Fine-Grained Satisfaction Analysis of In-Vehicle Infotainment Systems Using Improved Kano Model and Cumulative Prospect Theory

Abstract

In-vehicle infotainment system (IVIS) plays an increasingly important role in vehicle intelligence. New energy vehicles have sufficient power, and their IVIS is developing in the direction of large size and multiple functions. By using online comments, thirteen attributes of IVIS were extracted through data clustering. Then, the attribute types and their corresponding contributions to the improvement of satisfaction and the reduction of dissatisfaction were determined by structured data transformation, correlation analysis, dual satisfaction analysis, and fine-grained satisfaction analysis using Kano model. Then, satisfaction and dissatisfaction influence indexes (IFIs) were calculated by combining attention, correlation coefficient and sentimental intensity. An improved cumulative prospect theory was then adopted to calculate the overall IFIs of different attributes. Sensitivity analyses of risk aversion and attention tendency were then conducted, and specific satisfaction optimization suggestions were then discussed. The results of the study have a guiding role in the optimization design of IVIS and the effectiveness of marketing promotion. The research methods contribute theoretically to improving the identification accuracy of online comments, coordinating the influence of sentimental tendency, integrating the analysis of attention’s influence and testing the effect of risk attitudes.