Logistics outsourcing and performance of manufacturing small and medium-sized enterprises in Nairobi
Bibliographic record
Abstract
Background: Logistics outsourcing has been accepted as a strategy through which small and medium-sized enterprises (SMEs) can access the logistics capabilities they lack internally at a lower cost. However, the actual effect of logistics outsourcing on firm performance, especially among the SMEs in Nairobi, remains unknown. Aim: The study aimed to investigate the relationship between logistics outsourcing and firm performance of manufacturing SMEs in Nairobi. Setting: The study sampled manufacturing SMEs in Nairobi City County. Method: In this study, a convergent parallel mixed methods design was applied. Survey data were collected from 163 manufacturing SMEs. The data were analysed using structural equation modelling to test the relationship between logistics outsourcing and firm performance. Additionally, in-depth interviews were conducted in five manufacturing SMEs. Thematic analysis was used to analyse interview data to provide more insight in the quantitative data. Results: The anticipated direct link between logistics outsourcing and performance of manufacturing SMEs was not statistically significant. However, the study revealed a statistically significant indirect positive effect of logistics outsourcing on the performance of manufacturing SMEs through logistics outsourcing performance as a mediator variable. This article further highlights reasons and the process of logistics outsourcing and deduces a logistics outsourcing model for manufacturing SMEs to help improve their firm’s performance. Conclusion: The established relationship and deduced logistics outsourcing model is likely to guide SME managers as to how to manage logistics outsourcing to improve performance. The finding that logistics outsourcing has a positive indirect effect on the performance of manufacturing SMEs through logistics outsourcing performance makes a significant contribution to theory.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".