Determinants determining the adoption of technological innovation of urban farming: Employing binary logistic regression model in examining Rogers' framework

Abstract

The aim of this study was to identify key factors influencing the adoption of technological innovation in urban farming (ITUF) in Makassar City, South Sulawesi Province, Indonesia. Employing a quantitative approach for data analysis, primary data was gathered through a structured survey of 334 individuals randomly selected from 14 sub-districts. The survey instrument, based on Rogers's framework, included eight parameters and 22 research variables, analyzed through Binary Logistic Regression to explore their impacts on ITUF adoption. The study revealed eight significant variables influencing the adoption rate: knowledge and experience in farming, education, involvement in Urban Farming organizations, continuity of ITUF information dissemination, belief in the role of urban farming in job transition, availability of resources for ITUF implementation, environmental support, and observation of urban farming technology. The research affirmed that six out of the eight parameters from Rogers's Framework significantly affected ITUF Adoption. The influential parameters included personal characteristics, communication behavior, relative advantage, complexity, trialability, and observability. In contrast, socio-economic factors and compatibility did not significantly impact ITUF Adoption in this study. The study's findings led to four key recommendations for enhancing the sustainability of urban farming. These recommendations encompass strengthening diverse training and education initiatives, providing incentives for urban farming organizations, implementing informational campaigns, and facilitating vocational training in urban agriculture. Additionally, the city's government should ensure accessibility to urban farming resources, endorse resource-efficient methodologies, and utilize media channels for disseminating successful urban farming narratives, fostering increased public awareness and engagement.

Keywords

• Technology innovation
• Urban farming
• Technology adoption
• Binary logistic regression