Towards a Resource-Based View of Internal Capital Markets
Bibliographic record
Abstract
The literature on internal capital markets assumes that allocations are efficient if a headquarters shifts relatively more capital to subsidiaries with higher market growth opportunities (i.e., picks the winners). However, the empirical work has failed to consistently link deviations from winner-picking logic to firms’ value destruction. In this paper, we underscore the connection between the firms’ dominant view of strategy and their capital allocation patterns. We argue that winner-picking logic only corresponds to the industrial organization (IO) view of strategy (Porter, 1981) and neglects firms’ idiosyncrasies. Taking a resource-based view (RBV) of strategy, we theorize and empirically show that internal and external idiosyncrasies of firms lead them to evaluate their external opportunities differently and, therefore, deviate from the winner- picking logic. Our paper also contributes to the literature by helping to resolve the observed inconsistency in value implications of deviation from winner-picking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".