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Examining the Factors Contributing to Fintech Peer-to-peer Lending Adoption

Journal of Information Systems Engineering and Business Intelligence · Vol. 7 No. 2 (2021) · pp. 91–101

DOI
10.20473/jisebi.7.2.91-101
ISSN
2443-2555, 2598-6333
Published
2021-10-28
Publisher
Universitas Airlangga
Language
eng

Abstract

Background: Peer-to-peer (P2P) lending platform is one of key disruptive business models in financial technology. It bridges lenders and borrowers directly. Researchers have studied the leverage mechanism behind the P2P lending platform.Objective: This research proposes an enhanced technology acceptance model (TAM) to investigate how consumers embrace P2P lending platforms using quality of service and perceived risk as drivers of trust.Methods: This research uses structural equation modeling (SEM) to test the hypothesised connections between the latent variables.Results: The findings show that users' trust, perceived usefulness, and perceived ease of use in P2P lending platforms significantly influence attitudes towards adoption. Meanwhile, consumers' perceived risk in using P2P lending platforms is unaffected by the quality of service.Conclusion: The estimated model is consistent with the results shown in previous studies. The findings of the current research are useful for fine-tuning platform marketing plans and putting strategic goals into actions. For future research, we suggest including more variables to better understand the adoption intention of P2P lending platforms.Keywords: Adoption intention, Peer-to-peer lending, Structural equation modeling, Technology acceptance model

Collaboration network of the authors

40 authors · 97 co-authorship links · 7 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • State University of Jakarta
  • Universitas Halu Oleo
  • State University of Malang
  • Hasanuddin University
  • Universitas Airlangga
  • Universitas Bina Bangsa
  • Tanjungpura University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Dewi GathmyrAffiliation unknown3
Herlitah HerlitahAffiliation unknown3
Mahfuz Rizqi MubarakAffiliation unknown2
Nurul WahdahAffiliation unknown2
Tjipto SuwandiAffiliation unknown2
Miswan MiswanUniversitas Halu Oleo2
Budiman BudimanAffiliation unknown2
Ratna Tri Hari SafariningsihAffiliation unknown2
Joseph WilsonAffiliation unknown2
Ahmad SyukriansyahAffiliation unknown1
Dewi Retno SuminarAffiliation unknown1
Hanggara Budi UtomoState University of Malang1
Juliana JulianaAffiliation unknown1
Inomjon QudratovAffiliation unknown1
Uum SumiratAffiliation unknown1
Lazuardi Imani HakamAffiliation unknown1
Mawan SetiantoAffiliation unknown1
Hadi PrayitnoAffiliation unknown1
Lalu Muhammad SalehHasanuddin University1
Oedojo SoedirhamUniversitas Airlangga1
Syamilatul KharirohAffiliation unknown1
Endang AbdullahAffiliation unknown1
Aulia Mustika IlmianiAffiliation unknown1
Noor Amalina AudinaAffiliation unknown1
Aulia Mustika Ilmiani AuliaAffiliation unknown1
Adelina Dewi NuryamanAffiliation unknown1
Soukaina SamdouniAffiliation unknown1
Isnaini MahudaUniversitas Bina Bangsa1
Zuhri ZuhriAffiliation unknown1
Sinta Verawati DewiAffiliation unknown1
Jaka Wijaya KusumaAffiliation unknown1
Sri RafiqohAffiliation unknown1
Sri JumiyatiAffiliation unknown1
Puput PuputAffiliation unknown1
Rajindra RajindraAffiliation unknown1
Nur AfifahTanjungpura University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://e-journal.unair.ac.id/JISEBI/oai, first harvested 29 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Examining the Factors Contributing to Fintech Peer-to-peer Lending Adoption</dc:title>
	<dc:creator>Sunardi, Rudy</dc:creator>
	<dc:creator>Suhud, Usep</dc:creator>
	<dc:creator>Purwana, Dedi</dc:creator>
	<dc:creator>Hamidah, Hamidah</dc:creator>
	<dc:description xml:lang="en-US">Background: Peer-to-peer (P2P) lending platform is one of key disruptive business models in financial technology. It bridges lenders and borrowers directly. Researchers have studied the leverage mechanism behind the P2P lending platform.Objective: This research proposes an enhanced technology acceptance model (TAM) to investigate how consumers embrace P2P lending platforms using quality of service and perceived risk as drivers of trust.Methods: This research uses structural equation modeling (SEM) to test the hypothesised connections between the latent variables.Results: The findings show that users' trust, perceived usefulness, and perceived ease of use in P2P lending platforms significantly influence attitudes towards adoption. Meanwhile, consumers' perceived risk in using P2P lending platforms is unaffected by the quality of service.Conclusion: The estimated model is consistent with the results shown in previous studies.  The findings of the current research are useful for fine-tuning platform marketing plans and putting strategic goals into actions. For future research, we suggest including more variables to better understand the adoption intention of P2P lending platforms.Keywords: Adoption intention, Peer-to-peer lending, Structural equation modeling, Technology acceptance model</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2021-10-28</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/JISEBI/article/view/28292</dc:identifier>
	<dc:identifier>10.20473/jisebi.7.2.91-101</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Information Systems Engineering and Business Intelligence; Vol. 7 No. 2 (2021): October; 91-101</dc:source>
	<dc:source>2443-2555</dc:source>
	<dc:source>2598-6333</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JISEBI/article/view/28292/15932</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 The Authors. Published by Universitas Airlangga.</dc:rights>
</oai_dc:dc>