scholarly journals The Influence of Alcohol Warning Labels on Consumers’ Choices of Wine and Beer

2020 ◽  
Vol 9 (2) ◽  
pp. 3-21
Author(s):  
Azzurra Annunziata ◽  
Lara Agnoli ◽  
Riccardo Vecchio ◽  
Steve Charters ◽  
Angela Mariani
Keyword(s):  
Alcohol Abuse ◽  
Discrete Choice ◽  
Latent Class ◽  
Choice Models ◽  
Discrete Choice Experiments ◽  
Health Warnings ◽  
Negative Effects ◽  
Excessive Consumption ◽  
Targeted Messages ◽  
Different Levels

This study aims to analyse the influence of alternative formats of health warnings on French and Italian Millennial consumers’ choices of beer and wine. Two Discrete Choice Experiments were built for wine and beer and two Latent Class choice models were applied in order to verify the existence of different consumer profiles. Results show that young consumers’ choices for wine and beer are influenced by framing, design and visibility of warnings. In both countries, the acceptance of warnings is higher for beer than for wine and in both cases consumers show higher utility for a logo on the front label: on the neck with a neutral message in the case of beer; on the front, without a message for wine. Latent Class choice models highlight the existence of different consumers’ groups with different levels of warning influencing their choices. In order to apply policies conducting to health benefits, our results suggest the need to focus on young individuals to communicate the risks of alcohol abuse through targeted messages and, more generally, to make them aware of the potential negative effects of excessive consumption of both wine and beer.

scholarly journals Public, medical professionals’ and patients’ preferences for the allocation of donor organs for transplantation: study protocol for discrete choice experiments

BMJ Open ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. e026040 ◽  
Author(s):  
Carina Oedingen ◽  
Tim Bartling ◽  
Christian Krauth
Keyword(s):  
Discrete Choice ◽  
Study Protocol ◽  
Systematic Reviews ◽  
Latent Class ◽  
Choice Experiments ◽  
Fractional Factorial ◽  
Discrete Choice Experiments ◽  
Medical Professionals ◽  
The Public ◽  
Donor Organs

IntroductionOrgan transplantation is the treatment of choice for patients with severe organ failure. Nevertheless, donor organs are a scarce resource resulting in a large mismatch between supply and demand. Therefore, priority-setting leads to the dilemma of how these scarce organs should be allocated and who should be considered eligible to receive a suitable organ. In order to improve the supply–demand mismatch in transplantation medicine, this study explores preferences of different stakeholders (general public, medical professionals and patients) for the allocation of donor organs for transplantation in Germany. The aims are (1) to determine criteria and preferences, which are relevant for the allocation of scarce donor organs and (2) to compare the results between the three target groups to derive strategies for health policy.Methods and analysisWe outline the study protocol for discrete choice experiments, where respondents are presented with different choices including attributes with varied attribute levels. They were asked to choose between these choice sets. First, systematic reviews will be conducted to identify the state of art. Subsequently, focus group discussions with the public and patients as well as expert interviews with medical professionals will follow to establish the attributes that are going to be included in the experiments and to verify the results of the systematic reviews. Using this qualitative exploratory work, discrete choice studies will be designed to quantitatively assess preferences. We will use a D-efficient fractional factorial design to survey a total sample of 600 respondents according to the public, medical professionals and patients each. Multinomial conditional logit model and latent class model will be analysed to estimate the final results.Ethics and disseminationThis study has received Ethics Approval from the Hannover Medical School Human Ethics Committee (Vote number: 7921_BO_K_2018). Findings will be disseminated through conference presentations, workshops with stakeholders and peer-reviewed journal articles.

scholarly journals A protocol for a discrete choice experiment: understanding patient medicine preferences for managing chronic non-cancer pain

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e027153 ◽  
Author(s):  
Marian Shanahan ◽  
Briony Larance ◽  
Suzanne Nielsen ◽  
Milton Cohen ◽  
Maria Schaffer ◽  
...  
Keyword(s):  
Focus Group ◽  
Cancer Pain ◽  
Discrete Choice ◽  
Online Survey ◽  
Latent Class ◽  
Management Strategies ◽  
Discrete Choice Experiments ◽  
Research Centre ◽  
Treatment Preferences ◽  
Convenience Sample

