Exploring shippers' motivations to adopt collaborative truck-sharing initiatives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose A seaport is an essential part of a supply chain, but many ports experience truck shortages, creating pressure for port authorities from shippers who need more trucks that move cargo. This study explores and ranks the motives for adopting a truck-sharing concept (where shippers share the same truck for delivery) as a mechanism to improve transport capacity. Design/methodology/approach This study adopts a multi-method approach – both interviews and surveys. Interviews are first conducted with shippers to explore truck-sharing usage motives. Next, quantitative surveys of both shippers and carriers are conducted to rank those motives. Findings The study identifies five motives (operational efficiency goal, quick transport solution, sustainability policy, convenience-seeking behavior and secure transport process) for truck-sharing, four critical transport attributes (lower charges for freight, distance travelled, full capacity utilization and environmental recognition), four psychological consequences (monetary savings, greater safety, instant availability of trips and clarification of environmental values), and six core values (secure transport process, being careful of money, ease of doing business, sustainability, status in the community and recognition by customers of shippers). Research limitations/implications The qualitative results will help researchers better understand how usage motives influence shippers' willingness to share a truck for transport needs. The quantitative results are useful for ranking truck-sharing motives by their importance. Practical implications Based on the findings, managers of carriers can categorize shippers according to their specific needs and thereby customize promotions to attract more shippers. Originality/value The findings provide the first, exploratory insights into shippers' motives.
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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 | 0.000 | 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 | 0.000 |
| 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 it