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Record W4415981851 · doi:10.1111/joms.70014

Reclaiming Relevance Through Problem‐Driven Interdisciplinary Research

2025· article· en· W4415981851 on OpenAlexaff
Pratima Bansal, Jin‐Su Kang

Bibliographic record

VenueJournal of Management Studies · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsWestern University
FundersNational Science and Technology Council
KeywordsRelevance (law)DisciplineLegitimacyField (mathematics)Organization studiesValue (mathematics)Interdisciplinarity

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.378
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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