The Effect of Customer–Contractor Alignment in Forest Harvesting Services on Contractor Profitability and the Risk for Relationship Breakdown
Bibliographic record
Abstract
In forest operations, the interface between forest companies and harvesting contractors is of special importance, considering that it is the first link in the forest industry’s supply chains. Supply operations account for a significant share of the final costs of wood products (up to 50%). This study investigates the effect of customer–contractor alignment on contractors’ profit margins and on the risk for business relationship breakdown. Alignment is empirically measured for a Swedish forest company and 74 of its harvesting contractors, who were monitored during a four-year period. Two measures of alignment are employed: (1) the customer-perceived value of the contractors’ services; and (2) the contractors’ perceived alignment with the forest company expectations. Results indicate that the two measures of alignment are largely independent from each other, and that customer-perceived value affects both contractor profitability and the risk of relationship breakdown. Conflict between the two parties and lack of trust for the customer were found to be common complaints among contractors who ceased working for the studied forest company. Consequently, customer–contractor alignment should be considered a key objective by contractors who strive for business success, and also by forest companies who wish to improve their supply chain performance.
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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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".