Companies operating in the service sector have provided a shared economy platform to optimize the use of resources. The concepts like acceptance and use of technology (UTAUT2) and network effect were used to analyse how the network effect moderates the acceptance of a peer-to-peer application. A cross-sectional survey was used as a research method through self-administered Internet questionnaires which were adapted and applied to guests and hosts, as both need to accept the technology. It was decided to use SmartPLS software. Users are more interested in the opinions of their reference group when deciding to use Airbnb. The belief that there is technical infrastructure that supports the use of the app influences the desire to use it, especially when users begin to realize the increased benefits generated by the growth of other users. Price imposes a significant effect on behavioural intention; however, when it is added the network effect as a moderator, the relationship loses significance. It indicates that the value of hosting does not influence platform acceptance when it already has a significant number of users. This study used one of the most influential and comprehensive theories in the adoption of technology as a basis for conceptualization and expanded it with an important construct originating from studies on economic theories. In addition, this study presents its uniqueness by introducing a theoretical construct into a moderating action and using structural equation modelling to understand the consumer behaviour of these technologies, having, above all, a potential for theoretical contributions relevant to the research area.
AlalwanA. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28–44.
2.
AlbinssonP. A., & PereraB. Y. (2012). Alternative marketplaces in the 21st century: Building community through sharing events. Journal of Consumer Behaviour, 11(4), 303–315. https://doi.org/10.1002/cb
3.
AndersonJ. C., & GerbingD. W. (1988). Strctural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411
4.
AndersonL., OstromA. L., CorusC., FiskR. P., GallanA. S., GiraldoM., MendeM., MulderM., RayburnS. W., RosenbaumM. S., ShirahadaK., & WilliamsJ. D. (2013). Transformative service research: An agenda for the future. Journal of Business Research, 66(8), 1203–1210. https://doi.org/10.1016/j.jbusres.2012.08.013
5.
Airbnb. (2019). Study finds that Airbnb hosts and guests have major positive effect on city economies. Airbnb, San Francisco. https://news.airbnb.com/br/
6.
AttuquayefioS., & AddoH. (2014). Review of studies with UTAUT as conceptual framework. European Scientific Journal, 10(8), 249–258.
7.
BaishyaK., & SamaliaH. V. (2019). Factors influencing smartphone adoption: A study in the Indian bottom of the pyramid context. Global Business Review, 2, 1387–1405.
8.
BaptistaG., & OliveiraT. (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50, 418–430. https://doi.org/10.1016/j.chb.2015.04.024
9.
BenklerY. (2004). Sharing nicely: On shareable goods and the emergence of sharing as a modality of economic production. The Yale Law Journal Company, Inc., 114(2), 273–358.
10.
BiswasB., SenguptaP., & ChatterjeeD. (2020). Examining the determinants of the count of customer reviews in peer-to-peer home-sharing platforms using clustering and count regression techniques. Decision Support Systems, 113324. https://doi.org/10.1016/j.dss.2020.113324
11.
BotsmanR., & RogersR. (2010). What’ s mine is yours: The rise of collaborative consumption. Harper Collins, Inc.
12.
BrymanA. (1989). Research methods and organization studies. In BulmerM. (Ed.), Contemporary social research (p. 239). Routledge Taylor & Francis Group.
13.
ChenY., WolframS., & AsifA. (2017). User acceptance in sharing economy—A study of transportation network companies in China. Proceedings of the Mediterranean Conference on Information Systems 2017 (pp. 1–17). https://aisel.aisnet.org/mcis2017/24
14.
ChoudaryS. P. (2015). Platform scale: How an emerging business model helps startups build large empires with minimum investment. Platform Thinking Labs Pte. Ltd.
15.
ChungN., NamK., & KooC. (2016). Examining information sharing in social networking communities: Applying theories of social capital and attachment. Telematics and Informatics, 33(1), 77–91. https://doi.org/10.1016/j.tele.2015.05.005
16.
CodagnoneC., & MartensB. (2016). Scoping the sharing economy: Origins, definitions, impact and regulatory issues (JRC100369). https://doi.org/10.2139/ssrn.2783662
17.
EconomidesN. (1996). Network externalities, complementarities, and invitations to enter. European Journal of Political Economy, 12, 211–233. http://s3.amazonaws.com/academia.edu.documents/30844584/Economides_Network_externalities_EJPE_1996.pdf?AWSAccessKeyId=AKIAIR6FSIMDFXPEERSA&Expires=1380619606&Signature=c%2Fa7K2mIBijVQ4fTy%2FP4wGrBKhE%3D&response-content-disposition=inline
18.
FinkA., & KosecoffJ. (1998). How to conduct survey: A step-by-step guide. Sage Publications.
19.
FornellC., & LarckerD. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50. https://doi.org/10.2307/3150979
20.
GangwarH., & DateH. (2016). Critical factors of cloud computing adoption in organizations: An empirical study. Global Business Review, 17(4), 886–904.
21.
GawerA., & EvansP. C. (2016). The rise of the platform enterprise: A global survey. The Center for Global Enterprise (Report). https://doi.org/10.4236/ajibm.2014.49051
22.
GeissingerA., LaurellC., & SandströmC. (2020). Digital disruption beyond Uber and Airbnb—Tracking the long tail of the sharing economy. Technological Forecasting and Social Change, 155, 119323.
23.
