scholarly journals Rural sustainability and food choice: the effect of territorial characteristics on the consumers’ preferences for organic lentils

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Chiara Paffarini ◽  
Biancamaria Torquati ◽  
Tiziano Tempesta ◽  
Sonia Venanzi ◽  
Daniel Vecchiato
Keyword(s):  
Logit Model ◽  
Agricultural Land ◽  
Mixed Logit ◽  
Multinomial Logit Model ◽  
Natura 2000 ◽  
Rural Landscape ◽  
Natural Environments ◽  
Rural Sustainability ◽  
Umbria Region ◽  
Organic Solidarity

AbstractThe importance of pulse cultivation and consumption is recognized by the scientific community in terms of human nutrition, food security, biodiversity and a valid substitute for animal protein. In some marginal areas, pulse cultivation represents also a protection against the abandonment of agricultural land, the preservation of traditional landscape and the maintenance of natural environments, besides contributing to the safeguard of traditional gastronomy and culture.This study explores how some characteristics connected with rural sustainability, like the preservation of the traditional rural landscape, production area in a Natura 2000 Site of Community Importance (SCI) and EU quality labels (PDO and PGI), might influence organic consumers’ choice of lentils. Data were collected in the Umbria region (Italy) in 2014 by interviewing 213 consumers’ members of Organic Solidarity Purchase Groups (O-SPGs). The Discrete Choice Experiment methodology was used, and three different models (Multinomial Logit Model (MNL), Mixed Logit Model (RPL) and Endogenous Attribute Attendance (EAA)) were applied to verify the reliability of the estimates. Attribute non-attendance (ANA) behaviour was taken into account. Results reveal that the presence of ANA had an impact on both the relative importance of the estimated attributes and the magnitude of the estimated mean WTP. Therefore, this study suggests that WTP mean estimates should be considered with caution for marketing purposes if ANA is not considered. Looking at pulses, the results help to understand the importance in monetary terms of the relationship between lentil choice and rural sustainability.

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 Performance Analysis of Mixed Logit Models for Discrete Choice Models

2019 ◽  
pp. 563-575
Author(s):  
Jaka Nugraha
Keyword(s):  
Discrete Choice ◽  
Logit Model ◽  
Mixed Logit ◽  
Likelihood Estimation ◽  
Multinomial Logit Model ◽  
Discrete Choice Models ◽  
Logit Models ◽  
Nominal Data ◽  
Mixed Logit Models ◽  
Mnl Model

Mixed Logit model  (MXL) is generated from Multinomial Logit model (MNL) for discrete, i.e. nominal, data. It eliminates its limitations particularly on estimating the correlation among responses.  In the MNL, the probability equations are presented in the closed form and it is contrary with in the MXL. Consequently, the calculation of the probability value of each alternative get simpler in the MNL, meanwhile it needs the numerical methods for estimation in the MXL.  In this study, we investigated the performance of maximum likelihood estimation (MLE) in the MXL and MNL into two cases, the low and high correlation circumstances among responses. The performance is measured based on differencing actual and estimation value.  The simulation study and real cases show that the MXL model is more accurate than the MNL model. This model can estimates the correlation among response as well. The study concludes that the MXL model is suggested to be used if there is a high correlation among responses. 

scholarly journals Determinant Factors of Farmer’s Willingness to Pay for Sustainable Land Management Practices Evidence with Homosha Benishangul Gumuz Region: Application with Multinomial Logit Model, Mixed Logit Model, and Conditional Logit Model

2020 ◽  
Author(s):  
Melaku Tarekegn Takele ◽  
Mehammed Ibrahim Umer
Keyword(s):  
Willingness To Pay ◽  
Land Management ◽  
Logit Model ◽  
Management Practices ◽  
Mixed Logit ◽  
Multinomial Logit Model ◽  
Mixed Logit Model ◽  
Sustainable Land Management ◽  
Conditional Logit Model ◽  
Conditional Logit

Abstract The study examines factors affecting farmer’s willingness to pay for sustainable land management practices in Ethiopia, The study uses primary data collected from 200 households randomly selected from four kebeles of districts in Ethiopia’s Benishangul-Gumuz regional state with 4,800 observations (eight choices for each household). The choice experiment design was done using the R software to efficiently generate an attribute and level combination using fractional factorial design. Data were analyzed using discrete choice models including multinomial logit model, mixed logit model, and conditional logit model using STAT-14. The findings showed that households were aware of the effects of using SLM and benefits of using a bundle of SLM. However, they were challenged by the costs of implementing a bundle of SLM and technologies related to it. Moreover, mean willingness to pay estimates is about 844 to 2540 birr and in case of total willingness to pay households is not less than 66% for a bundle of SLM. Crop-rotation attributes levels are negatively and significantly affect decision for SLM, while conserve-agriculture positively and significantly affects households' decision to adopt a bundle of SLM. Socio-economic (the type of crop, land size, land form, livestock, awareness about SLM and technologies) variables are found to be factors that determine decision to adopt SLM.

Assortment Optimization under the Multinomial Logit Model with Random Choice Parameters

2014 ◽  
Vol 23 (11) ◽  
pp. 2023-2039 ◽  
Author(s):  
Paat Rusmevichientong ◽  
David Shmoys ◽  
Chaoxu Tong ◽  
Huseyin Topaloglu
Keyword(s):  
Logit Model ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Random Choice ◽  
Assortment Optimization

Preferences Toward COVID-19 Diagnostic Testing Features: Results From a National Cross-Sectional Survey

2021 ◽  
pp. 089011712110340
Author(s):  
Bhagyashree Katare ◽  
Shuoli Zhao ◽  
Joel Cuffey ◽  
Maria I. Marshall ◽  
Corinne Valdivia
Keyword(s):  
Logit Model ◽  
Nasal Swab ◽  
Latent Class ◽  
Diagnostic Testing ◽  
Mixed Logit ◽  
Monetary Incentive ◽  
Monetary Incentives ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Nationally Representative

Purpose: Describe preferences toward COVID-19 testing features (method, location, hypothetical monetary incentive) and simulate the effect of monetary incentives on willingness to test. Design: Online cross-sectional survey administered in July 2020. Subjects: 1,505 nationally representative U.S. respondents. Measures: Choice of preferred COVID-19 testing options in discrete choice experiment. Options differed by method (nasal-swab, saliva), location (hospital/clinic, drive-through, at-home), and monetary incentive ($0, $10, $20). Analysis: Latent class conditional logit model to classify preferences, mixed logit model to simulate incentive effectiveness. Results: Preferences were categorized into 4 groups: 34% (n = 517) considered testing comfort (saliva versus nasal swab) most important, 27% (n = 408) were willing to trade comfort for monetary incentives, 19% (n = 287) would only test at convenient locations, 20% (n = 293) avoided testing altogether. Relative to no monetary incentives, incentives of $100 increased the percent of testing avoiders (16%) and convenience seekers (70%) that were willing to test. Conclusion: Preferences toward different COVID-19 testing features vary, highlighting the need to match testing features with individuals to monitor the spread of COVID-19.

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