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Government investment in preparing for pandemics has never been more relevant. The COVID-19 pandemic has stimulated debate regarding the trade-offs societies are prepared to make between health and economic activity. What is not known is: (1) how much the public in different countries are prepared to pay in forgone GDP to avoid mortality from future pandemics; and (2) which health and economic policies the public in different countries want their government to invest in to prepare for and respond to the next pandemic. Using a future-focused, multi-national discrete choice experiment, we quantify these trade-offs and find that the tax-paying public is prepared to pay $3.92 million USD (Canada), $4.39 million USD (UK), $5.57 million USD (US) and $7.19 million USD (Australia) in forgone GDP per death avoided in the next pandemic. We find the health policies that taxpayers want to invest in before the next pandemic and the economic policies they want activated once the next pandemic hits are relatively consistent across the countries, with some exceptions. Such results can inform economic policy responses and government investment in health policies to reduce the adverse impacts of the next pandemic.

Background: Discrete choice experiments (DCEs) are a powerful tool that potentially offer a method to quantify individual and joint care preferences of persons with dementia and their informal caregivers. DCE studies that have included persons with dementia revealed that choice task complexity is a serious challenge to this population, which can lead to heuristic decision making. This feasibility study aims to determine best-in-practice dyadic DCE design to enable both persons with dementia and informal caregivers to participate in health care decision-making. Method: This study included three DCE rounds: (1) persons with dementia alone, (2) informal caregiver only, and (3) persons with dementia and their informal caregivers in dyads. A flexible DCE design was employed, in which choice task complexity was increased systematically to explore individual cognitive limitations in DCE decision making. Choice tasks were presented by illustrations and simplified language to decrease cognitive burden on persons with dementia. A think aloud approach of survey administration was used to provide insights into individual and joint decision-making processes. The Telephone Interview for Cognitive Status was used for cognitive screening. Results: 15 persons with dementia, 15 informal caregivers, and 14 dyadic discrete choice experiments were conducted. The majority of persons with dementia (53.3%) was able to complete choice tasks including two scenarios characterized by three attributes and completed on average 6.4 (SD 4.1) choice tasks before dropping out. The dyadic DCEs enabled persons with dementia to complete surveys that were somewhat more complex, and on average a higher number of choice tasks (7.9, SD 3.9). In the dyadic DCEs, informal caregivers assisted the person with dementia to understand the choice task, elicited choice motivation, and provided the means to discuss each other’s preferences. Conclusion: Persons with dementia that have a mild to severe cognitive impairment can use discrete choice experiments with low choice task complexity. Dyadic DCEs can enable greater empowerment of the person with dementia while promoting joint decision making. Future work should further explore the mapping of severity of dementia and feasible choice task complexity, to enhance our understanding about designing choice elicitation tasks.

When estimating random coefficients models from choice data, decisions relating to the multivariate density function assumed to describe preference heterogeneity across the population raise questions about stochastic (in)dependence between preference dimensions, uni- vs. multi-modality, potential point masses, bounds and/or constraints on support regions, among other concerns. Parametric representations of population distributions have generally implied uncomfortable compromises to achieve estimation tractability. It would seem preferable to sidestep such issues by estimating individual preferences in a distribution-free manner, but this freedom of form implies a large number of parameters since we lose the parsimony enabled by parametric densities and must deal directly with estimation of individual decision maker preferences. I propose a hybrid distribution-free estimator for individual parameter logit models that uses a genetic algorithm as first stage, the solution from which becomes a starting point for a gradient-based search to obtain the final posterior maximum likelihood estimates of individual preferences. This estimator is described in detail, its parameter recovery capability is tested with Monte Carlo data generation simulations, and a case study is developed in some detail to illustrate its use in policy analysis. The estimator can be applied to both stated and revealed preference data, requiring only that sufficient choice replications be available for individual observation units consistent with extant estimation methods. Computational experience shows the estimator to require CPU times comparable to extant simulation-based estimation methods, meaning that its use is practical for the exploration of the parameter space through multiple trials.

Previous economics literature has explored the role of visual attention on choice in isolation without accounting for other influences such as habits and goals or learning effects, nor their interrelationship. In this paper, we: (i) develop a novel joint framework to explore the relationship between visual attention, observed heterogeneity from stated habits and goals, and choice outcomes while accounting for shorter- and longer-term learning effects; and (ii) investigate whether accounting for these relationships improves model predictive power and behavioral insights. The empirical analysis used an eye-tracked discrete choice experiment on sugar-sweetened beverage purchasing (n = 152 adults with 20 choice tasks). Results suggest that habits, goals, and shorter-term learning are key drivers of information acquisition whereas cumulative choices (longer-term learning) affect subsequent choice outcome. Importantly, ignoring the joint relationship between habits, visual attention and choice may exaggerate the role of visual attention, leading to incorrect behavioral insights and reduced prediction accuracy.

Brands develop strategies based on forecasts that allow for individual differences, usually attributed empirically to heterogeneity in consumers' preferences. Behavioral theories propose choice process heterogeneity as the conditioning stage for choice outcomes, and suggest that not accounting for it causes biases in parameters and policy measures. We conduct a Monte Carlo simulation to study how underlying choice process heterogeneity generates substantively significant biases in different market contexts if analysts (erroneously) channel heterogeneity solely into tastes. We extend the literature by using a game theoretical analysis, driven by the results from the demand simulation, to explore demand mis-specification effects on brands' profitability and market equilibrium. Through mixed strategies we examine necessary conditions for market equilibrium when managers have access to different demand representations but are uncertain about which is true. We demonstrate that biases generated by representing consumer response heterogeneity solely through preference heterogeneity are enough to significantly jeopardize brands' profits due to misalignment of firms' products and resources with demand. Our work forcefully demonstrates to both marketers and econometricians/data scientists the necessity of modeling choice process heterogeneity given its impacts on brands’ performance.

