Skip to main content
. 2022 Sep 16;8(9):e10662. doi: 10.1016/j.heliyon.2022.e10662

Table 1.

A critical overview of related works.

Study Methodology Context/Setting Sample frame Sample Size Main Variables Outcomes Critical Overview
Pal et al. (2021) Cross-sectional; survey Smart voice assistant Alexa users and Google Assistant users 244 User engagement; trust; privacy risk; satisfaction; slowness of adoption; skepticism; attitude Continuance usage Pal et al. (2021) reflected the utilitarian attitude and the hedonic attitude as exogenous variables. On the other hand, the current study observes the formation process of continuance intention more elaborately by presenting the evidence factors that determine the utilitarian value and the hedonic value.
McLean and Osei-Frimpong (2019) Cross-sectional; survey AI In-home voice assistant Amazon Echo users 724 Utilitarian benefits; hedonic benefits; symbolic benefits; social presence; social interaction; perceived privacy risk Usage McLean and Osei-Frimpong (2019) explicated the use of voice assistants based on utilitarian benefits and hedonic benefits. The present research approaches the user behavior of AIPA more delicately by observing the formation mechanism of utilitarian value and hedonic value.
Nguyen et al. (2021) Cross-sectional; survey Chatbot Bank's chatbot users 359 Information quality; system quality; service quality; trust; user satisfaction; confirmation expectations; perceived usefulness Continuance intention Nguyen et al. (2021) reflected robust variables, but they have been overused in IT contexts. The current study is different in that it includes AIPA-specific variables while maintaining the major determinants of IT use.
Nguyen et al. (2019) Cross-sectional; survey Voice-user interface Voice-user interface users 414 Gender; information quality; information satisfaction; system quality; system satisfaction; perceived usefulness; perceived ease of use; perceived enjoyment; mobile self-efficacy; trust; perceived risk; attitude Continuance intention Nguyen et al. (2019) introduced representative variables of the IS success model and technology acceptance model. They did not reflect the unique characteristics of AIPA, which communicates through voice. The present research employs AIPA-specific factors and systematically structured them into two aspects: utilitarian value and hedonic value.
Pillai et al. (2020) Cross-sectional; survey AI-powered automated retail stores AI-powered store consumers 1250 Optimism; innovativeness; discomfort; insecurity; perceived usefulness; perceived ease of use; perceived enjoyment; customization; interactivity Intention to shop Pillai et al. (2020) focused on AI in the shopping environment by considering the tendencies of consumers. On the other hand, the current work aims to study AIPA, which is most easily encountered by people. It can derive implications that can be applied to detailed AI subjects (e.g., shopping, game, education, etc.).
Jang (2020) Cross-sectional; survey Virtual personal assistant Smart speaker users 534 Parasocial interaction; Personification type; Loneliness Satisfaction Jang (2020) investigated only how parasocial interaction, types of assistants, and loneliness affect satisfaction. The present paper has limitations in that it does not consider the function, technology, and value of assistants. To overcome it, this study measured related factors from the users' cognitive perspective.
Hasan et al. (2021) Cross-sectional; survey Voice-controlled AI Siri users 675 Trust; interaction; perceived risk; novelty value; employment; brand involvement; consumer innovativeness Brand loyalty While Hasan et al. (2021) explained brand loyalty by examining only Siri, the current article describes the general intention of using AIPA by investigating multiple assistants. Hasan et al. (2021) suggested basic interaction as the antecedent of brand loyalty. This study introducesparasocial interaction based on the human-like behavior of AIPA.
Xu et al. (2020) Study 3; Experimental study AI customer service Bank's AI Online service 51 Online customer service; perceived problem-solving ability; task-complexity Usage intention In explaining the use of AI customer service, Xu et al. (2020) considered only task complexity and problem-solving ability. The subjects of the study are used only for utility purposes. Because AIPA can provide both utilitarian value and hedonic value, this study considered both utility and hedonic aspects.