The internet and emerging technologies have increased the utilisation of digital platforms. This study aims to draw on performance expectancy, social influence, and satisfaction to investigate a proposed model which is geared toward social media healthcare information (SOHI) adoption. The proposed model uses a structured online questionnaire, and 300 responses were evaluated using partial least squares and structural equation modelling [PLS-SEM]. From the findings, performance expectancy of social media (PESM) and satisfaction with social media (SATSM) were revealed to be significant predictors of behavioural intention towards social media (BISM). Satisfaction with social media (SATSM) had the greatest impact on BISM, accounting for 64.7 per cent of the variance in users' intentions to utilise SOHI. Similarly, PESM and social influence on social media (SISM) had the most predictive influence on SATSM, accounting for 50.5 per cent of the variance in users' social media satisfaction, which led to SOHI adoption. Unlike others, the social influence on social media (SISM) did not have a significant effect on BISM. BISM and satisfaction with social media (SATSM) were significant predictors of SOHI adoption, accounting for 54.5 per cent of the variance in SOHI adoption. The recommendations in this study would help healthcare professionals change their approach to digital healthcare engagement.
AcheampongPZhiwenLHiranKK, et al. (2018) Examining the Intervening Role of Age and Gender on mobile payment Acceptance in Ghana: UTAUT Model. 6(1): 141–151.
2.
AgnihotriRDingusRHuMY, et al. (2016) Social media: Influencing customer satisfaction in B2B sales. Industrial Marketing Management53: 172–180.
3.
AleemNKhanamMHaqueIU (2021) Customer satisfaction as a mediator on the impact of service indications in international business in the light of social Media. Pakistan Journal of International Affairs4(2): 318–350.
4.
AlkazemiMFBayramzadehSAlkhubaiziNB, et al. (2019), The physical environment and patient satisfaction ratings on social media: an exploratory study. Emeraldinsight (November) 38(1/2): 86–97. DOI: https://doi.org/10.1108/F-11-2018-0138
5.
AlmuammarSANoorsaeedASAlafifRA, et al. (2021) The use of internet and social Media for health information and its consequences among the population in Saudi Arabia. Cureus13(9): 1–12. DOI: 10.7759/cureus.18338.
6.
AluriASlevitchLLarzelereR (2016) The influence of embedded social Media channels on travelers’; gratifications, satisfaction, and purchase intentions. Cornell Hospitality Quarterly57(3): 250–267.
7.
ArghashiVBozbayZKaramiA (2021) An integrated model of social Media brand love: Mediators of brand attitude and consumer satisfaction. Journal of Relationship Marketing20(4): 319–348.
8.
AvundukY (2021) The relationship between leisure satisfaction and social Media addictions of individuals at university. Journal of Educational Issues7(1): 07.
9.
BanjarnahorHHatabaratWSibueauAM, et al. (2018) Job satisfaction as a mediator between directive and participatory leadership styles toward organizational commitment humuntal. International Journal of Instruction11(4): 869–888.
10.
BarnesSSKaulVKudchadkarSR (2018) Social Media engagement and the critical care medicine community. Journal of Intensive Care Medicine 34(3): 175–182. DOI: 10.1177/0885066618769599.
11.
BaruchYHoltomBC (2008) Survey response rate levels and trends in organizational research. Human Relations61(8): 1139–1160.
12.
Ben ArfiWBen NasrIKhvatovaT, et al. (2021) Understanding acceptance of EHealthcare by IoT natives and IoT immigrants: An integrated model of UTAUT, perceived risk, and financial costTechnological Forecasting and Social Change163: 120437.
13.
BouznifMM (2018) Business students; continuance intention toward blackboard usage: An empirical investigation of UTAUT model. International Journal of Business and Management13(1): 120–130.
14.
Burns N andGrove SK (2001) The practice of nursing research, conduct, critique, and utilization, 4th Ed.Philadelphia: W.B. Saunders Company.
15.
CastellsM (2014) The impact of the internet on society: A global perspective. Open Mind: 1–13.
16.
ChaoC (2019) Factors determining the behavioral intention to use Mobile learning : An application and extension of the UTAUT model. Frontiers in Psychology10(1652): 1–14.
17.
ChinWW (1998). The partial least squares approach to structural equation modeling. In: MarcoulidesGA (Ed.), Modern Methods for Business Research. Mahwah, NJ: Lawrence Erlbaum Associates, pp. 295–358.
18.
CreswellJW (2014) Education Research: Planning, Conducing and Evaluating Quantitative and Qualitative Research. fourth Edi.
19.
