Analysing drivers of Lean Six Sigma adoption in Tanzania’s manufacturing sector: a PLS-SEM approach

Juma Mohamed Matindana & Francis D. Sinkamba

International Journal of Lean Six Sigma2026https://doi.org/10.1108/ijlss-07-2025-0195article
AJG 1ABDC B
Weight
0.37

Abstract

Purpose The performance of the manufacturing sector in Tanzania is not convincing, with its contribution to the gross domestic product (GDP) standing at 8.4%. The sector’s low performance is due to the growing competition that manufacturing industries face from imported goods. To improve performance, manufacturing industries globally are adopting continuous improvement philosophies, such as Lean Six Sigma (LSS), although adoption is relatively low in Tanzania. Therefore, this study aims to identify the drivers for the adoption of LSS. Design/methodology/approach This study used a survey research design with purposive sampling; data were collected by using questionnaires from 388 industries located in Dar es Salaam, Arusha and Mbeya. Data were analysed by using SPSS and smart PLS for PLS SEM. Descriptive analysis was used for demographic information. For the measurement model, reliability was assessed using factor loadings = 0.6, Cronbach’s alpha = 0.6 (if composite reliability is = 0.7) and AVE = 0.5. Discriminant validity used Heterotrait–-Monotrait = 0.9. For the structural model, path coefficients (t-tests = 1.96 and p-values = 0.05), Standardised Root Mean Square Residual = 0.08 and Normed Fit Index (0–0.1). Findings The results have shown that three categories of drivers out of five, which are financial performance, customer focus and people and culture, have an impact on the adoption of LSS, while environmental sustainability and operational performance have no impact on the adoption of LSS. The drivers with impact are customer satisfaction, fast delivery and strong market competition for customer focus; cost savings, increasing profit and reducing labour cost for financial performance and employee satisfaction, reduced turnover rates and increasing team morale for people and culture. Research limitations/implications The study was conducted in the context of Tanzania to reflect the situation of developing countries with similar characteristics. Moreover, the study used respondents with an educational level of diploma or above. Practical implications The identified drivers will assist practitioners and owners of manufacturing industries in setting a strategy for adopting LSS for the successful implementation, which will enhance the improvement of the operational and financial performance of the manufacturing industries. Furthermore, since customer focus and financial performance drive the adoption of LSS, it will motivate manufacturing organisations to document quality-related problems and set standard operating procedures for waste minimisation. Moreover, it will impress manufacturing organisations to calculate Return on Investment (ROI). Originality/value To the best of the authors’ knowledge, this is the first study to be conducted in Tanzania. The study is important as it will assist policymakers in setting policies for improving the adoption of LSS.

1 citation

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1108/ijlss-07-2025-0195

Or copy a formatted citation

@article{juma2026,
  title        = {{Analysing drivers of Lean Six Sigma adoption in Tanzania’s manufacturing sector: a PLS-SEM approach}},
  author       = {Juma Mohamed Matindana & Francis D. Sinkamba},
  journal      = {International Journal of Lean Six Sigma},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/ijlss-07-2025-0195},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Analysing drivers of Lean Six Sigma adoption in Tanzania’s manufacturing sector: a PLS-SEM approach

Flags are reviewed by the Arbiter methodology team within 5 business days.


Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.