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In the information system research, a question of particular interest is tointerpret and to predict the probability of a firm to adopt a new technologysuch that market promotions are targeted to only those firms that were morelikely to adopt the technology. Typically, there exists significant differencebetween the observed number of ``adopters'' and ``nonadopters,'' which isusually coded as binary response. A critical issue involved in modeling suchbinary response data is the appropriate choice of link functions in aregression model. In this paper we introduce a new flexible skewed linkfunction for modeling binary response data based on the generalized extremevalue (GEV) distribution. We show how the proposed GEV links provide moreflexible and improved skewed link regression models than the existing skewedlinks, especially when dealing with imbalance between the observed number of0's and 1's in a data. The flexibility of the proposed model is illustratedthrough simulated data sets and a billing data set of the electronic paymentssystem adoption from a Fortune 100 company in 2005.

Generalized extreme value regression for binary response data: An application to B2B electronic payments system adoption