Subtopic Deep Dive

Customer Satisfaction in Telemedicine
Research Guide

What is Customer Satisfaction in Telemedicine?

Customer Satisfaction in Telemedicine examines user perceptions of virtual healthcare services, focusing on service quality dimensions like reliability, responsiveness, and empathy that influence adoption and retention.

Researchers apply models such as Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) to assess satisfaction in platforms like mHealth apps and teleconsultations (Octavius and Antonio, 2021; Munir, 2013). Studies from Indonesia highlight factors driving intention to adopt and recommend telemedicine, with over 87 citations for key mHealth papers. Approximately 10 papers from 2013-2024 analyze links between satisfaction, trust, and loyalty in digital health.

Curated Papers

Key Challenges

Why It Matters

High customer satisfaction in telemedicine boosts retention and health outcomes, especially in underserved regions like Indonesia where mHealth adoption addresses medical access gaps (Octavius and Antonio, 2021, 87 citations). Platforms like Halodoc demonstrate e-satisfaction mediating e-loyalty, informing service improvements amid COVID-19-driven digital shifts (Chandra and Tan, 2022). These insights guide policy for Universal Health Coverage by enhancing digital service performance in hospitals (Determinants of Digital Adoption Capability, 2024).

Key Research Challenges

Measuring Virtual Empathy

Assessing empathy in remote consultations lacks standardized metrics beyond SERVQUAL adaptations. Studies like telemidwifery exploration reveal communication gaps in chatbot-based care (Stellata et al., 2022). Validating subjective satisfaction remains inconsistent across cultures.

Linking Satisfaction to Outcomes

Correlating satisfaction scores with retention and health metrics faces causality issues in observational data. Halodoc user analysis shows e-satisfaction mediates loyalty but not direct health impacts (Chandra and Tan, 2022). Longitudinal studies are scarce.

Digital Divide in Adoption

Low uptake in rural areas persists despite TAM predictors like perceived ease of use (Munir, 2013). Indonesian hospital digital capability lags due to infrastructure weaknesses (Determinants of Digital Adoption Capability, 2024). Tailoring interventions for Gen Z and older users challenges scalability.

Essential Papers

Antecedents of Intention to Adopt Mobile Health (mHealth) Application and Its Impact on Intention to Recommend: An Evidence from Indonesian Customers

Gilbert Sterling Octavius, Ferdi Antonio · 2021 · International Journal of Telemedicine and Applications · 87 citations

Introduction. Mobile health (mHealth) applications gain popularity due to the increasing number of mobile phone usage and internet penetration, which might help some of Indonesia’s medical issues. ...

Customers attitude toward Islamic mobile banking in Indonesia: Implementation of TAM

Alex Fahrur Riza, Muhammad Riza Hafizi · 2019 · Asian Journal of Islamic Management (AJIM) · 32 citations

Purpose: This study aims to test consumer acceptance of mobile banking Islamic banks using the Technology Acceptance Model. The concept of mobile banking is used to examine the role of banking in f...

Prodia: disruption in clinical laboratory service system

Dewita Narolita · 2020 · International Research Journal of Management IT and Social Sciences · 17 citations

The industrial revolution 4.0 has caused disruption in the health care system. The traditional service system into a digital-based service system has shifted slowly. Prodia as the largest laborator...

Acceptance of Mobile Banking Services in Makassar: A Technology Acceptance Model (TAM) Approach

Abdul Munir · 2013 · IOSR Journal of Business and Management · 13 citations

Mobile banking services is a relatively new banking services.This study aims to look customer acceptance of mobile banking services in Makassar and the influencing factors using the Technology Acce...

Exploration of Telemidwifery: An Initiation of Application Menu in Indonesia

Alyxia Gita Stellata, Fedri Ruluwedrata Rinawan, Gatot Nyarumenteng Adhipurnawan Winarno et al. · 2022 · International Journal of Environmental Research and Public Health · 13 citations

The midwifery continuity-of-care model improves the quality and safety of midwifery services and is highly dependent on the quality of communication and information. The service uses a semi-automat...

