by Alexander van Deursen
The era of Web 4.0 has arrived and its most prominent development, the IoT, is now widely available for consumers. Almost half of the Dutch adult population owns an IoT device. Although this may sound like the IoT is firmly rooted in people’s daily lives, ownership can be ascribed to a relatively limited set of devices: activity trackers, heart rate monitors, sport watches, smart thermostats, and lightning systems. The important features of such appliances are that a large amount of data is being collected, there is less autonomy from a user’s perspective, the devices work in the background and are invisible to the user, and there are substantial risks. Crucially, the IoT is directed by artificial intelligence, as decisions are not only automatically made by users but – once initial configurations are set – primarily by algorithms. These features have important consequences to the research on digital inequality. For example, although the devices are relatively cheap and daily use of IoT does not require extensive user skills as far as basic operations and functioning is concerned (precisely because IoT operates ‘on its own’), the story becomes more complex once these devices become part of an interconnected system in which they are connected to a multitude of other devices. Apart from a more complex process of appropriation (the process might have to be reiterated until amotivational threshold is reached), use might occur without any understanding of how functioning of a particular device influences functioning of other devices, and perhaps more importantly how data gathered in the background are shared across devices within and outside of the network (e.g., physical exercise data being shared with medical specialist or health insurance companies). In other words, although pragmatic use might be easy and straightforward, implications of use are far more complex and hence might require more advanced strategic skills. It is at this point that future research should clarify how such implications will affect (existing notions of) digital inequality. Taking a step back, in a recent article, resources and appropriation theory was used to study inequalities in the use of IoT in the Netherlands.
Following the appropriation process, we can first confirm the important role of IoT attitude. A positive attitude towards IoT increases the likelihood of IoT ownership and IoT skills and eventually leads to a wider diversity of IoT use. IoT skills, in turn, are important for IoT usage, although we did not find an effect for security related IoT activities. The adoption of security devices and related activities might be undertaken (regardless of skill levels), as they are important to wellbeing of one’s self and family members, issues which relate to basic needs. Resource and appropriation theory then argues that inequalities in the appropriation stages are caused by inequalities in resources, positions, and personal characteristics. Income surfaces as an important resource in relation to IoT attitude. People with low incomes that cannot afford IoT devices are less likely to develop favorable attitudes. Income remains important for material IoT access, especially for home-related IoT that appeals to (less basic) hedonic needs that are related to comfort and luxury, and safety-related IoT. The resource of social support only played a role in relation to security. Those with fewer support networks are more likely to buy security related IoT devices, maybe because they feel more insecure.
Among the IoT owners, next to the income resource, the position of educational attainment is associated with IoT attitudes. Education is also important for health related IoT uses. Both income and education were important predictors in Internet research that studied initial attitudes and uptake. As resource and appropriation theory posits IoT attitude at the start of the appropriation process, followed by material access, those with higher incomes and education will be the first to develop the necessary IoT skills and engage in diverse use of IoT devices. They are more likely to benefit from IoT developments. In terms of inequality, those that are already in more privileged positions are the first to further strengthen their resources by using the IoT or, in other words, to improve their health, living conditions at home, and security. Similar conclusions can be drawn for age: younger people tend to have the most material IoT access and have higher levels of IoT skills.
All in all, the investigation presented sufficient evidence to support beginning to focus digital inequality research on the IoT. In relation to Internet use, it took a long time before the emphasis started to shift away from having a connection to more elaborate explanations of skills and usage. For studying inequalities in the IoT, we stress that we should start incorporating these steps in research and policy at the start, even though material access rates are far from being saturated. Our results reveal that several inequalities emerge among those already using IoT devices.