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Where voice-assistant interviews feel acceptable

Source reportMethodology

Overview

Digital voice assistants are familiar to most adults. About 82% are at least somewhat familiar with tools like Google Assistant, Alexa, or Siri.

Participation in a voice-assistant interview is more situational. Home and household settings are more acceptable than public places or public transit.

Stacked breakdown

82% are at least somewhat familiar with digital voice assistants.

How familiar are you with using digital voice assistants like Google Assistant, Amazon’s Alexa or Apple’s Siri?

Extremely familiar
30.0%
Very familiar
26.5%
Somewhat familiar
25.6%
Slightly familiar
10.1%
Not at all familiar
7.9%

2025 · base n 1,000 · +/- 3.1%

Verasight AAPOR Omnibus Survey #2025-040

View source data

Familiarity is broad

About 30% say they are extremely familiar with digital voice assistants, and another 27% are very familiar.

Only about 8% say they are not at all familiar.

Topline

63% would participate while relaxing at home.

Regardless of whether or not your use a voice assistant in your daily life, if you were invited to an interview with a digital voice assistant in which of the following situations would you participate?

  • While relaxing at home 63.4%
  • While doing household chores, like cooking, cleaning, washing 43.8%
  • While driving in a car 30.9%
  • None of the above 20.7%
  • While staying in public places 17.1%
  • While commuting with public transport 14.3%

2025 · base n 1,000 · +/- 3.1%

Verasight AAPOR Omnibus Survey #2025-040

View source data

Private settings work better for interviews

The most selected interview setting is relaxing at home, chosen by about 63%.

Household chores are next at 44%, followed by driving in a car at 31%.

Public settings are less inviting

Public transportation and public places are much lower, selected by about 14% and 17%, respectively.

About 21% say none of the listed situations would be acceptable.

Methodology

Full methodology
Mode
Verasight panel recruited via random address-based sampling, random person-to-person text messaging, and dynamic online targeting
Population
US adults age 18+
Field dates
2025-05-22 → 2025-05-28
Base (unweighted)
1,000
Margin of error
+/- 3.1%
Module
Verasight AAPOR Omnibus Survey #2025-040
Sponsor
Verasight
Weight variable
weight
Weighting targets
age, race/ethnicity, sex, income, education, region, metropolitan status

Sources

[2]
  • 01
    How familiar are you with using digital voice assistants like Google Assistant, Amazon’s Alexa or Apple’s Siri?Shows baseline familiarity with digital voice assistants.reports.verasight.io/reports/verasight-aapor-omnibus-survey-2025-040
  • 02
    Regardless of whether or not your use a voice assistant in your daily life, if you were invited to an interview with a digital voice assistant in which of the following situations would you participate?Shows where adults would participate in a voice-assistant interview.reports.verasight.io/reports/verasight-aapor-omnibus-survey-2025-040

Citation

Verasight AAPOR Omnibus Survey #2025-040, fielded May 22-28, 2025, N=1,000 US adults age 18+, +/- 3.1%.

https://reports.verasight.io/reports/verasight-aapor-omnibus-survey-2025-040#how-familiar-are-you-with-using-digital-voice-assistants-like-google-assistant-amazon-s-alexa-or-apple-s-siri

Verasight survey methodology

How Verasight conducts surveys.

This page describes the Verasight general survey contract, separate from how the Data Library packages it. Each wave's specific field dates, sample sizes, and module breakdown are listed in that wave's report.

Mode
Verasight panel recruited via random address-based sampling, random person-to-person text messaging, and dynamic online targeting.
Population
US adults age 18+.
Sample design
Surveys are run as omnibus or single-topic waves. Omnibus waves are split into modules with their own respondent set, typically around one thousand respondents per module.
Field window
Each wave specifies its own field dates. Most omnibus waves field across roughly two weeks.
Weighting
Per-module weighting to CPS targets including age, race and ethnicity, sex, income, education, region, and metropolitan status.
Partisanship benchmark
Pew Research Center's NPORS benchmarking surveys, three-year running average.
Vote benchmark
2024 presidential vote population benchmarks.
Margin of error
Typically about plus or minus 3.4 to 3.6 percent per module at standard module sizes. Question-level MoE is recomputed when a base shrinks materially below the module baseline.
Reporting
Every wave is published as a standalone report at verasight.io/reports with full instrument and methodology.
Transparency
AAPOR transparency standards.

Wave-specific methodology, full weighting variable lists, and verbatim instrument text live in each report at verasight.io/reports.