Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data

Mode choice behaviour is often modelled by discrete choice models, in which the utility of each mode is characterized by mode-specific parameters reflecting how strongly the utility of that mode depends on attributes such as travel speed and cost, and a mode-specific constant value. For new modes, the mode-specific parameters and the constant in the utility function of discrete choice models are not known and are difficult to estimate on the basis of stated preferences data/choice experiments and cannot be estimated on the basis of revealed preference data. This paper demonstrates how revealed preference data can be used to estimate a discrete mode choice model without using mode-specific constants and mode-specific parameters. This establishes a method that can be used to analyze any new mode using revealed preference data and discrete choice models and is demonstrated using the OViN 2017 dataset with trips throughout the Netherlands using a multinomial and nested logit model. This results in a utility function without any alternative specific constants or parameters, with a rho-squared of 0.828 and an accuracy of 0.758. The parameters from this model are used to calculate the future modal split of shared autonomous vehicles and electric steps, leading to a potential modal split range of 24–30% and 37–44% when using a multinomial logit model, and 15–20% and 33–40% when using a nested logit model. An overestimation of the future modal split occurs due to the partial similarities between different transport modes when using a multinomial logit model. It can therefore be concluded that a nested logit model is better suited for estimating the potential modal split of a future mode than a multinomial logit model. To the authors’ knowledge, this is the first time that the future modal split of shared autonomous vehicles and electric steps has been calculated using revealed preference data from existing modes using an unlabelled mode modelling approach.

Most researchers in health economics cite random utility theory (RUT) when analysing discrete choice experiments (DCEs). Under RUT, the error term is associated with the analyst's inability to properly capture the true choice processes of the respondent as well as the inconsistency or mistakes arising from the respondent themselves. Under such assumptions, it stands to reason that analysts should explore more complex nonlinear indirect utility functions, than currently used in healthcare, to strive for better estimates of preferences in healthcare. To test whether complex indirect utility functions decrease error variance for models that either implicitly (i.e. the multinomial logit (MNL) model) or explicitly (i.e. entropy multinomial logit (EMNL) model) account for error variance in health(care)-related DCEs; and to determine the impact of complex indirect utility functions on willingness-to-pay (WTP) measures. Using data from DCEs aimed at healthcare-related decisions, we empirically compared (1) complex and simple indirect utility specifications in terms of goodness of fit, (2) their impact on WTP measures, including confidence intervals (CIs) based on the Delta method, the Krinsky and Robb-procedure, and Bootstrapping, and (3) MNL and EMNL model results. Complex indirect utility functions had a better model fit than simple specifications (p<0.05). WTP estimates were quite similar across alternative specifications. The Delta method produced the most narrow CIs. The EMNL model showed that respondents apply simplifying strategies when answering DCE questions. Complex indirect utility functions reduce error arisen from researchers, which can have important implications for measures in healthcare such as the WTP, whereas EMNL provides insights into the behaviour of respondents when answering DCEs. Understanding how respondents answer DCE questions may allow researchers to construct DCEs that minimise scale differences, so that the decision error made across respondents is more homogeneous and therefore taken out as additional noise in the data. Hence, better estimates of preferences in healthcare can be provided.

The most commonly used variant of conjoint analysis is choice-based conjoint (CBC). Here, hierarchical Bayesian (HB) multinomial logit (MNL) models are widely used for preference estimation at the individual respondent level. A new and very flexible approach to address multimodal and skewed preference heterogeneity in the context of CBC is the Dirichlet Process Mixture (DPM) MNL model. The number and masses of components do not have to be predisposed like in the latent class (LC) MNL model or in the mixture-of-normals (MoN) MNL model. The aim of this Monte Carlo study is to evaluate the performance of Bayesian choice models (basic MNL, HB-MNL, MoN-MNL, LC-MNL and DPM-MNL models) under varying data conditions (especially under multimodal heterogeneity structures) using statistical criteria for parameter recovery, goodness-of-fit and predictive accuracy. The core finding from this Monte Carlo study is that the standard HB-MNL model appears to be highly robust in multimodal preference settings.

