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Record W4389848047 · doi:10.5267/j.dsl.2023.11.001

The effect of management control systems on business performance and innovation organizational as moderating and mediating variable

2023· article· en· W4389848047 on OpenAlexvenueno aff
Agus Setiyawan, Tubagus Ismail, Munawar Muchlish, Ina Indriana

Bibliographic record

VenueDecision Science Letters · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsnot available
Fundersnot available
KeywordsModerationMediationScope (computer science)Knowledge managementOrganizational performanceControl (management)Management control systemStructural equation modelingDependency (UML)Empirical researchLatent variableVariable (mathematics)Dual (grammatical number)PsychologyComputer scienceSocial psychologyMathematicsSociologyStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

There were still contradictory results from earlier research on the relationship between organizational innovation, performance, and management control systems (MCS). In order to account for these contradictory results, future studies ought to concentrate on the empirical analysis of Simon's MCS theory. The purpose of the study was to assess the mediation and moderation model in relation to performance, innovation, and MCS. The study intends to broaden the scope by employing a more thorough definition and measurement of research variables. Because the Partial Least Square (PLS) can concurrently assess the existence of a dual dependency relationship of a latent variable, the study used PLS to test the hypothesis. The mediated hypothesis which holds that MCS indirectly affects performance through innovation seems to be supported by these data. All things considered, this study contributes to the understanding of the ambiguous and contradictory conclusions of earlier studies that examined the connection between MCS, innovation, and performance.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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 designSimulation or modeling
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

Citations4
Published2023
Admission routes1
Has abstractyes

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