Smart voices, smarter choices: Integrating information foraging and technology acceptance to examine retrieval efficiency in AI voice assistants

Voice assistants have become important mediators of digital information access, yet their evaluation continues to focus primarily on technical performance metrics such as response accuracy and latency. This study examines the retrieval efficiency of three leading artificial intelligence–driven voice assistants; Amazon Alexa, Google Assistant and Apple Siri, using an integrated analytical perspective based on information foraging theory and the technology acceptance model. A total of 30 validated factual queries across six knowledge domains were used to evaluate system performance in terms of response latency, factual accuracy, answer modality and dependence on external sources. The results indicate clear differences across systems. Amazon Alexa demonstrates the highest overall retrieval efficiency, characterized by accurate and direct responses with relatively low latency. Google Assistant shows balanced performance with moderate latency and more elaborated responses, while Apple Siri relies more frequently on external links, introducing additional interaction steps. Interpreted through the integrated information foraging theory–technology acceptance model framework, these findings suggest that system-level performance differences may be understood in terms of interaction efficiency and their potential relationship to user-oriented evaluation. However, such interpretations remain conceptual, as user perceptions are not directly measured in this study. While the analysis is limited to factual queries and controlled conditions, the study contributes by introducing retrieval efficiency as a composite measure for evaluating voice assistant performance and by demonstrating the value of integrating cognitive and behavioural perspectives. The findings offer implications for the design of more efficient and interaction-oriented voice-based information systems.