自從OpenAI推出GPT(Generative Pre-trained Transformer)系列模型以來，生成式AI在全球範圍內引起了廣泛關注，這項技術是人工智慧發展的一個重要突破，能夠自動生成如文字、圖像、音樂等多樣化內容。隨著技術的快速發展，生成式 AI 的應用場景逐漸擴展至各行各業。然而，企業在導入此新技術的過程中，可能會受遇到許多影響因素與挑戰。

本研究以資訊系統成功模式為理論基礎，探討系統品質、資訊品質、服務品質、使用者滿意度、再次使用意圖等構面對生成式 AI 導入過程的影響，並特別加入信任作為新增構面，討論其與另外五個構面之間的交互作用。本研究採用問卷調查的方法，透過蒐集大眾對生成式AI的使用經驗，進一步驗證各項假說與模型的適用性。

研究結果顯示，生成式 AI 與傳統資訊系統在影響構面上存在顯著差異。對於生成式 AI 而言，資訊品質與服務品質對使用者的信任與滿意度具有顯著的正向影響，顯示出使用者更重視系統提供資訊的準確性、相關性及即時性。然而，系統品質對使用者信任的影響未達顯著，表示使用者在評估生成式 AI 系統時，系統技術層面的表現並非關鍵的影響因素。

基於研究結果，本研究提出實務建議，企業在導入生成式 AI 系統時，應優先聚焦於提升資訊品質與服務品質下的準確性、即時性等特性，確保生成內容能滿足使用者的實際需求與應用情境，而操作的流暢度及介面設計等系統品質的特性在資源有限的情況下可作為次要優化項目，將有限資源投入在使用者最為關注的核心需求上，有助於提高生成式 AI 系統的導入成效。

Since OpenAI introduced the GPT (Generative Pre-trained Transformer) series models, generative AI has gained widespread attention worldwide. This technology represents a significant breakthrough in artificial intelligence, enabling the automatic generation of diverse content, such as text, images, and music. With the rapid advancement of technology, the applications of generative AI have gradually expanded across various industries. However, during the adoption of this new technology, businesses may encounter numerous influencing factors and challenges. This study is based on the Information Systems Success Model and examines the impact of factors such as system quality, information quality, service quality, user satisfaction, and intention to reuse on the adoption process of generative AI. Additionally, trust is introduced as a new dimension to explore its interactions with the other five dimensions. A questionnaire survey method is employed in this study to collect public experiences with generative AI, further validating hypotheses and assessing the applicability of the proposed model. The study results indicate significant differences between generative AI and traditional information systems in terms of influencing factors. For generative AI, information quality and service quality have a significant positive impact on user trust and satisfaction, demonstrating that users place greater importance on the accuracy, relevance, and timeliness of the information provided by the system. However, the impact of system quality on user trust is not significant, suggesting that the technical performance of the system is not a key determining factor when users evaluate generative AI systems. Based on the research findings, this study provides practical recommendations. When businesses adopt generative AI systems, they should prioritize improving the accuracy and timeliness of information quality and service quality to ensure that the generated content meets users' actual needs and application scenarios. Meanwhile, aspects related to system quality, such as operational smoothness and interface design, can be considered secondary optimization items when resources are limited. Allocating resources to address users' most critical concerns will help enhance the effectiveness of generative AI system adoption.

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