Where can capabilities come from? network ties and capability acquisition in business groups
Bibliographic record
Abstract
Abstract While strategy researchers have devoted considerable attention to the role of firm‐specific capabilities in the pursuit of competitive advantage, less attention has been directed at how firms obtain these capabilities from outside their boundaries. In this study, we examine how firms' multiplex network ties in business groups represent one important source of capability acquisition. Our focus allows us to go beyond the traditional focus on network structure and offer a novel contingency model that specifies how different types of network ties (e.g., buyer‐supplier, equity, and director), individually and in complementary combination, will differentially affect the process of R&D capability acquisition. We also offer an original analysis of how other aspects of network structure (i.e., network density) in business groups affect the efficacy of network ties on R&D capability. Empirically, we provide an original contribution to the capabilities literature by utilizing a stochastic frontier estimation to rigorously measure firm capabilities, and we demonstrate the value of this approach using longitudinal data on business groups in emerging economies. We close by discussing the implications of our supportive results for future research on firm capabilities, organizational networks, and business groups. Copyright © 2010 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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".