Measuring Artificial Intelligence Customer Experience: Scale Development and Validation
Ching‐Jui Keng, Ping-Fang Sung, Yu-Hsin Chen
Abstract
Artificial intelligence (AI) optimizing customer experience is recognized by enterprises as an important determinant of customer service success. This research investigates the perspectives of AI conversational intelligence, AI social intelligence, AI object intelligence, and AI anthropomorphic intelligence to develop and validate an artificial intelligence customer experience (AICE) scale and through five stages: item generation, scale purification, scale refinement, scale validity, and nomological validity. The validity and reliability tests were conducted using three distinct data collection phases (N = 888). The results identify AICE scale comprises 29 items across five dimensions: AI autonomy, AI uniqueness, AI parasocial ability, AI reliability, and AI dialogue ability. The nomological validity of the scale was established by demonstrating the examining the relationships between AICE and post-use confirmation, satisfaction, and willingness to continue use. This research contributes to a parsimonious and valid scale to measure customer experience. Managers can effectively use AICE to optimize service design.