The Past and Future of Absorptive Capacity
Bibliographic record
Abstract
“Absorptive capacity,” a widely studied construct in management research, remains underexplored in terms of its theoretical roots and development. In this curated collection, we present and analyze articles from the Academy of Management’s family of journals, shedding light on organizations’ abilities to recognize, assimilate, and apply external knowledge. We enhance our collective understanding of absorptive capacity by delineating four schools of thought that have been advanced through the articles in our collection. In addition to opportunities for further developing and integrating the ideas of the four schools of thought, we identify three themes for future research that span the schools and that also have the potential to leverage progress in this area of research. Building on the curated work, we explain opportunities for future research to tackle unresolved issues to offer insights regarding the roles of individuals and the microfoundations in the process and multidimensionality of absorptive capacity. We further explain the need to examine how advanced artificial intelligence may affect absorptive capacity, and call for future work to add to our understanding in this domain.
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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".