| Method overview
Briefly describe the methodologies and methods (data collection, statistical models, and analysis) that will be used to conduct the study.
|The proposed method is a quantitative study that is non-experimental. Quantifiable data is used to derive a solution to the research problem as a quantitative study. A meaningful research design would be considered for the quantitative study. The design is based on targeting major corporations in the manufacturing industry approach on a sample of 2*102 respondents from 10 leading major corporations in the manufacturing sector, specifically those operating in the US. This implies that from each corporation, 20 respondents would be selected from 10 corporations through random sampling and the final list maintained for the research. Permission will be obtained from the responsible management. This sample size would be large enough to minimize biasness of the research results. Only the corporations having information systems security measures in place would be considered. An online survey tool, specifically Survey Monkey, will be used. The link for the survey will be distributed via e-mail to potential respondents. Questionnaires will be used for the survey. Each of the respondents would be chosen randomly and completely by chance. All the respondents would have an equal probability of being selected from any of the 10 corporationuring the sampling process. The technique would be a surveying technique that is hardly biased (Yates, David, Daren, 2008).
Partial Least Squares (PLS), which is an element-based structural equation modeling method, will be used to analyze the data collected. PLS graph, build 1126, version 3 will be used for data analysis. The guidelines specified in Chin et al. (2003) shall be followed while analyzing the data; additionally, other exemplars in Information Systems research will also be followed. Modeling of the constructs will be done using reflective indicators. Employee attitude will be coded as per the score of each participant, while perception will be coded as an ordinal variable; this will be consistent with Davis (2000).
Ordinal data would be tested for normality in order to be used for parametric testing with respect to the numerical scores placed on the variable (Sheskin, 2007). Prior to indicating the terms of interaction, the variables will be mean-centered at the indicator level. This will help limit certain multicollinearity. To test the various PLS models, bootstrapping method will be employed.
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