Apps. Gerard Goggin
ecologies of the hardware, let’s turn now to considering apps as software. Apps are programs written in code. They consist of a collection of files that are downloaded by users and installed on devices. Once installed, apps execute code to gather resources, initiate events, and make things happen. In doing so, they marshal the capabilities of smartphones and the power of computers. They do so via mediating layers of codes, services, application frameworks, application programming interfaces (APIs), and so on. Central to these software environments is the operating system (OS), which orchestrates the software, the code and its compilation, and the hardware.
We can grasp these OSs as a series or stack of layers that allow apps (and their developers and users) to best avail themselves of the capabilities and affordances of the smartphone and, through it, of the various devices, networks, software, things, data, and so on to which it is connected. Increasingly, smartphones are a critical and generative node in wider platforms. What we, as users, experience as apps is a veritable tip of the iceberg. The breakthrough in mobile apps was the creation of these platforms as powerful, supportive, easy-to-use app development environments, typically operated by companies that own or are custodians of an OS. As we shall explore further, especially in chapter 3, companies such as Google, Apple, and others allow developers to avail themselves of their software developer kits, their OS environment, and their services and then to offer apps via an app store (often associated with an OS owner, too). This is the kind of thing introduced in software and app development manuals that target the novice developer; these manuals typically set an exercise such as making a flashlight app or a “beer advisor” app.
The app development environments offered by OS providers have evolved, over the 15 or so years of their existence, to offer comprehensive support for a wide range of tedious, difficult, or costly aspects of developing, versioning, upgrading, and deploying apps across multiple device configurations. So much of the hard work of app development is “blackboxed”—especially for small developers, who can take many things off the shelf, as it were. There are many unrealized, largely invisible aspects of these hinterlands of app software and computing environments; and they are due to the growing complexity, scale, and yoking together of different technologies in the digital platforms. So a customer seeking to grow her or his business by developing her or his own app is reliant on an assemblage of digital media and communication. Thus the single app and its potential users and communities fit into a global picture, which is of course much larger. More on this later.
So far in this section I have been sketching an anatomy of an app. Apps are software that rests upon layers of other software, all ultimately written in code, and all collectively drive the machine of smartphones and other devices to undertake what Lucy Suchman famously called “situated actions” (Suchman, 2007). Among the many things that apps marshal, one that looms large is data. Data from smartphones have special links with personal and collective information.
If we recall the predecessor technology of the telephone, information about subscribers was most systematically known by the phone company, and it was gathered and made available in directories. The calling patterns were typically studied by engineers in telephone companies to inform their design and planning of network capacity and distribution. The content of conversations held during telephone calls and the calling parties themselves could either be overheard, on party wires or via the operator, or listened in through telephone interception or phone tapping (Goggin, 2006). Such interception was possible with mobile phones as well, although encryption made it more difficult. However, with mobile phones came the widespread sharing and collection of telephone numbers: the preciousness of this identifying personal information is underscored by its role in money transfer apps or in messaging apps such as WhatsApp or WeChat. As they evolved, mobile phones gathered and brought to maturation many other sources of data in the smartphone era.
Especially important are location data, which are obtained via technologies such as cellular network triangulation, GPS, and Bluetooth. Thanks to their portability and intimate relationship with their users, smartphones offer rich data for following these users’ daily journeys and for approximating their locations. Many apps have been developed to take advantage of location data, the general arc moving from dedicated apps (e.g. check-in apps such as Foursquare, or map apps such as Waze) to incorporation of location data features in a wide range of other apps, especially social media ones.
Then there are data about people’s bodies and bodily states. These are the kinds of data used by health and wellness apps. Such data are directly gathered from the sensors contained in smartphones, as we have just seen. Many of them are used inferentially—for instance in apps that monitor, gauge, and arbitrate sleeping patterns, the amounts and quality of exercise and physical activity, health, well-being, or any kind of behavior; and they often do so problematically (Barnett et al., 2018). During the COVID-19 pandemic, health researchers, medical practitioners, and developers sought to develop apps that would assist in the diagnosis of positive cases on the basis of data from sensors. Some apps encourage people to enter these data themselves, as in a diary or journal.
One of the major axes of smartphone data is the connection between the app that runs on the smartphone and what is accessed, transferred, and collected, be it via networks, via databases, or via people and other things. Apps have a communicative function, accessing data from elsewhere or sending their data to a server, a database, or a repository elsewhere—which is dramatized in the development and discussion of cloud computing. The rise of apps has been enabled by the rise of cloud computing. Many apps are designed for, and rely upon, the cloud (Sitaram & Manjunath, 2012). Key cloud-based apps include the Google suite of apps, Microsoft Office, and many health apps (Woodward, 2016). So the “appification” of mobile communication has been powered by the rise of the cloud (Stawski, 2015). There is a spectrum of such implementations, from the many apps running on mobile and other devices that draw data from and use the services and capabilities (e.g. machine learning, AI, virtual machines) of cloud-based platforms such as Amazon Web Services (AWS) (Mishra, 2018), to cloud apps and cloud app marketplaces (Nguyen et al., 2016).
Another category of data is transactional data, which are generated when we make a purchase or book a ticket. There are also data on the activities we perform with apps. Watching a video via Netflix on a smartphone or tablet generates data that are held remotely as well as locally, and are “synced up” (i.e. updated) with one’s account. More and more areas of everyday life require apps for participation: there is now, for example, check-in to places via quick response (QR) code, which is designed to enable infectious disease tracing in the COVID-19 pandemic through social media and search apps; and there can be requirements to book a swimming pool spot or do banking or money transfer via an app. Given such developments, many more data about people, their lives, and their environments are gathered by or pass through apps. This dataphilic quality of apps is not only defining, by now it is well nigh constitutional of apps.
Hence the constant struggle to staunch the flow of “leaky apps” (Ball, 2014; Cadwalladr & Graham-Harrison, 2014), and to put in place safeguards that can regulate the data gathering, data use, and data sharing done by people’s main devices or by apps operated by better known brands and by companies with third-party apps or providers. This was (and remains) the nub of the problem with the 2018 revelations that exposed Facebook’s sharing of user data with the Cambridge Analytica company. The Facebook scandal was but one of many instances of data breaches, poor practices, and lack of adequate legal and regulatory frameworks and redress that have made privacy and data governance a burning issue of our time. By turns, apps are at the frontline of concerns about both private companies’ and the government’s use of personal data for profiling, tracking, and surveillance.
Histories of Apps
So far I have provided a working definition of “app” and showed you what an app typically does and looks like. To gain a better appreciation of apps as a socio-technical system, it is useful to look at the processes by which they emerged.
The classic predecessors of smartphone apps can be found in the so-called handheld devices, which gained their markets in the 1980s and 1990s and persisted until the early 00s. Handheld devices can be seen as part of