Uses and gratifications of social networking sites for bridging and bonding social capital: A comparison of Facebook, Twitter, Instagram, and Snapchat
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The U&G framework has been applied to diverse media and interactive technologies, including TV, social media, apps, and chatbots.
“It has been applied to diverse media and interactive technologies, including TV (Rubin, 1983), social media (Phua, et al. , 2017), apps (Ray, et al. , 2019), and chatbots (Brandtzaeg and Følstad, 2017; Cheng and Jiang, 2020a; C. Xie, et al. , 2022).”
From Why do people use ChatGPT? Exploring user motivations for generative conversational AI (2024) · cites this paper as Phua, et al., 2017
User interactions with social networks (virtual presence, high consumption of attention and perception, sensation of flow) and the characteristics of these media (simultaneous networks, instant notifications, endless content scrolling, variable reward) are related to problematic use.
“User interactions with social networks (virtual presence, high consumption of attention and perception, sensation of flow) and the characteristics of these media (the ability to be on different networks simultaneously, instant notifications, endless content scrolling, variable reward) are related to problematic use (Eyal & Hoover, 2014;Moretta et al., 2020;Phua et al., 2017;Rodríguez-García et al.”
From Problematic use of social media in adolescents or excessive social gratification? The mediating role of nomophobia (2024) · cites this paper as Phua et al., 2017
Cites
Social Capital: Prospects for a New Concept
Adler, P. S., & Kwon, S.-W. (2002) · The Academy of Management Review · doi:10.2307/4134367 · Cited by 9,665 · cited 2× · see citing works
“Social capital not only serve as contact points through which people receive information and opportunities (Adler and Kwon, 2002; Burt, 1999), but also entail personal relationships with a system of norms and reciprocity that can be activated by individuals to receive benefits in the same way as…”
BackgroundValenzuela, S., Park, N., & Kee, K. F. (2009) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2009.01474.x · Cited by 2,229 · see citing works
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“…recently, scholarly research has drawn upon UGT to examine audiences’ goal- directed consumption behavior in the computer-mediated communication context (e.g., Gil de Zuniga et al., 2012; Papacharissi and Mendelson, 2011; Raacke and Bonds-Raacke, 2008; Sundar and Limperos, 2013).”
BackgroundUses and gratifications of social media: A comparison of Facebook and instant messaging
Quan-Haase, A., & Young, A. L. (2010) · Bulletin of Science, Technology & Society · doi:10.1177/0270467610380009 · Cited by 1,205 · cited 4× · see citing works
“In particular, two trends of SNS use have been identified: first, the majority of people use two or more SNSs simultaneously because each has its unique features and purposes (Brandtzaeg, 2012); and second, people increasingly embrace SNSs as tools for both communication and information, which help them fulfill their…”
BackgroundInfluentialUses and Grats 2.0: New Gratifications for New Media
Sundar, S. S., & Limperos, A. M. (2013) · Journal of Broadcasting & Electronic Media · doi:10.1080/08838151.2013.845827 · Cited by 1,070 · see citing works
Ellison, N. B., Vitak, J., Gray, R., & Lampe, C. (2014) · Journal of Computer-Mediated Communication · doi:10.1111/jcc4.12078 · Cited by 916 · see citing works
Social networking on smartphones: When mobile phones become addictive
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Individual differences as predictors of social networking
Orchard, L. J., Fullwood, C., Galbraith, N., & Morris, N. (2014) · Journal of Computer-Mediated Communication · doi:10.1111/jcc4.12068 · Cited by 120 · cited 2× · see citing works
“Studies applying UGT have found that audience members’ individual differences—including media self-efficacy, habitual behavior, prior attitudes, and self-regulation—serve to moderate their media selections (e.g., Humphreys et al., 2013; LaRose and Eastin, 2004; Orchard et al., 2014).”
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Lim, M. S. C., Molenaar, A., Brennan, L., Reid, M., & McCaffrey, T. (2022) · Journal of Medical Internet Research · doi:10.2196/23656 · Cited by 294 · see citing works
Why do people use ChatGPT? Exploring user motivations for generative conversational AI
Skjuve, M., Brandtzaeg, P. B., & Følstad, A. (2024) · First Monday · doi:10.5210/fm.v29i1.13541 · Cited by 95 · see citing works
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Islam, T., Mahmood, K., Sadiq, M., Usman, B., & Yousaf, S. U. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.572526 · Cited by 54 · see citing works
“Given that, healthcare professionals should release relevant and sufficient information on social media through different channels, such as WhatsApp, Twitter, Facebook, or Snapchat, etc.”
BackgroundInfluentialPredictors of problematic social media use: personality and life-position indicators
Sheldon, P., Antony, M. G., & Sykes, B. (2021) · Psychological Reports · doi:10.1177/0033294120934706 · Cited by 55 · see citing works
Uses and gratifications of photo sharing on Instagram
Menon, D. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102917 · Cited by 52 · see citing works
Social Media and the Changing Information Environment
Kraft, P. W., Krupnikov, Y., Milita, K., Ryan, J. B., & Soroka, S. (2020) · Public Opinion Quarterly · doi:10.1093/poq/nfaa015 · Cited by 46 · see citing works
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Short video marketing strategy: evidence from successful entrepreneurs on TikTok
Chen, H., Ma, D., & Sharma, B. (2024) · Journal of Research in Marketing and Entrepreneurship · doi:10.1108/jrme-11-2022-0134 · Cited by 35 · see citing works
Trust for online social media direct-to-consumer prescription medication advertisements
Fogel, J., & Adnan, M. (2019) · Health Policy and Technology · doi:10.1016/j.hlpt.2019.08.009 · Cited by 8 · see citing works
Pérez-Torres, V. (2024) · Cyberpsychology: Journal of Psychosocial Research on Cyberspace · doi:10.5817/cp2024-4-2 · Cited by 6 · see citing works
Facebook Addiction among Afghan University Students: A Structural Equation Modelling
Mathematics Department, F. O. N. S., & Ghulami, H. R. (2021) · Indian Journal of Data Communication and Networking · doi:10.54105/ijdcn.b5001.041221 · Cited by 3 · see citing works
- Authors
- Phua, J., Jin, S. V., & Kim, J. (.
- Affiliations
- University of Georgia (US)
- ORCID
- Phua, Joe
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2017-07
- Volume
- 72
- Pages
- 115-122
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W2590123299
- Cited by
- 625
- References
- 8
- Citations by year
- 2026: 53 · 2025: 52 · 2024: 70 · 2023: 77 · 2022: 85 · 2021: 108 · 2020: 76 · 2019: 59 · 2018: 40 · 2017: 3 · 2016: 1
- FWCI
- 116.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers in Human Behavior (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Social Capital and Networks
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Physical Sciences › Computer Science › Computer Networks and Communications › Media and Digital Communication
- Keywords
- uses and gratifications theory, social capital, bridging social capital, bonding social capital, social networking sites, Facebook, Twitter, Instagram, Snapchat, social comparison, trust, tie strength, homophily, privacy concerns, introversion
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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