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Technopreneurship in Healthcare: Evaluating User Satisfaction and Trust in AI-Driven Safe Entry Stations

Aptisi Transactions on Technopreneurship (ATT) · Vol. 6 No. 3 (2024)

DOI
10.34306/att.v6i3.489
ISSN
2656-8888, 2655-8807
Published
2024-09-13
Publisher
Pandawan
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

The development of technology in the health sector has encouraged the adoption of technopreneurship, especially in the application of artificial intelligence (AI) to support the safety and efficiency of health services. One of the innovations that has emerged is the AI-driven Safe Entry Station, which is designed to improve the safety and comfort of patients and medical personnel. However, the success of implementing this technology is highly dependent on the level of user satisfaction and trust. This study aims to evaluate the level of user satisfaction and trust in Safe Entry Stations in the health care environment and also explore the variables that influence the acceptance of this technology among users. This research method uses a quantitative approach with a survey involving 673 respondents from various health institutions that have used Safe Entry Stations. Data were analyzed using Structural Equation Modeling (SEM) with SmartPLS 4.0 software to identify the relationship between User Satisfaction (US), trust (TR), behaviour intention (BI), usage behaviour (SB) and technopreneurial impac (TI). The results showed that US and TR significantly influences BI and UB. Additionally, BI strongly impacts TI, suggesting that stronger intentions lead to a greater perceived impact on technopreneurship. This study found that AI-driven Safe Entry Stations has great potential for widespread adoption in the healthcare sector. These findings provide important insights for further development of this technology as well as technopreneurship strategies in the healthcare sector.

Keywords

Collaboration network of the authors

40 authors · 157 co-authorship links · 8 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.

  • Universitas Raharja
  • Satya Wacana Christian University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Danny ManonggaSatya Wacana Christian University15
Ninda LutfianiUniversitas Raharja14
Irwan SembiringSatya Wacana Christian University12
Nuke Puji Lestari SantosoUniversitas Raharja11
Alfiah KhoirunisaUniversitas Raharja9
Dwi ApriliasariAffiliation unknown6
Agung RizkyAffiliation unknown5
Sutarto WijonoAffiliation unknown4
Ihsan Nuril HikamAffiliation unknown4
Adam FaturahmanAffiliation unknown4
Untung RahardjaUniversitas Raharja3
Muhtarom MuhtaromAffiliation unknown3
Nesti Anggraini SantosoAffiliation unknown3
Natasya Aprila YusufAffiliation unknown3
Alwiyah AlwiyahAffiliation unknown3
Eka Purnama HarahapUniversitas Raharja3
Marviola HardiniAffiliation unknown3
Nuke Puji Lestari SantosoAffiliation unknown3
Yulia Putri Ayu SanjayaAffiliation unknown3
Desy AprianiAffiliation unknown2
Mohamad RakhmansyahAffiliation unknown2
Umi NarimawatiAffiliation unknown2
Joko SiswantoAffiliation unknown2
Hindriyanto Dwi PurnomoSatya Wacana Christian University2
Fitra Putri OgandaUniversitas Raharja2
Tarisya RamadhanAffiliation unknown2
Aan KaniviaAffiliation unknown2
Muhammad Faizal FazriAffiliation unknown2
Izwan AmsyarAffiliation unknown2
Haznah LatifahAffiliation unknown2
Zaleha FauziahAffiliation unknown2
Raihan RaihanAffiliation unknown2
Nanda SeptianiAffiliation unknown2
Fauziyyah Husna Nurdiayanah LestariAffiliation unknown2
Farida AgustinAffiliation unknown2

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://att.aptisi.or.id/index.php/att/oai, first harvested 6 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Technopreneurship in Healthcare: Evaluating User Satisfaction and Trust in AI-Driven Safe Entry Stations</dc:title>
	<dc:creator xml:lang="en">Rahardja, Untung</dc:creator>
	<dc:creator xml:lang="en">Sunarya, Po Abas</dc:creator>
	<dc:creator xml:lang="en">Aini, Qurotul</dc:creator>
	<dc:creator xml:lang="en">Millah, Shofiyul</dc:creator>
	<dc:creator xml:lang="en">Maulana, Sabda</dc:creator>
	<dc:subject xml:lang="en">Technopreneurship</dc:subject>
	<dc:subject xml:lang="en">Healthcare</dc:subject>
	<dc:subject xml:lang="en">Satisfaction</dc:subject>
	<dc:subject xml:lang="en">Trust</dc:subject>
	<dc:subject xml:lang="en">Safe Entry Stations</dc:subject>
	<dc:description xml:lang="en">The development of technology in the health sector has encouraged the adoption of technopreneurship, especially in the application of artificial intelligence (AI) to support the safety and efficiency of health services. One of the innovations that has emerged is the AI-driven Safe Entry Station, which is designed to improve the safety and comfort of patients and medical personnel. However, the success of implementing this technology is highly dependent on the level of user satisfaction and trust. This study aims to evaluate the level of user satisfaction and trust in Safe Entry Stations in the health care environment and also explore the variables that influence the acceptance of this technology among users. This research method uses a quantitative approach with a survey involving 673 respondents from various health institutions that have used Safe Entry Stations. Data were analyzed using Structural Equation Modeling (SEM) with SmartPLS 4.0 software to identify the relationship between User Satisfaction (US), trust (TR), behaviour intention (BI), usage behaviour (SB) and technopreneurial impac (TI). The results showed that US and TR significantly influences BI and UB. Additionally, BI strongly impacts TI, suggesting that stronger intentions lead to a greater perceived impact on technopreneurship. This study found that AI-driven Safe Entry Stations has great potential for widespread adoption in the healthcare sector. These findings provide important insights for further development of this technology as well as technopreneurship strategies in the healthcare sector.</dc:description>
	<dc:publisher xml:lang="en">Pandawan</dc:publisher>
	<dc:date>2024-09-13</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://att.aptisi.or.id/index.php/att/article/view/489</dc:identifier>
	<dc:identifier>10.34306/att.v6i3.489</dc:identifier>
	<dc:source xml:lang="en">Aptisi Transactions on Technopreneurship (ATT); Vol. 6 No. 3 (2024): November; 404−417</dc:source>
	<dc:source xml:lang="id">Aptisi Transactions On Technopreneurship (ATT); Vol 6 No 3 (2024): November; 404−417</dc:source>
	<dc:source>2656-8888</dc:source>
	<dc:source>2655-8807</dc:source>
	<dc:source>10.34306/att.v6i3</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://att.aptisi.or.id/index.php/att/article/view/489/284</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Untung Rahardja, Po Abas Sunarya, Qurotul Aini, Shofiyul Millah, Sabda Maulana</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>