Which traits drive consumer preferences for gene-edited foods in Spain

Either a random-parameters logit (RPL) or latent class (LC) model can be used to model or explain preference heterogeneity in discrete-choice experiment (DCE) data. The former assumes continuous distribution of preferences across the sample, while the latter assumes a discrete distribution. This study compared RPL and LC models to explore preference heterogeneity when analyzing patient preferences for psoriasis treatments. Using DCE data collected from respondents with moderate-to-severe plaque psoriasis, we calculated and compared preference weights derived from RPL and LC models. We then compared how RPL and LC explain preference heterogeneity by exploring differences across subgroups defined by observed characteristics (i.e.,country, age, gender, marital status, and psoriasis severity). While RPL and LC models resulted in the same mean preference weights, different preference-heterogeneity patterns emerged from the two approaches. In both models, country of residence and self-reported disease severity could be linked to systematic differences in preferences. The RPL also identified gender and marital status, but not age, as sources of heterogeneity; the LC membership probability model indicated that age was a significant factor, but not gender or marital status. Using data from a psoriasis patient survey to compare two widely used methods for exploring heterogeneity identified differences in results between stated-preferences: subgroup analysis in the RPL model and inclusion of subgroup characteristics in the class membership probability function of the LC model. Researchers should model data using the most adaptable approach to address the initial study question.

Effectively assessing public preferences for air quality improvement policies is extremely important to environmental policy formulation, but developing policies that cater to public tastes is a great challenge. Although the random parameters logit (RPL) model in the choice experiment is widely used in relevant studies, it remains limited in revealing additional preference heterogeneity. Given this, the study applies two extended models in exploring public preference heterogeneity for air quality policies. An RPL model with heterogeneity in means and variances (RPL-HMV) and an RPL model with correlated random parameters (RPL-CRP) are used to provide more beneficial insights for policy analysis. The study shows that better-educated groups are more willing to pay for increasing urban green coverage, and income increases the randomness of such preferences’ distribution among groups. From the perspective of preferences, reducing heavy pollution days is positively associated with decreasing morbidity of respiratory diseases caused by outdoor air pollution and negatively correlated with improving urban green coverage. In addition, compared to the RPL-CRP model, the willingness to pay in the RPL model is overestimated by 14.72%. The study further clarifies public preferences for air quality policies, and the extra information revealed by extended models provides more valuable references for policy-making.

Understanding stakeholder preferences is essential for designing effective agricultural policies, promoting technology adoption, and aligning market strategies with consumer demand. Best–Worst Scaling (BWS) is a robust stated preference method that captures preferences by choosing the most and least important attributes within a choice set. This review highlights the statistical foundations of BWS, including Random Utility Theory, and common analytical models such as Multinomial Logit, Latent Class Analysis, Random Parameter Logit, and Hierarchical Bayesian frameworks to estimate preference heterogeneity. A bibliometric analysis of BWS applications in agricultural research highlights increasing adoption, publication trends, and leading contributors in the field. The findings reveal that BWS provides actionable insights into consumer and farmer preferences, informing product development, policy formulation, and sustainable decision-making, and demonstrates its growing relevance as a rigorous tool for evidence-based agricultural research.

Preferences for Pre-exposure Prophylaxis (PrEP) among people who use drugs in Thailand: A discrete choice experiment.

Background and Aims: Best–worst scaling (BWS) is compared to standard hedonic scaling for measuring consumer wine preferences. BWS is a relatively new method for producing ratio-level scales and has gained recent attention for application in sensory research, but has not been applied to wine. Methods and Results: Regular wine consumers (112) evaluated eight designed wines with both scaling methods in an intra-subject design over two test periods. The methods did not result in comparable product liking results. The eight wines could almost be differentiated on an aggregated level with hedonic ratings (P = 0.076); there was no significant difference with BWS. Latent class analysis was used to identify two clusters, which differed on the preferences for the designed sensory components. The BWS design had to be split into several blocks, so no complete individual measures were available, which prevented analysing heterogeneity for this method. Conclusions: BWS needs more wines to be assessed per person in order to discriminate between red wines and to allow modelling of consumer preference heterogeneity. Respondents would have to accomplish complete individual BWS designs, which requires repeated exposure to the same set of wines over several tasting sessions. Significance of the Study: This study demonstrates that BWS is not as suitable for sensory consumer preference measurement of red wine as hedonic rating. While BWS has shown a higher discriminative ability for different products and in non-sensory research, the factors of alcohol, tannin and memory fatigue make it less practical for red wine sensory measurement compared to hedonic rating.

