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Service type conditions stance attribution effects on switching after generative AI failures

nature.com 11.09.2026 02:00 5 views

Generative artificial intelligence (GenAI) service failures can prompt users to abandon a provider. Drawing on the Stereotype Content Model, with the Associative-Propositional Evaluation Model as a complementary process perspective, this research examines how intentional and design stance cues shape warmth, competence, and switching intention across service types. Study 1 used an ERP experiment and found stance-cue x service-type interactions for P2 and P3.

Holm-adjusted follow-up tests showed that no P2 simple contrast remained significant, whereas mechanical failures elicited a larger P3 than emotional failures under design stance cues. Study 2 used a scenario experiment and found that intentional stance cues increased perceived warmth and competence, but only competence mediated lower switching intention. This conditional indirect effect was significant for mechanical tasks but not emotional tasks, and the warmth pathway was not significant.

Together, the findings identify competence as a task-contingent pathway linking stance cues to switching intention and show that the effectiveness of stance framing depends on the service context. This research project was supported by the National Social Sciences funded general projects, PRC (Grant No. 22BGL006). School of Business, Huaqiao University, Quanzhou, 362021, China Chen Shouren College of Business, Quanzhou Normal University, Quanzhou, 362021, China The authors declare no competing interests.

The present study was reviewed under the Huaqiao University School of Medicine Medical Ethics Committee Approval No. M2025021 (approved on 31 March 2025; the application form was dated 11 March 2025). All methods were performed in accordance with the ethical standards of the Declaration of Helsinki and its later amendments.

Participants in Study 1 and Study 2 signed written informed consent forms before participation. Participation was voluntary, and compensation was provided as described in the respective Methods sections. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Below is the link to the electronic supplementary material. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it.

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