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Factors Influencing the Diffusion of the Internet in China: 1997-2001
Unformatted Document Text:  14 The second factor could be viewed as the industry structure, which specifically refers to the scale of the service industry including educational sectors. The development level of the educational industry is reflected in the number of schools and student enrollment in the area. The third factor is related to urban people’s wealth. The more money they make, the more money they will spend on telecommunication. We noticed that the rural residents’ income is not categorized in this factor; rather, it is more correlated with the region’s overall development level, which was categorized as “cosmopolitanism” in the first factor. It indicates that the rural resident’s income and expenditure do not significantly affect Internet adoption at the present stage. Correlation does not provide prediction of dependent variables. Since most of predictors are highly correlated with each other, stepwise regression is adopted to build regression model. Stepwise regression is typically used to develop a subset of IVS that is useful in predicting the Dependent variable, and to eliminate those Independent variables that do not provide additional prediction to the predictors already in the equation. As one methods of statistical regression, forward selection adds one variable at a time provided they meet the statistical criteria for entry (Tabachnick &Fidell, 2002) Table 7 presents the results of OLS regression models on Internet population in 2001. Rate of home computer ownership is one of the indicators for the factor of “level of cosmopolitism” (table 4), number of schools is one the indicator for the factor of “industry structure”, and the income of urban residents is one of the indicator of “Urban residents’ wealth”. The first model shows that the

Authors: Lin, Jia.
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14
The second factor could be viewed as the industry structure, which
specifically refers to the scale of the service industry including educational
sectors. The development level of the educational industry is reflected in the
number of schools and student enrollment in the area.
The third factor is related to urban people’s wealth. The more money they
make, the more money they will spend on telecommunication. We noticed that
the rural residents’ income is not categorized in this factor; rather, it is more
correlated with the region’s overall development level, which was categorized as
“cosmopolitanism” in the first factor. It indicates that the rural resident’s income
and expenditure do not significantly affect Internet adoption at the present stage.
Correlation does not provide prediction of dependent variables. Since
most of predictors are highly correlated with each other, stepwise regression is
adopted to build regression model. Stepwise regression is typically used to
develop a subset of IVS that is useful in predicting the Dependent variable, and
to eliminate those Independent variables that do not provide additional prediction
to the predictors already in the equation. As one methods of statistical
regression, forward selection adds one variable at a time provided they meet the
statistical criteria for entry (Tabachnick &Fidell, 2002)
Table 7 presents the results of OLS regression models on Internet
population in 2001. Rate of home computer ownership is one of the indicators for
the factor of “level of cosmopolitism” (table 4), number of schools is one the
indicator for the factor of “industry structure”, and the income of urban residents
is one of the indicator of “Urban residents’ wealth”. The first model shows that the


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