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Record W4408171656 · doi:10.5267/j.jpm.2025.1.006

The influence of organizational culture on project portfolio management practices within the healthcare sector

2025· article· en· W4408171656 on OpenAlexvenueno aff
Oswin Newton Kakumanu

Bibliographic record

VenueJournal of Project Management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHuman Resource and Talent Management
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessOrganizational cultureHealth carePortfolioProject portfolio managementKnowledge managementBusiness administrationProcess managementManagementProject managementEconomicsFinanceComputer scienceEconomic growth

Abstract

fetched live from OpenAlex

This study examines the impact of organizational culture on the effectiveness of Project Portfolio Management (PPM) practices. Organizational culture influences employee behavior and their way of working by providing a conducive work environment. Thus, managers are able to delegate better while ensuring a balanced workload, collaborative team efforts, and prudent resource allocation to achieve desired project portfolio deliverables. By shaping practices and values, a supportive culture enables efficient task performance, effective delegation, teamwork, and resource allocation toward project goals. Conducted with 35 individuals in a healthcare organization’s data and digital unit in New Zealand, the study used convenience sampling and email surveys. Regression analysis was performed in IBM SPSS Statistics to test the research hypothesis. Findings suggest that supportive organizational culture significantly enhances PPM execution and informs policy-making for improved PPM practices. This study facilitates directors, strategists, and managers in taking steps to form a culture that ensures effective execution of PPM practices to achieve better results. Future research could explore this topic with larger samples and alternative methodologies to deepen insights into culture’s role in PPM effectiveness.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.021
GPT teacher head0.292
Teacher spread0.272 · 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
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

Citations2
Published2025
Admission routes1
Has abstractyes

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