The emergence of personal assistants in the form of smart speakers has begun to significantly alter people’s everyday experiences with technology. The rate at which household Intelligent Personal Assistants such as Amazon’s Echo and Google Home emerged in household spaces has been rapid. They have begun to move human–computer interaction from text-based to voice-activated input, offering a multiplicity of features through speech. The supporting infrastructure connects with artificial intelligence and the internet of things, allowing digital interfaces with domestic appliances, lighting systems, thermostats, media devices and more. Yet this also constitutes a significant new production of situated and sensitive data. This study focuses on how (potential) users negotiate and make choices about household Intelligent Personal Assistant use in connection with their data. This study is based on empirical research in Europe with early adopters in Germany and potential users in the Netherlands. This examination of users’ early stage technology acceptance considerations highlights particular practices and choices of users to either preserve their privacy or determine what is acceptable use for their data. Drawing on a simplified version of Unified Theory of Acceptance and Use of Technology 2, a quantitative model for technology acceptance, we demonstrate how acceptance of a household Intelligent Personal Assistants does not imply access to all household data, how users see usefulness in relation to a proliferation of devices, and note the recognition by users regarding the efforts needed for full use and acceptance. The study highlights the complexity of data production at a household level and how these devices produce myopic views of users for platforms.

Additional Metadata
Keywords Smart speakers, household Intelligent Personal Assistants, Amazon Alexa, Google Home, Technology Acceptance, Models, privacy, surveillance capitalism
Persistent URL,
Journal Big Data & Society
Organisation Department of Media and Communication
Pridmore, J.H, & Mols, A.E. (2020). Personal choices and situated data. Big Data & Society. doi:10.1177/2053951719891748