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Gender heterogeneity in couriers' mode choice behaviours: Crowd-shipping for E-groceries

2025· article· en· W4416263978 on OpenAlexaff
Oleksandr Rossolov, Anastasiia Botsman, Serhii Lyfenko, Yusak O. Susilo

Bibliographic record

VenueJournal of Transport Geography · 2025
Typearticle
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsDalhousie University
FundersÖsterreichische ForschungsförderungsgesellschaftUniversität für Bodenkultur WienBundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation und Technologie
KeywordsMode choiceMode (computer interface)Willingness to payDiscrete choiceChoice modellingCyclingMate choiceFocus (optics)

Abstract

fetched live from OpenAlex

This paper examines the mode choice behaviour of occasional couriers providing crowd-shipping (CS) deliveries for e-groceries, with a particular focus on gender heterogeneity. Using a behavioural survey conducted in Kharkiv, Ukraine, in early 2021, combined with simulated travel attributes and discrete choice modelling based on random utility maximisation theory, this study explores how gender influences mode preferences and willingness to pay (WTP) across six transport modes within a crowd-shipping context. The results reveal significant gender-based differences in both mode choice and WTP. Female couriers consistently exhibit a higher WTP across all transport modes compared to their male counterparts. For instance, women's WTP for cycling (90 UAH/h) is substantially higher than men's (59.79 UAH/h), while for car-based deliveries, women's WTP reaches 87.16 UAH/h, compared to 54.94 UAH/h for men. These findings suggest that women require higher compensation, particularly for non-motorised modes, likely due to differences in physical effort required and perceived comfort.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.257
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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