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Elucidating user behavior of mobile learning A perspective of the extended technology acceptance model

机译:阐明移动学习的用户行为扩展技术接受模型的视角

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Purpose - The purpose of this paper is to propose and verify that the technology acceptance model (TAM) can be employed to explain and predict the acceptance of mobile learning (M-learning); an activity in which users access learning material with their mobile devices. The study identifies two factors that account for individual differences, i.e. perceived enjoyment (PE) and perceived mobility value (PMV), to enhance the explanatory power of the model.rnDesign/methodology/approach - An online survey was conducted to collect data. A total of 313 undergraduate and graduate students in two Taiwan universities answered the questionnaire. Most of the constructs in the model were measured using existing scales, while some measurement items were created specifically for this research. Structural equation modeling was employed to examine the fit of the data with the model by using the LISREL software.rnFindings - The results of the data analysis shows that the data fit the extended TAM model well. Consumers hold positive attitudes for M-learning, viewing M-learning as an efficient tool. Specifically, the results show that individual differences have a great impact on user acceptance and that the perceived enjoyment and perceived mobility can predict user intentions of using M-learning. Originality/value - There is scant research available in the literature on user acceptance of M-learning from a customer's perspective. The present research shows that TAM can predict user acceptance of this new technology. Perceived enjoyment and perceived mobility value are antecedents of user acceptance. The model enhances our understanding of consumer motivation of using M-learning. This understanding can aid our efforts when promoting M-learning.
机译:目的-本文的目的是提出并验证技术接受模型(TAM)可以用于解释和预测移动学习(M-学习)的接受;用户使用其移动设备访问学习资料的活动。该研究确定了两个因素来解释个体差异,即感知的享受(PE)和感知的移动性价值(PMV),以增强模型的解释力。设计/方法/方法-进行了在线调查以收集数据。台湾两所大学共有313名本科生和研究生回答了问卷。该模型中的大多数构造都是使用现有的比例尺进行测量的,而一些测量项目是专门为此研究创建的。通过使用LISREL软件,使用结构方程建模来检查数据与模型的拟合。rn结果-数据分析的结果表明,数据与扩展的TAM模型非常吻合。消费者对移动学习持积极态度,认为移动学习是一种有效的工具。具体而言,结果表明,个体差异对用户接受度有很大影响,并且感知的享受和感知的移动性可以预测用户使用M学习的意图。原创性/价值-从客户的角度来看,关于用户对M学习的接受程度的文献研究很少。本研究表明,TAM可以预测用户对该新技术的接受程度。感知的享受和感知的移动性价值是用户接受的前提。该模型增强了我们对使用M学习的消费者动机的理解。这种理解有助于我们促进M学习的努力。

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