“We do what we must, and call it by the best names”: Can deliberate names offset the consequences of organizational atypicality?
Bibliographic record
Abstract
Research summary: This article focuses on organizational naming as a strategic choice organizations make to overcome liabilities of atypicality. We argue that, in markets presenting an “illegitimacy discount,” atypical organizations may use deliberate names—names that communicate the market categories to which organizations claim membership—to offset the consequences of atypicality. Using data from the global hedge fund industry, we show that atypical hedge funds are more likely than typical funds to have deliberate names. Importantly, the selection of a deliberate name is economically significant. First, funds with deliberate names grow faster than funds without deliberate names, especially among atypical funds. Second, while atypicality heightened the likelihood of failure during the recent financial crisis—even after controlling for fund performance—having a deliberate name mitigated this effect . Managerial summary: Differentiation is a core element of many organizations' competitive advantage. Nevertheless, as differentiation implies being atypical among one's competitors, differentiation strategies can also lead to an “illegitimacy discount” whereby differentiators are at risk of being misunderstood, miscategorized, and ignored by consumers. Here we investigate how atypical hedge funds—funds that differentiate themselves from their competitors by investing in notably unique ways—use names to offset the potential consequences associated with the “illegitimacy discount.” Our analysis of more than 12,000 hedge funds over 12 years highlighted a trend whereby atypical hedge funds were more likely to choose names that unambiguously associated them with a known investment strategy—for instance, choosing the name “Apex Global Macro Capital” over simply “Apex Capital.” Importantly, name selection proved to be economically significant. For example, among atypical hedge funds, those with unambiguous names grew faster than those without. Furthermore, while being atypical increased the level of disinvestment during the recent financial crisis, having an unambiguous name reversed this effect. Organizational names play an important communication role with consumers, which, while highly symbolic, may also help resolve the dual organizational need to both conform to consumer expectations and differentiate from market competitors . Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.020 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".