Innovation and experience goods: a critical appraisal of a missing dimension in innovation theory
Bibliographic record
Abstract
This paper discusses how the concept of experience goods could be integrated conceptually into innovation studies. Experience goods are distinguishable in that their value or utility cannot be determined until after they have been consumed. The concept encompasses an enormous variety of consumer goods whose value is determined largely or entirely by subjective and non-rational factors that are difficult to accommodate in the established framework of innovation theory. This theory has a strong historical orientation to manufactured goods and to technology producer goods. The paper provides some critical perspectives on the conceptual evolution of ‘value’ in innovation theory. It then introduces the experience goods dimension, demonstrating its potential for exploring how historical, social, cultural and economic factors combine in the construction of value-producing innovations. Drawing on the literature of marketing, consumer research, and cultural economics, various dimensions of experience as a factor in innovation are mapped onto Schumpeter’s innovation typology. The paper concludes by discussing some of the implications for future research.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.004 | 0.066 |
| Scholarly communication | 0.010 | 0.031 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".