本研究探討人工智慧驅動的資訊品質（IQ）、顧客AI素養（AIL）以及AI焦慮（AIA）對於物流服務品質（LSQ）與顧客體驗（CX）的影響，並採用偏最小平方法結構方程模型（PLS-SEM）進行實證分析。研究透過結構式問卷蒐集156份有效樣本，並利用 SMARTPLS 驗證各假設間的關係。研究結果顯示，AIL 對 LSQ 與 CX 皆具有顯著且正向的影響，並成為最強且最一致的預測因子。IQ 對 CX 具有顯著正向影響，但與 LSQ 之間並無顯著關聯。相對地，AIA 對 LSQ 與 CX 皆無顯著影響，顯示顧客對 AI 的焦慮在此情境下並不會直接影響其對服務品質或整體體驗的感受。此外，在控制 AI 相關變數後，LSQ 對 CX 並無顯著直接效果，意味著顧客在評估體驗時，更傾向依據 AI 驅動的服務屬性，而非傳統的服務品質指標。在理論層面，本研究凸顯了 AI 素養在塑造物流服務品質與顧客體驗上的核心角色，同時指出高品質的 AI 生成資訊對提升顧客參與具有重要意義。在實務層面，研究建議物流企業應優先考量顧客培訓、友善的系統設計，以及 AI 操作的透明性，以優化營運成果並提升顧客滿意度

This research is aiming at the investigation of the effects of AI-driven Information Quality (IQ), Customer AI Literacy (AIL), and AI Anxiety (AIA) on Logistics Service Quality (LSQ) and Customer Experience (CX) within the freight forwarding industry, using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected from 156 valid respondents through a structured survey. The analysis was conducted by using SmartPLS to exam the relationship between all hypothesis thoroughly.
The results show that AIL significantly and with positive influence on both LSQ and CX, emerging as the most consistent and strongest predictor. IQ also exerts a significant positive effect on CX but shows no significant relationship with LSQ. AIA has no significant impact on LSQ and does not demonstrate an important relationship with CX under statistical concern, and indicating that customer’s anxiety toward AI may not directly shape service quality perceptions or overall experience in this context. Furthermore, LSQ shows no meaningful direct impact on CX after controlling for AI-related variables, suggesting that customers may evaluate their overall experience primarily through AI-driven service attributes rather than traditional service quality metrics.
Theoretically, this study contributes to the literature by highlighting the central role of AI literacy in shaping both service quality and customer experience, as well as the importance of high-quality AI-generated information in enhancing customer engagement. Practically, the findings shows that logistics firms should put customer training, user-friendly system design, and transparency in AI operations to optimize both operational outcomes and customer satisfaction into consideration for prioritization. Future research should further examine potential moderating factors such as customer expectations, prior AI experience, and trust in technology to provide a deeper understanding of human-AI interaction in service settings.

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