Influence of external organizational environment on performance of community - based h I v and aids organizations in Nairobi County, Kenya
Bibliographic record
Abstract
As the external organizational environment continue to be turbulent, \naffecting activities for both profit and non-profit organizations, organizations \nare becoming more concerned with their performance so as to ensure that the \navailable but limited resources are utilized efficiently and effectively. Over \ntime, attention has been focused on profit-making organizations with little \nemphasis on performance of community-based organizations (CBOs) \nespecially those located in Sub-Saharan Africa. Theoretically, scholars in \nmanagement discipline indicate that the external environment of an \norganization influences its performance. Thus, an organization’s level of \nperformance is dependent on dynamism and complexity of the external \nenvironment, heterogeneity as well as capacity and domain consensus of the \nexisting organizations. This paper examines the impact of the external \norganizational environment on performance of community-based HIV and \nAIDS organizations in Nairobi County, Kenya. The authors empirically \nassess the predicted relationship using survey data from 163 Community \nBased HIV and AIDS Organizations, in Nairobi County, Kenya between \nJanuary and March 2013. The study findings indicate that the external \nenvironment of an organization has an impact on an organization’s \neffectiveness, efficiency, relevance and financial viability with higher \nimpacts on the relevance performance indicators. External environment was \nevaluated from dimensions of uncertainty, domain consensus, heterogeneity, \ncapacity and dynamism. The findings of this study indicate statistically \nsignificant positive relationship between external environment and CBOs \nexternal environment and effectiveness (beta 0.541, p-value=0.000), efficiency (beta 0.695, p-value=0.000), relevance (beta 0.707, pvalue= \n0.000) and financial viability (beta 0.578, p-value=0.000) leading to \nan argument that proper scanning of external environment influences all \nactivities of an organization from planning to implementation. However, \nextra attention should be paid to external environment in program \nidentification and planning as this shapes implementation. This study has \nimportant implications for managers of CBOs on the relevance of proper \nscanning of external environment as it influences all activities of an \norganization from program conceptualization to implementation
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".