Influencing factors of using behaviour for computational advertising under the theoretical model of UTAUT

Abstract

Based on the UTAUT theoretical model, this paper discusses the applicability of the model in computational advertising situations. Computational advertising relies on artificial intelligence technologies such as big data, cloud computing, new algorithms, and blockchain. Results show that: performance expectation, effort expectation, and social impact have a positive influence on the acceptance intention of computational advertising; stimulus has no significant influence on the using behaviour of computational advertising; the acceptance intention of computational advertising has a positive influence on the using behaviour of computational advertising. Four points should be paid attention to in the future computational advertising: paying attention to the needs of users and realising accurate advertising; improving efforts and expectations and optimising user experience; enhancing user interaction and user stickiness; protecting user privacy and improving user satisfaction. The core of computational advertising research in the field of humanities and social sciences is not dislocated from the traditional user-centred problem consciousness.