Artificially intelligent interactive voice assistants (AIIVAs) are developed to understand language, but there is limited insight into their ability to understand accents. While there have been substantial advancements in understanding multiple languages by AIIVAs, having an understanding of variety of accents is an emerging concern. To address these concerns, we contextualised our study in India, one of the world's most populated and diverse countries with varying accents and dialects. Study 1 collected qualitative data through semi structured interviews with participants, data was subsequently thematically analysed, and a typology was developed with respect to the context of use and consumers' emotional and rational reactions towards AIIVAs when interacting with accents. For Study 2, we implemented the quantitative research method. This was done to reiterate the conceptual model formulated from the qualitative research findings. Findings suggest that positive emotional action has emerged as the most significant factor, followed by rational action and negative emotional action. This study contributes significantly to the theoretical understanding of future consumer behaviour and human‐computer interaction trends. It provides practical implications for managers, tech developers, and other companies working and using speech‐to‐text automatic speech recognition to know that while they train their algorithms with languages, they should be mindful of the diverse accents of their consumers.

There are multiple studies establishing the importance of Business Intelligence (BI), in the Big Data Analytics context. Voice is yet to be seen as a contributing channel. Voice enabled assistants are at the forefront of conversational AI advancement. As humans speak to devices, brands and business are investing in engagement through voice channel. This voice engagement is resulting in both intangible and tangible benefits and generating voice commerce. The resultant voice data should be integral to BI, leading to Voice BI. This paper proposes a conceptual framework from engagement to intelligence, with support of five propositions to realise voice business intelligence. Type of applications and their engagement characterisation is segregated to create better understanding using Cross-Cases Observation Technique. Along with future research agenda to strengthen the propositions, this investigation observes building voice business intelligence by tracking relevant metrics which enable informed decisions.