Three essays on informatics decision support systems for products selection

Resumen

Abstract Product selection has become more important since online consumers are given more product choices than they can find in traditional shops. Expert systems play a key role in providing an intelligent decision support system in the phases of commerce, including the pre-sale, sale, and post-sale phases. There are many techniques for building a product selection expert system. This thesis is interested in case-based reasoning (CBR), the analytic hierarchy process (AHP), and the fuzzy AHP (FAHP). The purpose of this thesis is to understand and investigate how people select based on two different decision support system models, CBR and FAHP. This thesis is comprised of three studies related to product selection. The first study discusses the effects gender has on product selection based on two conducted experiments, and the effect of the informatics advice. The second study experimentally and statistically studies the difference between conventional AHP and FAHP and evaluates gender-specific product selection based on both models. The third study is motivated by the results of the first two studies and proposes an expert system for product selection using FAHP and CBR-based approaches.