Push-pull framework for digital technology adoption in agri-food innovation ecosystems

Overview

This research initiative develops a push-pull framework for understanding how digital technologies are adopted by farmers and agri-food producers. The framework distinguishes between push factors, which are the conditions that enable technology development and transfer within an innovation ecosystem, and pull factors, which are the incentives that motivate or discourage adoption. By mapping these dynamics, the project aims to explain why technology adoption is shaped not only by economics but also by social, cultural, and political influences. The outcome will be a conceptual model that supports quantitative analysis of adoption patterns across Canada's diverse agricultural landscape, helping stakeholders design more effective strategies for technology transfer in precision agriculture and smart farming.

Technical specifications

Research approach:

• Develops a conceptual push-pull framework linking ecosystem conditions to producer-level adoption decisions
• Applies quantitative survey methodology across Canadian farms, segmented by geographic region, farm size, sector, and commodity type
• Measures five key dimensions: extent of digital technology adoption, barriers and drivers to adoption, achievement of original investment objectives, strategies for overcoming barriers, and actionable steps to accelerate technology transfer
• Produces data-driven insights into farmer perceptions of emerging digital trends and their impact on business performance
• Designed to inform predictive models that explain variables influencing adoption of automation, robotics, and cyber-physical platforms in agriculture

Key features:

• Sector-wide scope covering the breadth of Canadian agri-food production
• Focus on smart precision agriculture and digital cultivation technologies
• Emphasis on sustainability and resilience within agri-food ecosystems
• Structured to capture both positive and negative adoption outcomes

Technology readiness level

This project is at an early research stage. The current phase involves framework development and survey design, with planned data collection through a nationwide quantitative survey of Canadian farmers. The one-year timeline supports initial conceptual validation and identification of adoption variables. Future phases will extend the framework toward predictive modeling and broader empirical testing. The research is positioned to generate foundational evidence that can guide subsequent technology transfer programs, policy interventions, and industry-led digital adoption initiatives across the agri-food sector.