Conciliating nature conservation and tourism development is an increasingly important task for authorities in charge of managing protected areas and requires an adequate knowledge of visitors′ preferences and recreational behavior. In this light, we used data collected by means of a choice experiment to investigate recreational preferences at Dolomiti Bellunesi National Park, a protected area located in Northeastern Italy. More specifically, we analyzed the determinants of visitors’ decisions to engage with different activities in the park. This is important information for park managers, as different recreational activities have both different impact on the natural heritage and different capability to generate revenue for nature conservation and for enhancing the quality of life of local communities. The findings of our study suggest that the choice of recreational activities is mainly driven by the features of recreational sites and by visitors’ personal characteristics. Concerning park features, visitors’ choices seem to be mostly driven by features directly related to each activity, such as thematic trails for hiking and climbing routes for rock climbing. Among visitors′ characteristics, we found that both previous experience with the activity and socio-demographic characteristics had a significant effect on activity choice. Overall, the results of our study can help park authorities in developing management plans aimed specifically at attracting a larger number of visitors of a certain type, which is an important tool to foster the more sustainable forms of tourism.

Several disciplines, among them health, sociology, and economics, provide strong evidence that social context is important to individual choices. It is therefore surprising that relatively little research has been focused on integrating the effect of social influence into choice models, especially given the importance of such choices in healthcare. This study developed and empirically tested a choice model that accounts for social network influences in a discrete choice experiment (DCE). We focused on maternal choices for childhood vaccination in Australia, and used an econometric choice model that explicitly 1) incorporated vaccine schedule characteristics, benefits and costs, and 2) represented up to ten different identifiable key influencer types (e.g., partner, parents, friends, healthcare professionals, inter alia), allowing for the attribution of directional importance of each influencer on the gravid woman’s decision to adhere to or reject childhood vaccination. Pregnant women (N = 604) aged 18 years and older recruited from an online panel completed a survey, including a DCE and questions about key influencers. A two-class ordered latent class model was conducted to analyse the DCE data, which assumes that the underlying latent driver (in our case the WHO vaccine hesitancy scale) is ordered, to give a practical interpretation of the meaning of the classes. When the choice model considered both childhood vaccination attributes and key influencers, a very high model fit was reached. The impact of key influencers on maternal choice for childhood vaccination was massive compared to the impact of childhood vaccination attributes. The marginal impact differed between key influencers. Our DCE study showed that the maternal decision for childhood vaccination was essentially almost completely socially driven, suggesting that the potential impact of social network influences can and should be considered in health-related DCEs, particular those where there are likely to be strong underlying social norms dictating decision maker behaviour.

This paper aims to explicitly account for the impact of inertia (or habit) on departure time decisions, and explore (1) to what extent departure time is influenced by inertia, (2) what influences individuals’ inertia with respect to departure time decisions, and (3) to what extent it impacts transport policies. We estimate an integrated choice and latent variable (ICLV) model using a stated preference survey for morning car commuters in the Greater Copenhagen Area. We interact the rescheduling components in the Scheduling Model (SM) with the latent variable Inertia. The modelling results show that higher levels of inertia yields higher rescheduling penalties and lower willing to shift departure time. Furthermore, we find that inertia in departure time is influenced by gender, presence of children in the household as well as work type. We test the behavioral responses to demand management policies for segments with different inertia, and find that the least inertial segment showed the highest substitution patterns, while the most inertial segment show the lowest substitution patterns. Finally, we compared the ICLV model to a reference model without inertia, and find that the effects of the demand management strategy is overestimated if inertia is neglected.

© 2019 ISPOR–The Professional Society for Health Economics and Outcomes Research Background: Lack of evidence about the external validity of discrete choice experiments (DCEs) is one of the barriers that inhibit greater use of DCEs in healthcare decision making. Objectives: To determine whether the number of alternatives in a DCE choice task should reflect the actual decision context, and how complex the choice model needs to be to be able to predict real-world healthcare choices. Methods: Six DCEs were used, which varied in (1) medical condition (involving choices for influenza vaccination or colorectal cancer screening) and (2) the number of alternatives per choice task. For each medical condition, 1200 respondents were randomized to one of the DCE formats. The data were analyzed in a systematic way using random-utility-maximization choice processes. Results: Irrespective of the number of alternatives per choice task, the choice for influenza vaccination and colorectal cancer screening was correctly predicted by DCE at an aggregate level, if scale and preference heterogeneity were taken into account. At an individual level, 3 alternatives per choice task and the use of a heteroskedastic error component model plus observed preference heterogeneity seemed to be most promising (correctly predicting >93% of choices). Conclusions: Our study shows that DCEs are able to predict choices—mimicking real-world decisions—if at least scale and preference heterogeneity are taken into account. Patient characteristics (eg, numeracy, decision-making style, and general attitude for and experience with the health intervention) seem to play a crucial role. Further research is needed to determine whether this result remains in other contexts.

