scholarly journals Beef Producer Preferences and Purchase Decisions for Livestock Price Insurance

2008 ◽  
Vol 40 (3) ◽  
pp. 789-803 ◽  
Author(s):  
Deacue Fields ◽  
Jeffrey Gillespie
Keyword(s):  
Beef Cattle ◽  
Conjoint Analysis ◽  
Tobit Model ◽  
Purchase Decisions ◽  
Utility Values ◽  
Insurance Policies ◽  
Probit Models ◽  
Price Insurance

Personal interviews were conducted with beef cattle producers in Louisiana to determine their preferences and purchase decisions for livestock price insurance. Conjoint analysis was utilized to determine the importance of selected attributes of insurance policies for these producers. The characteristics of producers who prefer given attributes were also identified. Producers rated products given four economic situations to evaluate. A two-limit tobit model was used to estimate the part worth utility values for each attribute. Univariate probit models were estimated to evaluate the influence of producer characteristics on purchase decisions.

scholarly journals An Analysis of Consumer Preferences for Value-Added Seafood Products Derived from Crawfish

2002 ◽  
Vol 31 (2) ◽  
pp. 157-170 ◽  
Author(s):  
R. Wes Harrison ◽  
Timothy Stringer ◽  
Witoon Prinyawiwatkul
Keyword(s):  
Conjoint Analysis ◽  
Consumer Preferences ◽  
Value Added ◽  
Utility Functions ◽  
Ordered Probit ◽  
Probit Models ◽  
Seafood Products ◽  
Ordered Probit Models ◽  
Old Men

Conjoint analysis is used to evaluate consumer preferences for three consumer-ready products derived from crawfish. Utility functions are estimated using two-limit tobit and ordered probit models. The results show women prefer a baked nugget or popper type product, whereas 35- to 44-year-old men prefer a microwavable nugget or patty type product. The results also show little difference between part-worth estimates or predicted rankings for the tobit and ordered probit models, implying the results are not sensitive to assumptions regarding the ordinal and cardinal nature of respondent preferences.

scholarly journals Consumer Likelihood to Purchase Chickens with Novel Production Attributes

2007 ◽  
Vol 39 (3) ◽  
pp. 581-596 ◽  
Author(s):  
John C. Bernard ◽  
John D. Pesek ◽  
Xiqian Pan
Keyword(s):  
Conjoint Analysis ◽  
Consumer Preferences ◽  
Genetically Modified ◽  
Analysis Data ◽  
Tobit Model ◽  
The South ◽  
Consumer Interest ◽  
Free Range ◽  
Purchase Likelihood ◽  
Natural Foods

Typical supermarket chickens are produced with novel or controversial attributes. This continues despite contrasting growth in consumer interest in organic and natural foods. This study surveyed Delaware consumers' likelihood to purchase chicken given different attributes: free range, given antibiotics, irradiated, fed genetically modified (GM) feed, GM chicken, and price. Examining conjoint analysis data with a heteroskedastic two-limit tobit model, GM chicken and other novel attributes were found to lower purchase likelihood significantly. Understanding these results should help the industry meet consumer preferences while aiding its continued expansion to benefit workers and growers across the South.

Assessing Mass Consumer Information Quality Requirements Using Conjoint Analysis

2011 ◽  
pp. 77-98
Author(s):  
Elizabeth M. Pierce
Keyword(s):  
Conjoint Analysis ◽  
Information Quality ◽  
Consumer Information ◽  
Purchase Decisions ◽  
Buyer Behavior ◽  
Quality Requirements ◽  
Product Features ◽  
Information Systems Professionals ◽  
Importance Ratings ◽  
Information Products

This chapter demonstrates how conjoint analysis can be used to improve the design and delivery of mass consumer information products. Conjoint analysis is a technique that market researchers have used since the 1960’s to better understand how buyers make complex purchase decisions, to estimate preferences and importance ratings for product features, and to predict buyer behavior. This chapter describes the steps for performing a conjoint analysis to assess information quality preferences of potential home buyers interested in using a real estate website to help them locate properties for sale. The author hopes that this tutorial will convince information systems professionals of the usefulness of conjoint analysis as a tool for discerning how to prioritize information quality requirements so that the resulting systems produce information products that better serve the needs of their customers.

scholarly journals Estimating Preference Heterogeneity for Grass-fed Beef Cattle Traits

