Based on the stimulus–response framework, this study examines the external environmental stimuli influencing online rumour sharing about COVID-19 and considers the contingent effect of fear. A large-scale online survey was used to test the proposed research model and hypotheses. The final data set comprised 2807 valid responses. The results indicate that perceptions of community safety and infection risk negatively affect online rumour sharing, while social influence positively affects online rumour sharing. Fear weakens the negative effects of community safety on online rumour sharing but strengthens the positive effect of social influence on online rumour sharing. This study provides a comprehensive analysis by applying the stimulus–response framework to explore the underlying drivers of online rumour sharing with regard to COVID-19 and the moderating effects of fear in the Chinese context.
LinTCHuangSLLiaoWX. Examining the antecedents of everyday rumor retransmission. Technol People2022; 35: 1326–1345.
2.
PanSLZhangS. From fighting COVID-19 pandemic to tackling sustainable development goals: an opportunity for responsible information systems research. Int J Inf Manag2020; 55: 102196.
3.
LeongCMLPanSLRacthamPet al. ICT-enabled community empowerment in crisis response: social media in Thailand flooding 2011. J Assoc Inf Syst2015; 16: 174–212.
4.
MirbabaieMStieglitzSBrünkerF. Dynamics of convergence behaviour in social media crisis communication – a complexity perspective. Inf Technol People2022; 35: 232–258.
5.
IslamANLaatoSTalukderSet al. Misinformation sharing and social media fatigue during COVID-19: an affordance and cognitive load perspective. Technol Forecast Soc Change2020; 159: 120201.
6.
NaeemMOzuemW. Understanding misinformation and rumors that generated panic buying as a social practice during COVID-19 pandemic: evidence from Twitter, YouTube and focus group interviews. Inf Technol People. Epub ahead of print 27October2021. DOI: 10.1108/ITP-1101-2021-0061.
7.
WangXLiYLiJet al. A rumor reversal model of online health information during the Covid-19 epidemic. Inf Process Manag2021; 58: 102731.
8.
LuoYMaJYeoCK. Identification of rumour stances by considering network topology and social media comments. J Inf Sci2020; 48: 118–130.
9.
MaJLuoY. The classification of rumour standpoints in online social network based on combinatorial classifiers. J Inf Sci2019; 46: 191–204.
10.
ZhuH. Rumour detection based on deep hybrid structural and sequential representation networks. J Inf Sci. Epub ahead of print 13December2021. DOI: 10.1177/01655515211056579.
11.
AndersonRMHeesterbeekHKlinkenbergDet al. How will country-based mitigation measures influence the course of the COVID-19 epidemic?Lancet2020; 395: 931–934.
12.
SuperioDLAndersonKLOducadoRMFet al. The information-seeking behavior and levels of knowledge, precaution, and fear of college students in Iloilo, Philippines amidst the COVID-19 pandemic. Int J Disast Risk Reduct2021; 62: 102414.
13.
LiXZhouYWongYDet al. What influences panic buying behaviour? A model based on dual-system theory and stimulus-organism-response framework. Int J Disast Risk Reduct2021; 64: 102484.
14.
ChiangYCChuMLinSet al. Capturing the trajectory of psychological status and analyzing online public reactions during the coronavirus disease 2019 pandemic through Weibo posts in China. Front Psychol2021; 12: 744691.
15.
PalAChuaAYGohDH-L. How do users respond to online rumor rebuttals?Comput Human Behav2020; 106: 106243.
16.
XuFLuoXHsuC. Anger or fear? Effects of discrete emotions on employee’s computer-related deviant behavior. Inf Manag2020; 57: 103180.
17.
HassanSBSolimanM. COVID-19 and repeat visitation: assessing the role of destination social responsibility, destination reputation, holidaymakers’ trust and fear arousal. J Destin Market Manag2021; 19: 100495.
18.
ZhengDLuoQRitchieBW. Afraid to travel after COVID-19? Self-protection, coping and resilience against pandemic ‘travel fear’. Tour Manag2021; 83: 104261.
19.
BordiaPDifonzoN. Problem solving in social interactions on the Internet: rumor as social cognition. Soc Psychol Quart2016; 67: 33–49.
