A Framework for Choice Modeling in Usage Context-Based Design

2010 ◽  
Author(s):  
Lin He ◽  
Christopher Hoyle ◽  
Wei Chen ◽  
Jiliang Wang ◽  
Bernard Yannou
Keyword(s):  
Data Collection ◽  
Product Design ◽  
Consumer Choice ◽  
Choice Model ◽  
Consumer Preferences ◽  
Choice Modeling ◽  
Product Performance ◽  
Choice Data ◽  
Modeling Framework ◽  
Usage Context

Usage Context-Based Design (UCBD) is an area of growing interest within the design community. A framework and a step-by-step procedure for implementing consumer choice modeling in UCBD are presented in this work. To implement the proposed approach, methods for common usage identification, data collection, linking performance with usage context, and choice model estimation are developed. For data collection, a method of try-it-out choice experiments is presented. This method is necessary to account for the different choices respondents make conditional on the given usage context, which allows us to examine the influence of product design, customer profile, usage context attributes, and their interactions, on the choice process. Methods of data analysis are used to understand the collected choice data, as well as to understand clusters of similar customers and similar usage contexts. The choice modeling framework, which considers the influence of usage context on both the product performance, choice set and the consumer preferences, is presented as the key element of a quantitative usage context-based design process. In this framework, product performance is modeled as a function of both the product design and the usage context. Additionally, usage context enters into an individual customer’s utility function directly to capture its influence on product preferences. The entire process is illustrated with a case study of the design of a jigsaw.

scholarly journals Choice Modeling for Usage Context-Based Design

2012 ◽  
Vol 134 (3) ◽  
Author(s):  
Lin He ◽  
Wei Chen ◽  
Christopher Hoyle ◽  
Bernard Yannou
Keyword(s):  
Hybrid Electric Vehicle ◽  
Consumer Preferences ◽  
Stated Preference ◽  
Revealed Preference ◽  
Choice Modeling ◽  
Modeling Framework ◽  
Preference Data ◽  
Design Paradigm ◽  
Usage Context ◽  
The Impact

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.

scholarly journals Evaluating On-Road Emissions Impacts of HEVs, PHEVs and EVs: An Integrated Modeling Framework

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Kejia Hu ◽  
Jianyou Zhao ◽  
Yuche Chen ◽  
L.D. White
Keyword(s):  
Electric Vehicles ◽  
Consumer Choice ◽  
Choice Model ◽  
Government Policies ◽  
Harris County ◽  
Road Transportation ◽  
Modeling Framework ◽  
Transportation Emissions ◽  
Carbon Dioxide Co2

This paper develops a framework to evaluate HEVs, PHEVs and EVs on-road emissions impact, by integrating endogenous vehicle consumer choice model and MOVES-based regional emission transportation model. A case study based on Harris County, Texas data is implemented to examine the on-road emissions under different market penetrations (due to different future energy price) and government policies. The results show different on-road transportation emissions level for Carbon Dioxide (CO2), Carbon Monoxide (CO), Nitrogen Oxide (NOx) and Total Hydrocarbon (THC). In addition, cost effectiveness of reducing on-road emissions by extending tax credit for plug-in electric vehicles (PEV) is calculated and reported. 

On Usage Context of Hybrid Electric Vehicles in Choice Studies

2011 ◽  
Author(s):  
Lin He ◽  
Wei Chen ◽  
Guenter Conzelmann
Keyword(s):  
Hybrid Electric Vehicle ◽  
Choice Model ◽  
Principal Component ◽  
Choice Modeling ◽  
Multiple Sources ◽  
Hybrid Electric ◽  
Using Data ◽  
Household Travel ◽  
Usage Context ◽  
The Relationship

Considering usage context attributes in choice modeling has been shown to be important when product performance highly depends on the usage context. To build a reliable choice model, it is critical to first understand the relationship between usage context attributes and customer profile attributes, then to identify the market segmentation characterized by both sets of attributes, and finally to construct a choice model by integrating data from multiple sources. This is a complex procedure especially when a large number of customer attributes are potentially influential to the product choice. Using the hybrid electric vehicle (HEV) as an example, this paper presents a systematic procedure and the associated data analysis techniques for implementing each of the above steps. Usage context and customer profile attributes extracted from both National Household Travel Survey (NHTS) and Vehicle Quality Survey (VQS) data are first analyzed to understand the relationship between usage context attributes and customer profile attributes. Next the principal component analysis is utilized to identify the key characteristics of hybrid vehicle drivers, and to determine the market segmentations of HEV and the critical attributes to include in choice models. Before the two sets of data are combined for choice modeling, statistical analysis is used to test the compatibility of the two datasets. A pooled choice model created by incorporating usage context attributes illustrates the benefits of context-based choice modeling using data from multiple sources. Even though NHTS and VQS have been used in the literature to study transportation patterns and vehicle quality ratings, respectively, this work is the first to explore how they may be used together to benefit the study of customer preference for HEVs.

