Data mining and mall users profile

Marketing scholars have suggested a need for more empirical research on consumer response to malls, in order to have a better understanding of the variables that explain the behavior of the consumers. The segmentation methodology CHAID (Chi-square automatic interaction detection) was used in order to identify the profiles of consumers with regard to their activities at malls, on the basis of socio-demographic variables and behavioral variables (how and with whom they go to the malls). A sample of 790 subjects answered an online questionnaire. The CHAID analysis of the results was used to identify the profiles of consumers with regard to their activities at malls. In the set of variables analyzed the transport used in order to go shopping and the frequency of visits to centers are the main predictors of behavior in malls. The results provide guidelines for the development of effective strategies to attract consumers to malls and retain them there.

Cohesive Elements for Shells

A cohesive element for shell analysis is presented. The element can be used to simulate the initiation and growth of delaminations between stacked, non-coincident layers of shell elements. The procedure to construct the element accounts for the thickness offset by applying the kinematic relations of shell deformation to transform the stiffness and internal force of a zero-thickness cohesive element such that interfacial continuity between the layers is enforced. The procedure is demonstrated by simulating the response and failure of the Mixed Mode Bending test and a skin-stiffener debond specimen. In addition, it is shown that stacks of shell elements can be used to create effective models to predict the inplane and delamination failure modes of thick components. The results indicate that simple shell models can retain many of the necessary predictive attributes of much more complex 3D models while providing the computational efficiency that is necessary for design