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Record W2765141962 · doi:10.5539/ijef.v9n12p24

The Impact of Applying Time Driven Activity-Based Costing on Improving the Efficiency of Performance in Jordanian Industrial Corporations: A Survey Study

2017· article· en· W2765141962 on OpenAlexvenueno aff
Nabil Bashir Al-Halabi, Yazan Almnadheh

Bibliographic record

VenueInternational Journal of Economics and Finance · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsnot available
FundersZarqa University
KeywordsActivity-based costingProfitability indexSample (material)BusinessAccountingCost accountingStock exchangeManagement accountingIndustrial organizationOperations managementFinanceEconomics

Abstract

fetched live from OpenAlex

The paper explored the impact of applying the time driven activity-based costing-TDABC- model (independent variables) on improving the efficiency of performance (dependent variables) in Jordanian industrial corporations. Based on a questionnaire form data from a sample of 73 participants at different managerial positions, from 30 industrial corporations listed in Amman stock exchange (ASE), were gathered and processed using the statistical package of social sciences. The main results showed that there are significant impacts of applying TDABC on improving the efficiency of performance in Jordanian industrial corporations. The main conclusion indicated that TDABC has the ability to benefit from technological developments on the basis of the activities’ charts and reflected on pricing decision making processes in industrial corporations. The research also concluded that senior management and cost accountants of the sample studied did not desire in the short run to change the current cost accounting system due to the additional costs of using the new cost accounting system. The study recommended the application of TDABC in corporations where their operations relied on TDABC’s constituents as proved its impact on reducing costs of products and increasing corporations profitability.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.033
GPT teacher head0.249
Teacher spread0.217 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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