Overchoice and Assortment Type: When and Why Variety Backfires
Bibliographic record
Abstract
Almost universally, research and practice suggest that a brand that increases its product assortment, or variety, should benefit through increased market share. In this paper, we show this is not always the case. We introduce the construct “assortment type” and demonstrate that the effect of assortment size on brand share is systematically moderated by assortment type. We define an “alignable” assortment as a set of brand variants that differ along a single, compensatory dimension such that choosing from that assortment only requires within-attribute trade-offs. In contrast, we define a “nonalignable” assortment as a set of brand variants that simultaneously vary along multiple, noncompensatory dimensions, demanding between-attribute trade-offs. In turn, we argue that an alignable assortment can efficiently meet the diverse tastes of consumers, thereby increasing brand share, but that a nonalignable assortment increases both the cognitive effort and the potential for regret faced by a consumer, thereby decreasing brand share. We term this effect “overchoice.” Across three studies, we provide evidence of overchoice and tie the effect to the effort and regret brought about by nonalignability. In the process, we demonstrate that simplification of information presentation, reversibility of choice, and a reduction in underlying nonalignability serve to reduce or eliminate this effect.
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.
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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".