Bibliographic record
Abstract
Purpose This study aims to investigate the influence of social capital on firms’ business strategies, focusing on Miles and Snow (1978) dichotomy between “prospector” and “defender” strategies. Design/methodology/approach The authors perform multivariate regression analyses using a sample of US firms spanning the period from 1995 to 2021. The authors use a two-stage least squares model to alleviate endogeneity concerns and perform several cross-sectional tests and path analyses. Findings The authors find a significant and positive association between social capital and defender-type business strategies. Results from cross-sectional analyses reveal that this relationship is more pronounced in highly competitive product markets and among firms led by highly qualified CEOs. In addition, the authors find that CEO compensation mediates the effect of social capital on business strategy. Overall, the results suggest that low social capital regions foster prospector strategies due to managers’ self-maximizing incentives. Finally, the authors find that business strategy acts as a mediating factor, connecting social capital to firms’ financial reporting outcomes. Social implications In light of recent public concerns over declining social capital in major economies and the growing globalization and multiculturism in societies, the findings are of interest to policymakers and the wider society by highlighting the far-reaching implications of social capital on businesses and the capital market. Originality/value To the best of the authors’ knowledge, this study documents the first empirical evidence on the association between a society’s social capital and firms’ business strategies. The study contributes to the research on the determinants of a firm’s business strategy and extends the literature on the relationship between social capital and firm behavior.
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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 | 0.001 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".