Product Assortment and Consumer Choice: An Interdisciplinary Review
Bibliographic record
Abstract
The topic of product assortment has generated a plethora of research across various domains, including economics, analytical and empirical modeling, individual and group decision making, and social psychology. Despite the voluminous assortment research, however, the key findings have remained scattered across domains. In fact, the very domain of assortment research has not been clearly defined, thus complicating the understanding of the current state of assortment research. The goal of this review, therefore, is to define the field of assortment research and outline its key findings. In this context, this review delineates three key domains of assortment research: (1) how consumers perceive the variety of items in an assortment, (2) how consumers choose an item from a given assortment, and (3) how consumers choose among assortments. The key findings in each of these three areas are synthesized in the form of specific research propositions that build on the existing findings and provide guidance for further empirical investigation. By outlining the key findings in each of these three areas, this review offers an integrative framework for understanding the impact of assortment on consumer choice.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".