Modelling the factors that determine online news-sharing behaviour of social media users: The role of perceived message and online environmental characteristics
Restricted accessResearch articleFirst published online 2023
Modelling the factors that determine online news-sharing behaviour of social media users: The role of perceived message and online environmental characteristics
This study modelled the online environment characteristics and message characteristics that predict news sharing among social media users. This study’s data were obtained from a cross-sectional national survey conducted in Nigeria. Qualtrics was used to recruit data from 1320 participants in Nigeria. The participants were recruited via a stratified quota sampling which reflected the country’s census statistics for gender and age. We found the message characteristics to predict news-sharing behaviour among social media users in Nigeria. By implication, the characteristics of a message encountered online influence the news-sharing behaviour of social media users. We also found that the online environment characteristics predict news-sharing behaviour, which implies that the external factor, that is, the relationship a user has with his network members, predicts sharing behaviour. Some theoretical and practical implications were provided to conclude the study.
PennycookGMcPhetresJZhangY, et al. Fighting COVID-19 misinformation on social media: experimental evidence for a scalable accuracy-nudge intervention. Psychol Sci2020; 31(7): 770–780.
KarnowskiVLeonhardLKümpelAS.Why users share the news: a theory of reasoned action-based study on the antecedents of news-sharing behavior. Commun Res Rep2018; 35(2): 91–100.
5.
ShoemakerPJ.News and newsworthiness: a commentary. Communications2006; 31(1): 105–111.
6.
KümpelASKarnowskiVKeylingT.News sharing in social media: a review of current research on news sharing users, content, and networks. Soc Media Soc2015; 1(2): 1–14.
7.
ThompsonNWangXDayaP.Determinants of news sharing behavior on social media. J Comput Inf Syst2019; 60: 593–601.
8.
LeeCSMaL.News sharing in social media: the effect of gratifications and prior experience. Comput Human Behav2012; 28(2): 331–339.
MaLLeeCSGohDHL. Understanding news sharing in social media: an explanation from the diffusion of innovations theory. Online Inf Rev2014; 38(5): 598–615.
11.
SihombingSO.Predicting intention to share news through social media: an empirical analysis in Indonesian youth context. Bus Econ Horizons2017; 13(4): 468–477.
12.
MukherjeeDLimWMKumarS, et al. Guidelines for advancing theory and practice through bibliometric research. J Bus Res2022; 148: 101–115.
13.
GerhartNSidorovaA.The effect of network characteristics on online identity management practices. J Comput Inf Syst2017; 57(3): 229–237.
14.
MaLLeeCSGohDH.Understanding news sharing in social media from the diffusion of innovations perspective. In: 2013 IEEE international conference on Green computing and communications and IEEE internet of things and IEEE cyber, physical and social computing, 2013, pp. 1013–1020, https://ieeexplore.ieee.org/document/6682187
15.
HussainSKilagwaRTWokS.The effect of message credibility on media use and perception of fake news among students. Hum Commun2019; 1(2): 1–21.
16.
ChoiJ.Why do people use news differently on SNSs? An investigation of the role of motivations, media repertoires, and technology cluster on citizens’ news-related activities. Comput Human Behav2016; 54: 249–256.
ApukeODTuncaEA.A comparative content analysis of television and blog coverage of internally displaced persons in Nigeria. Soc Sci J2019; 56(2): 168–182.
GyaiseyAPAfful-DadzieABoatengR.Individuals Internet security perceptions and behaviors: polycontextual contrast between Ghana and Nigeria, 2019, https://ugspace.ug.edu.gh/handle/123456789/34209
21.
WeiLGongJXuJ, et al. Do social media literacy skills help in combating fake news spread? Modelling the moderating role of social media literacy skills in the relationship between rational choice factors and fake news sharing behaviour. Telemat Informatics2023; 76: 101910.
22.
ApukeODOmarB.Fake news proliferation in Nigeria: consequences, motivations, and prevention through awareness. Humanit Soc Sci Rev2020; 8(2): 318–327.
23.
ApukeODOmarB.What drives news sharing behaviour among social media users? A relational communication model from the social capital perspective. Int Sociol2021; 36(3): 339–361.
