Abstract
This study investigates how Generative AI integration affects personalized customer support performance, with emphasis on the mediating role of tool acceptance. Using a quantitative cross‑sectional design, data were collected from 119 Schneider Electric Philippines employees through a UTAUT‑ and TTF‑based survey. Four factors—Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions—were assessed, with Effort Expectancy emerging as the strongest indicator of ease of use. PLS‑SEM results showed significant direct effects of GenAI integration on both tool acceptance and personalized performance, with acceptance partially mediating the relationship. Although respondents recognized improvements in productivity and personalization, gaps in triage, prediction, and system compatibility persist. Overall, effective integration supported by strong employee acceptance greatly enhances personalized support outcomes.
Keywords: Effort Expectancy, Facilitating Conditions, Performance Expectancy, Social Influence, Task-Technology Fit (TTF) Theory, Unified Theory of Acceptance and Use of Technology (UTAUT)
https://doi.org/10.65494/pinagpalapublishing.158
