Reclaiming Relevance Through Problem‐Driven Interdisciplinary Research
Bibliographic record
Abstract
Abstract Management studies initially emerged as an applied field, uniquely positioned to tackle practical organizational problems through interdisciplinary research. Over time, however, the field has prioritized abstract theoretical contributions over real‐world engagement, fragmenting into disciplinary silos ill‐equipped to address complex contemporary management problems, such as climate change, technological disruptions, and social inequalities. Although this theoretical turn has deepened the rigor of the field, it has eroded the field’s distinct value and relevance. We call for management studies to reclaim its applied origins through interdisciplinary, problem‐driven research, which leverages the field’s unique integrative capacity while preserving theoretical depth. Drawing on Herbert Simon’s distinction between well‐ and ill‐structured problems, we propose a framework that matches problem types to appropriate forms of interdisciplinary engagement: coordination for moderately well‐structured issues, collaboration for moderately ill‐structured ones, and co‐creation for highly ill‐structured challenges. This framework offers a conceptual scaffold for scholars seeking to engage with pressing organizational and societal challenges while navigating the institutional and epistemic barriers to interdisciplinary research. By returning to its applied origins through problem‐driven, interdisciplinary research, management studies can restore both its scholarly legitimacy and societal relevance.
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 | 0.101 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.011 | 0.119 |
| Scholarly communication | 0.036 | 0.029 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".