scholarly journals Farmer preference for improved corn seeds in Chiapas, Mexico: A choice experiment approach

2017 ◽  
Vol 15 (3) ◽  
pp. e0116 ◽  
Author(s):  
Blanca I. Sánchez-Toledano ◽  
Zein Kallas ◽  
José M. Gil-Roig
Keyword(s):  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Model ◽  
Multinomial Logit Model ◽  
Marketing Strategies ◽  
Preference Heterogeneity ◽  
Agricultural Policies ◽  
Class Model ◽  
Maize Seeds ◽  
Choice Variable

Appropriate technologies must be developed for adoption of improved seeds based on the farmers’ preferences and needs. Our research identified the farmers’ willingness to pay (WTP) as a key determinant for selecting the improved varieties of maize seeds and landraces in Chiapas, Mexico. This work also analyzed the farmers’ observed heterogeneity on the basis of their socio-economic characteristics. Data were collected using a semi-structured questionnaire from 200 farmers. A proportional choice experiment approach was applied using a proportional choice variable, where farmers were asked to state the percentage of preference for different alternative varieties in a choice set. The generalized multinomial logit model in WTP-space approach was used. The results suggest that the improved seed varieties are preferred over the Creole alternatives, thereby ensuring higher yields, resistance to diseases, and larger ear size. For the preference heterogeneity analyses, a latent class model was applied. Three types of farmers were identified: innovators (60.5%), transition farmers (29.4%), and conservative farmers (10%). An understanding of farmers’ preferences is useful in designing agricultural policies and creating pricing and marketing strategies for the dissemination of quality seeds.

scholarly journals Customers’ Mode Choice Behaviors of Express Service Based on Latent Class Analysis and Logit Model

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Lian Lian ◽  
Shuo Zhang ◽  
Zhong Wang ◽  
Kai Liu ◽  
Lihuan Cao
Keyword(s):  
Logit Model ◽  
Choice Behavior ◽  
Latent Class ◽  
Latent Class Model ◽  
Policy Research ◽  
Multinomial Logit Model ◽  
Class Model ◽  
Taste Heterogeneity ◽  
Using Data ◽  
Service Choice

As the parcel delivery service is booming in China, the competition among express companies intensifies. This paper employed multinomial logit model (MNL) and latent class model (LCM) to investigate customers’ express service choice behavior, using data from a SP survey. The attributes and attribute levels that matter most to express customers are identified. Meanwhile, the customers are divided into two segments (penny pincher segment and high-end segment) characterized by their taste heterogeneity. The results indicate that the LCM performs statistically better than MNL in our sample. Therefore, more attention should be paid to the taste heterogeneity, especially for further academic and policy research in freight choice behavior.

Exploring Differences in Preference Heterogeneity Representation and Their Influence in Product Family Design

2011 ◽  
Author(s):  
Eric Sullivan ◽  
Scott Ferguson ◽  
Joseph Donndelinger
Keyword(s):  
Logit Model ◽  
Latent Class ◽  
Latent Class Model ◽  
Mixed Logit ◽  
Multinomial Logit Model ◽  
Hierarchical Bayes ◽  
Product Line ◽  
Combinatorial Design ◽  
Preference Heterogeneity ◽  
Target Market

When using conjoint studies for market-based design, two model types can be fit to represent the heterogeneity present in a target market, discrete or continuous. In this paper, data from a choice-based conjoint study with 2275 respondents is analyzed for a 19-attribute combinatorial design problem with over 1 billion possible product configurations. Customer preferences are inferred from the choice task data using both representations of heterogeneity. The hierarchical Bayes mixed logit model exemplifies the continuous representation of heterogeneity, while the latent class multinomial logit model corresponds to the discrete representation. Product line solutions are generated by each of these model forms and are then explored to determine why differences are observed in both product solutions and market share estimates. These results reveal some potential limitations of the Latent Class model in the masking of preference heterogeneity. Finally, the ramifications of these results on the market-based design process are discussed.

scholarly journals Exploring Social Desirability Bias in Perceptions of Dog Adoption: All’s Well that Ends Well? Or Does the Method of Adoption Matter?

