The Moderating Effects of Demographic Factors: The Usage of M-Learning Services among Jordanian Students

Lana Yasin Al Aesa, N. Al-Sammarraie
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引用次数: 1

Abstract

This study attempts to suggest a model that will increase the usage of M-Learning Services in developing countries. This study intends to study the aspect of Jordanian university students’ acceptance of M-learning services by looking into the students’ intention to use & the Continued Usage Intention of the M-learning services. The proposed model may facilitate the understanding of how certain factors can affect the level of usage of M-Learning Services in Jordan. The main objective of this study is identifying the effects of Effort Expectancy (EE), Facilitating Condition (FC), Performance Expectancy (PE), Social Influence (SI), Culture Factors (FC), Quality of Service (QoS) and Student Readiness (SR) on the Usage Behaviour (UB) of m-learning services. Determining the significant factors influencing students’ acceptance is aimed at reducing students’ resistance to the use of m-learning system. This study applied a quantitative research methodology, which includes a numerical measurement and analysis of the factors which influence acceptance. The findings of this study practically contribute to solving the research problem, namely students' acceptance of m-learning. The descriptive statistics revealed that, respondents indicated of this study have expertise and good experience with using m-learning in Jordan. More importantly, the study has offered results of PLS-SEM analysis which was obtained from evaluating hypotheses testing, the measurement model, and structural model.
人口统计学因素的调节作用:约旦学生移动学习服务的使用
本研究试图提出一种在发展中国家增加移动学习服务使用的模式。本研究拟从移动学习服务的学生使用意向和持续使用意向两个方面来研究约旦大学生对移动学习服务的接受程度。所提出的模型可能有助于理解某些因素如何影响约旦移动学习服务的使用水平。本研究的主要目的是确定努力期望(EE)、促进条件(FC)、绩效期望(PE)、社会影响(SI)、文化因素(FC)、服务质量(QoS)和学生准备(SR)对移动学习服务使用行为(UB)的影响。确定影响学生接受度的重要因素,旨在减少学生对使用移动学习系统的抗拒。本研究采用定量研究方法,包括对影响接受度的因素进行数值测量和分析。本研究的结果实际上有助于解决研究的问题,即学生对移动学习的接受程度。描述性统计数据显示,本研究的受访者在约旦使用移动学习方面具有专业知识和良好经验。更重要的是,本研究提供了PLS-SEM分析结果,该结果来自于评估假设检验、测量模型和结构模型。
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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