本研究以適地性服務概念為基礎，提供行動資訊創新服務之新穎性(Novelty)內容，並參考近年來常應用於消費者行為研究中的沉浸理論(Flow Theory)(Huang, 2003)，及廣泛運用於解釋個人行為的計畫行為理論(TPB)，探討使用者使用創新資訊服務行為意圖及沉浸經驗之影響。本研究實驗設計主要以行動裝置為傳輸媒介，在人群流量大的大眾運輸五個據點傳遞行動數位資訊服務內容，其資訊服務內容設計依據「使用者需求不確定性」高低以及「數位資訊內容廣度」寬窄分五種不同配適組合，並藉由發放問卷探討使用行動數位內容，使用者不確定性高低及行動資訊服務內容廣度所形成之配適度組合。

本研究發放紙本問卷600份，全數回收，有效問卷為596份。並採用線性結構方程模式(Partial Least Squares, PLS)對研究模型進行分析。分析結果顯示，高配適度組之信效度(AVE值與CR值)皆合乎標準，其迴歸可解釋變異量(R2值)也較高，使得模型解釋能力較高。本研究之學術貢獻在於綜合了新穎性、沉浸經驗及計畫行為理論，應用於行動裝置資訊服務上的創新，企業可依此作為未來發展新行動資訊服務時的參考依據，預先推測使用者在使用服務時，主要是受到哪些因素影響其沉浸經驗，及該如何針對不同使用者族群之需求設計其資訊服務內容，以提升服務品質及效用。

This paper based on the concept of Location-based services (LBS) to supply novelty contents for the mobile digital innovation service. We referred to the Flow Experience model (Huang, 2003) in recent years often used in consumer behavior researches and also referred to the Theory of Planed Behavior (TPB) model which both widely applied to explain individual behavior to explore what factors will affect individual's behavioral intentions and flow experience by use innovation information service. Our experimental design use the mobile devices as a medium to deliver service contents, and choice five different public transportations that have a large number of people to pass through as our deliver location. We also considered the height of “uncertain customers’ needs” and the range of “digital contents” to design five mobile digital service contents, and further to test and verify the goodness of fit by using questionnaires.

A total of 600 questionnaires were sent and all were collected, 596 responses were completed. The collected data were analyzed by Partial Least Squares (PLS) and to test the model. The results reveal that high goodness of fit group has standard AVE and CR value, which also has higher R square make the model more interpretability. This research combines Novelty, Flow Experience and TPB theory, and apply on mobile digital devices service innovation. Companies can use this research as their base to development new service in mobile digital content. In addition, they will able to predict what factors will affect different user group in order to design the contents to a specific group and to facilitate service quality and efficiency.

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