2019 ◽  
Vol 49 (3) ◽  
pp. 492-516
Author(s):  
Isaac Sitienei ◽  
Jeffrey Gillespie ◽  
Robert W. Harrison ◽  
Guillermo Scaglia
Keyword(s):  
Standard Deviation ◽  
Beef Cattle ◽  
Conjoint Analysis ◽  
Mixed Logit ◽  
Mail Survey ◽  
Kernel Density ◽  
Likert Scale ◽  
Preference Heterogeneity ◽  
Beef Producers ◽  
Using Data

This paper examines grass-fed beef producer preferences for cattle traits using data from a mail survey of 384 U.S. grass-fed beef producers. Conjoint analysis and Likert scale questions were used to determine preferences. Generally, results indicated that producers preferred easy-to-handle, heavy, black, and relatively lower-priced feeders raised from their own cows. The Kernel density figures for source, color, and temperament confirm the mixed logit standard deviation estimates that suggest heterogeneity in producer preferences.

Consideration Sets in Conjoint Analysis

1996 ◽  
Vol 33 (3) ◽  
pp. 364-372 ◽  
Author(s):  
Kamel Jedidi ◽  
Rajeev Kohli ◽  
Wayne S. Desarbo
Keyword(s):  
Conjoint Analysis ◽  
Latent Class ◽  
Estimation Procedure ◽  
Tobit Model ◽  
Consideration Sets ◽  
Parameter Estimates ◽  
Individual Level ◽  
Proposed Model ◽  
Preference Functions ◽  
Product Profile

The authors model product consideration as preceding choice in a segment-level conjoint model. They propose a latent-class tobit model to estimate cardinal, segment-level preference functions based on consumers’ preference ratings for product concepts considered worth adding to consumers’ self-explicated consideration sets. The probability with which the utility of a product profile exceeds an unobserved threshold corresponds to its consideration probability, which is assumed to be independent across product profiles and common to consumers in a segment. A market-share simulation compares the predictions of the proposed model with those obtained from an individual-level tobit model and from traditional ratings-based conjoint analysis. The authors also report simulations that assess the robustness of the proposed estimation procedure, which uses an E-M algorithm to obtain maximum likelihood parameter estimates.

Can Catastrophic Long-Term Care Insurance Policies Increase Private Insurance Coverage and Reduce Medicaid Expenditure?

2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Guoxuan Ma ◽  
Wei Sun
Keyword(s):  
Long Term Care ◽  
Private Insurance ◽  
Insurance Coverage ◽  
Purchase Decisions ◽  
Term Care ◽  
Middle Income ◽  
Insurance Policies ◽  
Long Term Care Insurance ◽  
Care Costs

Abstract Using an inter-temporal optimization model of long-term care insurance purchase decisions, we evaluate catastrophic long-term care insurance policies that cover the tail risk of long-term care costs at affordable premiums. Under our baseline model, we show theoretically that introducing catastrophic policies will induce 11 percent of middle-income men and 3 percent of middle-income women to initiate private insurance coverage. As a result, Medicaid costs will be reduced by 0.20 percent and 0.19 percent for men and women, respectively.

scholarly journals Analysis of Cardinal and Ordinal Assumptions in Conjoint Analysis

2005 ◽  
Vol 34 (2) ◽  
pp. 238-252 ◽  
Author(s):  
R. Wes Harrison ◽  
Jeffrey Gillespie ◽  
Deacue Fields
Keyword(s):  
Conjoint Analysis ◽  
Degrees Of Freedom ◽  
Rating Scales ◽  
Probit Model ◽  
Tobit Model ◽  
Agricultural Economics ◽  
Ordered Probit ◽  
Ordered Probit Model ◽  
Individual Level ◽  
Utility Scale

Of twenty-three agricultural economics conjoint analyses conducted between 1990 and 2001, seventeen used interval-rating scales, with estimation procedures varying widely. This study tests cardinality assumptions in conjoint analysis when interval-rating scales are used, and tests whether the ordered probit or two-limit tobit model is the most valid. Results indicate that cardinality assumptions are invalid, but estimates of the underlying utility scale for the two models do not differ. Thus, while the ordered probit model is theoretically more appealing, the two-limit tobit model may be more useful in practice, especially in cases with limited degrees of freedom, such as with individual-level conjoint models.

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