第五章 結論與建議
第二節 限制與建議
本次資料收集大多來自台灣而有研究上的限制，可能導致資料有所偏差。也因樣 本在台灣藉此建議台灣餐飲產業之商家欲採用科技菜單，可針對模型特性來作為店家 菜單的設計，來增加顧客價值進而促進收益。另一個較受關注的焦點在於本研究大部 分的參與者沒有使用過 iPad (85.5%)，由於研究在 iPad 發行同時欲搶得先機，除了目 前 iPad 在台灣尚未發行之外，也可能影響研究的結果。雖然大部份參與者沒使用過 iPad，但過去的經驗強烈影響著未來消費的預期(Kwun & Oh, 2004)，大部分的人在過 去有使用過智慧型手機(61.1%)兩年以上的經驗(46.3%)，且在問卷填答前皆有請填答
者先行看 iPad 相關影片，因此在模擬使用 iPad 這方陎並不是太大的問題。雖然本研 究欲選取經常使用資訊科技的樣本而採用網路問卷的方式進行發放，這種方式相當方 便不過在回答問題上還是與實際現場發放有些出入，施測者憑著記憶去想像有關餐廳 採用 iPad 的情境，因人力物力關係無法實際到國外採納的餐廳發放，待 iPad 在台灣 正式上市後，後續研究也可以探討其相關研究發展。
雖然新的科技迅速融入到每個人的生活中，還是有證據顯示使用者對於新的科技 感到失望(Alsop, 1999)，許多新科技對消費者來說太過於先進(Garcia & Calantone, 2002)，也就是說並非所有使用者都願意接受新的科技。雖然新科技提供了便利，但 關於科技服務產品還是有兩極化的意見(Mick & Fournier, 1998)，也因為每個人的個性 不盡相同，而對不同自助服務會有不同的感受(Bradley & Sparks 2002)，也是 Holbrook (1999)所強調，個人的相關性會影響不同的知覺價值。另一方陎，個人創新性(Personal innovativeness)也被發現會影響消費者對於新產品的接受採用(Agarwal & Karahanna, 2000)，因此建議後續研究可以針對消費者分割成不同群體或不同人格特質來分析其 中的看法，甚至探討個人的創新程度與比較分析其中的差異性。
Teo et al. (1999)使用知覺享樂來解釋使用者對網際網路的接受模式，對於網路使 用者而言，提升工作效率之餘使用網路也能感到享樂與容易使用。比較傳統訓練與遊 戲訓練環境兩種方法下，Venkatesh and Speier (2000)發現以遊戲為基礎的訓練方法，
比起傳統的訓練方式有更高的內在動機引發高度的知覺享樂與知覺易用性，結果也顯 示，知覺易用性的程度跟使用系統感到愉快也是有關係的。Agarwal and Karahanna (2000)的研究中享樂因素包含在內的認知吸收，是知覺實用與知覺易用性的潛在決定 因素，最終都會影響行為意圖。Venkatesh et al. (2002)主張，相較於知覺享樂性高的 人對於使用新電腦科技不會有太多的困難，知覺享樂性高的人也比較享受使用中的過 程而不在乎其中的障礙，因此會覺得在新科技學習上不頇耗費太多心力。知覺易用性 相對於知覺享樂亦有正向的相關，也就是系統越覺得容易使用，那麼也會感受到越多 的樂趣(Van der Heijden, 2003)。一個互動系統易於使用可以消除中間的障礙，並方便 的使用其功能，因此一個容易使用的系統，在沒有障礙的情況下可以很方便的獲得其 固有品質，讓使用者享樂在其中(Schaik & Ling, 2011)。也就是知覺享樂的因子跟知覺 易用性有緊密的關係，但本研究中並沒有討論這兩個因子間的影響。探討知覺易用性 的同時除了以往資訊科技較不發達難以了解外，目前科技越來越進步的時空背景下，
知覺易用性的角色是否如往常般重要在未來研究中可再度探討，知覺享樂的研究也日 益增加，知覺易用性與知覺享樂間的關係也可在未來研究裡討論。
之前研究提到了，熟悉度也可能跟顧客的滿意度與行為意圖有關，不同的熟悉度 會帶給顧客不同的參考評價(Söderlund, 2002)，像是消費者經常使用一個產品，而高 度的熟悉度會減少未來在購買時不確定的情況(Flavián, Guinalíu & Gurrea, 2006)。近 年來，關於涉入程度的研究也成為行銷領域的主流，也有研究指出涉入程度對行為意 圖具有影響效果(Schiffman & Kanuk, 2000)，因此待 iPad 在台發行與民眾接觸後，後 續研究也建議可以考慮往熟悉度以及涉入程度這方陎來探討。
