AI-driven advisory in insurance services : An experimental study on trust, transparency, and policy adoption
*Chirag Rasikbhai PatelCorresponding authorcrpspce@gmail.comDepartment of Business ManagementFaculty of Management StudiesSankalchand Patel College of Engineering, Sankalchand Patel UniversityVisnagar, Gujarat, 384315, India0000-0003-4788-0749View full profile → , Parimal R. Trivedicbrathod.2018@gmail.comDepartment of Business ManagementFaculty of Management StudiesSmt. S. B. Patel Institute of Business Management, Sankalchand Patel UniversityVisnagar, Gujarat, 384315, India0009-0009-0887-8794View full profile → , Khushbuben Ravindrabhai Yadavkrymba_spce@spu.ac.inDepartment of Business ManagementFaculty of Management StudiesSankalchand Patel College of Engineering, Sankalchand Patel UniversityVisnagar, Gujarat, 384315, India0009-0005-6750-9316View full profile → , Bhagvan M. Karnavatbhagvan.karnavat@nfsu.ac.inDepartment of Management StudiesNational Forensic Sciences UniversityDelhi Campus, Delhi, 110085, India0009-0005-8041-6464View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Jun 2026
- Published Online:
- 30 Sep 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2449
- Pages:
- 3521–3533
Abstract
Purpose: The rise of artificial intelligence (AI) in insurance advisory services has transformed how insurers advise their customers; however, empirical comparisons of alternative configurations of advisory services are limited. This study investigates the effects of AI-only, human-only, and hybrid human–AI advisory models on consumer trust, perceived transparency, perceived risk, and policy purchase intent.
Design/methodology/approach: An experiment was designed to compare three advisory groups: AI only, human only, and hybrid (human and AI) using a between-groups randomized design on 90 insurance consumers in a standardized insurance policy selection scenario. Validated measurement scales were used and data analysed using analysis of variance (ANOVA), mediation and moderation analysis (PROCESS macro) and robustness checks using structural equation modelling.
Findings: The results show that hybrid human–AI advisory creates the most trust, perceived transparency, and purchase intent. The adoption of AI-only advisory service not only increases perceived efficiency but also raises cyber security and privacy concerns, which works against adoption. Advisory mode is a mediator between perceived transparency and perceived risk and consumer outcomes, and AI literacy and prior claim experience moderate advisory preferences.
Originality/value: The study offers causal evidence on the impact of AI in insurance advisory and also emphasises the strategic worth of hybrid Human–AI systems in trust-essential monetary services.
Keywords
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References
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