Quantified Vehicles

Three trends have shown significant impact in recent years: (1) The Internet of Things (Wortmann and Flüchter 2015) has become an enabler for a connected world full of smart objects equipped with sensors and supplies enormous and still rising amounts of (2) Big Data (Schönberger and Cukier 2013), which can be analyzed and then turned into business value in various areas, including (3) the Quantified Self movement as a popular example for everyday life big data analytics (Swan 2009). On a more abstract level, capturing real world events and digitizing them into machine-readable data to satisfy needs or assist humans and machines in decision making, evaluation and comparison of physical world events has become increasingly important. Even a new branch of business has emerged through such big data analytics for data-driven innovations, while information overload has wiped off its negative image and has become the beautiful bride everybody wants to dance with. Schönberger and Cukier’s (2013) pragmatic book on the capacity of big data to change the world has become an international bestseller and was referenced by researchers more than 1000 times according to Google Scholar. In line with these developments, consumer products are increasingly connected to the Internet and have become a major source of data, too. So-called smart, connected products (Porter and Heppelmann 2014, 2015) are capable of capturing an increasing amount of data about their product life through all kinds of embedded sensors. The archetype of a smart, connected product is the well-known, widely used, and constantly switched on smartphone. The smartphone has become an interesting hub for sensors of all kinds, and has therefore kicked off the development of new services encapsulated in mobile applications. Some of those applications promise an additional value for the smartphone user by applying algorithms for sensor data analysis if the user is willing to share the required sensor data. In the age of computing humans have become datagenerating subjects, because they consciously or unconsciously leave behind ‘electronic traces’ when using their computer (Wolf 2013). ‘Quantified self-tracking’ (later shortened to ‘quantified self’) is a more current term, referring to an intended collection of any data about the self that can be measured, including biological, physical, behavioral, or environmental information (Swan 2009). Quantified-selfers are a diverse group of early adopters including life hackers, data analysts, computer scientists, health enthusiasts, gamers, productivity gurus, and patients, who track many kinds of data about themselves (Choe et al. 2014). Making use of this data collected through smartphones or wearables in the private domain to learn more about one’s body and leisure behavior is an emerging topic and has become a major creator of value (PWC 2016). Accepted after one revision by Prof. Dr. Sinz.