In modern everyday life, individuals experience an abundance of digital information and communication options, and pressure to use them effectively and constantly. While there are many benefits attainable through the use of digital information and communication technologies (ICTs), digital overuse needs to be explored as it may impair individual well-being. A nationally representative survey explored the extent of perceived digital overuse (PDO) and tested its relation to social digital pressure, digital coping skills, and, to assess everyday offline relevance, to individual subjective well-being. Results indicated that 28% of Swiss Internet users pereived digital overuse, which was strongly and negatively associated with well-being. Social pressure was positively related to overuse. Differences in experiencing and dealing with digital overabundance are highly relevant to general well-being and need to be further researched in light of social change and ICT innovations.
People's sense of being subject to digital dataveillance can cause them to restrict their digital communication behavior. Such a chilling effect is essentially a form of self-censorship in everyday digital media use with the attendant risks of undermining individual autonomy and well-being. This article combines the existing theoretical and limited empirical work on surveillance and chilling effects across fields with an analysis of novel data toward a research agenda. The institutional practice of dataveillance—the automated, continuous, and unspecific collection, retention, and analysis of digital traces—affects individual behavior. A mechanism-based causal model based on the theory of planned behavior is proposed for the micro level: An individual's increased sense of dataveillance causes their subjective probability assigned to negative outcomes of digital communication behavior to increase and attitudes toward this communication to become less favorable, ultimately decreasing the intention to engage in it. In aggregate and triggered through successive salience shocks such as data scandals, dataveillance is accordingly hypothesized to lower the baseline of free digital communication in a society through the chilling effects mechanism. From the developed theoretical model, a set of methodological consequences and questions for future studies are derived.
Algorithmic governance affects individuals' reality construction and consequently social order in societies. Vague concepts of algorithmic governance and the lack of comprehensive empirical insights into this kind of institutional steering by software from a user perspective may, however, lead to unrealistic risk assessments and premature policy conclusions. Therefore, this paper offers a theoretical model to measure the significance of algorithmic governance and an empirical mixed-methods approach to test it in different life domains. Applying this guideline should lead to a more nuanced understanding of the actual significance of algorithmic governance, thus contributing to an empirically better-informed risk assessment and governance of algorithms.
The term algorithmic governance describes institutional steering effects of algorithmic-selection applications that increasingly pervade all domains of everyday life. Empirical evidence on algorithmic governance is lacking and mostly limited to information services. This article compares the significance of algorithmic governancemeasured by use, subjective significance, awareness, risk awareness, and coping practicesfor four pivotal life domains (information, recreation, commercial transactions, and socializing). Drawing on qualitative, semi-structured interviews with Internet users, this article reveals important nuances in how differently users engage with algorithmic-selection applications across life domains and functional types like search or recommendation. While awareness of algorithmic selection and related risks is comparatively higher for information services, the findings reveal a significant lack of knowledge for algorithmic selection in other life domains and for specific algorithmic modes of operation. This article provides input for an evidence-based development of suitable regulation of algorithmic-selection applications, taking everyday practices of their users into account.
Testing communication theories requires a valid empirical basis, yet especially for usage time measures, retrospective self-reports have shown to be biased. This study draws on a unique data set of 923 Swiss internet users who had their internet use tracked for at least 30 days on mobile and desktop devices and took part in a survey covering internet usage as well as person-level background variables. The analysis focuses on active usage time overall and on the major services Google Search, YouTube, WhatsApp, Instagram, Facebook, and the online newspaper 20 Minuten. The results showed that overall internet usage time was lower for older and higher-educated users based on both the tracking and survey data, and the reported usage time was consistently higher than the tracked usage time. The tracking data further revealed that internet users in all social groups spent the majority of their time online on a mobile device. The number of users of the major services varied mainly between age groups. These differences were less pronounced when it came to the time users spent engaging with these services. Over the course of a day, the major services varied in their frequency of use: for example, messaging peaked before noon and in the late afternoon, whereas online news use was comparably constant at a lower level.
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