This paper considers decision contexts wherein consumers make choices among alternatives that contain a manifest feature-based attribute: i.e., a discrete, salient and important attribute that describes a dichotomous quality, such as “genetically modified”, “organic”, or “locally grown”. We propose a choice model that can explicitly account for a) perception bias with respect to such an attribute when its information is present, and b) inference formation if this attribute information is missing for some alternatives. The impact of different information presentation formats on consumers' perception bias and inference formation is then examined by applying theories from social psychology. Our model outperforms standard Random Utility models that omit explicit representation of these phenomena. Consistent with theories considered, we find significant evidence of perception bias and inference in the choice data. Our results also provide insights on how consumers may infer the quality of a missing attribute in different competitive framing contexts. Finally, our welfare estimates show that consumers may benefit simply from the information improvement regarding government labeling policies.

The estimation of choice models that explicitly incorporate economic agents’ goal-related information has yet to receive focused attention from practitioners. Despite recent advances on spatial analysis in stated preference studies, there is still no evidence on how spatial effects interact with goal pursuit. In this study we propose a modelling framework to analyse how goal pursuit influences choices and query whether pursuit of important goals makes individuals less distance-sensitive. We estimate a hybrid choice model with latent variables to investigate the role of goals and distance on preferences for recreational site attributes. We use data from a choice experiment involving selection among different sites in Dolomiti Bellunesi National Park (Italy). Our results show that goal pursuit has a significant effect on site choice probability and that distance disutility is decreased when individuals visit recreational sites due to pursuit of important goals. This result indicates that willingness to pay inferences concerning spatially distributed activities depend crucially on the spatial distribution of motivations for participation.

Preference heterogeneity is one of the central behavioral concepts in applied econometrics. Its centrality is particularly evident in the choice modeling literature, notably in its widespread application to environmental and health economics, marketing, and transport. Despite conceptual and empirical advances in modeling preference heterogeneity, the generalizability of preference heterogeneity to different decision contexts and different data generation processes remains an open question. The basic premise of this paper is that latent sources of preference heterogeneity can be decomposed into components general to decision contexts and others specific to them. We study the structure of preference heterogeneity in different data generation processes with the goal of reliably identifying common (presumably generalizable) and specific (presumably not generalizable) sources of preference heterogeneity. The contribution of the paper is both conceptual and methodological, leading to the testing of five rival model specifications which together elucidate the heterogeneity structure present in two preference data sources of the same choice behavior. In the empirical application, we find that the multitrait-multimethod model of preference heterogeneity has the best fit and most sensible interpretations, indicating that while each data source contributes uniquely to certain heterogeneity components, both data sources contribute also to common (generalizable) preference heterogeneity. Recognition of the separability of the common versus source-specific preference heterogeneity will lead to more reliable and accurate demand model forecasts and assessments of welfare impacts.

Workforce participation decisions involve multiple stages: search, screening and offer evaluation. Standard econometric practice focusses on these stages in isolation. We conceptualize the focal behaviours as separate sequential decision stages, and provide a stated preference measurement framework for online job search and choice with a behaviourally consistent modelling approach. We demonstrate this approach in an empirical application of 275 dentists who completed an online survey including two Discrete Choice Experiments: the first mimicked an online job search site in which dentists decided which jobs they would apply to and the second presented dentists with a job offer which they accepted or rejected. Modelling these tasks requires a two-stage econometric model that incorporates the likelihood of application (first stage) into the job offer choice (second stage). The model detects differences in preferences (hence behaviours) across stages, facilitating the differentiation of policy aimed at search and job choice behaviours. Job screening occurs during search and the marginal propensity to apply for a job-type differs from the offer stage. We suggest that the approach presented provides a valuable way to investigate how dentists particularly, and perhaps the health workforce more generally, respond at different stages of workforce participation decisions and discuss practical implications.

In this paper, we analyze the implications for the economic valuation of the provision of public goods, considering respondents’ perceptions of the institution(s) that provide the service. The specific behavioral mechanism whereby institutional distrust (ID) shows itself is through the activation of screening of choice options (choice set formation). However, ID-induced choice set formation might be confounded with the consumer budget constraint, especially in a developing country context, leading to biased welfare estimates for service improvement. We formulate a semi-compensatory hybrid choice set formation (SC-HCSF) model that enables us to 1) discriminate the effect of a budget constraint from that of ID-induced choice set formation and 2) characterize their separate impacts on welfare estimates using a spatial framework. We compare our model results to those from a standard Random Parameters Logit (RPL) Model. The RPL underestimates (overestimates) welfare when individuals have a low (high) ID. Based on our empirical model results, we demonstrate that the impacts of ignoring institutional trust issues can be highly deleterious to project appraisals, particularly in settings where legislative and regulatory institutions are perceived to be endemically corrupt.

Previous choice studies have proposed a way to condition the utility of each alternative in a choice set on experience with the alternatives accumulated over previous periods, defined either as a mode used or not in a most recent trip, or the mode chosen in their most recent trip and the number of similar one-way trips made during the last week. The paper found that the overall statistical performance of the mixed logit model improved significantly, suggesting that this conditioning idea has merit. Experience was treated as an exogenous influence linked to the scale of the random component, and to that extent it captures some amount of the heterogeneity in unobserved effects, purging them of potential endogeneity. The current paper continues to investigate the matter of endogeneity versus exogeneity. The proposed approach implements the control function method through the experience conditioning feature in a choice model. We develop two choice models, both using stated preference data. The paper extends the received contribution in that we allow for the endogenous variable to have an impact on the attributes through a two stage method, called the Multiple Indicator Solution, originally implemented in a different context and for a single (quality) attribute, in which stage two is the popular control function method. In the first stage, the entire utility expression associated with all observed attributes is conditioned on the prior experience with an alternative. Hence, we are capturing possible correlates associated with each and every attribute and not just one selected attribute. We find evidence of potential endogeneity. The purging exercise however, results in both statistical similarities and differences in time and cost choice elasticities and mean estimates of the value of travel time savings. We are able to identify a very practical method to correct for possible endogeneity under experience conditioning that will encourage researchers and practitioners to use such an approach in more advanced non-linear discrete choice models as a matter of routine.

