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 Prioritization of farm success factors by commercial farm managers

2018 ◽  
Vol 21 (6) ◽  
pp. 817-832 ◽  
Author(s):  
John Lai ◽  
Nicole J. Olynk Widmar ◽  
Michael A. Gunderson ◽  
David A. Widmar ◽  
David L. Ortega
Keyword(s):  
Key Management ◽  
Success Factors ◽  
Latent Class ◽  
Latent Class Model ◽  
Significant Heterogeneity ◽  
Relative Importance ◽  
Class Model ◽  
Commercial Farm ◽  
Agricultural Producer ◽  
Functional Areas

This study elicits U.S. agricultural producer preferences for five key management success factors: managing output prices; managing production; controlling costs; managing land/equipment/facilities; and managing people. The objective of this analysis was to determine the relative importance of each of the five profit-centric functional areas of management among U.S. farm managers. Significant heterogeneity in preferences was observed over the management areas. Farm managers, on average, placed the highest importance in controlling costs (29% preference share). Differences emerged among groups of farmers in a latent class model where managing people became relatively important to the viability of the agribusiness.

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 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 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 Non–Small Cell Lung Cancer Patient Preferences for First-Line Treatment: A Discrete Choice Experiment

2020 ◽  
Vol 5 (1) ◽  
pp. 238146832092220
Author(s):  
Joanna P. MacEwan ◽  
Komal Gupte-Singh ◽  
Lauren M. Zhao ◽  
Karen L. Reckamp
Keyword(s):  
Lung Cancer ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Model ◽  
Small Cell ◽  
Small Cell Lung ◽  
First Line ◽  
Class Model ◽  
Treatment Delays

Background. There has been much innovation in the treatment of non–small cell lung cancer (NSCLC) in recent years. In particular, use of immuno-oncology (IO) therapies has been growing. Methods. Patients with NSCLC in the United States were surveyed online using a discrete choice experiment to elicit first-line (1L) treatment preferences across six treatment attributes: survival, adverse events (AEs), mechanism of action (MOA), subsequent treatment options (STOs), genetic testing treatment delay, and out-of-pocket cost (OOPC). Preferences were estimated using a latent-class model. Preference shares were estimated for IO-IO, IO-chemo, and chemo-like regimens. Results. Of the 199 patients who completed the survey, 55% were male, 76% were white, 19% had not begun or were on 1L treatment, and the median age was 43 years. Based on a latent-class model with 3 preference classes, 53.0% of patients considered survival and OOPC alone and were less likely to choose an option with a higher OOPC and lower survival, 12.7% of patients were likely to choose the more expensive option, and for 34.3% of patients, survival, AE risk, and treatment delays all significantly influenced choices. MOA and STOs did not significantly influence treatment choices in any preference class. Approximately 53%, 27%, and 20% of patients preferred IO-IO-like, IO-chemo-like, and chemo-like regimens in 1L, respectively. Respondents were younger, more likely to be Caucasian, and more likely to speak English than the general NSCLC patient population. Conclusions. OOPC, effectiveness, treatment delays, and safety influenced NSCLC patients’ 1L treatment decisions, and most patients preferred an IO-IO followed by IO-chemo-like regimen in 1L. Cancer treatment decisions are complex and patient preferences are unique; therefore, patients’ treatment objectives should be discussed in shared treatment decision making.

scholarly journals Is India Ready for Alt-Meat? Preferences and Willingness to Pay for Meat Alternatives

2020 ◽  
Vol 12 (11) ◽  
pp. 4377 ◽  
Author(s):  
Rashmit S. Arora ◽  
Daniel A. Brent ◽  
Edward C. Jaenicke
Keyword(s):  
Willingness To Pay ◽  
Choice Experiment ◽  
Latent Class ◽  
Consumer Preferences ◽  
Latent Class Model ◽  
Future Price ◽  
Indian Market ◽  
Viable Option ◽  
Class Model ◽  
Generation Plant

Little is known about the consumer preferences of next-generation plant-based and cell-based meat alternatives, two food technologies that offer a demand-side solution to the environmental, nutritional, and other societal concerns associated with animal-intensive agriculture. To address this gap, this paper estimates consumers’ willingness to pay for four sources of protein (conventional meat, plant-based meat, cell-based meat, and chickpeas) in a developing country with rising demand for meat—India. A latent class model of a discrete choice experiment conducted in Mumbai identifies four heterogeneous segments in the Indian market. Aggregating across all four segments, respondents are willing to pay a premium for plant-based meat and a smaller premium for cell-based meat over the price of conventional meat. However, our main findings show that these premiums strongly differ across the four consumer-class segments. The results offer important insights into future price points and policy options that might make these meat alternatives commercially successful, and therefore, a viable option in addressing societal concerns.

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