IntroductionHigh rates of chronic non-cancer pain (CNCP), concerns about adverse effects including dependence among those prescribed potent pain medicines, the recent evidence supporting active rather than passive management strategies and a lack of funding for holistic programme have resulted in challenges around decision making for treatment among clinicians and their patients. Discrete choice experiments (DCEs) are one way of assessing and valuing treatment preferences. Here, we outline a protocol for a study that assesses patient preferences for CNCP treatment.Methods and analysisA final list of attributes (and their levels) for the DCE was generated using a detailed iterative process. This included a literature review, a focus group and individual interviews with those with CNCP and clinicians who treat people with CNCP. From this process a list of attributes was obtained. Following a review by study investigators including pain and addiction specialists, pharmacists and epidemiologists, the final list of attributes was selected (number of medications, risk of addiction, side effects, pain interference, activity goals, source of information on pain, provider of pain care and out-of-pocket costs). Specialised software was used to construct an experimental design for the survey. The survey will be administered to two groups of participants, those from a longitudinal cohort of patients receiving opioids for CNCP and a convenience sample of patients recruited through Australia’s leading pain advocacy body (Painaustralia) and their social media and website. The data from the two participant groups will be initially analysed separately, as their demographic and clinical characteristics may differ substantially (in terms of age, duration of pain and current treatment modality). Mixed logit and latent class analysis will be used to explore heterogeneity of responses.Ethics and disseminationEthics approval was obtained from the University of New South Wales Sydney Human Ethics committee HC16511 (for the focus group discussions, the one-on-one interviews and online survey) and HC16916 (for the cohort). A lay summary will be made available on the National Drug and Alcohol Research Centre website and Painaustralia’s website. Peer review papers will be submitted, and it is expected the results will be presented at relevant pain management conferences nationally and internationally. These results will also be used to improve understanding of treatment goals between clinicians and those with CNCP.

Using Both Time Tradeoff and Discrete Choice Experiments in Valuing the EQ-5D: Impact of Model Misspecification on Value Sets

2020 ◽  
Vol 40 (4) ◽  
pp. 483-497
Author(s):  
Ian Waudby-Smith ◽  
A. Simon Pickard ◽  
Feng Xie ◽  
Eleanor M. Pullenayegum
Keyword(s):  
Linear Relationship ◽  
Hybrid Model ◽  
Discrete Choice ◽  
Latent Class ◽  
Model Misspecification ◽  
Choice Experiments ◽  
Discrete Choice Experiments ◽  
Time Tradeoff ◽  
Value Sets ◽  
The Impact

Introduction. The EQ-5D-5L valuation protocol contains both time tradeoff (TTO) tasks and discrete choice experiments (DCE), raising the question of how to best use these in creating a value set. The hybrid model, which combines TTO and DCE data, has emerged as a commonly used approach. However, this model assumes independence among responses from the same individual, a linear relationship between TTO and DCE utilities, and, in many implementations, homoscedastic residuals. The aims of this study are to examine alternatives to these assumptions and determine the impact of misspecification on value sets. Methods. We performed a simulation study, parameterized using the US EQ-5D-5L valuation study, to assess the impact of model misspecification. We simulated TTO and DCE data with nonlinear relationships between TTO and DCE utilities, heteroscedastic errors, and correlated responses. Simulated data were analyzed using hybrid models with and without heteroscedasticity, Tobit models with and without heteroscedasticity, a latent class model, and a mixed model. Results. Mean absolute errors (MAEs) for correctly specified models were <0.05, whereas models that incorrectly assumed a linear relationship between TTO and DCE utilities or homoscedasticity of TTO responses featured states with an MAE >0.1. When a linear relationship between TTO and DCE utilities held, using both TTO and DCE data under correct specification yielded smaller MAEs compared with using TTO data alone but yielded larger MAEs when a linear relationship did not hold. Mistakenly assuming homoscedasticity led to increased MAEs, whereas ignoring dependence did not. Conclusions. Because heteroscedasticity in TTO utilities and nonlinear associations between DCE and TTO utilities have been noted, we recommend careful assessment of scedasticity and linearity to ascertain the suitability of a hybrid model.

