Relationship between farmers’ profile characteristics and the role of ICT in adoption of agricultural innovations

Introduction

The role of Information and Communication Technology (ICT) in adoption of agricultural innovations is influenced not only by the availability of digital technologies and advisories but also by the socioeconomic, communication and psychological characteristics of farmers. Farmers differ considerably in their ability and willingness to access, understand and utilize ICT-based agricultural information. Previous studies have reported that characteristics such as age, education, farm size, information-seeking behaviour, innovativeness, risk orientation, scientific orientation and economic status influence farmers' use and selection of ICTbased information sources. Mittal and Mehar (2016) [1] , for instance, reported that age, education and farm size significantly influenced farmers' choice of agricultural information sources in India. Similarly, Samatha et al. (2014) [2] found significant positive relationships between ICT use and education, information-seeking behaviour, economic status, achievement motivation, scientific orientation and innovativeness. The relationship between farmers' characteristics and ICT utilization has also been observed in different regions of India. Chandra et al. (2023) [3] found that farmers' profile characteristics explained a substantial proportion of variation in ICT utilization, with education showing the highest direct effect and extension contact having an important indirect effect. In Gujarat, Ganvit and Chandravadia (2024) [4] reported that education, annual family income, extension participation, innovativeness and scientific orientation were positively associated with ICT utilization, whereas age and farming experience showed negative relationships. Further, evidence from Gujarat on agricultural technology adoption indicates that education, income, mass media exposure, innovativeness, achievement motivation, risk orientation and scientific orientation are important factors associated with farmers' awareness and adoption of new agricultural technologies (Mallappa and Pathak, 2023) [5] . Thus, understanding the relationship between farmers' profile characteristics and the dependent variable is important for identifying the characteristics that facilitate or constrain the role of ICT in adoption of agricultural innovations. Such analysis can help extension agencies design more targeted ICT-based interventions according to farmers' educational level, economic condition, information-seeking behaviour and psychological orientation. Therefore, the present study examined the relationship between selected farmers' profile characteristics and the role of ICT in the adoption of www.extensionjournal.com agricultural innovations.

Methodology

The study was conducted in South Gujarat region of Gujarat State during the year 2023-26. The present study was conducted using an ex-post facto research design. The South Gujarat region has seven districts namely Surat, Bharuch, Narmada, Navsari, The Dangs, Valsad and Tapi. All districts were included in the study. Further, three talukas were randomly selected from each district and three villages were randomly selected from each selected taluka. Thus, twentyone talukas and sixty-three villages were selected. Five ICTusing farmers were randomly selected from each village by following the Simple Random Sampling (SRS) technique. Therefore, a total sample of 315 respondents was obtained for the study. For the present study, age (X₁), education (X₂), annual income (X₃), land holding (X₄), information seeking behaviour (X₅), mass media exposure (X₆), innovativeness (X₇), risk orientation (X₈), scientific orientation (X₉), economic motivation (X₁₀), achievement motivation (X₁₁) and decision-making ability (X₁₂) were selected as independent variables. The role of Information and Communication Technology (ICT) in adoption of agricultural innovations was the dependent variable (Y). The data were collected through personal interview using a structured interview schedule. Pearson's correlation coefficient was used to ascertain the relationship between the independent variables and the dependent variable. In the actual condition, no variable occurs independently without the influence of other variables. The stepwise multiple linear regression method was used to determine the most significant independent variables to include in a multiple linear regression model. It involves a systematic process of including or excluding variables based on their statistical significance or contribution to the model's performance. The path analysis was used to assess the causal influences of a set of independent variables on the dependent variable. Direct effects and total indirect effects can be known of individual independent variables on the dependent variable. Additionally, the substantial effect among the independent variables can also be determined. The step wise multiple regression analysis was performed in SPSS while path analysis was performed in OPSTAT. Economic motivation exhibited a positive and highly significant relationship with the role of ICT in adoption of agricultural innovations. Farmers with greater economic motivation may actively seek information and technologies capable of improving productivity, reducing costs and increasing farm returns. ICT can facilitate access to such information and therefore may be seen as an important means of achieving economic objectives. Achievement motivation was positively and highly significantly associated with the role of ICT in adoption of agricultural innovations. Farmers with higher achievement motivation may continuously seek improved methods and opportunities for enhancing farm performance. ICT provides convenient access to technical knowledge, innovations and successful farming experiences, which may support their achievement-oriented behaviour. Decision-making ability also showed a positive and highly significant relationship with the role of ICT in adoption of agricultural innovations. Farmers with better decisionmaking ability may be more capable of evaluating information from different sources and selecting agricultural innovations suited to their farm conditions. Thus, ICT may serve as an important source of timely information supporting informed agricultural decisions. Overall, all the selected profile characteristics exhibited significant relationships with the role of ICT in adoption of agricultural innovations, except that the direction of association was negative for age and positive for the remaining variables. Among the variables, age showed the strongest negative association (r = -0.689), whereas education showed the strongest positive association (r = 0.618) followed by economic motivation (r = 0.581) and information seeking behaviour (r = 0.519). Path analysis revealed that innovativeness (0.216) exerted the highest positive direct effect followed by risk orientation (0.172), education (0.151), land holding (0.149), mass media exposure (0.123) and decision-making ability (0.121), whereas age (-0.496) exerted the highest negative direct effect. In terms of total indirect effects, economic motivation (0.673) recorded the highest positive effect followed by information seeking behaviour (0.579), achievement motivation (0.492), annual income (0.480) and education (0.467), while age (-0.193) showed a negative indirect effect. The substantial indirect effects indicated that age and risk orientation were important pathways through which several independent variables influenced the role of ICT. Education showed the highest substantial indirect effect through age (0.315) followed by economic motivation (0.276), annual income (0.219) and achievement motivation (0.195) through age.

Results and Discussion

Conclusion

The study demonstrates that individual socio-psychological and demographic characteristics-most notably age, education, innovativeness, and economic motivationsubstantially dictate farmers' reliance on ICT for adoption of agricultural innovations, with younger and more digitally literate farmers exhibiting significantly higher adoption intent. Overall, key determinants like age, innovativeness, land holding, risk orientation, decision-making ability, education, and mass media exposure account for nearly 70 percent of the variation in ICT integration. These findings highlights that digital agricultural interventions are most effective when tailored to younger, resource-rich, and risktolerant early adopters, while older or less educated farmers require targeted digital literacy support and indirect channels.

References

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