What is a <i>Leather Iron</i> or a <i>Bird Phone?</i> Using Conceptual Combinations to Generate and Understand New Product Concepts
Bibliographic record
Abstract
This article introduces the framework of conceptual combinations, which underlies the creative ability to combine existing concepts to create new ones. Using this framework, two creative processes are identified, namely, (a) property mapping (PM), which entails combining concepts by transferring a property from one concept to another (e.g., shape in the case of notebook computers); and (b) relation linking (RL), which entails linking the two combining concepts by a thematic relation (e.g., the “locative” relation in desktop computers). The effect of these processes on the comprehension of new product concepts is investigated in two experimental studies. In Study 1 it is shown that novel products created by RL are easier to interpret than the ones created by PM. In Study 2 it is found that new products combining concepts from different super‐ordinate categories are more likely interpreted by RL, and are easier to comprehend than the ones from the same super‐ordinate category, which use PM. The theoretical and managerial implications of using conceptual combinations in the context of new product ideation are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".