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Record W1976184907 · doi:10.1108/17538371211192946

Project management maturity: a critical analysis of existing and emergent factors

2012· article· en· W1976184907 on OpenAlexaboutno aff
Beverly Pasian, Shankar Sankaran, Spike Boydell

Bibliographic record

VenueInternational Journal of Managing Projects in Business · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsMaturity (psychological)Capability Maturity ModelKnowledge managementContext (archaeology)Project managementOPM3Process managementExploratory researchComputer scienceProcess (computing)Flexibility (engineering)PsychologyProject management triangleSociologyEngineeringManagementSystems engineering

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to report on the findings of a doctoral thesis examining the limitations of project management maturity and associated models. It examines the management of undefined projects where the definition, repeatability and predictability of processes cannot be reasonably expected. The challenge to project management maturity theorists is to recognize the possibility of project management maturity in an environment characterized by undefined project elements and the requirement for greater flexibility in their management. Design/methodology/approach This inquiry was supported by a multimethod (MXM) research design with two stages: a content/textual analysis of two different collections of maturity models, and an exploratory case study of two university sites. The analysis (supported by grounded theory techniques) contributed to the development of a 4‐node conceptual framework that was used as the primary data collection instrument at two Canadian university sites. Findings Results indicate that multiple non‐process factors can contribute to a mature project management capability. These can include context‐specific values, specialized bodies of knowledge (instructional design), customer involvement, third‐party influence, and tacit “human factors” such as trust and creativity. The demands of this inquiry also demonstrated the need for a new data collection sequence in multimethod research design theory. Practical implications Practitioners are encouraged to consider customer involvement, organizational dynamics and adaptable variables such as leadership (among other non‐process factors) in their assessment of the maturity of their project management capability, and designers of future models could explore a multi‐dimensional approach that includes context‐specific factors to assessing and defining project management maturity. Originality/value This research expands the conceptual view and practical assessment of project management maturity; offers new analysis of the current generation of project management maturity models; documents e‐Learning project management; and defines a new data collection sequencing model.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.059
metaresearch head score (Gemma)0.128
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.311

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.128
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.005
Science and technology studies0.0050.007
Scholarly communication0.0110.013
Open science0.0010.005
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.199
GPT teacher head0.452
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations30
Published2012
Admission routes1
Has abstractyes

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