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Cultural Learning by Hiring New Leaders: Perpetuating Effect of Cultural Tightness in Groups

2017· article· en· W2765948315 on OpenAlexaff
Yeun Joon Kim, Soo Min Toh

Bibliographic record

VenueAcademy of Management Proceedings · 2017
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOrganizational cultureSocial psychologyPublic relationsWorking groupWork (physics)Social groupPsychologySociologyPolitical scienceBusinessLaw

Abstract

fetched live from OpenAlex

Organizational cultures form the social fabric of employees’ work lives. How employees respond to transitions, whether leaving an organization to join a new one as a leader, or receiving a new leader from outside of the organization, has important implications for an organization’s culture and work group cultures. With multi-wave and multi-source data collected from 372 employees in 91 work groups of a single firm, our study examined an organization’s cultural learning by hiring new leaders. In other words, group leaders were hired from outside, essentially newcomers to the organization, while group members were organizational insiders. We found that new groups acquired the culture of their leaders’ former groups; cultural tightness that leaders experienced in their former groups had enduring effects on the culture of their new groups, which in turn influenced negative and positive deviant behavior of members in these groups. The effects were stronger when the leaders identified or had longer tenures with their former groups. On the other hand, the effects disappeared when group identification or tenure with the former groups were low.

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)0.004
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.353
Teacher spread0.321 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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