Deriving significant factors for managing change in UN
Bibliographic record
Abstract
Purpose A consequence of the UN general assembly resolution calling for increased efficiency and better utilization of resources in all its agencies, is a mandate for change. As a response to this resolution, the purpose of this paper is to identify the key factors for managing change. Design/methodology/approach A survey methodology approach was used where officials representing department heads, middle managers, professionals and specialists were the target participants. Exploratory factor analysis was done for factor validation and reduction, followed by confirmatory factor analysis to identify the relationships between those factors. Findings Three significant factors, communication, temporal sensitivity and knowledge were found to represent a shared common theoretical propositions from Kotter’s, Lewin’s and ADKAR models. Extracted factor explain the proposed United Nations (UN) model. Research limitations/implications Due to political and cultural reasons, characteristics of participants could not be revealed. Also, a larger pool of participants spanning across all the UN agencies would provide more comprehensive view. The final UN model proposed herein would need to be further validated and tested within each agency as well as across them. Practical implications The study urges the UN to utilize its findings, with the hope of standardizing an effective change management model for all its agencies. Originality/value While change management literature primarily focuses on the private sector, few are applicable in the public sector. Research effort on managing change in UN is scarce. This study advocates the need for UN research to fill this very important gap. As such, the authors test existing theoretical model and then adapt it for the UN context.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".