Oeldorf-HirschASundarSS.Posting, commenting, and tagging: effects of sharing news stories on Facebook. Comput Human Behav2015; 44: 240–249.
27.
ParkILeeD.Understanding news-sharing across different platforms: the effects of newsworthiness and gratifications from news-sharing. Inf Res an Int Electron J2020; 25(4), http://informationr.net/ir/25-4/paper882.html
GohDLingRHuangL, et al. News sharing as reciprocal exchanges in social cohesion maintenance. Inf Commun Soc2019; 22(8): 1128–1144.
30.
SuMHLiuJMcLeodDM.Pathways to news sharing: issue frame perceptions and the likelihood of sharing. Comput Human Behav2019; 91: 201–210.
31.
BergerJMilkmanKL.What makes online content viral? Emotion and virality. J Mark Res Artic2012; 49: 192–205.
32.
KimHS.Attracting views and going viral: how message features and news-sharing channels affect health news diffusion. J Commun2015; 65(3): 512–534.
33.
ZhangXAkhterSNassaniAA, et al. Impact of news overload on social media news curation: mediating role of news avoidance. Front Psychol2022; 13: 865246.
34.
WeeksBEHolbertRL.Predicting dissemination of news content in social media: a focus on reception, friending, and partisanship. Journal Mass Commun Q2013; 90(2): 212–232.
35.
IhmJKimEM.The hidden side of news diffusion: understanding online news sharing as an interpersonal behavior. New Media Soc2018; 20(11): 4346–4365.
36.
RogersEM.Diffusion of innovations. 5th ed.New York: Free Press, 2003.
37.
MaL.A multi-theoretical approach towards understanding news sharing in social media. PhD Thesis, Nanyang Technological University, Singapore, 2015.
38.
ApleniASmutsH.Responsible design, implementation and use of information and communication technology. In: HattinghMMattheeM.SmutsH. (eds) International federation for information processing (Lecture Notes in Computer Science), vol. 12067. Cham: Springer, 2020, pp. 15–27.
39.
ChenYHHCorkindaleD. Towards an understanding of the behavioral intention to use online news services: an exploratory study. Internet Res2008; 18(3): 286–312.
40.
GladneyGAShapiroICastaldoJ.Online editors rate web news quality criteria. Newsp Res J2007; 28(1): 55–69.
41.
NguyenA.The contribution of online news attributes to its diffusion: an empirical exploration based on a proposed theoretical model for the micro-process of online news adoption/use. First Monday2008; 13(4), https://firstmonday.org/ojs/index.php/fm/article/view/2127/1952
42.
BastosMT.Shares, pins, and tweets: news readership from daily papers to social media. Journal Stud2015; 16(3): 305–325.
43.
KoohikamaliMSidorovaA.Information re-sharing on social network sites in the age of fake news. Informing Sci2017; 20: 215–235.
44.
SingerJB.User-generated visibility: secondary gatekeeping in a shared media space. New Media Soc2014; 16: 55–73.
45.
OmarBApukeODNorZM.The intrinsic and extrinsic factors predicting fake news sharing among social media users: the moderating role of fake news awareness. Curr Psychol. Epub ahead of print 21February2023. DOI: 10.1007/s12144-023-04343-4.
46.
ColemanJS.Social capital in the creation of human capital. Am J Sociol1988; 94: S95–S120.
47.
HungSDurcikovaALaiH, et al. The influence of intrinsic and extrinsic motivation on individuals’ knowledge sharing behavior. J Hum Comput Stud2011; 69(6): 415–427.
48.
Widén-WulffGGinmanM.Explaining knowledge sharing in organizations through the dimensions of social capital. J Inf Sci2004; 30(5): 448–458.
49.
OkoliCOhW.Investigating recognition-based performance in an aridge to knowledge transfer. J Bus Res2006; 59: 130–139.
50.
ChiuCMHsuMHWangETG. Understanding knowledge sharing in virtual communities: an integration of social capital and social cognitive theories. Decis Support Syst2006; 42(3): 1872–1888.
51.
NahapietJGhoshalS.Social capital, intellectual capital, and the organizational advantage. Acad Manag Rev1998; 23(2): 242–266.
52.
BuchananTBensonV.Spreading disinformation on Facebook: do trust in message source, risk propensity, or personality affect the organic reach of ‘fake news’?Soc Media Soc2019; 5(4): 1–13.
