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Consumer resistance to AI chatbots: barriers and impacts on negative word-of-mouth

Ahmed Taher Esawe

Spanish Journal of Marketing - ESIC2025https://doi.org/10.1108/sjme-07-2024-0187article
AJG 1
Weight
0.46

What the paper says

Purpose This study aims to investigate the effects of functional and psychological barriers on consumer resistance to adopting AI-powered conversational agents (AICAs) in financial services and their implications for negative word-of-mouth (NWOM). Design/methodology/approach This study used an online survey to collect data from a sample of 294 AICA users. This study uses partial least squares structural equation modeling to evaluate the study’s hypotheses. Findings The findings of this study reveal that usage, risk and tradition barriers significantly influence consumer resistance, in contrast to value and image barriers. Furthermore, consumer resistance significantly influences NWOM. Additionally, resistance fully mediates the relationships between usage and risk barriers, and NWOM partially mediates the relationship between tradition barriers and NWOM. Originality/value This study extends the Innovation Resistance Theory into the domain of AICA adoption, exploring the nuanced effects of IRT barriers on consumer resistance and NWOM. This study highlights the mediating role of consumer resistance in the relationship between barriers and NWOM, providing actionable insights for service providers and advancing technology adoption and resistance research.

4 citations

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Cite this paper

https://doi.org/https://doi.org/10.1108/sjme-07-2024-0187

Or copy a formatted citation

@article{ahmed2025,
  title        = {{Consumer resistance to AI chatbots: barriers and impacts on negative word-of-mouth}},
  author       = {Ahmed Taher Esawe},
  journal      = {Spanish Journal of Marketing - ESIC},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1108/sjme-07-2024-0187},
}

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Consumer resistance to AI chatbots: barriers and impacts on negative word-of-mouth

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Evidence weight

0.46

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.37 × 0.4 = 0.15
M · momentum0.60 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.