Emotions, behaviour and belief regulation in an intelligent guide with attitude

Abstract unavailable please refer to PD

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制度:新 ; 報告番号:甲2735号 ; 学位の種類:博士(人間科学) ; 授与年月日:2009/3/15 ; 早大学位記番号:新493

Website Personalization Based on Demographic Data

This study focuses on websites personalization based on user's demographic data. The main demographic data that used in this study are age, gender, race and occupation. These data is obtained through user profiling technique conducted during the study. Analysis of the data gathered is done to find the relationship between the user's demographic data and their preferences for a website design. These data will be used as a guideline in order to develop a website that will fulfill the visitor's need. The topic chose was Obesity. HCI issues are considered as one of the important factors in this study which are effectiveness and satisfaction. The methodologies used are website personalization process, incremental model, combination of these two methods and Cascading Style Sheet (CSS) which discussed detail in Chapter 3. After that, we will be discussing the effectiveness and evaluation of the personalization website that have been built. Last but not least, there will be conclusion that present the result of evaluation of the websites made by the respondents

Unveiling AI Aversion: Understanding Antecedents and Task Complexity Effects

Artificial Intelligence (AI) has generated significant interest due to its potential to augment human intelligence. However, user attitudes towards AI are diverse, with some individuals embracing it enthusiastically while others harbor concerns and actively avoid its use. This two essays\u27 dissertation explores the reasons behind user aversion to AI. In the first essay, I develop a concise research model to explain users\u27 AI aversion based on the theory of effective use and the adaptive structuration theory. I then employ an online experiment to test my hypotheses empirically. The multigroup analysis by Structural Equation Modeling shows that users\u27 perceptions of human dissimilarity, AI bias, and social influence strongly drive AI aversion. Moreover, I find a significant difference between the simple and the complex task groups. This study reveals why users avert using AI by systematically examining the factors related to technology, user, task, and environment, thus making a significant contribution to the emerging field of AI aversion research. Next, while trust and distrust have been recognized as influential factors shaping users\u27 attitudes towards IT artifacts, their intricate relationship with task characteristics and their impact on AI aversion remains largely unexplored. In my second essay, I conduct an online randomized controlled experiment on Amazon Mechanical Turk to bridge this critical research gap. My comprehensive analytic approach, including structural equation modeling (SEM), ANOVA, and PROCESS conditional analysis, allowed me to shed light on the intricate web of factors influencing users\u27 AI aversion. I discovered that distrust and trust mediate between task complexity and AI aversion. Moreover, this study unveiled intriguing differences in these mediated relationships between subjective and objective task groups. Specifically, my findings demonstrate that, for objective tasks, task complexity can significantly increase aversion by reducing trust and significantly decrease aversion by reducing distrust. In contrast, for subjective tasks, task complexity only significantly increases aversion by enhancing distrust. By considering various task characteristics and recognizing trust and distrust as vital mediators, my research not only pushes the boundaries of the human-AI literature but also significantly contributes to the field of AI aversion

Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This research aims to identify a feasible model to predict a learner’s stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner’s emotions. The few signals produced by mouse and keyboard could enable such solution to measure real world individual’s affective states. It is also important to ensure that the measurement can be applied regardless the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes of duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification