The emergence of wireless technologies has facilitated immense opportunities for optimal and efficient communication system. WSN is an effective communication technique which has expanded the application space ranging from public utilities, industrial monitoring and control, and defense and military operations. In general, WSN plays a significant role in wireless communication tool, and it has a great potential to serve as the next generation communication technology and for the future ubiquitous network. Such robustness have widened the usability of WSNs into various major applications and as such the technological development is expanding rapidly across the scientific horizon.
Currently, research on the theme of smart city and smart village continues to be done.This study aims to determine the comparison of characteristics of smart cities, and smart villages.The benefit of this research is to know the characteristics, what is needed by smart city, and smart village to be able to answer the need for smart city, and smart village.The needs of the city are smart, and the village is smartly different, but there are standards.To create economic balance and economic growth, it is necessary to focus on creating intelligent systems, in order to promote overall economic growth.
(1) Background: Due to the rapid growth of Massive Online Open Courses (MOOCs), higher educational institutions across the world are investing heavily in MOOCs to support their traditional teaching, their students’ learning experience, and their performance. However, the success of MOOCs highly depends on several factors that influence their success in higher education. Prior studies have attempted to investigate and predict user acceptance of MOOCs in higher education by using a variety of theoretical viewpoints. Nonetheless, these studies have yielded conflicting findings and are inconclusive. (2) Purpose: This study aims to develop a model that integrates the Theory of Planned Behavior (TPB), the Unified Theory of Acceptance and Use of Technology (UTAUT), as well as the Task-Technology Fit (TTF) to explore the factors that influence the acceptance and use of MOOCs in higher education institutions, while synthesizing previous empirical findings in the field. (3) Methods: The model was tested using Meta-analytic Structural Equation Modelling (MASEM) based on the data gathered from 43 studies (k = 45 samples, n = 16,774). (4) Results: Effort expectancy (EE), attitude (ATT), performance expectancy (PE), and TTF—determined by several task and technology characteristics—were identified as the direct predictors of behavioral intention (BI) to continue using MOOCs. (5) Conclusions: This model provides a cohesive view of MOOCs’ acceptance in higher educational institutions, and it helps to identify potential research opportunities in this area. (6) Implications: Results from MASEM offer managerial guidance for the effective implementation of MOOCs and provide directions for further research, to augment current knowledge of MOOCs’ adoption, by higher education institutions.
This study reports the findings of a research involving 400 respondents from all over the country to investigate the effect of industrialisation, modernisation and urbanisation to social cohesion of the
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