OP340 Kidney Patients’ Preferences For A Wearable Digital Health Technology To Support Self-Management Of Chronic Kidney Disease - A Discrete Choice Experiment

2021 ◽  
Vol 37 (S1) ◽  
pp. 14-15
Author(s):  
Vijay S. Gc ◽  
Cynthia Iglesias ◽  
Seda Erdem ◽  
Lamiece Hassan ◽  
Andrea Manca
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Discrete Choice ◽  
Latent Class ◽  
Digital Health ◽  
Latent Class Models ◽  
User Preferences ◽  
Discrete Choice Experiments ◽  
Self Management ◽  
Health Technologies

IntroductionWearable Digital Health Technologies (WDHTs) can support and enhance self-management by giving individuals with chronic conditions more control over their health, safety and wellbeing. Involving patients early on in the design of these technologies facilitates the development of person-centered products. It may increase the potential uptake of (and adherence to) any intervention they are designed to deliver. This research aims to elicit chronic kidney disease (CKD) patients’ preferences for WDHTs that may help patients manage their conditions.MethodsWe used discrete choice experiments (DCE) to elicit preferences for WDHTs characterized by their generalizable characteristics. The study design was informed by a multi-stage mixed-method approach (MSMMA). This included a review of the published literature, focus group interviews and one-to-one interactions with CKD patients to identify relevant characteristics (that is, attributes and levels) associated with wearable DHTs. We collected the data from 113 patients (age ≥18 years) with stage 3 or above CKD. The analysis started with a conventional multinomial logit model and was extended by investigating heterogeneity in preferences via latent class models.ResultsOur MSMMA yielded ten potential attributes for consideration in a choice task. The final list included five attributes, cross-checked and validated by the research team, and patient representatives. The most preferred attributes of WDHTs were device appearance, format and type of information provided, and mode of engagement with patients. Respondents preferred a discreet device, which offered options that individuals could choose from and provided medical information.ConclusionsWe show how to use MSMMA to elicit user preferences in (and to inform the) early stages of the development of WDHTs. Individuals with CKD preferred specific characteristics that would make them more likely to engage with the self-management support WDHT. Our results provide valuable insights that can be used to inform the development of different WDHTs for different segments of the CKD patients population, moving away from a one-size-fits-all provision and resulting in population health gains.

A Comparative Empirical Study of Discrete Choice Models in Retail Operations

2021 ◽  
Author(s):  
Gerardo Berbeglia ◽  
Agustín Garassino ◽  
Gustavo Vulcano
Keyword(s):  
Empirical Study ◽  
Discrete Choice ◽  
Operations Management ◽  
Latent Class ◽  
Markov Chain Model ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Chain Model ◽  
Retail Operations ◽  
Demand Models

Choice-based demand estimation is a fundamental task in retail operations and revenue management, providing necessary input data for inventory control, assortment, and price-optimization models. The task is particularly difficult in operational contexts where product availability varies over time and customers may substitute into the available options. In addition to the classical multinomial logit (MNL) model and extensions (e.g., nested logit, mixed logit, and latent-class MNL), new demand models have been proposed (e.g., the Markov chain model), and others have been recently revisited (e.g., the rank list-based and exponomial models). At the same time, new computational approaches were developed to ease the estimation function (e.g., column-generation and expectation-maximization (EM) algorithms). In this paper, we conduct a systematic, empirical study of different choice-based demand models and estimation algorithms, including both maximum-likelihood and least-squares criteria. Through an exhaustive set of numerical experiments on synthetic, semisynthetic, and real data, we provide comparative statistics of the predictive power and derived revenue performance of an ample collection of choice models and characterize operational environments suitable for different model/estimation implementations. We also provide a survey of all the discrete choice models evaluated and share all our estimation codes and data sets as part of the online appendix. This paper was accepted by Vishal Gaur, operations management.

scholarly journals Are Consumers Willing to Pay a Premium for Pure Rice Noodles? A Study of Discrete Choice Experiments in Taiwan

