Subtopic Deep Dive
Mobile Payment Adoption in Healthcare
Research Guide
What is Mobile Payment Adoption in Healthcare?
Mobile Payment Adoption in Healthcare examines factors influencing consumer and provider acceptance of digital payment systems integrated into health applications and services.
Studies apply models like TAM and UTAUT to analyze performance expectancy, effort expectancy, and security perceptions in mHealth payment contexts (Octavius and Antonio, 2021; 87 citations). Empirical surveys from Indonesia and Malaysia test intentions for cashless transactions in clinics and pharmacies during COVID-19 (Utomo et al., 2021; 68 citations). About 10 key papers since 2021 focus on behavioral models and e-wallet security.
Why It Matters
Mobile payments in healthcare streamline clinic transactions, reducing wait times and administrative costs in resource-limited settings (Fahlevi and Alharbi, 2021). During COVID-19, cashless adoption supported physical distancing in pharmacies and telehealth (Yusoff et al., 2022). Security perceptions drive sustained use of QRIS in health payments, enhancing patient trust (Wiryawan et al., 2023). Hybrid TAM-ISSM models predict loyalty in e-health systems (Krisdina et al., 2022).
Key Research Challenges
Security Perception Barriers
Users perceive high risks in e-wallet transactions for health data, limiting adoption despite convenience (Wiryawan et al., 2023). Studies show trust gaps in QRIS for sensitive payments (4 citations). Empirical models need validation across cultures.
Habit and Facilitating Conditions
Low habit formation and infrastructure limits hinder behavioral intention in mHealth payments (Utomo et al., 2021; 68 citations). Rural areas face device and network barriers. Interventions must address expectancy gaps.
Model Integration Complexity
Combining TAM with ISSM yields fragmented predictions for e-health payment loyalty (Krisdina et al., 2022; 4 citations). Mediating factors like satisfaction vary by context (Haykal et al., 2023). Standardized hybrid approaches are lacking.
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. ...
The Effects of Performance Expectancy, Effort Expectancy, Facilitating Condition, and Habit on Behavior Intention in Using Mobile Healthcare Application
Prio Utomo, Florentina Kurniasari, Purnamaningsih Purnamaningsih · 2021 · International Journal of Community Service & Engagement · 68 citations
South Tangerang Health Office had the responsibility in giving outstanding healthcare services to its resident’s despite of its limitation due Covid-19 pandemic. Some programs were initiated to red...
Adoption of e-payment system to support health social security agency
Mochammad Fahlevi, Nouf Alharbi · 2021 · International Journal of Data and Network Science · 17 citations
The development of the existing information technology era has helped the performance of BPJS more and more. The application of information technology in the work environment includes meeting the e...
Pengaruh Kualitas Sistem, Kualitas Informasi, dan Kualitas Layanan terhadap Loyalitas Konsumen yang dimediasi oleh Kepuasan Konsumen dalam Berbelanja Online
Azzahrah Putri Haykal, Ika Febrilia, Terrylina Arvinta Monoarfa · 2023 · Jurnal Bisnis Manajemen dan Keuangan · 5 citations
ABSTRACT
 The purpose of this research is to find out whether there is an influence of system quality, information quality, service quality on customer satisfaction and customer loyalty. This ...
Hybrid Model Based on Technology Acceptance Model (TAM) & Information System Success Model (ISSM) in Analyzing the Use of E-Health
Shinta Krisdina, Oky Dwi Nurhayati, Dinar Mutiara Kusumo Nugraheni · 2022 · E3S Web of Conferences · 4 citations
Electronic health or commonly known as e-health is defined as the use of information and communication technology in supporting the health and health-related fields. The outbreak of the Covid-19 vi...
Factors influencing e-wallet users' perception on payment transaction security: Evaluation on quick response Indonesia standard
Drajad Wiryawan, Joni Suhartono, Siti Elda Hiererra et al. · 2023 · AIP conference proceedings · 4 citations
QRIS (Quick Response Indonesian Standard) is a standard set by the Central Bank of Indonesia and the Indonesian Payment System Association (ASPI) for digital transactions. This study tries to find ...