Choice data generation using usage scenarios and discounted cash flow analysis

formula omitted]-optimal conjoint choice designs with no-choice options for a nested logit model

Discrete choice models estimated using hypothetical choices made in a survey setting (i.e., choice experiments) are widely used to estimate the importance of product attributes in order to make product design and marketing mix decisions. Choice experiments allow the researcher to estimate preferences for product features that do not yet exist in the market. However, parameters estimated from experimental data often show marked inconsistencies with those inferred from the market, reducing their usefulness in forecasting and decision making. We propose an approach for combining choice-based conjoint data with individual-level purchase data to produce estimates that are more consistent with the market. Unlike prior approaches for calibrating conjoint models so that they correctly predict aggregate market shares for a “baseline” market, the proposed approach is designed to produce parameters that are more consistent with those that can be inferred from individual-level market data.The proposed method relies on a new general framework for combining two or more sources of individual-level choice data to estimate a hierarchical discrete choice model. Past approaches to combining choice data assume that the population mean for the parameters is the same across both data sets and require that data sets are sampled from the same population. In contrast, we incorporate in the model individual characteristic variables, and assert only that the mapping between individuals' characteristics and their preferences is the same across the data sets. This allows the model to be applied even if the sample of individuals observed in each data set is not representative of the population as a whole, so long as appropriate product-use variables are collected that can explain the systematic deviations between them. The framework also explicitly incorporates a model for the individual characteristics, which allows us to use Bayesian missing-data techniques to handle the situation where each data set contains different demographic variables. This makes the method useful in practice for a wide range of existing market and conjoint data sets. We apply the method to a set of conjoint and market data for minivan choice and find that the proposed method predicts holdout market choices better than a model estimated from conjoint data alone or a model that does not include demographic variables.

Mandalay, as a major commercial and industrial hub of Myanmar, is now facing great challenges due to a substantial increase in automobile, traffic volume, air pollution, and urban sprawl. The rapid urban growth can be seen by the high-density housing development in many parts of the city. However, the current development of high-density housing that is going on in the city center may not best match with people’s preference, on the contrary it will even make the problem more severe resulting in traffic congestion and accelerating the urban sprawl. This paper presents a Stated-Preference (SP) experiment of housing location choice in Mandalay. A multinomial logit model is developed based on the SP survey data. Although the discrete choice model has a long history of application in the economic, transportation, marketing and geography fields, it is not well developed in location analysis. The results reveal that people are considering factors not only house size, house price, but also locational convenience in terms of commuting time, and neighborhood quality. It is also found that different socio-economic groups, i.e., ethnicity, exhibit different location preferences.

Chapter 12 - The Orthogonality Issue in Stated Choice Designs

Sustainability Label (SL) have emerged as an important product attribute in recent decades, and have evolved into various types as products with social, environmental, and economic benefits have become more prevalent in the marketplace. With the information of SL in products, consumers are encouraged to embrace environmental sustainability principles and to make environmentally sustainable choices and actions. SL helps alignment between consumers and the industry by enhancing consumers’ nderstanding of company’s act, and serve as an effective marketing message. However, there is limited research on consumer preferences for different types of sustainable labels or benefits. This study addresses this knowledge gap by applying choice experiment method to assess consumers’ choice behavior for products associated with various SL. Choice experiment designs are separately developed for two most relevant sustainable products (i.e. soymilk and EV) in China. A stated preference method (SPM) consumer survey was conducted in 2022 across six major cities in China, namely Beijing, Shanghai, Guangzhou, Shenzhen, Chengdu, and Xi’an, and a total of 840 valid responses were collected for assessing multinomial logit (MNL) model. Findings show that Chinese consumers prefer SL with environmental benefits, foreign COO/Brand in consuming soymilk, and prefer SL with employee friendliness, domestic COO/Brand for Electric Vehicle (EV). These findings provide insights for marketers and researchers Chinese consumers’ preference for specific SL and brand for two selected product categories.

Screening for interactions between design factors and demographics in choice-based conjoint

The study addressed the increasing complexity faced by students when selecting a university major at the Universidad de las Fuerzas Armadas – ESPE, considering the expansion of academic offerings and the changing demands of the labor market. Its objective was to identify the key determinants guiding this decision through the application of discrete choice models. The Multinomial Logit and Hierarchical Logit models were employed to examine the influence of personal, institutional, and expectation-related variables, organizing program alternatives into disciplinary categories to capture preference heterogeneity. The results showed that personal factors mainly vocation, individual interests, and long-term professional aspirations exerted the strongest influence on major selection, while institutional and economic considerations played a secondary role. The Hierarchical Logit model exhibited a superior fit compared with the Multinomial Logit model, indicating that disciplinary nesting allowed a more accurate representation of decision patterns. Additionally, expected employability and opportunities for career advancement emerged as critical motivations driving students’ choices. In conclusion, the study demonstrated that discrete choice models provided a robust methodological framework to explain educational decision-making processes, offering valuable evidence to strengthen vocational guidance, align academic programs with market demands, and support institutional planning based on empirical insights.