2020 ◽  
Vol 12 (15) ◽  
pp. 6144
Author(s):  
Yu-Hui Chen ◽  
Kai-Han Qiu ◽  
Kang Ernest Liu ◽  
Chun-Yuan Chiang
Keyword(s):  
Willingness To Pay ◽  
Marketing Strategy ◽  
Discrete Choice ◽  
Metropolitan Area ◽  
Latent Class ◽  
Corn Starch ◽  
Latent Class Model ◽  
Choice Experiments ◽  
Discrete Choice Experiments ◽  
Class Model

Most consumers in Taiwan have never eaten pure rice noodles (PRNs) and some may mistakenly treat corn starch-based rice noodles as PRNs. This study examines consumers’ willingness to pay (WTP) for PRNs using discrete choice (DC) experiments with a blind tasting test to understand consumers’ ability to identify PRNs with varying rice content on the basis of their appearance and taste. Collecting data from the Taipei metropolitan area, our DC experimental results of both pre- and post-experiment conditions show that Taiwanese consumers do prefer PRNs and their WTP for PRNs was strengthened. A latent class model highlights that attribute preferences tend to differ by group and thus rice content ratios should be properly labeled so that consumers can make a better choice according to their preferences. Our WTP estimates also imply that offering tasting trials to consumers is an effective marketing strategy to encourage potential purchases of PRNs for the rice noodle industry.

Exploring Marketing to Engineering Information Mapping in Mass Customization: A Presentation of Ideas, Challenges and Resulting Questions

2011 ◽  
Author(s):  
Scott Ferguson ◽  
Andrew Olewnik ◽  
Phil Cormier
Keyword(s):  
Discrete Choice ◽  
Mass Customization ◽  
Latent Class ◽  
Mixed Logit ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Hierarchical Bayes ◽  
Customer Preferences ◽  
Lack Of Information ◽  
Engineering Information

The paradigm of mass customization strives to minimize the tradeoffs between an ‘ideal’ product and products that are currently available. However, the lack of information relation mechanisms that connect the domains of marketing, engineering, and distribution have caused significant challenges when designing products for mass customization. For example, the bridge connecting the marketing and engineering domains is complicated by the lack of proven tools and methodologies that allow customer needs and preferences to be understood at a level discrete enough to support true mass customization. Discrete choice models have recently gained significant attention in engineering design literature as a way of expressing customer preferences. This paper explores how information from choice-based conjoint surveys might be used to assist the development of a mass customizable MP3 player, starting from 140 student surveys. The authors investigate the challenges of fielding discrete choice surveys for the purpose of mass customization, and explore how hierarchical Bayes mixed logit and latent class multinomial logit models might be used to understand the market for customizable attributes. The potential of using discrete choice models as a foundation for mathematically formulating mass customization problems is evaluated through an investigation of strengths and limitations.

scholarly journals Using discrete choice experiments to explore how bioecological attributes of sites drive birders’ preferences and willingness to travel

2021 ◽  
Author(s):  
Maria De Salvo ◽  
Giuseppe Cucuzza ◽  
Giovanni Signorello
Keyword(s):  
Discrete Choice ◽  
Latent Class ◽  
Bird Species ◽  
Choice Experiments ◽  
Latent Class Models ◽  
Discrete Choice Experiments ◽  
Biological Attributes ◽  
Birding Sites ◽  
Welfare Changes ◽  
Class Models

AbstractA study based on discrete choice experiments is conducted to investigate how bioecological attributes of birding sites enter the utility functions of specialized birders and affect their travel intentions. Estimates are based on generalized multinomial and scales-adjusted latent class models. We find that the probability of observing a rare or a new bird species, and the numerosity of species significantly affect birders’ choice destination. We also find that individual preferences among attributes are correlated and affected by scale and taste heterogeneity. We identify two latent classes of birders. In the first class fall birders attaching a strong interest in qualitative aspects of sites and low importance on distance from home. Class 2 groups birders addicted both on all qualitative and quantitative bioecological attributes of sites as well as on the distance. In general, we assess that the majority of birders prefer to travel short distances, also when the goal is viewing rare or new birds. Finally, we estimate marginal welfare changes in biological attributes of sites in terms of willingness to travel.

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