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Record W3125215576

A Conceptual Development of Simons’ Levers of Control Framework

2012· article· en· W3125215576 on OpenAlexaff
Sophie Tessier, David Otley

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsAmbiguityControl (management)Field (mathematics)Conceptual frameworkPunishment (psychology)Management control systemKnowledge managementManagement sciencePerceptionComputer scienceStrategic controlEpistemologyProcess managementSociologyPsychologyStrategic planningManagementSocial psychologyBusinessStrategic thinkingArtificial intelligenceSocial scienceEngineeringEconomicsMathematics
DOInot available

Abstract

fetched live from OpenAlex

The management control literature has been criticised for having concepts that are ill-defined. This causes mixed empirical results and makes it difficult to build a coherent body of knowledge. The paper addresses this issue by developing an important framework, that of Simons’ Levers of Control, which has been criticised in the past for its vague and ambiguous definitions. Using methods of concept analysis, the paper analyses prior literature to identify ambiguities with the different levers of control and uses examples from prior field studies to illustrate these ambiguities. The paper also analyses the positive and negative dimensions of controls which, although part of Simons’ framework, have remained unexplored. For each ambiguity identified, the paper proposes a solution to improve concept definitions or to clarify the relationship between concepts. The result is a revised framework that explicitly separates managerial intentions for controls and employee perceptions of controls. Managerial intentions are comprised of three levels: 1) types of controls (social and technical) 2) which are organised as four control systems (strategic performance, operational performance, strategic boundaries and operational boundaries) and 3) which can be used diagnostically or interactively, have an enabling or constraining role and can lead to either reward or punishment. Finally, after defining the framework’s concepts and explaining how they interact, the paper concludes by offering avenues for future research.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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 designTheoretical or conceptual
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

Citations10
Published2012
Admission routes1
Has abstractyes

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