Hussein, Safaa, Abdelhamid, Asmaa. (2021). Technology Acceptance Model for Pervasive Computing: Mobile Business Intelligence Applications in Egypt. مجلة جامعة الإسکندرية للعلوم الإدارية, 58(2), 291-326. doi: 10.21608/acj.2021.167940
Safaa Hussein; Asmaa Abdelhamid. "Technology Acceptance Model for Pervasive Computing: Mobile Business Intelligence Applications in Egypt". مجلة جامعة الإسکندرية للعلوم الإدارية, 58, 2, 2021, 291-326. doi: 10.21608/acj.2021.167940
Hussein, Safaa, Abdelhamid, Asmaa. (2021). 'Technology Acceptance Model for Pervasive Computing: Mobile Business Intelligence Applications in Egypt', مجلة جامعة الإسکندرية للعلوم الإدارية, 58(2), pp. 291-326. doi: 10.21608/acj.2021.167940
Hussein, Safaa, Abdelhamid, Asmaa. Technology Acceptance Model for Pervasive Computing: Mobile Business Intelligence Applications in Egypt. مجلة جامعة الإسکندرية للعلوم الإدارية, 2021; 58(2): 291-326. doi: 10.21608/acj.2021.167940
Technology Acceptance Model for Pervasive Computing: Mobile Business Intelligence Applications in Egypt
1Faculty of Commerce, Alexandria University, Alexandria, Egypt
2Teaching Assistant at Information Systems and Computers Department, Faculty of Commerce, Alexandria University Alexandria Egypt
المستخلص
The Pervasive Computing is a new paradigm in information technology. Over the past two decades, many of Pervasive Computing and Internet of Things applications were developed to facilitate our life. The current study will mainly focus on pervasive computing applications at work settings and empirically validate the proposed model. The Mobile Business Intelligence application was chosen as an initial stage technology of pervasive computing. The study developed a proposed model which included the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model along with the Trust and Perceived Convenience variables to investigate behavioral intention to use pervasive computing applications. Using Questionnaire survey, data was collected from 277 employees in some companies in Egypt. The correlation, regression and Structural Equation Modeling (SEM) were used for analysis. The results showed that effort expectancy explained 17.9% of the variations in Perceived Convenience, 28.9% of the variations in Performance Expectancy were justified by Perceived Convenience. The results also indicated that Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Perceived Convenience and Trust explained 46.7% of the variations in behavioral intention to use pervasive computing applications.
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