Examining user switching intention between generative AI platforms: A push-pull-mooring perspective

Due to the intense competition among generative AI platforms, users may switch from a platform to an alternative one. This may lead to user attrition and undermine the competitive advantage. The purpose of this research is to examine user switching intention between generative AI platforms based on the push-pull-mooring (PPM). Push factors include information hallucination, privacy risk, and dissatisfaction, while pull factors include perceived interactivity, perceived anthropomorphism, perceived personalization, and user experience. Mooring factor is switching costs. Both structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) were used to conduct data analysis. The results validate the hypotheses. The fsQCA found that dissatisfaction is a common core condition triggering switching intention.

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Publication Details

Journal
Information Development
Published
2024-12-20
DOI
https://doi.org/10.1177/02666669241306735
Citations
3
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
1.04

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article

Examining user switching intention between generative AI platforms: A push-pull-mooring perspective

Tao Zhou, Songtao Li
3 citations
Information Development
AI in Service Interactions
1.04
article

Examining user switching intention between generative AI platforms: A push-pull-mooring perspective

Tao Zhou, Songtao Li
article en
3 citations

Abstract

Due to the intense competition among generative AI platforms, users may switch from a platform to an alternative one. This may lead to user attrition and undermine the competitive advantage. The purpose of this research is to examine user switching intention between generative AI platforms based on the push-pull-mooring (PPM). Push factors include information hallucination, privacy risk, and dissatisfaction, while pull factors include perceived interactivity, perceived anthropomorphism, perceived personalization, and user experience. Mooring factor is switching costs. Both structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) were used to conduct data analysis. The results validate the hypotheses. The fsQCA found that dissatisfaction is a common core condition triggering switching intention.

Information DevelopmentVol. 41(3)
Hangzhou Dianzi University (CN)
National Social Science Fund of China
Openalex Percentile: Top 18%
AI in Service Interactions
1.04
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