Information Technology Use & Acceptance

 
 
 
 
PERCEIVED USEFULNESS, PERCEIVED EASE OF USE, AND USER ACCEPTANCE OF INFORMATION TECHNOLOGY: AN ARTICLE CRITIQUE

The article "Perceived usefulness, perceived ease of use, and user acceptance of information technology" is critiqued in this research. The critique covers validity, variance, procedures, factor analysis, internal consistency, moderator variable, multiple regression, and reliability.

Validity is critiqued in five contexts. These contexts are face, content, predictive or criterion, construct, and concurrent.

Face validity is that quality of an indicator that makes the indicator at least appear to be a reasonable measure of some variable. The principle explanatory variables involved in the research reported in the critiqued article were user perceived usefulness of information technology and user perceived ease of use of information technology. Potential indicators of these variables were identified through the literature. This procedure provided face validity in the search for indicator measures of the two explanatory variables.

Content validity is the degree to which a measure covers the range of meanings included in the concept of some variable. A pre-test and pre-test interviews were used in the research reported in the critiqued article to assess the content validity of the selected indicator measures.

Criterion validity is the degree to which a measure of some variable relates with some external criterion. Criterion validity w


     
 
 
 
    

 

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ollinearity. In most such instances, if the data are to be used effectively, it is necessary to reduce the number of explanatory variables to more manageable proportions. Factor analysis is a general descriptor for a group of specific computational procedures. Each of the procedures included in the group, however, are intended to reduce a large number of measures to a smaller number that provides a more efficient and powerful measure of the same thing. The three general objectives of factor analysis are to study the correlations of a large number of variables by clustering those variables into factors in a way that the variables included in each factor are highly correlated to interpret each factor identified according the variables included in the factor, and to summarize many variables in to a few factors. Factor analysis is a statistical procedure with data-reduction capabilities that is used to determine the underlying pattern of relationships among a set of variables or conditions which may be taken as source variables, accounting for the observed interrelations in the data. Factor loadings are the coefficients of factors identified in factor analysis, and are used as measures of the degree of the relationships between

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