From interaction to impact: developing the AI touchpoint experience scale
Heng Chu, Chunli Ji, Yan Tang, Catherine Prentice, Xiaojun LiuPurpose
This study aims to develop and validate a scale to measure the effectiveness of customer-centric artificial intelligence (AI) touchpoints throughout the customer journey in hospitality and tourism.
Design/methodology/approach
This study followed established scale development procedures using a multiphase approach. An initial pool of items was generated through a combination of deductive literature review and inductive qualitative interviews. Two studies were conducted to perform exploratory and confirmatory factor, as well as to refine and validate the AI touchpoint experience scale, which assesses the effectiveness of AI-driven touchpoints across the customer journey. The scale was finalized with confirming convergent, discriminant, nomological and predictive validities.
Findings
The final scale consists of 22 items with six dimensions: functional efficacy, proactive resonance, effortless expertise, relational authenticity, ethical assurance and behavioral enabling. The scale demonstrates strong psychometric properties and criterion validities, explaining significant variance in customer satisfaction and reuse intention. These findings confirm the effectiveness of AI touchpoints.
Originality/value
To the best of the authors’ knowledge, this is the first study to develop a multidimensional, customer-centric scale to assess AI touchpoint effectiveness in hospitality and tourism. It addresses gaps in fragmented measurement approaches and advances both theoretical understanding and practical application of AI-enabled customer experiences.