ALLIANCE-BASED COMPETITIVE DYNAMICS BRIAN S.
Bibliographic record
Abstract
Do rivals ' alliances increase or decrease the competitive pressure experienced by a firm? Linking ecological and economic research on organizations, we propose that the effects of rivals ' horizontal, upstream, and downstream alliances are determined by the degree to which they (1) foreclose a focal firm's alliance opportunities and (2) increase industry carrying capacity. We also hypothesize that firms can co-opt rivals' alliances by partnering with well-linked rivals. An analysis of Canadian biotechnology firms supports these predictions. In technology-based industries, do rivals ' alliances increase or decrease the competitive pressure experienced by a firm? If rivals ' alliances increase competitive pressure, what if anything can a firm do to counteract these effects? Although the last decade has witnessed an explosion of research concerning the effects of alliances on the firms that participate in them, the literature is virtually silent regarding their competitive effects on rivals. Yet these implications are crucial for scholarly understanding of the competitive dynamics of technolo-gy-based industries, as well as for managers competing in such environments. There is by now some consensus, based on research rooted in transaction cost economics (TCE) and the resource-based view (RBV), that firms pursue collaborative arrangements to gain more efficient or timely access to scarce resources (Hennart, 1988; Kogut, 1988; Williamson, 1991). But to the extent that competitive dynamics of alliances have been considered, the literature has produced varying theoretical predictions. According to one line of reasoning, in a world of limited potential partners, a firm's alliances weaken its rivals by denying them access to desirable partners and resources (Gomes-Casseres, 1994). An alternate line of argument,
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".