Formal institutional context in global strategy research: A layer cake perspective
Bibliographic record
Abstract
Abstract Research summary We offer a novel view of formal institutions as a layer cake, suggesting a structural relationship between higher‐level and lower‐level institutions. In this context, inter‐layer conflict imposes complex pressures on multinational corporations (MNCs). These tensions have become more rife amid the growth in global connectedness and the commensurate increase in the importance of within‐country differences. Drawing on political science and economic geography research, we introduce regime type and the distribution of economic resources as conditions under which inter‐layer conflict is most likely to arise. We leverage two caselets to illustrate the inter‐layer conflict and the novel response options MNCs can deploy. Our perspective advances the theoretical understanding of intra‐national institutional diversity, laying the groundwork for future research at the nexus of institutional theory and global strategy. Managerial summary Firms often encounter opposing pressures in their operating environments because institutions within the nation‐state impose misaligned policies. Despite acknowledging that such interactions exist, firms traditionally did not make it an integral part of their strategy. We demarcate how formal institutions cascade, forming a layer cake of relevant influences whereby the structural relationship between higher‐level and lower‐level institutions may impose complex pressures when in conflict. We turn to political science and economic geography literatures for explanations of when such conflict is most likely and offer a window into the responses by multinational firms using caselets within the COVID‐19 pandemic context. We offer new avenues for research on the ways in which institutions function to affect multinational firms in a global economy increasingly characterized by institutional complexity.
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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".