How far can you rely on a concept test: the generalizability of testing over occasions
Bibliographic record
Abstract
In practice, product managers have to assume consumer evaluations of concepts generalise from the time (and research environment) of concept testing to the time (and market environment) of market introduction. However, little is known about the temporal stability or generalisability of the results of concept testing over occasions. Rarely have concept-testing studies incorporated testing of the same concepts on the same respondents on more than one occasion. This research investigates the importance of occasions as a source of error variance in estimates of the generalisability of concept test scores for both minor and major innovations within the context of Generalisability theory. The study collected concept evaluations of ten innovations from members of an online panel on three occasions, approximately a month apart. The results show that the three-way interaction among subjects, concepts and occasions is a substantial contributor to variation in concept testing of both major and minor innovations, with the contribution for major innovations even more substantial than for minor innovations. Moreover, failure to recognize occasions as an explicit source of variance in the generalisability analyses will lead managers to overestimate the generalisability of their decision studies. However, the impact of neglecting occasions varies by purpose of measurement and associated object of measurement. This research provides insight about how well concept testing can generalise over occasions. Concept test evaluations provided on an initial exposure are more favourable than will be received on any later occasions, and apparent differences in consumer evaluations of a particular concept in an initial test do not provide a generalisable basis for identifying which consumers will respond most favourably to it on a later occasion. For concept testing to be used for targeting or segmentation, more occasions will need to be sampled.
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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 | 0.275 | 0.673 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.006 | 0.019 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".