Artificial Intelligence Based Travel Recommendations and Tourist Decision Making: The Mediating Role of Trust

Abstract

Travel suggestions which are artificial intelligence based give the tourist information and an alternative of suggestions as they are planning on their travels. The level of their participation in decision making is based on the perceived recommendation performance and trust in the recommending system. This research paper looks at the correlation between the recommendation quality, personalization, trust and tourist decision making through a provided survey analysis of 560 qualified records. Twenty statements measured on a five-point agreement scale were used to measure four reflective constructs. The analytical workbook has reported partial least squares structural equation modelling and 5,000 bootstrap resamples. Trust had a positive association with the quality of the recommendation and personalization with standardized coefficients of 0.3548 and 0.3456, respectively. They were directly correlated to tourist decision making with a value of 0.2186 and 0.1953. The most direct relation was observed between trust and decision making that was 0.4312. The indirect relationships based on trust were positive in case of recommendation quality 0.1530 and personalization 0.1490. Confidence intervals not equal to zero supported all the seven hypotheses. The model accounted 34.07% of the trust variances and 47.88% of the decision-making variances. The findings show the existence of complementary partial mediation, and the importance of information quality and preference compatibility to facilitate decision support in part via trust. It can only be interpreted on the basis of provided analytical records since the provenance of fieldwork and independent outcomes of behaviour were not recorded.