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The rapid growth of artificial intelligence (AI)-based voice-assistant systems (VASs) has created many opportunities for individuals to use VASs for various purposes in…
The rapid growth of artificial intelligence (AI)-based voice-assistant systems (VASs) has created many opportunities for individuals to use VASs for various purposes in their daily lives. However, traditional quality success factors, such as information quality and system quality, may not be sufficient in explaining the adoption and use of AI-based VASs. This study aims to propose interaction quality as an additional, yet more important quality measure that leads to trust in an AI-based VAS and its adoption.
The authors propose a research model that highlights the importance of interaction quality and trust as underlying mechanisms in the adoption of AI-based VASs. Based on survey methodology and data from 221 respondents, the proposed research model is tested with a partial least squares approach.
The results suggest that interaction quality and trust are critical factors influencing the adoption of AI-based VASs. The findings also indicate that the impacts of traditional quality factors (i.e. information quality and system quality) occur through interaction quality in the context of AI-based VASs.
This research adds interaction quality as a new quality factor to the traditional quality factors in the information systems success model. Further, given the interactive nature of VASs, the authors use social response theory to explain the importance of the trust mechanism when individuals interact with AI-based VASs.
Contribution to Impact
Consumers often encounter issues of perceived ambiguity and performance risk when attempting to evaluate experience goods being offered online. Sellers try to alleviate…
Consumers often encounter issues of perceived ambiguity and performance risk when attempting to evaluate experience goods being offered online. Sellers try to alleviate this knowledge gap often seen in a medium of low naturalness by engaging in effective compensatory adaptation. This research theoretically looks into three primary aspects of compensatory adaption and their potential in securing communication of high-quality information between the online seller and consumer.
Utilizing survey data and structural equation modeling, this study tests the effectiveness of different aspects of compensatory adaption to alleviate the knowledge gap in a medium of low naturalness.
Drawing on media naturalness theory and the tripartite model of attitude, this paper identifies three theoretical components that significantly affect the effectiveness of compensatory adaption. They are information retrieval capability from the cognitive/logical aspect, information richness from the affective/audiovisual aspect and interactivity from the behavioral aspect. The effectiveness of compensatory adaptation proves to have a positive impact on perceived information quality.
To the best of our knowledge, this is the first paper in the information systems literature to examine the compensatory adaptation tools for effective transfer of information. This study contributes to the academics by providing three handles to improve effectiveness of compensatory adaptation toward information quality. We focus on three compensatory adaptation tools in cognitive/logical, affective/audiovisual and behavioral aspects, and this compensation perspective leads to three practical factors that affect effective transfer of information between online sellers and consumers. The result of this study complements the nomological network of the enablers and impediments of e-commerce.