Barriers to shippers’ resistance in adopting truck-sharing services
Bibliographic record
Abstract
Purpose Truck-sharing stands out as an impactful strategy for minimizing emissions and optimizing the streamlined transport of goods. This study seeks to address a gap in understanding by investigating the barriers shippers face in adopting truck-sharing services. Design/methodology/approach This study employs the innovation resistance theory to examine a range of potential barriers. A total of seven potential barriers are included in the investigation. Survey data from Bangladeshis are analyzed using an artificial neural network. Findings The barriers, ranked in importance, include image, tradition, value, usage, risk, psychological ownership and privacy concerns. Thus, psychological barriers (image and tradition) mostly underpin resistance to change, showing that the issue is more rooted in shippers' perceptions than operations. Also, they often do not find a financial cause to use truck-sharing services. Usage barriers, explicitly addressing the practical application of truck-sharing services, have now assumed the third position, underscoring their significance in overcoming the barriers. Research limitations/implications The findings provide valuable insights for policymakers to reconsider their approaches in addressing the most formidable truck-sharing barriers. Practical implications This insight holds implications for shippers and transport companies, offering strategic guidance to optimize their engagement with and support for such services. Originality/value To the best of our knowledge, this study examines shippers' reluctance to adopt truck-sharing services in a developing country.
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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 | 0.004 | 0.018 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".