Green Lean Six Sigma (GLSS) is an emerging approach that combines the principles of environmental sustainability, lean management and Six Sigma to enhance organisational performance. However, previous studies have highlighted the potential benefits of this approach in improving process efficiency, reducing waste and enhancing environmental sustainability, but limited research has been conducted in the context of the health-care sector. To fill in the research gap, this study aims to examine the impact of GLSS on health-care performance (HP) through the mediating effects of value-added time (VAT) and employee professional development (EPD). The proposed study uses a quantitative research approach and a purposive sampling method to conduct a survey-based questionnaire. This study used ADANCO 2.4.0 for partial least squares structural equation modelling (PLS-SEM). The Python and SPSS software are used for artificial neural network modelling and predictive analysis. The PLS-SEM is employed to examine the relationships among variables using a natural resource-based view (NRBV) theory to support the conceptual model of this research. The findings of this study indicate that GLSS practices, including waste reduction, variability reduction and measurement matrices, have a strong positive and significant effect on HP. Moreover, the findings show that GLSS influences HP through the partial mediating effect of VAT and EPD. There is a lack of empirical studies exploring the role of GLSS, VAT, EPD and HP in the health-care sector of developing countries. This study extends the literature on the NRBV theory in GLSS and HP. It provides theoretical and practical implications for healthcare policymakers and practitioners in encouraging GLSS practices in health-care organisations.
Exploring the impact of the adoption of Green Lean Six Sigma practices on healthcare performance
F. Khan,Yang Wang,Lin Zhang,Muhammad Adeel Anjum,Dr. Sidra Ramzan
Published 2025 in International Journal of Lean Six Sigma
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- Publication year
2025
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International Journal of Lean Six Sigma
- Publication date
2025-11-13
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