<scp>Product‐Line Length as a Competitive Tool</scp>
Bibliographic record
Abstract
The increasing number of consumer goods and services offered in recent years suggests that product‐line extensions have become a favored strategy of product managers. A larger assortment, it is often argued, keeps customers loyal and allows firms to charge higher prices. There is disagreement, however, about the extent to which a longer product line translates into higher profits. We develop an econometric model derived from a game‐theoretic perspective that explicitly considers firms' use of product‐line length as a competitive tool. On the demand side, we analytically establish the link between consumer choice and the length of the product line. Based on our derivations, we include a measure of line length in the utility function to investigate consumer preference for variety using a brand‐level discrete‐choice model. The supply side is characterized by price and line length competition between oligopolistic firms. For the empirical analysis we use market‐level data for the yogurt category. We find that there are decreasing returns to product‐line length. Based on a series of “what‐if” experiments, we derive recommendations for effective product line decisions in a competitive environment.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.007 |
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".