Selecting a Customization Strategy Under Competition: Mass Customization, Targeted Mass Customization, and Product Proliferation
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
Customization requires not only an implementation of proper manufacturing systems but also a proper strategy regarding when firms should offer customized products and what the nature of customization should be. This paper questions 1)whether customization is better than no customization, and, if so, 2) what kind of customization strategy firms should adopt under competition. We find that customization is not optimal when the cost of soliciting customer preference information is sufficiently high. When competing firms choose to customize, we show that firms target only certain customer segments with customized products. We also find that the optimal customization strategy may require firms to offer only a few discrete product varieties. Despite the concern that customization may initiate price wars because customization reduces product differentiation, we find that customization does not escalate the price competition, because aggressive price competition exacerbates cannibalization. Although customers within the product line of a firm are charged higher prices, we show that on average customers are better off when firms adopt customization. However, unless the customization is quite cheap, when firms choose to customize, we find that firms cannot generate more profits than when firms offer only a single product
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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