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Record W2566489875 · doi:10.1111/joms.12258

Rethinking ‘Top‐Down’ and ‘Bottom‐Up’ Roles of Top and Middle Managers in Organizational Change: Implications for Employee Support

2016· article· en· W2566489875 on OpenAlexaff
Mariano L.M. Heyden, Sebastian Fourné, Bastiaan A.S. Koene, R.A. Werkman, Shahzad Ansari

Bibliographic record

VenueJournal of Management Studies · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsOrganizational changeChange management (ITSM)Top-down and bottom-up designBusinessMiddle managementChange detectionKnowledge managementPsychologyMarketingComputer sciencePublic relationsPolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract In this study we integrate insights from ‘top‐down’ and ‘bottom‐up’ traditions in organizational change research to understand employees’ varying dispositions to support change. We distinguish between change initiation and change execution roles and identify four possible role configurations in which top managers (TMs) and middle managers (MMs) can feature in change. We contend that both TMs and MMs can play change initiation and/or change execution roles, TMs and MMs have different strengths and limitations for taking on different change roles, and their relative strengths and limitations are compounded or attenuated based on the specific configuration of change roles. We subsequently hypothesize employee support for change in relation to different TM‐MM change role configurations. Our findings show that change initiated by TMs does not engender above‐average level of employee support. However, change initiated by MMs engenders above‐average level of employee support, and even more so, if TMs handle the change execution.

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)0.014
metaresearch head score (Gemma)0.034
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.008
Scholarly communication0.0070.006
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.053
GPT teacher head0.272
Teacher spread0.219 · 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
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

Citations251
Published2016
Admission routes1
Has abstractyes

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