Melola Discovery

← Melola Discovery

Open access Journal

Understanding Data-Driven Analytic Decision Making on Air Quality Monitoring an Empirical Study

Aptisi Transactions on Technopreneurship (ATT) · Vol. 6 No. 3 (2024) · pp. 418–431

DOI
10.34306/att.v6i3.459
ISSN
2656-8888, 2655-8807
Published
2024-09-13
Publisher
Pandawan
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

Air quality monitoring is increasingly relying on data-driven analytic decision-making tools to provide accurate and timely information, forming the background of this study. The objective is to understand the factors influencing the adoption and usage behavior of these tools using the Unified Theory of Acceptance and Use of Technology (UTAUT2) model. The method involves incorporating UTAUT2 constructs Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Price Value (PV), Hedonic Motivation (HM), and Habit (H), alongside external variables such as Considered Risk (CR) and Considered Trust (CT). Data from 287 respondents were analyzed to assess their impact on Behavior Intention (BI) and Usage Behavior (UB). The results demonstrate that both trust and risk considerations significantly affect user behavior, underscoring the need to address these factors to enhance the adoption of air quality monitoring systems. In conclusion, this research provides valuable insights for developers and policymakers on improving the implementation and acceptance of data-driven technologies in environmental monitoring, thereby contributing to more effective air quality management.

Keywords

Collaboration network of the authors

40 authors · 154 co-authorship links · 20 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.

  • Satya Wacana Christian University
  • Universitas Raharja
  • Atma Jaya Catholic University of Indonesia
  • Universitas Buddhi Dharma
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nuke Puji Lestari SantosoUniversitas Raharja13
Ninda LutfianiUniversitas Raharja13
Hindriyanto Dwi PurnomoSatya Wacana Christian University9
Po Abas SunaryaAffiliation unknown9
Alfiah KhoirunisaUniversitas Raharja8
Dwi ApriliasariAffiliation unknown8
Sutarto WijonoAffiliation unknown6
Ade IrianiSatya Wacana Christian University6
Untung RahardjaUniversitas Raharja5
Agung RizkyAffiliation unknown5
Shofiyul MillahAffiliation unknown5
Adam FaturahmanAffiliation unknown5
Hendry HendrySatya Wacana Christian University4
HenderiAffiliation unknown4
Muhtarom MuhtaromAffiliation unknown3
Iwan SetyawanSatya Wacana Christian University3
Theophilus WellemAffiliation unknown3
Nesti Anggraini SantosoAffiliation unknown3
Ihsan Nuril HikamAffiliation unknown3
Natasya Aprila YusufAffiliation unknown3
Joko SiswantoAffiliation unknown3
Alwiyah AlwiyahAffiliation unknown3
Eko SediyonoSatya Wacana Christian University3
Kristoko Dwi HartomoSatya Wacana Christian University2
Desy AprianiAffiliation unknown2
Mohamad RakhmansyahAffiliation unknown2
Adi SetiawanAffiliation unknown2
Umi NarimawatiAffiliation unknown2
Eka Purnama HarahapUniversitas Raharja2
Yerik Afrianto SinggalenAtma Jaya Catholic University of Indonesia2
Fitra Putri OgandaUniversitas Raharja2
Benny DaniawanUniversitas Buddhi Dharma2
Tarisya RamadhanAffiliation unknown2
Raihan RaihanAffiliation unknown2
Farida AgustinAffiliation unknown2
Marviola HardiniAffiliation 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) 
<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">Understanding Data-Driven Analytic Decision Making on Air Quality Monitoring an Empirical Study</dc:title>
	<dc:creator xml:lang="en">Sembiring, Irwan</dc:creator>
	<dc:creator xml:lang="en">Manongga, Danny</dc:creator>
	<dc:creator xml:lang="en">Rahardja, Untung</dc:creator>
	<dc:creator xml:lang="en">Aini, Qurotul</dc:creator>
	<dc:subject xml:lang="en">Air Quality Monitoring</dc:subject>
	<dc:subject xml:lang="en">UTAUT2</dc:subject>
	<dc:subject xml:lang="en">Usage Behavior</dc:subject>
	<dc:subject xml:lang="en">Technology Adoption</dc:subject>
	<dc:subject xml:lang="en">Behavioral Intention</dc:subject>
	<dc:description xml:lang="en">Air quality monitoring is increasingly relying on data-driven analytic decision-making tools to provide accurate and timely information, forming the background of this study. The objective is to understand the factors influencing the adoption and usage behavior of these tools using the Unified Theory of Acceptance and Use of Technology (UTAUT2) model. The method involves incorporating UTAUT2 constructs Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Price Value (PV), Hedonic Motivation (HM), and Habit (H), alongside external variables such as Considered Risk (CR) and Considered Trust (CT). Data from 287 respondents were analyzed to assess their impact on Behavior Intention (BI) and Usage Behavior (UB). The results demonstrate that both trust and risk considerations significantly affect user behavior, underscoring the need to address these factors to enhance the adoption of air quality monitoring systems. In conclusion, this research provides valuable insights for developers and policymakers on improving the implementation and acceptance of data-driven technologies in environmental monitoring, thereby contributing to more effective air quality management.</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/459</dc:identifier>
	<dc:identifier>10.34306/att.v6i3.459</dc:identifier>
	<dc:source xml:lang="en">Aptisi Transactions on Technopreneurship (ATT); Vol. 6 No. 3 (2024): November; 418-431</dc:source>
	<dc:source xml:lang="id">Aptisi Transactions On Technopreneurship (ATT); Vol 6 No 3 (2024): November; 418-431</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/459/285</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Irwan Sembiring, Danny Manongga, Untung Rahardja, Qurotul Aini</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>