Application of the Model: Case-Based Studies

Application of the Model: Case-Based Studies

DOI: 10.4018/978-1-4666-4201-0.ch011
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It is argued that models are conceptualized, designed, developed, and validated to understand complex behaviour of larger entities. Models provide indicative measurements, and their validations in real life situation need careful considerations of relevant ambient conditions. Models also provide suggestive and causal relationships among their qualitative and quantitative influencers for better predictability. Generally, predictive models provide structural equations, measurement equations with associated random errors. These errors do play vital roles in relating abstracted behavior of the model outputs with the real life situations. In order to reduce these errors to an agreed level, case-based validations of models are quite important. This chapter discusses derived measurement and structural equations that the model has produced and presents some cases to examine the appropriateness of the application of the model developed.
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Derived Measurement Equations Of The Model

Measurement equations show the relationships among latent and exogenous variables. “Classical test theory” is the basis for establishing this relationship (Cronbach et al., 1972; Linn and Werts, 1979; Susan et al., 2008; España et al., 2010). The general form of the measurement equation is shown in Figure 1.

Figure 1.

Measurement equation (SEM) (España et al., 2010)


The measurement equation here is X =978-1-4666-4201-0.ch011.m01 + 978-1-4666-4201-0.ch011.m02 where X is the exogenous variables which is the mean of the items summated through dummy coding as explained in chapters eight and nine. 978-1-4666-4201-0.ch011.m03 represents the endogenous variable in the equation. Each variable X thus represents a set of independent indicators (questions) administered to the respondents and ordinal data are acquired. Classical test theory considers measurement errors (978-1-4666-4201-0.ch011.m04) and provides a predictive ability to the relationship between exogenous variable and predictors. Table 1 describes the relationships and displays the foundation for such predictions.

Table 1.
Measurement equation
Stratified Independent Indicators
Dummy Codes Used
Summated Independent Indicator
Description of Exogenous Variable
Latent Endogenous Variables
Measurement Equation
(Classical Test Theory)
(X =978-1-4666-4201-0.ch011.m08 + 978-1-4666-4201-0.ch011.m09)
U101-U106U1UUser PreparednessP
Pre-Acquisition Preparedness
U = PU + 978-1-4666-4201-0.ch011.m10
I = PI + 978-1-4666-4201-0.ch011.m11
T = PT + 978-1-4666-4201-0.ch011.m12
(Pmean) = 978-1-4666-4201-0.ch011.m13
I101-I106I1IIS Preparedness
T101-T107T1TTechnology Preparedness
C101-C108C1C1User Perception on OrganizationC
Climate Preparedness
C1 = Cc1 + 978-1-4666-4201-0.ch011.m14
C2 = C c2+ 978-1-4666-4201-0.ch011.m15
C 3= Cc 3+ 978-1-4666-4201-0.ch011.m16
(Cmean) = 978-1-4666-4201-0.ch011.m17
C201-C207C2C2User Perception on IT
C301-C307C3C3User Perception on Decision Making Process
VC01-VC06VCVCVendor ManagementAQ
Acquisition Process
VC = AQ + 978-1-4666-4201-0.ch011.m18
AT = AQ + 978-1-4666-4201-0.ch011.m19
(AQmean) = 978-1-4666-4201-0.ch011.m20
AT01-A06ATATAcquisition Process

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