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

本研究将探讨新兴技术如何改变职业和组织，探索数字农业在奶牛养殖中的重要例子。这项研究的结果将为有关技术使用和工作性质变化的研究提供信息。虽然研究表明，新的数字技术在社会背景下得到了最好的理解，但我们对人、传感器、数据、动物、数据科学模型和其他重要参与者之间不断变化的关系是如何演变的知之甚少。通过对数字农业奶牛养殖相关人员的研究，了解他们如何、何时以及为什么相互作用，本研究将有助于讨论如何有效地应用数据驱动技术。这些发现还将通过记录大数据对职业和组织的影响，发展出关于大数据和资源管理之间关系的新理论。这项研究强调了数据科学模型如何促进这种不断发展的关系，以及关系转换如何反过来塑造模型。在提出研究数字农业的关系框架之前，本项目的理论框架建立在探索技术，数据和组织的奖学金的关键贡献之上。这项工作采用比较混合方法研究，有三个主要项目目标：(1)探索数字农业中不断发展的关系及其对职业和组织的影响；(2)总结研究结果，并为数据驱动技术的兴起可能促使类似关系转变的其他背景得出理论见解；(3)评估关于不断发展的关系的见解如何有助于数据科学模型的设计和部署，以及传感器、数据流等。研究生和本科生的指导将作为如何在研究项目中整合数据科学和社会科学调查的例子，同时为学生提供必要的实地考察、分析和出版技能。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.