Digital government is a complex socio-technical phenomenon, which is affected by technical, managerial, institutional, and environmental factors (Dawes & Pardo, 2002; Fountain, 2001; Gant, 2003; Garson, 2000; Heeks, 2005; Kraemer, King, Dunkle et al., 1989; Landsbergen & Wolken, 2001; Laudon, 1985; Rocheleau, 2003). Recent studies have greatly contributed to developing the necessary knowledge about e-government benefits and success factors (Barrett & Greene, 2000; Dawes, 1996; Gil-García & Pardo, 2005; Heeks, 2003; Holmes, 2001; O’Looney, 2002; Rocheleau, 1999; West, 2005; Zhang et al., 2002). However, an important portion of this research has used a single measure of e-government and relatively simple assumptions about the relationships between information technologies and organizational, institutional, and contextual factors (Gil-García, 2005b).
Key Terms in this Chapter
Indicator: It is a variable that can be directly observed and measured and, in a SEM framework, it is used to form or reflect a construct or latent variable.
Construct: It is a variable that cannot be observed and measured directly, but has theoretical relevance. In a SEM framework, multiple indicators are used to form or reflect a construct or latent variable.
Partial Least Squares (PLS): SEM technique that attempts to minimize the differences between the observed and predicted values of endogenous constructs (dependent variables).
Indirect Effect: In a causal model, this is the impact of one variable on another, through the impact of the former on a third variable that is called mediating variable. In complex models, several indirect-effect paths may exist between two variables.
Measurement Error: This type of error is derived from the quality of the indicators used to operationalize a variable. SEM techniques allow researchers to model the measurement error, while other statistical techniques, such as linear regression, assume no measurement error.
Direct Effect: In a causal model, this is the direct impact of one variable on another and is represented by an arrow from the independent to the dependent variable.
Covariance-Based SEM: SEM techniques that attempt to minimize the differences between the observed and predicted covariance matrices.
Structural Equation Modeling (SEM): Statistical technique that allows simultaneously testing the measurement and structural models. Two of the main types of SEM are covariance-based and variance-based.6