Whose Crystal Ball to Choose? Individual Difference in the Generalizability of Concept Testing
Notice bibliographique
Résumé
The product development literature has identified several individual characteristics that could influence how subjects respond to new products in concept tests. Few of these characteristics have been thoroughly investigated. The purpose of this research is to examine whether a number of personality traits (1) do influence concept evaluation scores and (2) can be used to identify respondents who provide substantially higher‐quality data in concept testing and whether the answers to these questions change for major versus minor innovations. The data quality of the concept testing data is defined using the generalizability theory, which provides a decision‐specific G‐coefficient. Higher quality means a G‐coefficient closer to 1 for a particular managerial decision. A Web‐based study to concept test 10 appliance innovations on multiple occasions was conducted among 105 panelists from the Institute for Online Consumer Studies (IOCS). During the concept testing, respondents' innovativeness, change‐seeking tendency, and propensity to exert cognitive effort were also measured. The results showed that the respondent characteristics influence the mean evaluation of the concepts and the psychometric quality of the concept testing data: (1) there is a significant linear relationship between concept scores and all of the innovativeness scales and change‐seeking measures; (2) the effect of innovativeness on concept testing outcomes is even more substantial for major innovations than for minor innovations; (3) the study provides evidence that the quality of concept testing data provided by respondents varies substantially with their innovativeness, whereas the differences are more modest when scaling just minor innovations; (5) there are also strong effects on data quality for the Need to Evaluate scale used to capture cognitive effort characteristics; and (6) there is little effect of segmenting on social desirability on data quality. Managerially, the current results indicate that a product manager wanting to concept test a pool of appliance concepts can benefit from screening for the respondents who will provide higher‐quality concept testing data. For example, respondents who are high on domain‐specific innovativeness provide the highest‐quality concept testing data for both minor and major innovations. The effects of traits are stronger for major innovations, supporting the claim that subject selection is a more critical issue in concept testing of major innovations. Product managers can improve the quality of their concept testing data without an increase in cost by screening the subjects they use in concept testing.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».