Factors Influencing Practice of Cashless Purchase During COVID-19 Movement Control Order (MCO) in Malaysian Society
Nur Hafizah Yusoff, Muhammad Ridhwan Sarifin, Azlina Zainal Abidin · 2022 · International Journal of Academic Research in Business and Social Sciences · 4 citations
The pandemic of COVID19 required Malaysian government to implement the Movement Restricted Order (MCO) to avoid mass gahthering and maintain the physical distancing. The purpose of this article was...
Reading Guide
Foundational Papers
No pre-2015 papers available; start with highest-cited Octavius and Antonio (2021; 87 citations) for mHealth adoption baseline and Utomo et al. (2021; 68 citations) for UTAUT in healthcare payments.
Recent Advances
Wiryawan et al. (2023) on QRIS security; Sulistyaningrum et al. (2023) on telepharmacy intentions; Haykal et al. (2023) on loyalty mediation.
Core Methods
UTAUT/TAM surveys with SEM (Utomo et al., 2021); hybrid TAM-ISSM (Krisdina et al., 2022); QR code security perception scales (Wiryawan et al., 2023).
How PapersFlow Helps You Research Mobile Payment Adoption in Healthcare
Discover & Search
Research Agent uses searchPapers with query 'mobile payment TAM healthcare Indonesia' to retrieve Octavius and Antonio (2021; 87 citations), then citationGraph maps UTAUT extensions to Utomo et al. (2021). findSimilarPapers expands to 20+ regional studies; exaSearch uncovers gray literature on QRIS health adoption.
Analyze & Verify
Analysis Agent applies readPaperContent to extract TAM constructs from Fahlevi and Alharbi (2021), then verifyResponse with CoVe cross-checks security claims against Wiryawan et al. (2023). runPythonAnalysis performs meta-regression on citation counts and effect sizes using pandas for expectancy factors; GRADE grading scores evidence quality as moderate for Indonesian surveys.
Synthesize & Write
Synthesis Agent detects gaps in post-COVID habit models via contradiction flagging between Utomo (2021) and Yusoff (2022). Writing Agent uses latexEditText for TAM diagrams, latexSyncCitations to integrate 10 papers, and latexCompile for a review manuscript; exportMermaid visualizes adoption model flows.
Use Cases
"Run meta-analysis on performance expectancy effect sizes in mHealth payment papers."
Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas meta-regression on 8 papers) → GRADE scoring → CSV export of pooled OR=2.1 (95% CI).
"Draft LaTeX section on TAM factors for mobile payment in telepharmacy."
Research Agent → findSimilarPapers (Sulistyaningrum et al., 2023) → Synthesis → gap detection → Writing Agent → latexEditText + latexSyncCitations + latexCompile → PDF with UTAUT figure.
"Find GitHub repos with TAM survey code for health payment studies."
Research Agent → paperExtractUrls (Utomo 2021) → Code Discovery → paperFindGithubRepo → githubRepoInspect → Python scripts for SEM analysis shared via exportBibtex.
Automated Workflows
Deep Research workflow conducts systematic review: searchPapers (50+ TAM papers) → citationGraph → DeepScan (7-step CoVe analysis with GRADE) → structured report on adoption barriers. Theorizer generates hypotheses like 'QRIS security moderates habit in clinics' from Wiryawan (2023) + Octavius (2021). DeepScan verifies cross-study contradictions on effort expectancy.
Frequently Asked Questions
What defines Mobile Payment Adoption in Healthcare?
It covers fintech integration in health apps, focusing on security, convenience, and UTAUT/TAM-driven intentions (Octavius and Antonio, 2021).
What methods dominate this subtopic?
Quantitative surveys use TAM/UTAUT hybrids and SEM; examples include performance expectancy analysis (Utomo et al., 2021) and QRIS security factors (Wiryawan et al., 2023).
What are key papers?
Octavius and Antonio (2021; 87 citations) on mHealth intentions; Utomo et al. (2021; 68 citations) on habit in mobile healthcare; Fahlevi and Alharbi (2021; 17 citations) on e-payment in social security.
What open problems exist?
Cross-cultural validation of hybrid models; rural infrastructure impacts; post-pandemic habit decay (Krisdina et al., 2022; Yusoff et al., 2022).
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