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Record W2885252983 · doi:10.5539/ibr.v11n9p66

Activating Balanced Scorecard Importance as a Way to Improve the Accounting Education in Jordanian Universities

2018· article· en· W2885252983 on OpenAlexvenueno aff
Yaser Saleh Al Frijat

Bibliographic record

VenueInternational Business Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsnot available
Fundersnot available
KeywordsBalanced scorecardAccountingGraduation (instrument)RevenueSample (material)Quality (philosophy)Management accountingBusinessPsychologyMarketingEngineering

Abstract

fetched live from OpenAlex

This study discussed activating Balanced Scorecard (BScs) importance as a way to improve the Accounting Education in Jordanian Universities. Data analysis was conducted using multiple regression models, a sample 134 academic staff in the Accounting departments and Managers in Jordanian Universities. The findings of regressions indicated that there is a statistically significant positive relationship between activating of BScs and improve Accounting Education, where a asserted that financial indicators, students, internal processes, learning’s and innovation contribute in performance success of accounting education, in terms of activating internal controls of all revenues and expenditures, and achievement principle of operational efficiency. It also emphasized to pay attention to students, and on providing academic services, and provide students with intellectual skills, personal, ethical, and communication with others, also surveying students, accepts complaints, continuous communication with students after graduation, meeting admission requirements for accounting students. The study also concluded that supporting scientific research culture for academics, paying attention to quality standards that deal with education and updating technology means related to teaching processes.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.020
GPT teacher head0.315
Teacher spread0.295 · 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.

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

Citations3
Published2018
Admission routes1
Has abstractyes

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