The Dual Impact of Product Line Length on Consumer Choice
Bibliographic record
Abstract
Although extant literature has argued for both positive and negative effects of product line length on choice, i.e. a “dual impact,” such a possibility has not been empirically investigated. This is the first study to address this issue, using a multiple discrete choice model for horizontally differentiated goods. The authors argue that the dual impact of product line length is due to competing effects of the two constituent dimensions of product line structure: a positive effect of product line width (total number of product configurations offered) and a negative effect of average line depth (average number of SKUs per product configuration). They also examine the moderating role of choice diversification propensity manifested in multiple discreteness. An empirical application in the potato chip market confirms the expectations regarding the competing effects of the two product line dimensions and hence the dual impact of product line length. Furthermore, the negative effect of average line depth is found to be more pronounced for households with higher choice diversification propensity. These findings are not only novel but also meaningful since simulations show that the corresponding effects influence product line management decisions and new product design.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".