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TRUVACE RECORD VERSION
record: TRV-2026-2658
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T16:57:26.526189Z
status: published
lens: g_space
sector: lifestyle
headline: Consumer response to emotion-expressing artificial intelligence in customer services: the role of brand relationship norms and expectation (dis)confirmation
dek: Purpose Emotion-expressing AI in customer service can produce mixed, context-dependent outcomes. This study investigates the joint effect of AI emotional expression and consumer brand relationship norms on consumer satisfaction and continuance intention and tests expectation confirmation as the mediating mechanism and AI mind perception as the boundary condition. Design/methodology/approach This research adopts a multi-experiment design comprising three between-subjects experiments with a combined total sample o…
gain_title: In three experiments with 677 participants, customers with communal brand relationships reported higher satisfaction and continuance intention when customer service AI expressed high rather than low emotion, but only when they saw the AI as having a mind.
problem_title: (none)
trace_subject: (none)
gain_reading: In three experiments with 677 participants, customers with communal brand relationships reported higher satisfaction and continuance intention when customer service AI expressed high rather than low emotion, but only when they saw the AI as having a mind.
gain_evidence: For communal consumers, high (vs low) AI emotional expression elevates satisfaction and continuance intention | the positive impact of high AI emotional expression on communal consumers emerges only under high AI mind perception
problem_reading: (none)
problem_evidence: (none)
quick_read: Three between-subjects experiments with 677 participants in total tested how emotional expression by customer service AI affects customer satisfaction and intention to keep using the service. The result depended on the kind of relationship customers expect with a brand.

For communal consumers, those who expect a caring, relationship-style bond with a brand, high emotional expression raised satisfaction and continuance intention compared with low expression. For exchange consumers, who treat dealings as transactions, the effect was non-significant.

The effect worked through expectation confirmation, meaning satisfaction rose when the AI's emotional behaviour matched what customers expected. It appeared for communal consumers only when they perceived the AI as having an experiential mind, so a blanket approach to emotional AI would not benefit all customers.
limitation: 
tag: Evidence-backed gain
key_points: The research used three between-subjects experiments with 677 participants combined. | AI emotional expression and brand relationship norms interacted to affect satisfaction and continuance intention. | Among communal consumers, high emotional expression increased both satisfaction and continuance intention compared with low expression. | Among exchange consumers, the effect of emotional expression was non-significant. | Expectation confirmation mediated the interaction between emotional expression and relationship norms. | The benefit for communal consumers appeared only when they had high mind perception of the AI.
rundown: The study examined emotion-expressing AI in customer service, where earlier outcomes have been mixed and dependent on context. It tested the joint effect of how emotional the AI appeared and the type of relationship consumers expect with a brand, communal or exchange.

The outcomes measured were consumer satisfaction and continuance intention. A significant interaction showed emotional expression did not work the same way for everyone: it helped communal consumers and made no significant difference for exchange consumers.

The authors identify expectation confirmation as the mechanism and AI mind perception as the boundary condition. Their practical advice is to calibrate rather than standardize emotional expression by relationship norms, and to raise customers' perception of the AI's experiential capacity through interaction design and user education.
sources:
- peer_reviewed | Asia Pacific Journal of Marketing and Logistics | https://doi.org/10.1108/apjml-12-2025-2751 | 2026-08-28
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