Several discrete choice experiment studies have investigated issues in the design of incentive programs to enhance the provision of ecosystem services. In these studies, ownership of land is usually private, and landowners make decisions independently of each other. However, the assumption of independence may be invalid when decision making involves a spatial setting and social networks. This study presents a new approach that accounts for social cooperation and preference interdependence across farmers in a land management context with mixed ownership. We formulate an econometric model of implicit choice set formation that accounts for (1) farmers’ expectations regarding mutual positive benefits from cooperation in an integrated land management system, and (2) the potential interdependence of preferences across farmers within the same socio-spatial group. We show that cooperation expectations matter for the decision of whether to engage in cooperative management and also for welfare estimates. Our model can identify sources of heterogeneities arising from cooperation expectations in ways that would not be possible using a reduced-form choice model. The assumption of independence should be checked routinely in similar settings to avoid potential endogeneity problems in discrete choice models when dealing with data that have a social-spatial dimension. Several discrete choice experiment studies have investigated issues in the design of incentive programs to enhance the provision of ecosystem services. In these studies, ownership of land is usually private, and landowners make decisions independently of each other. However, the assumption of independence may be invalid when decision making involves a spatial setting and social networks. This study presents a new approach that accounts for social cooperation and preference interdependence across farmers in a land management context with mixed ownership. We formulate an econometric model of implicit choice set formation that accounts for (1) farmers’ expectations regarding mutual positive benefits from cooperation in an integrated land management system, and (2) the potential interdependence of preferences across farmers within the same socio-spatial group. We show that cooperation expectations matter for the decision of whether to engage in cooperative management and also for welfare estimates. Our model can identify sources of heterogeneities arising from cooperation expectations in ways that would not be possible using a reduced-form choice model. The assumption of independence should be checked routinely in similar settings to avoid potential endogeneity problems in discrete choice models when dealing with data that have a social-spatial dimension.

Lack of evidence about the external validity of Discrete Choice Experiments (DCEs)-sourced preferences inhibits greater use of DCEs in healthcare decision-making. This study examines the external validity of such preferences, unravels its determinants, and provides evidence of whether healthcare choice is predictable. We focused on influenza vaccination and used a six-step approach: i) literature study, ii) expert interviews, iii) focus groups, iv) survey including a DCE, v) field data, and vi) in-depth interviews with respondents who showed discordance between stated choices and actual healthcare utilization. Respondents without missing values in the survey and the actual healthcare utilization (377/499 = 76%) were included in the analyses. Random-utility-maximization and random-regret-minimization models were used to analyze the DCE data, whereas the in-depth interviews combined five scientific theories to explain discordance. When models took into account both scale and preference heterogeneity, real-world choices to opt for influenza vaccination were correctly predicted by DCE at an aggregate level, and 91% of choices were correctly predicted at an individual level. There was 13% (49/377) discordance between stated choices and actual healthcare utilization. In-depth interviews showed that several dimensions played a role in clarifying this discordance: attitude, social support, action of planning, barriers, and intention. Evidence was found that our DCE yields accurate actual healthcare choice predictions if at least scale and preference heterogeneity are taken into account. Analysis of discordant subjects showed that we can even do better. The DCE measures an important part of preferences by focusing on attribute tradeoffs that people make in their decision to participate in a healthcare intervention. Inhibitors may be among these attributes, but it is more likely that inhibitors have to do with exogenous factors like goals, religion, and social norms. Con-ducting upfront work on constraints/inhibitors of the focal behavior, not just what promotes the behavior, might further improve predictive ability.

We develop and estimate a statistical model of neighborhood choice that draws on insights from cognitive science and decision theory as well as qualitative studies of housing search. The model allows for a sequential decision process and the possibility that people consider a small and selective subset of all potential destinations. When combined with data from the Los Angeles Family and Neighborhood Survey, our model reveals that affordability constraints and households’ tendency toward short-distance moves lead blacks and Hispanics to have racially stratified choice sets in which their own group is disproportionately represented. We use an agent-based model to assess how racially stratified choice sets contribute to segregation outcomes. Our results show that cognitive decision strategies can amplify patterns of segregation and inequality.

Coastal erosion is a global and pervasive phenomenon that predicates a need for a strategic approach to the future management of coastal assets (both built and natural): should we invest in protective structures such as seawalls that aim to preserve specific coastal features, or allow natural coastline retreat to preserve sandy beaches and other coastal ecosystems? Determining the most suitable management approach in a specific context requires a better understanding of the full suite of economic values the population holds for coastal assets, including non-market values. In this study, we characterise New South Wales residents' willingness to pay to maintain sandy beaches (width and length) in the face of coastal erosion along the Australian state's nearly 2200 km coastline. The measurement instrument is a stated preference referendum task administered state-wide to a sample of 2014 respondents, with the payment mechanism defined as a purpose-specific incremental levy of a fixed amount over a set period of years. We use an innovative application of a Latent Class Binary Logit model to deal with “Yea-sayers” and “Nay-sayers”, as well as revealing the latent heterogeneity among sample members. We find that 65% of the population would be willing to pay some amount of levy, dependent on the policy setting. In most cases, there is no effect of degree of beach deterioration – characterised as loss of width and/or length of sandy beaches of between 5% and 100% - on respondents' willingness to pay for a management levy. This suggests that respondents who agreed to pay a management levy were motivated to preserve sandy beaches in their current state irrespective of the severity of sand loss likely to occur as a result of coastal erosion. Willingness to pay also varies according to beach type (amongst Iconic, Main, Bay and Surf beaches) – a finding that can assist with spatial prioritisation of coastal management. Not recognizing the presence of nay-sayers in the data or recognizing them but eliminating them from the estimation will result in biased WTP results and, consequently, biased policy propositions by coastal managers.

