AI in Service Interactions
Peer reviewed
Abstract
Abstract
Purpose This study aims to examine how generative artificial intelligence (AI) chatbots influence tourists’ purchase intention (TPI) in Iran’s tourism sector. Iran is a developing economy with distinct socio-cultural and technological features. The study uses the Stimulus-Organism-Response (S-O-R) framework. It explores technological stimuli: prompt quality (PQ), personal innovativeness (INN), personalization (PER) and interactivity (INT). These shape psychological responses: tourist attitude (TATT), tourist trust (TT) and tourist engagement (TE). The responses drive TPI. Digital literacy (DL) moderates these relationships. Design/methodology/approach This study used a quantitative approach. Data came from 400 Iranian tourists who used ChatGPT for travel activities. An online survey was conducted in November–December 2024. The authors applied partial least squares structural equation modeling (PLS-SEM) to test the hypotheses. Findings PQ, INN, PER and INT positively affect TATT, TT and TE (with one exception for PQ-TE). These psychological factors mediate the path to TPI. TT is the strongest driver of TPI. This highlights its importance in low-trust contexts. DL strengthens the link between TATT and TPI. Its effect on TT and TE is limited. The model shows strong explanatory and predictive power. Originality/value This study extends the S-O-R framework to generative AI chatbots in a developing tourism market. It introduces PQ and DL as key elements. It addresses literature gaps by considering socio-cultural nuances. The study provides theoretical insights and practical recommendations for chatbot design. These can boost engagement, trust and sustainable tourism in similar economies.
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@article{Ghorbanzadeh2026Leveraging,
title = {Leveraging generative AI for tourist purchase intention: an S-O-R model moderated by digital literacy},
author = {Davood Ghorbanzadeh and Hitmi Khalifa Alhitmi and Biju Theruvil Sayed and Teddy Chandra and S Senthil kumar and K D V Prasad},
journal = {Nankai Business Review International},
year = {2026},
doi = {10.1108/nbri-07-2025-0084},
url = {https://doi.org/10.1108/nbri-07-2025-0084}
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