Subtopic Deep Dive
Trust and Security in Technology Adoption
Research Guide
What is Trust and Security in Technology Adoption?
Trust and Security in Technology Adoption examines how privacy concerns, perceived security risks, and institutional trust influence user acceptance of technologies, especially in fintech, health IT, and protective systems.
This subtopic extends models like UTAUT and TAM to incorporate trust and risk factors as key barriers to adoption (Venkatesh et al., 2016; 2101 citations). Studies apply multi-method approaches including surveys and structural equation modeling to develop scales for measuring security perceptions in mobile payments and health informatics (Slade et al., 2015; 864 citations; Rahimi et al., 2018; 621 citations). Over 10 papers from the list focus on these dynamics across domains.
Why It Matters
In fintech, trust and risk perceptions determine remote mobile payment adoption, as shown in Slade et al. (2015) extending UTAUT with innovativeness, risk, and trust for UK consumers. Health IT deployment faces barriers from provider security concerns, per Rahimi et al. (2018) review of TAM applications. Dinev and Hu (2007) highlight awareness's role in protective technology intentions, impacting cybersecurity tool uptake in organizations.
Key Research Challenges
Measuring Perceived Security Risks
Developing reliable scales for subjective security perceptions remains difficult amid evolving threats. Slade et al. (2015) extended UTAUT to include risk in mobile payments but noted context-specific variations. Rahimi et al. (2018) found inconsistent TAM adaptations in health informatics due to unvalidated risk measures.
Integrating Trust in Adoption Models
Standard models like UTAUT underemphasize trust dynamics in AI and voice assistants. Pitardi and Marriott (2021) identified unique trust drivers for voice-based AI like Alexa. Venkatesh et al. (2016) called for synthesizing trust extensions in UTAUT for future roadmaps.
Overcoming Resistance in Sensitive Domains
Users resist IoT and health innovations due to privacy fears despite utility. Mani and Chouk (2018) applied Ram and Sheth's framework to IoT service resistance. Dinev and Hu (2007) emphasized awareness gaps in protective tech adoption.
Essential Papers
Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead
Viswanath Venkatesh, James Y.L. Thong, Xu Xin · 2016 · Journal of the Association for Information Systems · 2.1K citations
The unified theory of acceptance and use of technology (UTAUT) is a little over a decade old and has been used extensively in information systems (IS) and other fields, as the large number of citat...
Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model
Cheng‐Min Chao · 2019 · Frontiers in Psychology · 965 citations
This study developed and empirically tested a model to predict the factors affecting students' behavioral intentions toward using mobile learning (m-learning). This study explored the behavioral in...
Modeling Consumers’ Adoption Intentions of Remote Mobile Payments in the United Kingdom: Extending UTAUT with Innovativeness, Risk, and Trust
Emma Slade, Yogesh K. Dwivedi, Niall Piercy et al. · 2015 · Psychology and Marketing · 864 citations
ABSTRACT Mobile payments (MPs) are predicted to be one of the future's most successful mobile services but have achieved limited acceptance in developed countries to date. PCs are still the preferr...
A Systematic Review of the Technology Acceptance Model in Health Informatics
Bahlol Rahimi, Hamed Nadri, Hadi Lotfnezhad Afshar et al. · 2018 · Applied Clinical Informatics · 621 citations
Background One common model utilized to understand clinical staff and patients' technology adoption is the technology acceptance model (TAM). Objective This article reviews published research on TA...
Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model
Carlos Tam, Diogo Soares dos Santos, Tiago Oliveira · 2018 · Information Systems Frontiers · 522 citations
“Extending the Technology Acceptance Model (TAM) to Predict University Students’ Intentions to Use Metaverse-Based Learning Platforms”
Ahmad Samed Al‐Adwan, Na Li, Amer Al-Adwan et al. · 2023 · Education and Information Technologies · 477 citations
The Centrality of Awareness in the Formation of User Behavioral Intention toward Protective Information Technologies
Tamara Dinev, Qing Hu · 2007 · Journal of the Association for Information Systems · 411 citations
While there is a rich body of literature on user acceptance of technologies with positive outcomes, little is known about user behavior toward what we call protective technologies: information tech...
