The future of anticompetitive self-preferencing: analysis of hypernudging by voice assistants under article 102 TFEU

Morozovaite, Viktorija

doi:10.1080/17441056.2023.2200623

European Competition Journal

2023

DOI: 10.1080/17441056.2023.2200623

|View full text |Cite

The future of anticompetitive self-preferencing: analysis of hypernudging by voice assistants under article 102 TFEU

Viktorija Morozovaite

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Digital Lyrebirds: Experimental Evidence That Voice-Based Deep Fakes Influence Trust

Schanke,

Burtch,

Ray

2024

Management Science

View full text Add to dashboard Cite

We consider the pairing of audio chatbot technologies with voice-based deep fakes, that is, voice clones, examining the potential of this combination to induce consumer trust. We report on a set of controlled experiments based on the investment game, evaluating how voice cloning and chatbot disclosure jointly affect participants’ trust, reflected by their willingness to play with an autonomous, AI-enabled partner. We observe evidence that voice-based agents garner significantly greater trust from subjects when imbued with a clone of the subject’s voice. Recognizing that these technologies present not only opportunities but also the potential for misuse, we further consider the moderating impact of AI disclosure, a recent regulatory proposal advocated by some policymakers. We find no evidence that AI disclosure attenuates the trust-inducing effect of voice clones. Finally, we explore underlying mechanisms and contextual moderators for the trust-inducing effects, with an eye toward informing future efforts to manage and regulate voice-cloning applications. We find that a voice clone’s effects operate, at least in part, by inducing a perception of homophily and that the effects are increasing in the clarity and quality of generated audio. Implications of these results for consumers, policymakers, and society are discussed. This paper has been This paper was accepted by D. J. Wu for the Special Issue on the Human-Algorithm Connection. Funding: This work was supported by funding from the University of Wisconsin-Milwaukee Research Assistance Fund. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03316 .

show abstract

Digital Lyrebirds: Experimental Evidence That Voice-Based Deep Fakes Influence Trust

Schanke,

Burtch,

Ray

2024

Management Science

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

The future of anticompetitive self-preferencing: analysis of hypernudging by voice assistants under article 102 TFEU

Cited by 1 publication

References 0 publications

Digital Lyrebirds: Experimental Evidence That Voice-Based Deep Fakes Influence Trust

Digital Lyrebirds: Experimental Evidence That Voice-Based Deep Fakes Influence Trust

Contact Info

Product

Resources

About