Intelligent Attributes of Voice Assistants and User’s Love for AI: A SEM-Based Study

Abstract

Adoption of Voice Assistants (VA) has been a hot topic of research currently. However, the existing studies are based on traditional models like the Technology Acceptance Model that is less suitable for investigating adoption of AI-based technologies like VAs. This is because the conventional models do not consider the anthropomorphic nature of AI-based technologies that has the potential to form emotional bonds between man and machines. Therefore, in this work we propose artificial autonomy as an intelligent AIbased attribute founded on three task primitives of sensing, thinking, and action autonomy. Further, we use the concept of interpersonal love and apply it in the context of human-AI relationship based on Sternberg's Triangular Theory of love. Data is collected from 607 participants across two Asian countries. Partial Least Squares (PLS) based Structural Equation Modelling (SEM) has been used for the purpose of model building and testing. The results highlight the importance of artificial autonomy on all the three aspects of love, indicating the presence of consummate love. ''Love for AI'' is different from ''brand love'' since in the later case some of the love aspects of passion, intimacy, and commitment are missing. Theoretically, this study improves our understanding of artificial autonomy as an intelligent AI-attribute and the perceptions of love in Information Systems research. The findings can help technology companies develop novel strategies for improving the human-likeliness of these VAs.

The Role of Artificial Intelligence Autonomy in Higher Education: A Uses and Gratification Perspective

“…That is, the task performers first sense and identify the environmental factors, then think and reflect to generate a decision or plan, and finally act based on the plan to solve the problem. Consistent with previous studies [33,39,[80][81][82][83], we categorize artificial autonomy, based on the STA paradigm, into sense autonomy, Drawing on the U&G theory [51,52], the literature on artificial autonomy [33,37,38], and the sense-think-act paradigm [64,65], this study provides a new perspective for understanding how AI educators' artificial autonomy can improve their usage intention. The model proposed in this study takes into account that users proactively select AI educators when motivated by the U&G benefits.…”

“…That is, the task performers first sense and identify the environmental factors, then think and reflect to generate a decision or plan, and finally act based on the plan to solve the problem. Consistent with previous studies [33,39,[80][81][82][83], we categorize artificial autonomy, based on the STA paradigm, into sense autonomy, thought autonomy, and action autonomy to capture the autonomous ability of AI educators in each step in the process of problem-solving.…”

“…When students seek information, AI educators can autonomously sense their information needs and enable users to perceive the implementation of hands-free commands through their real-time grasp of the environment and users' statuses. This type of autonomy allows users to perceive fast, accurate, efficient, effective, and even hands-free information responses [33,80], resulting in enhanced information-seeking gratification. For example, when students find it difficult to understand a teaching video, AI educators may monitor students' confused expressions (such as frowning), actions (such as pausing the video or repeatedly watching), or voice (such as the voice of students asking others).…”

“…Accessed on 23 December 2023, from https://xiaoai.mi.com/AboutXiaoai?tab=link003). Previous studies have shown that this type of autonomy causes users to clearly perceive themselves as being cared for by AI agents, and believe that AI agents are always with them, which leads to the belief that AI agents are friendly, enthusiastic, intimate, and loyal to the students [80]. Meanwhile, a large number of studies demonstrated that friendliness [96], social presence [97], intimacy [98], and loyalty [99] have significant positive impacts on relationship establishment and maintenance, which, in turn, leads to user engagement in social interactions.…”

“…For example, users may voice-command the AI educator to perform specific tasks, request that an AI educator answers questions, ask an AI educator about the course schedule, receive proactive care and reminders from the AI educator, etc. Because AI educators can interact with users in an anthropomorphic [30,40,102], personalized [30,46,101], authentic [46], and responsive [45,103,104] manner, users can clearly perceive the friendliness, care, and warmth of the AI educator [80,101,105]. Therefore, such human-computer interactions may meet or even exceed users' expectations for social interaction.…”