DaiBLarnyoETettehEA, et al. (2019) Factors affecting Caregivers’ acceptance of the use of wearable devices by patients with dementia: An extension of the unified theory of acceptance and use of technology model. American Journal of Alzheimer's Disease and Other Dementias 35(1): 1–11. DOI: 10.1177/1533317519883493.
20.
DavisFD (1989) Perceived usefulnessm perceived ease of use, and user acceptance of information technology. MIS Quarterly13(3): 319–340.
21.
de OñaJ (2021) Understanding the mediator role of satisfaction in public transport: A cross-country analysis. Transport Policy100: 129–149.
22.
De RosisSBarsantiS (2016) Patient satisfaction, e-health and the evolution of the patient–general practitioner relationship: Evidence from an Italian survey. Health Policy120(11): 1279–1292.
23.
De VosASStrydomH (1998) Research at Grass Roots: A Primer for the Caring Professions, 1st edPretoria: J.L. van Schaik Academic.
24.
DincMSKuzeyCStetaN (2018) Nurses’ job satisfaction as a mediator of the relationship between organizational commitment components and job performance. Journal of Workplace Behavioral Health33(2): 75–95.
25.
DouglassMMoyB (2019) Evaluation of the impact of a social media – focused intervention on reducing mental health stigma among pharmacy students. Ment Health Clin 9(3): 110–115. DOI: 10.9740/mhc.2019.05.110.
26.
EdingtonDWSchultzABPittsJS, et al. (2015) The future of health promotion in the 21st century: A focus on the working population. American Journal of Lifestlye10(4): 242–252.
27.
FaulFErdfelderEBuchnerA, et al. (2009) Statistical power analyses using G*power 3.1: Tests for correlation and regression analyses. Behavior Research Methods41(4): 1149–1160.
28.
FornellCLarckerDF (1981) Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research18(1): 39–50.
GabarronEBradwayMFernandez-LuqueL, et al. (2018) Social media for health promotion in diabetes: Study protocol for a participatory public health intervention design. BMC Health Services Research18(1): –5.
31.
GaravandASamadbeikMNadriH, et al. (2019) Effective factors in adoption of mobile health applications between medical sciences students using the UTAUT model. Methods of Information in Medicine58(4–5): 131–139.
32.
GeisserS (1975) The predictive sample reuse method with applications. Journal of the American Statistical Association, 70(350): 320–328. DOI: https://doi.org/10.1080/01621459.1975.10479865
33.
GruzdAStavesKWilkA (2012) Connected scholars: Examining the role of social media in research practices of faculty using the UTAUT model. Computers in Human Behavior28(6): 2340–2350.
34.
HairJFSarstedtMPieperTM, et al. (2012) The use of partial least squares structural equation modeling in strategic management research : A review of past practices and recommendations for future applications. Long Range Planning45(5–6): 320–340.
35.
HairJFRingleCMStarstedtM (2013) Editorial partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. International Journal of Strategic Management46: 1–12.
36.
HairJFHultGTMRingleCM, et al. (2014) A Primer on Partial Lease Squares Structural Equation Modeling (PLS-SEM). DOI: 10.1007/978-3-319-05542-8.
37.
HausmannJSTouloumtzisCWhiteMT, et al. (2017) Adolescent and young adult use of social media for health and its implications. Journal of Adolescent Health60(6): 714–719.
38.
HazzamJLahrechA (2018) Health care professionals’ social Media behavior and the underlying factors of social media adoption and use: Quantitative study. Journal of Medical Internet Research20(11): e12035.
39.
HenselerJRingleCMSarstedtM (2015) A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci43: 115–135.
40.
HiltzSRJohnsonK (1990) User satisfaction with computer-mediated communication systems. Management Science36(6): 739–764.
41.
HolmbergCBergCLissnerL, et al. (2018) Health literacy in a complex digital media landscape: Pediatric obesity patients ‘ experiences with online weight, food, and health information. Health Informatics Journal 25(4): 1343–1357. DOI: 10.1177/1460458218759699.
42.
HoqueRSorwarG (2017) Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics101: 75–84.
43.
IfinedoP (2016) Applying uses and gratifications theory and social influence processes to understand students’ pervasive adoption of social networking sites: Perspectives from the americas. International Journal of Information Management36(2): 192–206.
44.
ImlawiJGreggD (2020) Understanding the satisfaction and continuance intention of knowledge contribution by health professionals in online health communities. Informatics for Health and Social Care45(2): 151–167.
45.
IskandarAAmrianiTN (2021) Determinant analysis of the net benefits of the use of sakti : User satisfaction mediation. International Journal of Economics, Business and Accounting Research2021(2): 109–128.
