Choice Modeling for Usage Context-Based Design

Abstract

Usage context-based design (UCBD) is an emerging design paradigm where usage context is considered as a critical part of driving factors behind customers’ choices. Here, usage context is defined as all aspects describing the context of product use that vary under different use conditions and affect product performance and/or consumer preferences for the product attributes. In this paper, we propose a choice modeling framework for UCBD to quantify the impact of usage context on customer choices. We start with defining a taxonomy for UCBD. By explicitly modeling usage context’s influence on both product performances and customer preferences, a step-by-step choice modeling procedure is proposed to support UCBD. Two case studies, a jigsaw example with stated preference data and a hybrid electric vehicle example with revealed preference data, demonstrate the needs and benefits of incorporating usage context in choice modeling.

Analysis of some Physical Parameters under Workers' Optimal Performance in Wet and Hot Tropical Climates; A Literature Review

“…Performance is the product of efficiency and effectiveness [71].The relationship between the indoor environment on the one hand and well-being and performance on the other hand, and the systems and concepts that influence this relationship, offers the possibility to design in such a way that productivity improvements and a comfortable working environment can be created [72][73][74][75]. Air temperature is the commonly used indicator of thermal environment in indoor air quality and productivity research [76][77].…”

Sensitivity of Vehicle Market Share Predictions to Alternative Discrete Choice Model Specifications

“…The more flexible mixed and nested logit models did not offer meaningful improvements for the new vehicle market using the types of covariates that have been used in the literature, although we only examined mixed logit models with diagonal covariance matrices. We caution that these types of models may not be well suited to predict vehicle shares for new design or policy evaluation [7,[9][10][11] and that these same limitations may apply to other product design domains which use discrete choice models [4][5][6]. Reducing the data set to include only midsize vehicles improved the predictive capabilities of the model.…”

“…Motivated by a call to base design decisions explicitly on predictions of their downstream consequences for the firm [1], researchers have proposed a variety of methods to predict the influence of design decisions on firm profit. The majority of these efforts apply discrete choice methods [2] to predict consumer choice as a function of product attributes and price, using choice predictions to guide or even optimize design decisions [3][4][5][6][7][8][9][10][11]. Such methods rely on the accuracy of choice predictions: uncertainty in choice predictions creates uncertainty about which designs are best [5,12].…”

Analyzing and Predicting Heterogeneous Customer Preferences in China's Auto Market Using Choice Modeling and Network Analysis

“…Other applications of discrete choice models for vehicle demand are essentially variations of the preceding literature. Also, the engineering community has implemented and extended DCA broadly to model demand for an automotive system, in particular in the context of product design optimization [18,19,20,21,22,23,24,25]. Built on the extant literature on preference demand modeling, in this paper, we use mixed logit to examine the underlying drivers of consumer heterogeneity in vehicle preferences.…”