Bibliographic record
Abstract
Purpose This article develops a benefit curve and a cost curve that relate the strength of a CEO’s social tie to its benefits and costs respectively, and thereby develops a cost-benefit framework for understanding the strengths of CEO social ties. In particular, this framework helps address the basic, yet largely unanswered questions of why one tie is stronger than another and why a CEO utilizes social ties to a greater extent in one context than in another. Design/methodology/approach As a conceptual paper, this article develops a cost-benefit framework for understanding the strengths of CEO social ties. Findings This article suggests an important shift of research focus and a different way of thinking regarding tie strength. Specifically, it suggests that the more fundamental question might not be whether a social tie is beneficial or one tie is more beneficial than another, but rather what its optimal strength is, given the underlying relational factors such as resource dependence and demographic similarity. Relatedly, the question might not be whether a CEO’s level of utilization of social ties has a more positive effect on firm performance in one context than in another, but rather what the optimal level of utilization is, given the contextual factors such as environmental uncertainty. Originality/value This article addresses a widely accepted, yet potentially misleading understanding of the relationship between a tie’s strength and its benefits (i.e. the strength of weak ties argument). By doing so, it develops a benefit curve that integrates into a coherent, parsimonious function three seemingly conflicting key ideas in the literature (i.e. the overall notion that social ties are beneficial, the strength of weak ties argument, and the liability of strong ties argument). Relatedly, it develops a coherent framework for understanding the strengths of CEO social ties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".