Customer preference modelling has been widely used to aid engineering design decisions on the selection and configuration of design attributes. Recently, network analysis approaches, such as the exponential random graph model (ERGM), have been increasingly used in this field. While the ERGM-based approach has the new capability of modelling the effects of interactions and interdependencies (e.g., social relationships among customers) on customers’ decisions via network structures (e.g., using triangles to model peer influence), existing research can only model customers’ consideration decisions, and it cannot predict individual customer’s choices, as what the traditional utility-based discrete choice models (DCMs) do. However, the ability to make choice predictions is essential to predicting market demand, which forms the basis of decision-based design (DBD). This paper fills this gap by developing a novel ERGM-based approach for choice prediction. This is the first time that a network-based model can explicitly compute the probability of an alternative being chosen from a choice set. Using a large-scale customer-revealed choice database, this research studies the customer preferences estimated from the ERGM-based choice models with and without network structures and evaluates their predictive performance of market demand, benchmarking the multinomial logit (MNL) model, a traditional DCM. The results show that the proposed ERGM-based choice modelling achieves higher accuracy in predicting both individual choice behaviours and market share ranking than the MNL model, which is mathematically equivalent to ERGM when no network structures are included. The insights obtained from this study further extend the DBD framework by allowing explicit modelling of interactions among entities (i.e., customers and products) using network representations.

This paper investigates freight market segmentation to seek the potential market of modal shift from road to rail-based intermodal (only pre- and end- haulage are transported by road), using discrete choice analysis. The Defficient design approach was used to generate SP scenarios. The data, including 2160 SP observations of 135 companies, was collected in China's Yangtze River Delta region. The multinomial logit (MNL) model, the mixed logit (ML) model, and the latent class (LC) model were constructed successively. Freight mode attributes (cost, time and reliability), shipment characteristics (distance, time threshold), cargo characteristics (value and density) were considered. In addition, the logistics elements such as the stages of production process and psychological characteristics such as choice inertia were also introduced in this paper. The results show that the LC model with segment variables performs better than the counterpart without segment variables, the MNL model and the ML model. Using the LC model, shippers are classified into four market segments. There are significant differences in shippers' preference among different classes. Based on the class membership function, the characteristic of each class can be identified by a combination of different attributes. The difficulty of achieving the modal shift from road to rail-based intermodal is ranked according to the values of attributes, further determining the priority of the potential market. This study helps to understand the shippers' freight mode choice better. Rail service suppliers can design transportation products that match the shippers' demand characteristics for different classes to improve the market share.

IntroductionThe optimal approach to engage the public in healthcare decision-making is unclear. Approaches range from deliberative citizens’ juries to large population surveys using discrete choice experiments. This study promotes public...

The rapid growth of quick commerce (q-commerce) is changing urban grocery retailing by guaranteeing delivery of essentials, including fresh fruits and vegetables in 10-30 min. However, speed of delivery has implications for postharvest management of highly perishable food items such as leafy greens and climacteric fruits (i.e., bananas, mangoes, tomatoes and peaches). There is a significant lack of research examining how physiological quality preservation methods influence consumer preferences for highly perishable items such as leafy greens and climacteric fruits. This research fills this gap by investigating urban consumers' preferences regarding the trade-off between delivery speed, price and quality preservation on q-commerce platforms. The study was conducted in the Coimbatore district in Tamil Nadu. A structured survey of 110 urban consumers in Coimbatore, Tamil Nadu, was conducted using convenience sampling. Choice-Based Conjoint (CBC) analysis under the Random Utility Theory framework was applied using an orthogonal design with three attributes: delivery speed, quality preservation methods and pricing. Consumer utility values were estimated using the Multinomial logit model (MNL) with significance testing at p&lt;0. 05. The results indicated that delivery speed held the highest relative importance (38 %, p&lt;0.001) in consumer preferences, followed by quality preservation methods (32 %, p&lt;0.001) and price sensitivity (30 %, p&lt;0.001). Exploratory Factor Analysis (EFA) revealed that physiological quality indicators such as freshness, ripeness and shelf life were the most influential latent factor (explaining 37.5 % of variance). Garrett Ranking identified inconsistent ripening (mean = 69.68) and high delivery charges (mean = 56.60) as the top barriers to purchasing fresh produce via quick commerce platforms.