近十年來金融環境革命性的改變，導致對資訊系統使用者之便利性及服務內容的質變。投信業募集基金運用AI架構，打造智慧募資模式，達到公司的目標，將成為重要的議題。
本研究以任務-科技配適模型為基礎，探討科技特性滿足支援的任務特性的適配度時，對於績效成正向的成長。再運用知識管理系統探討歷史的募集資金模式，將成功的知識管理透過AI得以延續，提高資訊系統使用者滿意度，以達到公司所期望的募集資金目標，使基金規模之增長績效，持續成長。
過往文獻未將三項理論關係做結合，本研究結合科技適配理論、知識管理系統、期望確認理論來探討投信募集資金運用AI模式研究，並透過問卷調查法為研究方法，並以結構方程模式(SEM)作為統計分析。
研究結論為投信業募集資金AI模式，以此研究對投信業產業提出主要之策略方向，並將研究成果概念可以應用於台灣政府四大基金。
The revolutionary changes in the financial environment in the past decade have led to changes in the convenience of information system users and the quality of the services. The investment industry fund raising fund uses the AI framework to create a smart fundraising model for wireless network applications.
The use of AI framework by the investment fund raising foundation to create a smart model of fundraising for wireless network applications to meet the goals set by the executives of the company will become an important issue. This study is based on the task-technical fitness model and explores the positive growth of performance when the technical characteristics meet the goodness of fit of the task characteristics it supports.
Then, using the knowledge management system to investigate the historical fundraising model for wireless network applications, and continue the successful knowledge management through AI, improve the user satisfaction of the information system, to achieve the company's desired fundraising goal, so that the growing performance of the fund can continue. The past literature does not combine the three theoretical relationships. This study combines the Task Technology Fit theory, the Knowledge Management System, and the Expectation Confirmation theory to explore the use of the deep learning model for the asset management industry. The key factors for the successful funds raised from the various enterprises are extracted. Through statistical analysis, these serve as a reference for relevant decision-making, increase the performance of raised funds and the efficiency of corporate organization, and to achieve the desired goal of raised funds set by the management. The subjects of this study are those who have not used the deep learning model structure and the users of the AI system, and use the online questionnaire to conduct convenient sampling, using questionnaires and SEM structural equation models for analysis.
The research result is to construct the deep learning model of fund-raising in the asset management industry , and proposes the main strategic direction for the asset management industry and the concept of the research results can be applied to the four major funds of the Taiwan government.
