Day-to-day routines of media platform use in the digital age: A structuration perspective

ABSTRACT

Using Giddens's structuration theory, this study examines how the routinized use of traditional and new media platforms differently align with the structures of everyday life. We analyzed data from a quantitative diary study in Germany to find that new media platforms specifically affect societal structuration by blurring the lines between obligations and leisure time. The part played by routines in the use of new media platforms was less strongly connected to clock time compared to traditional media platforms. Consequently, the findings indicate both a vanishing potential for media platform use as a social zeitgeber and the relevance of rules as structuring elements.

KEYWORDS:

• Media use
• routines
• structuration
• clock time
• quantitative diary study
• multilevel analysis

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Acknowledgements

We would like to thank Marko Bachl, Christina Viehmann, Jens Vogelgesang, the editor Paul Schrodt, and reviewers for their very helpful comments on earlier versions of the article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes on contributor

Anna Schnauber-Stockmann is a postdoctoral researcher at the Department of Communication, University of Mainz. Frank Mangold is a postdoctoral researcher at the Department of Communication, University of Hohenheim.

Notes

1 H1 is a generalization of the more specific notions that television use is more likely when people have leisure time and that radio use is less likely when people have leisure time. H2 is a generalization of the more specific notions that television use and computer use are more probable when people are at home.

2 Original categories: at home, in a car, on a bus/train, at another person’s home, at work/school/university, outdoors, at a bar/pub/restaurant, somewhere else.

3 Original categories: commuting/travelling from one place to another, working/learning, grocery shopping, eating, homework, hobby, taking care of children, cooking/preparing food, personal hygiene, (private) media platform use, sleep, sports/working out, meeting with others/talking to others, something else.

4 Original categories: alone, with partner/spouse, with family member(s), with friend(s), with colleague(s), and with another person/other people.

5 Because the change rates of platform-use-probability were fairly constant across time (see also Figure S1 in the Online Appendix), linear models proved to be a good and readily interpretable approximation. More complex models including, for example, additional quadratic or cubic time predictors did not produce further insights. Across days, no consistent time dependencies of media platform use other than the ones identified by linear probability models were identifiable. Thus, linear and more complex models led to the same practical and statistical inferences. The same held true for the use of alternative non-linear linkage functions.

6 Given the parameterization of the linear probability models (i.e., hours as time units), the positive total association translated into an increase in the probability of television use of 12% points per hour.

7 Television use (r = .14; p > .05) and radio use (r = .17; p < .05) was positively correlated with age (i.e., more prevalent among older respondents). Smartphone use (r = −.34; p < .05) was negatively correlated with age. Computer use was not significantly correlated with age (r = .02; p > .05).

8 Stated alternatively, older respondents did not merely use their smartphone generally less often, but specifically used it less often when they were at home.