SPATIAL CONSISTENCY AND TEMPORAL PERSISTENCE IN MNEs’ REPETITIVE STRATEGIC RESOURCE ALLOCATIONS
Bibliographic record
Abstract
Although scholarship regarding dynamic capability has provided meaningful contributions to our understanding of the patterns of strategic decisions, less is known about their manifestations in the context of multinational enterprises (MNEs). By focusing on recursive, high-stake strategic resource allocation decisions, we disentangle the time and space dimensions of the deployment of capabilities. More specifically, we examine the stability patterns in MNEs and their subsidiaries as a result of the application of capabilities manifested as simple organizational rules. We develop two complementary core constructs for our purpose: temporal persistence and spatial consistency. Utilizing two primary dimensions of international strategy, namely expatriate assignment and equity ownership level decisions, respectively representing repetitive and quasi-repetitive decisions, we consider the role of degree of repetitiveness in the stability and dynamism of decisions and their influence on firm performance. We find a positive effect on performance for MNEs’ spatial consistency across subsidiaries for expatriation (as a repetitive decision), and a negative effect for spatial consistency in equity ownership (as a quasi-repetitive decision). We also observe for temporal persistence in expatriation, a positive effect on performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".