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Record W3082813023 · doi:10.5539/ibr.v13n9p166

Management and Cost Control of Construction Projects in Jordan

2020· article· en· W3082813023 on OpenAlexvenueno aff
Sireen Mamoun Arabiyat, Ayman H. Al‐Momani

Bibliographic record

VenueInternational Business Research · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsDimension (graph theory)Control (management)BusinessProject managerProject planningSample (material)Operations managementData collectionRelevance (law)Quality (philosophy)Project managementWorkforceCost contingencyCost accountingCost engineeringAccountingEconomicsManagement

Abstract

fetched live from OpenAlex

The variations in time and quality are vital to the projects' success. Though the cost variation is the most impacting variation, the objective of this study is to recognise the significance of the cost controls in the construction projects in Jordan. Moreover, it aims to determine the factors that lead to reduce these costs. For the methodology, the study used the questionnaire instrument for the data collection. The study sample entailed 154 respondents who hold the responsibility in relevance positions with experiences in contracting and consultants engineering field in Jordan under first and second-grade classification. The study results revealed that there is a significant relationship between cost reduction and all investigated factors, namely, the demographic variables, the pre-execution conditions and specification, and managerial, technical, or financial dimensions. Further, the results showed a significant impact of proper resource planning. In summary, the key factors affecting the project cost during the pre-execution stage involves the appropriate resources planning (i.e., workforce, funds, data). In details, the most critical factors affecting the project cost from managerial dimension are the project manager assignment and integrity of consultant. For the technical dimension, the elements are the skilled workers, the applied methods, the statements and specifications. Lastly, for the fund allocation dimension, the most critical factors that affect the project cost from a financial point of view. Lastly, a set of recommendations are proposed to the project managers to reduce the cost.

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)0.002
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 score0.366
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.182
GPT teacher head0.434
Teacher spread0.252 · 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
Published2020
Admission routes1
Has abstractyes

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