研究生: |
陳純晶 Chun-Ching Chen |
---|---|
論文名稱: |
企業導入新興科技關鍵成功要素-擴展AI賦能IoT應用架構之協同演進過程 Key Success Factors for Enterprises to Adopt Emerging Technologies - The Coevolutionary Process of Expanding AI-empowered IoT Application Architecture |
指導教授: |
羅乃維
Nai-Wei Lo |
口試委員: |
楊維寧
Wei-Ning Yang 查士朝 Shi-Cho Cha |
學位類別: |
碩士 Master |
系所名稱: |
管理學院 - 資訊管理系 Department of Information Management |
論文出版年: | 2022 |
畢業學年度: | 110 |
語文別: | 中文 |
論文頁數: | 78 |
中文關鍵詞: | AIoT 、資料探勘 、因素分析 、區別分析 、專家問卷 |
外文關鍵詞: | AIoT, data mining, factor analysis, discriminat analysis, expert questionnaires |
相關次數: | 點閱:390 下載:0 |
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新的產業趨勢造成經濟版圖的變化,近幾年AIoT(Artificial Intelligence of Things,智聯網)的議題迅速展開數位轉型與價值創新,重要性讓各國政府制定國家策略倡議其影響層級,興起的動能帶給臺灣擅於設計與製造的資通訊業產生龐大挑戰及契機,面對這波長期趨勢改變,大型企業必須進行優化數位能力來因應。
新數位科技物聯網結合人工智慧,兩種技術宛如神經元與大腦相輔而成,物聯感測及行動裝置提供可用地巨量資料,藉由不同網路拓樸將資料數據傳送到平台層伺服器群集,提供給AI進行學習、演算法分析與判斷、以及產生精準預測模擬,衍生的服務應用涵蓋各行業領域,像是:智慧工廠、智慧城市、智慧家庭、智慧醫療等,大型企業商業策略必須根據產業變動而改變,採擷新興數位資訊科技來適應。
本研究旨在探討目前AIoT數位科技對大型企業之影響及實行現況,透過學者文獻與AIoT演化歷程以TOE理論框架建構問卷量表題項,有效回收的專家問卷經因素分析得到三個高解釋量的共同因子,再運用區別分析法檢測兩分群,得到領先組企業與一般組企業的標準化區別函數,研究結果歸納出實施AIoT關鍵成功要素,提供企業實務上調整商業策略參考。
A novel industrial trend has ushered in dramatic changes to the global economic landscape. In recent years, AIoT (Artificial Intelligence of Things) related topics have rapidly triggered the megatrends of digital transformation and value innovation. Thus, it is indispensable for global governments to develop national strategies and advocate the significant influence. The emerging momentum would bring business opportunities as well as challenges for Taiwan ITC industry that is good at designing and manufacturing. To address the long-term and drastic global changes, enterprises must optimize their digital capabilities to tackle the impending transformation.
New digital technology of IoT leveraged with AI can be likened to be the biological synergy created by neurons and brains. IoT sensors work in tandem with mobile devices to provide tremendous amount of raw data transmitted through individual network topology to the server clusters over the platform layer, where AI programs undertake tasks of learning, algorithm analysis and decision making, allowing to develop a model of simulation with diverse services & applications across multiple industries such as smart manufacturing, smart cities, smart homes, smart healthcare etc... Large enterprises need to step up meaningful innovative digital strategies to usher in the new industrial era.
The purpose of this study is to delve into the current impact of AIoT digital technologies on large enterprises and the status of its implementation. The questionnaires were based on the framework of TOE theory and developed through scholars’literature alongside with the evolution of AIoT. Three highly-explainable common factors found through the Factor Analysis from the collected valid data of expert questionnaires. The Discriminant Analysis was adopted to come up with the Standardized Discriminant Function Coefficients of two categories:significantly ahead and on par. Eventually, this research investigation has illustrated the key factors to the implementation of AIoT technologies, providing a meaningful reference for enterprises to adapt their business strategies.
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