Validation of Model: The Process for Tier-II Variables

Validation of Model: The Process for Tier-II Variables

DOI: 10.4018/978-1-4666-4201-0.ch009
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The proposed IT acquisition model builds on the predictive behavior of Tier-I influencers and suggests that Tier-II influencers need to collectively contribute to attain organizational synergy. The most critical aspect of collectiveness is heterogeneous organizational behavior across the hierarchy in the organization. It is believed that strategic, tactical, and operational layers in the organization have different tasks, motivations, roles, and responsibilities. However, collective orientation of this heterogeneity needs to be achieved for this model for IT acquisition for its holistic success. Therefore, the model considers it important to identify the controlling agency in the hierarchy so that controlled elements contribute effectively in the IT acquisition process. Identification of “controlling” and “controlled” elements for assessment of collective contributions of users, information systems, and information technologies in the IT acquisition process needs in-depth studies through an appropriate stratified and unequal sampling plan for the proposed model. This chapter discusses validation of Tier-II influencers with quantitative methods.
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Validation Of Pre-Acquisition Stage Variables

It has been discussed earlier that stratified sampling processes provide enormous challenge to validate overall preparedness of the organization through sampling techniques and quantitative methods. In earlier chapters it was discussed and verified that methods adopted for the model validation are reliable since the data collected through the sampling process for Tier-I predictors have shown accepted Cronbach alpha coefficient (α). The next challenge is to validate the higher layer of predictors assigned to the model. These are Tier-II predictors. These predictors use the reliable and validated data filtered from the Tier-I data sets. However, these data sets are governed by the variables that the model uses are “User Preparedness (U)”, “IS preparedness (I)”, “Technology Preparedness (T)”, Climate Preparedness (C) ”. These variables display locus of control in the hierarchy explained earlier (Nonaka,1988). Such a situation recommends dummy coding methods in which locus of control is identified and then other controlled data are used for overall fitness of the variables so that further analyses can be carried for the model (Spector,1988; Carmines & Zeller, 1994; Mcliver & Edward 1994;). For example, in Table 1, user preparedness (U) represents the overall group behavior constituted by U1, U2, U3 representing strategic, tactical and operational subject groups respectively. In this case U3 is considered as the control group to hypothesize that operational group is likely to influence the IT acquisition process the most in the organization (Pearce & Robinson, 1996; Pedhazur, 1997). It is thus imperative for U12 and U2 subjects respect the preparedness of U3 for successful IT acquisition.

Table 1.
Dummy coding for predicting user preparedness (U)
No. of ItemsUser Preparedness
Dummy Code
Dummy Code
U1746U10U = ∑Uij /6; i=1, j=1-6 (Subject wise item- response mean)
U22107U01U = ∑Uij /7; i=2, j=1-7 (Subject wise item- response mean)
3207U00U = ∑Uij /7; i=3, j=1-7 (Subject wise item- response mean)

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