What is a <i>Leather Iron</i> or a <i>Bird Phone?</i> Using Conceptual Combinations to Generate and Understand New Product Concepts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it