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Record W4320153321 · doi:10.31355/87

The PMO as Performance Driver for Non-Profit Organizations – From Operational Data to Strategic Goals

2022· article· en· W4320153321 on OpenAlexaff
Celso Sawaia

Bibliographic record

VenueJournal of Digital Innovation for Humanity · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsOPM3Process managementBusinessProject managementOrganizational performanceProject stakeholderKnowledge managementProject management triangleProject portfolio managementProject charterComputer scienceEngineeringSystems engineering

Abstract

fetched live from OpenAlex

Aim/Purpose: The need to measure and report on performance is an acute necessity for any organization, especially non-profit organizations. However, measuring the impact of operations and projects on non-tangible goals can be challenging given the diversity of objectives and project structures adopted by these organizations. This paper proposes that the Project Management Office (PMO), as the central body for project excellence in an organization, takes an assertive role as one of the drivers of organizational performance, when adopting a KPI measuring strategy associated with the organizational strategic objectives. By adopting project operational indicators aligned with organizational strategy and the data derived from delivered projects, one can trace a direct relation between project data and strategic success. Besides the quantitative associations between project data and meeting organizational objectives, the author also briefly evaluates the contribution of qualitative, human aspects of project management to those objectives. Background: Since the early 90’s, the concept of project management offices (PMO) has been spreading and their implementation in organizations of different sizes and complexity is increasing significantly. There are several definitions and approaches to what a PMO is. The Association for Project Management defines it simply as “An organizational structure that provides support for projects, programs, and/or portfolios”. The Project Management Institute defines it as “An organizational body or entity assigned various responsibilities related to the centralized and coordinated management of those projects under its domain”; while others define it as “The decision-enabling and support business model for all business changes in the organization”. Whatever view or definition is used, the PMO aims to enhance resource use and management, minimize project and program risks, as well as to re-engineer processes and internal structures that deal with the delivery of projects and programs. An organization that properly implements a PMO was found to be more effective. When well-integrated into the organizational structure, the PMO brings adequate control of an organization’s portfolio allowing focused investments that will contribute to the company’s performance reflected by leaner resource management, higher quality of products, and a more aggressive time-to-market strategy. This increment in performance, obtained from the investments in project management practices can lead to the rapid growth of the organization. Although the PMO's contribution to strategy can be seen through targeted measures, monitoring activities, and standardizing methods, the lack of empirical evidence of success is generally attributed to the lack of its recognition as a method for enhanced performance. This disconnection is even more evident when evaluating the performance of non-profit organizations, which usually lag in formal organizational structure and do not have a formal process to track and measure long-term benefits. This gap between the measured benefits of a PMO and the organizational strategic objectives is the primary motivation for this study where we propose a performance measurement method that links tangible outputs (project goals and objectives) to non-tangible benefits (the organization strategic objectives) defining organizational strategic KPIs focused on project and program performance. Methodology: The first step is to define the relationship between the five major areas of activities for the non-profit. These relationships are depicted in a Map of Impact Drivers for a fictional non-profit with the vision of improving the quality of life of poor communities. We then identify some KPIs, such as the number of projects completed, the number of services implemented, and the number of communities with active projects, to name a few. The mission to the impacts transforming all mapped activities into measurable quantities are then tabulated. Findings: A causal linkage map supported by a strong set of performance indicators for measuring financial and non-financial aspects of the organization's outputs is proposed. The choice of which indicators to be used is highly dependent on the organization’s goals and operations, to which the PMO must adapt to properly meet its monitoring goals. As such, the PMO becomes an intrinsic part of the organization’s effort to be more effective in achieving its strategic goals. This interdependence between the PMO and the other areas of the organization, especially human resources, implies that changes dictated by the PMO will directly affect other areas of the organization that also contribute to the overall performance.

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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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
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 head0.258
GPT teacher head0.404
Teacher spread0.147 · 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
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

Citations1
Published2022
Admission routes1
Has abstractyes

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