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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93208| Title: | 人工智慧驅動早期即時檢測冠心病的診斷工具產品接受度 Product Acceptance of an AI-Driven Point-of-Care Diagnostic Tool for Early Detection of Coronary Heart Diseases |
| Authors: | 羅捷 Jieh Lou |
| Advisor: | 簡怡雯 Yi-Wen Chien |
| Keyword: | 人工智慧,互聯網,中介變數,線性回歸, AI,IoT,Mediators,Linear regression, |
| Publication Year : | 2024 |
| Degree: | 碩士 |
| Abstract: | 隨著時代的演進,AI與IoT產品逐漸成為市場的主流,深受廣大消費者的喜愛。然而,即便生物技術產業積極採用AI科技開發各種終端產品,這些創新卻未能像其他領域那樣輕易地獲得廣泛推廣。本研究旨在探究消費者對於生技產業中與生命疾病相關的AIoT產品使用意向會受到哪些因素影響,以及可能存在的中介變數。透過問卷調查深入了解消費者心理,藉此幫助企業優化未來的決策和優先次序。
本研究有三個主要發現。首先,社會影響、專家建議、績效期望、感知價值和便利條件都是影響冠心病AI健康監測產品使用意向的關鍵因素。其次,信任在社會影響和專家建議對使用意向的影響中起到了部分中介作用。然而,採納AI傾向和健康識能不應視為年齡和使用意向中的中介變數,而應直接作為影響使用意向的自變數。最後,一個包含信任、績效期望、感知價值、採納AI傾向、便利條件和社會影響的綜合模型,被證實是解釋冠心病AI健康監測產品使用意向的最佳模型。 As time progresses, AI and IoT products have gradually become mainstream in the market, enjoying widespread consumer popularity. However, even though the biotechnology industry has also actively adopted AI technology to develop a variety of end products, these innovations have not been as easily promoted as in other fields. This study aims to explore the factors that influence consumers' intentions to use AIoT products related to life diseases within the biotechnology industry, as well as possible mediating variables. Through a questionnaire survey to deeply understand consumer psychology, this research aims to help companies optimize future decisions and priorities. There are three main findings in this study. First, social influence, expert recommendations, performance expectations, perceived value, and facility conditions are key factors affecting the behavioral intentions of AI health monitoring products for coronary heart disease. Second, trust plays a partial mediating role in the impact of social influence and expert recommendations on behavioral intentions. However, the AI tech acceptance and health literacy should not be seen as mediating variables between age and behavioral intentions but should be directly considered as independent variables affecting behavioral intentions. Finally, a comprehensive model that includes trust, performance expectations, perceived value, the inclination to adopt AI, convenience conditions, and social influence has been proven to be the best model for explaining the intention to use AI health monitoring products for coronary heart disease. |
| URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93208 |
| DOI: | 10.6342/NTU202401716 |
| Fulltext Rights: | 同意授權(全球公開) |
| Appears in Collections: | 商學研究所 |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-112-2.pdf | 1.56 MB | Adobe PDF | View/Open |
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