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Record W2621135535 · doi:10.1017/9781316662243.011

Project Portfolio Management

2017· book-chapter· en· W2621135535 on OpenAlexaff
Julian Kopmann, Alexander Kock, Catherine P. Killen

Bibliographic record

VenueCambridge University Press eBooks · 2017
Typebook-chapter
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsProject portfolio managementPortfolioComputer scienceContent (measure theory)BusinessProcess managementEngineering managementOperations researchProject managementFinanceEngineeringSystems engineeringMathematics

Abstract

fetched live from OpenAlex

© Cambridge University Press 2017. Introduction Project portfolio management (PPM) is a central component of organizational project management (OPM), especially through its role in both the formulation and the delivery of organizational strategy. Corporate activities are increasingly carried out in the form of projects, in a trend that has been called “projectification” (Midler, 1995). In particular, for the implementation of complex innovations, it is not enough for organizations to focus on the successful management of individual innovation projects; they must also manage a large number of interdependent projects from a portfolio perspective. In today's dynamic environment, organizing by projects has become the rule rather than the exception, and organizations face challenges in managing these large project landscapes (programs and portfolios). The management of project portfolios is closely linked to the implementation of strategies. As strategies are ultimately implemented by projects, PPM – as a link between corporate strategy and projects – plays a central role (Meskendahl, 2010). In most research and practice this role is considered from a top-down perspective: strategies are considered to be a given yardstick for the prioritization and selection of projects and the allocation of resources. From such perspectives, PPM acts as the recipient of strategic goals and requirements that need only to be operationalized. However, the strategic management literature has long recognized the importance of emergent strategy; and that the realized strategy (the strategy that is actually implemented) often strays from the intended strategy (Mintzberg, 1978). Surprisingly, this is hardly considered in existing research models and standards for PPM (PMI, 2013). While there is some empirical evidence to suggest that hierarchical, formal, top-down approaches are not the actual practice of PPM (Christiansen & Varnes, 2009; Jerbrant & Gustavsson, 2013; Martinsuo, 2013), a much broader debate is needed to fully explore the role of PPM in the context of emergent strategies. The goal of this chapter is therefore to explore the role of PPM in the relationship between the formulation and implementation of strategy and consider both the top-down approach as well as the bottom-up strategy emergence. We first discuss emergence in the context of strategy implementation and the role of different phases in the PPM process that affect strategy implementation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.706
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.002
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 head0.102
GPT teacher head0.303
Teacher spread0.201 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations5
Published2017
Admission routes1
Has abstractyes

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