Thinking strategically about professional sports
Bibliographic record
Abstract
In this paper, we examine the potential value that Professional Sport Franchises (PSFs) have for firms that seek to earn economic rents and obtain a persistent competitive advantage. To do so, we discuss PSFs from two perspectives: 1) Structure‐Conduct‐Performance (most closely associated with Porter's [1979] five‐forces model) and 2) the resource‐based view (RBV) of the firm (Barney, 1991). We argue that, despite operating in a less munificent market than in previous decades, sport franchises should continue to provide a means for corporations to attain a competitive advantage and earn superior economic rents. However, the likelihood of success will be directly dependent upon the particular strategy being pursued, which, we argue here, must combine the characteristics of each franchise with other valuable resources unique to the specific corporation. We go on to argue that such strategy should focus on developing, maintaining, and sustaining a strong, committed fan base. The contribution the paper makes to the sport management literature is as follows. First, by distinguishing a PSF as a strategic economic asset, we show how we can avoid the ambiguity of the term “team”, which we argue is really only a subset of employees who work together to produce the league product. Second, we identify the need for research to move from examining sport organizations'individual business strategies to how sport organizations fit into broader corporate strategies. Finally, we show how a resource‐based view allows for a better understanding of why, despite the apparent financial woes of PSFs, franchises continue to escalate in value and remain as resources that corporations can employ to attain superior economic rents and a persistent competitive advantage.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".