A Network Perspective of Digital Competition in Online Advertising Industries: A Simulation-Based Approach
Bibliographic record
Abstract
Using agent-based simulation experiments, we investigate the outcome of SAs between two smaller online search engine companies in competition with a dominant market leader in settings where an advertiser's decision making is the consequence of a combination of NI (e.g., an individual's willingness to follow others' decisions) and IP. In particular, we focus on a context in which the combined search engine company competes with a market leader holding a larger share of the market than the two runner-up “underdogs” combined. Our results indicate that, with the presence of NI and cascading effects, an alliance with “only” 35%–40% combined market share could compete with a leader whose market share, at the time of an alliance, is 60%–65%. Although important, size alone might be insufficient to build the market as suggested by the “vanilla” network effect theory. Another noteworthy finding is that a nonlinear association exists between NI and an alliance outcome; the combined runner-up companies have the best chance of success when the extent of NI is midrange, rather than on the high or low end of continuum. Contrary to the conventional view, this finding might also stimulate discussions among network science researchers. Furthermore, our results suggest that NI substantially moderates the relationship between the combined market share at the time of an alliance and the likelihood of resulting alliance success.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".