The Factors Affected m-Services Adoption in Airports

This research focus on the factors which affected m-Services utilization in airport from airport’s functionaries point of view. Also, the background of this research is related to some former researches which defined some models that may increase the exertion of electronic mobile based service application (m-Services) such as: TAM, UTAUT, and UTAUT2. However, the former literature reviews only elucidate the factors which affect the increasing of m-Services or mobile technologies/ self service technology exertion from customer point of view. While, there are less of them apprising the factors affected m-Services adoption from airport’s functionaries point of view. The purpose of this study is fulfilling the lack in the literature review by apprising the study towards airport’s functionaries point of view. To reach the purpose of the study, this study applies literature review method. The literature review’s source of this study is originated from some international journals which discuss the factors of airport’s functionaries side. The result of this study are the Explanation of the factors affected m-Service adoption or mobile technologies/ self-services technology from airport’s functionaries point of view and the development of conceptual model related to the factors affected m-Services adoption from airport’s function perspective

ノウハ ニ モトズク ネムケ ケンシュツ スイテイ オヨビ ヨソク ニ ムケタ サポート ベクター アプローチ

熊本大

Gazing Time Analysis for Drowsiness Assessment Using Eye Gaze Tracker

From several investigations, it has been shown that most of the traffic accidents were due to drowsy driving. In order to address this issue, many related works have been conducted. One study was able to capture the driver’s facial expression and estimate their drowsiness. Instead of measuring the driver’s physiological condition, the results of such measurements were also used to predict their drowsiness level in this study. We investigated the relationship between the drowsiness and physiological condition by employing an eye gaze signal utilizing an eye gaze tracker and the Japanese version of the Karolinska sleepiness scale (KSS-J) within the driving simulator environment. The results showed that the gazing time has a significant statistical difference in relation to the drowsiness level: alert (1−5), weak drowsiness (6−7), and strong drowsiness (8−9), with P<0.001. Therefore, we suggested the potential of using the eye gaze to assess the drowsiness under a driving condition.