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Record W2144197439 · doi:10.5539/ass.v10n9p82

Target Costing Evolution: A Review of the Literature from IFAC’s (1998) Perspective Model

2014· review· en· W2144197439 on OpenAlexvenueno aff
Hussein H. Sharaf-Addin, Normah Omar, Suzana Sulaiman

Bibliographic record

VenueAsian Social Science · 2014
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsnot available
Fundersnot available
KeywordsActivity-based costingManagement accountingCost accountingCompetition (biology)Target costingProfit (economics)Perspective (graphical)BusinessAccountingIndustrial organizationProcess managementOperations managementEconomicsComputer scienceMicroeconomics

Abstract

fetched live from OpenAlex

During the last two decades, with increasing competition in highly changing business environment, literature has cautioned against the efficiency and capability of traditional management accounting techniques in providing sufficient information needed for decision making. In coping with continuing changes in business environment and increasing pressure of competition, cost and management accounting techniques have been changed and new techniques have been developed, especially in the last two decades. This is to enable organizations to stay competitive by producing better quality products at lower costs. As Target Costing (TC) has been innovatively adopted to achieve this objective by Japanese companies in the 1960s, this paper attempts to show the historical development of TC. Premised on the review of forty (42) refereed journal articles published in various accounting journals during the periods of 1984 to 2013, this paper shows how, over times, the TC practices have undergone a three-phase evolution from cost reduction and control to cost and profit management and to quality and functionality improvement. Specifically, the paper examines the evolutionary facet of TC by using the 1998 IFAC-based management accounting evolution framework.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.898
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.004
Science and technology studies0.0010.000
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 head0.016
GPT teacher head0.275
Teacher spread0.260 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2014
Admission routes1
Has abstractyes

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