Determinants of Behavioral Intention Toward Telemedicine Services in Indonesia Post-Pandemic COVID-19

The COVID-19 pandemic has significantly disrupted daily life & accelerated the adoption of digital technologies across various sectors, including healthcare. In Indonesia, this shift has prompted the rapid development and implementation of Telemedicine services by both start-ups and established healthcare institutions. This study aims to identify the factors that influence customer intention to use Telemedicine services in the post-pandemic period, using the Unified Theory of Acceptance and Use of Technology (UTAUT2) model. The research focuses on five key variables: Price Value, Effort Expectancy, Geographical Location, Performance Expectancy, and Social Influence. Data collected from 507 respondents across Indonesia through an online questionnaire reveals that user satisfaction is critical for further use of Telemedicine services. The findings indicate that improvements in the affordability, user-friendliness, accessibility, effectiveness, and social endorsement of Telemedicine services significantly enhance user satisfaction. The study provides practical recommendations for Telemedicine providers to improve these factors, thereby increasing customer retention and adoption rates. This research contributes to the understanding and offers insights for optimizing Telemedicine services in Indonesia in the evolving post-pandemic behaviors

Authors

Institutions

Publication Details

Journal
Syntax Idea
Published
2024-09-09
DOI
https://doi.org/10.46799/syntax-idea.v6i9.4451
Citations
1
Primary Topic
COVID-19 Pandemic Impacts
Type
article
Field-Weighted Citation Impact
1.13
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Determinants of Behavioral Intention Toward Telemedicine Services in Indonesia Post-Pandemic COVID-19

Fajri Satria Wika, Desi S.W. Utami, Oscar Hertinur Betthy S.
1 citations
Syntax Idea
COVID-19 Pandemic Impacts
1.13
article

Determinants of Behavioral Intention Toward Telemedicine Services in Indonesia Post-Pandemic COVID-19

Fajri Satria Wika, Desi S.W. Utami, Oscar Hertinur Betthy S.
article en
1 citations

Abstract

The COVID-19 pandemic has significantly disrupted daily life & accelerated the adoption of digital technologies across various sectors, including healthcare. In Indonesia, this shift has prompted the rapid development and implementation of Telemedicine services by both start-ups and established healthcare institutions. This study aims to identify the factors that influence customer intention to use Telemedicine services in the post-pandemic period, using the Unified Theory of Acceptance and Use of Technology (UTAUT2) model. The research focuses on five key variables: Price Value, Effort Expectancy, Geographical Location, Performance Expectancy, and Social Influence. Data collected from 507 respondents across Indonesia through an online questionnaire reveals that user satisfaction is critical for further use of Telemedicine services. The findings indicate that improvements in the affordability, user-friendliness, accessibility, effectiveness, and social endorsement of Telemedicine services significantly enhance user satisfaction. The study provides practical recommendations for Telemedicine providers to improve these factors, thereby increasing customer retention and adoption rates. This research contributes to the understanding and offers insights for optimizing Telemedicine services in Indonesia in the evolving post-pandemic behaviors

Syntax IdeaVol. 6(9)
Universitas Prasetiya Mulya
Openalex Percentile: Top 12%
COVID-19 Pandemic Impacts
1.13
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.