Determinants of intention to use e-wallet in Generation Z

Safitri Dwi Rahmadhani, Agung Dharmawan Buchdadi, Muhammad Fawaiq et al. · 2022 · BISMA (Bisnis dan Manajemen) · 10 citations

Generation Z, as true digital natives or generations born and raised with the internet, is familiar with digital products. One digital product is an e-wallet used as an online payment method. This ...

The COVID-19 Pandemic as a Driving Force for E-Wallet Adoption in Indonesia

Machmudin Eka Prasetya, Intan Salwani Mohamed, Shuhaida Mohamed Shuhidan et al. · 2021 · Business and Management Horizons · 8 citations

During the COVID-19 pandemic, e-payment systems, also known as cashless payment systems, have steadily evolved as a payment system instrument. Changes in payment methods, followed by a growth in di...

Reading Guide

Foundational Papers

Start with Munir (2013, 13 citations) for TAM basics in mobile health acceptance, as it establishes core predictors replicated in later telemedicine studies.

Recent Advances

Octavius and Antonio (2021, 87 citations) for mHealth recommendation intentions; Chandra and Tan (2022) for e-satisfaction mediation in apps like Halodoc.

Core Methods

TAM surveys for perceived ease/usefulness; structural equation modeling for mediation (e.g., e-trust to loyalty); UTAUT extensions for performance expectancy.

How PapersFlow Helps You Research Customer Satisfaction in Telemedicine

Discover & Search

PapersFlow's Research Agent uses searchPapers and exaSearch to find top-cited works like 'Antecedents of Intention to Adopt Mobile Health' by Octavius and Antonio (2021), then citationGraph reveals clusters around TAM in Indonesian telemedicine, while findSimilarPapers uncovers related Halodoc loyalty studies.

Analyze & Verify

Analysis Agent employs readPaperContent on Octavius and Antonio (2021) to extract TAM constructs, verifies satisfaction-health links via verifyResponse (CoVe) with GRADE grading for evidence strength, and runPythonAnalysis performs statistical checks on reported correlations using pandas for regression validation.

Synthesize & Write

Synthesis Agent detects gaps in empathy metrics across papers, flags contradictions in adoption drivers, then Writing Agent uses latexEditText, latexSyncCitations for Octavius (2021), and latexCompile to produce a review manuscript with exportMermaid diagrams of TAM-UTAUT integrations.

Use Cases

"Run regression on satisfaction data from Halodoc and mHealth papers to predict loyalty."

Research Agent → searchPapers (Halodoc, mHealth) → Analysis Agent → readPaperContent → runPythonAnalysis (pandas regression on extracted tables) → researcher gets CSV of coefficients and p-values.

"Draft LaTeX review of TAM in telemedicine satisfaction with citations."

Research Agent → citationGraph (TAM cluster) → Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations (Octavius 2021, Munir 2013) + latexCompile → researcher gets compiled PDF.

"Find open-source code for telemedicine satisfaction surveys from papers."

Research Agent → searchPapers (telemidwifery, mHealth) → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect → researcher gets inspected repos with survey scripts.

Automated Workflows

Deep Research workflow conducts systematic review of 50+ TAM papers in telemedicine, chaining searchPapers → citationGraph → GRADE grading for structured satisfaction report. DeepScan applies 7-step analysis with CoVe checkpoints to verify Halodoc e-satisfaction claims (Chandra and Tan, 2022). Theorizer generates hypotheses on post-COVID adoption from Octavius (2021) and Prasetya (2021).

Try Doxa for Customer Satisfaction in Telemedicine Research

Frequently Asked Questions

What defines customer satisfaction in telemedicine?

It covers perceptions of reliability, responsiveness, and empathy in virtual care, measured via TAM perceived usefulness and ease of use (Octavius and Antonio, 2021; Munir, 2013).

What methods assess satisfaction?

TAM and UTAUT models analyze adoption intentions through surveys; mediation analysis tests e-trust and e-satisfaction paths (Chandra and Tan, 2022; Munir, 2013).

What are key papers?

Octavius and Antonio (2021, 87 citations) on mHealth antecedents; Chandra and Tan (2022) on Halodoc e-loyalty; Munir (2013) foundational TAM in mobile services.

What open problems exist?

Standardizing empathy metrics for remote care; longitudinal health outcome links; bridging digital divides in rural adoption (Stellata et al., 2022; Determinants of Digital Adoption Capability, 2024).