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

Irwan Sembiring · Danny Manongga · Untung Rahardja · Qurotul Aini

12 citations0/0 files storedAptisi Transactions On Technopreneurship (ATT)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Technology Adoption and User Behaviour Big Data and Business Intelligence Innovative Human-Technology Interaction

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.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Imported from OpenAlex (CC0) — nothing here is generated.