Preference heterogeneity for renewable energy technology

This paper presents estimations of willingness-to-pay (WTP) values in the context of rural bus service in a developing country. Using stated choice data collected from rural bus users, WTP values are estimated by developing standard multinomial logit model (MNL) and three different random parameter logit (RPL) models: One with independent or uncorrelated responses, another taking into account the correlations among responses, and the other allowing heterogeneity around the mean of in-vehicle travel time. While developing the RPL models, successful application of the sparsely used constrained triangular distribution is demonstrated. WTP values are estimated separately for commuting and noncommuting trips. Higher WTP values for travel time are observed for commuting trips, while for noncommuting trips higher WTP values are found for qualitative attribute. Preference heterogeneity associated with the mean is investigated, and the “travel distance” is found to have a statistically significant decomposition effect on the mean of in-vehicle travel time for commuting trips. The traditional MNL model, in general, is found to underestimate the WTP values as compared to RPL models.

Can respondent uncertainty be ignored when exploring heterogeneity in public preferences for air pollution treatment policies? Comparative results from choice experiment analysis

Importance of Dwelling, Neighbourhood Attributes in Residential Location Modelling: Best Worst Scaling vs. Discrete Choice

In this paper, we explore the properties of a positive-part Stein-like estimator which is a stochastically weighted convex combination of a fully correlated parameter model estimator and uncorrelated parameter model estimator in the Random Parameters Logit (RPL) model. The results of our Monte Carlo experiments show that the positive-part Stein-like estimator provides smaller MSE than the pretest estimator in the fully correlated RPL model. Both of them outperform the fully correlated RPL model estimator and provide more accurate information on the share of population putting a positive or negative value on the alternative attributes than the fully correlated RPL model estimates. The Monte Carlo mean estimates of direct elasticity with pretest and positive-part Stein-like estimators are closer to the true value and have smaller standard errors than those with fully correlated RPL model estimator.

IntroductionPrice competition in the tomato market may lead Italian processing companies to adopt product differentiation strategies to attract consumers and raise revenues. Exploring consumers’ preferences for processed tomato products’ attributes can support companies in implementing product differentiation strategies and succeed in the market.MethodsThis study used a convenience sample of 602 Italian consumers to identify consumers’ preferences for processed tomato products’ attributes selected through a literature review and tested by the Best Worst Scaling (BWS) method. Also, a two-step cluster analysis was used to identify and size consumer groups with common preferences for tested product’s attributes.ResultsOverall, our results showed that Italian consumers of processed tomato products strongly preferred attributes such as Country of origin, Organic, and Social and economic sustainability labels. In addition, the analysis shows the existence of three distinct consumer segments such as “traditional,” “price-sensitive,” and “sustainable” consumers. The largest one was price-sensitive consumers followed by sustainable and traditional ones.DiscussionOur findings have practical implications for producers and policymakers. Producers may use claims about the product’s origin jointly with an organic production logo and ethical label to differentiate their products and raise revenues. Likewise, policymakers should enhance monitoring and control measures to ensure with environmental, social, and economic sustainable standards compliance. This will support building consumer trust for those credence attributes. Overall, this study adds valuable insights to the existing literature on consumer preferences for processed tomato products, providing results with high external validity using a large convenience sample.

IntroductionDespite recent advances in treatment for psoriatic arthritis (PsA), many patients experience inadequate response or intolerance to therapy, indicating that unmet treatment-related needs remain. To further characterize these unmet needs, we evaluated patients’ experiences regarding the burden of PsA symptoms and disease impacts, and patients’ preferences for treatment.MethodsPatients from ArthritisPower, a rheumatology research registry, completed a web-based survey. Object case best–worst scaling (BWS) was used to evaluate the relative burden of 11 PsA-related symptoms and the relative importance of improvement in nine PsA-related disease impacts. BWS data were analyzed using a random-parameters logit model. Patient demographics, preferences for mode and frequency of therapy, and preferences for methotrexate were analyzed descriptively.ResultsAmong the 332 participants, most were White (94%), female (80%), with mean age of 54 years (SD 11.4). In the BWS, joint pain was the most bothersome symptom, followed by other musculoskeletal pain and fatigue. The BWS for disease impacts found that improvements in the ability to perform physical activities were most important, followed by improvements in the ability to function independently, sleep quality, and the ability to perform daily activities. The most burdensome symptoms and desired disease impact improvements were similar in patients regardless of their experience with biologic disease-modifying antirheumatic drugs. The most preferred mode and frequency of treatment administration was oral, once-daily medication (preferred by 38% of respondents), and 74% prioritized therapies that significantly improved joint-related symptoms versus psoriasis-related symptoms. The majority of respondents (65%) preferred PsA treatment regimens that did not include methotrexate.ConclusionsPatients with PsA from a rheumatology registry found musculoskeletal pain symptoms to be the most bothersome and prioritized improvements to functional impacts of their disease. These findings can better inform development of new therapies and guide shared patient-provider treatment decision-making.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40744-022-00436-x.

