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Cited by 372

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

The “Myth” of Media Multitasking: Reciprocal Dynamics of Media Multitasking, Personal Needs, and Gratifications

Wang, Zheng, Tchernev, John M.·2012·Journal of Communication

Abstract

The increasing popularity of media multitasking is frequently reported in national surveys while laboratory research consistently confirms that multitasking impairs task performance. This study explores this apparent contradiction. Using dynamic panel analysis of time series data collected from college students across 4 weeks, this study examines dynamic reciprocal impacts of media multitasking, needs (emotional, cognitive, social, and habitual), and corresponding gratifications. Consistent with the laboratory research, cognitive needs are not satisfied by media multitasking even though they drive media multitasking in the first place. Instead, emotional gratifications are obtained despite not being actively sought. This helps explain why people increasingly multitask at the cost of cognitive needs. Importantly, this study provides evidence of the dynamic persistence of media multitasking behavior.

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Crossref · 10.1111/j.1460-2466.2012.01641.x

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Metadata

Authors
Wang, Z., & Tchernev, J. M.
Venue
Journal of Communication
Published
2012-04-25
Volume
62
Issue
3
Pages
493-513
ISSN
0021-9916 (ISSN-L)
OpenAlex
W2050870248
Cited by
372
References
8
FWCI
49.32
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The “Myth” of Media Multitasking: Reciprocal Dynamics of Media Multitasking, Personal Needs, and Gratifications. https://app.cochat.ai/papers/10.1111/j.1460-2466.2012.01641.x
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Wang, Z., & Tchernev, J. M. (2012). The “Myth” of Media Multitasking: Reciprocal Dynamics of Media Multitasking, Personal Needs, and Gratifications. Journal of Communication. https://doi.org/10.1111/j.1460-2466.2012.01641.x

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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.