Determinants of group dynamics effectiveness among women farmers’ FIGs under ATMA in Navsari District of Gujarat State

Introduction

Agriculture remains the largest unorganized sector in India, where the absence of strong institutional mechanisms often limits the collective bargaining power of smallholders. In this context, farmers' organizations play a crucial role in fostering collective action and improving rural livelihoods. Under the Agricultural Technology Management Agency (ATMA), the promotion and mobilization of Farmers' Interest Groups (FIGs) serve as an important strategy for decentralized technology dissemination and farmer empowerment (Anonymous, 2018; Patel et al., 2018) [1,11] . Women farmers, who constitute a significant segment of the agricultural workforce, increasingly participate in such groups, where effectiveness largely depends on the quality of internal group functioning. In social psychology, a group is defined as individuals connected through social interaction and communication, working together to achieve common goals while recognizing their membership within the group (Levi, 2007) [7] . Group dynamics refer to the forces that emerge as members interact, including communication processes, cohesion and social integration (Dommata & Konagala, 2014) [4] . Effective groups are characterized by shared leadership, open communication, mutual trust, and coordinated efforts (Johnson & Johnson, 2003; Larson & LaFasto, 1989; Zander, 1982) [5,6,13] . Failure to strengthen these dynamics often results in weak group performance and member dissatisfaction (Dommata & Konagala, 2014) [4] . Collective action is essential because individual farmers often find it difficult to overcome production and market constraints independently. Group-based approaches in developing regions have been emphasized to enhance productivity and socio-economic outcomes (Otieno, 2012) [8] . Socio-Economic Status (SES), defined as the position individuals occupy in relation to prevailing standards of income, material possessions and participation in community activities (Akinbile, 2003; Oyeyinka, 2002) [2,10] , is influenced by such collective efforts. SES also affects access to services and opportunities (Ovwigho, 2014; Ramesh & Gangaboraiah, 2013) [9,12] . Therefore, strengthening group dynamics is fundamental for improving the socio-economic conditions of women farmers.

Methodology

The study was conducted in Navsari district of Gujarat using an ex-post facto research design. Four talukas having a higher number of women FIGs under ATMA, namely Jalalpor, Gandevi, Navsari and Vansada, were selected purposively. From each taluka, two FIGs were selected randomly, and 15 members from each FIG were selected through random sampling, constituting a total sample of 120 women FIG members. The independent variables comprised selected personal (age, education, annual income, training received, occupation and market facility), social (social participation and extension participation) and psychological characteristics (economic motivation, market orientation, innovativeness, achievement motivation and attitude towards collectivism). Standardized scales developed by previous researchers were used with suitable modifications wherever required. The dependent variable, Group Dynamics Effectiveness, was measured using the Group Dynamics Effectiveness Index developed by Darji (2018) [3] , covering key dimensions such as participation, teamwork, cohesiveness, leadership, communication and decision-making. Data were collected through a pre-tested structured interview schedule and analysed using appropriate statistical tools to determine the influence of selected independent variables on Group Dynamics Effectiveness. Adjusted R 2 =. 608 F = 15.218** ** Significant ('F' at (K-1) and (N-K-1) df) at 1% level of probability * Significant at 0.05 level of probability, ** Significant at 0.01 level of probability From Table 1, it could be observed that the coefficient of determination (R²) was significant. The R² value of 0.651 indicated that all the thirteen independent variables taken together explained about 65.10% of the variation in the dependent variable, i.e., group dynamics effectiveness among women farmers' FIGs. The remaining 34.90% of variation might be attributed to other extraneous variables not included in the present study. The F value (15.218**) was significant at one% level of probability, indicating that the overall regression model was a good fit. Among the independent variables, attitude towards collectivism (X13) had a strong positive and highly significant influence on group dynamics effectiveness at one per cent level of probability (β = 0.624; p = 0.000). This implies that the higher the positive attitude towards collectivism among women farmers, the greater would be the effectiveness of group dynamics within FIGs. Education (X2) was found to be positive and significant at one% level of probability (β = 0.184; p = 0.003), indicating that better educated members tend to contribute more effectively to group functioning and coordination. Market orientation (X10) was significant at five% level of probability (β = 0.124; p = 0.050), suggesting that women farmers with higher market orientation exhibited better group dynamics effectiveness. Other variables such as age, annual income, training received, occupation, market facility, social participation, extension participation, economic motivation, innovativeness and achievement motivation were found to be non-significant in the full model.

Results and Discussion

Determinants of Group Dynamics Effectiveness among Women Farmers' FIGs Linear regression analysis of determinants of Group Dynamics Effectiveness

Step down regression analysis

In step-down regression analysis, independent variables were eliminated sequentially based on their least contribution to R² (coefficient of multiple determination). The model was refined through successive steps to identify the most influential determinants of group dynamics effectiveness. www.extensionjournal.com From Table 2, it could be inferred that the final model (Step 5) recorded an R² value of 0.621 with an adjusted R² of 0.604 and F value of 37.36, indicating that the selected variables together explained about 62.10% of the variation in group dynamics effectiveness among women farmers' FIGs. The results of the final step-down regression model (Table 3) revealed that attitude towards collectivism (X13) was highly significant at one% level of probability (b = 0.694; p = 0.000). A unit increase in attitude towards collectivism would result in an increase of 0.694 units in group dynamics effectiveness, ceteris paribus, indicating that stronger collective orientation substantially enhances the functioning and cohesion of women farmers' FIGs. Education (X2) was also found to be significant at one% level (b = 0.163; p = 0.006), suggesting that higher educational status improves members' understanding, participation, communication and coordination within the group, thereby contributing positively to group dynamics effectiveness. Extension participation (X8) was significant at five% level (b = 0.119; p = 0.050), implying that increased interaction with extension agencies facilitates exposure, knowledge sharing and collective decision-making, which in turn strengthens group functioning. Similarly, market orientation (X10) was significant at five% level (b = 0.122; p = 0.045), indicating that women farmers with a progressive market outlook tend to align better with group objectives and cooperate more effectively. Achievement motivation (X12), though positive in direction, was found to be non-significant (p = 0.070); however, it was retained in the model due to its relative contribution to the explained variance. Overall, the findings highlight that attitudinal and institutional factors play a crucial role in enhancing group dynamics effectiveness among women farmers' FIGs.

Conclusion

The study concludes that the group dynamics effectiveness within women Farmers' Interest Groups (FIGs) under ATMA is not a result of chance, but is significantly determined by a combination of personal, social, and psychological factors. Among the thirteen variables studied, attitude towards collectivism emerged as the most powerful determinant, indicating that the success of these groups depends heavily on members' psychological inclination to work together and share common goals. Furthermore, the significant roles of education, market orientation, and extension participation demonstrate that cognitive empowerment and active engagement with external support systems are vital for smooth group functioning. The findings imply that while providing infrastructure or financial aid is important, the "human element" specifically fostering a collective mindset and enhancing educational awareness is what truly drives group efficacy. To ensure the sustainability of women-led FIGs, it is recommended that ATMA and other extension agencies focus on attitudinal training and strengthening marketextension linkages. Promoting these key determinants will lead to more cohesive, well-coordinated, and effective farmers' groups, ultimately contributing to the socioeconomic empowerment of women farmers.

Acknowledgement

AcknowledgementNone.

References

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