Study on the influence mechanism of adoption of smart agriculture technology behavior

Abstract

Smart agricultural (SA) technology has become a technological support for modern agriculture. By exploring the decision-making process and psychological motivation of farmers in adopting SA technology, it is conducive to achieving the popularisation of SA technology and promoting the modernisation of agriculture. Based on microscopic research data, a Structural Equation Model (SEM) is used to analyse the influencing factors and extent of cotton farmers’ adoption of SA technologies, using Deconstructive Theory of Planned Behavior (DTPB) as the analytical framework. This was combined with in-depth interviews to further reveal the motivations and influencing mechanisms of cotton farmers’ adoption of SA technologies. The results show that under the behavioural belief dimension, cotton farmers value the positive effect of perceived usefulness even though the risk of the technology itself has a dampening effect on adoption intentions. Under the normative belief dimension, superior influence influenced the willingness to adopt SA technologies to a greater extent than peer influence. Under the control belief dimension, factors such as self-efficacy and information channels influence willingness to adopt technology and behaviour. In addition, behavioural attitudes, subjective norms, and perceived behavioural control all contribute to cotton farmers’ willingness to adopt SA technologies, and can also influence behaviour directly or indirectly through willingness to adopt. Policy and technology satisfaction positively moderate the transition from willingness to behaviour. Therefore, preferential policies are proposed to reduce the cost of adopting SA technologies; to continuously improve the level of SA technologies; to establish SA technology test plots to provide a reference base; and to increase knowledge training on SA and expand access to information.

Investigating Farmers’ Intentions to Reduce Water Waste through Water-Smart Farming Technologies

“…Lazaridou et al [15] confirmed that farmers' willingness to use recycled water is linked to their perception of its environmental benefits. Additionally, previous research has shown that perceived usefulness is significantly associated with farmers' willingness to adopt smart farming technologies [64][65][66]. Based on these findings, this study will test the hypothesis that: H4: Perceived usefulness of water-smart farming has a significant influence on farmers' intentions to reduce water waste.…”

“…The social influence scale was adapted from Schrank et al [50] and Qalati et al [30]. Additionally, the PBC scale was drawn from Mouloudj et al [19] and Wang et al [53], and the perceived usefulness scale was obtained from Li et al [66]. As for the water waste reduction-related knowledge scale, it was modified from the scales used by Njoku et al [68] and Zhang et al [75].…”

Factors Affecting the Adoption of Digital Technology by Farmers in China: A Systematic Literature Review

“…A risk-neutral farmer is likely to adopt a technology that results in net increases in operating profits [42]. Many studies show that the perception of profitability with new technologies significantly drives the intention of adoption [31,34,37,38]. In addition to the profitability of alternative technologies, the riskiness of those profits can also impact the decisions of risk-averse farmers [33,35,38].…”

“…Many studies show that the perception of profitability with new technologies significantly drives the intention of adoption [31,34,37,38]. In addition to the profitability of alternative technologies, the riskiness of those profits can also impact the decisions of risk-averse farmers [33,35,38]. Since novel digital technologies often come with more economic uncertainty and technical difficulty than traditional ones, research has confirmed that the perceived ease of utilizing these technologies increases farmers' adoption of digital technologies [33,34].…”

Driving rural industry revitalization in the digital economy era: Exploring strategies and pathways in China

“…However, while industrial digitization accelerates across sectors, the agricultural domain lags behind, with a meager 8.9% penetration rate of the digital economy. In contrast to the levels of digitization witnessed in secondary and tertiary sectors, the application of digital technologies in agriculture remains relatively limited in terms of quality and depth [ 2 ]. Certain areas with lower levels of development experience slower progress in digital rural development, while remote regions face challenges in establishing a conducive environment for digital infrastructure and Digital financial environment(DFE) [ 3 ].…”