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Predicting the adoption of cloud-based technology using fuzzy analytic hierarchy process and structural equation modelling approaches

机译:采用模糊分析层次工艺及结构方程建模方法预测采用基于云的技术

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摘要

With the emergence of cloud-based technology, personalized learning mechanism has increasingly become a fundamental requirement for most learning systems. This study aimed to identify the key factors that influence user adoption of cloud-based collaborative learning technology in the educational context. Grounded on the Unified Theory of Acceptance and Use of Technology (UTAUT), personalization construct was linked to the behavioral intention, performance expectancy and effort expectancy. This research applied a new methodological approach combining both Fuzzy Analytic Hierarchy Process (FAHP) and Structural Equation Modelling (SEM) to determine the relative weight and importance of the factors as well as to test the proposed hypotheses in the research model. Using a survey questionnaire, data was collected from 150 students of four Malaysian public universities. The findings of FAHP demonstrated that performance expectancy, social influence, and personalization were the most important factors predicting behavioral intention to adopt cloud-based collaborative learning technology from experts' point of view. The results of the SEM showed that users' behavioral intention was significantly influenced by performance expectancy, effort expectancy, social influence and personalization. Although, personalization performed a direct influence on behavioral intention, its indirect influence through performance expectancy and effort expectancy was also considerable. This study and its findings can serve as a baseline by which cloud service providers, ministry of education, and educational institutions can make strategic and strong decisions about adoption of cloud-based technology in educational environments. (C) 2018 Elsevier B.V. All rights reserved.
机译:随着基于云技术的出现,个性化学习机制越来越成为大多数学习系统的根本要求。本研究旨在确定影响教育背景下基于云的协作学习技术的关键因素。基于统一的接受和使用技术理论(Utaut),个性化构建与行为意图相关联,性能预期和常规率。该研究应用了一种新的方法论方法,将模糊分析层次处理(FAHP)和结构方程模型(SEM)相结合,以确定因素的相对重量和重要性以及测试研究模型中提出的假设。使用调查问卷,从150名马来西亚公共大学的学生收集数据。 FAHP的调查结果表明,性能期望,社会影响和个性化是预测从专家的角度采用基于云的协作学习技术的行为意图最重要的因素。 SEM的结果表明,用户的行为意图受到性能预期,努力预期,社会影响和个性化的显着影响。虽然个性化对行为意图进行了直接影响,但其间接影响通过性能预期和努力期望也很大。本研究及其调查结果可以作为基准作为云服务提供商,教育部和教育机构可以做出关于通过在教育环境中采用基于云技术的战略和强烈决定。 (c)2018 Elsevier B.v.保留所有权利。

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