Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model

Abstract

This study investigates the factors that build resistance and attitude towards AI voice assistants (AIVA). A theoretical model is proposed using the dual-factor framework by integrating status quo bias factors (sunk cost, regret avoidance, inertia, perceived value, switching costs, and perceived threat) and Technology Acceptance Model (TAM; perceived ease of use and perceived usefulness) variables. The study model investigates the relationship between the status quo factors and resistance towards adoption of AIVA, and the relationship between TAM factors and attitudes towards AIVA. A sample of four hundred and twenty was analysed using structural equation modeling to investigate the proposed hypotheses. The results indicate an insignificant relationship between inertia and resistance to AIVA. Perceived value was found to have a negative but significant relationship with resistance to AIVA. Further, the study also found that inertia significantly differs across gender (male/female) and age groupings. The study's framework and results are posited as adding value to the extant literature and practice, directly related to status quo bias theory, dual-factor model and TAM.

Voice Assistant vs. Chatbot – Examining the Fit Between Conversational Agents’ Interaction Modalities and Information Search Tasks

“…Existing information systems (IS) research on VA use distinguishes between utilitarian and hedonic drivers and inhibitors. As speech is a natural and intuitive way of interacting (Kock, 2004), various studies stress its utilitarian benefits of convenience, ease of use, and time saving through hands-free possibilities (Balakrishnan et al, 2021;McLean & Osei-Frimpong, 2019). In addition, users enjoy talking to their VAs, thus deriving hedonic value from their interactions (Pal et al, 2020;Rzepka, 2019;Yang & Lee, 2019).…”

Perceived value of AI-based recommendations service: the case of voice assistants

“…Prior research into VAs has been limited mostly to existing general technology adoption frameworks, such as the technology acceptance model (TAM; Davis 1989;Balakrishnan et al 2021) and the unified theory of acceptance and use of technology (UTAUT; Venkatesh and Davis 2000;Vimalkumar et al 2021). Hence, research into VAs needs to look beyond the traditional adoption models.…”

Examining the context‐specific reasons and adoption of artificial intelligence‐based voice assistants: A behavioural reasoning theory approach

“…In this AI‐influenced environment, consumers' decision‐making processes are changing (Klaus & Zaichkowsky, 2021). Following the emergence of AI, technology businesses began creating their own voice assistants, and within a few years, nearly all tech giants had released AI‐based voice assistants to the public (Balakrishnan et al, 2021). Apple's Siri, Amazon's Alexa, Samsung's Bixby, Microsoft's Cortana, and Google Assistant are voice assistants developed by top tech companies.…”

“…The adoption of AI‐based voice assistants is also influencing other domains, such as consumer buying behaviour, as consumers' attitudes and purchasing habits are changing due to their responsiveness to AI technologies (V. Kumar et al, 2019). Therefore, besides the technical aspects and usage of AI‐based voice assistants, consumer behaviour towards this advancement is another significant factor to investigate (Balakrishnan et al, 2021).…”