46.
JayaseelanRKoothoorPPichandyC (2020) Technology acceptance by medical doctors in India: An analysis with UTAUT model. International Journal of Scientific and Technology Research9(1): 3854–3857.
47.
JinXLZhouZYuX (2019) Predicting Users’ willingness to diffuse healthcare knowledge in social media: A communicative ecology perspective?Information Technology and People32(4): 1044–1064.
48.
KalinicZMarinkovićVDjordjevicA, et al. (2019) What drives customer satisfaction and word of mouth in mobile commerce servicesA UTAUT2-based analytical approach. Journal of Enterprise Information Management33(1): 71–94.
49.
KhanMILohJ(MI) (2021) Benefits, challenges, and social impact of health care Providers’ adoption of social Media. Social Science Computer Review 39: 1–17 089443932110257. DOI: 10.1177/08944393211025758.
50.
KhatoonSZhengliangXHussainH (2020) The mediating effect of customer satisfaction on the relationship between electronic banking service quality and customer purchase intention: Evidence from the Qatar banking sector. SAGE Open10(2): 1–12. DOI: 10.1177/2158244020935887.
51.
KhechineHRaymondBAugierM (2020) The adoption of a social learning system: Intrinsic value in the UTAUT model. British Journal of Educational Technology51(6): 2306–2325.
52.
KimSSMalhotraNK (2005) A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management Science51(5): 741–755.
53.
KissiJDaiBDogbeCSK, et al. (2019) Predictive factors of physicians’ satisfaction with telemedicine services acceptance. Health Informatics Journal26(3): 1866–1880.
54.
KlineRB (2016) Principles and practice of structural equation modeling. Methodology in the Social Sciences, 4th Ed. New York, NY, US: Guilford Press, pp. 1–534.
55.
KockNLynnGS (2012) Journal of the association for information lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations lateral collinearity and misleading results in variance-. Journal of the Association for Information Systems13(7): 546–580.
56.
KockFBerbekovaAAssafAG (2021) Understanding and managing the threat of common method bias: Detection, prevention and control. Tourism Management86(December 2020): 104330.
57.
KrishenASBerezanOAgarwalS, et al. (2019) Social media networking satisfaction in the US and Vietnam: Content versus connection. Journal of Business Research101(October 2018): 93–103.
58.
LiXLiuQ (2020) Social Media use, eHealth literacy, disease knowledge, and preventive behaviors in the COVID-19 pandemic : Cross-sectional study on Chinese netizens. Journal of Medical Internet Research22(10): e19684.
59.
LimXJAwECTeohKG (2018) Factors influencing repurchase intention in online shopping context: The mediating role of satisfaction. Journal of Applied Structural Equation Modeling2(January): 29–43.
60.
LouCYuanS (2019) Influencer marketing: How message value and credibility affect consumer trust of branded content on social media influencer marketing : How message value and credibility affect consumer. Journal of Interactive Advertising19(1): 58–73.
61.
LulinZOwusu-MarfoJAsante AntwiH, et al. (2020a) Nurses’ readiness in the adoption of hospital electronic information management systems in Ghana: The application of the structural equation modeling and the UTAUT model. SAGE Open10(2): 1–11.
62.
LulinZOwusu-MarfoJAntwiHA, et al. (2020b) The contributing factors to nurses’ behavioral intention to use hospital information technologies in Ghana. SAGE Open Nursing6(301): 1–12. DOI: 10.1177/2377960820922024.
63.
MailletÉMathieuLSicotteC (2015) Modeling factors explaining the acceptance, actual use and satisfaction of nurses using an electronic patient record in acute care settings: An extension of the UTAUT. International Journal of Medical Informatics84(1): 36–47.
64.
MarinkovićVĐorđevićAKalinićZ (2019) The moderating effects of gender on customer satisfaction and continuance intention in mobile commerce : A UTAUT-based perspective. Technology Analysis & Strategic Management0(0): 1–13.
65.
MeilatinovaN (2021) International Journal of information management social commerce: Factors affecting customer repurchase and word-of-mouth intentions. International Journal of Information Management57(December 2020): 1–12.
66.
NapitupuluDYacubRPutraAHPK (2021) Factor influencing of telehealth acceptance during COVID-19 outbreak: Extending UTAUT model. International Journal of Intelligent Engineering and Systems14(3): 267–281.
67.
Ndumbe-EyohSMazzuccoA (2016) Social media, knowledge translation, and action on the social determinants of health and health equity: A survey of public health practices. Journal of Public Health Policy37(2): 249–259.
68.
