近年來隨著雲端科技與智慧型裝置的普及，產業陸續建置以「軟體即服務」 之雲端服務，並透過網站、軟體與行動應用程式等形式，讓消費者得以隨時、隨地 利用各種智慧型裝置觸接其服務，同時開啟了消費者與多個裝置之間特有的互動方 式-「多螢互動」。過去多螢議題的相關研究，多著重在裝置間的資訊同步與操作使 用性，鮮少著墨於多螢互動帶給人們的在認知與情感上的感受及影響;多螢服務之 研究亦侷限在特定的情境，研究成果難以類推到其他服務上，及無法促發新的互動 形式產生。
為了解多螢互動對使用者的影響，本研究將定義多螢互動類別、尋求多螢互 動的重要構面，以及探索多螢互動影響使用意圖等步驟進行。首先，為聚焦多螢互 動研究議題，本研究由文獻探討將多螢互動定義為「一致性」、「連續性」與「互 補性」三類;並交叉比對過去研究，尋求對三種多螢互動有重要影響之構面與因子; 接續為探索多螢互動對於消費者使用之意圖，本研究將結合科技接受模型 (TAM)、 計劃行為理論 (TPB) 與前述之重要構面，分別構建了三個多螢互動之研究模型。由 調查兩百多個多螢服務使用者之結果發現影響多螢服務使用者意圖的構面，感受到 介面之「連貫」，與因應不同裝置特性之「調適」，為構成一致性互動之重要構面; 而連續性互動則受到是否感受到「同步」及裝置間的「無縫移轉」影響;是否感受 到「多螢配置」、「同步使用性」及裝置間的「補強」，則是構成互補性互動之重 要構面。同時，本研究藉由偏最小平方法 (PLS)，驗證三個多螢互動之研究模型皆對 於消費者之使用意圖有顯著之效果，說明重要構面對於三種多螢互動具足夠之解釋 力，並進一步提出由確認功能性之重要構面及其設計涵蓋指標，提出對於設計多螢 互動之指引，以提升設計師與研究人員投入多螢互動設計之指引。

The proliferation of cloud computing and the internet allow people to easy access and share data across a variety of devices. This ecosystem approach has fully promoted the development of services like mobile applications and streaming platforms, also known as the multi-screen service. The availability of variety of digital devices has changed the way people interact with digital services and new interaction patterns have emerged to facilitate people in better achieving their goals. Multi-screen service has unique interaction patterns and previous research in multi-screen interaction pertaining users’ acceptance to such services have remain scarce.
This study synthesizes previous literature reviews and proposed research models to understand the users’ acceptance of multi-screen service regarding the potential impacts of consistent interaction, continuous interaction and complementary interaction. The purpose of this study aims to identify the determinants that affect user to perform multi-screen interaction and how specific multi-screen interaction affect users’ intentions toward using multi-screen service. Three research model was constructed based on the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) to conceptualize multi-screen interaction with user intention and to reveal significant relationships with behavioral constructs. Data collected from more than 200 multi-screen users were used to test the proposed research models.
The results make several contributions to the understanding of multi-screen interaction and user intention: First, this study developed three conceptual models for multi- screen service acceptance, including identifying determinants for each interaction that impact users’ intentions. Secondly, this study compared the differences between these three acceptance models and revealed significant relationships between them and provide design suggestion that guide the design of multi-screen interactions to enhance user acceptance of multi-screen services. To conclude, this study is important in finding the gaps between multi- screen interaction and user intention, as well as for providing models and guidelines for designing better multi-screen services.

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