隨著行動網路技術日益發展與智慧型手機的普及，越來越多用戶透過手機執行各項日常工作，其中行動支付提供消費者便捷的支付服務，吸引許多消費者使用。臺灣使用行動支付人口逐漸增加，市場已從導入期進入成長期，業者無不希望提高使用量及市佔率。各業者除吸引新的行動支付使用者外，還必須吸引其他平台的使用者，並保留住現有用戶，因此，了解消費者的轉換行為相當重要。
本研究分為兩部分，第一、參考人口遷徙理論中的「推力－拉力－鎖住力模型」，及過去行動支付相關文獻，探討影響用戶轉換供應商的變數。第二、運用前述之重要變數以類神經網路進行預測分析。研究結果發現(1)拉力效果(替代品吸引力、網路外部性、主觀規範)正向影響使用者的轉換意圖；(2)推力效果(低服務品質、低滿意度)正向影響使用者的轉換意圖；(3)轉換意圖正向影響消費者的轉換行為。此外運用類神經網路以推力因素、拉力因素及轉換意圖，預測消費者的轉換行為有78.3%的正確率。

Following the development of mobile network technology and the common use of smart phones, more and more users use their phones to manage daily work. Among a variety of functions, mobile payments provide convenient payment services for consumers and attract many of them to use. Because the population using mobile payments in Taiwan has increased, the mobile payment market is moving from start-up stage into the growth stage. All service providers hope to raise the amount of usage and their company’s market share. Each service provider not only attracts new mobile payment users, but also have to appeal to users on other platforms, and maintain current users. Thus, understanding consumers’ switching behavior is imperative. 
This study includes two parts: firstly, to discuss the variables which influence users to switch service providers with reference of Push-Pull-Mooring Model in Migration Theory of Geography as well as other related papers in mobile payment. Secondly, using those important variables mentioned above, along with neural network, to conduct predictive analysis. There are three conclusions: 1. The pulling effect (Alternative attractiveness, Network Externality, and Subjective Norms) affects users’ switching intensions positively. 2. The pushing effect (Low Serving Quality and Low Sense of Satisfaction) affects users’ switching intensions positively. 3. Switching intensions have a positive impact on consumers’ switching behavior. Furthermore, applying neural network, in consideration of pushing factor, pulling factor, and switching intensions, to predict consumers’ switching behaviors would result in a 78.3% of accuracy.

目錄
目錄	I
圖目錄	III
表目錄	IV
第一章	緒論	1
第一節　研究背景與動機	1
第二節　研究目的	4
第三節　研究範圍與對象	4
第四節　研究流程	5
第二章	文獻探討	6
第一節　行動支付	6
第二節　轉換行為與PPM模型之相關文獻	12
第三節　推力因素	16
第四節　拉力因素	21
第五節　鎖住力因素	24
第六節　類神經網路	25
第三章	研究方法	28
第一節　研究架構	28
第二節　研究假說	30
第三節　研究假設相關變項操作型定義與衡量	32
第四節　研究設計	39
第五節　資料分析方法	41
第六節　前測分析	44
第四章	資料分析與研究結果	48
第一節　敘述統計	48
第二節　結構方程模型分析	59
第三節　應用類神經網路於轉換行為分析	70
第五章	結論與建議	77
第一節　結論	77
第二節　管理意涵與研究貢獻	79
第三節　研究限制與未來研究建議	81
參考文獻	82
附錄一 第一階段問卷	96
附錄二 第二階段問卷	102
附錄三 類神經網路權重值	104

圖目錄
圖1-1研究流程圖	5
圖2-1類神經網路處理單元	26
圖2-2類神經網路的網路結構	27
圖3-1 PPM模型研究架	28
圖3-2 本研究流程架構	29
圖4-1 路徑分析	69
圖4-2 本研究網路架構	72

表目錄
表2-1 行動支付生態系	7
表2-2 PPM模型相關文獻彙整表	14
表2-3 行動支付服務品質要素	19
表2-4 BURNHAM ET AL. (2003)之轉換成本類型	25
表3-1 服務品質及子構面之操作型定義	33
表3-2 服務品質構面之衡量問項	33
表3-3 滿意度之衡量問項	35
表3-4 替代品吸引力之衡量問項	35
表3-5 網路外部性之衡量問項	36
表3-6 主觀規範之衡量問項	37
表3-7 轉換成本子構面之操作型定義	37
表3-8 轉換成本之衡量問項	38
表3-9 轉換意圖、轉換行為之衡量問項	39
表3-10 問卷設計	41
表3-11 前測各構面信度分析	44
表3-12 前測各構面信度分析(修改後)	47
表4-1 受訪者基本資料敘述統計	49
表4-2 受訪使用現況次數統計	51
表4-3 受訪者最常使用的行動支付兩階段問卷次數統計	53
表4-4 服務品質敘述性統計	54
表4-5 滿意度敘述性統計	56
表4-6 替代品吸引力敘述性統計	56
表4-7 網路外部性及主觀規範敘述性統計	57
表4-8 轉換成本敘述性統計	58
表4-9 轉換意圖敘述性統計	58
表4-10 各構面驗證性因素分析(CFA)	60
表4-11各主構面驗證性因素分析(CFA)	62
表4-12 子構面區別效度檢驗結果表	63
表4-13 主構面區別效度檢驗結果表	64
表4-14 結構模型配適度標準暨本研究模型配適度－整體模式	67
表4-15 結構方程式模型權重係數與假說驗證彙整表	68
表4-16 運用WEKA建構本研究網路之參數輸入值	71
表4-17 本研究所得之最佳網路架構模型內容	73
表4-18 測試分類結果	73
表4-19 各構面之權重值	74
表4-20 網路訓練效果之多變量變異數分析	75
表4-21 隱藏層處理單元數對辨識率及RMS ERROR之變異數分析	75
表4-22 學習速率對辨識率及RMS ERROR之變異數分析	76
表5-1 研究假說驗證彙整表	77

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