生物辨識技術在智慧行動裝置之科技評估與採用分析

Abstract

面臨安全性與隱私性的挑戰，許多智慧行動裝置(smart mobile devices)廠商與研究機構積極嘗試在其產品上運用生物辨識(biometrics)，例如蘋果(Apple Inc.)在iPhone 5S機種上面設計指紋辨識(fingerprint recognition)，因生物辨識可以提供可靠耐用的個人識別機制來確認與偵測其身分。 本研究目的主要透過科技管理聯結生物辨識在智慧行動裝置的應用上做兩個規劃，第一，透過典型的技術評估考量要素結合生物辨識科技的特色來進行衡量，並獲得應用於智慧行動裝置(smart mobile devices)之最佳生物辨識技術選擇方案。為了達成此研究目的，我們運用模糊層級分析法(the fuzzy analytic hierarchy process，FAHP)，最佳非模糊績效值法(best non-fuzzy performance，BNP)，分析結果顯示選擇生物辨識方案時，最重要的構面為技術評估(technology assessment)構面，接著為生物辨識性能(biometrics performance)構面、生物辨識要素(key elements of biometrics)構面，其也透露研究目標的技術特色扮演重要角色。進一步結果指出在六種生物辨識中，最有評選優勢的依序為指紋辨識、虹膜辨識(iris recognition)以及臉型辨識(face recognition)。 其次，根據第一階段的結果，我們繼續探討指紋辨識方案運用在智慧行動裝置的接受度，設計以整合消費者與技術觀點的科技接受模式(technology acceptance model)來進行分析，其中導入三個外部變數構面：生物辨識要素、生物辨識性能以及技術評估。以研究模式的結構方程式(structural equation modeling)分析結果說明「生物辨識要素正面影響知覺易用性(perceived ease of use)」、「生物辨識性能正面影響知覺有用性(perceived usefulness)」以及「技術評估正面影響使用意願 (intent to use)」。整體視之，此模式的適配度檢定符合結構方程式所要求之水準，大部分的假設都得到支持，除了知覺易用性與使用意願的正相關程度並不顯著。 綜上所述，本研究可以提供給管理研究學者面臨前瞻科技評選議題時作為參考，本研究首先運用FAHP和BNP了解影響生物辨識評選的關鍵構面，特別是關於目標技術的特色需要被考量進去，最後得到指紋辨識為最適方案，其可以幫助理解智慧行動裝置採用生物辨識技術的評估因素。另外，TAM分析結果可以說明促進消費者在智慧行動裝置上接受指紋辨識的重要環節，據此可以擴大生物辨識技術的滲透率與普及程度。

Faced with security and privacy challenges, many smart mobile devices manufacturers and research institutions are engaged in designing their products using biometrics, such as Apple launched iPhone 5S with fingerprint authentication. Although biometrics has been applied in some specific fields for decades, biometrics has gradually proliferated in consumer and mobile electronic devices to enhance security and privacy concerns because that biometrics can provide a durable and reliable personal identification and detection mechanisms to confirm his identity. There are two main purposes linking technology management with marketing biometrics adoption of this study plan; first, we evaluate biometrics through conventional technology assessment considerations combining the measurement elements of the particularity of biometric technologies with the optimal biometric technology selection to get in smart mobile devices. In order to achieve biometric technology assessment, we use fuzzy analytic hierarchy process (FAHP) and best non-fuzzy performance (BNP) for our research purposes. The outcome reveals that technology assessment is the most emphasized key object in selecting biometric technologies followed by biometrics performance and key elements of biometrics. It also reveals that the technic features of the concerned technologies play an important role in research goals. Further results indicate that in the six kinds of biometrics, the most advantageous selection order is the fingerprint recognition, followed by the iris recognition and the facial recognition. Secondly, according to the results of the first stage, we continue to explore the use of the fingerprint recognition in the acceptance of smart mobile devices via technology acceptance model, designed to integrate with consumer and technology views for analysis with three extrinsic variables, ‘key elements of biometrics’, ‘biometrics performance’, and ‘technology assessment’. The research results of structural equation modeling indicate that ‘key elements of biometrics’ positively impacts perceived ease of use, ‘biometrics performance’ positively impacts perceived usefulness, and ‘technology assessment’ positively affect intent to use biometrics. Over all, the fitness of the model meets requirements, and the most hypotheses are supported except that the degree of positive correlation between perceived ease of use and intent to use is not significant. In conclusion, this study can provide as a reference when management researchers faced when managing the use of forward-looking technology selection issues. This study applies FAHP and BNP analysis in evaluating biometric technologies to simulate how different evaluation objects affect biometric technology selecting. This study enables researchers to recognize that technology assessment should give more consideration to the particularity of target technologies. In this way, it could help them more comprehensively evaluate and select the biometrics applied in smart mobile devices. In addition, the TAM analysis results illustrate the chances to stimulate consumer acceptance of biometrics in smart mobile devices whereby to expand the penetration and popularity of biometric technology.