20.
ChuaAYKBanerjeeS. Intentions to trust and share online health rumors: an experiment with medical professionals. Comput Human Behav2018; 87: 1–9.
21.
ErogluSAMachleitKADavisLM. Empirical testing of a model of online store atmospherics and shopper responses. Psychol Market2003; 20: 139–150.
22.
TsengSYWangCN. Perceived risk influence on dual-route information adoption processes on travel websites. J Bus Res2016; 69: 2289–2296.
SukiNMSukiNM. Examination of peer influence as a moderator and predictor in explaining green purchase behaviour in a developing country. J Clean Product2019; 228: 833–844.
25.
BristolTMangleburgTF. Not telling the whole story: teen deception in purchasing. J Acad Market Sci2005; 33: 79–95.
26.
QuintalVALeeJASoutarGN. Tourists’ information search: the differential impact of risk and uncertainty avoidance. Int J Tour Res2010; 12: 321–333.
27.
ReisingerYMavondoF. Travel anxiety and intentions to travel internationally: implications of travel risk perception. J Travel Res2016; 43: 212–225.
28.
DiFonzoNBordiaP. Rumor, gossip and urban legends. Diogenes2007; 54: 19–35.
29.
CenatJMBlais-RochetteCKokou-KpolouCKet al. Prevalence of symptoms of depression, anxiety, insomnia, posttraumatic stress disorder, and psychological distress among populations affected by the COVID-19 pandemic: a systematic review and meta-analysis. Psychiatr Res2021; 295: 113599.
30.
CislerJMOlatunjiBOLohrJM. Disgust, fear, and the anxiety disorders: a critical review. Clin Psychol Rev2009; 29: 34–46.
31.
VrhovecSMiheličA. Redefining threat appraisals of organizational insiders and exploring the moderating role of fear in cyberattack protection motivation. Comput Secur2021; 106: 102309.
32.
RatherRA. Demystifying the effects of perceived risk and fear on customer engagement, co-creation and revisit intention during COVID-19: a protection motivation theory approach. J Destin Market Manag2021; 20: 100564.
33.
BaruaZBaruaSAktarSet al. Effects of misinformation on COVID-19 individual responses and recommendations for resilience of disastrous consequences of misinformation. Progr Disast Sci2020; 8: 100119.
34.
AliSKhalidAZahidE. Is COVID-19 immune to misinformation? A brief overview. Asian Bioethics Rev2021; 13: 255–277.
35.
SongSYaoXWenN. What motivates Chinese consumers to avoid information about the COVID-19 pandemic? The perspective of the stimulus-organism-response model. Inf Process Manag2021; 58: 102407.
36.
PianWChiJMaF. The causes, impacts and countermeasures of COVID-19 ‘infodemic’: a systematic review using narrative synthesis. Inf Process Manag2021; 58: 102713.
37.
NguyenTNLiCNiederéeC. On early-stage debunking rumors on Twitter: leveraging the wisdom of weak learners. In: GCiampagliaAMashhadiTYasseri (eds) Social informatics – lecture notes in computer science. Cham: Springer, 2017, pp. 141–158.
38.
VarshneyDVishwakarmaDK. A review on rumour prediction and veracity assessment in online social network. Expert Syst Appl2021; 168: 114208.
39.
ZhangXDongXXuXet al. What influences physicians’ online knowledge sharing? A stimulus–response perspective. Front Psychol2022; 12: 808432.
40.
JacobyJ. Stimulus-organism-response reconsidered: an evolutionary step in modeling (consumer) behavior. J Consum Psychol2002; 12: 51–57.
41.
PengCKimYG. Application of the stimuli-organism-response (S-O-R) framework to online shopping behavior. J Internet Commer2014; 13: 159–176.
42.
SpenceKW. Cognitive versus stimulus-response theories of learning. Psychol Rev1950; 57: 159–172.
43.
DuJvan KoningsbruggenGMKerkhofP. Spontaneous approach reactions toward social media cues. Comput Human Behav2020; 103: 101–108.
44.
YildirimMGulerA. Positivity explains how COVID-19 perceived risk increases death distress and reduces happiness. Personal Indiv Differ2021; 168: 110347.
