» Articles » PMID: 36329748

Application and Extension of the UTAUT2 Model for Determining Behavioral Intention Factors in Use of the Artificial Intelligence Virtual Assistants

Overview
Journal Front Psychol
Date 2022 Nov 4
PMID 36329748
Authors
Affiliations
Soon will be listed here.
Abstract

Virtual Assistants, also known as conversational artificial intelligence, are transforming the reality around us. These virtual assistants have challenged our daily lives by assisting us in the different dimensions of our lives, such as health, entertainment, home, and education, among others. The main purpose of this study is to develop and empirically test a model to predict factors that affect users' behavioral intentions when they use intelligent virtual assistants. As a theoretical basis for investigating behavioral intention of using virtual assistants from the consumers' perspective, researchers employed the extended Unified Theory of Acceptance and Use of Technology (UTAUT2). For this research paper, seven variables were analyzed: performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, habit, and price/value. In order to improve consumer behavior prediction, three additional factors were included in the study: perceived privacy risk, trust, and personal innovativeness. Researchers carried out an online survey with 304 responses. The obtained sample was analyzed with Structural Equation Modeling (SEM) through IBM SPSS V. 27.0 and AMOS V 27.0. The main study results reveal that factors, such as , and , have a significant impact on the adoption of virtual assistants. However, on the other side, , and were not significant factors in the users' intention to adopt this service. This research paper examines the effect of personal innovation, security, and trust variables in relation to the use of virtual assistants. It contributes to a more holistic understanding of the adoption of these intelligent devices and tries to fill the knowledge gap on this topic, as it is an emerging technology. This investigation also provides relevant information on how to successfully implement these technologies.

Citing Articles

Understanding older adults' acceptance of Chatbots in healthcare delivery: an extended UTAUT model.

Yu S, Chen T Front Public Health. 2024; 12:1435329.

PMID: 39628811 PMC: 11611720. DOI: 10.3389/fpubh.2024.1435329.


Utilization of, Perceptions on, and Intention to Use AI Chatbots Among Medical Students in China: National Cross-Sectional Study.

Tao W, Yang J, Qu X JMIR Med Educ. 2024; 10:e57132.

PMID: 39466038 PMC: 11533383. DOI: 10.2196/57132.


Acceptance of artificial intelligence in university contexts: A conceptual analysis based on UTAUT2 theory.

Acosta-Enriquez B, Ramos Farronan E, Villena Zapata L, Mogollon Garcia F, Rabanal-Leon H, Angaspilco J Heliyon. 2024; 10(19):e38315.

PMID: 39430455 PMC: 11489141. DOI: 10.1016/j.heliyon.2024.e38315.


Exploring the recycled water acceptance based on the technological perspective of UTAUT2: a hybrid analytical approach.

Xu X, Hu Y, Gao Y, Jia Q Front Psychol. 2024; 15:1384635.

PMID: 38957883 PMC: 11217519. DOI: 10.3389/fpsyg.2024.1384635.


Analyzing Preceding factors affecting behavioral intention on communicational artificial intelligence as an educational tool.

Cortez P, Ong A, Diaz J, German J, Singh Jagdeep S Heliyon. 2024; 10(3):e25896.

PMID: 38356557 PMC: 10865406. DOI: 10.1016/j.heliyon.2024.e25896.


References
1.
Juaneda-Ayensa E, Mosquera A, Sierra Murillo Y . Omnichannel Customer Behavior: Key Drivers of Technology Acceptance and Use and Their Effects on Purchase Intention. Front Psychol. 2016; 7:1117. PMC: 4963459. DOI: 10.3389/fpsyg.2016.01117. View

2.
Malarvizhi C, Al Mamun A, Jayashree S, Naznen F, Abir T . Predicting the Intention and Adoption of Near Field Communication Mobile Payment. Front Psychol. 2022; 13:870793. PMC: 9033273. DOI: 10.3389/fpsyg.2022.870793. View

3.
Zarouali B, Van den Broeck E, Walrave M, Poels K . Predicting Consumer Responses to a Chatbot on Facebook. Cyberpsychol Behav Soc Netw. 2018; 21(8):491-497. DOI: 10.1089/cyber.2017.0518. View

4.
Tsay D, Patterson C . From Machine Learning to Artificial Intelligence Applications in Cardiac Care. Circulation. 2018; 138(22):2569-2575. DOI: 10.1161/CIRCULATIONAHA.118.031734. View

5.
Schmitz A, Diaz-Martin A, Yague Guillen M . Modifying UTAUT2 for a cross-country comparison of telemedicine adoption. Comput Human Behav. 2022; 130:107183. PMC: 8739826. DOI: 10.1016/j.chb.2022.107183. View