Profiling shopping mobility in pre- and post-purchase phases: Latent class analysis of apparel trial and return trips
Bibliographic record
Abstract
This study explores the unobserved heterogeneity in shopping mobility by examining consumers’ apparel trial and return behaviors across the pre- and post-purchase phases. In the context of the rapid growth of e-commerce and rising return rates, understanding how trial and return behaviors interact within individual shopping journeys becomes critical. While prior research has explored online shopping’s impact on travel, limited attention has been paid to the diversity of these behaviors and their mobility implications. To address this gap, two latent class choice models are estimated using revealed preference data from 507 U.S. shoppers. Latent class membership is explained through attitudinal profiles derived from factor analysis (Bartlett scores), capturing environmental concerns, consumption habits, and convenience preferences. Three distinct segments are identified for both trial and return behaviors, each characterized by unique trip frequencies, trial and return method preferences, and socio-demographic traits. The interplay between pre- and post-purchase mobility is further examined through a segment probability matrix. Results show that most shoppers specialize in either trial or return behaviors, with limited overlap. For instance, “Active Trial Enthusiasts” comprise 51.2% of the sample, marked by frequent store visits and hybrid trial preferences. In contrast, 23.6% belong to the “Frequent & Opportunistic Returners,” who regularly return goods using both self-managed and carrier-based methods. These findings reveal diverse and complementary mobility patterns shaped by apparel shopping habits. The study provides valuable insights for transport planners and e-retailers seeking to address the environmental and operational impacts of evolving consumer behavior.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".