Extending relationship value: observations from a case study of the Canadian structural wood products industry
Bibliographic record
Abstract
Purpose The purpose of this paper is to present how the construct of relationship value (RV) has the potential to help suppliers understand how to create superior value in their customer relationships and ultimately improve their competitiveness. Nowhere is this truer than in the Canadian wood products industry, where sales to its most important market, the USA, have recently been dwindling. The paper seeks to present how RV was adapted in this research context and extended over elements of scope, range of potential applications, scale of measurement, and computational techniques. Design/methodology/approach A multiple‐case study was undertaken and consisted of the evaluation of 58 customer relationships for three wood products manufacturers. Findings Findings highlight the necessary distinction between “value for” and “value of” customers when measuring relationship value from a supply perspective. Based on the value and orientation of exchange, a new segmentation of customer relationships emerges which differentiates “questionable”, “supportive”, “promising”, and “strategic” relationships. Originality/value The case study will serve in the development of value‐driven relationship management approaches, which are likely to become a major source of competitive advantage, not only in the wood products industry, but in business in general.
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 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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".