Determinants of Green Cluster Supply Chain Adoption and Practice of Arabica Coffee Growers in Pang Ma-O and Pamiang Areas

Chanita Panmanee ； Aree Wiboonpongse

《International Journal of Intelligent Technologies and Applied Statistics》 9卷2期 (2016/06) Pp. 169-189

https://doi.org/10.6148/IJITAS.2016.0902.06

並列摘要

This paper focuses on the determinants and practices of Arabica coffee growers in Pang Ma-O and Pamiang villages for planning and developing green cluster supply chains (GCSC). Samples selected in this paper are coffee growers in two villages, staff of the Highland Research and Development Institution (HDRI), and the Royal Project Foundation (RPF), and officers of the Pang Ma-O Extension Project, and the Pamiang Royal Project Development Center. Multivariate probit (MVP) model, cluster mapping and modified GEM model were used as tools for analyzing. The findings revealed that education, age and input cost have significantly positive influences on the green adoption whereas farm size has a negative impact on it. Considering cluster mapping, it is not complicated so the plan and development of GCSC is not too difficult. For the determinants involving in GCSC, the results not only showed the macroeconomic environmental determinants such as government policies, geographical position and total domestic demand, but also the microeconomic environmental determinants, namely, green relation, structure and strategies, and resources which are the strength factors confirming that it is possible to develop the GCSC. However, the external market is a relative weakness determinant that is implied as the drawback of GCSC development. The findings of this paper are useful for planning and developing GCSC to enhance the competitiveness of Arabica coffee farmers in the highland area bringing about the strength of community, the ability to be self-reliance, and the enhancement of income and well-being of farmers in the selected area, and as a helpful resource for expanding the similar projects in other areas.

並列關鍵字

Arabica coffee ； Green cluster supply chain ； Adoption ； Multivariate probit model ； Modified GEM model