Chatbot Technology Use and Acceptance Using Educational Personas

IF 3.4 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Fatima Ali Amer jid Almahri, David Bell, Zameer Gulzar
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Abstract

Chatbots are computer programs that mimic human conversation using text or voice or both. Users’ acceptance of chatbots is highly influenced by their persona. Users develop a sense of familiarity with chatbots as they use them, so they become more approachable, and this encourages them to interact with the chatbots more readily by fostering favorable opinions of the technology. In this study, we examine the moderating effects of persona traits on students’ acceptance and use of chatbot technology at higher educational institutions in the UK. We use an Extended Unified Theory of Acceptance and Use of Technology (Extended UTAUT2). Through a self-administrated survey using a questionnaire, data were collected from 431 undergraduate and postgraduate computer science students. This study employed a Likert scale to measure the variables associated with chatbot acceptance. To evaluate the gathered data, Structural Equation Modelling (SEM) coupled with multi-group analysis (MGA) using SmartPLS3 were used. The estimated Cronbach’s alpha highlighted the accuracy and legitimacy of the findings. The results showed that the emerging factors that influence students’ adoption and use of chatbot technology were habit, effort expectancy, and performance expectancy. Additionally, it was discovered that the Extended UTAUT2 model did not require grades or educational level to moderate the correlations. These results are important for improving user experience and they have implications for academics, researchers, and organizations, especially in the context of native chatbots.
使用教育人物角色的聊天机器人技术使用和接受情况
聊天机器人是模仿人类对话的计算机程序,使用文本或语音,或两者兼用。用户对聊天机器人的接受程度受其角色的影响很大。用户在使用聊天机器人的过程中会产生一种熟悉感,因此聊天机器人会变得更加平易近人,这将鼓励他们与聊天机器人进行互动,从而对聊天机器人技术产生好感。在本研究中,我们研究了角色特质对英国高等教育机构学生接受和使用聊天机器人技术的调节作用。我们采用了技术接受和使用扩展统一理论(Extended UTAUT2)。通过使用问卷进行自我管理调查,我们从 431 名计算机科学专业的本科生和研究生中收集了数据。本研究采用李克特量表来测量与聊天机器人接受度相关的变量。为了评估收集到的数据,我们使用了结构方程建模(SEM)和使用 SmartPLS3 的多组分析(MGA)。估计的 Cronbach's alpha 强调了研究结果的准确性和合理性。结果显示,影响学生采用和使用聊天机器人技术的新因素是习惯、努力期望和表现期望。此外,研究还发现,扩展UTAUT2 模型并不需要成绩或教育水平来调节相关性。这些结果对于改善用户体验非常重要,对学术界、研究人员和组织机构都有意义,尤其是在原生聊天机器人方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Informatics
Informatics Social Sciences-Communication
CiteScore
6.60
自引率
6.50%
发文量
88
审稿时长
6 weeks
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