The National Disability Insurance Scheme (NDIS) is an ambitious Australian welfare program, with the goal of supporting 460,000 people living with a disability. To promote individual choices, the NDIS allocates personalised budgets based on the goals of Participants (or their nominated Carers). This paper examines (a) the heterogeneity of personal preferences for support services in the disability sector, (b) whether the program is subject to dissonance in the evaluation of supports for allowing different decision makers as plan managers and (c) how the central role of personal goals impacts the choice outcomes of eligible NDIS Participants (and their nominated Carers). Our findings reveal the adoption of two distinct mindsets: (i) one that favours immediacy and disability-specific services and (ii) one that pursues higher-quality services and general life skills. Although being a Participant or Carer in itself did not influence the adoption of either mindset, the goals they pursued did. To ensure the NDIS delivers on its intended Participant-focused outcomes, our findings suggest the need to understand the goals pursued by NDIS decision makers. Our study contributes to the body of research that promotes customer-centric approaches in policymaking and develops a methodology that produces insights for policy makers, service providers, advocacy groups and other NDIS stakeholders.

Effective public expenditure currently dominates the management focus of many protected areas. This calls for explicit modeling of constraints and motivations that, respectively, obstruct and stimulate visits to selected outdoor destinations. Choice set formation is the result of screening and/or inclusion of specific sites (alternatives) to form the set of sites considered in real choices. Evidence shows that the omission of a structural representation of choice set formation is harmful to econometric inference. Yet, the literature has largely ignored the underlying behavioral phenomenon. We show, using a discrete choice experiment involving selection among seven recreational sites in an Italian national park, that choice set formation is behaviorally relevant, even after controlling for preference discrimination. Motivations (why visit?) are important determinants of preliminary site screening for choice set inclusion, as well as site selection, justifying the additional value of such modeling extension. Refereed/Peer-reviewed

In contrast to the recent proliferation of studies incorporating ordinal methods to generate health state values from adults, to date relatively few studies have utilised ordinal methods to generate health state values from adolescents. This paper reports upon a study to apply profile case best worst scaling methods to derive a new adolescent specific scoring algorithm for the Child Health Utility 9D (CHU9D), a generic preference based instrument that has been specifically designed for the estimation of quality adjusted life years for the economic evaluation of health care treatment and preventive programs targeted at young people. A survey was developed for administration in an on-line format in which consenting community based Australian adolescents aged 11-17 years (N = 1982) indicated the best and worst features of a series of 10 health states derived from the CHU9D descriptive system. The data were analyzed using latent class conditional logit models to estimate values (part worth utilities) for each level of the nine attributes relating to the CHU9D. A marginal utility matrix was then estimated to generate an adolescent-specific scoring algorithm on the full health = 1 and dead = 0 scale required for the calculation of QALYs. It was evident that different decision processes were being used in the best and worst choices. Whilst respondents appeared readily able to choose 'best' attribute levels for the CHU9D health states, a large amount of random variability and indeed different decision rules were evident for the choice of 'worst' attribute levels, to the extent that the best and worst data should not be pooled from the statistical perspective. The optimal adolescent-specific scoring algorithm was therefore derived using data obtained from the best choices only. The study provides important insights into the use of profile case best worst scaling methods to generate health state values with adolescent populations. Refereed/Peer-reviewed

Random utility models have been widely employed in environmental valuation. But stochastic choice set formation models in the random utility framework are rarely applied in this literature, although previous research has shown that ignoring choice set formation (when it exists) leads to biased parameter estimates and welfare measures. This paper conducts Monte Carlo (MC) experiments to investigate the directionality and magnitude of welfare measure bias arising from ignoring or misspecifying choice set formation. We find that when attribute cutoff-based choice set formation exists in the data generation process, typical RUM models ignoring or misspecifying choice set formation underestimate welfare measures by 30-50%. Models that approximate choice set formation may produce unbiased welfare measures, but constitute a promising area for future research. This paper illustrates the importance of applying choice set formation in environmental valuation and provides practical guidance about the usage of stochastic choice set formation models. Refereed/Peer-reviewed

Traditional stated choice (SC) experiments are the most widely used method for measuring individual preferences. Notwithstanding, the issue of differentiating between the impacts of the attributes per se vs. the impact of the attribute levels shown in the experiment has been something of a challenge for researchers. Recent studies suggest that Best–Worst scaling (B–W) experiments would allow for the possibility of making this discrimination. In this study we combine B–W data with more traditional SC data to model the choice of residential location; we also allow for observed and unobserved heterogeneity in the combined models. To the best of our knowledge, this type of data fusion has not been used before in this context. Our dataset is fairly unique and corresponds to a sample of individuals interested in acquiring a flat in the centre of Santiago, Chile, from whom both types of responses were elicited. In addition, we have available a larger independent validation sample that responded to a second SC exercise in the same location and time period. We found that the best-fit model corresponded to pooling the ‘best’ and SC responses (i.e., ignoring the ‘worst’ data), while accounting for systematic taste variations (observable heterogeneity) and allowing for a correct treatment of the pseudo-panel nature of the data through error components (unobserved heterogeneity). Our results imply that analysts could get valuable additional information to increase their understanding on individuals’ behaviour when considering the joint use of B–W Scaling and SC data incorporating heterogeneity. Refereed/Peer-reviewed