45.
OhOAgrawalMRaoHR. Community intelligence and social media services: a rumor theoretic analysis of tweets during social crises. MIS Quart2013; 37: 407–426.
46.
KimSCHawkinsKH. The psychology of social media communication in influencing prevention intentions during the 2019 U.S. measles outbreak. Comput Human Behav2020; 111: 106428.
47.
YooWChoiDHParkK. The effects of SNS communication: how expressing and receiving information predict MERS-preventive behavioral intentions in South Korea. Comput Human Behav2016; 62: 34–43.
48.
YoonCKimH. Understanding computer security behavioral intention in the workplace. Inf Technol People2013; 26: 401–419.
49.
GoyalAVermaP. Do it yourself effect on restaurants – the pandemic effect: driven by the fear appeal theory. J Hosp Tour Manag2021; 48: 76–87.
50.
KimJBaeJHastakM. Emergency information diffusion on online social media during storm Cindy in U.S. Int J Inf Manag2018; 40: 153–165.
51.
GiesenCGSchmidtJRRothermundK. The law of recency: an episodic stimulus-response retrieval account of habit acquisition. Front Psychol2020; 10: 2927.
52.
ZhangXMZhouHEZhangWWet al. Assessment of coronavirus disease 2019 community containment strategies in Shenzhen, China. JAMA Netw Open2020; 3: e2012934.
53.
BaoYSunYMengSet al. 2019-nCoV epidemic: address mental health care to empower society. Lancet2020; 395: e37–e38.
54.
WangCHorbyPWHaydenFGet al. A novel coronavirus outbreak of global health concern. Lancet2020; 395: 470–473.
55.
RosnowRLFineGA. Rumor and gossip: the social psychology of hearsay. New York: Elsevier, 1976.
56.
WangQYangXXiW. Effects of group arguments on rumor belief and transmission in online communities: an information cascade and group polarization perspective. Inf Manag2018; 55: 441–449.
57.
TsarenkoYFerraroCSandsSet al. Environmentally conscious consumption: the role of retailers and peers as external influences. J Retail Cons Serv2013; 20: 302–310.
KatzEShibutaniT. Improvised news: a sociological study of rumor. Am Sociol Rev1969; 34: 781–782.
60.
WenHXWangCNiePY. Acceleration of rural households’ conversion to cleaner cooking fuels: the importance and mechanisms of peer effects. Energ Policy2021; 154: 112301.
61.
JinXLZhouZYuX. Predicting users’ willingness to diffuse healthcare knowledge in social media. Inf Technol People2019; 32: 1044–1064.
62.
OhOGuptaPAgrawalMet al. ICT mediated rumor beliefs and resulting user actions during a community crisis. Gov Inf Quart2018; 35: 243–258.
63.
Hazel KwonKRaghav RaoH. Cyber-rumor sharing under a homeland security threat in the context of government Internet surveillance: the case of South-North Korea conflict. Gov Inf Quart2017; 34: 307–316.
64.
GarrettRK. Troubling consequences of online political rumoring. Human Commun Res2011; 37: 255–274.
65.
JooDXuWLeeJet al. Residents’ perceived risk, emotional solidarity, and support for tourism amidst the COVID-19 pandemic. J Destin Market Manag2021; 19: 100553.
66.
MaserBWeiermairK. Travel decision-making: from the vantage point of perceived risk and information preferences. J Travel Tour Market1998; 7: 107–121.
67.
WilliamsAMBalážV. Tourism, risk tolerance and competences: travel organization and tourism hazards. Tour Manag2013; 35: 209–221.
68.
WenHLeeYM. Effects of message framing on food allergy communication: a cross-sectional study of restaurant customers with food allergies. Int J Hosp Manag2020; 89: 102401.
69.
BossSGallettaDLowryPBet al. What do systems users have to fear? Using fear appeals to engender threats and fear that motivate protective security behaviors. MIS Quart2015; 39: 837–864.
RydellRJMackieDMMaitnerATet al. Arousal, processing, and risk taking: consequences of intergroup anger. Pers Soc Psychol Bullet2008; 34: 1141–1152.
72.
