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Does Voice Kill the Text Star? No, It Does Not! An Online Experiment on the Trustworthiness of the Text-Based and Voice-Based Variants of Google Assistant.

Authors:
Katrin Etzrodt at TU Dresden
Katrin Etzrodt
Sophie Wagner at TU Dresden
Sophie Wagner
Sven Engesser
Sven Engesser
Sven Engesser
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Abstract

RQ1: What characteristics do people attribute to conversational social agents? RQ2: Which characteristics predict the trustworthiness of conversational social agents?
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Program for the Promotion of
Early-Career Female Scientists of
TU Dresden
Does Voice Kill the Text Star? No, It Does Not!
An Online Experiment on the Trustworthiness of the Text-Based and
Voice-Based Variants of Google Assistant
Katrin Etzrodt // Sophie Wagner // Sven Engesser /// Corresponding Address: katrin.etzrodt@tu-dresden.de
References:
Daft, R. L., & Lengel, R. H. (1986). Organizational Information Requirements, Media Richness and Structural Design. Management Science, 32(5), 554–571.
Kock, N. (2005). Media Richness or Media Naturalness? The Evolution of Our Biological Communication Apparatus and Its Influence on Our Behavior Toward E-Communication Tools.
IEEE Transactions on Professional Communication, 48(2), 117 –130.
Reeves, B. & Nass, C. I. (1996). The media equation: How people treat computers, television, and new media like real people and places. Cambridge: Cambridge University Press.
Media Richness. Richer information presentation
increases social reactions
(Daft & Lengel, 1986).
incompetent competent
unsocial social
unemotional
emotional **
does not resemble resembles
not trustworthy
trustworthy *
1 2 3 4 5 6 7
Google Home (n=60) Google Allo (n=62)
Attributes of Text-Based and Voice-Based Google Assistant
Predictors of Trustworthiness of Google Assistant
Discussion
•Criterion Variable (Gong, 2008)
•Trustworthiness (trustworthy, reliable, honest, confidential, truthful)
•Predictor Variables
•Resemblance (resembling me, behaves like me, thinks like me)
•PFA, promax (KMO = .70):
•Competence (competent, informative, intelligent)
•Sociability (entertaining, funny, sociable)
•Emotional (sensitive, warm-hearted)
•Control Variables
•Personality (Rammstedt et al., 2013)
•User Experience (Johnson et al., 2004)
•Technical Affinity (Karrer et al., 2009)
Measures
• N = 122, Google Home (n=60), Google Allo (n=62)
• Aged between 15 and 69 (M = 27, SD = 12)
• 62 % women
Sample
•Online-survey
• Simulation of interaction through activating video input
• 2x1 between-subjects design
• Google Home (voice) vs. Google Allo (text)
• Manipulation check: 83 % perceived the interaction as close-to-reality
Quasi-
experiment
Media Equation. People show social reactions – e.g.
trust – towards conversational social agents (CSA)
(Reeves & Nass, 1996).
What characteristics do people attribute to conversational social agents?
Which characteristics predict the trustworthiness of conversational social agents?
Media Naturalness. More natural information
presentation increases social reactions
(Kock, 2005).
Media Equation. Respondents perceive both
assistants as competent, social and trustworthy.
Media Richness. The richer medium is not perceived
as more competent, social or trustworthy.
Media Naturalness. The text-based CSA appears
more natural: Google Allo is perceived as more
sensitive and warm-hearted, and thus more
trustworthy. The assistant's voice apparently
emphasized his artificiality more clearly.
Mode (Text)
Resemblance
Emotionality **
Socialbility
Competence ***
Technological Affinity
Extraversion
Agreeableness
Openness
User Experience *
Gender (women)
Age
A S S I S T A N T U S E R
+p< .1, * p< .05, ** p< .01 , *** p< .001
Standardized Regression Coefficient
RQ1:
RQ2:
-1,00
0,00
1,00
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