53.
SterrettDMalatoDBenzJ, et al. Who shared it? Deciding what news to trust on social media. Digit J2019; 7(6): 783–801.
54.
WilliamsEJBeardmoreAJoinsonAN.Individual differences in susceptibility to online influence: a theoretical review. Comput Human Behav2017; 72: 412–421.
55.
PrasetioA.Understanding knowledge sharing and social capital in social network sites. Int J Sci Res2014; 3(3): 760–766.
56.
El RayessMCheblCMhannaJ, et al. Fake news judgement: the case of undergraduate students at Notre Dame University-Louaize, Lebanon. Ref Serv Rev2018; 46(1): 146–149.
57.
LiYYeFSheuC.Social capital, information sharing and performance evidence from China. Int J Oper Prod Manag2014; 34(11): 1440–1462.
58.
DuffyATandocELingR.Too good to be true, too good not to share: the social utility of fake news. Inf Commun Soc2020; 23: 1965–1979.
59.
AhmedS.Disinformation sharing thrives with fear of missing out among low cognitive news users: a cross-national examination of intentional sharing of deep fakes. J Broadcast Electron Media2022; 66: 1–21.
60.
KimADennisAR.Says who? The effects of presentation format and source rating on fake news in social media. MIS Q2019; 43(3): 1025–1039.
61.
IslamAKMNLaatoSTalukderS, et al. Misinformation sharing and social media fatigue during COVID-19: an affordance and cognitive load perspective. Technol Forecast Soc Change2020; 159: 120201.
62.
BhagatSKimDJKimDJ.Examining users’ news sharing behaviour on social media: role of perception of online civic engagement and dual social influences. Behav Inf Technol2023; 42: 1194–1215.
63.
RamayahTCheahJChuahF, et al. Partial least squares structural equation modeling (PLS-SEM) using smartPLS 3.0: an updated guide and practical guide to statistical analysis. Kuala Lumpur, Malaysia: Pearson, 2018.
64.
SarstedtMHairJFCheahJH, et al. How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australas Mark J2019; 27(3): 197–211.
65.
DuartePAmaroS.Methods for modelling reflective-formative second order constructs in PLS: an application to online travel shopping. J Hosp Tour Technol2018; 9(3): 295–313.
66.
KhanGFSarstedtMShiauWL, et al. Methodological research on partial least squares structural equation modeling (PLS-SEM): an analysis based on social network approaches. Internet Res2019; 29(3): 407–429.
67.
HairJFRingleCMGuderganSP, et al. Partial least squares structural equation modeling-based discrete choice modeling: an illustration in modeling retailer choice. Bus Res2019; 12(1): 115–142.
68.
HairJFJrHultGTMRingleC, et al. A primer on partial least squares structural equation modeling (PLS-SEM). 2nd ed.Thousand Oaks, CA: SAGE, 2017.
69.
CohenJ.Statistical power analysis for the behavioral sciences. 2nd ed.Hillsdale, NJ: Lawrence Erlbaum Associates, 1988.
70.
HairJFRingleCMSarstedtM.Partial least squares: the better approach to structural equation modeling?Long Range Plann2012; 45(5–6): 312–319.
71.
VosoughiSRoyDAralS.False news is big news. The spread of true and false news online. Science2018; 359: 1146–1151.
72.
KimJNamkoongKChenJ.Predictors of online news-sharing intention in the U.S and South Korea: an application of the theory of reasoned action. Commun Stud2020; 71(2): 315–331.
73.
KorantengFNWiafeI.Factors that promote knowledge sharing on academic social networking sites: an empirical study. Educ Inf Technol2019; 24(2): 1211–1236.
74.
LiuLCheungCMKLeeMKO. An empirical investigation of information sharing behavior on social commerce sites. Int J Inf Manage2016; 36(5): 686–699.
75.
KhanMLIdrisIK.Recognise misinformation and verify before sharing: a reasoned action and information literacy perspective. Behav Inf Technol2019; 38(12): 1194–1212.
76.
TalwarSDhirAKaurP, et al. Why do people share fake news? Associations between the dark side of social media use and fake news sharing behavior. J Retail Consum Serv2019; 51: 72–82.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