This paper develops new directions on how individuals’ use of multiple goals can be incorporated in econometric models of individual decision making. We start by outlining key components of multiple, simultaneous goal pursuit and multi-stage choice. Since different goals are often only partially compatible, such a multiple goal-based approach implies balancing goals, leading to a deliberate goal-level choice strategy on the part of the decision maker. Accordingly, we introduce a conceptual framework to classify different aspects of individuals’ decisions in the presence of multiple goals. Based on this framework we propose a formalization of individual decision making when pursuing multiple goals. We briefly review different previous streams on goal-based decision making and how the proposed goal-driven conceptual framework relates to earlier research in discrete choice models. The framework is illustrated using examples from different domains, in particular marketing, environmental economics, transportation and sociology. Finally, we discuss identification and modeling needs for goal-based choice strategies and opportunities for further research. Refereed/Peer-reviewed

Goals are constructs that direct choice behavior by guiding a decision maker toward desirable (or away from undesirable) end states. Often, consumers are motivated to satisfy multiple goals within a single choice. Although previous research has recognized this possibility, it has not directly formulated models of choice as a multigoal problem. The authors develop such a model, referred to as the multiple-goal-based choice model, which incorporates (1) simultaneous multiple goal pursuit and (2) context-driven goal adaptation but (3) does not require a priori identification of the number or nature of the goals. Goal adaptation within a single choice instance, allied to repeated choices, is the key to empirical identification of multiple latent goals. The proposed model is tested and supported using discrete choice experimental data on digital cameras through multiple validation exercises. The model can lead to significantly different policy implications with regard to consumers’ valuation for new product designs, compared with extant utility-based choice models.

This paper contributes to our understanding of individual decision making by testing the proposal that differential weighting of 2 (or more) goals can be an important factor leading to stochastic (probabilistic) choice. The tested models follow from the endogenous maximum entropy program (eMEP) paradigm (Swait & Marley, 2013), which proposes that stochastic choice is (partially or entirely) a consequence of balancing multiple goals. That framework leads to an interpretation of the scale factor in classic random utility models (such as the multinomial logit [MNL]) as an endogenous property of a decision maker—an interpretation that is in stark contrast to the standard interpretation of the scale as due to heterogeneity or other “noise.” The new perspective is supported by data from a task that manipulates (by a prime) an individual’s propensity to be either consistent or to seek variety, suggesting that balanced pursuit of exploitation and exploration goals is a reasonable interpretation of stochastic choice. Refereed/Peer-reviewed

Traditional discrete choice experiments do not differentiate between the intrinsic importance of an attribute and that associated with its levels of variation. It has been suggested recently that best-worst (B-W) scaling (Case 2) allows for this differentiation. Here we pool B-W answers with binary stated choice (SC) data to study the importance of dwelling and neighbourhood attributes for apartment seekers in the centre of Santiago, Chile. Previous research had shown how these diverse elicitation methods can be pooled (albeit without including any type of heterogeneity), suggesting that the “best” (as opposed to “worst”) responses are most compatible with the binary SC data. In this paper we extend this work to allow for heterogeneity in preferences through (a) systematic taste variations alone, (b) correct treatment of panel effects alone, and finally, (c) the combined effect of both. In all cases the best resulting model is obtained by pooling the “best” answers with the binary SC, under the assumption of common and specific attributes to each dataset. Nevertheless, when the model included only unobserved heterogeneity through error components (to treat the panel effect), the datasets did not pool as well as when we did not consider it. The joint model had half of the attributes specific to each dataset while in the previous case only two were specific. On the other hand, when considering observed and unobserved heterogeneity, the two datasets pooled better than in the other cases, needing to consider only one attribute as specific to each set (and the remaining seven as common). We also analysed how the inclusion of heterogeneity changed the attribute estimates. In particular, the scales that can be constructed for the attributes and for their levels of variation do not change their relative order, but the magnitudes do, especially in cases where the estimates were not significantly different from zero at the 95% confidence level. Refereed/Peer-reviewed

External validity is a crucial but under-researched topic when considering using discrete choice experiment (DCE) results to inform decision making in clinical, commercial or policy contexts. We present the theory and tests traditionally used to explore external validity that focus on a comparison of final outcomes and review how this traditional definition has been empirically tested in health economics and other sectors (such as transport, environment and marketing) in which DCE methods are applied. While an important component, we argue that the investigation of external validity should be much broader than a comparison of final outcomes. In doing so, we introduce a new and more comprehensive conceptualisation of external validity, closely linked to process validity, that moves us from the simple characterisation of a model as being or not being externally valid on the basis of predictive performance, to the concept that external validity should be an objective pursued from the initial conceptualisation and design of any DCE. We discuss how such a broader definition of external validity can be fruitfully used and suggest innovative ways in which it can be explored in practice. Refereed/Peer-reviewed

