Learning investments and organizational capabilities
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".