scholarly journals POSITIONING VIETNAM’S PANGASIUS CATFISH IN THE FRENCH MARKET USING DISCRETE CHOICE MODEL

2014 ◽  
Vol 17 (4) ◽  
pp. 96-111
Author(s):  
Thong Tien Nguyen ◽  
Hung Manh Nguyen
Keyword(s):  
Discrete Choice ◽  
Low Income ◽  
Latent Class ◽  
Choice Model ◽  
Latent Class Model ◽  
Random Parameter ◽  
Discrete Choice Model ◽  
Class Model ◽  
Additional Information ◽  
Random Parameter Model

The study used discrete choice model to investigate the position of Vietnam’s Pangasius catfish in the French market. Data was collected via a choice experiment designed for 12 aquaculture species familiar to French consumers. The random parameter model was estimated and used to calculate the share elasticity. The market position of the aquaculture products in this study was calculated based on the competitive clout, vulnerability scores, and ranked-order implicit values. The results show that Vietnam’s Pangasius has a low competitive clout, high vulnarability score, and low ranked-order implicit value. A latent class model was also estimated for comparison and acquisition of additional information. A strong segment of Pangasius (11.9%) is described by low income and education consumers, women at mid-age dominated, and family with children. To improve the Pangasius position and image in the international market, Vietnam needs promotional and marketing campaigns at global level for the product.

scholarly journals Integrated Model of Joint Residence-Workplace Location Choice and Commute Behavior Using Latent Class and Mixed Logit Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Pengpeng Jiao ◽  
Meiqi Liu ◽  
Jin Guo
Keyword(s):  
Latent Class ◽  
Choice Model ◽  
Location Choice ◽  
Rapid Development ◽  
Latent Class Model ◽  
Mixed Logit ◽  
Integrated Model ◽  
Model Framework ◽  
Class Model ◽  
Residence Location

With the rapid development of urbanization and motorization, urban commute trips are becoming increasingly serious due to the unbalanced distribution of residence and workplace land-use types in most Chinese cities. To explore the inherent interrelations among residence location, workplace, and commute trip, an integrated model framework of joint residence-workplace location choice and commute behavior is put forward based on the personal trip survey data of Beijing in 2005. First, to extract households’ different choice characteristics, this paper presents a latent class model, clusters all households into several groups, and analyzes the conditional probability of each group. Second, the paper integrates the residence location and workplace together as the joint choice alternative, employs the socioeconomic factors, individual attributes, household attributes, and trip characteristics as explanatory variables, and formulates the joint residence-workplace location choice model using mixed logit method. Estimations of the latent class model show that four latent groups fit the data best. Further results of the joint residence-workplace location choice model indicate that there exist significantly different choice characteristics in each latent group. Generally, the integrated model framework outperforms traditional location choice methods.

Minority households’ willingness to pay for public and private wildfire risk reduction in Florida

2017 ◽  
Vol 26 (8) ◽  
pp. 744 ◽  
Author(s):  
Armando González-Cabán ◽  
José J. Sánchez
Keyword(s):  
Willingness To Pay ◽  
Risk Reduction ◽  
Low Income ◽  
Cost Sharing ◽  
Latent Class ◽  
Latent Class Model ◽  
Random Parameter ◽  
Cost Share ◽  
Wildfire Risk ◽  
Private And Public

The purpose of this work is to estimate willingness to pay (WTP) for minority (African-American and Hispanic) homeowners in Florida for private and public wildfire risk-reduction programs and also to test for differences in response between the two groups. A random parameter logit and latent class model allowed us to determine if there is a difference in wildfire mitigation program preferences, whether WTP is higher for public or private actions for wildfire risk reduction, and whether households with personal experience and who perceive that they live in higher-risk areas have significantly higher WTP. We also compare Florida minority homeowners’ WTP values with Florida original homeowners’ estimates. Results suggest that Florida minority homeowners are willing to invest in public programs, with African-Americans WTP values at a higher rate than Hispanics. In addition, the highest priority for cost-sharing funds would go to low-income homeowners, especially to cost-share private actions on their own land. These results may help fire managers optimise allocation of scarce cost-sharing funds for public v. private actions.

scholarly journals Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment

10.2196/22841 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e22841
Author(s):  
Taoran Liu ◽  
Winghei Tsang ◽  
Fengqiu Huang ◽  
Oi Ying Lau ◽  
Yanhui Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Model ◽  
Disease Diagnosis ◽  
Class Model ◽  
Class 1 ◽  
Time Accuracy