Using discrete choice modelling, the study investigates 946 American consumers’ willingness-to-pay and preferences for diverse beef products. A novel experiment was used to elicit the number of beef products that each consumer would purchase. The range of products explored in this study included ground, diced, roast, and six cuts of steaks (sirloin, tenderloin, flank, flap, New York and cowboy/rib-eye). The outcome of the study suggests that US consumers vary in their preferences for beef products by season. The presence of a USDA certification logo is by far the most important factor affecting consumer’s willingness to pay for all beef cuts, which is also heavily dependent on season. In relation to packaging, US consumers have mixed preference for different beef products by season. The results from a scaled adjusted ordered logit model showed that after price, safety-related attributes such as certification logos, types of packaging, and antibiotic free and organic products are a stronger influence on American consumers’ choice. Furthermore, US consumers on average purchase diced and roast products more often in winter “slow cooking season”, than in summer; whereas New York strip and flank steak are more popular in the summer “grilling season”. This study provides valuable insights for businesses as well as policymakers to make inform decisions while considering how consumers relatively value among different labelling and product attributes by season and better address any ethical, safety and aesthetic concerns that consumers might have. Refereed/Peer-reviewed

Goals direct decision making, from the most abstract levels of motivation to the multitudinous details of evaluation of options available for choice. However, the pervasive influence of goals in decision processes is generally not explicitly recognized at the level of demand model formulation and specification. In applied economics generally, and transportation specifically, demand models re-late product/service attributes directly to behavior, using utility (or value) asa shorthand representation for the impact of goals. In this paper we argue that this is a limiting view that restricts our thinking about decision making and, hence, our representation and inference-making about that behavior. We support this argument by reinterpreting and/or extending various applications of hybrid models in transportation to a goal-based framework and formulating goal-based choice models which recognize that goals 1) are drivers of choice, 2)explain the choice of strategy, 3) are part of the constraint set and 4) contribute to explaining impacts of the decision context on the allocation of cognitive re-sources by the decision maker Refereed/Peer-reviewed

This paper develops new directions on how individuals’ use of multiple goals can be incorporated in econometric models of individual decision making. We start by outlining key components of multiple, simultaneous goal pursuit and multi-stage choice. Since different goals are often only partially compatible, such a multiple goal-based approach implies balancing goals, leading to a deliberate goal-level choice strategy on the part of the decision maker. Accordingly, we introduce a conceptual framework to classify different aspects of individuals’ decisions in the presence of multiple goals. Based on this framework we propose a formalization of individual decision making when pursuing multiple goals. We briefly review different previous streams on goal-based decision making and how the proposed goal-driven conceptual framework relates to earlier research in discrete choice models. The framework is illustrated using examples from different domains, in particular marketing, environmental economics, transportation and sociology. Finally, we discuss identification and modeling needs for goal-based choice strategies and opportunities for further research.

We conceptualize probabilistic choice as the result of the simultaneous pursuit of multiple goals in a vector optimization representation, which is reduced to a scalar optimization that implies goal balancing. The majority of prior theoretical and empirical work on such probabilistic choice is based on random utility models, the most basic of which assume that each choice option has a valuation that has a deterministic (systematic) component plus a random component determined by some specified distribution. An alternate approach to probabilistic choice has considered maximization of one quantity (e.g., utility), subject to constraints on one or more other quantities (e.g., cost). The multiple goal perspective integrates the results regarding the well-studied multinomial logit model of probabilistic choice that has been derived from each of the above approaches; extends the results to other models in the generalized extreme value (GEV) class; and relates them to recent axiomatic work on the utility of gambling. Refereed/Peer-reviewed

Given the large number of food choices that consumers make each day it seems likely that they will adopt decision strategies that minimize cognitive effort. To examine this issue, we develop a conceptual and empirical model of habitual choice, and the factors that result in transitions to two strategies other than habitual selection: utility-maximizing choice and a variety-seeking strategy. Our approach provides an alternative to traditional state dependence methods used in this type of panel data. We apply this framework to the choice of two food products that illustrate the heterogeneity across types of products in decision strategies and routine choice patterns. Copyright 2013, Oxford University Press.

Objectives Increased use of discrete choice experiments (DCEs) in healthcare requires establishing whether stated preferences are predictive of observed healthcare utilization. This study aimed to determine whether the number of alternatives in a DCE choice task should reflect the actual decision context, and how complex the choice model needs to be to predict real-world choices correctly at an aggregate and individual level in healthcare. Methods Two randomized controlled trials (RCTs) involving choices for influenza vaccination and colorectal cancer screening were used. Each RCT had three study conditions: DCE choice tasks with (i) two alternatives, (ii) three alternatives, or (iii) both. Two samples of 1,200 respondents each were randomly assigned to one of the conditions. Each respondent answered 16 DCE choice tasks (for the derivation of the decision model) plus a choice task mimicking the real-world choice (to keep the decision context the same). The data was analysed in a systematic way using random-utility-maximization (RUM) and random-regret-minimization (RRM) choice processes with scale and/or preference heterogeneity. Results Irrespective of the number of alternatives per choice task, the choice to opt for influenza vaccination and colorectal cancer screening was correctly predicted by DCE at an aggregate level, if scale and preference heterogeneity were taken into account. At an individual level, three alternatives per choice task and using heteroscedastic model plus preference heterogeneity seemed to be most promising, correctly predicting the real-world choice in 81.7% and 87.9% of the cases for influenza vaccination and colorectal cancer screening respectively. No evidence was found that RRM outperformed RUM. Conclusions Our study shows that DCEs hold the potential of being externally valid at an aggregate level if at least scale and preference heterogeneity are taken into account. Further research is needed to determine if this result remains in other contexts, and to optimise choice prediction at an individual level.

