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Record W2528696846 · doi:10.1007/s00187-022-00342-x

The use of management controls in different cultural regions: an empirical study of Anglo-Saxon, Germanic and Nordic practices

2022· article· en· W2528696846 on OpenAlexaboutno aff
Teemu Malmi, David S. Bedford, Rolf Brühl, Johan Dergård, Sophie Hoozée, Otto Janschek, Jeanette Willert

Bibliographic record

VenueJournal of Management Control · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsnot available
FundersAalto-Yliopisto
KeywordsSBusTactGermanic languagesGermanBusinessHistoryGeographyPolitical sciencePsychologyArchaeology

Abstract

fetched live from OpenAlex

Abstract Most cross-cultural studies on management control have compared Anglo-Saxon firms to Asian firms, leaving us with limited understanding of potential variations between developed Western societies. This study addresses differences and similarities in a wide variety of management control practices in Anglo-Saxon (Australia, English Canada), Germanic (Austria, non-Walloon Belgium, Germany) and Nordic firms (Denmark, Finland, Norway, Sweden). Unique data is collected through structured interviews from 584 strategic business units (SBUs). We find that management control structures in Anglo-Saxon SBUs, relative to those from Germanic and Nordic regions, are more decentralized and participative and place greater emphasis on performance-based pay. Comparing Germanic SBUs to Nordic ones, we find Germanic SBUs to rely more on individual behaviour in performance evaluation, whereas Nordic SBUs rely more on quantitative measures and value alignment in employee selection. We also observe numerous similarities in MC practices between the three cultural regions. The implications of these findings for theory development are outlined.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.041
GPT teacher head0.283
Teacher spread0.242 · 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 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

Citations18
Published2022
Admission routes1
Has abstractyes

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