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Record W4379646125 · doi:10.1504/ijaf.2022.10056830

Enabling management control systems in a service organisation: an exploratory case study

2022· article· en· W4379646125 on OpenAlexaff
Rafik Kurji, Ruoyi Wang, Kalinga Jagoda

Bibliographic record

VenueInternational Journal of Accounting and Finance · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsUniversity of GuelphMount Royal University
Fundersnot available
KeywordsBusinessExploratory researchControl (management)Service (business)Process managementManagement control systemKnowledge managementComputer scienceSociologyManagementMarketingEconomicsSocial science

Abstract

fetched live from OpenAlex

Management control is a tool to assist organisations to the attainment of their goals and objectives. Over the past four decades, management control theories and applications have been developed extensively. However, there are still some discussions regarding the nature of management control systems (MCSs) and how exactly they would add value to an organisation. This paper investigates how managers in a car rental company utilise various management control tools to achieve their objectives. We tested a previous model on management control systems and proposed a revised model to be applied to the service industry. This paper aims to provide an understanding of how MCS would assist any organisation in strategic planning, operational controls and financial control. We believe this model would be a useful aid to policy makers, researchers and entrepreneurs.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.004
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.255
Teacher spread0.231 · 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 designQualitative
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
Published2022
Admission routes1
Has abstractyes

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