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Record W4408171586 · doi:10.5267/j.jpm.2025.2.004

The influence of balanced scorecard dimension on total quality management and sustainable performance as a mediating variable: An empirical study in KSA services projects ,

2025· article· en· W4408171586 on OpenAlexvenueno aff
Heba Mousa Mousa Hikal, Omer Tajelsir Omer Elnour, Abdelmjeed Abdelrahim Ali Alajab, Nagwa Mohamed Bahreldin Abubaker, Yosra Azhari Elamin Elboukhari, Asaad Mubarak Hussien Musa

Bibliographic record

VenueJournal of Project Management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsnot available
Fundersnot available
KeywordsBalanced scorecardDimension (graph theory)BusinessEmpirical researchVariable (mathematics)Process managementTotal quality managementOperations managementEnvironmental economicsMathematicsStatisticsEngineeringEconomicsMarketingLean manufacturing

Abstract

fetched live from OpenAlex

This study aims to analyze the influence of the Balanced Scorecard (BSC) dimensions (financial, customer, internal processes, and learning and growth) on Total Quality Management (TQM), with sustainability performance as a mediating variable. The study utilized a cross-sectional survey method, distributing 400 questionnaires to employees in the service projects in KSA. Out of these, 340 questionnaires were deemed valid for final analysis. Data analysis was conducted using the SmartPls program. The study found that all BSC dimensions, except for the learning and growth dimension, had a negative direct effect on TQM, and the BSC dimensions through sustainability performance positively affected TQM, except for the learning and growth dimension. The main contribution of the research is to identify the BSC dimensions that best predict TQM in service projects in Saudi Arabia. The extended model test shows that sustainability performance is a good mediator in the causal relationship between the BSC dimensions and TQM. Future research can validate these findings in projects such as capital construction and industrial projects using a longitudinal survey design. Organizations can apply these research findings by leveraging the Balanced Scorecard as a management framework for predicting and improving TQM.

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)0.004
metaresearch head score (Gemma)0.000
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 score0.045
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
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 head0.011
GPT teacher head0.288
Teacher spread0.278 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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