Background Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industry across the world. These factors can contribute to patient anxiety about misdiagnosis by clinicians. However, with the increasing growth in use of big data in biomedical and health care communities, the performance of artificial intelligence (Al) techniques of diagnosis is improving and can help avoid medical practice errors, including under the current circumstance of COVID-19. Objective This study aims to visualize and measure patients’ heterogeneous preferences from various angles of AI diagnosis versus clinicians in the context of the COVID-19 epidemic in China. We also aim to illustrate the different decision-making factors of the latent class of a discrete choice experiment (DCE) and prospects for the application of AI techniques in judgment and management during the pandemic of SARS-CoV-2 and in the future. Methods A DCE approach was the main analysis method applied in this paper. Attributes from different dimensions were hypothesized: diagnostic method, outpatient waiting time, diagnosis time, accuracy, follow-up after diagnosis, and diagnostic expense. After that, a questionnaire is formed. With collected data from the DCE questionnaire, we apply Sawtooth software to construct a generalized multinomial logit (GMNL) model, mixed logit model, and latent class model with the data sets. Moreover, we calculate the variables’ coefficients, standard error, P value, and odds ratio (OR) and form a utility report to present the importance and weighted percentage of attributes. Results A total of 55.8% of the respondents (428 out of 767) opted for AI diagnosis regardless of the description of the clinicians. In the GMNL model, we found that people prefer the 100% accuracy level the most (OR 4.548, 95% CI 4.048-5.110, P<.001). For the latent class model, the most acceptable model consists of 3 latent classes of respondents. The attributes with the most substantial effects and highest percentage weights are the accuracy (39.29% in general) and expense of diagnosis (21.69% in general), especially the preferences for the diagnosis “accuracy” attribute, which is constant across classes. For class 1 and class 3, people prefer the AI + clinicians method (class 1: OR 1.247, 95% CI 1.036-1.463, P<.001; class 3: OR 1.958, 95% CI 1.769-2.167, P<.001). For class 2, people prefer the AI method (OR 1.546, 95% CI 0.883-2.707, P=.37). The OR of levels of attributes increases with the increase of accuracy across all classes. Conclusions Latent class analysis was prominent and useful in quantifying preferences for attributes of diagnosis choice. People’s preferences for the “accuracy” and “diagnostic expenses” attributes are palpable. AI will have a potential market. However, accuracy and diagnosis expenses need to be taken into consideration.

scholarly journals Identifying Dietary Consumption Patterns from Survey Data: a Bayesian Nonparametric Latent Class Model

2021 ◽  
Author(s):  
Briana Joy Kennedy Stephenson ◽  
Francesca Dominici
Keyword(s):  
Dietary Intake ◽  
Survey Data ◽  
Low Income ◽  
Dietary Patterns ◽  
Latent Class ◽  
Latent Class Model ◽  
The United States ◽  
Sampling Weights ◽  
Class Model ◽  
The Impact

Dietary intake is one of the largest contributing factors to cardiovascular health in the United States. Amongst low-income adults, the impact is even more devastating.Dietary assessments, such as 24-hour recalls, provide snapshots of dietary habits in a study population. Questions remain on how generalizable those snapshots are in nationally representative survey data, where certain subgroups are sampled disproportionately to comprehensively examine the population. Many of the models that derive dietary patterns account for study design by incorporating the sampling weights to the derived model parameter estimates post hoc. We propose a Bayesian overfitted latent class model that accounts for survey design and sampling variability to derive dietary patterns in adults aged 20 and older. We compare these results with a subset of the population, adults considered low-income (at or below the 130% poverty income threshold) to understand if and how these patterns generalize in a smaller subpopulation. Using dietary intake data from the National Health and Nutrition Examination Surveys, we identified six dietary patterns in the US adult population. These differed in consumption features found in the five dietary patterns derived in low-income adults. Reproducible code/data are provided on GitHub to encourage further research and application in this area.

scholarly journals Patients’ Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment (Preprint)

2020 ◽  
Author(s):  
Taoran Liu ◽  
Winghei Tsang ◽  
Fengqiu Huang ◽  
Oi Ying Lau ◽  
Yanhui Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Model ◽  
Disease Diagnosis ◽  
Class Model ◽  
Class 1 ◽  
Time Accuracy

BACKGROUND Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industry across the world. These factors can contribute to patient anxiety about misdiagnosis by clinicians. However, with the increasing growth in use of big data in biomedical and health care communities, the performance of artificial intelligence (Al) techniques of diagnosis is improving and can help avoid medical practice errors, including under the current circumstance of COVID-19. OBJECTIVE This study aims to visualize and measure patients’ heterogeneous preferences from various angles of AI diagnosis versus clinicians in the context of the COVID-19 epidemic in China. We also aim to illustrate the different decision-making factors of the latent class of a discrete choice experiment (DCE) and prospects for the application of AI techniques in judgment and management during the pandemic of SARS-CoV-2 and in the future. METHODS A DCE approach was the main analysis method applied in this paper. Attributes from different dimensions were hypothesized: diagnostic method, outpatient waiting time, diagnosis time, accuracy, follow-up after diagnosis, and diagnostic expense. After that, a questionnaire is formed. With collected data from the DCE questionnaire, we apply Sawtooth software to construct a generalized multinomial logit (GMNL) model, mixed logit model, and latent class model with the data sets. Moreover, we calculate the variables’ coefficients, standard error, <i>P</i> value, and odds ratio (OR) and form a utility report to present the importance and weighted percentage of attributes. RESULTS A total of 55.8% of the respondents (428 out of 767) opted for AI diagnosis regardless of the description of the clinicians. In the GMNL model, we found that people prefer the 100% accuracy level the most (OR 4.548, 95% CI 4.048-5.110, <i>P</i>&lt;.001). For the latent class model, the most acceptable model consists of 3 latent classes of respondents. The attributes with the most substantial effects and highest percentage weights are the accuracy (39.29% in general) and expense of diagnosis (21.69% in general), especially the preferences for the diagnosis “accuracy” attribute, which is constant across classes. For class 1 and class 3, people prefer the AI + clinicians method (class 1: OR 1.247, 95% CI 1.036-1.463, <i>P</i>&lt;.001; class 3: OR 1.958, 95% CI 1.769-2.167, <i>P</i>&lt;.001). For class 2, people prefer the AI method (OR 1.546, 95% CI 0.883-2.707, <i>P</i>=.37). The OR of levels of attributes increases with the increase of accuracy across all classes. CONCLUSIONS Latent class analysis was prominent and useful in quantifying preferences for attributes of diagnosis choice. People’s preferences for the “accuracy” and “diagnostic expenses” attributes are palpable. AI will have a potential market. However, accuracy and diagnosis expenses need to be taken into consideration.

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.

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