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Integrative Interactions: The Microfoundations of Transformative Hybrid Organizing

2024· article· en· W4400444504 on OpenAlexaff
Yi Ming Ng, Alwyn Lim

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal and Cross-Cultural Management
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsMicrofoundationsTransformative learningSociologyEpistemologyPsychologyEconomicsPhilosophyPedagogy

Abstract

fetched live from OpenAlex

Hybrid organizations are key players in the movement to mainstream stakeholder capitalism. Yet, their influence on institutional market logics remains under studied. Turning to hitherto overlooked microfoundations of hybrid organizations, we employ a strategic interactionist and discourse ethics approach to posit that the routine stakeholder interactions of hybrids are key mechanisms of transformative hybrid organizing. Through in-depth interviews with 30 chief executives of social enterprises, B Corporations, and service co-operatives in Singapore, we identify a typology of interaction frames hybrids adopt that align with traditional or progressive market logics. A cross-case analysis finds that while progressive frames are associated with transformative impact, hybrids that integrate both traditional and progressive frames in their stakeholder interactions reported transformative outcomes the most. Through this, we theorize the role of integrative interactions – bringing together diverse business and societal values, norms, and engagement forms beyond transactional relations – in advancing hybrid organizing in the market . We further discuss the role of organizational and business network innovation for such integrative responsible leadership and implications for the hybrid organizing movement.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.023
Scholarly communication0.0060.007
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.338
Teacher spread0.315 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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