NeunerJFeddersMCaravellaM, et al. (2015) Meaningful use and the patient portal: Patient enrollment, use, and satisfaction with patient portals at a later-adopting center. American Journal of Medical Quality30(2): 105–113.
69.
NunesALimpoTCastroSL (2019) Acceptance of Mobile health applications: Examining key determinants and moderators. Frontiers in Psychology10(December): –9.
70.
OforiPPAntwiEAOduro-AsanteA (2021a) The behavioral intention in accessing digital healthcare information on social media. International Journal of Scientific Research in Science and Technology8(6): 510–521.
71.
OforiPPAntwiEAOwusu-ansahKA (2021b) The mediating effects of satisfaction and attitude on consumers ‘ intent toward adoption of social media healthcare information. Journal of Health and Social Sciences6(3): 391–402.
72.
OtooAAAZhiwenLOtooACO, et al. (2019) Business value and E-commerce adoption. International Journal of Scientific Research in Computer Science, Engineering and Information Technology5(5): 262–271.
73.
OzelS (2018) Enhancing end-user satisfaction through technology compatibility: an assessment on health information system. Health Policy and Technology7(3): 265–274. DOI: 10.1016/j.hlpt.2018.06.001.
74.
PodsakoffPMMackenzieSBLeeJ, et al. (2003) Common method biases in behavioral research : A critical review of the literature and recommended remedies. Journal of Applied Psychology88(5): 879–903.
75.
PrasannaRHugginsTJ (2016) Computers in human behavior factors affecting the acceptance of information systems supporting emergency operations centres. Computers in Human Behavior57: 168–181.
76.
PriyankaAFibriaI (2018) The influence of UTAUT factors on E-retention with E-satisfaction as mediating variable in E-learning. Hasanuddin Economics and Business Review2(1): 19–33.
77.
PurwantoELoisaJ (2020) The Intention and Use Behaviour of the Mobile Banking System in indonesia: UTAUT Model. Technology Reports of Kansai University (July).
78.
PuspitasariIFirdauzyA (2019) Characterizing consumer behavior in leveraging social media for e-patient and health-related activities. International Journal of Environmental Research and Public Health16(18): –5.
RamanathanUSubramanianNParrottG (2017) Role of social media in retail network operations and marketing to enhance customer satisfaction. International Journal of Operations and Production Management37(1): 105–123.
81.
RamnundSBaloyiOBNkwanyanaNM, et al. (2021) Midwives’ acceptance of mHealth applications in the dissemination of maternal health education in primary healthcare clinics. Information Development DOI: 10.1177/02666669211049140.
82.
RawashdehAMElayanMShamoutMD, et al. (2020) Job satisfaction as a mediator between transformational leadership and employee performance: Evidence from a developing country. Management Science Letters10(16): 3855–3864.
83.
ReinartzWJHaenleinMHenselerJ (2009) An Empirical Comparison of the Efficacy of Covariance-based and An Empirical Comparison of the Efficacy of Covariance-based by.
84.
RichterJPKazleyAS (2020) Social media : How hospital Facebook activity may influence patient satisfaction. Health Marketing Quarterly37(1): –9.
85.
RoscoeJT (1975) Fundamental Research Statistics for the Behavioral Sciences. New York: Holt, Rinehart and Winston.
86.
RoySKEshqhiASarkarA (2013) Antecedents and consequences of brand love. Journal of Brand Management20(4): 325–332.
87.
RuedaLBenitezJBraojosJ (2017) From traditional education technologies to student satisfaction in management education: A theory of the role of social media applications. Information & Management 54(8): 1059–1071. DOI: 10.1016/j.im.2017.06.002.
88.
ShahFTSyedZImamA, et al. (2020) The impact of airline service quality on passengers’ behavioral intentions using passenger satisfaction as a mediator. Journal of Air Transport Management85(March): 101815.
89.
ShangLZhouJZuoM (2020) Understanding older adults’ intention to share health information on social media: The role of health belief and information processing. Internet Research 31(1): 100–122. DOI: 10.1108/INTR-12-2019-0512.
90.
ShehataA (2020) Health information behaviour during COVID-19 outbreak among Egyptian library and information science undergraduate students. Information Development 37(3): 417–430. DOI: 10.1177/0266666920976181.
91.
ShiferawKBMehariEA (2019) Modeling predictors of acceptance and use of electronic medical record system in a resource limited setting: Using modified UTAUT model. Informatics in Medicine Unlocked17(April): 100182.
92.
