Revisiting Internal Capital Market Efficiency: A Strategic View
Bibliographic record
Abstract
The empirical literature on internal capital markets (ICM) implicitly has assumed that capital allocations are efficient when headquarters shift more capital to divisions operating in industries with more attractive investment opportunities. In this study, we challenge this one-size-fits-all industry-comparing logic, arguing that it overlooks the role of firm-specific idiosyncrasies in shaping capital allocation decisions. We introduce firm-specific industry effects (FSIE) as a novel firm-level construct that captures the extent to which the performance of a firm’s divisions is driven by industry factors rather than firm-level characteristics. This measure reflects the relevance of industry trends for a firm’s capital allocation decisions and, consequently, the extent to which the firm is expected to strategically—that is, purposefully and with the aim of creating value—conform to (or deviate from) industry-comparing logics of capital allocation. Our empirical results show that FSIE is associated with firms’ conformity to such a logic of capital allocation. A post hoc analysis further demonstrates that the value-creating effect of such a conformity depends on the extent to which it is systematically driven by FSIE. Specifically, firm value is associated with conformity to the industry-comparing logic of allocation only when such a conformity systematically covaries with FSIE. These findings suggest that the prevailing literature on ICM may have overestimated inefficiency and value destruction in multi-business firms by overlooking how firm idiosyncrasies affect capital allocation strategies. More broadly, our study highlights the importance of considering FSIE in understanding when industry-level characteristics provide a relevant basis for corporate-level decisions. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsc.2022.0053 .
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".