Not All Ties Are Equal: CEO Outside Directorships and Strategic Imitation in R&D Investment
Bibliographic record
Abstract
Prior research has identified two different sources of strategic imitation—through perceived organizational cluster similarity (cluster effects) and direct social connections (tied-to effects). In the research on tied-to effects, top executives’ social ties, such as outside directorships, have long been studied as a mechanism through which strategic imitation develops. However, are all ties the same? There has been little examination of whether some social ties have more influence than others. Using the attention-based view of the firm, we argue that certain social ties garner more attention by being salient to top executives. We empirically test this assertion by examining the effects of CEO outside directorships on R&D spending. Using panel data from large U.S. manufacturing firms, we find that CEOs imitate the R&D intensity of tied-to firms (i.e., a firm in which the CEO serves as an outside board member) in their own firm’s R&D decisions. Consistent with attention-based arguments, our results show evidence of selective imitation, as imitating relationships are stronger when the CEO has longer tenure as a director of a tied-to firm and the tied-to firm is performing well. In contrast to conventional institutional theory, our findings also show that CEOs imitate relatively smaller tied-to firms when they make R&D investment decisions. Not all social ties have equal influence on imitative strategic decision making; thus, they have different strategic implications.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".