Skip to content
#explainable ai Review Open access

Digital insurance adoption and financial inclusion among urban consumers in Chennai: Mediating effects of awareness, digital literacy, and trust

Oct 2026 · Insurance Markets and Companies · 0 citations · 51 references
Technology Adoption and User Behaviour

Abstract

Type of the article: Research ArticleAbstractInsurance penetration in India remains critically low (2.7 per cent life, 1.0 per cent general) despite two decades of market liberalization and substantial digital infrastructure expansion. This study investigates how insurance awareness and digital literacy translate into digital insurance adoption among Chennai's urban consumers, with perceived usefulness, perceived ease of use, and trust as cognitive and relational pathways linking capability to market action. Data were collected in August-September 2024 from 405 purposively sampled consumers with digital device access, comprising existing insurance holders and potential first-time buyers, using a structured questionnaire. Chennai, a theoretically relevant research setting with mature digital infrastructure and diverse urban demographics, allows perceptual and informational barriers to be examined independently of connectivity constraints, offering transferable insights for comparable Indian urban markets. Structural equation modeling showed excellent measurement model fit across standard indices, explaining 50.2 per cent of adoption intention variance. Awareness was the strongest predictor of perceived ease of use (β = 0.758, R2 = 57.5 per cent) and digital literacy of perceived usefulness (β = 0.752, R2 = 56.5 per cent). Both influenced adoption intention entirely through their respective mediators without significant direct effects, consistent with complete statistical mediation. Trust added serial mediation pathways, reinforcing the role of institutional confidence in credence good markets. Awareness campaigns and literacy programs yield negligible adoption gains unless designed to strengthen perceived platform utility and navigability. Insurers and policymakers should prioritize interface design optimizing functional value and operational ease, with transparent claims processes converting platform confidence into durable institutional trust.AcknowledgmentsThe authors used an AI language tool (Claude, Anthropic) for assistance with language editing, readability improvement, and proofreading of this manuscript. All intellectual content, research design, data collection, analysis, interpretation, and conclusions are solely the work of the authors. The authors reviewed and verified all AI-assisted text and take full responsibility for the accuracy and integrity of the final manuscript.

Read PDF

Similar papers

#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.

Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al. · 62 citations · ⚡3
#computer vision Open access Mar 2024

LLM-based agents for automating the enhancement of user story quality: An early report

The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.

Zheying Zhang, M. Rayhan, Tomas Herda et al. · 48 citations · ⚡4
#computer vision Review Mar 2024

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.

Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al. · 44 citations · ⚡2
#computer vision Feb 2024

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.

Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al. · 41 citations
#artificial intelligence Conference Open access Jun 2018

The Key Concepts of Ethics of Artificial Intelligence

It is suggested that the focus on finding keywords is the first step in guiding and providing direction for future research in the AI ethics field.

Ville Vakkuri, P. Abrahamsson · 39 citations · ⚡2

Related blog posts

MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.