Reading Guide
Foundational Papers
Start with Dinev and Hu (2007) for protective tech awareness centrality, then Venkatesh (2006) for adoption decision-making directions, as they establish trust and risk as core before UTAUT synthesis.
Recent Advances
Study Venkatesh et al. (2016) UTAUT roadmap, Pitardi and Marriott (2021) on voice AI trust, and Al-Adwan et al. (2023) metaverse TAM extensions for current model evolutions.
Core Methods
Core techniques are UTAUT/TAM extensions via SEM (Venkatesh et al., 2016; Slade et al., 2015), expectation-confirmation for continuance (Tam et al., 2018), and resistance frameworks (Mani and Chouk, 2018).
How PapersFlow Helps You Research Trust and Security in Technology Adoption
Discover & Search
Research Agent uses searchPapers and exaSearch to find UTAUT extensions on trust, like Venkatesh et al. (2016), then citationGraph reveals 2101 citing works on security in fintech. findSimilarPapers links Slade et al. (2015) to health IT parallels in Rahimi et al. (2018).
Analyze & Verify
Analysis Agent applies readPaperContent to extract risk scales from Slade et al. (2015), verifies UTAUT syntheses via verifyResponse (CoVe), and uses runPythonAnalysis for meta-analysis of citation impacts with GRADE grading on evidence strength in adoption models.
Synthesize & Write
Synthesis Agent detects gaps in trust modeling post-Venkatesh et al. (2016), flags contradictions between mobile and health findings, while Writing Agent uses latexEditText, latexSyncCitations for Venkatesh papers, and latexCompile to produce adoption model reports with exportMermaid for UTAUT diagrams.
Use Cases
"Run meta-analysis on trust factors in UTAUT across fintech papers."
Research Agent → searchPapers('UTAUT trust fintech') → Analysis Agent → runPythonAnalysis(pandas meta-regression on effect sizes from Slade 2015, Venkatesh 2016) → GRADE-graded statistical summary table.
"Draft LaTeX review on security barriers in health IT adoption."
Research Agent → citationGraph(Rahimi 2018) → Synthesis Agent → gap detection → Writing Agent → latexEditText(structure review) → latexSyncCitations(10 papers) → latexCompile(PDF with UTAUT figure via latexGenerateFigure).
"Find code for simulating adoption models with security variables."
Research Agent → paperExtractUrls(Venkatesh 2016) → Code Discovery → paperFindGithubRepo → githubRepoInspect(UTAUT simulation scripts) → runPythonAnalysis(test risk-trust interactions).
Automated Workflows
Deep Research workflow conducts systematic review of 50+ UTAUT trust papers: searchPapers → citationGraph → DeepScan(7-step verification with CoVe checkpoints). Theorizer generates theory extensions from Dinev and Hu (2007) awareness model to IoT via literature synthesis. DeepScan analyzes continuance intentions in Tam et al. (2018) with statistical critiques.
Frequently Asked Questions
What is the definition of Trust and Security in Technology Adoption?
It examines privacy concerns, perceived security, and institutional trust as barriers to technology acceptance, particularly in fintech and health IT using models like UTAUT.
What are key methods used?
Methods include structural equation modeling for UTAUT extensions (Venkatesh et al., 2016), surveys for risk scales (Slade et al., 2015), and systematic reviews of TAM in health (Rahimi et al., 2018).
What are the most cited papers?
Venkatesh et al. (2016) UTAUT synthesis (2101 citations), Slade et al. (2015) mobile payments trust (864 citations), and Dinev and Hu (2007) protective tech awareness (411 citations).
What are open problems?
Challenges include context-specific risk measurement, AI trust integration beyond voice assistants (Pitardi and Marriott, 2021), and resistance barriers in IoT services (Mani and Chouk, 2018).
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