A study published in the Journal of Retailing outlines a framework for businesses to measure how customers perceive and accept AI-powered shopping assistants.
Key facts
- •The study identified eight dimensions of AI acceptance: personalization, accuracy, safety, speed, convenience, humanization, friendliness, and emotionality.
- •Researchers developed a 32-item scale and an eight-item condensed version to help retailers diagnose and improve AI service performance.
- •Higher customer acceptance of AI tools is associated with increased word-of-mouth, usage share, and willingness to pay.
- •The research team tested their framework across eight separate studies.
- •The study, titled 'Customer Acceptance of Artificial Intelligence (CAAI): Applications, Scale Development, and Testing,' was published in 2026.
Researchers have developed a new method to evaluate how customers accept AI-enabled retail services, identifying eight key dimensions of the user experience. The study, published in the Journal of Retailing, suggests that retailers must prioritize reliability and accuracy alongside technological innovation to ensure long-term adoption. The findings highlight that while AI tools can offer speed and convenience, they often fail to meet customer needs in areas like safety and personalization.
Dimensions of customer acceptance
The researchers identified eight distinct dimensions that influence whether customers accept AI retail services: personalization, accuracy, safety, speed, convenience, humanization, friendliness, and emotionality. The study notes that an AI tool might excel in one area, such as speed, while failing in another, such as accuracy or data privacy. The research team developed a 32-item measure for detailed diagnosis and an eight-item version for quicker assessments to help businesses identify specific weaknesses.
Risks of rapid AI implementation
The authors warn that retailers risk a backlash if they prioritize rapid deployment over customer experience. They point to the 2024 conclusion of a McDonald's U.S. trial for AI-powered drive-thru ordering, which faced reports of incorrect orders, as an example of why reliability is critical. The study emphasizes that measuring customer satisfaction across different stages of the shopping journey—such as browsing, complaining, or checking out—is essential for managers to prevent service failures from damaging a brand's reputation.
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This article was independently rewritten by ManyPress editorial AI from reporting originally published by Phys.org.
