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Endorsement or Detachment? Examining Individual Responses to Institutional Decoupling

2024· article· en· W4400439651 on OpenAlexaff
Yunsung Lee, Sangchan Park

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsDecoupling (probability)PsychologyEngineering

Abstract

fetched live from OpenAlex

Scholars within institutional theory have consistently directed their focus toward the institutional and organizational determinants that explain why certain organizations engage in institutional decoupling. While these studies offer valuable insights, there remains a significant gap in understanding how individual employees respond to institutional decoupling. This study addresses this theoretical gap by examining South Korean organizations ostensibly adopting the 40-hour work policy while discreetly maintaining overtime work to sustain productivity. Through a multi-level analysis involving 6,576 individual employees across 209 organizations, our findings reveal a negative relationship between institutional decoupling, organizational identification, and proactive performance, attributed to the cognitive dissonance arising from the incongruence between external expressions and internal values. We argue that cognitive dissonance can be mitigated through normative attitudes and behaviors associated with social identities, along with individual motivations for self-enhancement. As organizations gain greater appeal in the labor market, employees rationalize institutional decoupling as a means to enhance their identity and status. Additionally, when organizations make efforts to achieve a desirable future image, they interpret institutional decoupling as a transitional phase aimed at reinforcing their social identity. These findings highlight the role of social identity in shaping how individual employees address cognitive dissonance arising from institutional decoupling.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.952
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.114
GPT teacher head0.369
Teacher spread0.256 · 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
GenreEmpirical

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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