自助服務能夠符合消費者需求時，消費者會感到滿意，如果這些服務可以在消費 者風險承擔的範圍內，消費者會更樂意的使用(Meuter et al., 2000)。也由於市場上提 供各種品質與價格的產品，對高價值的產品消費者會更加謹慎的開銷 (Duman &
Mattila, 2005)。由於 iPad 這種高科技與價位的關係，可以預想採用 iPad 菜單的餐廳 在消費額度上會有一定的水準，Hui and Bateson (1991)認為消費者的滿意度會受到個 人知覺風險的影響，這部分探討滿意度及知覺風險也是未來研究可行的方向之一。另 外影響品牌零售的研究中，Webster (2000)提到消費者知覺價值與知名品牌有正向的 影響；Gupta and Stewart (1996)也發現品牌是預測行為意圖和產品類別的重要期望，
就以 iPad 來說蘋果公司的品牌已是社會大眾所認同的品牌之一，才會有每次新產品 推出時蘋果迷爭相搶購的現象，所以品牌效應、行為意圖與期望程度也是之後研究可 探討的一個方向。
服務行業中，口碑(Word-of-mouth)是最強大的溝通方式，顧客在尋求相關資訊和 搜索過程中，經常會看到口碑的訊息，因為是來自第三方體驗的意見，所以更加來得 可靠(Ha & Jang, 2010)。此外顧客的價值觀念，似乎也影響著未來的行為，如回購意 圖和推薦意願(Brady & Cronin, 2001; Cronin et al., 2000)，也有研究證實知覺價值的消 費體驗對推薦意願正向影響著行為意圖(Ladhari, Brun & Morales, 2008)。另外提到消 費者在制定決策過程中，會因親朋好友的建議或是外在資訊而做出回應，這種社會影 響左右消費者採用新資訊科技(Tan & Teo, 2000)的行為，對新資訊科技而言，Venkatesh and Davis (2000)建議社會影響應列入考慮。所以未來研究中，回購意圖、推薦意願與 社會影響也是可以考慮的研究方向。
等候時間的問題也是服務業的一個重大挑戰，因為過長的等待也可能是一種成
本，如員工呆滯也是種浪費，或造成顧客失望及其他成本的損失。在這方陎若採用 iPad 電子菜單來提供自助科技服務，即時更新的資訊將顧客點餐的資料直接送至櫃檯與內 場，在損耗員工呆滯或等待時間上並沒有太大的疑慮。若等待時間為人為因素（如內 場來不及製作或員工無法負荷）時，Larson (1987)指出如果服務業者能夠對等待中的 顧客提供相關等候時間的資訊將可減少顧客的不滿，例如提供說明訂單已傳至內場，
目前處理進度或等候順序，也可表明本餐點為現點現做之佳餚，餐點製作手續繁雜請 予耐心等待或相關資訊。也是 Larson (1987)指出服務業者對等待中的顧客適時提供等 待資訊可以改善顧客在等待時的知覺，結合了 iPad 的功能性，在等候餐點的同時可 以給予 iPad 系統上的娛樂或進行遊戲、無線上網及其他應用程式的使用，讓顧客不 再那麼無聊而專注在等待時間上。
Zeithaml, Berry and Parasuraman (1996)也指出員工所提供的服務是無形性的，所 以顧客會依照實體環境這類有形性的指標，做為衡量標準。且當消費者處於一個令人 愉快的環境時，會傾向較正向的評價產品(Laroche, Teng, Michon & Chebat, 2005)，因 此除了應用電子菜單外，餐廳經營者應該要營造一個讓消費者愉悅的環境，像是在裝 潢設計上動些巧思或是背景音樂周邊產品的搭配可以讓顧客的消費有更高的價值。網 路自助服務的消費者如果認為使用上容易操作，那將預計有較高的滿意度與服務品質 (Yen, 2005)。因此待 iPad 上市與餐廳實際應用後，相關研究也可以繼續延伸探討滿意 度與服務品質的學術課題。
最後研究提出的新模式在驗證分析後得以成立，新模型可供未來研究參考及應 用。建議後續研究者可以考慮使用其他知覺因子來探討新科技技術與餐飲產業之間的 研究，或針對採用電子菜單的商家進行深度訪談，以及套用別種理論模式在研究當中。
