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Project, program and portfolio management as modes of organizing: Theorising at the intersection between mergers and acquisitions and project studies

2022· article· en· W4220767401 on OpenAlexaff
Joana Geraldi, Satu Teerikangas, Gustavo Birollo

Bibliographic record

VenueInternational Journal of Project Management · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsProject portfolio managementPortfolioProject sponsorshipPosition (finance)Bridge (graph theory)Project managementIntersection (aeronautics)Mergers and acquisitionsProject management triangleProject charterBusinessProcess managementManagement scienceEngineering managementEngineeringManagementEconomicsFinanceTransport engineering

Abstract

fetched live from OpenAlex

Although the management of mergers and acquisitions (M&As) and of projects are connected in practice, they remain disjoined in academia. In this paper, we conceptually bridge the literature on projects and M&As to discuss the transitory nature of organisations by mobilising the concepts of project, programme, and portfolio as alternative modes of organising M&As. As a project, the managerial effort in M&A focuses on completion on time and budget. As a programme, M&As are managed as complex processes of convergence between organisations. As a portfolio, M&A management is part of the ongoing integration efforts within organisations that have grown via M&As. Our contribution to project studies is to position projects, programmes, and portfolios as modes of organising, hence, not as phenomena but as managerial choices used to shape strategic change initiatives, such as M&As. We conclude with implications beyond project studies, thereby drafting a project-based theory of the firm.

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)0.008
metaresearch head score (Gemma)0.009
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0020.022
Scholarly communication0.0100.014
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.109
GPT teacher head0.448
Teacher spread0.339 · 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

Citations35
Published2022
Admission routes1
Has abstractyes

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