Animals ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 154 ◽  
Author(s):  
Courtney Bir ◽  
Nicole Olynk Widmar ◽  
Candace Croney
Keyword(s):  
Social Desirability ◽  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Model ◽  
The United States ◽  
Social Desirability Bias ◽  
Random Parameters ◽  
Class Model ◽  
Random Parameters Logit ◽  
Nationally Representative

Dogs are a popular companion animal in the United States; however, dog acquisition is often a contentious subject. Adoption is often cited as an ethical and popular method of acquisition but interpretation of the term ‘adoption’ may vary. In a nationally representative survey of the U.S., 767 respondents were asked questions regarding their opinions of dog acquisition and adoption. Within the sample, 45% had a dog; of those, 40% had adopted a dog, and 47% visited a veterinarian once a year. A best-worst choice experiment, where respondents were asked to choose the most ethical and least ethical method of acquiring a dog from a statistically determined set of choices, was used to elicit respondents’ preferences for the most ethical method of dog adoption. A random parameters logit and a latent class model were used to estimate relative rankings of dog adoption methods. In the random parameters logit model, the largest preference share was for adoption from a municipal animal shelter (56%) and the smallest preference share was for adoption from a pet store (3%). Dog acquisition was further evaluated by creating an index of social desirability bias using how important respondents believed certain dog characteristics were compared to how important respondents believed others would rate/rank the same dog characteristics. The highest incidences of social desirability bias occurred for the dog characteristics of appearance and breed.

scholarly journals What Makes Consumers Purchase Fresh Eggs in Supermarkets: The Effect of Unrealistic Choice Set Matters

Animals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 3542
Author(s):  
Shang-Ho Yang ◽  
Widya Satya Nugraha
Keyword(s):  
Data Collection ◽  
Animal Welfare ◽  
Market Segmentation ◽  
Latent Class ◽  
Latent Class Model ◽  
Marketing Strategies ◽  
Class Model ◽  
Crucial Component ◽  
Egg Products ◽  
Choice Set

Eggs are the crucial component of daily meals for almost everyone in Taiwan, while the multi-attributes of fresh egg products generate the challenges of marketing and promotions in supermarkets. This study analyzes the market segmentation and consumer willingness-to-pay (WTP) for fresh egg attributes (i.e., color, traceability, animal welfare, brand, and price). In particular, the effect of the unrealistic choice set is considered in this study. The data collection was distributed near markets, schools, and train stations across Taiwan from July to September in 2020. A total of 1115 valid responses were collected, and the Latent Class Model was used. Results show that fresh egg products in supermarkets reveal a strong preference for animal welfare label with the highest WTP, which is about 64.2 NT$ (≈US$ 2.29). Furthermore, traceability label, farm brand, and brown-color egg still exhibit positive WTP of about 33.4 NT$ (≈US$ 1.19), 32.6 NT$ (≈US$ 1.16), and 32.5 NT$ (≈US$ 1.16) in supermarkets, respectively. However, including the unrealistic choice set can potentially alter the final outcomes, and it provides a good example for researchers who may have the same situation. This research helps to know more about the complexity of attributes for fresh egg products in supermarkets, so marketers would be able to adopt the effective marketing strategies for fresh egg products in supermarkets.

scholarly journals Assessing the willingness of non-members to invest in new financial products in agricultural producer cooperatives: A choice experiment

2017 ◽  
Vol 26 (4) ◽  
Author(s):  
Eeva Alho
Keyword(s):  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Model ◽  
Control Rights ◽  
Class Model ◽  
New Organizational Forms ◽  
Experiment Method ◽  
Investment Capital ◽  
Agricultural Producer ◽  
And Control

