Cross-Market Network Effect with Asymmetric Customer Loyalty: Implications for Competitive Advantage
Bibliographic record
Abstract
A cross-market network effect exists in many industries (e.g., newspaper publishing, media, software) in which a seller sells both a primary and a secondary product (e.g., a newspaper publisher sells newspapers to readers and advertising space to advertisers), and the value of the secondary product depends on the size of the user base of the primary product. This paper examines the competitive implications of asymmetric customer loyalty in such markets. In traditional markets, an advantage in customer loyalty generates a profit advantage. We show here, however, that in the presence of a cross-market network effect, a midlevel of loyalty advantage in the primary product market can lead to an overall profit disadvantage. This surprising result is derived from the interdependence of the two markets, whereby a profit in one market may be gained at the cost of the other, and by the positive relationship between a larger loyalty segment and a higher opportunity cost of price competition in the product of the primary market. Extending our model to a two-period entry game also shows that under certain conditions, the entrant with disadvantage in customer loyalty can outperform the incumbent in profit and market share. This result suggests that asymmetry in customer loyalty can be a source of “first-mover” advantage or disadvantage.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".