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Cited by 231

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Psychological escapism: predicting the amount of television viewing by need for cognition

Henning, B·2001·Journal of Communication

Abstract

Germans spend an average of more than 3 hours watching television each day. Among them, there are many who turn on their TV sets for less than 1.5 hours a day, and others who do this for more than 8 hours a day. What accounts for these differences? The central thesis in this paper is that individuals may be distinguished by their attitude toward thinking and that differences in their need for cognition explain the differences in their time spent with TV. The lower viewers' need for cognition is, the less pleasant they feel when they have nothing to do because there is nothing left to do but think. The easiest way for individuals to escape this pressure to think is by watching TV. Thus, individuals will watch more TV when they have a lower need for cognition. Results of a survey study show that the concept of escapism proves to be useful in explaining TV use when it takes over a psychological perspective as well as a sociological one.

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Crossref · 10.1093/joc/51.1.100

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Metadata

Authors
Henning, B.
Venue
Journal of Communication
Published
2001-03-01
Volume
51
Issue
1
Pages
100-120
ISSN
  • 0021-9916 (ISSN-L)
  • 1460-2466
OpenAlex
W2100256341
Cited by
231
References
5
FWCI
60.86
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Psychological escapism: predicting the amount of television viewing by need for cognition. https://app.cochat.ai/papers/10.1093/joc/51.1.100
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Henning, B. (2001). Psychological escapism: predicting the amount of television viewing by need for cognition. Journal of Communication. https://doi.org/10.1093/joc/51.1.100

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.