Alexa, know your limits: developing a framework for the accepted and desired degree of product smartness for digital voice assistants - Erika Graf; Denise Zessinger (SN Business & Economics, 2022)
This research investigates the conditions for the acceptance of digital voice user interfaces focusing on the accepted and desired degree of product smartness. We argue that digital voice assistants (DVAs) are different from other smart products because DVAs are not self-contained products. Smart products also work without DVAs. Therefore, the decision to buy and use a DVA is different. DVAs are not designed to work in isolation. Of course, they can be used only to talk to, but that greatly restricts what the assistants are capable of. The existing literature lacks research on the critical characteristics and properties of DVAs, as well as a categorization of their smartness in the light of the advances in artificial intelligence. The qualitative research design is based on interviews with users and non-users of DVAs. Using a qualitative content analysis, a category system for the degree of product smartness (PS) of DVAs is developed. This paper contributes to the existing literature by exploring the attributes that influence the perception of DVAs and providing a graduated framework for organizing the accepted and desired degree of smartness for DVAs. The framework suggests four gradations each representing an advanced application of artificial intelligence. Red lines appear for some applications, indicating that they are technically feasible but, at least currently, rejected. Rejection relates to the device's autonomous decision-making and privacy control capabilities, as well as the style of interaction, when the DVA acts as though it was a friend. Future research should quantitatively investigate the relationships between user profiles and acceptance. For designers, the model provides guidance for offering user-customized settings for DVAs, according to user preferences.
Keywords: M31; M15; O33; 330; Digital voice assistants; Product smartness; Autonomous products; Technological acceptance; Qualitative content analysis