The Application of Uninorms in Importance-Performance Analysis

In the field of marketing, Importance-Performance Analysis is a useful technique for evaluating the elements of a marketing program. The importance dimension of this technique is often determined in a regression based approach. However, this approach has certain problems and limitations. A new approach, based on uninorms, is suggested. This article shows that the uninorm approach possesses several strengths for this type of analysis and matches particularly well with the customer satisfaction theory

A decision support tool for evaluating customer intentions

It is crucial for any manager to keep a close watch on customer satisfaction, customer loyalty and the customer’s intention to recommend the company. In this article, a new decision support tool is developed to support a manager with this task. This tool has been developed with companies in mind that posses limited customer satisfaction data. It uses model-based knowledge discovery to extract the customer’s expectation and the expectation-performance compatibility from the data. Two hypotheses are formulated which posit that compatibility between product performance and customer expectation have a positive influence on the customer’s intentions. Both hypotheses are supported by the data. Finally, a decision support tool is developed which visualizes the impact of customer satisfaction, product performance and expectation-performance compatibility on the customer’s intentions. The decision support tool contains three views which offer the manager important information at a glance.Decision support tool; Dombi’s uninorm; Expectancy disconfirmation paradigm; Customer expectation; Participatory learning paradigm; Expectation-performance compatibilit

PSO driven collaborative clustering: a clustering algorithm for ubiquitous environments

The goal of this article is to introduce two existing clustering approaches into the domain of ubiquitous knowledge discovery. First we demonstrate how horizontal collaborative clustering can be performed in a ubiquitous environment and discuss the ability of these two clustering techniques to cope with privacy constraints. Next, we illustrate how a particle swarm optimization driven version of this clustering algorithm can be used in KDUbiq research and we introduce a ¯tness functions whose objective is to ¯nd similar cluster composition across data locations. Finally, we run an experiment which shows the potential of PSO driven collaborative clustering in a ubiquitous environment with privacy issue

PSO driven collaborative clustering: a clustering algorithm for ubiquitous environments

The goal of this article is to introduce two existing clustering approaches into the domain of ubiquitous knowledge discovery. First we demonstrate how horizontal collaborative clustering can be performed in a ubiquitous environment and discuss the ability of these two clustering techniques to cope with privacy constraints. Next, we illustrate how a particle swarm optimization driven version of this clustering algorithm can be used in KDUbiq research and we introduce a ¯tness functions whose objective is to ¯nd similar cluster composition across data locations. Finally, we run an experiment which shows the potential of PSO driven collaborative clustering in a ubiquitous environment with privacy issue

An Interactive Computer-Based Interface to Support the Discovery of Individuals’ Mental Representations and Preferences in Decisions Problems: An Application to Travel Behavior

Growing emphasis is currently given in decision modeling on process data to capture behavioral mechanisms that ground decision-making processes. Nevertheless, advanced applications to elicit such data are still lacking. The Causal Network Elicitation Technique interview and card-game, both face-to-face interviews, are examples of a behavioral process method to obtain individuals’ decision-making by eliciting temporary mental representations of particular problems. However, to portray and model these representations into formal modeling approaches, such as Bayesian decision networks, an extensive set of parameters has to be gathered for each individual. Thus, data collection procedures for large sample groups can be costly and time consuming. This paper reports on the methodological conversion and enhancement of the existing elicitation methods into a computer-based interface that allows to not only uncover individuals’ mental representations but also to automate the generation of preference parameter elicitation questions. Results of such studies can be used to understand individuals’ constructs and beliefs with respect to decision alternatives, predict individuals’ decision behavior at a disaggregate level, and to assess behavioral changes due to differences in contexts and constraints