The objective of discriminant analysis in such scenarios is to understand the group differences and to predict the likelihood that an entity that will distinguish adopters and non-adopters of ecommerce based on their perception of the complexity involved in using ecommerce.
Discriminant analysis involves deriving a variate; in this case the variate is a linear combination of nine independent variables –
v Perceived complexity
v Perceived benefits
v Organizational competence
v Perceived compatibility
v Supporting industries e-readiness
v Top management support
v Market e-readiness
v IT capability
Government e-readiness that was used to discriminate between adopters and non-adopters of ecommerce. Discrimination is achieved by setting the variate’s weight for each variable to maximize the between-group variance relative to the within group variance.
The two groups (adopters/non-adopters) of this research are independent in the sense that they are separate samples containing different sets of individual subjects.
The group variable is adoption of ecommerce. The independent variable is Management support. It discriminates between adopters and non-adopters of ecommerce. Moreover, the adopters of ecommerce had greater support from management.
All the items within this variable were significant discriminators. The mean value for adopters was significantly larger than the mean value for non-adopters. This is consistent with assertion that top management support is crucial in the acquisition and diffusion of innovation.
A number of conclusions can be drawn from these results. Firstly, banks with a strong support and commitment to ecommerce from top management are more likely to adopt ecommerce. Secondly, banks that have the requisite IT and business resources (infrastructure and skills) for ecommerce adoption stand a better chance at adopting ecommerce. Thirdly, banks that have sound IT infrastructure in place are in a better position to adopt ecommerce.