The sourcing of outside investment capital from non-members has motivated the emergence of innovative cooperative structures, but the literature on these new organizational forms omits the perspective of an outside investor. This paper reports a study that applied a choice experiment method in a novel setting to increase understanding of the preferences of investors in agricultural firms. A large questionnaire dataset consisting of 845 financially literate subjects enabled testing of the form in which residual and control rights provide incentives for non-producer investors to invest in agricultural firms. The choice experiment data were analyzed using a latent class model. The results demonstrate that the subjects were interested in the currently hypothetical, new types of investment instruments in agricultural producer cooperatives. Three investor classes were distinguished based on the preferences: return-seeking, ownership-oriented and risk-averse investors. Who controls the firm appears to be irrelevant concerning willingness to invest, while the rural ties of the respondent are positively related to the preference for voting rights.

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 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 How do chocolate lovers balance taste and ethical considerations?

2016 ◽  
Vol 118 (2) ◽  
pp. 343-361 ◽  
Author(s):  
Eline Poelmans ◽  
Sandra Rousseau
Keyword(s):  
Latent Class ◽  
Latent Class Model ◽  
Preference Heterogeneity ◽  
Conditional Logit Model ◽  
Ethical Considerations ◽  
Class Model ◽  
Data Set ◽  
Content Type ◽  
Conditional Logit ◽  
Percent Response Rate

Purpose – The purpose of this paper is to investigate how chocolate lovers balance taste and ethical considerations when selecting chocolate products. Design/methodology/approach – The data set was collected through a survey at the 2014 “Salon du Chocolat” in Brussels, Belgium. The authors distributed 700 copies and received 456 complete responses (65 percent response rate). Choice experiments were used to estimate the relative importance of different chocolate characteristics and to predict respondents’ willingness to pay for marginal changes in those characteristics. The authors estimate both a conditional logit model and a latent class model to take possible preference heterogeneity into account. Findings – On average, respondents were willing to pay 11 euros more for 250 g fairtrade labeled chocolate compared to conventional chocolate. However, taste clearly dominates ethical considerations. The authors could distinguish three consumer segments, each with a different tradeoff between taste and fairtrade. One group clearly valued fairtrade positively, a second group valued fairtrade to a lesser extent and a third group did not seem to value fairtrade. Originality/value – Chocolate can be seen as a self-indulgent treat where taste is likely to dominate other characteristics. Therefore it is unsure to what extent ethical factors are included in consumer decisions. Interestingly the results indicate that a significant share of chocolate buyers still positively value fairtrade characteristics when selecting chocolate varieties.

scholarly journals Domestic airport passenger access mode choice decisions in a multi-airport region of South Africa

2014 ◽  
Vol 8 (1) ◽  
Author(s):  
Stephen Carstens
Keyword(s):  
South Africa ◽  
Willingness To Pay ◽  
Latent Class ◽  
Mode Choice ◽  
Latent Class Model ◽  
Preference Heterogeneity ◽  
Access Time ◽  
Class Model ◽  
Access Cost ◽  
Access Mode

The ground access mode used by air passengers to an airport has a vital impact on infrastructural and environmental decisions. An important aspect of a passenger’s mode choice is the sensitivity to factors such as access time and access cost. The objective of this research was to analyse air passenger’s sensitivity to access mode choice attributes, that is,access time, access cost, parking time and parking cost at two airports in Johannesburg, South Africa. A stated choice experiment was used to obtain the information and a latent class model was estimated. In general, discrete choice experiments are designed to reveal respondent(preference) heterogeneity and the latent class model allows for this heterogeneity to be modelled discretely. The estimated results indicated that three latent classes provided the best fit with preference heterogeneity evident from the set of parameter estimates. The access mode used was found to be the only significant covariate in the class assignment model. The respondents’ willingness to pay for a reduction in access time was estimated and it indicated that respondents had the highest access time willingness-to-pay value for the taxi as access mode. In addition, it was estimated that passengers being dropped off at the airport had a higher access time willingness-to-pay than passengers that used their own vehicles to the airport. The research results confirmed the presence of respondent heterogeneity (according to access mode) which resulted in different access time willingness-to-pay values.

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