Robust Learning of Consumer Preferences

2021 ◽  
Author(s):  
Yifan Feng ◽  
René Caldentey ◽  
Christopher Thomas Ryan
Keyword(s):  
Consumer Choice ◽  
Choice Model ◽  
Consumer Preferences ◽  
Numerical Study ◽  
Real Data ◽  
Worst Case ◽  
Robust Learning ◽  
Special Cases ◽  
Data Driven Approach ◽  
Practical Performance

When companies develop new products, there are often competing designs from which to choose to take to market. How to decide? Traditional methods, such as focus groups, do not scale to the modern marketplace in which tastes evolve rapidly. In “Robust Learning of Consumer Preferences,” Feng, Caldentey, and Ryan develop a data-driven approach to deciding which design to produce by displaying a sequence of subsets of possible designs to potential customers. Their framework finds solutions that are robust to any model of consumer choice within a broad family that includes common choice models studied in the literature as special cases. Previous research focuses on algorithms whose performances are tied to a given choice model. Their algorithm is shown to be asymptotically optimal in a worst-case sense. The promising practical performance of the algorithm is demonstrated through a comprehensive numerical study using real data.

Exploring Product Solution Differences Due to Choice Model Selection in the Presence of Noncompensatory Decisions With Conjunctive Screening Rules

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
Jaekwan Shin ◽  
Scott Ferguson
Keyword(s):  
Product Design ◽  
Optimization Problems ◽  
Choice Model ◽  
Marketing Research ◽  
Research Literature ◽  
Product Line ◽  
Additive Models ◽  
Choice Data ◽  
Compensatory Model ◽  
Line Design

Research in market-based product design has often used compensatory preference models that assume an additive part-worth rule. These additive models have a simple, usable form and their parameters can be estimated using existing software packages. However, marketing research literature has demonstrated that consumers sometimes use noncompensatory-derived heuristics to simplify their choice decisions. This paper explores the quality of optimal solution obtained to a product line design search when using a compensatory model in the presence of noncompensatory choices and a noncompensatory model with conjunctive screening rules. Motivation for this work comes from the challenges posed by Bayesian-based noncompensatory models: the need for screening rule assumptions, probabilistic representations of noncompensatory choices, and discontinuous choice probability functions. This paper demonstrates how respondents making noncompensatory choices with conjunctive rules can lead to compensatory model estimations with distinct respondent segmentation and relative, large absolute part-worth values. Results from a product design problem suggest that using a compensatory model can provide benefits of smaller design errors and reduced computational costs. Product design optimization problems using real choice data confirm that the compensatory model and the noncompensatory model with conjunctive rules provide comparable solutions that have similar likelihoods of not being screened out when using a consideration set verifier. While many different noncompensatory heuristic rules exist, the presented study is limited to conjunctive screening rules.

scholarly journals Evaluating On-Road Emissions Impacts of HEVs, PHEVs and EVs: An Integrated Modeling Framework

2018 ◽  
Author(s):  
Kejia Hu ◽  
Jianyou Zhao ◽  
Yuche Chen ◽  
L.D. White
Keyword(s):  
Electric Vehicles ◽  
Consumer Choice ◽  
Choice Model ◽  
Government Policies ◽  
Harris County ◽  
Road Transportation ◽  
Modeling Framework ◽  
Transportation Emissions ◽  
Carbon Dioxide Co2

This paper develops a framework to evaluate HEVs, PHEVs and EVs on-road emissions impact, by integrating endogenous vehicle consumer choice model and MOVES-based regional emission transportation model. A case study based on Harris County, Texas data is implemented to examine the on-road emissions under different market penetrations (due to different future energy price) and government policies. The results show different on-road transportation emissions level for Carbon Dioxide (CO2), Carbon Monoxide (CO), Nitrogen Oxide (NOx) and Total Hydrocarbon (THC). In addition, cost effectiveness of reducing on-road emissions by extending tax credit for plug-in electric vehicles (PEV) is calculated and reported. 

scholarly journals Consumer Choice Modelling in Sharing Economy

2021 ◽  
Vol 11 (2) ◽  
pp. 1153-1161
Author(s):  
A.O. Gostilovich
Keyword(s):  
Consumer Choice ◽  
Consumer Behaviour ◽  
Choice Model ◽  
Consumer Preferences ◽  
Sharing Economy ◽  
Target Market ◽  
Manufacturing Companies ◽  
Choice Modelling ◽  
Industrial Enterprises ◽  
New Business

Development of sharing economy creates new challenges and opens unprecedented business opportunities. In this economic environment, industrial enterprises can expand their direct selling strategies with the new business model “product as a service”. This option is the result of a shift in consumer preferences among clients of industrial enterprises. The development of the consumer choice model applied to sharing economy is a topical agenda, perhaps now more than ever. Such a model, if available, would help predict multiple scenarios of consumer behaviour and prepare the manufacturing companies for better interaction with their target market. This article makes an attempt to offer a consumer choice model in sharing economy, based on 4 types of possible consumer behaviour. The results of the article serve as a foundation of multi-agent modelling and quantitative assessment of abstract situations in the business-to-consumer market.

