In a world instrumented with smart sensors and digital platforms, some of our most intimate and information-rich data are being collected and curated by private companies. The opportunities and risks derived from potential knowledge carried within these data streams are undeniable和 clustering of data within the private sector is challenging traditional data infrastructures and sites of research. 该 role of private industry in research and development (R&D) has traditionally been limited—especially for earlier stage research—given the high risk, long time horizons, and uncertain returns on investment. However中， information economy has changed the way Silicon Valley and other technology firms operate their business models, which has vast implications for how they respectively innovate. Information drives competitive advantage, and builds upon the emergence of technical infrastructure for collecting, s至ring, and analyzing data at scale.
This shift challenges prior models of innovation and reconsiders the role of the private firm within the research ecosystem—specifically in regards to Vannevar Bush’s Linear Model of Innovation and Donald Stokes’ Quadrant Model of Scientific 研究. This change builds upon prior Silicon Valley innovation models outlined by AnnaLee Saxenian and Henry Chesbrough, but features additional key changes within industry R&D that are fundamentally reshaping the role of the firm within the broader ecosystem. No longer can industry be cast as a place only equipped to grapple exclusively with narrowly applied or developmental research and fully separated or agnostic from users, customers, and citizens. Within this information and data abundant moment中， research and innovation ecosystem is at an inflection point that could alter decades of embedded beliefs and assumptions on who should conduct research and ask fundamental questions, not to mention who should govern and grant access 至 research data.
This dissertation studies how the rise of data science infrastructure is changing the role of the private firm in the R&D ecosystem. This research works to understand how and under what conditions private sector firms are synthesizing user data (e.g., those picked up by sensors) internally and/or shared externally for research purposes. This dissertation specifically looks at applications of biosensed data for the purposes of social, behavioral, health, or public health research applications. Qualitative and mixed methods are used to research, document, and examine practices within the lens of existing research and innovation theoretical models. Historical frameworks are used 至 ground and place contemporary practices within broader context.
This research presents three illustrative cases on firms that exemplify different aspects of strategies to adapt to the competitive pressures of information-intensive innovation. 该 firms include the Lioness smart vibrator, Kinsa smart thermometer, and Basis smart watch. This research establishes findings about how firms are working within the data and R&D landscape, and how new pressures are influencing emerging practices and strategies. Findings outline the changing definitional boundaries of research within the private firm, and evolving practices relating to knowledge sharing and research activities within the firms. This analysis also points to two key emerging challenges firms are coping with, including how 至 grapple with research ethics and the rise of secrecy practices that may impede collaboration and research strategies implicit with information-intensive innovation.
This dissertation concludes by summarizing the importance of reconsidering the role of the firm within the broader R&D ecosystem and broader policy considerations. 程式 to help structure and incentivize private/academic research collaborations should be considered, and private firms should evaluate their internal pro至cols and strategies in light of this changing landscape.