ChenLYangX. Using EPPM to evaluate the effectiveness of fear appeal messages across different media outlets to increase the intention of breast self-examination among Chinese women. Health Commun2019; 34: 1369–1376.
73.
WitteKBerkowitzJMCameronKAet al. Preventing the spread of genital warts: using fear appeals to promote self-protective behaviors. Health Educ Behav1998; 25: 571–585.
74.
LiASunYGuoXet al. Understanding how and when user inertia matters in fitness app exploration: a moderated mediation model. Inf Process Manag2021; 58: 102458.
75.
SunYLiuDChenSet al. Understanding users’ switching behavior of mobile instant messaging applications: an empirical study from the perspective of push-pull-mooring framework. Comput Human Behav2017; 75: 727–738.
76.
CooperBEvaNFazlelahiFZet al. Addressing common method variance and endogeneity in vocational behavior research: a review of the literature and suggestions for future research. J Vocat Behav2020; 121: 103472.
77.
MullinsRAgnihotriR. Digital selling: organizational and managerial influences for frontline readiness and effectiveness. J Acad Market Sci2022; 50: 800–821.
78.
VenkateshVMorrisMGDavisGBet al. User acceptance of information technology: toward a unified view. MIS Quart2003; 27: 425–478.
79.
ColindresCBryceECoral-RoseroPet al. Effect of effort-reward imbalance and burnout on infection control among Ecuadorian nurses. Int Nurs Rev2018; 65: 190–199.
80.
EmersonEKrnjackiLLlewellynGet al. Perceptions of safety and exposure to violence in public places among working age adults with disabilities or long-term health conditions in the UK: cross sectional study. Publ Health2016; 135: 91–96.
81.
FornellCLarckerDF. Evaluating structural equation models with unobservable variables and measurement error. J Market Res1981; 18: 39–50.
82.
GuoFRestubogSLDCuiLet al. What determines the entrepreneurial success of academics? Navigating multiple social identities in the hybrid career of academic entrepreneurs. J Vocat Behav2019; 112: 241–254.
83.
YangYZhuXSongRet al. Not just for the money? An examination of the motives behind physicians’ sharing of paid health information. J Inf Sci. Epub ahead of print 14February2021. DOI: 10.1177/0165551521991029.
84.
CohenJCohenPWestSGet al. Applied multiple regression/correlation analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum, 2013.
85.
PodsakoffPMOrganDW. Self-reports in organizational research: problems and prospects. J Manag1986; 12: 531–544.
86.
PodsakoffPMMacKenzieSBLeeJ-Yet al. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol2003; 88: 879–903.
87.
LiangHSarafNHuQet al. Assimilation of enterprise systems: the effect of institutional pressures and the mediating role of top management. MIS Quart2007; 31: 59–87.
88.
AhorsuDKLinCYImaniVet al. The fear of COVID-19 Scale: development and initial validation. Int J Ment Health Addict2020; 20: 1537–1545.
89.
XiaoC. A novel approach of consultation on 2019 novel coronavirus (COVID-19)-related psychological and mental problems: structured letter therapy. Psychiatr Invest2020; 17: 175–176.
90.
GargRK. Structural equation modeling of E-supplier selection criteria in mechanical manufacturing industries. J Clean Product2021; 311: 127597.
91.
ÖzgürH. Relationships between teachers’ technostress, technological pedagogical content knowledge (TPACK), school support and demographic variables: a structural equation modeling. Comput Human Behav2020; 112: 106468.
AhmadMIramKJabeenG. Perception-based influence factors of intention to adopt COVID-19 epidemic prevention in China. Environ Res2020; 190: 109995.
94.
YildirimMGecerEAkgulO. The impacts of vulnerability, perceived risk, and fear on preventive behaviours against COVID-19. Psychol Health Med2021; 26: 35–43.
95.
Rodriguez-LlanesJMVosFGuha-SapirD. Measuring psychological resilience to disasters: are evidence-based indicators an achievable goal?Environ Health2013; 12: 1–10.
96.
LuuTPFollmannR. The relationship between sentiment score and COVID-19 cases in the United States. J Inf Sci. Epub ahead of print 8January2022. DOI: 10.1177/01655515211068167.
