base. The author verifies the proposed effects in an experimental study conducted with a large-scale, representative sample of the target market population. Further research on other (moderating) factors affecting the evaluation of CPs is also suggested.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".