Voice-based interfaces provide new opportunities for firms to interact with consumers along the customer journey. The current work demonstrates across four studies that voice-based (as opposed to text-based) interfaces promote more flow-like user experiences, resulting in more positively-valenced service experiences, and ultimately more favorable behavioral firm outcomes (i.e., contract renewal, conversion rates, and consumer sentiment). Moreover, we also provide evidence for two important boundary conditions that reduce such flow-like user experiences in voice-based interfaces (i.e., semantic disfluency and the amount of conversational turns). The findings of this research highlight how fundamental theories of human communication can be harnessed to create more experiential service experiences with positive downstream consequences for consumers and firms. These findings have important practical implications for firms that aim at leveraging the potential of voice-based interfaces to improve consumers’ service experiences and the theory-driven “conversational design” of voice-based interfaces.
A critical aspect for the continuity of family businesses (FB) is the existence of willing successors. In this article, we use the imprinting theory to ground an explanation of how the family imprinting process shapes siblings’ willingness to become a successor in the FB. We stylize three family imprinting types based mainly on different family motivations: the FB-centric, the entrepreneurship-centric, and the free-will-centric. The uniqueness of this approach is that it offers an imprinting-based process model capable of explaining the origins of homogeneity and heterogeneity in succession willingness at both the inter- and the intra-family levels.
The use of voice recordings in both research and industry practice has increased dramatically in recent years—from diagnosing a COVID-19 infection based on patients’ self-recorded voice samples to predicting customer emotions during a service center call. Crowdsourced audio data collection in participants’ natural environment using their own recording device has opened up new avenues for researchers and practitioners to conduct research at scale across a broad range of disciplines. The current research examines whether fundamental properties of the human voice are reliably and validly captured through common consumer-grade audio-recording devices in current medical, behavioral science, business, and computer science research. Specifically, this work provides evidence from a tightly controlled laboratory experiment analyzing 1800 voice samples and subsequent simulations that recording devices with high proximity to a speaker (such as a headset or a lavalier microphone) lead to inflated measures of amplitude compared to a benchmark studio-quality microphone while recording devices with lower proximity to a speaker (such as a laptop or a smartphone in front of the speaker) systematically reduce measures of amplitude and can lead to biased measures of the speaker’s true fundamental frequency. We further demonstrate through simulation studies that these differences can lead to biased and ultimately invalid conclusions in, for example, an emotion detection task. Finally, we outline a set of recording guidelines to ensure reliable and valid voice recordings and offer initial evidence for a machine-learning approach to bias correction in the case of distorted speech signals.
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