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Record W4247493439 · doi:10.1108/00197850910927705

Performance evaluation in a matrix organization: a case study (part 3)

2009· article· en· W4247493439 on OpenAlexaff
Steven H. Appelbaum, David Nadeau, Michael Cyr

Bibliographic record

VenueIndustrial and Commercial Training · 2009
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsCAE (Canada)Concordia University
Fundersnot available
KeywordsConstructiveOrder (exchange)Process managementComputer scienceKnowledge managementBusinessProcess (computing)

Abstract

fetched live from OpenAlex

Purpose The purpose of this article is to examine and ultimately suggest the most effective method with which to evaluate employees operating within a matrix organization. The authors will demonstrate the tools, corporate participation and acceptance levels required in order to ensure employee and manager “buy in” and implementation. Design/methodology/approach This article consists of a comprehensive review of literature demonstrating functional areas within a matrix organization as well as employee evaluation methods within various organizations. It is presented in three sections: defining a matrix organization; demonstrating effective evaluation methods and strategies; and finally how the two should work together. Critical incidents are interspersed throughout the article in order to demonstrate how the research compares to the methods employed by a leading aviation engineering firm. Findings Ineffective evaluation methods within matrix organizations can lead to lower employee morale as well as an ambiguous understanding of employee roles within such an organization. Employee and management buy in and support of an evaluation system and its goals are crucial to the success of the program. The multi‐rater system appeared to be most effective. Practical implications Several tools exist to help employers effectively evaluate their employees in a constructive and effective manner. Among them are clear job description and corporate structure, followed by a review of performance by both functional and project managers. Additionally, peer evaluations can prove to be constructive and contribute positively to the development of the employee. This article can be a practical aid for managers in a matrix organization that need to successfully and constructively evaluate employees, but are having difficulty doing so in an effective method. Originality/value Given the limited research with respect to evaluations within a matrix structure, this paper demonstrates an understanding of a subject that has not been adequately explored. The article demonstrated in “real time” the critical synthesis for PA and the matrix organization – an absence noted in the literature.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.419
GPT teacher head0.428
Teacher spread0.009 · 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

Citations6
Published2009
Admission routes1
Has abstractyes

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