Benchmarks for Discrete Choice Models

1994 ◽  
Vol 31 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Manohar U. Kalwani ◽  
Robert J. Meyer ◽  
Donald G. Morrison
Keyword(s):  
Discrete Choice ◽  
Consumer Choice ◽  
Repeated Measures ◽  
Choice Model ◽  
Simulated Data ◽  
Brand Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Choice Data ◽  
Choice Probabilities

In assessing the performance of a choice model, we have to answer the question, “Compared with what?” Analyses of consumer brand choice data historically have measured fit by comparing a model's performance with that of a naive model that assumes a household's choice probability on each occasion equals the aggregate market share of each brand. The authors suggest that this benchmark could form an overly naive point of reference in assessing the fit of a choice model calibrated on scanner-panel data, or any repeated-measures analysis of choice. They propose that fairer benchmarks for discrete choice models in marketing should incorporate heterogeneity in consumer choice probabilities, evidence for which is by now well documented in the marketing literature. They use simulated data to compare the performance of parametric and nonparametric benchmark models, which allow for heterogeneity in consumer choice probabilities, with the performance of the aggregate share-based benchmark model, which assumes consumers are homogeneous in their choice probabilities. They also assess the performance of two previously published consumer behavior models against the proposed fairer benchmark models that allow for heterogeneity in consumer choice probabilities. They find that one provides a significantly better fit than their more conservative benchmark models and the other performs less favorably.

scholarly journals A Conceptual Discourse on Consumer’s Preference of Brandy

2019 ◽  
Vol 1 (2) ◽  
pp. 296-307
Author(s):  
Raj Maharjan
Keyword(s):  
Consumer Behavior ◽  
Consumer Choice ◽  
Reasoned Action ◽  
Action Theory ◽  
Factor Model ◽  
Choice Model ◽  
Consumer Preferences ◽  
Consumer Preference ◽  
Alcoholic Beverages ◽  
Hedonic Price

Background: Liquor industry is growing to become a global giant by empowering its competitiveness. Nowadays, alcohol has been accepted and welcomed as a normal part of everyday life with innovatively embedded alcohol development and promotion. Alcohol products consist of a range of offerings including Gin, wine, vodka and Scotch, among which brandy has been gaining higher importance. Objectives: This paper explores the consumers’ preferences for brandy, their knowledge on brandy and also the factors determining the consumer choice on consumption of brandy.This study aims to contribute to the brandy consumer behavior-responsive managerial implications, especially in hospitality industry by identifying the attributes that are perceived important for the marketing of brandy to a large segment of dynamic market. Methods: The academic discourse on this paper includes exploration of multiple dimensions related to the study of consumer behavior. Theories concerning consumer preferences, with specific focus on Reasoned Action Theory, Engel Kollat Blackwell Model, Hybrid Choice Model, Hedonic Price Model, Consumer Perception Factor Model and Conjoint Analysis are reviewed.The study on brandy, along with the differences from other alcoholic beverages, has also been included. Findings: Brandy represents a wide category and the bases of differences among types of brandy are studied along with the review of brandy products available worldwide. This study highlights brandy consumption practices in the world, benefits of brandy consumption and people’s perception towards brandy among other alcoholic beverages. Conclusions: Alcohol is the fastest growing industry and requires consumer preference for the promotions and penetration of the product into the market, and for developingthe product and improving it further.

Integrated Bayesian Hierarchical Choice Modeling to Capture Heterogeneous Consumer Preferences in Engineering Design

2010 ◽  
Vol 132 (12) ◽  
Author(s):  
Christopher Hoyle ◽  
Wei Chen ◽  
Nanxin Wang ◽  
Frank S. Koppelman
Keyword(s):  
Complex System ◽  
Engineering Design ◽  
Consumer Preferences ◽  
Critical Role ◽  
Choice Modeling ◽  
System Level ◽  
Multiple Model ◽  
Modeling Framework ◽  
Bayesian Hierarchical ◽  
Modeling Approach

Choice models play a critical role in enterprise-driven design by providing a link between engineering design attributes and customer preferences. However, existing approaches do not sufficiently capture heterogeneous consumer preferences nor address the needs of complex design artifacts, which typically consist of many subsystems and components. An integrated Bayesian hierarchical choice modeling (IBHCM) approach is developed in this work, which provides an integrated solution procedure and a highly flexible choice modeling approach for complex system design. The hierarchical choice modeling framework utilizes multiple model levels corresponding to the complex system hierarchy to create a link between qualitative attributes considered by consumers when selecting a product and quantitative attributes used for engineering design. To capture heterogeneous and stochastic consumer preferences, the mixed logit choice model is used to predict consumer system-level choices, and the random-effects ordered logit model is used to model consumer evaluations of system and subsystem level design features. In the proposed approach, both systematic and random consumer heterogeneity are explicitly considered, the ability to combine multiple sources of data for model estimation and updating is provided using the Bayesian estimation methodology, and an integrated estimation procedure is introduced to mitigate error propagated throughout the model hierarchy. The new modeling approach is validated using several metrics and validation techniques for behavior models. The benefits of the IBHCM method are demonstrated in the design of an automobile occupant package.

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