Assessing the effect of innovation diffusion and technology readiness theories on attitude, behavioral intention and implementation of smart learning

Khadija Alhammadi, Hazem Marashdeh, M. Hussain
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Abstract

PurposeThis study assesses the impact of innovation diffusion theory (IDT), technology readiness index (TRI) and technology acceptance model (TAM) on the actual use of smart learning. This impact also accounts for the country-digital culture by moderating the effects of resistance to change (RTC) and mediating the role of attitude.Design/methodology/approachThe authors gather data from 301 respondents from various academic institutions in the United Arab Emirates (UAE) by operationalizing established theoretical constructs. The authors adopt a covariance-based structural equation modeling (SEM) approach.FindingsThe results reveal that IDT and TRI significantly and positively affect attitudes toward implementing smart learning. Besides, the attitude fully mediates the relationship between IDT, TRI constructs and behavioral intention (BI). Moreover, this study proves that RTC plays a major role in converging BI to place smart learning into actual use.Research limitations/implicationsThe major limitation of the authors' work is that this work employs cross-sectional data from UAE only, and the data were gathered during the coronavirus disease 2019 (COVID-19) pandemic.Practical implicationsThe stakeholders and administrators in government can benefit from the study findings to improve the efficiency and effectiveness of the implementation of smart learning, which will contribute to achieving stakeholders and administrators' strategic objectives.Originality/valueThe originality of this work stems from the incorporation of IDT, TRI and TAM constructs in the case of smart learning in UAE in post-COVID-19 scenarios.
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评估创新扩散和技术准备理论对智能学习态度、行为意向和实施的影响
目的本研究评估创新扩散理论(IDT)、技术就绪指数(TRI)和技术接受模型(TAM)对智能学习实际使用的影响。这种影响也解释了国家数字文化通过缓和抵制变化(RTC)的影响和中介态度的作用。设计/方法/方法作者通过对已建立的理论结构进行操作,从阿拉伯联合酋长国(UAE)各学术机构的301名受访者中收集数据。作者采用了基于协方差的结构方程建模方法。研究结果显示,IDT和TRI显著正向影响实施智能学习的态度。此外,态度在IDT、TRI构念与行为意向(BI)之间的关系中起着完全中介作用。此外,本研究证明,RTC在将BI融合到智能学习中发挥了重要作用。研究局限性/意义作者工作的主要局限性是本工作仅使用阿联酋的横断面数据,并且数据是在2019年冠状病毒病(COVID-19)大流行期间收集的。实践意义政府的持份者和行政人员可以从研究结果中获益,以提高实施智能学习的效率和效果,从而有助于实现持份者和行政人员的战略目标。独创性/价值这项工作的独创性源于将IDT、TRI和TAM结构结合到阿联酋后covid -19情景下的智能学习中。
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