This study tests and contrasts the ability of multidimen
sional scaling (MDS) and nonlinear mapping (NLM) in re
covering complex data structures in attribute space, and
aiding researchers and practitioners in making neighbor
hood interpretations. The relative merits of both MDS and
NLM for product positioning are explored and discussed. A
formal comparison of the performance of NLM versus MDS
is presented using both simulated and actual data. The re
sults of this study provide direction as to the conditions
under which a nonlinear mapping algorithm is preferable
over MDS.
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