ShiferawKBMengisteSAGullslettMK, et al. (2021) Healthcare providers’ acceptance of telemedicine and preference of modalities during COVID-19 pandemics in a low-resource setting: An extended UTAUT model. PLoS ONE16(4 April 2021): 1–15.
93.
SilverRASubramaniamCStylianouA (2020) The impact of portal satisfaction on portal use and health-seeking behavior: Structural equation analysis. Journal of Medical Internet Research22(3): 1–13.
94.
SimRWenSLeeH (2021) Patient preference and satisfaction with the use of telemedicine for glycemic control in patients with type 2 diabetes : A review. Patient Preference and Adherence15: 283–298.
95.
SmailhodzicEHooijsmaWBoonstraA, et al. (2016) Social media use in healthcare : A systematic review of effects on patients and on their relationship with healthcare professionals. BMC Health Services Research 16: 1–14. DOI: 10.1186/s12913-016-1691-0.
96.
StoneM (1974) Cross-Validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological36(2): 111–133.
97.
SudibjoNSuwarliMBN (2020) Job embeddedness and job satisfaction as a mediator between work-life balance and intention to stay. International Journal of Innovation, Creativity and Change11(8): 311–331.
98.
TavaresJGoulãoAOliveiraT (2018) Electronic health record portals adoption: Empirical model based on UTAUT2. Informatics for Health and Social Care43(2): 109–125.
99.
TeoTSHSrivastavaSCJiangLI (2009) Trust and electronic government success : An empirical study. Journal of Management Information Systems25(3): 99–131.
100.
TerrasseMGorinMSistiD (2019) Social media, E-health, and medical ethics. Hastings Center Report49(1): 24–33.
101.
TrivediSKYadavM (2017) VINE Journal of Information and Knowledge Management Systems.
102.
VenkateshVMorrisMGDavisGB, et al. (2003) User acceptance of information technology: Toward a unified view. MIS Quarterly27(3): 425–478.
103.
VenkateshVThongJYLChanFKY, et al. (2011) Extending the two-stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Information Systems Journal21: 527–555.
104.
VenkateshVThongJYLXuX (2016) Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems17(5): 328–376.
105.
VichitkraivinPNaennaT (2020). Factors of healthcare robot adoption by medical staff in Thai government hospitals. Health and Technology11: 139–151. DOI: https://doi.org/10.1007/s12553-020-00489-4
106.
WalkerTPalermoCKlassenK (2019) Considering the impact of social media on contemporary improvement of Australian aboriginal health: Scoping review. JMIR Public Health and Surveillance5(1): e11573.
107.
WangNSunY (2016) Social influence or personal preference? Examining the determinants of usage intention across social media with different sociability. Information Development32(5): 1442–1456.
108.
WebbDSoutarGNMazzarolT, et al. (2013) Self-determination theory and consumer behavioural change: Evidence fromahousehold energy-saving behaviour study. Journal of Environmental Psychology35: 59–66.
109.
WendlandJLunardiGLDolciDB (2019) Adoption of health information technology in the mobile emergency care service. RAUSP Management Journal54(3): 287–304.
110.
WijayantiRPHandayaniPWAzzahroF (2022) Intention to seek health information on social media in Indonesia. Procedia Computer Science197(2021): 118–125.
111.
ZerrweckCAranaSCallejaC, et al. (2019) Social media, advertising, and internet use among general and bariatric surgeons. Surgical Endoscopy 34: 1634–1640. DOI: 10.1007/s00464-019-06933-5.
112.
ZhangXWenDLiangJ, et al. (2017) How the public uses social media WeChat to obtain health information in China: A survey study. BMC Medical Informatics and Decision Making17(Suppl 2): 71-79. DOI: 10.1186/s12911-017-0470-0.
113.
ZhangHZhangRLuX, et al. (2020) Impact of personal trust tendency on patient compliance based on internet health information seeking. Telemedicine and e-Health26(3): 294–303.
114.
ZhaoYZhangJ (2017) Review article consumer health information seeking in social media : A literature review. Health Information and Libraries Journal 34(3): 268–283. DOI: 10.1111/hir.12192.
115.
ZhouL-HNtoumanisNThøgersen-NtoumaniC (2019) Effects of perceived autonomy support from social agents on motivation and engagement of Chinese primary school students: Psychological need satisfaction as mediator. Contemporary Educational Psychology58(299): 323–330.
116.
ZhouLLOwusu-marfoJAntwiHA, et al. (2019) Assessment of the social influence and facilitating conditions that support nurses’ adoption of hospital electronic information management systems (HEIMS) in Ghana using the unified theory of acceptance and use of technology (UTAUT) model. BMC Medical Informatics and Decision Making 19: 1–9.
