Riding on customer coattails: Canadian B2B technology companies and enhanced reputation through customer advocacy
Bibliographic record
Abstract
This case study explores how fast-growing Canadian technology companies in the business-to-business (B2B) sector leverage their customers for reputation building. Literature on the under-studied topic of customer referencing practices as well as reputation research provide a framework for the companies’ practices and objectives. A content analysis of key texts from 25 Canadian technology startups and 30 member companies of the Customer Reference Professionals Association (CRPA) provide a bases for comparison. Interviews with three technology startups and two established companies offer further insight into the role that customer advocacy plays in their reputation-building strategies. The findings shed light on the increasingly important role of customer advocates in these companies’ reputation-building toolkits. The study concludes that despite inherent challenges, Canadian companies recognise that harnessing the customers as a strategic communication vehicle for building reputation offer significant returns by demonstrating competence, establishing credibility, and reducing risk in the eyes of prospective customers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".