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Investigations of Key Factors Affecting Market Channel Choice Decision by Saba Banana Farmers Using A Multinomial Logistic Model

Rokhman Permadi, Ahmad Alim Bachri, Muhammad Fauzi, Hamdani

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Int. J. Biosci.21(1), 64-75, July 2022

DOI: http://dx.doi.org/10.12692/ijb/21.1.64-75


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e of the essential roles in improving farmers’ welfare is connecting small farmers to high-value markets. Therefore, it is urgent to focus on opening access to markets and selecting the proper marketing channels. This research provides knowledge of what factors to be addressed to help saba banana farmers in the study area, especially in determining marketing channels. Multinomial logistic regression has been used to determine the factors influencing the decision to select market outlets for Saba banana farmers in Sungai Bakau, Pematang Panjang, and Bangun Harja villages. 254 Saba banana farmers were used as respondents to obtain cross-sectional data, which were then analysed (descriptive statistics and econometric analysis) using the STATA 17 statistical package. The results reveal that, although saba banana farmers in the research locations most often deliver to collectors at the farm gate, differences in farmer characteristics and asset specificity increasingly encourage many farmers to choose other, more valuable marketing channels. The significant factors that influence the choice of market channels were: the level of education, farm size, quantity produced, own transportation, access to credit, and access to market information. The education level and quantity produced have a negative effect. Meanwhile, farm size, own transportation, access to credit, and access to market information positively affect the decision of saba banana farmers to choose the best market channel. This study highlights the need for future research to conduct a more in-depth investigation of the role of information and credit on the level of farmer’s market participation.


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Investigations of Key Factors Affecting Market Channel Choice Decision by Saba Banana Farmers Using A Multinomial Logistic Model

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