Microfoundations of firm internationalization: The owner CEO effect
Bibliographic record
Abstract
Research Summary In this article, we examine the influence of owner CEOs’ motivations and authority on strategic risk‐taking behavior of firms as reflected by their investments in foreign markets. We theorize that owner CEOs, aided by their strategic leadership, long‐term orientation, and less‐restricted decision‐making powers, will facilitate their firms’ strategic decisions that are exploratory in nature and, thus, are more risky. We further propose that the owner CEO effect is likely to differentially interact with performance aspirations and governance structures of firms in influencing internationalization. We test our predictions on a longitudinal panel dataset of 226 Indian manufacturing firms over the 10‐year period from 2002 to 2011 and find support for our hypotheses. We contribute to the emerging literature on microfoundations and behavioral strategy. Managerial Summary Given that a large number of firms around the world are characterized by concentrated ownership and owners who also assume CEO roles, we explore the influence of owner CEOs on firms’ strategic risk‐taking behavior. We propose that firms with owner CEOs, particularly founder owner CEOs, are likely to exhibit a higher degree of internationalization as compared to firms with professional CEOs. Further, we propose that the positive owner CEO effect is stronger when the firm performance is above its aspirations and also in stand‐alone firms when compared to firms affiliated to business groups. We test our predictions using a sample of 226 Indian manufacturing firms over the 10‐year period from 2002 to 2011 and find support for our predictions.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".