The Utilization of Interorganizational Relations in an Uncertain Institutional Field: A Case Study of the Fiesta Bowl’s Ascension
Bibliographic record
Abstract
This study utilized a historical institutionalism frame to examine three questions. First, what environmental conditions must be present to prompt a city or region to support the creation of a college football bowl game when the failure rate (50%) of bowl games is so high? Second, how does a bowl game survive in an uncertain institutional field? Third, how does a bowl game improve its standing in a competitive institutional field? Focusing on the collective history of the Fiesta Bowl (i.e., 1968 to 2015) as a theoretical sample, this work utilizes Barringer and Harrison’s interorganizational relationship (IR) typology (i.e., joint ventures, networks, consortia, alliances, trade associations, and interlocking directorates) and Oliver’s IR environmental determinants (i.e., necessity, asymmetry, reciprocity, efficiency, stability, and legitimacy) to explain the Fiesta Bowl’s ascension to a top-tier event. Specifically, we found conference affiliations, corporate sponsors, and television broadcast agreements were major areas involving IR. These relationships show IR helped improve the market position and value creation of the Fiesta Bowl in a competitive institutional field. Furthermore, this works uniquely demonstrates that a bowl game’s timeliness to use emerging IR may enhance their institutional position (i.e., tier-status) and ability to help create a new product.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".