Date of Award

23-5-2024

Document Type

Thesis

School

School of Management

First Advisor

Dr.R.Renganathan

Keywords

Farm Mechanization, Cauvery Delta, Farm Hiring Decisions, Intention, Farmers

Abstract

Agriculture is critical to any country's economic development. It not only benefits the primary and secondary sectors but also helps the economy flourish. In addition to ensuring food security, the Indian agricultural sector provides work to huge populations, both directly and indirectly. Traditional farming practices are now overused, labour is no longer abundant, and the power demand is rising steadily. The rapid development of technology has given rise to new opportunities in agriculture (Balafoutis, Evert, & Fountas, 2020). Technology aids agricultural growth by allowing more effective utilization of inputs. It has the potential to make an immense contribution to agricultural development. A significant number of farmers are facing numerous difficulties, such as varied topography, small and fragmented farm holdings, and lack of investments and technology to follow subsistence agriculture and use conventional farm machinery. Agricultural productivity is affected due to the prevalence of impediments like land fragmentation, labour scarcity, increase in wages for farm labour, climate change, lack of infrastructure, credit facilities, etc., In addition to combating these impediments, a technologically driven concept called 'Farm Mechanization' is a crucial farming approach that helps to improve agricultural productivity.

This study used a few constructs from the Innovation Diffusion Theory, Technology Acceptance Model, and Theory of Planned Behaviour models, which affect farmers' hiring decisions for mechanisation services. The constructs used for the Technological attributes include Relative Advantage, Perceived Convenience, Perceived Economic Cost Benefits, Perceived Usefulness, Trialability, and Observability. The Psychological factors have constructs: Attitude, Subjective Norms, Perceived Behavioural Control, and Values. The institutional factors have constructs Access to Informational sources, Service Providers, Access to credit through Financial Institutions, Government Support, Extension Services, and Environmental factors. The Socio-economic variables used for the study include Age, Education, annual income, farm experience and Land Holding.

The researcher has collected the data from the farmers of the Cauvery Delta zone in Tamil Nadu, which is considered as the population for the study, by applying the multistage cluster sampling method. The structured questionnaires were distributed to 1010 farmers across the Cauvery Delta Zone consisting of Thanjavur, Tiruvarur, Nagapattinam, Mayiladuthurai, Tiruchirapalli, and Ariyalur districts. Kulithalai Taluk of Karur district, Aranthangi Taluk of Pudukottai district, and Chidambaram and Kattumannarkoil Taluks of Cuddalore District. A total of 854 questionnaires were received, with a response rate of 84.55%. 44 questionnaires were found with some missing information after the cleaning process. Finally, 810 questionnaires were found to be fit and included in the analysis.

Out of eighteen factors of hire purchase decision, The topmost five factors are environmental factors (3.827), subjective norms (3.703), followed by farmers' intention on hire purchase(3.697), farmers' actual hiring (3.681) and perceived behaviour control (3.656). Whereas the least inflowing factors are Values (3.543), Perceived convenience (3.511), Economic Cost benefits (3.458), Extension Services (3.455) and Service Providers (3.362).

All sub-factors of technological and institutional factors show a high positive correlation with farmers' Intention to hire. The respective Pearson correlation values for the Psychological factors such as ‘Attitude’, ‘Subjective Norms’ and ‘Values’ denote that they have a high positive correlation with ‘Intention’ and ‘Perceived Behavioral Control’ has a moderate positive correlation with ‘Intention’.

Environmental factors, Access to Informational sources, Perceived Behavioural Control, Perceived Economic Cost Benefits, Observability, Relative Advantage and Trialability have significant influence on Intention. Further, Intention is found to have a significant influence on Actual hiring decisions. The R-Square value of 0.708 represents that 70.8% of the variation in the 'Intention' can be explained and caused by technological attributes, psychological factors, and institutional factors. Likewise, the R-Square value of 0.635 of the second-order construct represents that 63.5% of the variation in the Actual hiring decisions can be explained and caused by Intention.

There is a significant influence of Technological attributes, Psychological factors and Institutional factors on the dependent variable 'Intention', and there is a significant influence of Intention on Actual hiring decisions, respectively. The R-Square value of 0.646 represents that 64.6% of the variation in the 'Intention' can be explained and caused by variables such as Technological attributes, Psychological factors and Institutional factors. Likewise, the R-Square value of 0.634 of the second-order construct represents that 63.4% of the variation in the Actual hiring decisions can be explained and caused by Intention.

In terms of the moderating effect on socioeconomic factors towards actual hiring: annual income and landholding have a significant effect. In contrast, the other factors, age, education, and farming experience, have no significant moderating effect on actual hiring decisions.

The study confirms that different socio-economic profiles have different effects on these three factors. The correlation and structural equation analyses also confirmed that there is a constructive effect between these three factors: Technology, Psychological, and Institutional and the farmer's Intention and actual decision to hire machinery.

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