Subtopic Deep Dive

Service Quality in E-Health Platforms
Research Guide

What is Service Quality in E-Health Platforms?

Service Quality in E-Health Platforms evaluates multidimensional aspects like tangibles, assurance, and responsiveness in online health portals and apps using SERVQUAL adaptations to predict user trust and behavioral outcomes.

Researchers adapt SERVQUAL to measure e-health service quality, linking perceptions to adoption and repurchase intentions. Studies from Indonesia dominate, examining mHealth apps and digital health services. Over 20 papers since 2016 analyze factors like privacy, security, and chatbot integration, with Octavius and Antonio (2021) cited 87 times.

Curated Papers

Key Challenges

Why It Matters

High service quality in e-health platforms increases user trust and mHealth adoption, as shown in Octavius and Antonio (2021) where quality perceptions drove recommendation intentions among Indonesian users. Purnamasari and Suryandari (2023) found e-service quality mediated by satisfaction and trust boosts repurchase in online health shopping. Optimizing these factors enhances competitiveness in digital health markets, with Prodia's digital lab services disrupting traditional systems (Narolita, 2020).

Key Research Challenges

Adapting SERVQUAL to Digital Health

SERVQUAL dimensions like tangibles and assurance require customization for e-health contexts such as apps and chatbots. Octavius and Antonio (2021) highlight gaps in measuring mHealth-specific factors like data privacy. Validation across cultures remains limited, especially beyond Indonesia.

Linking Quality to Behavioral Outcomes

Quantifying how service quality influences trust, satisfaction, and loyalty in e-health needs longitudinal studies. Purnamasari and Suryandari (2023) identify mediation by e-satisfaction but call for causal models. Indonesian market dominance limits generalizability to global platforms.

Integrating Emerging Tech like Chatbots

E-health platforms increasingly use chatbots and telemidwifery, but quality metrics lag. Sanny et al. (2019) analyze chatbot acceptance factors, yet integration with SERVQUAL is underexplored. Scalability in low-resource settings poses implementation barriers (Stellata et al., 2022).

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. ...

Iterative development of Vegethon: a theory-based mobile app intervention to increase vegetable consumption

Sarah Ann Mummah, Abby C. King, Christopher D. Gardner et al. · 2016 · International Journal of Behavioral Nutrition and Physical Activity · 85 citations

The analysis of customer satisfaction factors which influence chatbot acceptance in Indonesia

Lim Sanny, Ari Clementin Susastra, Choky Roberts et al. · 2019 · Management Science Letters · 62 citations

Chatbot program has evolved in the Indonesian market as the representative of online service customer that provides immediate response and it is able to interact with customers by using Natural Lan...

Analysis of Factors Affecting Customer Satisfaction and Customer Loyalty in the Shopee Marketplace

Rejhi Yunus Siringo Ringo, Dihin Septyanto, Abdul Haeba Ramli · 2023 · Majalah Ilmiah Bijak · 25 citations

This study aims to find out what factors influence customer satisfaction and loyalty in the Shopee marketplace. The variables used in this study are price, product quality, information quality, pri...

Effectiveness of Digital Health Interventions Containing Game Components for the Self-management of Type 2 Diabetes: Systematic Review

Linda Ossenbrink, Tina Haase, Patrick Timpel et al. · 2023 · JMIR Serious Games · 22 citations

Background Games and game components have become a major trend in the realm of digital health research and practice as they are assumed to foster behavior change and thereby improve patient-reporte...

Effect of E-Service Quality on E-Repurchase Intention in Indonesia Online Shopping: E-Satisfaction and E-Trust as Mediation Variables

Ismawati Purnamasari, Retno Tanding Suryandari · 2023 · European Journal of Business Management and Research · 18 citations

The purpose of this study is to develop new knowledge in understanding the effect of electronic service quality on customer satisfaction, customer trust and customer purchase intention and to under...

LITERATURE REVIEW: THE IMPLEMENTATION OF E-HEALTH AT PRIMARY HEALTHCARE CENTERS IN SURABAYA CITY

Yesica Aprillia Putri Adian, Wasis Budiarto · 2020 · Jurnal Administrasi Kesehatan Indonesia · 17 citations

Background: The increasing number of patient visits in primary healthcare centers in Surabaya causes long duration of queue in a registration counter. To solve this problem, Surabaya Government has...

Reading Guide

Foundational Papers

Start with Octavius and Antonio (2021) for core mHealth SERVQUAL model (87 citations), then Purnamasari and Suryandari (2023) to understand mediation in repurchase.

Recent Advances

Study Sanny et al. (2019) for chatbot factors and Stellata et al. (2022) for telemidwifery apps to capture 2020s digital shifts.

Core Methods

SERVQUAL surveys with SEM analysis (Octavius 2021); mediation modeling (Purnamasari 2023); qualitative implementation reviews (Narolita 2020).

How PapersFlow Helps You Research Service Quality in E-Health Platforms

Discover & Search

Research Agent uses searchPapers and exaSearch to find SERVQUAL adaptations in e-health, pulling Octavius and Antonio (2021) as top-cited. citationGraph reveals clusters around Indonesian mHealth adoption, while findSimilarPapers links to Purnamasari and Suryandari (2023) for repurchase models.

Analyze & Verify

Analysis Agent employs readPaperContent on Octavius and Antonio (2021) to extract SERVQUAL factors, then verifyResponse with CoVe checks claims against citations. runPythonAnalysis runs statistical verification on satisfaction mediation models from Purnamasari and Suryandari (2023), with GRADE grading for evidence strength in adoption studies.

Synthesize & Write

Synthesis Agent detects gaps in chatbot-SERVQUAL integration from Sanny et al. (2019), flagging contradictions in trust metrics. Writing Agent uses latexEditText and latexSyncCitations to draft SERVQUAL frameworks, latexCompile for reports, and exportMermaid for quality dimension flowcharts.

Use Cases

"Run regression on e-service quality factors from Indonesian mHealth papers"

Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas regression on data from Octavius and Antonio 2021, Purnamasari and Suryandari 2023) → matplotlib plots of mediation effects.

"Draft LaTeX review of SERVQUAL in e-health adoption"

Synthesis Agent → gap detection → Writing Agent → latexEditText (SERVQUAL table) → latexSyncCitations (Octavius 2021 et al.) → latexCompile → PDF with e-health quality model.

"Find GitHub repos for telemidwifery chatbot code"

Research Agent → paperExtractUrls (Stellata et al. 2022) → Code Discovery → paperFindGithubRepo → githubRepoInspect → code snippets for SERVQUAL-integrated chatbots.

Automated Workflows

Deep Research workflow conducts systematic review of 50+ e-health quality papers, chaining searchPapers → citationGraph → GRADE grading for structured SERVQUAL report. DeepScan applies 7-step analysis with CoVe checkpoints to verify mediation models in Purnamasari and Suryandari (2023). Theorizer generates hypotheses linking chatbot quality to adoption from Sanny et al. (2019).

Try Doxa for Service Quality in E-Health Platforms Research

Frequently Asked Questions

What is Service Quality in E-Health Platforms?

It assesses SERVQUAL dimensions adapted for online health apps, portals, and chatbots to predict trust and usage.

What methods are used?

Researchers apply structural equation modeling on surveys, as in Octavius and Antonio (2021) for mHealth adoption and Purnamasari and Suryandari (2023) for repurchase mediation.

What are key papers?

Octavius and Antonio (2021, 87 citations) on mHealth intentions; Sanny et al. (2019, 62 citations) on chatbot satisfaction; Purnamasari and Suryandari (2023, 18 citations) on e-service quality mediation.