GoswamiS., & SarmaM. K. (2019). Modelling customer delight in hotel industry. Global Business Review, 20(2),:405–419.
24.
HairJ. F., HultG. T. M., RingleC. M., & SarstedtM. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications. https://doi.org/10.1016/j.lrp.2013.01.002
25.
HaruvyE., & PrasadA. (1998). Optimal product strategies in the presence of network externalities. Information Economics and Policy, 10(4), 489–499. https://doi.org/10.1016/S0167-6245(98)00014-6
26.
HongS. J., & TamK. Y. (2006). Understanding the adoption of multipurpose information appliances: The case of mobile data services. Information Systems Research, 17(2), 162–179. https://doi.org/10.1016/j.jhazmat.2005.10.004
27.
HossainM. (2020). Sharing economy: A comprehensive literature review. International Journal of Hospitality Management, 87, 102470.
28.
Indrawati HaryotoK. S. (2015, 11–13 August). The use of modified theory of acceptance and use of technology 2 to predict prospective users’ intention in adoption TV streaming. Proceedings of the 5th International Conference on Computing and Informatics. Istanbul, Turkey. http://icoci.cms.net.my/PROCEEDINGS/2015/PDF/PID125.pdf
29.
KatzM. L., & ShapiroC. (1985). Network externalities, competition and compatibility. The American Economic Review, 75(3), 424–440. https://www.jstor.org/stable/1814809?seq=1#page_scan_tab_contents
30.
KatzM. L., & ShapiroC. (1992). Product introduction with network externalities. The Journal of Industrial Economics, 40(1), 55–83. https://www.jstor.org/stable/2950627?seq=1#page_scan_tab_contents
31.
KimS. S., & MalhotraN. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management Science, 51(5), 741–755. https://doi.org/10.1287/mnsc.1040.0326
32.
Liébana-CabanillasF., Sánchez-FernándezJ., & Muñoz-LeivaF. (2014). Antecedents of the adoption of the new mobile payment systems: The moderating effect of age. Computers in Human Behavior, 35, 464–478. https://doi.org/10.1016/j.chb.2014.03.022
33.
LimayemM., HirtS. G., & CheungC. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705–737. https://doi.org/10.2307/25148817
34.
MatzlerK., VeiderV., & KathanW. (2015). Adapting to the sharing economy about the research. MIT Sloan Management Review, 56(2), 71–77. https://www.researchgate.net/publication/269683045_Adapting_to_the_Sharing_Economy
35.
MorosanC., & DeFrancoA. (2016). It’s about time: Revisiting UTAUT2 to examine consumers’ intentions to use NFC mobile payments in hotels. International Journal of Hospitality Management, 53, 17–29. https://doi.org/10.1016/j.ijhm.2015.11.003
36.
de MouraA. C., GoslingM. D. S., ChristinoJ. M. M., & MacedoS. B. (2017). Acceptance and use of technology for the choice of tourist destinations by elderly people: A study using UTAUT2. Brazilian Journal of Tourism Research, 11(2), 239–269.
37.
ParkerG. G., van AlstyneM. W., & ChoudaryS. P. (2016). Platform revolution: How networked markets are transforming the economy—And how to make them work for you. WW Norton & Company.
38.
ReillierL. C., & ReillierB. (2017). Platform strategy: How to unlock the power of communities and networks to grow your business. Taylor & Francis Group.
39.
ShapiroC., & VarianH. R. (1999). Information rules: A strategic guide to the network economy. Harvard Business School Press.
40.
ShethJ. N., SethiaN. K., & SrinivasS. (2011). Mindful consumption: A customer-centric approach to sustainability. Journal of the Academy of Marketing Science, 39(1), 21–39. https://doi.org/10.1007/s11747-010-0216-3
41.
SladeE. L., DwivediY. K., PiercyN. C., & WilliamsM. D. (2015). Modeling consumers’ adoption of remote mobile payments in the United Kingdom: Extending UTAUT with innovativeness, risk and trust. Psychology & Marketing, 32(8), 860–873. https://doi.org/10.1002/mar
42.
SuntornpithugN., & KhamalahJ. (2010). Machine and person interactivity: The driving forces behind influences on consumers’ willingness to purchase online. Journal of Electronic Commerce Research, 11(4), 299–325.
43.
VenkateshV., MorrisM. G., DavisG. B., & DavisF. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.1016/j.inoche.2016.03.015
44.
VenkateshV., ThongJ. Y. L., & XuX. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://misq.org/consumer-acceptance-and-use-of-information-technology-extending-the-unified-theory-of-acceptance-and-use-of-technology.html
45.
VieiraK. C., PaivaA. L., de RosaM., & SantosA. C. dos. (2017). Thirty years of multivariate data analysis in strategic management: A systematic review. XX SemeAd - Seminars in Administration (pp. 1–15). Sao Paulo. http://login.semead.com.br/20semead/arquivos/2048.pdf
46.
WangC.-C., LoS.-K., & FangW. (2008). Extending the technology acceptance model to mobile telecommunication innovation: The existence of network externalities. Journal of Consumer Behaviour, 7(2), 101–110. https://doi.org/10.1002/cb240
47.
WatanabeC., NaveedK., NeittaanmäkiP., & FoxB. (2017). Consolidated challenge to social demand for resilient platforms—Lessons from Uber’s global expansion. Technology in Society, 48, 33–53. https://doi.org/10.1016/j.techsoc.2016.10.006
