"... In PAGE 48: ... 4) Checkout, where shipping information and payment data were input. Factorial Design Table8 shows the conjoint labels used to represent the six variables in this experiment along with the stages they appeared in the actual online shopping simulation. The conjoint experiment had six attributes with two levels each for a total of 64 possible combinations (2x2x2x2x2x2) in a full factorial design.... ..."

"... In PAGE 2: ... In this paper the results for a sample of 99 respondents are reported. The design of the conjoint study is shown in Table1 . The levels of the... ..."

"... In PAGE 8: ...8 Current approaches for implementing a conjoint analysis project differ in terms of several features; some main features are: stimulus representation, formats of data collection, nature of data collection, and estimation methods. Table1 lays out some alternatives for these features. The approaches that are more commonly used are: ratings- based (or Full-profile) Conjoint Analysis; Choice-based Conjoint Analysis; Adaptive Conjoint Analysis; Self-explicated Conjoint Analysis.... In PAGE 9: ... I refer the reader to Green and Srinivasan (1978, 1990), Green and Carroll (1995), and Hauser and Rao (2003) for various details of these approaches. Insert Table1 about Here Typically, a linear, additive model is used to describe the evaluations (preferences) in a ratings-based conjoint study while a multinomial logit model is used to model the probability of choice of a profile for the choice-based conjoint studies. Undoubtedly, there are several variations of these basic models used in practice.... ..."

"... In PAGE 5: ... We estimated the FM conjoint models by increasing the number of components until the BIC was minimized. The results are given in Table1 . The BIC iden- tifies the true number of components in 95 (4 + 42 + 49) of the 144 data sets.... ..."

"... In PAGE 4: ... Green, et al. (2001) give four major types of preference input procedures used for conjoint analysis (see Table2 ). All essentially reflect a utility function, where k attributes are evaluated on some scale (usually something like 0 to 100), and each attribute i is weighted, often with each weight constrained to between 0 and 1 and the sum of the weights equal to 1.... ..."

"... In PAGE 8: ... We estimated one- to four-class models with and without covariates. Table1 reports the obtained test results. The BIC values indicate that the three-class model is the best model (BIC is lowest for this model) and that the inclusion of covariates significantly improves the model.... ..."

"... In PAGE 5: ...f classic conjoint analysis estimates was 53.5%, an improvement of 28.5% compare to chance. { Insert Table1 Here } ... ..."

"... In PAGE 81: ... Cluster part-worth utility scores for fraud protection Note: 1 = Time Savers, 2 = General Surfers, 3 = Information Seekers Results by Market Segment. Reexamination of the research hypotheses within the context of the three clusters showed that the attributes not important to the overall sample were also not important to members of all three clusters (see Table15 ). These included accessibility, price search information, and notification of personal information protection.... ..."

"... In PAGE 4: ... The physical perspective ties datalogical concepts and constructs to a particular organizational and technical environment. In Table1 we apply the perspectives to characterize the ME perspectives with relevant questions. The ISD perspectives can be described in the same way.... In PAGE 10: ... Next, new and promising ISD principles are searched for and integrated into the existing method. Some of these steps correspond to the ISDM RE workflow but because Song (1997) does not provide any guidance to requirements engineering of the method we locate these steps in the ISDM analysis workflow in Table1 . In the artifact model integration the existing method is enhanced with new artifact models (i.... ..."