Background and ObjectiveBest–worst scaling is a theory-driven method that can be used to prioritize objects in health. We sought to characterize all studies of best–worst scaling to prioritize objects in health, to assess trends of using best–worst scaling in prioritization over time, and to assess the relationship between a legacy measure of quality (PREFS) and a novel assessment of subjective quality and policy relevance.MethodsA systematic review identified studies published through to the end of 2021 that applied best–worst scaling to study priorities in health (PROSPERO CRD42020209745), updating a prior review published in 2016. The PubMed, EBSCOhost, Embase, Scopus, APA PsychInfo, Web of Science, and Google Scholar databases were used and were supplemented by a hand search. Data describing the application, development, design, administration/analysis, quality, and policy relevance were summarized and we tested for trends by comparing articles before and after 1 January, 2017. Multivariate statistics were then used to assess the relationships between PREFS, subjective quality, policy relevance, and other possible indicators.ResultsFrom a total of 2826 unique papers identified, 165 best–worst scaling studies were included in this review. Applications of best–worst scaling to study priorities in health have continued to grow (p < 0.01) and are now used in all regions of the world, most often to study the priorities of patients/consumers (67%). Several key trends can be observed over time: increased use of pretesting (p < 0.05); increased use of online administration (p < 0.01), and decreased use of paper self-administered surveys (p = 0.02); increased use of heterogeneity analysis (p = 0.02); an increase in having a clearly stated purpose (p < 0.01); and a decrease in comparing respondents to non-respondents (p = 0.01). The average sample size has more than doubled, from 228 to 472 respondents, but formal sample size justifications remain low (5.3%) and unchanged over time (p = 0.68). While the average PREFS score remained unchanged at 3.1/5, both subjective quality and policy relevance trended up, but changes were not statistically significant (p = 0.06 and p = 0.13). Most of the variation in subjective quality was driven by PREFS (R2 = 0.42), but it was also positively assosciated with policy relevance, heterogeneity analysis, and using a balanced incomplete block design, and was negatively associated with not using developmental methods and an increasing sample size.ConclusionsUsing best–worst scaling to prioritize objects is now commonly used around the world to assess the priorities of patients and other stakeholders in health. Best practices are clearly emerging for best–worst scaling. Although legacy measures (PREFS) to measure study quality are reasonable, there may need to be new tools to assess both study quality and policy relevance.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40273-022-01167-1.

PNS99 Using an Online Survey to Elicit Preferences in China: Comparison of Different Methods Based on SF-6DV2

Purpose This study explores wine consumer preferences in the Valencian Community through a territorial and segmented lens. While wine consumption has been widely examined, no research has systematically compared the provinces of Valencia, Castellón and Alicante. The aim is to identify key wine attributes, interprovincial differences and distinct consumer segments. Design/methodology/approach A survey was administered to 504 wine consumers across the three provinces of the Valencia Community. Best–Worst Scaling (BWS) was applied to measure the importance of seven wine attributes, and Latent Class Analysis (LCA) was used to identify consumer segments. Findings Four consumer segments were identified: inexperienced, experts, price-sensitive and eco-friendly. These groups differ significantly in their evaluation of attributes such as price, wine type, designation of origin (DO) and label information. Research limitations/implications The study is limited to the Valencian Community and may not be generalizable to other regions. Future research could apply the same approach in broader national or cross-cultural contexts. Originality/value This is the first study to integrate BWS and LCA to examine wine preferences in the Valencian Community, offering a novel understanding of consumer heterogeneity based on psychographic and territorial dimensions.