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附錄 A
測量模型分析過程
執行結構方程模式之前頇先進行測量模型的驗證性因素分析，首先驗證每個構陎 的適配度，待每個構陎完成後再予執行整體測量模型的檢測。結構模型圖如圖 1 所示 接著要執行各構陎測量模型的檢測，因此先從知覺實用性(PU)構陎以及五個觀察變項 開始分析。表 10 中可以看見 PU 第一次測量模型的適配度並不是很好，因此在經過 MI 修正指標(Modification indices, MI)的建議以及其他數據（顯著、標準化因素負荷 量、題意…等）的考量下把知覺實用性(PU)構陎中第二題刪除，接著用剩下四題來做 第二次 PU 構陎的驗證性因素分析。以下各構陎以代號簡稱：知覺實用性(Perceived usefulness, PU)；知覺易用性(Perceived ease of use, PEOU)；知覺控制(Perceived control, PC)；知覺享樂(Perceived enjoyment, PE)；知覺新奇(Perceived novelty, PN)；知覺價值 (Perceived value, PV)；光臨意圖(Behavioral intention, BI)。
表 10
PU 第一次測量模型適配度
適配度指標 建議值 實際值
χ2/degree of freedom ≦3.00 18.928 Goodness-of-fit index (GFI) ≧0.80 0.663 Adjusted goodness-of-fit index (AGFI) ≧0.80 0.888 Normed fit index (NFI) ≧0.90 0.891 Comparative fit index (CFI) ≧0.90 0.896 Root mean square residual (RMSR) ≦0.10 0.233
圖 4 PU 第一次測量模型標準化結果
知覺實用性
PU3 PU
PU4 PU5 e
e e e PU1
e PU1 0.678
0.713 0.878
0.804 0.718
第二次 PU 的驗證分析中（表 11），雖然可以看出卡方自由度值下降且其他數值 也有好轉的跡象，但很顯然的許多模型適配度的指標還是不符合標準，因此在指標及 數據建議下把 PU 構陎第一題刪除並繼續下一輪的驗證分析。
表 11
PU 第二次測量模型適配度
適配度指標 建議值 實際值
χ2/degree of freedom ≦3.00 15.264 Goodness-of-fit index (GFI) ≧0.80 0.953 Adjusted goodness-of-fit index (AGFI) ≧0.80 0.766 Normed fit index (NFI) ≧0.90 0.951 Comparative fit index (CFI) ≧0.90 0.954 Root mean square residual (RMSR) ≦0.10 0.208
圖 5 PU 第二次測量模型標準化結果
觀察變項的共變數矩陣的元素數目減去要估計參數的數目即是自由度，當自由度 為 0 時，模型稱之為飽和模型(Saturated model)。當模型為飽和模型時適配度都是完 美的，此時模型只有一種解釋而軟體無法去評估實際適配度的好壞（徐聖訓，2009，
頁 90）。計算觀察變項共變數矩陣的元素數目上，可以用公式 n(n+1)/2 算出（n: 觀察
變項的數目）。經過兩輪驗證性因素分析後 PU 構陎第三次執行時觀察變項由原本的
五題只剩下三題觀察變項，其中共變數矩陣的元素數目利用上述公式計算出為 6，要 估計的參數數目也是 6（見圖 6，0 為參照指標），因此相減過後自由度為 0，所以此 時 PU 構陎的模型即為飽和模型，表現出來的適配度指標皆為 0 或 1.0 而不具參考意 義，在此與後序過程中既不附上飽和模型的適配度指標。
知覺實用性 PU
PU3 PU4 PU5 e
e e
e PU1 0.603 0.821
0.872 0.774