Community-based business models for agricultural and forestry data ecosystems: A systematic literature review
Year: 2025
Type: review
Study design: Systematic Review
Abstract: Data-driven solutions are becoming essential to modern business models, changing traditional business practices and complex value chains in multi-stakeholder and community-based sectors such as agriculture and forestry. Nevertheless, there is a lack of consolidated knowledge about the benefits and challenges that data-driven community-based business models may present in these domains. This study conducts a systematic literature review of scientific publications to identify the benefits and barriers that community-based business models for agriculture and forestry data ecosystems present. The articles included are in English and peer-reviewed and were published between 2014 and 2024. The search was conducted in Scopus, Web of Science, and IEEE Xplore, and the query resulted in 387 studies. This review has followed the PRISMA methodology, and the final number of reviewed papers was 51. Ultimately, it is found that the benefits outweigh the barriers in terms of their repetition across the literature. Significant benefits identified are interconnectedness and interactivity, resource availability, and multidirectional knowledge transfer, while the high cost of implementation and the complexity of integration and implementation of data-driven community-based business models are among the major barriers. The findings from this work can bridge the existing gap of attention to data-driven community-based business models in agriculture and forestry, help with future research work, and act as guidelines for implementing such business models.
Source: Smart Agricultural Technology
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDGs: Life on land, Climate action
Open Access status: gold
Estimated APC paid: $1,770
OpenAlex ID: W4409582557