The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
Antecedents and effects of social network fatigue
Abstract
Guided by literature on “fatigue” from within the domains of clinical and occupational studies, the present article seeks to define the phenomenon termed social network fatigue in the context of one of the popular uses of social networks, namely, to stay socially connected. This is achieved through an identification of the antecedents and effects of experiences that contribute to negative emotions or to a reduction in interest in using social networks with the help of a mixed‐methods study. Five generic antecedents and varying effects of these antecedents on individual user activities have been identified. Fatigue experiences could result from social dynamics or social interactions of the members of the community, content made available on social networks, unwanted changes to the platform that hosts the network, self‐detected immersive tendencies of the users themselves, or a natural maturing of the life cycle of the community to which the user belongs. The intensity of the fatigue experience varies along a continuum ranging from a mild or transient experience to a more severe experience, which may eventually result in the user's decision to quit the environment that causes stress. Thus, users were found to take short rest breaks from the environment, moderate their activities downward, or suspend their social network activities altogether as a result of fatigue experiences.
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Cites
Graneheim, U. H., & Lundman, B. (2004) · Nurse Education Today · doi:10.1016/j.nedt.2003.10.001 · Cited by 20,772
Basics of Qualitative Research
Ozanne, J. L., Strauss, A., & Corbin, J. (1992) · Journal of Marketing Research · doi:10.2307/3172751 · Cited by 17,850
Basics of Qualitative Research: Grounded Theory Procedures and Techniques
Long, D. R., Strauss, A., & Corbin, J. (1993) · The Modern Language Journal · doi:10.2307/328955 · Cited by 17,576
Social network sites: definition, history, and scholarship
boyd, D. M., & Ellison, N. B. (2007) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2007.00393.x · Cited by 16,251
(1998) · Cited by 13,501
(2013) · Cited by 12,072
Chiu, C.-M., Hsu, M.-H., & Wang, E. T. G. (2006) · Decision Support Systems · doi:10.1016/j.dss.2006.04.001 · Cited by 3,170
Content Analysis in Communication Research.
de Sola Pool, I., & Berelson, B. (1952) · American Sociological Review · doi:10.2307/2088029 · Cited by 3,099
Content analysis: Method, applications, and issues
Downe‐Wamboldt, B. (1992) · Health Care for Women International · doi:10.1080/07399339209516006 · Cited by 2,682
Eppler, M. J., & Mengis, J. (2008) · Kommunikationsmanagement im Wandel · doi:10.1007/978-3-8349-9772-2_15 · Cited by 2,177
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Lee, A. R., Son, S.-M., & Kim, K. K. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.08.011 · Cited by 814
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Metadata
- Authors
- Ravindran, T., Yeow Kuan, A. C., & Hoe Lian, D. G.
- Venue
- Journal of the Association for Information Science and Technology
- Publisher
- Wiley
- Published
- 2014-04-16
- Volume
- 65
- Issue
- 11
- Pages
- 2306-2320
- ISSN
- 2330-1635 (ISSN-L)
- 2330-1643
- OpenAlex
- W1606325646
- Cited by
- 330
- References
- 72
- FWCI
- 17.30
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ravindran, T., Yeow Kuan, A. C., & Hoe Lian, D. G. (2014). Antecedents and effects of social network fatigue. Journal of the Association for Information Science and Technology. https://doi.org/10.1002/asi.23122
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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.