Bibliographic record
Abstract
Purpose Previous research in the context of feature fit has examined the effects of congruence (i.e. more specifically, the extent to which a new feature and the product are similar in the hedonic-utilitarian benefits they provide to consumers). The purpose of this paper is to examine a second dimension of feature fit: complementarity (i.e. the extent to which a new feature is related and contributing to the main functionality of the product). Design/methodology/approach The role of feature fit is examined in two experimental studies (n=593) in the context of feature additions, and also for feature deletions. Findings The results showed that complementarity adds value to a product as an additional dimension of feature fit beyond congruence, complementarity matters more for a hedonic than for a utilitarian product, and complementarity can compensate for lack of congruence. Originality/value For a product developer, adding new features to a product offers an array of choices in terms of what feature(s) to include. Although having a large pool of potential features to choose from is attractive it can also prove problematic, as products may become overly complex and features do not fit well together. The results demonstrate the importance of both congruence and complementarity as predictors of feature fit when features are added to or deleted from products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.004 |
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".