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Record W1501445758 · doi:10.1108/17538370810883800

Learning investments and organizational capabilities

2008· article· en· W1501445758 on OpenAlexaff
Catherine P. Killen, Robert Hunt, Elko J. Kleinschmidt

Bibliographic record

VenueInternational Journal of Managing Projects in Business · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsKnowledge managementTacit knowledgeOrganizational learningCompetitive advantageDynamic capabilitiesBusinessProcess (computing)PortfolioProcess managementComputer scienceMarketing

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to improve understanding and provide guidance for investments in organizational learning mechanisms for the establishment and evolution of organizational capabilities such as project portfolio management (PPM) and project management capabilities. Design/methodology/approach A multiple‐case study research project investigates the development of PPM capabilities in six successful organizations across diverse industries. Findings The research indicates that PPM and organizational learning are dynamic capabilities that enhance an organization's ability to achieve and maintain competitive advantage in dynamic environments. PPM capabilities are shown to co‐evolve through a combination of tacit experience accumulation, explicit knowledge articulation and explicit knowledge codification learning mechanisms. Although all three learning mechanisms are important throughout the establishment and evolution of PPM capability development, the research indicates that the development of an effective PPM capability will require particularly strong investments in enhancing tacit experience accumulation mechanisms and explicit knowledge codification mechanisms during the initial establishment or during periods of radical change to the PPM process. Research limitations/implications The research includes a sample of six case studies and the results may not be generalisable. In addition, the research was conducted over a short period of time whereas a longitudinal study would be required to gain more detailed information about the development of capabilities over time. Practical implications The findings suggest that managers can enhance and sustain competitive advantage by investing in tacit experience accumulation as well as explicit knowledge articulation and codification learning mechanisms to develop their PPM capability. Strengthened investment in experience accumulation and knowledge codification learning mechanisms is recommended during establishment of the PPM capability. Originality/value This paper contributes to the understanding of the links between organizational learning and the development of dynamic capabilities. Original hypotheses are proposed and some initial support for these hypotheses is provided through multiple‐case study research.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.233
Teacher spread0.213 · 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

Citations78
Published2008
Admission routes1
Has abstractyes

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