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Download 3-Designs from the Z 4-Goethals Codes via a New Kloosterman by Shin D.-J., Kumar P.V. PDF

By Shin D.-J., Kumar P.V.

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Pick, J. , & Azari, R. (2011). A global model of utilization of technology based on governmental, social, economic, and business investment factors. Journal of Management Information Systems, 28(1), 51–85. Pick, J. , & Zhang, X. (2013). Determinants of China’s technology utilization and availability 2006–2009: A spatial analysis. The Information Society, 29(1), 26–48. Pick, J. , & Johnson, J. (2014). United States digital divide: State level analysis of spatial clustering and multivariate determinants of ICT utilization.

This section concerns a small group of prior investigations that do recognize the geography of technological differences. They include studies of the world (Warf 2001; Takhteyev et al. S. (Grubesic 2004, 2006, 2010), Japan (Arai and Naganuma 2010), and China (Song 2008). They include the geography in a variety of ways, including thematic mapping of attributes, social media ties, local spatial autocorrelation (LISA analysis), and qualitative case studies of geographical settings and interventions to stimulate more technology development and use.

A further reason for not using SEM is that the interest in this book is to estimate and compare individual technology factors, such as mobile phone utilization or internet subscribers, rather than a combined latent factor, for which individual technologies cannot be distinguished. Lastly, it is not used because most of the sample sizes in this volume for states, provinces, and prefectures are too small to utilize with SEM. Two other literature examples of the SEM approach are a model of complex pathways of influence of technology acceptance by individuals that combines wellknown factors from the Technology Acceptance Model (TAM) with cultural factors from the Hofstede model (Srite and Karahanna 2006).

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