Capacity Gaps and Training Needs in Performance Evaluation: A Case of the Zanzibar M&E Workforce

Abstract This study examines capacity gaps and training needs in performance evaluation within Zanzibar’s Monitoring and Evaluation (M&E) workforce. Specifically, it investigates how capacity dimensions’ skills, knowledge, resources, institutional support and training program attributes frequency, coverage, and relevance influence the quality of performance evaluation in public institutions. The study adopted a mixed methods research design, integrating quantitative and qualitative approaches. Quantitative data were collected through structured questionnaires administered to 150 M&E officers drawn from various ministries, departments, and agencies under the Zanzibar Development Plan (ZADEP). Qualitative data were obtained through key informant interviews, which provided in-depth insights to complement the quantitative findings. Descriptive statistics summarized respondents’ perceptions, and multiple linear regression analysis tested the influence of the seven predictor variables on the quality of performance evaluation. The findings revealed that the seven predictors jointly were explained by 53.8% of the variance in evaluation quality (R² = 0.538, p < 0.001). At the variable level, skills (β = 0.178, p = 0.013), knowledge (β = 0.161, p = 0.020), institutional support (β = 0.147, p = 0.025), training frequency (β = 0.112, p = 0.043) and training relevance (β = 0.134, p = 0.028) emerged as statically significant predictors of evaluation quality. In contrast resources (p = 0.112) and training coverage (p = 0.089) did not show statistically significant effects. These results indicate that continuous professional development, supported by strong institutional frameworks and relevant training, is essential for improving the accuracy, timeliness, and utilization of performance evaluations in Zanzibar’s public sector. The study concluded that strengthening technical competencies, enhancing institutional support, and providing regular and relevant training are critical for improving the quality of performance evaluation in Zanzibar’s public sector. The findings underscore the importance of institutionalized M&E capacity development initiatives, stronger collaboration between government institutions, ZAMEA, and academic bodies, as well as the introduction of professional certification mechanisms to enhance evaluation practice and promote evidence-based decision-making in Zanzibar.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-12-23
DOI
https://doi.org/10.5281/zenodo.18038212
Primary Topic
Evaluation and Performance Assessment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Capacity Gaps and Training Needs in Performance Evaluation: A Case of the Zanzibar M&E Workforce

Dr. George Nyaronga, Farashuu Juma Moh'd
Zenodo (CERN European Organization for Nuclear Research)
Evaluation and Performance Assessment
article

Capacity Gaps and Training Needs in Performance Evaluation: A Case of the Zanzibar M&E Workforce

Dr. George Nyaronga, Farashuu Juma Moh'd
article en

Abstract

Abstract This study examines capacity gaps and training needs in performance evaluation within Zanzibar’s Monitoring and Evaluation (M&E) workforce. Specifically, it investigates how capacity dimensions’ skills, knowledge, resources, institutional support and training program attributes frequency, coverage, and relevance influence the quality of performance evaluation in public institutions. The study adopted a mixed methods research design, integrating quantitative and qualitative approaches. Quantitative data were collected through structured questionnaires administered to 150 M&E officers drawn from various ministries, departments, and agencies under the Zanzibar Development Plan (ZADEP). Qualitative data were obtained through key informant interviews, which provided in-depth insights to complement the quantitative findings. Descriptive statistics summarized respondents’ perceptions, and multiple linear regression analysis tested the influence of the seven predictor variables on the quality of performance evaluation. The findings revealed that the seven predictors jointly were explained by 53.8% of the variance in evaluation quality (R² = 0.538, p < 0.001). At the variable level, skills (β = 0.178, p = 0.013), knowledge (β = 0.161, p = 0.020), institutional support (β = 0.147, p = 0.025), training frequency (β = 0.112, p = 0.043) and training relevance (β = 0.134, p = 0.028) emerged as statically significant predictors of evaluation quality. In contrast resources (p = 0.112) and training coverage (p = 0.089) did not show statistically significant effects. These results indicate that continuous professional development, supported by strong institutional frameworks and relevant training, is essential for improving the accuracy, timeliness, and utilization of performance evaluations in Zanzibar’s public sector. The study concluded that strengthening technical competencies, enhancing institutional support, and providing regular and relevant training are critical for improving the quality of performance evaluation in Zanzibar’s public sector. The findings underscore the importance of institutionalized M&E capacity development initiatives, stronger collaboration between government institutions, ZAMEA, and academic bodies, as well as the introduction of professional certification mechanisms to enhance evaluation practice and promote evidence-based decision-making in Zanzibar.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 29%
Evaluation and Performance Assessment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.