Abstract
The purpose of this study is to provide an explanation of factors Affecting Agricultural Cooperatives Marketing Performance (In The Case Of Gedeb Hasasa District). In this study, descriptive statistics tools were used to give clear picture about the socio-demographic characteristics of respondents. To answer the research questions and measure the construct and predictors effects on Agricultural Cooperatives Marketing Performance (ACMP), both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used. EFA results showed that the Kaiser-Mayer Olkin of 0.90 and Bartlett test of 0.00 shows that factor analysis is appropriate. The principal component analysis extraction method was used to analyze the data with Promax Rotation Method; five factors were extracted and statistically significant. The first factor explained 32.11% of the variance, the second factor explained 11.92% of the variance, the third factor explained 8.08% of the variance, the fourth factor explained 7.07% of the variance and the fifth factor explained 4.56 %, of the variance. The obtained results from the EFA results revealed that five extracted factors explained 63.75 % of the variation of influencing factors on agricultural cooperatives. Under CFA, Measurement Model and structural modeling techniques were used with the aid of AMOS and Smart PLS3 statistical packages to explain the relationships among multiple manifested variables and exogenous and endogenous factors. CFA confirmed that five factors have a significant positive impact on ACMP. Implications of this research work will help ACs and CPO to identify the major factors that can affect ACMP. Key words: Agricultural cooperatives (ACs), agricultural cooperatives marketing performance (ACMP), cooperative governance factor (CGF), financial factor (FF), infrastructural factor (IF), marketing factor (MF) and members value factor (MVF).
Highlights
In the analysis of agricultural cooperatives marketing performance measurement model, the results showed that the model was fitted with the empirical data with the following values; Chi-square = 731.787, Degrees of freedom = 362, Probability level = .000, χ2 /df = 2.022, goodnessof-fit index (GFI)=0.857, comparative fit index (CFI)=0.916, incremental fit index (IFI)=0.916, Normed fit index (NFI)= 0.847, root mean square residual (RMR)= 0.066 and root mean square error of approximation (RMSEA)= 0.058
These results indicate that the models are acceptable
Cooperative promotion office, primary cooperatives themselves, unions and other concerned stakeholders should give attention to empower cooperative members awareness and members decision making abilities through open discussion and through short term and long-term well programmed trainings and educations
Summary
Cooperative enterprise born in the Agricultural and Industrial Revolutions of the 19 and 20th centuries, modern cooperatives bear a long and rich history. Founded in 1844, the Rochdale Society of Equitable Pioneers usually considered as the first successful cooperative enterprise, following the - Rochdale. Principles‖, used as a model for modern cooperatives. A group of 28 weavers in Rochdale, England, set up the society to open their own store selling items otherwise unaffordable. This first success was the one of a consumer co‐operative (Euro Coop, 2008)
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