Business-to-business referral as digital coopetition strategy
Bibliographic record
Abstract
Purpose Based on theories related to coopetition, the purpose of this paper is to examine the patterns of business-to-business digital referrals inscribed in businesses’ digital content. Design/methodology/approach A complete industry-wise digital data set is formed by extracting digital referrals in all the content pages. The authors outline how digital referrals are strategically used among peer businesses in the peer-to-peer digital network and in the augmented digital network, taking into consideration geographical framing and physical distance. Findings The authors reveal how geographical framing and physical distance influence peer-to-peer referral patterns in the digital space. Quite counter-intuitively, businesses are more likely to give digital referrals for peers residing in the same region, as well as for peers located in closer proximity. Further, results from the augmented digital network show that peer businesses in closer proximity exhibit greater strategic similarity in their digital referring strategy. Research limitations/implications The findings extend the understanding of business-to-business coopetition to the digital space and suggest that geographical framing and physical distance can induce reciprocated relationships between peers by offering each other digital referrals. Practical implications The findings shed light on the formation of a business-to-business digital coopetition strategy using digital referral marketing. Originality/value This study highlights the impact of digital referrals in business-to-business relationship management, especially in the digital coopetition context.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".