The Role of Kansei Engineering in Influencing Overall Satisfaction and Behavioral Intention in Service Encounters

Customers today concern themselves more on fulfilling their emotional needs rather than rationales and functionalities. In dealing with customer emotions in products/services, Kansei Engineering (KE) is applied. A comprehensive case study in luxury hotels was conducted. Eighty one Indonesian, 75 Singaporean, and 74 Japanese tourists participated in this survey. It aims to investigate the relationships among constructs during service encounter process. The finding shows that emotions (affective process) play a significant role as a complement to cognitive process in influencing customer satisfaction. Among 3 populations, Japanese was found to be more Kansei-oriented customer. Keywords: Kansei Engineering, emotional needs, customer satisfactio

Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution

It is not uncommon that meta-heuristic algorithms contain some intrinsic parameters, the optimal configuration of which is crucial for achieving their peak performance. However, evaluating the effectiveness of a configuration is expensive, as it involves many costly runs of the target algorithm. Perhaps surprisingly, it is possible to build a cheap-to-evaluate surrogate that models the algorithm's empirical performance as a function of its parameters. Such surrogates constitute an important building block for understanding algorithm performance, algorithm portfolio/selection, and the automatic algorithm configuration. In principle, many off-the-shelf machine learning techniques can be used to build surrogates. In this paper, we take the differential evolution (DE) as the baseline algorithm for proof-of-concept study. Regression models are trained to model the DE's empirical performance given a parameter configuration. In particular, we evaluate and compare four popular regression algorithms both in terms of how well they predict the empirical performance with respect to a particular parameter configuration, and also how well they approximate the parameter versus the empirical performance landscapes

Applying Kansei Engineering, the Kano model and QFD to services

This paper aims to present an integrative framework of Kansei Engineering (KE), the Kano model and quality function deployment (QFD) applied to services. An empirical study involving Indonesian and Singaporean tourists was conducted to showcase the framework’s applicability. The study utilises a sample of 100 Indonesian and 125 Singaporean tourists who stayed in luxury hotels and covers only services in luxury hotels. Interviews and face-to-face questionnaire surveys were carried out. Using stepwise linear regression analysis, this research models the effect of perceived hotel service performance on customer emotional needs (Kansei). House of quality (HOQ) is then used to formulate managerial strategies. We present the fruitfulness of integrating the Kano model, KE and QFD. Perceived attractive qualities have a direct significant impact on Kansei response. There is no analysis of the impact of cultural differences on Kansei. We provide insight on which service attributes deserve more attention with regard to their significant impact on customer emotions. It may guide service managers to provide and implement improvement strategies in satisfying customer emotional needs. The study proposes a unique methodology of integrative three concepts commonly used in manufacturing and service quality research to measure and model customer emotional needs

Cultural differences in applying Kansei Engineering to services

It is imperative for companies to provide competitive products and services at a competitive price. Products and services need to offer features and properties which can makethem distinguishable and attractive to customers. Emotions and feelings are prominent during product interaction and service encounter. Kansei Engineering (KE) enables interpretation and translation of customer emotions into design parameters. The application of KE covers both products and services design. Besides dealing with attractive exterior appearances, KE has an ability to optimize properties that are not directly detectable or visible, such as the comfort of hospital and concert hall. There are few empirical studies. Kansei management should recognize cultural differences in Kansei. However, for analysis of cultural values we need to understand the different needs of different customers. A study of luxury hotel services for Indonesian, Japanese and Singaporean tourists, was conducted using interviews and a tri-lingual face-to-face questionnaire. 425 responses were collected. Japanese tourists were found to be the most Kansei-oriented. They tended to value luxury hotels as “clean” and “quiet” places to stay. Indonesian and Singaporean tourists shared a common response to the Kansei word “elegant” which correlates with their common cultural dimension of “power distance”. Incorporation of cultural issues into Kansei studies can provide marketing strategies for customers of different cultural backgrounds

Jantzen filtration of Weyl modules, product of Young symmetrizers and denominator of Young's seminormal basis

Let $G$ be a connected reductive algebraic group over an algebraically closed field of characteristic $p>0$, $\Delta(\lambda)$ denote the Weyl module of $G$ of highest weight $\lambda$ and $\iota_{\lambda,\mu}:\Delta(\lambda+\mu)\to \Delta(\lambda)\otimes\Delta(\mu)$ be the canonical $G$-morphism. We study the split condition for $\iota_{\lambda,\mu}$ over $\mathbb{Z}_{(p)}$, and apply this as an approach to compare the Jantzen filtrations of the Weyl modules $\Delta(\lambda)$ and $\Delta(\lambda+\mu)$. In the case when $G$ is of type $A$, we show that the split condition is closely related to the product of certain Young symmetrizers and, under some mild conditions, is further characterized by the denominator of a certain Young's seminormal basis vector. We obtain explicit formulas for the split condition in some cases

Regression Modeling of Longitudinal Outcomes With Outcome-Dependent Observation Times

Conventional longitudinal data analysis methods typically assume that outcomes are independent of the data-collection schedule. However, the independence assumption may be violated when an event triggers outcome assessment in between prescheduled follow-up visits. For example, patients initiating warfarin therapy who experience poor anticoagulation control may have extra physician visits to monitor the impact of necessary dose changes. Observation times may therefore be associated with outcome values, which may introduce bias when estimating the effect of covariates on outcomes using standard longitudinal regression methods. We consider a joint model approach with two components: a semi-parametric regression model for longitudinal outcomes and a recurrent event model for observation times. The semi-parametric model includes a parametric specification for covariate effects, but allows the effect of time to be unspecified. We formulate a framework of outcome-observation dependence mechanisms to describe conditional independence between the outcome and observation-time processes given observed covariates or shared latent variables. We generalize existing methods for continuous outcomes by accommodating any combination of mechanisms through the use of observation-level weights and/or patient-level latent variables. We develop new methods for binary outcomes, while retaining the flexibility of a semi-parametric approach. We extend these methods to account for discontinuous risk intervals in which patients enter and leave the at-risk set multiple times during the study. Our methods are based on counting process approaches, rather than relying on possibly intractable likelihood-based or pseudo-likelihood-based approaches, and provide marginal, population-level inference. In simulations, we evaluate the statistical properties of our proposed methods. Comparisons are made to `naive\u27 approaches that do not account for outcome-dependent observation times. We illustrate the utility of our proposed methods using data from a randomized trial of interventions designed to improve adherence to warfarin therapy and a randomized trial of malaria vaccines among children in Mali