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TRUVACE RECORD VERSION
record: TRV-2026-2685
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:00:55.997999Z
status: published
lens: g_space
sector: lifestyle
headline: Avatars that engage: How visual role expectations, contextual congruence, user characteristics, and perceived sympathy shape responses to shopping chatbots
dek: In online shopping contexts, chatbot avatars with human-like features can conform to or conflict with users' contextual role expectations - schemas of what a salesperson in a given retail domain should look like. However, whether users' actual avatar preferences align with their articulated role expectations, and whether avatar–context congruence translates into stronger engagement intentions, remains unclear. In a large-scale between-subjects experiment (N = 5278; data collected 2024 in the United States, Unite…
gain_title: In an online experiment with 5,278 people in the US, UK and Switzerland, shopping-chatbot avatars matching a person's own idea of a salesperson earned slightly higher intention to use the chatbot than mismatched ones (3.54 vs 3.39 on a 6-point scale).
problem_title: (none)
trace_subject: (none)
gain_reading: In an online experiment with 5,278 people in the US, UK and Switzerland, shopping-chatbot avatars matching a person's own idea of a salesperson earned slightly higher intention to use the chatbot than mismatched ones (3.54 vs 3.39 on a 6-point scale).
gain_evidence: schema-congruent avatars elicited higher behavioral intention than schema-violating ones (Δ ≈ 0.15 on a 1–6 scale) | The stereotype-congruent condition yielded significantly higher Behavioral Intention than the stereotype-violating condition (p = .004)
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers at Zurich University of Applied Sciences randomly assigned 5,278 adults in the United States, United Kingdom and Switzerland to a scripted shopping chatbot in either a wine or a fashion scenario. Avatars were AI-generated humanoids; the avatar either matched the participant's own description of a typical salesperson, violated it, or was chosen by the participant. Matching avatars earned slightly higher intention to use the chatbot than violating ones: 3.54 versus 3.39 on a 6-point scale.

Expectations differed sharply by product. In the wine scenario 41.3% of 1,767 participants described the typical salesperson as older, formally dressed and male, while 51.4% of 1,773 fashion participants described a young, informally dressed woman. When free to choose, only 10.2% of wine participants picked the wine-typical avatar, a 31-point gap between stated expectation and actual choice.

The effect sizes were small, and how much the avatar liking drove the result is the main finding. Perceived sympathy toward the avatar carried the effect of avatar assignment on intention; adding it to the model raised explained variance from an adjusted R2 of .025 to .150. Self-selection did not beat a well-matched default.
limitation: Effects were small and intention was self-reported right after a scripted chatbot interaction, with sympathy and intention measured at the same time, so the link from sympathy to intention is not firmly established. The samples came from three Western countries only.
tag: Evidence-backed gain
key_points: The experiment used 5,278 participants recruited via Intervista (Switzerland) and Prolific (UK and US), with data collected March 26 to April 2, 2024. | Mean intention to use the chatbot was 3.59 with a self-selected avatar, 3.54 with a matching avatar and 3.39 with a violating avatar; self-selection did not differ significantly from matching (p = .929). | In the fashion scenario, both self-selected and matching avatars beat violating ones (p < .01 and p < .05); in wine the pattern pointed the same way but no pairwise difference survived correction (smallest p = .06). | Participants chose avatars differently from what they expected: in fashion, the young, female, informal avatar was expected by 51.4% but chosen by 35.3%, a 16-point gap. | Young female avatars were chosen most often overall (53%) and rated most sympathetic; older male avatars were rated least sympathetic. | Avatars that violated expectations lowered sympathy (β = −.53), while self-selection raised it (β = .39); sympathy predicted intention (β = .39). | Experimental factors explained little variance on their own (partial η2 .004 to .008), and age mattered more (partial η2 .013). | Expectations varied by user: 74% of male versus 62% of female participants expected a male wine salesperson, and 54% of German-speaking Swiss versus 30% of US participants expected an older, formal, male wine salesperson.
rundown: The study used a 3 × 2 between-subjects design. Participants first saw a scripted 'Virtual Shopping Assistant' named Alex in either a wine or a fashion setting. In the two assigned conditions they first said whether a typical salesperson was male or female, under 30 or over 50, and formally or informally dressed. The chatbot then appeared as a matching avatar or the exact opposite combination. Dialogue and product recommendations (up to three wines, or T-shirts and shoes) were held constant, so only appearance differed.

Eight AI-generated avatars covered every combination of gender expression, age appearance and clothing style. Behavioral intention was an average of two items: willingness to use such an assistant and willingness to recommend it. Sympathy was rated on a 0 to 100 slider for the avatar each person had actually seen.

Expectations differed by who was asked. Male participants held the male-wine-salesperson view more strongly than female participants (74% vs 62%), and female participants held the female-fashion view more strongly than male participants (93% vs 69%). Younger participants more often expected a young fashion salesperson (94% vs 77% of older participants). In free choice, male participants picked young female avatars more often than female participants did (59.0% vs 46.9%), while female participants picked older female avatars more often (31.3% vs 9.3%).

Sympathy ran in opposite directions at once. With sympathy held constant, violating avatars produced slightly higher intention (β = +.10) and self-selection slightly lower (β = −.12). Through sympathy, violating avatars cut intention (indirect β = −.21) and self-selection raised it (indirect β = +.15). The authors suggest context-appropriate defaults, sympathy-focused design and optional personalization.
sources:
- peer_reviewed | Computers in Human Behavior: Artificial Humans | https://doi.org/10.1016/j.chbah.2026.100335 | 2026-06-23
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