We examine a model of six latent constructs and propose that true brand loyalty can be explained as a result of five distinct antecedents: brand credibility, affective brand conviction, cognitive brand conviction, attitude strength, and brand commitment. Data from experimental conditions with manipulations of eight product classes and two involvement levels lend support for the proposed model, demonstrating that brand loyalty can be considered as truly loyal only when mediated by a high degree of affective and cognitive brand conviction, and attitude strength. Advertising and marketing implications for the relationships among the six constructs under different manipulation conditions are discussed.

We review the discussion at a workshop whose goal was to achieve a better integration among behavioral, economic, and statistical approaches to choice modeling. The workshop explored how current approaches to the specification, estimation, and application of choice models might be improved to better capture the diversity of processes that are postulated to explain how consumers make choices. Some specific challenges include how to capture and parsimoniously describe heterogeneous mixes of heuristic choice rules, methods for building realistic models of choice, and nontraditional methods for estimating models. An agenda for important future work in these areas is also proposed.

The authors offer a new conceptualization and operational model of consumer choice that allows context-sensitive information usage and preference heterogeneity to be separately and simultaneously captured, thus transforming the axiom of full information use into a testable hypothesis. A key contribution of the proposed framework is the integration of two previously disjointed and often antagonistic research paradigms: (1) the economic rationality perspective, which assumes stable preferences and full information usage, and (2) the psychological bounded-rationality perspective, which allows context-sensitive preferences and information selectivity. The authors demonstrate that the two paradigms can and do coexist in the same decision-making space, even at the level of individual consumer choices. The proposed information archetype mixture model is tested in four studies that span different product categories and levels of task complexity. The findings have ramifications for choice modeling theory and implementation, beyond the disciplinary boundaries of marketing to applied economics and choice-focused social sciences. Refereed/Peer-reviewed

This paper examines the effects of brand credibility, a central concept in information economics–based approaches to brand effects and brand equity, on consumer choice and choice set formation. We investigate the mechanisms through which credibility effects materialize, namely, through perceived quality, perceived risk, and information costs saved. The credibility of a brand as a signal is defined as the believability of the product position information contained in a brand, which depends on consumer perceptions of the willingness and ability of firms to deliver what they have promised. The choice set is defined as the collection of brands that have a nonzero probability of being chosen among those actually available for choice in a given context. Furthermore, we study the impact of brand credibility on the variance of the stochastic component of utility. Not only do choice model parameters capture the impact of systematic utility differences on choice probabilities, but also the magnitude of this systematic impact is moderated by the relative importance of the stochastic utility component in preference. We term this moderation phenomenon preference discrimination, which we conceptualize as the decision makers' capacity to effectively discriminate between products' utilities in choice situations. We estimate a discrete choice model of brand choice set formation and preference discrimination on experimental data in two categories—juice and personal computers—and find strong evidence for brand credibility effects and differential mechanisms through which brand credibility's impact materializes on brand choice conditional on choice set, choice set formation, and preference discrimination.

This article tests how well the information economics view of brand equity explains consumer brand choice in countries that represent different cultural dimensions. In this empirical analysis, the authors use survey and experimental data on orange juice and personal computers collected from respondents in Brazil, Germany, India, Japan, Spain, Turkey, and the United States. The results provide strong empirical evidence across countries for the role of brands as signals of product positions. In addition, the positive effect of brand credibility on choice is greater for consumers who rate high on either collectivism or uncertainty avoidance. Credible brands provide more value to collectivist consumers because such consumers perceive these brands as being of higher quality (i.e., reinforcing group identity). Credible brands provide more value to high-uncertainty-avoidance consumers because such brands have lower perceived risk and information costs.

From a practical perspective, (arguably) most consumer decisions are not made in isolation of the households in which consumers are inserted, yet we commonly treat them econometrically as if they were. The purpose of this workshop was to take some initial steps in defining needed research in household decision making that structurally accounts for goal sharing, utility interdependence, taste heterogeneity, choice set formation, power structures, group size and composition, and so forth. We also considered conditions under which aggregation of tastes, utility and choices might occur and make sense from both behavioral and modeling perspectives.

INTRODUCTION:Preference-based measures of health-related quality of life play a key role in the calculation of Quality-Adjusted Life Years (QALYs) for Health Technology Assessment (HTA). The Child Health Utility 9D (CHU9D) is a new preference-based instrument designed specifically for application in children and adolescents (aged 7 to 17 years). This study aimed to compare Chinese and Australian adolescent population preferences for CHU9D health states using profile case best worst scaling (BWS) methods.METHODS:Fifty CHU9D health states (blocked into five survey versions) were generated for valuation using a fractional factorial design. Study participants were recruited through an online panel company in Australia, and through primary and secondary schools in China. A latent class modelling framework was adopted for econometric analysis.RESULTS:A total of 1,982 respondents (51 percent female) in Australia and 902 respondents (43 percent female) in China provided useable survey responses. Latent class analysis indicated the existence of preference heterogeneity for both population groups. In the Australian sample, respondents in Class I placed the most importance on the mental health dimensions of the CHU9D (for example, Worried and Annoyed) and the least importance on daily activities (for example, Activities, Daily routine, Sleep), whilst respondents in Class II placed equal weights on all attributes. In the Chinese sample, respondents in Class I placed the most importance on the Activities dimension of the CHU9D and the least importance on the Annoyed dimension, whist Class II placed the most importance on the Schoolwork dimension and the least importance on Pain.CONCLUSIONS:This study has provided important cross-country insights into the use of profile case BWS methods to elicit health state preferences with young people for application in HTA in children and adolescents. The differential latent classes identified between Australia and China highlights the necessity to derive country-specific adolescent scoring algorithms for the CHU9D instrument for application in HTA.