Collaborative Research: HCC: Medium: Big Data on the Dairy Farm: Relational Transformations across Agricultural Occupations and Organizations with the Rise of Digital Technologies

University Of California-Santa Barbara, Santa Barbara CA

Investigators

Abstract

This research will investigate how emerging technologies are transforming occupations and organizations, exploring the significant example of digital agriculture in dairy farming. The findings of this study will inform research on technology use and the changing nature of work. While research has shown that new digital technologies are best understood in social contexts, we know little about how changing relationships between people, sensors, data, animals, data science models and other important participants evolve. By studying those involved in digital agriculture dairy farming, supported by an understanding of how, when, and why they interact, this research will contribute to the discussion on how to apply data-driven technologies productively. The findings will also develop new theory about the relationship between big data and resource management by documenting their impacts on occupations and organizations. This research highlights how data science models contribute to such evolving relations and how relational transformations, in turn, shape the models. This project's theoretical framework is built upon key contributions of the scholarship exploring technology, data, and organizing before proposing a relational framework for studying digital agriculture. The work employs a comparative mixed-method study with three main project goals: (1) to explore evolving relations in digital agriculture and the occupational and organizational consequences of them, (2) to generalize the findings and derive theoretical insights for other contexts where the rise of data-driven technologies may prompt similar relational transformations, and (3) to assess how insights about evolving relations might aid in design and deployment of the data science models and with them sensors, data streams, and the like. Mentoring of graduate and undergraduate students will serve as an example of how to integrate data science and social science inquiries in research projects while providing students with essential fieldwork, analytical, and publishing skills. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.