Consumer and Citizen Perspectives on Sustainability in Last-mile Deliveries
Bibliographic record
Abstract
Purpose: Consumers are a central stakeholder in last-mile deliveries (LMD), particularly with the increases in online ordering. However, consumers’ perspectives are not represented much in research regarding LMDs in urban areas. Therefore, this paper examines consumers’ perceptions regarding choices made for LMDs, sustainability (or lack) thereof and how increased deliveries impact urban space. Design/methodology/approach: The data was collected through six qualitative focus groups of international university students. Wooclap tool was used to collect the students’ views on four different themes of urban LMDs. Discussions were recorded, transcribed and analysed through coding. Findings: The results show that there is interest in sustainable options in LMDs, but consumers feel they are not given enough information to make informed choices. Convenience of deliveries, i.e. speed and price, as well as the option to choose a delivery time and/or location were considered important. The students were from four different universities in Canada, China, Denmark, and Finland, so where they were based influenced their responses. Research limitations/implications (if applicable): This study was made with a predominantly young demographic. A similar study with a different age group may yield different results, so we hope to expand the study with additional data. Originality: Consumer perspectives, choices, and demands have significant influence on choices companies make. The consequences of increased LMDs on urban space, particularly from a citizen point of view are rare and we explore this in the paper.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".